Nothing
R/nem_plot.R
In easynem: Nematode Community Analysis
################################################################################
#' Visualize the results of the calculation
#'
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_beta(pca, Treatments, method = "bray") |> nem_plot()
#' ```
#'
#' @param object \code{\link{beta-class}} or other types data (\code{\link{beta2-class}},
#' \code{\link{compare-class}}, \code{\link{compare2-class}}, \code{\link{ef-class}},
#' \code{\link{ef2-class}}, \code{\link{funguild-class}}, \code{\link{funguild2-class}},
#' \code{\link{mf-class}}, \code{\link{mf2-class}}, \code{\link{ter-class}},
#' \code{\link{ter2-class}}, etc.).
#' @param ... Other parameters to be expanded.
#'
#' @return A plot object. Typically a \code{ggplot} object for most classes,
#' or a \code{recordedplot} object for \code{\link{ter-class}} and
#' \code{\link{ter2-class}} ternary plots.
#'
#' @export
setGeneric("nem_plot", function(object, ...){
standardGeneric("nem_plot")
})
################################################################################
#' Visualization of beta diversity results (single factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{beta-class}}
#' and is used to visualize the single-factor beta diversity results.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_beta(pca, Treatments, method = "bray") |> nem_plot()
#' ```
#'
#' @param object A \code{\link{beta-class}} object.
#' @param level Used to adjust the size of the confidence ellipse. Default
#' \code{level = 0.6}. See \code{\link[ggplot2]{stat_ellipse}}.
#' @param type Method used to adjust the display of scatter area. \code{type = 1},
#' displays as a confidence ellipse; \code{type = 2}, displays as a polygon.
#' Default \code{type = 1}.
#' @param ... Other parameters to be expanded.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @aliases nem_plot,beta-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem2(tab = nemtab, tax = nemtax, meta = nemmeta)
#' nem_plot <- nem |>
#' calc_beta(pcoa, Treatments, method = "bray") |>
#' nem_plot(level = 0)
#' nem_plot
#' nem_plot <- nem |>
#' calc_beta(nmds, Treatments, method = "bray") |>
#' nem_plot(type = 2)
#' nem_plot
setMethod("nem_plot", signature("beta"), function(object, level = 0.6, type = 1, ...){
meta = object@meta
result = object@result
temp = object@temp
if (length(temp) == 1){
NMDS1 = meta[,3]
NMDS2 = meta[,4]
group = meta[,2]
p = ggplot2::ggplot(meta,ggplot2::aes(x=NMDS1,y=NMDS2,color=!!dplyr::sym(colnames(group))))+
ggplot2::labs(x=colnames(NMDS1),y = colnames(NMDS2), title=temp)+ggplot2::geom_point(size=4)+
ggplot2::theme(legend.title = ggplot2::element_blank())+
ggplot2::theme_test() + ggplot2::coord_fixed(1)
if (level > 0){
if (type == 1){
p = p+ggplot2::stat_ellipse(level=level)
} else if(type == 2){
p = p+geom_encircle(ggplot2::aes(fill=!!dplyr::sym(colnames(group))), alpha = 0.1, show.legend = F)
} else {
stop("Type value must be 1 or 2.")
}
} else if (level == 0){
p
} else {
stop("Invalid level value.")
}
} else {
PCoA1 = meta[,3]
PCoA2 = meta[,4]
group = meta[,2]
if (colnames(PCoA1)=="PCoA1"){
p = ggplot2::ggplot(meta,ggplot2::aes(x=PCoA1,y=PCoA2,color=!!dplyr::sym(colnames(group))))
} else {
PC1 = meta[,3]
PC2 = meta[,4]
group = meta[,2]
p = ggplot2::ggplot(meta,ggplot2::aes(x=PC1,y=PC2,color=!!dplyr::sym(colnames(group))))
}
p = p+ggplot2::labs(x = temp[1], y=temp[2], title=temp[3])+ggplot2::geom_point(size=4)+
ggplot2::theme(legend.title = ggplot2::element_blank())+
ggplot2::theme_test() + ggplot2::coord_fixed(1)
if (level > 0){
if (type == 1){
p = p+ggplot2::stat_ellipse(level=level)
} else if(type == 2){
p = p+geom_encircle(ggplot2::aes(fill=!!dplyr::sym(colnames(group))), alpha = 0.1, show.legend = F)
} else {
stop("Type value must be 1 or 2.")
}
} else if (level == 0){
p
} else {
stop("Invalid level value.")
}
}
p = p + ggplot2::geom_vline(xintercept = 0, linetype = "dashed") + ggplot2::geom_hline(yintercept = 0, linetype = "dashed")
})
################################################################################
#' Visualization of beta diversity results (two-factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{beta2-class}}
#' and is used to visualize the two-factor beta diversity results.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_beta2(pca, con_crop, season, method = "bray") |> nem_plot()
#' ```
#'
#' @param object A \code{\link{beta2-class}} object.
#' @param level Used to adjust the size of the confidence ellipse. Default
#' \code{level = 0.6}. See \code{\link[ggplot2]{stat_ellipse}}.
#' @param type Method used to adjust the display of scatter area. \code{type = 1},
#' displays as a confidence ellipse; \code{type = 2}, displays as a polygon.
#' Default \code{type = 1}.
#' @param ... Other parameters to be expanded.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @aliases nem_plot,beta2-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem(tab = easynem_example("nemtab1.csv"),
#' tax = easynem_example("nemtax1.csv"),
#' meta = easynem_example("nemmeta1.csv"))
#' nem_plot <- nem |>
#' calc_beta2(pcoa, con_crop, season, method = "bray") |>
#' nem_plot(level = 0)
#' nem_plot
#' nem_plot <- nem |>
#' calc_beta2(nmds, con_crop, season, method = "bray") |>
#' nem_plot(type = 2)
#' nem_plot
setMethod("nem_plot", signature("beta2"), function(object, level = 0.6, type = 1, ...){
meta = object@meta
result = object@result
temp = object@temp
if (length(temp) == 1){
NMDS1 = meta[,5]
NMDS2 = meta[,6]
group1 = meta[,2]
group2 = meta[,3]
group3 = meta[,4]
p = ggplot2::ggplot(meta,ggplot2::aes(x=NMDS1,y=NMDS2,color=!!dplyr::sym(colnames(group1)), shape=!!dplyr::sym(colnames(group2))))+
ggplot2::labs(x=colnames(NMDS1),y = colnames(NMDS2), title=temp)+ggplot2::geom_point(size=4)+
ggplot2::theme(legend.title = ggplot2::element_blank())+
ggplot2::theme_test() + ggplot2::coord_fixed(1)
if (level > 0){
if (type == 1){
p = p+ggplot2::stat_ellipse(level=level)
} else if(type == 2){
p = p+geom_encircle(ggplot2::aes(fill=!!dplyr::sym(colnames(group3))), alpha = 0.1, show.legend = F)
} else {
stop("Type value must be 1 or 2.")
}
} else if (level == 0){
p
} else {
stop("Invalid level value.")
}
} else {
PCoA1 = meta[,5]
PCoA2 = meta[,6]
group1 = meta[,2]
group2 = meta[,3]
group3 = meta[,4]
if (colnames(PCoA1)=="PCoA1"){
p = ggplot2::ggplot(meta,ggplot2::aes(x=PCoA1,y=PCoA2,color=!!dplyr::sym(colnames(group1)),shape=!!dplyr::sym(colnames(group2))))
} else {
PC1 = meta[,5]
PC2 = meta[,6]
group1 = meta[,2]
group2 = meta[,3]
group3 = meta[,4]
p = ggplot2::ggplot(meta,ggplot2::aes(x=PC1,y=PC2,color=!!dplyr::sym(colnames(group1)),shape=!!dplyr::sym(colnames(group2))))
}
p = p+ggplot2::labs(x = temp[1], y=temp[2], title=temp[3])+ggplot2::geom_point(size=4)+
ggplot2::theme(legend.title = ggplot2::element_blank())+
ggplot2::theme_test() + ggplot2::coord_fixed(1)
if (level > 0){
if (type == 1){
p = p+ggplot2::stat_ellipse(level=level)
} else if(type == 2){
p = p+geom_encircle(ggplot2::aes(fill=!!dplyr::sym(colnames(group3))), alpha = 0.1, show.legend = F)
} else {
stop("Type value must be 1 or 2.")
}
} else if (level == 0){
p
} else {
stop("Invalid level value.")
}
}
p = p + ggplot2::geom_vline(xintercept = 0, linetype = "dashed") + ggplot2::geom_hline(yintercept = 0, linetype = "dashed")
})
################################################################################
#' Visualizing the results of multiple comparisons (single factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{compare-class}}
#' and is used to visualize the results of single-factor multiple comparisons.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_compare(.group = con_crop, y = pH, method = LSD) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{compare-class}} object.
#' @param type \code{type = 1}, draws a box plot; \code{type = 2} draws a bar plot.
#' Default \code{type = 1}.
#' @param add Add standard deviation or standard error (only used when drawing a
#' bar plot).
#' @param ... Other parameters to be expanded.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @aliases nem_plot,compare-method
#' @import ggplot2
#' @import multcompView
#' @export
#' @examples
#' nem <- read_nem2(tab = nemtab, tax = nemtax, meta = nemmeta)
#' nem_plot <- nem |> calc_compare(.group = Treatments,
#' y = Mesorhabditis,
#' method = LSD) |>
#' nem_plot()
#' nem_plot
#' nem_plot <- nem |> calc_compare(.group = Treatments,
#' y = Mesorhabditis,
#' method = HSD) |>
#' nem_plot(type = 2, add = "mean_se")
#' nem_plot
setMethod("nem_plot", signature("compare"), function(object, type = 1, add, ...){
meta = object@meta
temp = object@temp
if (temp == "TTest") {
value = meta[,3]
group = meta[,2]
if(type == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = group, y = value)) +
ggplot2::geom_boxplot(ggplot2::aes(fill = group), width = 0.5) +
ggplot2::geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none") +
ggplot2::theme_test() +
ggplot2::xlab(NULL) +
ggplot2::ylab(colnames(meta)[3])
compar = list(unique(meta[,2]))
p = p + ggpubr::stat_compare_means(comparisons = compar, method = "t.test", label = "p.signif")
} else if (type == 2){
compar = list(unique(meta[,2]))
if (add == "mean_se"){
p = ggpubr::ggbarplot(meta, x = colnames(meta)[2], y = colnames(meta)[3], fill = colnames(meta)[2], width = 0.5, add = "mean_se") +
ggpubr::stat_compare_means(comparisons = compar, method = "t.test", label = "p.signif") + ggplot2::xlab(NULL)+
ggplot2::theme_test() +
ggplot2::geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none")
} else if (add == "mean_sd"){
p = ggpubr::ggbarplot(meta, x = colnames(meta)[2], y = colnames(meta)[3], fill = colnames(meta)[2], width = 0.5, add = "mean_sd") +
ggpubr::stat_compare_means(comparisons = compar, method = "t.test", label = "p.signif") + ggplot2::xlab(NULL)+
ggplot2::theme_test() +
ggplot2::geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none")
}
}
} else if (temp == "WilcoxTest") {
value = meta[,3]
group = meta[,2]
if(type == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = group, y = value)) +
ggplot2::geom_boxplot(ggplot2::aes(fill = group), width = 0.5) +
ggplot2::geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none") +
ggplot2::theme_test() +
ggplot2::xlab(NULL) +
ggplot2::ylab(colnames(meta)[3])
compar = list(unique(meta[,2]))
p = p + ggpubr::stat_compare_means(comparisons = compar, method = "wilcox.test", label = "p.signif")
} else if (type == 2){
compar = list(unique(meta[,2]))
if (add == "mean_se"){
p = ggpubr::ggbarplot(meta, x = colnames(meta)[2], y = colnames(meta)[3], fill = colnames(meta)[2], width = 0.5, add = "mean_se") +
ggpubr::stat_compare_means(comparisons = compar, method = "wilcox.test", label = "p.signif") + ggplot2::xlab(NULL)+
ggplot2::theme_test() +
ggplot2::geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none")
} else if (add == "mean_sd"){
p = ggpubr::ggbarplot(meta, x = colnames(meta)[2], y = colnames(meta)[3], fill = colnames(meta)[2], width = 0.5, add = "mean_sd") +
ggpubr::stat_compare_means(comparisons = compar, method = "wilcox.test", label = "p.signif") + ggplot2::xlab(NULL)+
ggplot2::theme_test() +
ggplot2::geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none")
}
}
} else if (temp == "KruskalTest") {
value = meta[,3]
group = meta[,2]
wmc = object@result$wmc
wmc = dplyr::select(dplyr::select(dplyr::mutate(wmc, names = paste(Group.1, Group.2, sep = "-")), names, everything()), -Group.1, -Group.2)
significant = wmc[,3]
names(significant) = wmc$names
dif = multcompView::multcompLetters(significant)
dif2 = as.data.frame(dif$monospacedLetters)
colnames(dif2) <- "label"
dif2$label <- gsub(" ", "", dif2$label)
dif2$group <- row.names(dif2)
if(type == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = group, y = value)) +
ggplot2::geom_boxplot(ggplot2::aes(fill = group), width = 0.5) +
ggplot2:: geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none") +
ggplot2::theme_test() +
ggplot2::xlab(NULL) +
ggplot2::ylab(colnames(meta)[3])
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(max = max(.data[[colnames(meta)[3]]]))
all = merge(dif2, meta2, by.x = "group", by.y = colnames(meta)[2])
p = p + ggplot2::geom_text(data = all, ggplot2::aes(x = group, y = max, label = label), vjust = -0.5)
} else if (type == 2){
if (add == "mean_se") {
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(mean = mean(!!rlang::sym(colnames(meta)[3])), er = stats::sd(!!rlang::sym(colnames(meta)[3]))/dplyr::n())
} else if (add == "mean_sd") {
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(mean = mean(!!rlang::sym(colnames(meta)[3])), er = stats::sd(!!rlang::sym(colnames(meta)[3])))
}
all = merge(dif2, meta2, by.x = "group", by.y = colnames(meta)[2])
colnames(all)[3] = colnames(meta)[3]
p = ggpubr::ggbarplot(meta, x = colnames(meta)[2], y = colnames(meta)[3], fill = colnames(meta)[2], width = 0.5, add = "mean") +
ggplot2::xlab(NULL)+
ggplot2::theme_test() +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(colnames(meta)[2])), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none")
all$mean = all[,3]
p = p + ggplot2::geom_errorbar(data = all, ggplot2::aes(x = group, y = mean, ymin = mean-er, ymax =mean+er), width = .2) +
ggplot2::geom_text(data = all, ggplot2::aes(x = group, y = mean + er, label = label), vjust = -0.5)
}
} else if (temp == "LSD") {
value = meta[,3]
group = meta[,2]
meta1 = object@result$LSD$means[,-1]
meta1$group = rownames(meta1)
meta2 = object@result$LSD$groups
meta2$group = rownames(meta2)
meta_all = merge(meta1,meta2,by = "group")
if (type == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = group, y = value)) +
ggplot2::geom_boxplot(ggplot2::aes(fill = group), width = 0.5) +
ggplot2:: geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none") +
ggplot2::theme_test() +
ggplot2::xlab(NULL) +
ggplot2::ylab(colnames(meta)[3])
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(max = max(.data[[colnames(meta)[3]]]))
all = merge(meta_all, meta2, by.x = "group", by.y = colnames(meta)[2])
all$mean = all[[colnames(meta)[3]]]
p = p + ggplot2::geom_text(data = all, ggplot2::aes(x = group, y = max, label = groups), vjust = -0.5)
} else if (type == 2){
if (add == "mean_se") {
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(mean = mean(!!rlang::sym(colnames(meta)[3])), er = stats::sd(!!rlang::sym(colnames(meta)[3]))/dplyr::n())
} else if (add == "mean_sd") {
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(mean = mean(!!rlang::sym(colnames(meta)[3])), er = stats::sd(!!rlang::sym(colnames(meta)[3])))
}
p = ggpubr::ggbarplot(meta, x = colnames(meta)[2], y = colnames(meta)[3], fill = colnames(meta)[2], width = 0.5, add = "mean") +
ggplot2::xlab(NULL)+
ggplot2::theme_test() +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(colnames(meta)[2])), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none")
all = merge(meta_all, meta2, by.x = "group", by.y = colnames(meta)[2])
all$mean = all[[colnames(meta)[3]]]
p = p + ggplot2::geom_errorbar(data = all, ggplot2::aes(x = group, y = mean, ymin = mean-er, ymax =mean+er), width = .2) +
ggplot2::geom_text(data = all, ggplot2::aes(x = group, y = mean + er, label = groups), vjust = -0.5)
}
} else if (temp == "HSD") {
value = meta[,3]
group = meta[,2]
wmc = as.data.frame(object@result$HSD[[colnames(object@meta)[2]]])
significant = wmc[,4]
names(significant) = rownames(wmc)
dif = multcompView::multcompLetters(significant)
dif2 = as.data.frame(dif$Letters)
colnames(dif2) <- "label"
dif2$group <- rownames(dif2)
if(type == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = group, y = value)) +
ggplot2::geom_boxplot(ggplot2::aes(fill = group), width = 0.5) +
ggplot2:: geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none") +
ggplot2::theme_test() +
ggplot2::xlab(NULL) +
ggplot2::ylab(colnames(meta)[3])
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(max = max(.data[[colnames(meta)[3]]]))
all = merge(dif2, meta2, by.x = "group", by.y = colnames(meta)[2])
p = p + ggplot2::geom_text(data = all, ggplot2::aes(x = group, y = max, label = label), vjust = -0.5)
} else if (type == 2){
if (add == "mean_se") {
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(mean = mean(!!rlang::sym(colnames(meta)[3])), er = stats::sd(!!rlang::sym(colnames(meta)[3]))/dplyr::n())
} else if (add == "mean_sd") {
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(mean = mean(!!rlang::sym(colnames(meta)[3])), er = stats::sd(!!rlang::sym(colnames(meta)[3])))
}
all = merge(dif2, meta2, by.x = "group", by.y = colnames(meta)[2])
colnames(all)[3] = colnames(meta)[3]
p = ggpubr::ggbarplot(meta, x = colnames(meta)[2], y = colnames(meta)[3], fill = colnames(meta)[2], width = 0.5, add = "mean") +
ggplot2::xlab(NULL)+
ggplot2::theme_test() +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(colnames(meta)[2])), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none")
all$mean = all[,3]
p = p + ggplot2::geom_errorbar(data = all, ggplot2::aes(x = group, y = mean, ymin = mean-er, ymax =mean+er), width = .2) +
ggplot2::geom_text(data = all, ggplot2::aes(x = group, y = mean + er, label = label), vjust = -0.5)
}
}
p
})
################################################################################
#' Visualizing the results of multiple comparisons (two-factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{compare2-class}}
#' and is used to visualize the results of two-factor multiple comparisons.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_compare2(.group1 = con_crop, .group2 = season, y = pH, method = LSD) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{compare2-class}} object.
#' @param type1 \code{type1 = 1}, draws a box plot; \code{type1 = 2}, draws a bar plot.
#' Default \code{type1 = 1}.
#' @param type2 \code{type2 = 1}, draw a cluster plot; \code{type2 = 2}, draws faceted plot.
#' Default \code{type2 = 1}.
#' @param add Add standard deviation or standard error (only used when drawing a
#' bar plot).
#' @param ... Other parameters to be expanded.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @aliases nem_plot,compare2-method
#' @import ggplot2
#' @import multcompView
#' @import ggpubr
#' @importFrom stats sd
#' @export
#' @examples
#' nem <- read_nem(tab = easynem_example("nemtab1.csv"),
#' tax = easynem_example("nemtax1.csv"),
#' meta = easynem_example("nemmeta1.csv"))
#' nem_plot <- nem |> calc_compare2(.group1 = con_crop,
#' .group2 = season, y = pH, method = LSD2) |>
#' nem_plot(type2 = 2)
#' nem_plot
#' nem_plot <- nem |> calc_compare2(.group1 = con_crop,
#' .group2 = season, y = pH, method = HSD2) |>
#' nem_plot(type1 = 2, type2 = 2, add = "mean_sd")
#' nem_plot
setMethod("nem_plot", signature("compare2"), function(object, type1 = 1, type2 = 1, add, ...){
meta = object@meta
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2]), !!rlang::sym(colnames(meta)[3])) |>
dplyr::summarise(max = max(.data[[colnames(meta)[4]]]),
mean = mean(!!rlang::sym(colnames(meta)[4])),
se = stats::sd(!!rlang::sym(colnames(meta)[4]))/dplyr::n(),
sd = stats::sd(!!rlang::sym(colnames(meta)[4])))
temp = object@temp
result = object@result
if(temp == "TTest2"){
for (i in 1:nrow(result)){
# i = 2
sig = result[i,c(1,6,12)]
significant = as.data.frame(sig)[,2]
names(significant) = sig$group
dif = multcompView::multcompLetters(significant)
dif2 = as.data.frame(dif$Letters)
colnames(dif2) <- "label"
dif2$label <- gsub(" ", "", dif2$label)
dif2[[colnames(meta)[2]]] = row.names(dif2)
dif2[[colnames(meta)[3]]] = sig[[colnames(meta)[3]]]
meta2 = dplyr::left_join(meta2, dif2, by = c(colnames(meta)[2], colnames(meta)[3]))
}
label_cols <- grep("^label\\.", colnames(meta2), value = TRUE)
meta2$label <- apply(meta2[label_cols], 1, function(row) {
paste(stats::na.omit(row), collapse = "")
})
meta2 <- meta2[, !colnames(meta2) %in% label_cols]
if (type1 == 1){
if (type2 == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]),
fill = !!rlang::sym(names(meta)[3]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),position = ggplot2::position_dodge(0.8)) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),
shape = 21, position = ggplot2::position_jitterdodge(0.8/nrow(meta2)))+
ggplot2::geom_text(data = meta2, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym(names(meta2)[3]),
label = !!rlang::sym(names(meta2)[7]),
vjust = -0.5),
position = ggplot2::position_dodge(0.8)) +
ggplot2::theme_test() +
ggplot2::xlab(NULL)
} else if (type2 == 2){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[2]))) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[2])), shape = 21, width = 1/nrow(meta2)/2) +
ggplot2::scale_fill_discrete(guide = "none")+
ggplot2::theme_test() +
ggplot2::xlab(NULL)
compar = list(unique(meta[,2]))
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggpubr::stat_compare_means(comparisons = compar, method = "t.test", label = "p.signif", ...) + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
} else if (type1 == 2){
if (type2 == 1){
p = ggplot2::ggplot(meta2, ggplot2:: aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym(names(meta2)[4]),
fill = !!rlang::sym(names(meta2)[2]))) +
ggplot2::geom_bar(stat = "identity",position = ggplot2::position_dodge(0.8), color = "black", width = 0.75) +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]) ,
fill = !!rlang::sym(names(meta)[3])),
shape = 21,
position = ggplot2::position_jitterdodge(0.8/nrow(meta2)))+
ggplot2::ylab(names(meta)[4]) +
ggplot2::xlab(NULL) +
ggplot2::theme_test()
if (add == "mean_se"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - se, ymax = mean+se), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + se,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
} else if (add == "mean_sd"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - sd, ymax = mean+sd), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + sd,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
}
} else if (type2 == 2){
formu = stats::as.formula(paste0(names(meta)[4], "~", names(meta)[2]))
stat.test = ggpubr::compare_means(formu, data = meta, group.by = names(meta)[3], method = "t.test", ...)
meta3 = meta2 |> dplyr::group_by(!!rlang::sym(names(meta2)[2])) |> dplyr::summarise(p.position = max(max))
stat.test = merge(stat.test, meta3, by = names(meta2)[2])
if (add == "mean_se"){
p = ggplot2::ggplot(meta2, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]), y = !!rlang::sym(names(meta2)[4]), fill = names(meta[2]))) +
ggplot2::geom_bar(stat = "identity",ggplot2::aes(fill = !!rlang::sym(names(meta2)[2])), color = "black",width = 0.75) +
ggplot2::geom_errorbar(ggplot2::aes(ymin = mean-se, ymax = mean+se), width = 0.75/4)+
ggplot2::theme_test() +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),y = !!rlang::sym(names(meta)[4]), fill = !!rlang::sym(names(meta)[3])), shape = 21, width = 0.75/4) +
ggplot2::scale_fill_discrete(guide = "none") +
ggpubr::stat_pvalue_manual(
stat.test, label = "p.signif", y.position = "p.position"
) +
ggplot2::xlab(NULL)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
} else if (add == "mean_sd"){
p = ggplot2::ggplot(meta2, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]), y = !!rlang::sym(names(meta2)[4]), fill = names(meta[2]))) +
ggplot2::geom_bar(stat = "identity",ggplot2::aes(fill = !!rlang::sym(names(meta2)[2])), color = "black",width = 0.75) +
ggplot2::geom_errorbar(ggplot2::aes(ymin = mean-sd, ymax = mean+sd), width = 0.75/4)+
ggplot2::theme_test() +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),y = !!rlang::sym(names(meta)[4]), fill = !!rlang::sym(names(meta)[3])), shape = 21, width = 0.75/4) +
ggplot2::scale_fill_discrete(guide = "none") +
ggpubr::stat_pvalue_manual(
stat.test, label = "p.signif", y.position = "p.position"
) +
ggplot2::xlab(NULL)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
}
}
} else if (temp == "WilcoxTest2"){
for (i in 1:nrow(result)){
# i = 2
sig = result[i,c(1,3,6)]
significant = as.data.frame(sig)[,2]
names(significant) = sig$group
dif = multcompView::multcompLetters(significant)
dif2 = as.data.frame(dif$Letters)
colnames(dif2) <- "label"
dif2$label <- gsub(" ", "", dif2$label)
dif2[[colnames(meta)[2]]] = row.names(dif2)
dif2[[colnames(meta)[3]]] = sig[[colnames(meta)[3]]]
meta2 = dplyr::left_join(meta2, dif2, by = c(colnames(meta)[2], colnames(meta)[3]))
}
label_cols <- grep("^label\\.", colnames(meta2), value = TRUE)
meta2$label <- apply(meta2[label_cols], 1, function(row) {
paste(stats::na.omit(row), collapse = "")
})
meta2 <- meta2[, !colnames(meta2) %in% label_cols]
if (type1 == 1){
if (type2 == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]),
fill = !!rlang::sym(names(meta)[3]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),position = ggplot2::position_dodge(0.8)) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),
shape = 21, position = ggplot2::position_jitterdodge(0.8/nrow(meta2)))+
ggplot2::geom_text(data = meta2, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym(names(meta2)[3]),
label = !!rlang::sym(names(meta2)[7]),
vjust = -0.5),
position = ggplot2::position_dodge(0.8)) +
ggplot2::theme_test() +
ggplot2::xlab(NULL)
} else if (type2 == 2){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[2]))) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[2])), shape = 21, width = 1/nrow(meta2)/2) +
ggplot2::scale_fill_discrete(guide = "none")+
ggplot2::theme_test() +
ggplot2::xlab(NULL)
compar = list(unique(meta[,2]))
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggpubr::stat_compare_means(comparisons = compar, method = "wilcox.test", label = "p.signif", ...) + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
} else if (type1 == 2){
if (type2 == 1){
p = ggplot2::ggplot(meta2, ggplot2:: aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym(names(meta2)[4]),
fill = !!rlang::sym(names(meta2)[2]))) +
ggplot2::geom_bar(stat = "identity",position = ggplot2::position_dodge(0.8), color = "black", width = 0.75) +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]) ,
fill = !!rlang::sym(names(meta)[3])),
shape = 21,
position = ggplot2::position_jitterdodge(0.8/nrow(meta2)))+
ggplot2::ylab(names(meta)[4]) +
ggplot2::xlab(NULL) +
ggplot2::theme_test()
if (add == "mean_se"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - se, ymax = mean+se), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + se,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
} else if (add == "mean_sd"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - sd, ymax = mean+sd), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + sd,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
}
} else if (type2 == 2){
formu = stats::as.formula(paste0(names(meta)[4], "~", names(meta)[2]))
stat.test = ggpubr::compare_means(formu, data = meta, group.by = names(meta)[3], method = "wilcox.test", ...)
meta3 = meta2 |> dplyr::group_by(!!rlang::sym(names(meta2)[2])) |> dplyr::summarise(p.position = max(max))
stat.test = merge(stat.test, meta3, by = names(meta2)[2])
if (add == "mean_se"){
p = ggplot2::ggplot(meta2, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]), y = !!rlang::sym(names(meta2)[4]), fill = names(meta[2]))) +
ggplot2::geom_bar(stat = "identity",ggplot2::aes(fill = !!rlang::sym(names(meta2)[2])), color = "black",width = 0.75) +
ggplot2::geom_errorbar(ggplot2::aes(ymin = mean-se, ymax = mean+se), width = 0.75/4)+
ggplot2::theme_test() +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),y = !!rlang::sym(names(meta)[4]), fill = !!rlang::sym(names(meta)[3])), shape = 21, width = 0.75/4) +
ggplot2::scale_fill_discrete(guide = "none") +
ggpubr::stat_pvalue_manual(
stat.test, label = "p.signif", y.position = "p.position"
) +
ggplot2::xlab(NULL)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
} else if (add == "mean_sd"){
p = ggplot2::ggplot(meta2, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]), y = !!rlang::sym(names(meta2)[4]), fill = names(meta[2]))) +
ggplot2::geom_bar(stat = "identity",ggplot2::aes(fill = !!rlang::sym(names(meta2)[2])), color = "black",width = 0.75) +
ggplot2::geom_errorbar(ggplot2::aes(ymin = mean-sd, ymax = mean+sd), width = 0.75/4)+
ggplot2::theme_test() +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),y = !!rlang::sym(names(meta)[4]), fill = !!rlang::sym(names(meta)[3])), shape = 21, width = 0.75/4) +
ggplot2::scale_fill_discrete(guide = "none") +
ggpubr::stat_pvalue_manual(
stat.test, label = "p.signif", y.position = "p.position"
) +
ggplot2::xlab(NULL)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
}
}
} else if (temp == "KruskalTest2"){
result = result$WilcoxTest
result$group <- paste(result$group1, result$group2, sep = "-")
result_grouped <- result |> dplyr::group_by(!!rlang::sym(names(meta)[3])) %>%
dplyr::group_split()
results <- lapply(result_grouped, function(group) {
sig <- group[, c(1, 9, 12)]
significant <- as.data.frame(sig)[, 2]
names(significant) <- sig$group
dif <- multcompView::multcompLetters(significant)
dif2 <- as.data.frame(dif$Letters)
colnames(dif2) <- "label"
dif2$label <- gsub(" ", "", dif2$label)
dif2[[colnames(meta)[2]]] <- row.names(dif2)
dif2[[colnames(meta)[3]]] <- unique(sig[[colnames(meta)[3]]])
dif2
})
results_combined <- do.call(rbind, results)
meta2 <- dplyr::left_join(meta2, results_combined, by = c(colnames(meta)[2], colnames(meta)[3]))
if (type1 == 1){
if (type2 == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]),
fill = !!rlang::sym(names(meta)[3]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),position = ggplot2::position_dodge(0.8)) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),
shape = 21, position = ggplot2::position_jitterdodge(0.8/nrow(meta2)))+
ggplot2::geom_text(data = meta2, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym(names(meta2)[3]),
label = !!rlang::sym(names(meta2)[7]),
vjust = -0.5),
position = ggplot2::position_dodge(0.8)) +
ggplot2::theme_test() +
ggplot2::xlab(NULL)
} else if (type2 == 2){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[3]))) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])), shape = 21, width = 1/nrow(meta2)/2) +
ggplot2::scale_fill_discrete(guide = "none")+
ggplot2::theme_test() +
ggplot2::xlab(NULL)
compar = list(unique(meta[,2]))
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::geom_text(data = meta2, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym(names(meta2)[3]),
label = !!rlang::sym(names(meta2)[7]),
vjust = -0.5)) + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
} else if (type1 == 2){
if (type2 == 1){
p = ggplot2::ggplot(meta2, ggplot2:: aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym(names(meta2)[4]),
fill = !!rlang::sym(names(meta2)[2]))) +
ggplot2::geom_bar(stat = "identity",position = ggplot2::position_dodge(0.8), color = "black", width = 0.75) +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]) ,
fill = !!rlang::sym(names(meta)[3])),
shape = 21,
position = ggplot2::position_jitterdodge(0.8/nrow(meta2)))+
ggplot2::ylab(names(meta)[4]) +
ggplot2::xlab(NULL) +
ggplot2::theme_test()
if (add == "mean_se"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - se, ymax = mean+se), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + se,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
} else if (add == "mean_sd"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - sd, ymax = mean+sd), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + sd,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
}
} else if (type2 == 2){
p = ggplot2::ggplot(meta2, ggplot2:: aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym(names(meta2)[4]),
fill = !!rlang::sym(names(meta2)[2]))) +
ggplot2::geom_bar(stat = "identity",color = "black", width = 0.75) +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]) ,
fill = !!rlang::sym(names(meta)[3])),
shape = 21)+
ggplot2::ylab(names(meta)[4]) +
ggplot2::xlab(NULL) +
ggplot2::theme_test()
if (add == "mean_se"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - se, ymax = mean+se), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + se,label = label),vjust = -0.5)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
} else if (add == "mean_sd"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - sd, ymax = mean+sd), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + sd,label = label),vjust = -0.5)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
}
}
} else if (temp == "LSD2"){
if (type1 == 1){
if (type2 == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]),
fill = !!rlang::sym(names(meta)[3]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),position = ggplot2::position_dodge(0.8)) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),
shape = 21, position = ggplot2::position_jitterdodge(0.8/nrow(meta2)))+
ggplot2::geom_text(data = result, ggplot2::aes(x = !!rlang::sym(names(result)[2]),
y = !!rlang::sym("Max"),
label = !!rlang::sym("label"),
vjust = -0.5),
position = ggplot2::position_dodge(0.8)) +
ggplot2::theme_test() +
ggplot2::xlab(NULL)
} else if (type2 == 2){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[3]))) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])), shape = 21, width = 1/nrow(meta2)/2) +
ggplot2::scale_fill_discrete(guide = "none")+
ggplot2::theme_test() +
ggplot2::xlab(NULL)
compar = list(unique(meta[,2]))
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::geom_text(data = result, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym("Max"),
label = !!rlang::sym("label"),
vjust = -0.5)) + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
} else if (type1 == 2){
if (type2 == 1){
names(result)[13] = "mean"
p = ggplot2::ggplot(result, ggplot2:: aes(x = !!rlang::sym(names(result)[2]),
y = !!rlang::sym("mean"),
fill = !!rlang::sym(names(result)[1]))) +
ggplot2::geom_bar(stat = "identity",position = ggplot2::position_dodge(0.8), color = "black", width = 0.75) +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]) ,
fill = !!rlang::sym(names(meta)[3])),
shape = 21,
position = ggplot2::position_jitterdodge(0.8/nrow(result)))+
ggplot2::ylab(names(meta)[4]) +
ggplot2::xlab(NULL) +
ggplot2::theme_test()
if (add == "mean_se"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - se, ymax = mean+se), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + se,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
} else if (add == "mean_sd"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - std, ymax = mean+std), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + std,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
}
} else if (type2 == 2){
names(result)[13] = "mean"
p = ggplot2::ggplot(result, ggplot2:: aes(x = !!rlang::sym(names(result)[2]),
y = !!rlang::sym(names(result)[13]),
fill = !!rlang::sym(names(result)[1]))) +
ggplot2::geom_bar(stat = "identity",color = "black", width = 0.75) +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]) ,
fill = !!rlang::sym(names(meta)[3])),
shape = 21)+
ggplot2::ylab(names(meta)[4]) +
ggplot2::xlab(NULL) +
ggplot2::theme_test()
if (add == "mean_se"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - se, ymax = mean+se), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + se,label = label),vjust = -0.5)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
} else if (add == "mean_sd"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - std, ymax = mean+std), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + std,label = label),vjust = -0.5)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
}
}
} else if (temp == "HSD2"){
if (type1 == 1){
if (type2 == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]),
fill = !!rlang::sym(names(meta)[3]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),position = ggplot2::position_dodge(0.8)) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),
shape = 21, position = ggplot2::position_jitterdodge(0.8/nrow(meta2)))+
ggplot2::geom_text(data = result, ggplot2::aes(x = !!rlang::sym(names(result)[2]),
y = !!rlang::sym("Max"),
label = !!rlang::sym("label"),
vjust = -0.5),
position = ggplot2::position_dodge(0.8)) +
ggplot2::theme_test() +
ggplot2::xlab(NULL)
} else if (type2 == 2){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[3]))) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])), shape = 21, width = 1/nrow(meta2)/2) +
ggplot2::scale_fill_discrete(guide = "none")+
ggplot2::theme_test() +
ggplot2::xlab(NULL)
compar = list(unique(meta[,2]))
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::geom_text(data = result, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym("Max"),
label = !!rlang::sym("label"),
vjust = -0.5)) + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
} else if (type1 == 2){
if (type2 == 1){
names(result)[11] = "mean"
p = ggplot2::ggplot(result, ggplot2:: aes(x = !!rlang::sym(names(result)[2]),
y = !!rlang::sym("mean"),
fill = !!rlang::sym(names(result)[1]))) +
ggplot2::geom_bar(stat = "identity",position = ggplot2::position_dodge(0.8), color = "black", width = 0.75) +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]) ,
fill = !!rlang::sym(names(meta)[3])),
shape = 21,
position = ggplot2::position_jitterdodge(0.8/nrow(result)))+
ggplot2::ylab(names(meta)[4]) +
ggplot2::xlab(NULL) +
ggplot2::theme_test()
if (add == "mean_se"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - se, ymax = mean+se), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + se,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
} else if (add == "mean_sd"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - std, ymax = mean+std), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + std,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
}
} else if (type2 == 2){
names(result)[11] = "mean"
p = ggplot2::ggplot(result, ggplot2:: aes(x = !!rlang::sym(names(result)[2]),
y = !!rlang::sym(names(result)[11]),
fill = !!rlang::sym(names(result)[1]))) +
ggplot2::geom_bar(stat = "identity",color = "black", width = 0.75) +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]) ,
fill = !!rlang::sym(names(meta)[3])),
shape = 21)+
ggplot2::ylab(names(meta)[4]) +
ggplot2::xlab(NULL) +
ggplot2::theme_test()
if (add == "mean_se"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - se, ymax = mean+se), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + se,label = label),vjust = -0.5)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
} else if (add == "mean_sd"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - std, ymax = mean+std), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + std,label = label),vjust = -0.5)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
}
}
}
p
})
################################################################################
#' Visualizing nematode functional guild data (single factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{funguild-class}}
#' and is used to visualize the nematode functional guild data.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_nemindex() |> calc_funguild(Treatments) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{funguild-class}} object.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @aliases nem_plot,funguild-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem2(tab = nemtab, tax = nemtax, meta = nemmeta)
#' nem_plot <- nem |>
#' calc_nemindex() |>
#' calc_funguild(Treatments) |>
#' nem_plot()
#' nem_plot
setMethod("nem_plot", signature("funguild"), function(object){
# object = nem
meta = object@result
meta2 = stats::na.omit(meta)
p = ggplot2::ggplot(meta2, ggplot2::aes(x = !!rlang::sym("SI"), y = !!rlang::sym("EI"), color = !!rlang::sym(names(meta2)[2]))) +
ggplot2::geom_hline(yintercept = 50) +
ggplot2::geom_vline(xintercept = 50) +
ggplot2::ylim(0, 100) +
ggplot2::xlim(0, 100) +
ggplot2::geom_point() + ggplot2::theme_test() +
ggplot2::ylab("Enrichment Index") +
ggplot2::xlab("Structure Index")
p
})
################################################################################
#' Visualizing nematode functional guild data (two-factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{funguild2-class}}
#' and is used to visualize the nematode functional guild data.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_nemindex() |> calc_funguild2(con_crop, season) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{funguild2-class}} object.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @aliases nem_plot,funguild2-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem(tab = easynem_example("nemtab1.csv"),
#' tax = easynem_example("nemtax1.csv"),
#' meta = easynem_example("nemmeta1.csv"))
#' nem_plot <- nem |>
#' calc_nemindex() |>
#' calc_funguild2(con_crop, season) |>
#' nem_plot()
#' nem_plot
setMethod("nem_plot", signature("funguild2"), function(object){
# object = hehe2
meta = object@result
meta2 = stats::na.omit(meta)
p = ggplot2::ggplot(meta2, ggplot2::aes(x = !!rlang::sym("SI"), y = !!rlang::sym("EI"),
color = !!rlang::sym(names(meta2)[2]),
shape = !!rlang::sym(names(meta2)[3]))) +
ggplot2::geom_hline(yintercept = 50) +
ggplot2::geom_vline(xintercept = 50) +
ggplot2::ylim(0, 100) +
ggplot2::xlim(0, 100) +
ggplot2::geom_point() + ggplot2::theme_test() +
ggplot2::ylab("Enrichment Index") +
ggplot2::xlab("Structure Index")
p
})
################################################################################
#' Visualizing the metabolic footprint of nematode communities (single factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{mf-class}}
#' and is used to visualize the metabolic footprint of nematode communities. Metabolic
#' footprints quantify the amplitude of Carbon utilisation by different food web
#' components. The point in the middle of a rhombus represents the intersection
#' of EI and SI and length of vertical and horizontal axes of the rhombus corresponds
#' to the footprints of enrichment and structure components respectively.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_nemindex() |> calc_mf(Treatments) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{mf-class}} object.
#' @param kei Adjust the width of the diamond, default \code{kei = 1}.
#' @param ksi Adjust the length of the diamond, default \code{ksi = 1}.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @references
#' * <https://shiny.wur.nl/ninja/>
#' * Ferris, Howard. "Form and function: metabolic footprints of nematodes in the
#' soil food web." European Journal of Soil Biology 46.2 (2010): 97-104.
#'
#' @aliases nem_plot,mf-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem2(tab = nemtab, tax = nemtax, meta = nemmeta)
#' nem_plot <- nem |>
#' calc_nemindex() |>
#' calc_mf(Treatments) |>
#' nem_plot(kei = 30, ksi = 20)
#' nem_plot
setMethod("nem_plot", signature("mf"), function(object, kei = 1, ksi = 1){
# object = hehe2
# kei = 3
# ksi = 3
meta = object@result
meta2 = stats::na.omit(meta)
output = meta2 |> dplyr::group_by(!!rlang::sym(names(meta2)[2])) |>
dplyr::summarise(meanei = mean(EI), meansi = mean(SI), meanefp = mean(EnrichmentFootprint), meansfp = mean(StructureFootprint))
output$meanfufp = (output$meanefp * output$meansfp) / 2
output$meanfufp_ = (output$meanfufp/sum(output$meanfufp))*100
output$meanei0.5p = output$meanei + 0.5 * output$meanefp/kei
output$meanei0.5n = output$meanei - 0.5 * output$meanefp/kei
output$meansi0.5p = output$meansi + 0.5 * output$meansfp/ksi
output$meansi0.5n = output$meansi - 0.5 * output$meansfp/ksi
output1 = output[ , c(names(output)[1], "meanei", "meansi")]
output2 = output[ , c(names(output)[1], "meanei", "meansi0.5p")]
colnames(output2) = colnames(output1)
output3 = output[ , c(names(output)[1], "meanei", "meansi0.5n")]
colnames(output3) = colnames(output1)
output4 = output[ , c(names(output)[1], "meanei0.5p", "meansi")]
colnames(output4) = colnames(output1)
output5 = output[ , c(names(output)[1],"meanei0.5n", "meansi")]
colnames(output5) = colnames(output1)
zoutput = rbind(output1, output5, output3, output4, output2)
output6 = output[ , c(names(output)[1], "meanfufp_")]
output6$meanfufp_ = round(output6$meanfufp_, 2)
output6$meanfufp_ = as.character(output6$meanfufp_)
output6$meanfufp_ = paste0(output6$meanfufp_,"%")
zoutput = merge(zoutput, output6, by = names(zoutput)[1])
zoutput[[names(zoutput)[1]]] = paste(zoutput[[names(zoutput)[1]]], zoutput$meanfufp, sep = "_")
zoutputsub = zoutput |> dplyr::group_by(zoutput[[names(zoutput)[1]]]) |> dplyr::slice(-1)
# return(zoutputsub)
p <- ggplot2::ggplot(zoutput, ggplot2::aes(x = meansi, y = meanei, color = !!rlang::sym(names(zoutput)[1]))) + ggplot2::geom_point(size = 3) +
ggplot2::xlab("Structure index") + ggplot2::ylab("Enrichment index") + ggplot2::scale_x_continuous(expand = c(0, 0), breaks = c(0, 50, 100)) +
ggplot2::scale_y_continuous(expand = c(0, 0), breaks = c(0, 50, 100)) + ggplot2::theme_test() +
ggplot2::geom_hline(yintercept = c(0, 50, 100)) + ggplot2::geom_vline(xintercept = c(0, 50, 100)) + ggplot2::geom_polygon(data = zoutputsub, ggplot2::aes(x = meansi, y = meanei, fill = !!rlang::sym(names(zoutput)[1])), alpha = 0.2)
p
})
################################################################################
#' Visualizing the metabolic footprint of nematode communities (two-factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{mf2-class}}
#' and is used to visualize the metabolic footprint of nematode communities. Metabolic
#' footprints quantify the amplitude of Carbon utilisation by different food web
#' components. The point in the middle of a rhombus represents the intersection
#' of EI and SI and length of vertical and horizontal axes of the rhombus corresponds
#' to the footprints of enrichment and structure components respectively.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_nemindex() |> calc_mf2(con_crop, season) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{mf2-class}} object.
#' @param kei Adjust the width of the diamond, default \code{kei = 1}.
#' @param ksi Adjust the length of the diamond, default \code{ksi = 1}.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @references
#' * <https://shiny.wur.nl/ninja/>
#' * Ferris, Howard. "Form and function: metabolic footprints of nematodes in the
#' soil food web." European Journal of Soil Biology 46.2 (2010): 97-104.
#'
#' @aliases nem_plot,mf2-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem(tab = easynem_example("nemtab1.csv"),
#' tax = easynem_example("nemtax1.csv"),
#' meta = easynem_example("nemmeta1.csv"))
#' nem_plot <- nem |>
#' calc_nemindex() |>
#' calc_mf2(con_crop, season) |>
#' nem_plot(kei = 35, ksi = 35)
#' nem_plot
setMethod("nem_plot", signature("mf2"), function(object, kei = 1, ksi = 1){
# object = hehe2
# kei = 3
# ksi = 3
meta = object@result
meta2 = stats::na.omit(meta)
output = meta2 |> dplyr::group_by(!!rlang::sym(names(meta2)[2])) |>
dplyr::summarise(meanei = mean(EI), meansi = mean(SI), meanefp = mean(EnrichmentFootprint), meansfp = mean(StructureFootprint))
output$meanfufp = (output$meanefp * output$meansfp) / 2
output$meanfufp_ = (output$meanfufp/sum(output$meanfufp))*100
output$meanfufp_ = round(output$meanfufp_, 2)
output$meanfufp_ = as.character(output$meanfufp_)
output$meanfufp_ = paste0(output$meanfufp_,"%")
output1 = output$meanfufp_
names(output1) = output[[1]]
output = meta2 |> dplyr::group_by(!!rlang::sym(names(meta2)[3])) |>
dplyr::summarise(meanei = mean(EI), meansi = mean(SI), meanefp = mean(EnrichmentFootprint), meansfp = mean(StructureFootprint))
output$meanfufp = (output$meanefp * output$meansfp) / 2
output$meanfufp_ = (output$meanfufp/sum(output$meanfufp))*100
output$meanfufp_ = round(output$meanfufp_, 2)
output$meanfufp_ = as.character(output$meanfufp_)
output$meanfufp_ = paste0(output$meanfufp_,"%")
output2 = output$meanfufp_
names(output2) = output[[1]]
output = meta2 |> dplyr::group_by(!!rlang::sym(names(meta2)[2]), !!rlang::sym(names(meta2)[3])) |>
dplyr::summarise(meanei = mean(EI), meansi = mean(SI), meanefp = mean(EnrichmentFootprint), meansfp = mean(StructureFootprint))
output[[1]] <- sapply(output[[1]], function(x) {
if (x %in% names(output1)) {
paste0(x, "_", output1[x])
} else {
x
}
})
output[[2]] <- sapply(output[[2]], function(x) {
if (x %in% names(output2)) {
paste0(x, "_", output2[x])
} else {
x
}
})
output$meanei0.5p = output$meanei + 0.5 * output$meanefp/kei
output$meanei0.5n = output$meanei - 0.5 * output$meanefp/kei
output$meansi0.5p = output$meansi + 0.5 * output$meansfp/ksi
output$meansi0.5n = output$meansi - 0.5 * output$meansfp/ksi
output1 = output[ , c(names(output)[1], names(output)[2], "meanei", "meansi")]
output2 = output[ , c(names(output)[1], names(output)[2], "meanei", "meansi0.5p")]
colnames(output2) = colnames(output1)
output3 = output[ , c(names(output)[1], names(output)[2], "meanei", "meansi0.5n")]
colnames(output3) = colnames(output1)
output4 = output[ , c(names(output)[1], names(output)[2], "meanei0.5p", "meansi")]
colnames(output4) = colnames(output1)
output5 = output[ , c(names(output)[1],names(output)[2], "meanei0.5n", "meansi")]
colnames(output5) = colnames(output1)
zoutput = rbind(output1, output5, output3, output4, output2)
zoutputsub = zoutput |> dplyr::group_by(zoutput[[names(zoutput)[1]]], zoutput[[names(zoutput)[2]]]) |> dplyr::slice(-1)
# return(zoutputsub)
p <- ggplot2::ggplot(zoutput, ggplot2::aes(x = meansi, y = meanei, color = !!rlang::sym(names(zoutput)[1]), shape = !!rlang::sym(names(zoutput)[2]))) + ggplot2::geom_point(size = 3) +
ggplot2::xlab("Structure index") + ggplot2::ylab("Enrichment index") + ggplot2::scale_x_continuous(expand = c(0, 0), breaks = c(0, 50, 100)) +
ggplot2::scale_y_continuous(expand = c(0, 0), breaks = c(0, 50, 100)) + ggplot2::theme_test() +
ggplot2::geom_hline(yintercept = c(0, 50, 100)) + ggplot2::geom_vline(xintercept = c(0, 50, 100)) + ggplot2::geom_polygon(data = zoutputsub, ggplot2::aes(x = meansi, y = meanei, fill = !!rlang::sym(names(zoutput)[1])), alpha = 0.2)
p
})
################################################################################
#' Visualizing the energy structure of nematode communities (single factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{ef-class}}
#' and is used to visualize the energy structure of nematode communities. a five-node
#' food web was constructed with bacterivores, fungivores and herbivores receiving
#' energy from basal resources (R), omnivores-carnivores receiving energy from other
#' nodes. Numbers along the lines represented energy flux (ug C / 100 g dry soil / day).
#' The size of nodes corresponds to the fresh biomass (ug / 100 g dry soil).
#' Uniformity (U) of soil nematode energetic structure (unitless, mean ± standard error)
#' was calculated as the ratio of the mean of summed energy flux through each
#' energy channel to the standard deviation of these mean values.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_nemindex() |> calc_ef(Treatments) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{ef-class}} object.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @references
#' * Wan, Bingbing, et al. "Organic amendments increase the flow uniformity of
#' energy across nematode food webs." Soil Biology and Biochemistry 170 (2022): 108695.
#' * Ferris, H., 2010. Form and function: metabolic footprints of nematodes in the soil
#' food web. European Journal of Soil Biology 46, 97–104.
#' * Van Den Hoogen, Johan, et al. "Soil nematode abundance and functional group
#' composition at a global scale." Nature 572.7768 (2019): 194-198.
#' * Barnes, A.D., Jochum, M., Mumme, S., Haneda, N.F., Farajallah, A., Widarto, T.H.,
#' Brose, U., 2014. Consequences of tropical land use for multitrophic biodiversity and
#' ecosystem functioning. Nature Communications 5, 1–7.
#' * De Ruiter, P.C., Van Veen, J.A., Moore, J.C., Brussaard, L., Hunt, H.W., 1993. Calculation
#' of nitrogen mineralization in soil food webs. Plant and Soil 157, 263–273.
#'
#' @aliases nem_plot,ef-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem2(tab = nemtab, tax = nemtax, meta = nemmeta)
#' nem_plot <- nem |>
#' calc_nemindex() |>
#' calc_ef(Treatments) |>
#' nem_plot()
#' nem_plot
setMethod("nem_plot", signature("ef"), function(object){
# object = hehe
result = object@result
result = result[,-1]
result2 = result |>
dplyr::group_by(!!rlang::sym(names(result)[1])) |>
dplyr::summarise(dplyr::across(dplyr::everything(), \(x) mean(x, na.rm = TRUE)))
result3 = result |>
dplyr::group_by(!!rlang::sym(names(result)[1])) |>
dplyr::summarise(Use = stats::sd(U)/sqrt(dplyr::n()))
result4 = merge(result2,result3, by = names(result)[1])
result4_U = result4[,c(names(result4)[1], "U", "Use")]
result4_U$U = format(result4_U$U, digits = 2, nsmall = 2)
result4_U$Use = format(result4_U$Use, digits = 2, nsmall = 2)
result4_U = result4_U |> dplyr::mutate(U = paste0("U = ", U, " (", "\u00B1", Use, ")"))
result4_nodes = result4[,c(names(result4)[1], "OM", "BM", "HM", "FM")]
result4_nodes$R = 600
names(result4_nodes) = c(names(result4_nodes)[1], "Omnivores_carnivores", "Bacterivores", "Herbivores", "Fungivores", "Resources")
result4_nodes_long = reshape2::melt(result4_nodes,id.vars = names(result4_nodes)[1], variable.name = "Feeding", value.name = "Fresh_biomass")
result4_edges = result4[,c(names(result4)[1],"fbo", "fho", "ffo", "frb", "frh", "frf")]
result4_edges_long = reshape2::melt(result4_edges,id.vars = names(result4_nodes)[1], variable.name = "group", value.name = "Energy_flow")
result4_edges_long = result4_edges_long |>
dplyr::mutate(group = dplyr::case_when(
group == "fbo" ~ "Bacterivores-Omnivores_carnivores",
group == "fho" ~ "Herbivores-Omnivores_carnivores",
group == "ffo" ~ "Fungivores-Omnivores_carnivores",
group == "frb" ~ "Resources-Bacterivores",
group == "frh" ~ "Resources-Herbivores",
group == "frf" ~ "Resources-Fungivores",
TRUE ~ group # 保留其他未匹配的值
))
result4_edges_long = result4_edges_long |>
tidyr::separate(group, into = c("from1", "to1"), sep = "-")
result4_edges_long = result4_edges_long |> dplyr::mutate(from = paste0(!!rlang::sym("from1"), !!rlang::sym(names(result4)[1])))
result4_edges_long = result4_edges_long |> dplyr::mutate(to = paste0(!!rlang::sym("to1"), !!rlang::sym(names(result4)[1])))
result4_edges_long = result4_edges_long |> dplyr::select(!!rlang::sym("from"), !!rlang::sym("to"), dplyr::everything())
nodes <- data.frame(
id = c("Resources", "Bacterivores", "Herbivores", "Fungivores", "Omnivores_carnivores"),
Feeding = c("Resources", "Bacterivores", "Herbivores", "Fungivores", "Omnivores_carnivores")
)
# nodes = dplyr::bind_rows(replicate(nrow(result4), nodes, simplify = FALSE))
# nodes[[names(result4)[1]]] = sort(rep(result4[[1]], times = 5))
nodes = merge(nodes, result4_nodes_long, by = "Feeding")
nodes = nodes |> dplyr::mutate(id = paste0(!!rlang::sym("id"), !!rlang::sym(names(result4)[1])))
nodes$Feeding = factor(nodes$Feeding, levels = c("Bacterivores", "Fungivores", "Herbivores", "Resources", "Omnivores_carnivores"))
result4_U = result4_U[, -which(names(result4_U) == "Use")]
nodes = merge(nodes, result4_U, by = names(result)[1])
# nodes = merge(nodes, result4_nodes_long, by = c(names(result4)[1], "Feeding"), all = TRUE)
nodes = dplyr::select(nodes, id, dplyr::everything())
nodes$Fresh_biomass = round(nodes$Fresh_biomass,2)
nodes$label = nodes$Fresh_biomass
nodes = nodes |> dplyr::mutate(label = ifelse(Feeding == "Resources", "R", label))
order = rep(c("Bacterivores", "Fungivores", "Herbivores", "Omnivores_carnivores", "Resources"), times = nrow(nodes)/5)
positions <- integer(length(order))
used_indices <- logical(length(order))
for (i in 1:length(order)){
for(j in 1:length(order)){
if(order[i] == nodes[["Feeding"]][j] && !used_indices[j]){
positions[i] = j
used_indices[j] <- TRUE
break
}
}
}
nodes = nodes[positions,]
# edges <- data.frame(
# from = c("Resources", "Resources", "Resources", "Bacterivores", "Herbivores", "Fungivores"),
# to = c("Bacterivores", "Herbivores", "Fungivores", "Omnivores_carnivores", "Omnivores_carnivores", "Omnivores_carnivores"),
# group = c("Bacterivores", "Herbivores", "Fungivores", "Omnivores_carnivores", "Omnivores_carnivores", "Omnivores_carnivores")
# )
# edges = dplyr::bind_rows(replicate(nrow(result4), edges, simplify = FALSE))
# edges[[names(result4)[1]]] = sort(rep(result4[[1]], times = 6))
# edges = edges |> dplyr::mutate(from = paste0(!!rlang::sym("from"), !!rlang::sym(names(result4)[1])))
# edges = edges |> dplyr::mutate(to = paste0(!!rlang::sym("to"), !!rlang::sym(names(result4)[1])))
# edges = merge(edges, result4_U, by = names(result)[1])
# edges = merge(edges, result4_edges_long, by = c("from", "to"), all = TRUE)
edges = result4_edges_long
edges$Energy_flow = round(edges$Energy_flow, 2)
graph = tidygraph::tbl_graph(nodes = nodes, edges = edges, directed = TRUE, node_key = "id")
# graph <- igraph::graph_from_data_frame(edges, directed = FALSE, vertices = nodes)
layout_matrix <- matrix(c(
0, 3,
3, 0,
1.5, 1.5,
3, 3,
0, 0
), ncol = 2, byrow = TRUE)
formu = stats::as.formula(paste0("~", names(result4)[1]))
p3 = ggraph::ggraph(graph, layout = layout_matrix) +
ggraph::geom_edge_fan(ggplot2::aes(colour = to1, width = Energy_flow, label = Energy_flow), family = "sans", show.legend = FALSE) +
ggraph::scale_edge_width(range=c(0.5, 4)) +
ggplot2::geom_text(ggplot2::aes(x = 2, y = 0.5, label = U),family = "sans", data = nodes)+
ggraph::geom_node_point(ggplot2::aes(colour = Feeding, size = Fresh_biomass)) +
ggraph::geom_node_text(aes(label = label), repel = TRUE) +
ggraph::theme_graph(foreground = 'steelblue', fg_text_colour = 'white', base_family = "sans") +
ggplot2::scale_size(range = c(5,13)) +
ggplot2::facet_wrap(formu, scales = "free")
p3
})
################################################################################
#' Visualizing the energy structure of nematode communities (two-factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{ef2-class}}
#' and is used to visualize the energy structure of nematode communities. a five-node
#' food web was constructed with bacterivores, fungivores and herbivores receiving
#' energy from basal resources (R), omnivores-carnivores receiving energy from other
#' nodes. Numbers along the lines represented energy flux (ug C / 100 g dry soil / day).
#' The size of nodes corresponds to the fresh biomass (ug / 100 g dry soil).
#' Uniformity (U) of soil nematode energetic structure (unitless, mean ± standard error)
#' was calculated as the ratio of the mean of summed energy flux through each
#' energy channel to the standard deviation of these mean values.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_nemindex() |> calc_ef2(con_crop, season) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{ef2-class}} object.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @references
#' * Wan, Bingbing, et al. "Organic amendments increase the flow uniformity of
#' energy across nematode food webs." Soil Biology and Biochemistry 170 (2022): 108695.
#' * Ferris, H., 2010. Form and function: metabolic footprints of nematodes in the soil
#' food web. European Journal of Soil Biology 46, 97–104.
#' * Van Den Hoogen, Johan, et al. "Soil nematode abundance and functional group
#' composition at a global scale." Nature 572.7768 (2019): 194-198.
#' * Barnes, A.D., Jochum, M., Mumme, S., Haneda, N.F., Farajallah, A., Widarto, T.H.,
#' Brose, U., 2014. Consequences of tropical land use for multitrophic biodiversity and
#' ecosystem functioning. Nature Communications 5, 1–7.
#' * De Ruiter, P.C., Van Veen, J.A., Moore, J.C., Brussaard, L., Hunt, H.W., 1993. Calculation
#' of nitrogen mineralization in soil food webs. Plant and Soil 157, 263–273.
#'
#' @aliases nem_plot,ef2-method
#' @import ggplot2
#' @import igraph
#' @import ggraph
#' @import reshape2
#' @export
#' @examples
#' nem <- read_nem(tab = easynem_example("nemtab1.csv"),
#' tax = easynem_example("nemtax1.csv"),
#' meta = easynem_example("nemmeta1.csv"))
#' nem_plot <- nem |>
#' calc_nemindex() |>
#' calc_ef2(con_crop, season) |>
#' nem_plot()
#' nem_plot
setMethod("nem_plot", signature("ef2"), function(object){
# object = hehe
result = object@result
result = result[,-1]
result2 = result |>
dplyr::group_by(!!rlang::sym(names(result)[1]),!!rlang::sym(names(result)[2])) |>
dplyr::summarise(dplyr::across(dplyr::everything(), \(x) mean(x, na.rm = TRUE)))
result3 = result |>
dplyr::group_by(!!rlang::sym(names(result)[1]),!!rlang::sym(names(result)[2])) |>
dplyr::summarise(Use = stats::sd(U)/sqrt(dplyr::n()))
result4 = merge(result2,result3, by = c(names(result)[1], names(result)[2]))
result4_U = result4[,c(names(result4)[1], names(result4)[2], "U", "Use")]
result4_U$U = format(result4_U$U, digits = 2, nsmall = 2)
result4_U$Use = format(result4_U$Use, digits = 2, nsmall = 2)
result4_U = result4_U |> dplyr::mutate(U = paste0("U = ", U, " (", "\u00B1", Use, ")"))
result4_nodes = result4[,c(names(result4)[1], names(result4)[2], "OM", "BM", "HM", "FM")]
result4_nodes$R = 600
names(result4_nodes) = c(names(result4_nodes)[1], names(result4_nodes)[2], "Omnivores_carnivores", "Bacterivores", "Herbivores", "Fungivores", "Resources")
result4_nodes_long = reshape2::melt(result4_nodes,id.vars = c(names(result4_nodes)[1], names(result4_nodes)[2]), variable.name = "Feeding", value.name = "Fresh_biomass")
result4_edges = result4[,c(names(result4)[1],names(result4)[2], "fbo", "fho", "ffo", "frb", "frh", "frf")]
result4_edges_long = reshape2::melt(result4_edges,id.vars = c(names(result4_nodes)[1],names(result4_nodes)[2]), variable.name = "group", value.name = "Energy_flow")
result4_edges_long = result4_edges_long |>
dplyr::mutate(group = dplyr::case_when(
group == "fbo" ~ "Bacterivores-Omnivores_carnivores",
group == "fho" ~ "Herbivores-Omnivores_carnivores",
group == "ffo" ~ "Fungivores-Omnivores_carnivores",
group == "frb" ~ "Resources-Bacterivores",
group == "frh" ~ "Resources-Herbivores",
group == "frf" ~ "Resources-Fungivores",
TRUE ~ group # 保留其他未匹配的值
))
result4_edges_long = result4_edges_long |>
tidyr::separate(group, into = c("from1", "to1"), sep = "-")
result4_edges_long = result4_edges_long |> dplyr::mutate(from = paste0(!!rlang::sym("from1"), !!rlang::sym(names(result4)[1]), !!rlang::sym(names(result4)[2])))
result4_edges_long = result4_edges_long |> dplyr::mutate(to = paste0(!!rlang::sym("to1"), !!rlang::sym(names(result4)[1]), !!rlang::sym(names(result4)[2])))
result4_edges_long = result4_edges_long |> dplyr::select(!!rlang::sym("from"), !!rlang::sym("to"), dplyr::everything())
nodes <- data.frame(
id = c("Resources", "Bacterivores", "Herbivores", "Fungivores", "Omnivores_carnivores"),
Feeding = c("Resources", "Bacterivores", "Herbivores", "Fungivores", "Omnivores_carnivores")
)
# nodes = dplyr::bind_rows(replicate(nrow(result4), nodes, simplify = FALSE))
# nodes[[names(result4)[1]]] = sort(rep(result4[[1]], times = 5))
# nodes[[names(result4)[2]]] = rep(unique(result4[[2]]), times = length(unique(result4[[1]]))*5)
nodes = merge(nodes, result4_nodes_long, by = "Feeding")
nodes = nodes |> dplyr::mutate(id = paste0(!!rlang::sym("id"), !!rlang::sym(names(result4)[1]), !!rlang::sym(names(result4)[2])))
nodes$Feeding = factor(nodes$Feeding, levels = c("Bacterivores", "Fungivores", "Herbivores", "Resources", "Omnivores_carnivores"))
result4_U = result4_U[, -which(names(result4_U) == "Use")]
nodes = merge(nodes, result4_U, by = c(names(result)[1], names(result)[2]))
# nodes = merge(nodes, result4_nodes_long, by = c(names(result4)[1], names(result4)[2], "Feeding"), all = TRUE)
nodes = dplyr::select(nodes, id, dplyr::everything())
nodes$Fresh_biomass = round(nodes$Fresh_biomass,2)
nodes$label = nodes$Fresh_biomass
nodes = nodes |> dplyr::mutate(label = ifelse(Feeding == "Resources", "R", label))
order = rep(c("Bacterivores", "Fungivores", "Herbivores", "Omnivores_carnivores", "Resources"), times = nrow(nodes)/5)
positions <- integer(length(order))
used_indices <- logical(length(order))
for (i in 1:length(order)){
for(j in 1:length(order)){
if(order[i] == nodes[["Feeding"]][j] && !used_indices[j]){
positions[i] = j
used_indices[j] <- TRUE
break
}
}
}
nodes = nodes[positions,]
# edges = dplyr::bind_rows(replicate(nrow(result4), edges, simplify = FALSE))
# edges[[names(result4)[1]]] = sort(rep(result4[[1]], times = 6))
# edges[[names(result4)[2]]] = rep(unique(result4[[2]]), times = length(unique(result4[[1]]))*6)
# edges = edges |> dplyr::mutate(from = paste0(!!rlang::sym("from"), !!rlang::sym(names(result4)[1]), !!rlang::sym(names(result4)[2])))
# edges = edges |> dplyr::mutate(to = paste0(!!rlang::sym("to"), !!rlang::sym(names(result4)[1]), !!rlang::sym(names(result4)[2])))
# edges = merge(edges, result4_U, by = c(names(result)[1], names(result)[2]))
# edges = merge(edges, result4_edges_long, by = c("from", "to"), all = TRUE)
edges = result4_edges_long
edges$Energy_flow = round(edges$Energy_flow, 2)
graph = tidygraph::tbl_graph(nodes = nodes, edges = edges, directed = TRUE, node_key = "id")
# graph <- igraph::graph_from_data_frame(edges, directed = FALSE, vertices = nodes)
layout_matrix <- matrix(c(
0, 3,
3, 0,
1.5, 1.5,
3, 3,
0, 0
), ncol = 2, byrow = TRUE)
formu = stats::as.formula(paste0(names(result4)[2], "~", names(result4)[1]))
p3 = ggraph::ggraph(graph, layout = layout_matrix) +
ggraph::geom_edge_fan(ggplot2::aes(colour = to1, width = Energy_flow, label = Energy_flow), family = "sans", show.legend = FALSE) +
ggraph::scale_edge_width(range=c(0.5, 4)) +
ggplot2::geom_text(ggplot2::aes(x = 2, y = 0.5, label = U),family = "sans", data = nodes)+
ggraph::geom_node_point(ggplot2::aes(colour = Feeding, size = Fresh_biomass)) +
ggraph::geom_node_text(ggplot2::aes(label = label), repel = TRUE) +
ggraph::theme_graph(foreground = 'steelblue', fg_text_colour = 'white', base_family = "sans") +
ggplot2::scale_size(range = c(5,13)) +
ggplot2::facet_grid(formu, scales = "free")
p3
})
################################################################################
#' Visualizing the results of the ternary analysis (single factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{ter-class}}
#' and is used to visualize the results of the ternary analysis. This function
#' visualizes the distribution of nematode communities using the relative abundance
#' of nematodes of \code{cp1}, \code{cp2}, and \code{cp3-5} or the relative biomass
#' of herbivorous nematodes, bacterivorous nematodes, and fungivorous nematodes
#' as the three axes of a ternary plot.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_ter(Treatments) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{ter-class}} object.
#' @param type Visualize the nematodes by their \code{feeding} habits or by their \code{cp} values.
#' @param point_size Size of the points. Default is 1.
#' @param legend_cex Size of the legend text. Default is 0.9
#' @param ... Additional parameters passed to \code{Ternary::TernaryPlot()}.
#'
#' @return A \code{recordedplot} object from \code{Ternary::TernaryPlot}.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @references
#' * <https://shiny.wur.nl/ninja/>
#' * Goede, RGM de, T. Bongers, and C. H. Ettema. "Graphical presentation and
#' interpretation of nematode community structure: cp triangles." (1993): 743-750.
#'
#' @aliases nem_plot,ter-method
#' @import ggplot2
#' @import Ternary
#' @export
#' @examples
#' nem <- read_nem2(tab = nemtab, tax = nemtax, meta = nemmeta)
#' nem_plot <- nem |>
#' calc_ter(Treatments) |>
#' nem_plot(type = feeding)
#' nem_plot
#' nem <- read_nem2(tab = nemtab, tax = nemtax, meta = nemmeta)
#' nem_plot <- nem |>
#' calc_ter(Treatments) |>
#' nem_plot(type = cp)
#' nem_plot
setMethod("nem_plot", signature("ter"), function(object, type, point_size = 1, legend_cex = 0.9, ...){
# object = nem_index
type = deparse(substitute(type))
result = object@result
result = as.data.frame(result)
color_factor <- factor(result[[names(result)[2]]])
color_levels <- levels(color_factor)
color_palette <- grDevices::hcl.colors(length(color_levels), palette = "viridis")
color_map <- stats::setNames(color_palette, color_levels)
point_colors <- color_map[color_factor]
# opar <- par(no.readonly = TRUE)
# on.exit(par(opar))
# par(mar = c(1, 1, 1, 1))
if (type == "cp"){
Ternary::TernaryPlot(
alab = "cp1%\n(Enrichment)",
blab = "cp3-5%\n(Stability)",
clab = "cp2%\n(Stress)",
...
)
Ternary::TernaryPoints(
coordinates = result[, c("cp1% (Enrichment)", "cp3-5% (Stability)", "cp2% (Stress)")],
col = point_colors,
# pch = point_shapes,
cex = point_size
)
graphics::legend(
"topleft",
legend = names(color_map),
col = color_map,
pch = 16,
pt.cex = 1,
bty = "n",
cex = legend_cex
)
recorded_plot <- grDevices::recordPlot()
return(recorded_plot)
# base = ggtern::ggtern(data=result,ggplot2::aes(x=`cp1% (Enrichment)`,y=`cp3-5% (Stability)`,z=`cp2% (Stress)`))
# p3 = base +
# ggplot2::geom_point(ggplot2::aes(color=!!rlang::sym(names(result)[2]), shape = !!rlang::sym(names(result)[3]))) +
# ggtern::theme_rgbw()
# p4 = p3 + ggplot2::labs(x = "cp1%\n(Enrichment)", y = "cp3-5%\n(Stability)", z = "cp2%\n(Stress)")
} else if (type == "feeding") {
Ternary::TernaryPlot(
alab = "Herbivorous%",
blab = "Bacteria%",
clab = "Fungus%",
...
)
Ternary::TernaryPoints(
coordinates = result[, c("Herbivorous nematodes%", "Bacteria-feeding nematodes%", "Fungus-feeding nematodes%")],
col = point_colors,
# pch = point_shapes,
cex = point_size
)
graphics::legend(
"topleft",
legend = names(color_map),
col = color_map,
pch = 16,
pt.cex = 1,
bty = "n",
cex = legend_cex
)
recorded_plot <- grDevices::recordPlot()
return(recorded_plot)
# result = object@result
# base = ggtern::ggtern(data=result,ggplot2::aes(x=`Herbivorous nematodes%`,y=`Bacteria-feeding nematodes%`,z=`Fungus-feeding nematodes%`))
# p3 = base +
# ggplot2::geom_point(ggplot2::aes(color=!!rlang::sym(names(result)[2]), shape = !!rlang::sym(names(result)[3]))) +
# ggtern::theme_rgbw()
# p4 = p3 + ggplot2::labs(x = "Herbivorous%", y = "Bacteria%", z = "Fungus%")
}
# p4
})
################################################################################
#' Visualizing the results of the ternary analysis (two-factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{ter2-class}}
#' and is used to visualize the results of the ternary analysis. This function
#' visualizes the distribution of nematode communities using the relative abundance
#' of nematodes of \code{cp1}, \code{cp2}, and \code{cp3-5} or the relative biomass
#' of herbivorous nematodes, bacterivorous nematodes, and fungivorous nematodes
#' as the three axes of a ternary plot.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_ter2(con_crop, season) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{ter2-class}} object.
#' @param type Visualize the nematodes by their \code{feeding} habits or by their \code{cp} values.
#' @param point_size Size of the points. Default is 1.
#' @param legend_cex Size of the legend text. Default is 0.9
#' @param ... Additional parameters passed to \code{Ternary::TernaryPlot()}.
#'
#' @return A \code{recordedplot} object from \code{Ternary::TernaryPlot}.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @references
#' * <https://shiny.wur.nl/ninja/>
#' * Goede, RGM de, T. Bongers, and C. H. Ettema. "Graphical presentation and
#' interpretation of nematode community structure: cp triangles." (1993): 743-750.
#'
#' @aliases nem_plot,ter2-method
#' @import ggplot2
#' @import Ternary
#' @export
#' @examples
#' nem <- read_nem(tab = easynem_example("nemtab1.csv"),
#' tax = easynem_example("nemtax1.csv"),
#' meta = easynem_example("nemmeta1.csv"))
#' nem_plot <- nem |>
#' calc_ter2(con_crop, season) |>
#' nem_plot(type = feeding)
#' nem_plot
#' nem_plot <- nem |>
#' calc_ter2(con_crop, season) |>
#' nem_plot(type = cp)
#' nem_plot
setMethod("nem_plot", signature("ter2"), function(object, type, point_size = 1, legend_cex = 0.9, ...){
# object = nem_index
type = deparse(substitute(type))
result = object@result
result = as.data.frame(result)
color_factor <- factor(result[[names(result)[2]]])
color_levels <- levels(color_factor)
color_palette <- grDevices::hcl.colors(length(color_levels), palette = "viridis")
color_map <- stats::setNames(color_palette, color_levels)
point_colors <- color_map[color_factor]
shape_factor <- factor(result[[names(result)[3]]])
shape_levels <- levels(shape_factor)
shape_palette <- c(16, 17, 15, 18, 3, 4, 8, 0, 1, 2, 5, 6)[1:length(shape_levels)]
shape_map <- stats::setNames(shape_palette, shape_levels)
point_shapes <- shape_map[shape_factor]
# opar <- par(no.readonly = TRUE)
# on.exit(par(opar))
# par(mar = c(1, 1, 1, 1))
if (type == "cp"){
Ternary::TernaryPlot(
alab = "cp1%\n(Enrichment)",
blab = "cp3-5%\n(Stability)",
clab = "cp2%\n(Stress)",
...
)
Ternary::TernaryPoints(
coordinates = result[, c("cp1% (Enrichment)", "cp3-5% (Stability)", "cp2% (Stress)")],
col = point_colors,
pch = point_shapes,
cex = point_size
)
graphics::legend(
"topleft",
legend = names(color_map),
col = color_map,
pch = 16,
pt.cex = 1,
bty = "n",
cex = legend_cex
)
graphics::legend(
"topright",
legend = names(shape_map),
pch = shape_map,
col = "black",
pt.cex = 1,
bty = "n",
cex = legend_cex
)
recorded_plot <- grDevices::recordPlot()
return(recorded_plot)
# base = ggtern::ggtern(data=result,ggplot2::aes(x=`cp1% (Enrichment)`,y=`cp3-5% (Stability)`,z=`cp2% (Stress)`))
# p3 = base +
# ggplot2::geom_point(ggplot2::aes(color=!!rlang::sym(names(result)[2]), shape = !!rlang::sym(names(result)[3]))) +
# ggtern::theme_rgbw()
# p4 = p3 + ggplot2::labs(x = "cp1%\n(Enrichment)", y = "cp3-5%\n(Stability)", z = "cp2%\n(Stress)")
} else if (type == "feeding") {
Ternary::TernaryPlot(
alab = "Herbivorous%",
blab = "Bacteria%",
clab = "Fungus%",
...
)
Ternary::TernaryPoints(
coordinates = result[, c("Herbivorous nematodes%", "Bacteria-feeding nematodes%", "Fungus-feeding nematodes%")],
col = point_colors,
pch = point_shapes,
cex = point_size
)
graphics::legend(
"topleft",
legend = names(color_map),
col = color_map,
pch = 16,
pt.cex = 1,
bty = "n",
cex = legend_cex
)
graphics::legend(
"topright",
legend = names(shape_map),
pch = shape_map,
col = "black",
pt.cex = 1,
bty = "n",
cex = legend_cex
)
recorded_plot <- grDevices::recordPlot()
return(recorded_plot)
# result = object@result
# base = ggtern::ggtern(data=result,ggplot2::aes(x=`Herbivorous nematodes%`,y=`Bacteria-feeding nematodes%`,z=`Fungus-feeding nematodes%`))
# p3 = base +
# ggplot2::geom_point(ggplot2::aes(color=!!rlang::sym(names(result)[2]), shape = !!rlang::sym(names(result)[3]))) +
# ggtern::theme_rgbw()
# p4 = p3 + ggplot2::labs(x = "Herbivorous%", y = "Bacteria%", z = "Fungus%")
}
# p4
})
################################################################################
#' Visualizing the results of linear regression (single factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{lme-class}}
#' and is used to visualize the results of linear regression.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_lm(Treatments, Chao1, TotalBiomass) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{lme-class}} object.
#' @param ... Other parameters of \code{\link[ggpubr]{stat_cor}} function.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @aliases nem_plot,lme-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem2(tab = nemtab, tax = nemtax, meta = nemmeta)
#' nem_plot <- nem |>
#' calc_alpha() |>
#' calc_nemindex() |>
#' calc_lm(group = Treatments,
#' x = Chao1,
#' y = TotalBiomass) |>
#' nem_plot()
#' nem_plot
setMethod("nem_plot", signature("lme"), function(object, ...){
# object = nem_index
meta = object@meta
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[3]), y = !!rlang::sym(names(meta)[4])))+
ggplot2::geom_point(ggplot2::aes(colour = !!rlang::sym(names(meta)[2])))+
ggplot2::geom_smooth(method = "lm")+
ggpubr::stat_cor(...)+
ggplot2::theme_test() +
ggplot2::theme(text = ggplot2::element_text(colour='black'), axis.text=ggplot2::element_text(colour='black'))
return(p)
})
################################################################################
#' Visualizing the results of linear regression (two-factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{lme2-class}}
#' and is used to visualize the results of linear regression.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_lm2(con_crop, season, x = SOC, y = pH) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{lme2-class}} object.
#' @param ... Other parameters of \code{\link[ggpubr]{stat_cor}} function.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @aliases nem_plot,lme2-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem(tab = easynem_example("nemtab1.csv"),
#' tax = easynem_example("nemtax1.csv"),
#' meta = easynem_example("nemmeta1.csv"))
#' nem_lm <- nem |> calc_lm2(con_crop, season, x = pH, y = Fe) |> nem_plot()
#' nem_lm
setMethod("nem_plot", signature("lme2"), function(object, ...){
# object = nem_lm
meta = object@meta
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[4]), y = !!rlang::sym(names(meta)[5])))+
ggplot2::geom_point(ggplot2::aes(colour = !!rlang::sym(names(meta)[3]), shape = !!rlang::sym(names(meta)[2])))+
ggplot2::geom_smooth(method = "lm", ggplot2::aes(colour = !!rlang::sym(names(meta)[3]), fill = !!rlang::sym(names(meta)[3])), alpha = 0.1)+
ggpubr::stat_cor(ggplot2::aes(colour = !!rlang::sym(names(meta)[3])), ...)+
ggplot2::theme_test() +
ggplot2::theme(text = ggplot2::element_text(colour='black'), axis.text=ggplot2::element_text(colour='black'))
return(p)
})
################################################################################
Try the easynem package in your browser
Any scripts or data that you put into this service are public.
easynem documentation built on Nov. 5, 2025, 7:39 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
################################################################################
#' Visualize the results of the calculation
#'
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_beta(pca, Treatments, method = "bray") |> nem_plot()
#' ```
#'
#' @param object \code{\link{beta-class}} or other types data (\code{\link{beta2-class}},
#' \code{\link{compare-class}}, \code{\link{compare2-class}}, \code{\link{ef-class}},
#' \code{\link{ef2-class}}, \code{\link{funguild-class}}, \code{\link{funguild2-class}},
#' \code{\link{mf-class}}, \code{\link{mf2-class}}, \code{\link{ter-class}},
#' \code{\link{ter2-class}}, etc.).
#' @param ... Other parameters to be expanded.
#'
#' @return A plot object. Typically a \code{ggplot} object for most classes,
#' or a \code{recordedplot} object for \code{\link{ter-class}} and
#' \code{\link{ter2-class}} ternary plots.
#'
#' @export
setGeneric("nem_plot", function(object, ...){
standardGeneric("nem_plot")
})
################################################################################
#' Visualization of beta diversity results (single factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{beta-class}}
#' and is used to visualize the single-factor beta diversity results.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_beta(pca, Treatments, method = "bray") |> nem_plot()
#' ```
#'
#' @param object A \code{\link{beta-class}} object.
#' @param level Used to adjust the size of the confidence ellipse. Default
#' \code{level = 0.6}. See \code{\link[ggplot2]{stat_ellipse}}.
#' @param type Method used to adjust the display of scatter area. \code{type = 1},
#' displays as a confidence ellipse; \code{type = 2}, displays as a polygon.
#' Default \code{type = 1}.
#' @param ... Other parameters to be expanded.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @aliases nem_plot,beta-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem2(tab = nemtab, tax = nemtax, meta = nemmeta)
#' nem_plot <- nem |>
#' calc_beta(pcoa, Treatments, method = "bray") |>
#' nem_plot(level = 0)
#' nem_plot
#' nem_plot <- nem |>
#' calc_beta(nmds, Treatments, method = "bray") |>
#' nem_plot(type = 2)
#' nem_plot
setMethod("nem_plot", signature("beta"), function(object, level = 0.6, type = 1, ...){
meta = object@meta
result = object@result
temp = object@temp
if (length(temp) == 1){
NMDS1 = meta[,3]
NMDS2 = meta[,4]
group = meta[,2]
p = ggplot2::ggplot(meta,ggplot2::aes(x=NMDS1,y=NMDS2,color=!!dplyr::sym(colnames(group))))+
ggplot2::labs(x=colnames(NMDS1),y = colnames(NMDS2), title=temp)+ggplot2::geom_point(size=4)+
ggplot2::theme(legend.title = ggplot2::element_blank())+
ggplot2::theme_test() + ggplot2::coord_fixed(1)
if (level > 0){
if (type == 1){
p = p+ggplot2::stat_ellipse(level=level)
} else if(type == 2){
p = p+geom_encircle(ggplot2::aes(fill=!!dplyr::sym(colnames(group))), alpha = 0.1, show.legend = F)
} else {
stop("Type value must be 1 or 2.")
}
} else if (level == 0){
p
} else {
stop("Invalid level value.")
}
} else {
PCoA1 = meta[,3]
PCoA2 = meta[,4]
group = meta[,2]
if (colnames(PCoA1)=="PCoA1"){
p = ggplot2::ggplot(meta,ggplot2::aes(x=PCoA1,y=PCoA2,color=!!dplyr::sym(colnames(group))))
} else {
PC1 = meta[,3]
PC2 = meta[,4]
group = meta[,2]
p = ggplot2::ggplot(meta,ggplot2::aes(x=PC1,y=PC2,color=!!dplyr::sym(colnames(group))))
}
p = p+ggplot2::labs(x = temp[1], y=temp[2], title=temp[3])+ggplot2::geom_point(size=4)+
ggplot2::theme(legend.title = ggplot2::element_blank())+
ggplot2::theme_test() + ggplot2::coord_fixed(1)
if (level > 0){
if (type == 1){
p = p+ggplot2::stat_ellipse(level=level)
} else if(type == 2){
p = p+geom_encircle(ggplot2::aes(fill=!!dplyr::sym(colnames(group))), alpha = 0.1, show.legend = F)
} else {
stop("Type value must be 1 or 2.")
}
} else if (level == 0){
p
} else {
stop("Invalid level value.")
}
}
p = p + ggplot2::geom_vline(xintercept = 0, linetype = "dashed") + ggplot2::geom_hline(yintercept = 0, linetype = "dashed")
})
################################################################################
#' Visualization of beta diversity results (two-factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{beta2-class}}
#' and is used to visualize the two-factor beta diversity results.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_beta2(pca, con_crop, season, method = "bray") |> nem_plot()
#' ```
#'
#' @param object A \code{\link{beta2-class}} object.
#' @param level Used to adjust the size of the confidence ellipse. Default
#' \code{level = 0.6}. See \code{\link[ggplot2]{stat_ellipse}}.
#' @param type Method used to adjust the display of scatter area. \code{type = 1},
#' displays as a confidence ellipse; \code{type = 2}, displays as a polygon.
#' Default \code{type = 1}.
#' @param ... Other parameters to be expanded.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @aliases nem_plot,beta2-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem(tab = easynem_example("nemtab1.csv"),
#' tax = easynem_example("nemtax1.csv"),
#' meta = easynem_example("nemmeta1.csv"))
#' nem_plot <- nem |>
#' calc_beta2(pcoa, con_crop, season, method = "bray") |>
#' nem_plot(level = 0)
#' nem_plot
#' nem_plot <- nem |>
#' calc_beta2(nmds, con_crop, season, method = "bray") |>
#' nem_plot(type = 2)
#' nem_plot
setMethod("nem_plot", signature("beta2"), function(object, level = 0.6, type = 1, ...){
meta = object@meta
result = object@result
temp = object@temp
if (length(temp) == 1){
NMDS1 = meta[,5]
NMDS2 = meta[,6]
group1 = meta[,2]
group2 = meta[,3]
group3 = meta[,4]
p = ggplot2::ggplot(meta,ggplot2::aes(x=NMDS1,y=NMDS2,color=!!dplyr::sym(colnames(group1)), shape=!!dplyr::sym(colnames(group2))))+
ggplot2::labs(x=colnames(NMDS1),y = colnames(NMDS2), title=temp)+ggplot2::geom_point(size=4)+
ggplot2::theme(legend.title = ggplot2::element_blank())+
ggplot2::theme_test() + ggplot2::coord_fixed(1)
if (level > 0){
if (type == 1){
p = p+ggplot2::stat_ellipse(level=level)
} else if(type == 2){
p = p+geom_encircle(ggplot2::aes(fill=!!dplyr::sym(colnames(group3))), alpha = 0.1, show.legend = F)
} else {
stop("Type value must be 1 or 2.")
}
} else if (level == 0){
p
} else {
stop("Invalid level value.")
}
} else {
PCoA1 = meta[,5]
PCoA2 = meta[,6]
group1 = meta[,2]
group2 = meta[,3]
group3 = meta[,4]
if (colnames(PCoA1)=="PCoA1"){
p = ggplot2::ggplot(meta,ggplot2::aes(x=PCoA1,y=PCoA2,color=!!dplyr::sym(colnames(group1)),shape=!!dplyr::sym(colnames(group2))))
} else {
PC1 = meta[,5]
PC2 = meta[,6]
group1 = meta[,2]
group2 = meta[,3]
group3 = meta[,4]
p = ggplot2::ggplot(meta,ggplot2::aes(x=PC1,y=PC2,color=!!dplyr::sym(colnames(group1)),shape=!!dplyr::sym(colnames(group2))))
}
p = p+ggplot2::labs(x = temp[1], y=temp[2], title=temp[3])+ggplot2::geom_point(size=4)+
ggplot2::theme(legend.title = ggplot2::element_blank())+
ggplot2::theme_test() + ggplot2::coord_fixed(1)
if (level > 0){
if (type == 1){
p = p+ggplot2::stat_ellipse(level=level)
} else if(type == 2){
p = p+geom_encircle(ggplot2::aes(fill=!!dplyr::sym(colnames(group3))), alpha = 0.1, show.legend = F)
} else {
stop("Type value must be 1 or 2.")
}
} else if (level == 0){
p
} else {
stop("Invalid level value.")
}
}
p = p + ggplot2::geom_vline(xintercept = 0, linetype = "dashed") + ggplot2::geom_hline(yintercept = 0, linetype = "dashed")
})
################################################################################
#' Visualizing the results of multiple comparisons (single factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{compare-class}}
#' and is used to visualize the results of single-factor multiple comparisons.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_compare(.group = con_crop, y = pH, method = LSD) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{compare-class}} object.
#' @param type \code{type = 1}, draws a box plot; \code{type = 2} draws a bar plot.
#' Default \code{type = 1}.
#' @param add Add standard deviation or standard error (only used when drawing a
#' bar plot).
#' @param ... Other parameters to be expanded.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @aliases nem_plot,compare-method
#' @import ggplot2
#' @import multcompView
#' @export
#' @examples
#' nem <- read_nem2(tab = nemtab, tax = nemtax, meta = nemmeta)
#' nem_plot <- nem |> calc_compare(.group = Treatments,
#' y = Mesorhabditis,
#' method = LSD) |>
#' nem_plot()
#' nem_plot
#' nem_plot <- nem |> calc_compare(.group = Treatments,
#' y = Mesorhabditis,
#' method = HSD) |>
#' nem_plot(type = 2, add = "mean_se")
#' nem_plot
setMethod("nem_plot", signature("compare"), function(object, type = 1, add, ...){
meta = object@meta
temp = object@temp
if (temp == "TTest") {
value = meta[,3]
group = meta[,2]
if(type == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = group, y = value)) +
ggplot2::geom_boxplot(ggplot2::aes(fill = group), width = 0.5) +
ggplot2::geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none") +
ggplot2::theme_test() +
ggplot2::xlab(NULL) +
ggplot2::ylab(colnames(meta)[3])
compar = list(unique(meta[,2]))
p = p + ggpubr::stat_compare_means(comparisons = compar, method = "t.test", label = "p.signif")
} else if (type == 2){
compar = list(unique(meta[,2]))
if (add == "mean_se"){
p = ggpubr::ggbarplot(meta, x = colnames(meta)[2], y = colnames(meta)[3], fill = colnames(meta)[2], width = 0.5, add = "mean_se") +
ggpubr::stat_compare_means(comparisons = compar, method = "t.test", label = "p.signif") + ggplot2::xlab(NULL)+
ggplot2::theme_test() +
ggplot2::geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none")
} else if (add == "mean_sd"){
p = ggpubr::ggbarplot(meta, x = colnames(meta)[2], y = colnames(meta)[3], fill = colnames(meta)[2], width = 0.5, add = "mean_sd") +
ggpubr::stat_compare_means(comparisons = compar, method = "t.test", label = "p.signif") + ggplot2::xlab(NULL)+
ggplot2::theme_test() +
ggplot2::geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none")
}
}
} else if (temp == "WilcoxTest") {
value = meta[,3]
group = meta[,2]
if(type == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = group, y = value)) +
ggplot2::geom_boxplot(ggplot2::aes(fill = group), width = 0.5) +
ggplot2::geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none") +
ggplot2::theme_test() +
ggplot2::xlab(NULL) +
ggplot2::ylab(colnames(meta)[3])
compar = list(unique(meta[,2]))
p = p + ggpubr::stat_compare_means(comparisons = compar, method = "wilcox.test", label = "p.signif")
} else if (type == 2){
compar = list(unique(meta[,2]))
if (add == "mean_se"){
p = ggpubr::ggbarplot(meta, x = colnames(meta)[2], y = colnames(meta)[3], fill = colnames(meta)[2], width = 0.5, add = "mean_se") +
ggpubr::stat_compare_means(comparisons = compar, method = "wilcox.test", label = "p.signif") + ggplot2::xlab(NULL)+
ggplot2::theme_test() +
ggplot2::geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none")
} else if (add == "mean_sd"){
p = ggpubr::ggbarplot(meta, x = colnames(meta)[2], y = colnames(meta)[3], fill = colnames(meta)[2], width = 0.5, add = "mean_sd") +
ggpubr::stat_compare_means(comparisons = compar, method = "wilcox.test", label = "p.signif") + ggplot2::xlab(NULL)+
ggplot2::theme_test() +
ggplot2::geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none")
}
}
} else if (temp == "KruskalTest") {
value = meta[,3]
group = meta[,2]
wmc = object@result$wmc
wmc = dplyr::select(dplyr::select(dplyr::mutate(wmc, names = paste(Group.1, Group.2, sep = "-")), names, everything()), -Group.1, -Group.2)
significant = wmc[,3]
names(significant) = wmc$names
dif = multcompView::multcompLetters(significant)
dif2 = as.data.frame(dif$monospacedLetters)
colnames(dif2) <- "label"
dif2$label <- gsub(" ", "", dif2$label)
dif2$group <- row.names(dif2)
if(type == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = group, y = value)) +
ggplot2::geom_boxplot(ggplot2::aes(fill = group), width = 0.5) +
ggplot2:: geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none") +
ggplot2::theme_test() +
ggplot2::xlab(NULL) +
ggplot2::ylab(colnames(meta)[3])
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(max = max(.data[[colnames(meta)[3]]]))
all = merge(dif2, meta2, by.x = "group", by.y = colnames(meta)[2])
p = p + ggplot2::geom_text(data = all, ggplot2::aes(x = group, y = max, label = label), vjust = -0.5)
} else if (type == 2){
if (add == "mean_se") {
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(mean = mean(!!rlang::sym(colnames(meta)[3])), er = stats::sd(!!rlang::sym(colnames(meta)[3]))/dplyr::n())
} else if (add == "mean_sd") {
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(mean = mean(!!rlang::sym(colnames(meta)[3])), er = stats::sd(!!rlang::sym(colnames(meta)[3])))
}
all = merge(dif2, meta2, by.x = "group", by.y = colnames(meta)[2])
colnames(all)[3] = colnames(meta)[3]
p = ggpubr::ggbarplot(meta, x = colnames(meta)[2], y = colnames(meta)[3], fill = colnames(meta)[2], width = 0.5, add = "mean") +
ggplot2::xlab(NULL)+
ggplot2::theme_test() +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(colnames(meta)[2])), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none")
all$mean = all[,3]
p = p + ggplot2::geom_errorbar(data = all, ggplot2::aes(x = group, y = mean, ymin = mean-er, ymax =mean+er), width = .2) +
ggplot2::geom_text(data = all, ggplot2::aes(x = group, y = mean + er, label = label), vjust = -0.5)
}
} else if (temp == "LSD") {
value = meta[,3]
group = meta[,2]
meta1 = object@result$LSD$means[,-1]
meta1$group = rownames(meta1)
meta2 = object@result$LSD$groups
meta2$group = rownames(meta2)
meta_all = merge(meta1,meta2,by = "group")
if (type == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = group, y = value)) +
ggplot2::geom_boxplot(ggplot2::aes(fill = group), width = 0.5) +
ggplot2:: geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none") +
ggplot2::theme_test() +
ggplot2::xlab(NULL) +
ggplot2::ylab(colnames(meta)[3])
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(max = max(.data[[colnames(meta)[3]]]))
all = merge(meta_all, meta2, by.x = "group", by.y = colnames(meta)[2])
all$mean = all[[colnames(meta)[3]]]
p = p + ggplot2::geom_text(data = all, ggplot2::aes(x = group, y = max, label = groups), vjust = -0.5)
} else if (type == 2){
if (add == "mean_se") {
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(mean = mean(!!rlang::sym(colnames(meta)[3])), er = stats::sd(!!rlang::sym(colnames(meta)[3]))/dplyr::n())
} else if (add == "mean_sd") {
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(mean = mean(!!rlang::sym(colnames(meta)[3])), er = stats::sd(!!rlang::sym(colnames(meta)[3])))
}
p = ggpubr::ggbarplot(meta, x = colnames(meta)[2], y = colnames(meta)[3], fill = colnames(meta)[2], width = 0.5, add = "mean") +
ggplot2::xlab(NULL)+
ggplot2::theme_test() +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(colnames(meta)[2])), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none")
all = merge(meta_all, meta2, by.x = "group", by.y = colnames(meta)[2])
all$mean = all[[colnames(meta)[3]]]
p = p + ggplot2::geom_errorbar(data = all, ggplot2::aes(x = group, y = mean, ymin = mean-er, ymax =mean+er), width = .2) +
ggplot2::geom_text(data = all, ggplot2::aes(x = group, y = mean + er, label = groups), vjust = -0.5)
}
} else if (temp == "HSD") {
value = meta[,3]
group = meta[,2]
wmc = as.data.frame(object@result$HSD[[colnames(object@meta)[2]]])
significant = wmc[,4]
names(significant) = rownames(wmc)
dif = multcompView::multcompLetters(significant)
dif2 = as.data.frame(dif$Letters)
colnames(dif2) <- "label"
dif2$group <- rownames(dif2)
if(type == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = group, y = value)) +
ggplot2::geom_boxplot(ggplot2::aes(fill = group), width = 0.5) +
ggplot2:: geom_jitter(ggplot2::aes(fill = group), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none") +
ggplot2::theme_test() +
ggplot2::xlab(NULL) +
ggplot2::ylab(colnames(meta)[3])
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(max = max(.data[[colnames(meta)[3]]]))
all = merge(dif2, meta2, by.x = "group", by.y = colnames(meta)[2])
p = p + ggplot2::geom_text(data = all, ggplot2::aes(x = group, y = max, label = label), vjust = -0.5)
} else if (type == 2){
if (add == "mean_se") {
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(mean = mean(!!rlang::sym(colnames(meta)[3])), er = stats::sd(!!rlang::sym(colnames(meta)[3]))/dplyr::n())
} else if (add == "mean_sd") {
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2])) |> dplyr::summarise(mean = mean(!!rlang::sym(colnames(meta)[3])), er = stats::sd(!!rlang::sym(colnames(meta)[3])))
}
all = merge(dif2, meta2, by.x = "group", by.y = colnames(meta)[2])
colnames(all)[3] = colnames(meta)[3]
p = ggpubr::ggbarplot(meta, x = colnames(meta)[2], y = colnames(meta)[3], fill = colnames(meta)[2], width = 0.5, add = "mean") +
ggplot2::xlab(NULL)+
ggplot2::theme_test() +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(colnames(meta)[2])), shape = 21, width = 1/length(unique(meta[,2]))) +
ggplot2::scale_fill_discrete(guide = "none")
all$mean = all[,3]
p = p + ggplot2::geom_errorbar(data = all, ggplot2::aes(x = group, y = mean, ymin = mean-er, ymax =mean+er), width = .2) +
ggplot2::geom_text(data = all, ggplot2::aes(x = group, y = mean + er, label = label), vjust = -0.5)
}
}
p
})
################################################################################
#' Visualizing the results of multiple comparisons (two-factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{compare2-class}}
#' and is used to visualize the results of two-factor multiple comparisons.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_compare2(.group1 = con_crop, .group2 = season, y = pH, method = LSD) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{compare2-class}} object.
#' @param type1 \code{type1 = 1}, draws a box plot; \code{type1 = 2}, draws a bar plot.
#' Default \code{type1 = 1}.
#' @param type2 \code{type2 = 1}, draw a cluster plot; \code{type2 = 2}, draws faceted plot.
#' Default \code{type2 = 1}.
#' @param add Add standard deviation or standard error (only used when drawing a
#' bar plot).
#' @param ... Other parameters to be expanded.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @aliases nem_plot,compare2-method
#' @import ggplot2
#' @import multcompView
#' @import ggpubr
#' @importFrom stats sd
#' @export
#' @examples
#' nem <- read_nem(tab = easynem_example("nemtab1.csv"),
#' tax = easynem_example("nemtax1.csv"),
#' meta = easynem_example("nemmeta1.csv"))
#' nem_plot <- nem |> calc_compare2(.group1 = con_crop,
#' .group2 = season, y = pH, method = LSD2) |>
#' nem_plot(type2 = 2)
#' nem_plot
#' nem_plot <- nem |> calc_compare2(.group1 = con_crop,
#' .group2 = season, y = pH, method = HSD2) |>
#' nem_plot(type1 = 2, type2 = 2, add = "mean_sd")
#' nem_plot
setMethod("nem_plot", signature("compare2"), function(object, type1 = 1, type2 = 1, add, ...){
meta = object@meta
meta2 = dplyr::group_by(meta, !!rlang::sym(colnames(meta)[2]), !!rlang::sym(colnames(meta)[3])) |>
dplyr::summarise(max = max(.data[[colnames(meta)[4]]]),
mean = mean(!!rlang::sym(colnames(meta)[4])),
se = stats::sd(!!rlang::sym(colnames(meta)[4]))/dplyr::n(),
sd = stats::sd(!!rlang::sym(colnames(meta)[4])))
temp = object@temp
result = object@result
if(temp == "TTest2"){
for (i in 1:nrow(result)){
# i = 2
sig = result[i,c(1,6,12)]
significant = as.data.frame(sig)[,2]
names(significant) = sig$group
dif = multcompView::multcompLetters(significant)
dif2 = as.data.frame(dif$Letters)
colnames(dif2) <- "label"
dif2$label <- gsub(" ", "", dif2$label)
dif2[[colnames(meta)[2]]] = row.names(dif2)
dif2[[colnames(meta)[3]]] = sig[[colnames(meta)[3]]]
meta2 = dplyr::left_join(meta2, dif2, by = c(colnames(meta)[2], colnames(meta)[3]))
}
label_cols <- grep("^label\\.", colnames(meta2), value = TRUE)
meta2$label <- apply(meta2[label_cols], 1, function(row) {
paste(stats::na.omit(row), collapse = "")
})
meta2 <- meta2[, !colnames(meta2) %in% label_cols]
if (type1 == 1){
if (type2 == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]),
fill = !!rlang::sym(names(meta)[3]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),position = ggplot2::position_dodge(0.8)) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),
shape = 21, position = ggplot2::position_jitterdodge(0.8/nrow(meta2)))+
ggplot2::geom_text(data = meta2, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym(names(meta2)[3]),
label = !!rlang::sym(names(meta2)[7]),
vjust = -0.5),
position = ggplot2::position_dodge(0.8)) +
ggplot2::theme_test() +
ggplot2::xlab(NULL)
} else if (type2 == 2){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[2]))) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[2])), shape = 21, width = 1/nrow(meta2)/2) +
ggplot2::scale_fill_discrete(guide = "none")+
ggplot2::theme_test() +
ggplot2::xlab(NULL)
compar = list(unique(meta[,2]))
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggpubr::stat_compare_means(comparisons = compar, method = "t.test", label = "p.signif", ...) + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
} else if (type1 == 2){
if (type2 == 1){
p = ggplot2::ggplot(meta2, ggplot2:: aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym(names(meta2)[4]),
fill = !!rlang::sym(names(meta2)[2]))) +
ggplot2::geom_bar(stat = "identity",position = ggplot2::position_dodge(0.8), color = "black", width = 0.75) +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]) ,
fill = !!rlang::sym(names(meta)[3])),
shape = 21,
position = ggplot2::position_jitterdodge(0.8/nrow(meta2)))+
ggplot2::ylab(names(meta)[4]) +
ggplot2::xlab(NULL) +
ggplot2::theme_test()
if (add == "mean_se"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - se, ymax = mean+se), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + se,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
} else if (add == "mean_sd"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - sd, ymax = mean+sd), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + sd,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
}
} else if (type2 == 2){
formu = stats::as.formula(paste0(names(meta)[4], "~", names(meta)[2]))
stat.test = ggpubr::compare_means(formu, data = meta, group.by = names(meta)[3], method = "t.test", ...)
meta3 = meta2 |> dplyr::group_by(!!rlang::sym(names(meta2)[2])) |> dplyr::summarise(p.position = max(max))
stat.test = merge(stat.test, meta3, by = names(meta2)[2])
if (add == "mean_se"){
p = ggplot2::ggplot(meta2, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]), y = !!rlang::sym(names(meta2)[4]), fill = names(meta[2]))) +
ggplot2::geom_bar(stat = "identity",ggplot2::aes(fill = !!rlang::sym(names(meta2)[2])), color = "black",width = 0.75) +
ggplot2::geom_errorbar(ggplot2::aes(ymin = mean-se, ymax = mean+se), width = 0.75/4)+
ggplot2::theme_test() +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),y = !!rlang::sym(names(meta)[4]), fill = !!rlang::sym(names(meta)[3])), shape = 21, width = 0.75/4) +
ggplot2::scale_fill_discrete(guide = "none") +
ggpubr::stat_pvalue_manual(
stat.test, label = "p.signif", y.position = "p.position"
) +
ggplot2::xlab(NULL)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
} else if (add == "mean_sd"){
p = ggplot2::ggplot(meta2, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]), y = !!rlang::sym(names(meta2)[4]), fill = names(meta[2]))) +
ggplot2::geom_bar(stat = "identity",ggplot2::aes(fill = !!rlang::sym(names(meta2)[2])), color = "black",width = 0.75) +
ggplot2::geom_errorbar(ggplot2::aes(ymin = mean-sd, ymax = mean+sd), width = 0.75/4)+
ggplot2::theme_test() +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),y = !!rlang::sym(names(meta)[4]), fill = !!rlang::sym(names(meta)[3])), shape = 21, width = 0.75/4) +
ggplot2::scale_fill_discrete(guide = "none") +
ggpubr::stat_pvalue_manual(
stat.test, label = "p.signif", y.position = "p.position"
) +
ggplot2::xlab(NULL)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
}
}
} else if (temp == "WilcoxTest2"){
for (i in 1:nrow(result)){
# i = 2
sig = result[i,c(1,3,6)]
significant = as.data.frame(sig)[,2]
names(significant) = sig$group
dif = multcompView::multcompLetters(significant)
dif2 = as.data.frame(dif$Letters)
colnames(dif2) <- "label"
dif2$label <- gsub(" ", "", dif2$label)
dif2[[colnames(meta)[2]]] = row.names(dif2)
dif2[[colnames(meta)[3]]] = sig[[colnames(meta)[3]]]
meta2 = dplyr::left_join(meta2, dif2, by = c(colnames(meta)[2], colnames(meta)[3]))
}
label_cols <- grep("^label\\.", colnames(meta2), value = TRUE)
meta2$label <- apply(meta2[label_cols], 1, function(row) {
paste(stats::na.omit(row), collapse = "")
})
meta2 <- meta2[, !colnames(meta2) %in% label_cols]
if (type1 == 1){
if (type2 == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]),
fill = !!rlang::sym(names(meta)[3]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),position = ggplot2::position_dodge(0.8)) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),
shape = 21, position = ggplot2::position_jitterdodge(0.8/nrow(meta2)))+
ggplot2::geom_text(data = meta2, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym(names(meta2)[3]),
label = !!rlang::sym(names(meta2)[7]),
vjust = -0.5),
position = ggplot2::position_dodge(0.8)) +
ggplot2::theme_test() +
ggplot2::xlab(NULL)
} else if (type2 == 2){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[2]))) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[2])), shape = 21, width = 1/nrow(meta2)/2) +
ggplot2::scale_fill_discrete(guide = "none")+
ggplot2::theme_test() +
ggplot2::xlab(NULL)
compar = list(unique(meta[,2]))
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggpubr::stat_compare_means(comparisons = compar, method = "wilcox.test", label = "p.signif", ...) + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
} else if (type1 == 2){
if (type2 == 1){
p = ggplot2::ggplot(meta2, ggplot2:: aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym(names(meta2)[4]),
fill = !!rlang::sym(names(meta2)[2]))) +
ggplot2::geom_bar(stat = "identity",position = ggplot2::position_dodge(0.8), color = "black", width = 0.75) +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]) ,
fill = !!rlang::sym(names(meta)[3])),
shape = 21,
position = ggplot2::position_jitterdodge(0.8/nrow(meta2)))+
ggplot2::ylab(names(meta)[4]) +
ggplot2::xlab(NULL) +
ggplot2::theme_test()
if (add == "mean_se"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - se, ymax = mean+se), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + se,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
} else if (add == "mean_sd"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - sd, ymax = mean+sd), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + sd,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
}
} else if (type2 == 2){
formu = stats::as.formula(paste0(names(meta)[4], "~", names(meta)[2]))
stat.test = ggpubr::compare_means(formu, data = meta, group.by = names(meta)[3], method = "wilcox.test", ...)
meta3 = meta2 |> dplyr::group_by(!!rlang::sym(names(meta2)[2])) |> dplyr::summarise(p.position = max(max))
stat.test = merge(stat.test, meta3, by = names(meta2)[2])
if (add == "mean_se"){
p = ggplot2::ggplot(meta2, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]), y = !!rlang::sym(names(meta2)[4]), fill = names(meta[2]))) +
ggplot2::geom_bar(stat = "identity",ggplot2::aes(fill = !!rlang::sym(names(meta2)[2])), color = "black",width = 0.75) +
ggplot2::geom_errorbar(ggplot2::aes(ymin = mean-se, ymax = mean+se), width = 0.75/4)+
ggplot2::theme_test() +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),y = !!rlang::sym(names(meta)[4]), fill = !!rlang::sym(names(meta)[3])), shape = 21, width = 0.75/4) +
ggplot2::scale_fill_discrete(guide = "none") +
ggpubr::stat_pvalue_manual(
stat.test, label = "p.signif", y.position = "p.position"
) +
ggplot2::xlab(NULL)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
} else if (add == "mean_sd"){
p = ggplot2::ggplot(meta2, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]), y = !!rlang::sym(names(meta2)[4]), fill = names(meta[2]))) +
ggplot2::geom_bar(stat = "identity",ggplot2::aes(fill = !!rlang::sym(names(meta2)[2])), color = "black",width = 0.75) +
ggplot2::geom_errorbar(ggplot2::aes(ymin = mean-sd, ymax = mean+sd), width = 0.75/4)+
ggplot2::theme_test() +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),y = !!rlang::sym(names(meta)[4]), fill = !!rlang::sym(names(meta)[3])), shape = 21, width = 0.75/4) +
ggplot2::scale_fill_discrete(guide = "none") +
ggpubr::stat_pvalue_manual(
stat.test, label = "p.signif", y.position = "p.position"
) +
ggplot2::xlab(NULL)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
}
}
} else if (temp == "KruskalTest2"){
result = result$WilcoxTest
result$group <- paste(result$group1, result$group2, sep = "-")
result_grouped <- result |> dplyr::group_by(!!rlang::sym(names(meta)[3])) %>%
dplyr::group_split()
results <- lapply(result_grouped, function(group) {
sig <- group[, c(1, 9, 12)]
significant <- as.data.frame(sig)[, 2]
names(significant) <- sig$group
dif <- multcompView::multcompLetters(significant)
dif2 <- as.data.frame(dif$Letters)
colnames(dif2) <- "label"
dif2$label <- gsub(" ", "", dif2$label)
dif2[[colnames(meta)[2]]] <- row.names(dif2)
dif2[[colnames(meta)[3]]] <- unique(sig[[colnames(meta)[3]]])
dif2
})
results_combined <- do.call(rbind, results)
meta2 <- dplyr::left_join(meta2, results_combined, by = c(colnames(meta)[2], colnames(meta)[3]))
if (type1 == 1){
if (type2 == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]),
fill = !!rlang::sym(names(meta)[3]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),position = ggplot2::position_dodge(0.8)) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),
shape = 21, position = ggplot2::position_jitterdodge(0.8/nrow(meta2)))+
ggplot2::geom_text(data = meta2, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym(names(meta2)[3]),
label = !!rlang::sym(names(meta2)[7]),
vjust = -0.5),
position = ggplot2::position_dodge(0.8)) +
ggplot2::theme_test() +
ggplot2::xlab(NULL)
} else if (type2 == 2){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[3]))) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])), shape = 21, width = 1/nrow(meta2)/2) +
ggplot2::scale_fill_discrete(guide = "none")+
ggplot2::theme_test() +
ggplot2::xlab(NULL)
compar = list(unique(meta[,2]))
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::geom_text(data = meta2, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym(names(meta2)[3]),
label = !!rlang::sym(names(meta2)[7]),
vjust = -0.5)) + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
} else if (type1 == 2){
if (type2 == 1){
p = ggplot2::ggplot(meta2, ggplot2:: aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym(names(meta2)[4]),
fill = !!rlang::sym(names(meta2)[2]))) +
ggplot2::geom_bar(stat = "identity",position = ggplot2::position_dodge(0.8), color = "black", width = 0.75) +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]) ,
fill = !!rlang::sym(names(meta)[3])),
shape = 21,
position = ggplot2::position_jitterdodge(0.8/nrow(meta2)))+
ggplot2::ylab(names(meta)[4]) +
ggplot2::xlab(NULL) +
ggplot2::theme_test()
if (add == "mean_se"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - se, ymax = mean+se), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + se,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
} else if (add == "mean_sd"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - sd, ymax = mean+sd), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + sd,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
}
} else if (type2 == 2){
p = ggplot2::ggplot(meta2, ggplot2:: aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym(names(meta2)[4]),
fill = !!rlang::sym(names(meta2)[2]))) +
ggplot2::geom_bar(stat = "identity",color = "black", width = 0.75) +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]) ,
fill = !!rlang::sym(names(meta)[3])),
shape = 21)+
ggplot2::ylab(names(meta)[4]) +
ggplot2::xlab(NULL) +
ggplot2::theme_test()
if (add == "mean_se"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - se, ymax = mean+se), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + se,label = label),vjust = -0.5)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
} else if (add == "mean_sd"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - sd, ymax = mean+sd), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + sd,label = label),vjust = -0.5)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
}
}
} else if (temp == "LSD2"){
if (type1 == 1){
if (type2 == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]),
fill = !!rlang::sym(names(meta)[3]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),position = ggplot2::position_dodge(0.8)) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),
shape = 21, position = ggplot2::position_jitterdodge(0.8/nrow(meta2)))+
ggplot2::geom_text(data = result, ggplot2::aes(x = !!rlang::sym(names(result)[2]),
y = !!rlang::sym("Max"),
label = !!rlang::sym("label"),
vjust = -0.5),
position = ggplot2::position_dodge(0.8)) +
ggplot2::theme_test() +
ggplot2::xlab(NULL)
} else if (type2 == 2){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[3]))) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])), shape = 21, width = 1/nrow(meta2)/2) +
ggplot2::scale_fill_discrete(guide = "none")+
ggplot2::theme_test() +
ggplot2::xlab(NULL)
compar = list(unique(meta[,2]))
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::geom_text(data = result, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym("Max"),
label = !!rlang::sym("label"),
vjust = -0.5)) + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
} else if (type1 == 2){
if (type2 == 1){
names(result)[13] = "mean"
p = ggplot2::ggplot(result, ggplot2:: aes(x = !!rlang::sym(names(result)[2]),
y = !!rlang::sym("mean"),
fill = !!rlang::sym(names(result)[1]))) +
ggplot2::geom_bar(stat = "identity",position = ggplot2::position_dodge(0.8), color = "black", width = 0.75) +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]) ,
fill = !!rlang::sym(names(meta)[3])),
shape = 21,
position = ggplot2::position_jitterdodge(0.8/nrow(result)))+
ggplot2::ylab(names(meta)[4]) +
ggplot2::xlab(NULL) +
ggplot2::theme_test()
if (add == "mean_se"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - se, ymax = mean+se), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + se,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
} else if (add == "mean_sd"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - std, ymax = mean+std), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + std,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
}
} else if (type2 == 2){
names(result)[13] = "mean"
p = ggplot2::ggplot(result, ggplot2:: aes(x = !!rlang::sym(names(result)[2]),
y = !!rlang::sym(names(result)[13]),
fill = !!rlang::sym(names(result)[1]))) +
ggplot2::geom_bar(stat = "identity",color = "black", width = 0.75) +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]) ,
fill = !!rlang::sym(names(meta)[3])),
shape = 21)+
ggplot2::ylab(names(meta)[4]) +
ggplot2::xlab(NULL) +
ggplot2::theme_test()
if (add == "mean_se"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - se, ymax = mean+se), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + se,label = label),vjust = -0.5)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
} else if (add == "mean_sd"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - std, ymax = mean+std), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + std,label = label),vjust = -0.5)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
}
}
} else if (temp == "HSD2"){
if (type1 == 1){
if (type2 == 1){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]),
fill = !!rlang::sym(names(meta)[3]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),position = ggplot2::position_dodge(0.8)) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])),
shape = 21, position = ggplot2::position_jitterdodge(0.8/nrow(meta2)))+
ggplot2::geom_text(data = result, ggplot2::aes(x = !!rlang::sym(names(result)[2]),
y = !!rlang::sym("Max"),
label = !!rlang::sym("label"),
vjust = -0.5),
position = ggplot2::position_dodge(0.8)) +
ggplot2::theme_test() +
ggplot2::xlab(NULL)
} else if (type2 == 2){
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]))) +
ggplot2::geom_boxplot(ggplot2::aes(fill = !!rlang::sym(names(meta)[3]))) +
ggplot2::geom_jitter(ggplot2::aes(fill = !!rlang::sym(names(meta)[3])), shape = 21, width = 1/nrow(meta2)/2) +
ggplot2::scale_fill_discrete(guide = "none")+
ggplot2::theme_test() +
ggplot2::xlab(NULL)
compar = list(unique(meta[,2]))
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::geom_text(data = result, ggplot2::aes(x = !!rlang::sym(names(meta2)[1]),
y = !!rlang::sym("Max"),
label = !!rlang::sym("label"),
vjust = -0.5)) + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
} else if (type1 == 2){
if (type2 == 1){
names(result)[11] = "mean"
p = ggplot2::ggplot(result, ggplot2:: aes(x = !!rlang::sym(names(result)[2]),
y = !!rlang::sym("mean"),
fill = !!rlang::sym(names(result)[1]))) +
ggplot2::geom_bar(stat = "identity",position = ggplot2::position_dodge(0.8), color = "black", width = 0.75) +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]) ,
fill = !!rlang::sym(names(meta)[3])),
shape = 21,
position = ggplot2::position_jitterdodge(0.8/nrow(result)))+
ggplot2::ylab(names(meta)[4]) +
ggplot2::xlab(NULL) +
ggplot2::theme_test()
if (add == "mean_se"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - se, ymax = mean+se), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + se,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
} else if (add == "mean_sd"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - std, ymax = mean+std), position = ggplot2::position_dodge(.8), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + std,label = label),vjust = -0.5,position = ggplot2::position_dodge(0.8))
}
} else if (type2 == 2){
names(result)[11] = "mean"
p = ggplot2::ggplot(result, ggplot2:: aes(x = !!rlang::sym(names(result)[2]),
y = !!rlang::sym(names(result)[11]),
fill = !!rlang::sym(names(result)[1]))) +
ggplot2::geom_bar(stat = "identity",color = "black", width = 0.75) +
ggplot2::geom_jitter(data = meta, ggplot2::aes(x = !!rlang::sym(names(meta)[2]),
y = !!rlang::sym(names(meta)[4]) ,
fill = !!rlang::sym(names(meta)[3])),
shape = 21)+
ggplot2::ylab(names(meta)[4]) +
ggplot2::xlab(NULL) +
ggplot2::theme_test()
if (add == "mean_se"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - se, ymax = mean+se), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + se,label = label),vjust = -0.5)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
} else if (add == "mean_sd"){
p = p + ggplot2::geom_errorbar(ggplot2::aes(ymin = mean - std, ymax = mean+std), width = 0.75/4) +
ggplot2::geom_text(ggplot2::aes(y = mean + std,label = label),vjust = -0.5)
formu = stats::as.formula(paste0(".", "~", names(meta)[3]))
p = p + ggplot2::facet_wrap(formu, scales = "free_y", nrow = 1)
}
}
}
}
p
})
################################################################################
#' Visualizing nematode functional guild data (single factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{funguild-class}}
#' and is used to visualize the nematode functional guild data.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_nemindex() |> calc_funguild(Treatments) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{funguild-class}} object.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @aliases nem_plot,funguild-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem2(tab = nemtab, tax = nemtax, meta = nemmeta)
#' nem_plot <- nem |>
#' calc_nemindex() |>
#' calc_funguild(Treatments) |>
#' nem_plot()
#' nem_plot
setMethod("nem_plot", signature("funguild"), function(object){
# object = nem
meta = object@result
meta2 = stats::na.omit(meta)
p = ggplot2::ggplot(meta2, ggplot2::aes(x = !!rlang::sym("SI"), y = !!rlang::sym("EI"), color = !!rlang::sym(names(meta2)[2]))) +
ggplot2::geom_hline(yintercept = 50) +
ggplot2::geom_vline(xintercept = 50) +
ggplot2::ylim(0, 100) +
ggplot2::xlim(0, 100) +
ggplot2::geom_point() + ggplot2::theme_test() +
ggplot2::ylab("Enrichment Index") +
ggplot2::xlab("Structure Index")
p
})
################################################################################
#' Visualizing nematode functional guild data (two-factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{funguild2-class}}
#' and is used to visualize the nematode functional guild data.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_nemindex() |> calc_funguild2(con_crop, season) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{funguild2-class}} object.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @aliases nem_plot,funguild2-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem(tab = easynem_example("nemtab1.csv"),
#' tax = easynem_example("nemtax1.csv"),
#' meta = easynem_example("nemmeta1.csv"))
#' nem_plot <- nem |>
#' calc_nemindex() |>
#' calc_funguild2(con_crop, season) |>
#' nem_plot()
#' nem_plot
setMethod("nem_plot", signature("funguild2"), function(object){
# object = hehe2
meta = object@result
meta2 = stats::na.omit(meta)
p = ggplot2::ggplot(meta2, ggplot2::aes(x = !!rlang::sym("SI"), y = !!rlang::sym("EI"),
color = !!rlang::sym(names(meta2)[2]),
shape = !!rlang::sym(names(meta2)[3]))) +
ggplot2::geom_hline(yintercept = 50) +
ggplot2::geom_vline(xintercept = 50) +
ggplot2::ylim(0, 100) +
ggplot2::xlim(0, 100) +
ggplot2::geom_point() + ggplot2::theme_test() +
ggplot2::ylab("Enrichment Index") +
ggplot2::xlab("Structure Index")
p
})
################################################################################
#' Visualizing the metabolic footprint of nematode communities (single factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{mf-class}}
#' and is used to visualize the metabolic footprint of nematode communities. Metabolic
#' footprints quantify the amplitude of Carbon utilisation by different food web
#' components. The point in the middle of a rhombus represents the intersection
#' of EI and SI and length of vertical and horizontal axes of the rhombus corresponds
#' to the footprints of enrichment and structure components respectively.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_nemindex() |> calc_mf(Treatments) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{mf-class}} object.
#' @param kei Adjust the width of the diamond, default \code{kei = 1}.
#' @param ksi Adjust the length of the diamond, default \code{ksi = 1}.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @references
#' * <https://shiny.wur.nl/ninja/>
#' * Ferris, Howard. "Form and function: metabolic footprints of nematodes in the
#' soil food web." European Journal of Soil Biology 46.2 (2010): 97-104.
#'
#' @aliases nem_plot,mf-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem2(tab = nemtab, tax = nemtax, meta = nemmeta)
#' nem_plot <- nem |>
#' calc_nemindex() |>
#' calc_mf(Treatments) |>
#' nem_plot(kei = 30, ksi = 20)
#' nem_plot
setMethod("nem_plot", signature("mf"), function(object, kei = 1, ksi = 1){
# object = hehe2
# kei = 3
# ksi = 3
meta = object@result
meta2 = stats::na.omit(meta)
output = meta2 |> dplyr::group_by(!!rlang::sym(names(meta2)[2])) |>
dplyr::summarise(meanei = mean(EI), meansi = mean(SI), meanefp = mean(EnrichmentFootprint), meansfp = mean(StructureFootprint))
output$meanfufp = (output$meanefp * output$meansfp) / 2
output$meanfufp_ = (output$meanfufp/sum(output$meanfufp))*100
output$meanei0.5p = output$meanei + 0.5 * output$meanefp/kei
output$meanei0.5n = output$meanei - 0.5 * output$meanefp/kei
output$meansi0.5p = output$meansi + 0.5 * output$meansfp/ksi
output$meansi0.5n = output$meansi - 0.5 * output$meansfp/ksi
output1 = output[ , c(names(output)[1], "meanei", "meansi")]
output2 = output[ , c(names(output)[1], "meanei", "meansi0.5p")]
colnames(output2) = colnames(output1)
output3 = output[ , c(names(output)[1], "meanei", "meansi0.5n")]
colnames(output3) = colnames(output1)
output4 = output[ , c(names(output)[1], "meanei0.5p", "meansi")]
colnames(output4) = colnames(output1)
output5 = output[ , c(names(output)[1],"meanei0.5n", "meansi")]
colnames(output5) = colnames(output1)
zoutput = rbind(output1, output5, output3, output4, output2)
output6 = output[ , c(names(output)[1], "meanfufp_")]
output6$meanfufp_ = round(output6$meanfufp_, 2)
output6$meanfufp_ = as.character(output6$meanfufp_)
output6$meanfufp_ = paste0(output6$meanfufp_,"%")
zoutput = merge(zoutput, output6, by = names(zoutput)[1])
zoutput[[names(zoutput)[1]]] = paste(zoutput[[names(zoutput)[1]]], zoutput$meanfufp, sep = "_")
zoutputsub = zoutput |> dplyr::group_by(zoutput[[names(zoutput)[1]]]) |> dplyr::slice(-1)
# return(zoutputsub)
p <- ggplot2::ggplot(zoutput, ggplot2::aes(x = meansi, y = meanei, color = !!rlang::sym(names(zoutput)[1]))) + ggplot2::geom_point(size = 3) +
ggplot2::xlab("Structure index") + ggplot2::ylab("Enrichment index") + ggplot2::scale_x_continuous(expand = c(0, 0), breaks = c(0, 50, 100)) +
ggplot2::scale_y_continuous(expand = c(0, 0), breaks = c(0, 50, 100)) + ggplot2::theme_test() +
ggplot2::geom_hline(yintercept = c(0, 50, 100)) + ggplot2::geom_vline(xintercept = c(0, 50, 100)) + ggplot2::geom_polygon(data = zoutputsub, ggplot2::aes(x = meansi, y = meanei, fill = !!rlang::sym(names(zoutput)[1])), alpha = 0.2)
p
})
################################################################################
#' Visualizing the metabolic footprint of nematode communities (two-factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{mf2-class}}
#' and is used to visualize the metabolic footprint of nematode communities. Metabolic
#' footprints quantify the amplitude of Carbon utilisation by different food web
#' components. The point in the middle of a rhombus represents the intersection
#' of EI and SI and length of vertical and horizontal axes of the rhombus corresponds
#' to the footprints of enrichment and structure components respectively.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_nemindex() |> calc_mf2(con_crop, season) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{mf2-class}} object.
#' @param kei Adjust the width of the diamond, default \code{kei = 1}.
#' @param ksi Adjust the length of the diamond, default \code{ksi = 1}.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @references
#' * <https://shiny.wur.nl/ninja/>
#' * Ferris, Howard. "Form and function: metabolic footprints of nematodes in the
#' soil food web." European Journal of Soil Biology 46.2 (2010): 97-104.
#'
#' @aliases nem_plot,mf2-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem(tab = easynem_example("nemtab1.csv"),
#' tax = easynem_example("nemtax1.csv"),
#' meta = easynem_example("nemmeta1.csv"))
#' nem_plot <- nem |>
#' calc_nemindex() |>
#' calc_mf2(con_crop, season) |>
#' nem_plot(kei = 35, ksi = 35)
#' nem_plot
setMethod("nem_plot", signature("mf2"), function(object, kei = 1, ksi = 1){
# object = hehe2
# kei = 3
# ksi = 3
meta = object@result
meta2 = stats::na.omit(meta)
output = meta2 |> dplyr::group_by(!!rlang::sym(names(meta2)[2])) |>
dplyr::summarise(meanei = mean(EI), meansi = mean(SI), meanefp = mean(EnrichmentFootprint), meansfp = mean(StructureFootprint))
output$meanfufp = (output$meanefp * output$meansfp) / 2
output$meanfufp_ = (output$meanfufp/sum(output$meanfufp))*100
output$meanfufp_ = round(output$meanfufp_, 2)
output$meanfufp_ = as.character(output$meanfufp_)
output$meanfufp_ = paste0(output$meanfufp_,"%")
output1 = output$meanfufp_
names(output1) = output[[1]]
output = meta2 |> dplyr::group_by(!!rlang::sym(names(meta2)[3])) |>
dplyr::summarise(meanei = mean(EI), meansi = mean(SI), meanefp = mean(EnrichmentFootprint), meansfp = mean(StructureFootprint))
output$meanfufp = (output$meanefp * output$meansfp) / 2
output$meanfufp_ = (output$meanfufp/sum(output$meanfufp))*100
output$meanfufp_ = round(output$meanfufp_, 2)
output$meanfufp_ = as.character(output$meanfufp_)
output$meanfufp_ = paste0(output$meanfufp_,"%")
output2 = output$meanfufp_
names(output2) = output[[1]]
output = meta2 |> dplyr::group_by(!!rlang::sym(names(meta2)[2]), !!rlang::sym(names(meta2)[3])) |>
dplyr::summarise(meanei = mean(EI), meansi = mean(SI), meanefp = mean(EnrichmentFootprint), meansfp = mean(StructureFootprint))
output[[1]] <- sapply(output[[1]], function(x) {
if (x %in% names(output1)) {
paste0(x, "_", output1[x])
} else {
x
}
})
output[[2]] <- sapply(output[[2]], function(x) {
if (x %in% names(output2)) {
paste0(x, "_", output2[x])
} else {
x
}
})
output$meanei0.5p = output$meanei + 0.5 * output$meanefp/kei
output$meanei0.5n = output$meanei - 0.5 * output$meanefp/kei
output$meansi0.5p = output$meansi + 0.5 * output$meansfp/ksi
output$meansi0.5n = output$meansi - 0.5 * output$meansfp/ksi
output1 = output[ , c(names(output)[1], names(output)[2], "meanei", "meansi")]
output2 = output[ , c(names(output)[1], names(output)[2], "meanei", "meansi0.5p")]
colnames(output2) = colnames(output1)
output3 = output[ , c(names(output)[1], names(output)[2], "meanei", "meansi0.5n")]
colnames(output3) = colnames(output1)
output4 = output[ , c(names(output)[1], names(output)[2], "meanei0.5p", "meansi")]
colnames(output4) = colnames(output1)
output5 = output[ , c(names(output)[1],names(output)[2], "meanei0.5n", "meansi")]
colnames(output5) = colnames(output1)
zoutput = rbind(output1, output5, output3, output4, output2)
zoutputsub = zoutput |> dplyr::group_by(zoutput[[names(zoutput)[1]]], zoutput[[names(zoutput)[2]]]) |> dplyr::slice(-1)
# return(zoutputsub)
p <- ggplot2::ggplot(zoutput, ggplot2::aes(x = meansi, y = meanei, color = !!rlang::sym(names(zoutput)[1]), shape = !!rlang::sym(names(zoutput)[2]))) + ggplot2::geom_point(size = 3) +
ggplot2::xlab("Structure index") + ggplot2::ylab("Enrichment index") + ggplot2::scale_x_continuous(expand = c(0, 0), breaks = c(0, 50, 100)) +
ggplot2::scale_y_continuous(expand = c(0, 0), breaks = c(0, 50, 100)) + ggplot2::theme_test() +
ggplot2::geom_hline(yintercept = c(0, 50, 100)) + ggplot2::geom_vline(xintercept = c(0, 50, 100)) + ggplot2::geom_polygon(data = zoutputsub, ggplot2::aes(x = meansi, y = meanei, fill = !!rlang::sym(names(zoutput)[1])), alpha = 0.2)
p
})
################################################################################
#' Visualizing the energy structure of nematode communities (single factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{ef-class}}
#' and is used to visualize the energy structure of nematode communities. a five-node
#' food web was constructed with bacterivores, fungivores and herbivores receiving
#' energy from basal resources (R), omnivores-carnivores receiving energy from other
#' nodes. Numbers along the lines represented energy flux (ug C / 100 g dry soil / day).
#' The size of nodes corresponds to the fresh biomass (ug / 100 g dry soil).
#' Uniformity (U) of soil nematode energetic structure (unitless, mean ± standard error)
#' was calculated as the ratio of the mean of summed energy flux through each
#' energy channel to the standard deviation of these mean values.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_nemindex() |> calc_ef(Treatments) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{ef-class}} object.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @references
#' * Wan, Bingbing, et al. "Organic amendments increase the flow uniformity of
#' energy across nematode food webs." Soil Biology and Biochemistry 170 (2022): 108695.
#' * Ferris, H., 2010. Form and function: metabolic footprints of nematodes in the soil
#' food web. European Journal of Soil Biology 46, 97–104.
#' * Van Den Hoogen, Johan, et al. "Soil nematode abundance and functional group
#' composition at a global scale." Nature 572.7768 (2019): 194-198.
#' * Barnes, A.D., Jochum, M., Mumme, S., Haneda, N.F., Farajallah, A., Widarto, T.H.,
#' Brose, U., 2014. Consequences of tropical land use for multitrophic biodiversity and
#' ecosystem functioning. Nature Communications 5, 1–7.
#' * De Ruiter, P.C., Van Veen, J.A., Moore, J.C., Brussaard, L., Hunt, H.W., 1993. Calculation
#' of nitrogen mineralization in soil food webs. Plant and Soil 157, 263–273.
#'
#' @aliases nem_plot,ef-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem2(tab = nemtab, tax = nemtax, meta = nemmeta)
#' nem_plot <- nem |>
#' calc_nemindex() |>
#' calc_ef(Treatments) |>
#' nem_plot()
#' nem_plot
setMethod("nem_plot", signature("ef"), function(object){
# object = hehe
result = object@result
result = result[,-1]
result2 = result |>
dplyr::group_by(!!rlang::sym(names(result)[1])) |>
dplyr::summarise(dplyr::across(dplyr::everything(), \(x) mean(x, na.rm = TRUE)))
result3 = result |>
dplyr::group_by(!!rlang::sym(names(result)[1])) |>
dplyr::summarise(Use = stats::sd(U)/sqrt(dplyr::n()))
result4 = merge(result2,result3, by = names(result)[1])
result4_U = result4[,c(names(result4)[1], "U", "Use")]
result4_U$U = format(result4_U$U, digits = 2, nsmall = 2)
result4_U$Use = format(result4_U$Use, digits = 2, nsmall = 2)
result4_U = result4_U |> dplyr::mutate(U = paste0("U = ", U, " (", "\u00B1", Use, ")"))
result4_nodes = result4[,c(names(result4)[1], "OM", "BM", "HM", "FM")]
result4_nodes$R = 600
names(result4_nodes) = c(names(result4_nodes)[1], "Omnivores_carnivores", "Bacterivores", "Herbivores", "Fungivores", "Resources")
result4_nodes_long = reshape2::melt(result4_nodes,id.vars = names(result4_nodes)[1], variable.name = "Feeding", value.name = "Fresh_biomass")
result4_edges = result4[,c(names(result4)[1],"fbo", "fho", "ffo", "frb", "frh", "frf")]
result4_edges_long = reshape2::melt(result4_edges,id.vars = names(result4_nodes)[1], variable.name = "group", value.name = "Energy_flow")
result4_edges_long = result4_edges_long |>
dplyr::mutate(group = dplyr::case_when(
group == "fbo" ~ "Bacterivores-Omnivores_carnivores",
group == "fho" ~ "Herbivores-Omnivores_carnivores",
group == "ffo" ~ "Fungivores-Omnivores_carnivores",
group == "frb" ~ "Resources-Bacterivores",
group == "frh" ~ "Resources-Herbivores",
group == "frf" ~ "Resources-Fungivores",
TRUE ~ group # 保留其他未匹配的值
))
result4_edges_long = result4_edges_long |>
tidyr::separate(group, into = c("from1", "to1"), sep = "-")
result4_edges_long = result4_edges_long |> dplyr::mutate(from = paste0(!!rlang::sym("from1"), !!rlang::sym(names(result4)[1])))
result4_edges_long = result4_edges_long |> dplyr::mutate(to = paste0(!!rlang::sym("to1"), !!rlang::sym(names(result4)[1])))
result4_edges_long = result4_edges_long |> dplyr::select(!!rlang::sym("from"), !!rlang::sym("to"), dplyr::everything())
nodes <- data.frame(
id = c("Resources", "Bacterivores", "Herbivores", "Fungivores", "Omnivores_carnivores"),
Feeding = c("Resources", "Bacterivores", "Herbivores", "Fungivores", "Omnivores_carnivores")
)
# nodes = dplyr::bind_rows(replicate(nrow(result4), nodes, simplify = FALSE))
# nodes[[names(result4)[1]]] = sort(rep(result4[[1]], times = 5))
nodes = merge(nodes, result4_nodes_long, by = "Feeding")
nodes = nodes |> dplyr::mutate(id = paste0(!!rlang::sym("id"), !!rlang::sym(names(result4)[1])))
nodes$Feeding = factor(nodes$Feeding, levels = c("Bacterivores", "Fungivores", "Herbivores", "Resources", "Omnivores_carnivores"))
result4_U = result4_U[, -which(names(result4_U) == "Use")]
nodes = merge(nodes, result4_U, by = names(result)[1])
# nodes = merge(nodes, result4_nodes_long, by = c(names(result4)[1], "Feeding"), all = TRUE)
nodes = dplyr::select(nodes, id, dplyr::everything())
nodes$Fresh_biomass = round(nodes$Fresh_biomass,2)
nodes$label = nodes$Fresh_biomass
nodes = nodes |> dplyr::mutate(label = ifelse(Feeding == "Resources", "R", label))
order = rep(c("Bacterivores", "Fungivores", "Herbivores", "Omnivores_carnivores", "Resources"), times = nrow(nodes)/5)
positions <- integer(length(order))
used_indices <- logical(length(order))
for (i in 1:length(order)){
for(j in 1:length(order)){
if(order[i] == nodes[["Feeding"]][j] && !used_indices[j]){
positions[i] = j
used_indices[j] <- TRUE
break
}
}
}
nodes = nodes[positions,]
# edges <- data.frame(
# from = c("Resources", "Resources", "Resources", "Bacterivores", "Herbivores", "Fungivores"),
# to = c("Bacterivores", "Herbivores", "Fungivores", "Omnivores_carnivores", "Omnivores_carnivores", "Omnivores_carnivores"),
# group = c("Bacterivores", "Herbivores", "Fungivores", "Omnivores_carnivores", "Omnivores_carnivores", "Omnivores_carnivores")
# )
# edges = dplyr::bind_rows(replicate(nrow(result4), edges, simplify = FALSE))
# edges[[names(result4)[1]]] = sort(rep(result4[[1]], times = 6))
# edges = edges |> dplyr::mutate(from = paste0(!!rlang::sym("from"), !!rlang::sym(names(result4)[1])))
# edges = edges |> dplyr::mutate(to = paste0(!!rlang::sym("to"), !!rlang::sym(names(result4)[1])))
# edges = merge(edges, result4_U, by = names(result)[1])
# edges = merge(edges, result4_edges_long, by = c("from", "to"), all = TRUE)
edges = result4_edges_long
edges$Energy_flow = round(edges$Energy_flow, 2)
graph = tidygraph::tbl_graph(nodes = nodes, edges = edges, directed = TRUE, node_key = "id")
# graph <- igraph::graph_from_data_frame(edges, directed = FALSE, vertices = nodes)
layout_matrix <- matrix(c(
0, 3,
3, 0,
1.5, 1.5,
3, 3,
0, 0
), ncol = 2, byrow = TRUE)
formu = stats::as.formula(paste0("~", names(result4)[1]))
p3 = ggraph::ggraph(graph, layout = layout_matrix) +
ggraph::geom_edge_fan(ggplot2::aes(colour = to1, width = Energy_flow, label = Energy_flow), family = "sans", show.legend = FALSE) +
ggraph::scale_edge_width(range=c(0.5, 4)) +
ggplot2::geom_text(ggplot2::aes(x = 2, y = 0.5, label = U),family = "sans", data = nodes)+
ggraph::geom_node_point(ggplot2::aes(colour = Feeding, size = Fresh_biomass)) +
ggraph::geom_node_text(aes(label = label), repel = TRUE) +
ggraph::theme_graph(foreground = 'steelblue', fg_text_colour = 'white', base_family = "sans") +
ggplot2::scale_size(range = c(5,13)) +
ggplot2::facet_wrap(formu, scales = "free")
p3
})
################################################################################
#' Visualizing the energy structure of nematode communities (two-factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{ef2-class}}
#' and is used to visualize the energy structure of nematode communities. a five-node
#' food web was constructed with bacterivores, fungivores and herbivores receiving
#' energy from basal resources (R), omnivores-carnivores receiving energy from other
#' nodes. Numbers along the lines represented energy flux (ug C / 100 g dry soil / day).
#' The size of nodes corresponds to the fresh biomass (ug / 100 g dry soil).
#' Uniformity (U) of soil nematode energetic structure (unitless, mean ± standard error)
#' was calculated as the ratio of the mean of summed energy flux through each
#' energy channel to the standard deviation of these mean values.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_nemindex() |> calc_ef2(con_crop, season) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{ef2-class}} object.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @references
#' * Wan, Bingbing, et al. "Organic amendments increase the flow uniformity of
#' energy across nematode food webs." Soil Biology and Biochemistry 170 (2022): 108695.
#' * Ferris, H., 2010. Form and function: metabolic footprints of nematodes in the soil
#' food web. European Journal of Soil Biology 46, 97–104.
#' * Van Den Hoogen, Johan, et al. "Soil nematode abundance and functional group
#' composition at a global scale." Nature 572.7768 (2019): 194-198.
#' * Barnes, A.D., Jochum, M., Mumme, S., Haneda, N.F., Farajallah, A., Widarto, T.H.,
#' Brose, U., 2014. Consequences of tropical land use for multitrophic biodiversity and
#' ecosystem functioning. Nature Communications 5, 1–7.
#' * De Ruiter, P.C., Van Veen, J.A., Moore, J.C., Brussaard, L., Hunt, H.W., 1993. Calculation
#' of nitrogen mineralization in soil food webs. Plant and Soil 157, 263–273.
#'
#' @aliases nem_plot,ef2-method
#' @import ggplot2
#' @import igraph
#' @import ggraph
#' @import reshape2
#' @export
#' @examples
#' nem <- read_nem(tab = easynem_example("nemtab1.csv"),
#' tax = easynem_example("nemtax1.csv"),
#' meta = easynem_example("nemmeta1.csv"))
#' nem_plot <- nem |>
#' calc_nemindex() |>
#' calc_ef2(con_crop, season) |>
#' nem_plot()
#' nem_plot
setMethod("nem_plot", signature("ef2"), function(object){
# object = hehe
result = object@result
result = result[,-1]
result2 = result |>
dplyr::group_by(!!rlang::sym(names(result)[1]),!!rlang::sym(names(result)[2])) |>
dplyr::summarise(dplyr::across(dplyr::everything(), \(x) mean(x, na.rm = TRUE)))
result3 = result |>
dplyr::group_by(!!rlang::sym(names(result)[1]),!!rlang::sym(names(result)[2])) |>
dplyr::summarise(Use = stats::sd(U)/sqrt(dplyr::n()))
result4 = merge(result2,result3, by = c(names(result)[1], names(result)[2]))
result4_U = result4[,c(names(result4)[1], names(result4)[2], "U", "Use")]
result4_U$U = format(result4_U$U, digits = 2, nsmall = 2)
result4_U$Use = format(result4_U$Use, digits = 2, nsmall = 2)
result4_U = result4_U |> dplyr::mutate(U = paste0("U = ", U, " (", "\u00B1", Use, ")"))
result4_nodes = result4[,c(names(result4)[1], names(result4)[2], "OM", "BM", "HM", "FM")]
result4_nodes$R = 600
names(result4_nodes) = c(names(result4_nodes)[1], names(result4_nodes)[2], "Omnivores_carnivores", "Bacterivores", "Herbivores", "Fungivores", "Resources")
result4_nodes_long = reshape2::melt(result4_nodes,id.vars = c(names(result4_nodes)[1], names(result4_nodes)[2]), variable.name = "Feeding", value.name = "Fresh_biomass")
result4_edges = result4[,c(names(result4)[1],names(result4)[2], "fbo", "fho", "ffo", "frb", "frh", "frf")]
result4_edges_long = reshape2::melt(result4_edges,id.vars = c(names(result4_nodes)[1],names(result4_nodes)[2]), variable.name = "group", value.name = "Energy_flow")
result4_edges_long = result4_edges_long |>
dplyr::mutate(group = dplyr::case_when(
group == "fbo" ~ "Bacterivores-Omnivores_carnivores",
group == "fho" ~ "Herbivores-Omnivores_carnivores",
group == "ffo" ~ "Fungivores-Omnivores_carnivores",
group == "frb" ~ "Resources-Bacterivores",
group == "frh" ~ "Resources-Herbivores",
group == "frf" ~ "Resources-Fungivores",
TRUE ~ group # 保留其他未匹配的值
))
result4_edges_long = result4_edges_long |>
tidyr::separate(group, into = c("from1", "to1"), sep = "-")
result4_edges_long = result4_edges_long |> dplyr::mutate(from = paste0(!!rlang::sym("from1"), !!rlang::sym(names(result4)[1]), !!rlang::sym(names(result4)[2])))
result4_edges_long = result4_edges_long |> dplyr::mutate(to = paste0(!!rlang::sym("to1"), !!rlang::sym(names(result4)[1]), !!rlang::sym(names(result4)[2])))
result4_edges_long = result4_edges_long |> dplyr::select(!!rlang::sym("from"), !!rlang::sym("to"), dplyr::everything())
nodes <- data.frame(
id = c("Resources", "Bacterivores", "Herbivores", "Fungivores", "Omnivores_carnivores"),
Feeding = c("Resources", "Bacterivores", "Herbivores", "Fungivores", "Omnivores_carnivores")
)
# nodes = dplyr::bind_rows(replicate(nrow(result4), nodes, simplify = FALSE))
# nodes[[names(result4)[1]]] = sort(rep(result4[[1]], times = 5))
# nodes[[names(result4)[2]]] = rep(unique(result4[[2]]), times = length(unique(result4[[1]]))*5)
nodes = merge(nodes, result4_nodes_long, by = "Feeding")
nodes = nodes |> dplyr::mutate(id = paste0(!!rlang::sym("id"), !!rlang::sym(names(result4)[1]), !!rlang::sym(names(result4)[2])))
nodes$Feeding = factor(nodes$Feeding, levels = c("Bacterivores", "Fungivores", "Herbivores", "Resources", "Omnivores_carnivores"))
result4_U = result4_U[, -which(names(result4_U) == "Use")]
nodes = merge(nodes, result4_U, by = c(names(result)[1], names(result)[2]))
# nodes = merge(nodes, result4_nodes_long, by = c(names(result4)[1], names(result4)[2], "Feeding"), all = TRUE)
nodes = dplyr::select(nodes, id, dplyr::everything())
nodes$Fresh_biomass = round(nodes$Fresh_biomass,2)
nodes$label = nodes$Fresh_biomass
nodes = nodes |> dplyr::mutate(label = ifelse(Feeding == "Resources", "R", label))
order = rep(c("Bacterivores", "Fungivores", "Herbivores", "Omnivores_carnivores", "Resources"), times = nrow(nodes)/5)
positions <- integer(length(order))
used_indices <- logical(length(order))
for (i in 1:length(order)){
for(j in 1:length(order)){
if(order[i] == nodes[["Feeding"]][j] && !used_indices[j]){
positions[i] = j
used_indices[j] <- TRUE
break
}
}
}
nodes = nodes[positions,]
# edges = dplyr::bind_rows(replicate(nrow(result4), edges, simplify = FALSE))
# edges[[names(result4)[1]]] = sort(rep(result4[[1]], times = 6))
# edges[[names(result4)[2]]] = rep(unique(result4[[2]]), times = length(unique(result4[[1]]))*6)
# edges = edges |> dplyr::mutate(from = paste0(!!rlang::sym("from"), !!rlang::sym(names(result4)[1]), !!rlang::sym(names(result4)[2])))
# edges = edges |> dplyr::mutate(to = paste0(!!rlang::sym("to"), !!rlang::sym(names(result4)[1]), !!rlang::sym(names(result4)[2])))
# edges = merge(edges, result4_U, by = c(names(result)[1], names(result)[2]))
# edges = merge(edges, result4_edges_long, by = c("from", "to"), all = TRUE)
edges = result4_edges_long
edges$Energy_flow = round(edges$Energy_flow, 2)
graph = tidygraph::tbl_graph(nodes = nodes, edges = edges, directed = TRUE, node_key = "id")
# graph <- igraph::graph_from_data_frame(edges, directed = FALSE, vertices = nodes)
layout_matrix <- matrix(c(
0, 3,
3, 0,
1.5, 1.5,
3, 3,
0, 0
), ncol = 2, byrow = TRUE)
formu = stats::as.formula(paste0(names(result4)[2], "~", names(result4)[1]))
p3 = ggraph::ggraph(graph, layout = layout_matrix) +
ggraph::geom_edge_fan(ggplot2::aes(colour = to1, width = Energy_flow, label = Energy_flow), family = "sans", show.legend = FALSE) +
ggraph::scale_edge_width(range=c(0.5, 4)) +
ggplot2::geom_text(ggplot2::aes(x = 2, y = 0.5, label = U),family = "sans", data = nodes)+
ggraph::geom_node_point(ggplot2::aes(colour = Feeding, size = Fresh_biomass)) +
ggraph::geom_node_text(ggplot2::aes(label = label), repel = TRUE) +
ggraph::theme_graph(foreground = 'steelblue', fg_text_colour = 'white', base_family = "sans") +
ggplot2::scale_size(range = c(5,13)) +
ggplot2::facet_grid(formu, scales = "free")
p3
})
################################################################################
#' Visualizing the results of the ternary analysis (single factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{ter-class}}
#' and is used to visualize the results of the ternary analysis. This function
#' visualizes the distribution of nematode communities using the relative abundance
#' of nematodes of \code{cp1}, \code{cp2}, and \code{cp3-5} or the relative biomass
#' of herbivorous nematodes, bacterivorous nematodes, and fungivorous nematodes
#' as the three axes of a ternary plot.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_ter(Treatments) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{ter-class}} object.
#' @param type Visualize the nematodes by their \code{feeding} habits or by their \code{cp} values.
#' @param point_size Size of the points. Default is 1.
#' @param legend_cex Size of the legend text. Default is 0.9
#' @param ... Additional parameters passed to \code{Ternary::TernaryPlot()}.
#'
#' @return A \code{recordedplot} object from \code{Ternary::TernaryPlot}.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @references
#' * <https://shiny.wur.nl/ninja/>
#' * Goede, RGM de, T. Bongers, and C. H. Ettema. "Graphical presentation and
#' interpretation of nematode community structure: cp triangles." (1993): 743-750.
#'
#' @aliases nem_plot,ter-method
#' @import ggplot2
#' @import Ternary
#' @export
#' @examples
#' nem <- read_nem2(tab = nemtab, tax = nemtax, meta = nemmeta)
#' nem_plot <- nem |>
#' calc_ter(Treatments) |>
#' nem_plot(type = feeding)
#' nem_plot
#' nem <- read_nem2(tab = nemtab, tax = nemtax, meta = nemmeta)
#' nem_plot <- nem |>
#' calc_ter(Treatments) |>
#' nem_plot(type = cp)
#' nem_plot
setMethod("nem_plot", signature("ter"), function(object, type, point_size = 1, legend_cex = 0.9, ...){
# object = nem_index
type = deparse(substitute(type))
result = object@result
result = as.data.frame(result)
color_factor <- factor(result[[names(result)[2]]])
color_levels <- levels(color_factor)
color_palette <- grDevices::hcl.colors(length(color_levels), palette = "viridis")
color_map <- stats::setNames(color_palette, color_levels)
point_colors <- color_map[color_factor]
# opar <- par(no.readonly = TRUE)
# on.exit(par(opar))
# par(mar = c(1, 1, 1, 1))
if (type == "cp"){
Ternary::TernaryPlot(
alab = "cp1%\n(Enrichment)",
blab = "cp3-5%\n(Stability)",
clab = "cp2%\n(Stress)",
...
)
Ternary::TernaryPoints(
coordinates = result[, c("cp1% (Enrichment)", "cp3-5% (Stability)", "cp2% (Stress)")],
col = point_colors,
# pch = point_shapes,
cex = point_size
)
graphics::legend(
"topleft",
legend = names(color_map),
col = color_map,
pch = 16,
pt.cex = 1,
bty = "n",
cex = legend_cex
)
recorded_plot <- grDevices::recordPlot()
return(recorded_plot)
# base = ggtern::ggtern(data=result,ggplot2::aes(x=`cp1% (Enrichment)`,y=`cp3-5% (Stability)`,z=`cp2% (Stress)`))
# p3 = base +
# ggplot2::geom_point(ggplot2::aes(color=!!rlang::sym(names(result)[2]), shape = !!rlang::sym(names(result)[3]))) +
# ggtern::theme_rgbw()
# p4 = p3 + ggplot2::labs(x = "cp1%\n(Enrichment)", y = "cp3-5%\n(Stability)", z = "cp2%\n(Stress)")
} else if (type == "feeding") {
Ternary::TernaryPlot(
alab = "Herbivorous%",
blab = "Bacteria%",
clab = "Fungus%",
...
)
Ternary::TernaryPoints(
coordinates = result[, c("Herbivorous nematodes%", "Bacteria-feeding nematodes%", "Fungus-feeding nematodes%")],
col = point_colors,
# pch = point_shapes,
cex = point_size
)
graphics::legend(
"topleft",
legend = names(color_map),
col = color_map,
pch = 16,
pt.cex = 1,
bty = "n",
cex = legend_cex
)
recorded_plot <- grDevices::recordPlot()
return(recorded_plot)
# result = object@result
# base = ggtern::ggtern(data=result,ggplot2::aes(x=`Herbivorous nematodes%`,y=`Bacteria-feeding nematodes%`,z=`Fungus-feeding nematodes%`))
# p3 = base +
# ggplot2::geom_point(ggplot2::aes(color=!!rlang::sym(names(result)[2]), shape = !!rlang::sym(names(result)[3]))) +
# ggtern::theme_rgbw()
# p4 = p3 + ggplot2::labs(x = "Herbivorous%", y = "Bacteria%", z = "Fungus%")
}
# p4
})
################################################################################
#' Visualizing the results of the ternary analysis (two-factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{ter2-class}}
#' and is used to visualize the results of the ternary analysis. This function
#' visualizes the distribution of nematode communities using the relative abundance
#' of nematodes of \code{cp1}, \code{cp2}, and \code{cp3-5} or the relative biomass
#' of herbivorous nematodes, bacterivorous nematodes, and fungivorous nematodes
#' as the three axes of a ternary plot.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_ter2(con_crop, season) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{ter2-class}} object.
#' @param type Visualize the nematodes by their \code{feeding} habits or by their \code{cp} values.
#' @param point_size Size of the points. Default is 1.
#' @param legend_cex Size of the legend text. Default is 0.9
#' @param ... Additional parameters passed to \code{Ternary::TernaryPlot()}.
#'
#' @return A \code{recordedplot} object from \code{Ternary::TernaryPlot}.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @references
#' * <https://shiny.wur.nl/ninja/>
#' * Goede, RGM de, T. Bongers, and C. H. Ettema. "Graphical presentation and
#' interpretation of nematode community structure: cp triangles." (1993): 743-750.
#'
#' @aliases nem_plot,ter2-method
#' @import ggplot2
#' @import Ternary
#' @export
#' @examples
#' nem <- read_nem(tab = easynem_example("nemtab1.csv"),
#' tax = easynem_example("nemtax1.csv"),
#' meta = easynem_example("nemmeta1.csv"))
#' nem_plot <- nem |>
#' calc_ter2(con_crop, season) |>
#' nem_plot(type = feeding)
#' nem_plot
#' nem_plot <- nem |>
#' calc_ter2(con_crop, season) |>
#' nem_plot(type = cp)
#' nem_plot
setMethod("nem_plot", signature("ter2"), function(object, type, point_size = 1, legend_cex = 0.9, ...){
# object = nem_index
type = deparse(substitute(type))
result = object@result
result = as.data.frame(result)
color_factor <- factor(result[[names(result)[2]]])
color_levels <- levels(color_factor)
color_palette <- grDevices::hcl.colors(length(color_levels), palette = "viridis")
color_map <- stats::setNames(color_palette, color_levels)
point_colors <- color_map[color_factor]
shape_factor <- factor(result[[names(result)[3]]])
shape_levels <- levels(shape_factor)
shape_palette <- c(16, 17, 15, 18, 3, 4, 8, 0, 1, 2, 5, 6)[1:length(shape_levels)]
shape_map <- stats::setNames(shape_palette, shape_levels)
point_shapes <- shape_map[shape_factor]
# opar <- par(no.readonly = TRUE)
# on.exit(par(opar))
# par(mar = c(1, 1, 1, 1))
if (type == "cp"){
Ternary::TernaryPlot(
alab = "cp1%\n(Enrichment)",
blab = "cp3-5%\n(Stability)",
clab = "cp2%\n(Stress)",
...
)
Ternary::TernaryPoints(
coordinates = result[, c("cp1% (Enrichment)", "cp3-5% (Stability)", "cp2% (Stress)")],
col = point_colors,
pch = point_shapes,
cex = point_size
)
graphics::legend(
"topleft",
legend = names(color_map),
col = color_map,
pch = 16,
pt.cex = 1,
bty = "n",
cex = legend_cex
)
graphics::legend(
"topright",
legend = names(shape_map),
pch = shape_map,
col = "black",
pt.cex = 1,
bty = "n",
cex = legend_cex
)
recorded_plot <- grDevices::recordPlot()
return(recorded_plot)
# base = ggtern::ggtern(data=result,ggplot2::aes(x=`cp1% (Enrichment)`,y=`cp3-5% (Stability)`,z=`cp2% (Stress)`))
# p3 = base +
# ggplot2::geom_point(ggplot2::aes(color=!!rlang::sym(names(result)[2]), shape = !!rlang::sym(names(result)[3]))) +
# ggtern::theme_rgbw()
# p4 = p3 + ggplot2::labs(x = "cp1%\n(Enrichment)", y = "cp3-5%\n(Stability)", z = "cp2%\n(Stress)")
} else if (type == "feeding") {
Ternary::TernaryPlot(
alab = "Herbivorous%",
blab = "Bacteria%",
clab = "Fungus%",
...
)
Ternary::TernaryPoints(
coordinates = result[, c("Herbivorous nematodes%", "Bacteria-feeding nematodes%", "Fungus-feeding nematodes%")],
col = point_colors,
pch = point_shapes,
cex = point_size
)
graphics::legend(
"topleft",
legend = names(color_map),
col = color_map,
pch = 16,
pt.cex = 1,
bty = "n",
cex = legend_cex
)
graphics::legend(
"topright",
legend = names(shape_map),
pch = shape_map,
col = "black",
pt.cex = 1,
bty = "n",
cex = legend_cex
)
recorded_plot <- grDevices::recordPlot()
return(recorded_plot)
# result = object@result
# base = ggtern::ggtern(data=result,ggplot2::aes(x=`Herbivorous nematodes%`,y=`Bacteria-feeding nematodes%`,z=`Fungus-feeding nematodes%`))
# p3 = base +
# ggplot2::geom_point(ggplot2::aes(color=!!rlang::sym(names(result)[2]), shape = !!rlang::sym(names(result)[3]))) +
# ggtern::theme_rgbw()
# p4 = p3 + ggplot2::labs(x = "Herbivorous%", y = "Bacteria%", z = "Fungus%")
}
# p4
})
################################################################################
#' Visualizing the results of linear regression (single factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{lme-class}}
#' and is used to visualize the results of linear regression.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_lm(Treatments, Chao1, TotalBiomass) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{lme-class}} object.
#' @param ... Other parameters of \code{\link[ggpubr]{stat_cor}} function.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @aliases nem_plot,lme-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem2(tab = nemtab, tax = nemtax, meta = nemmeta)
#' nem_plot <- nem |>
#' calc_alpha() |>
#' calc_nemindex() |>
#' calc_lm(group = Treatments,
#' x = Chao1,
#' y = TotalBiomass) |>
#' nem_plot()
#' nem_plot
setMethod("nem_plot", signature("lme"), function(object, ...){
# object = nem_index
meta = object@meta
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[3]), y = !!rlang::sym(names(meta)[4])))+
ggplot2::geom_point(ggplot2::aes(colour = !!rlang::sym(names(meta)[2])))+
ggplot2::geom_smooth(method = "lm")+
ggpubr::stat_cor(...)+
ggplot2::theme_test() +
ggplot2::theme(text = ggplot2::element_text(colour='black'), axis.text=ggplot2::element_text(colour='black'))
return(p)
})
################################################################################
#' Visualizing the results of linear regression (two-factor)
#'
#' The \code{\link{nem_plot}} function is generalized to the \code{\link{lme2-class}}
#' and is used to visualize the results of linear regression.
#'
#' To facilitate code interpretation, it is recommended to use the pipe symbol
#' [`|>`] to connect functions:
#'
#' ```
#' nem_plot <- nem |> calc_lm2(con_crop, season, x = SOC, y = pH) |> nem_plot()
#' ```
#'
#' @param object A \code{\link{lme2-class}} object.
#' @param ... Other parameters of \code{\link[ggpubr]{stat_cor}} function.
#'
#' @return An \code{gg} or \code{ggplot} object.
#'
#' @seealso
#' The \code{nem_plot()} is used to visualize the calculation results and is a
#' generalized function for multiple classes including \code{\link{beta-class}},
#' \code{\link{beta2-class}}, \code{\link{compare-class}}, \code{\link{compare2-class}},
#' \code{\link{ef-class}}, \code{\link{ef2-class}}, \code{\link{funguild-class}},
#' \code{\link{funguild2-class}}, \code{\link{mf-class}}, \code{\link{mf2-class}},
#' \code{\link{ter-class}}, \code{\link{ter2-class}}, etc.
#'
#' @aliases nem_plot,lme2-method
#' @import ggplot2
#' @export
#' @examples
#' nem <- read_nem(tab = easynem_example("nemtab1.csv"),
#' tax = easynem_example("nemtax1.csv"),
#' meta = easynem_example("nemmeta1.csv"))
#' nem_lm <- nem |> calc_lm2(con_crop, season, x = pH, y = Fe) |> nem_plot()
#' nem_lm
setMethod("nem_plot", signature("lme2"), function(object, ...){
# object = nem_lm
meta = object@meta
p = ggplot2::ggplot(meta, ggplot2::aes(x = !!rlang::sym(names(meta)[4]), y = !!rlang::sym(names(meta)[5])))+
ggplot2::geom_point(ggplot2::aes(colour = !!rlang::sym(names(meta)[3]), shape = !!rlang::sym(names(meta)[2])))+
ggplot2::geom_smooth(method = "lm", ggplot2::aes(colour = !!rlang::sym(names(meta)[3]), fill = !!rlang::sym(names(meta)[3])), alpha = 0.1)+
ggpubr::stat_cor(ggplot2::aes(colour = !!rlang::sym(names(meta)[3])), ...)+
ggplot2::theme_test() +
ggplot2::theme(text = ggplot2::element_text(colour='black'), axis.text=ggplot2::element_text(colour='black'))
return(p)
})
################################################################################
Try the easynem package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.