#' Chart of rows correlation with a selected dimension
#'
#' This function allows to calculate the correlation (sqrt(COS2)) of the row categories with the selected dimension.
#'
#' The function displays the correlation of the row categories with the selected dimension; the parameter sort=TRUE arrange the categories in decreasing order of correlation.
#' At the left-hand side, the categories' labels show a symbol (+ or -) according to which side of the selected dimension they are correlated, either positive or negative.
#' The categories are grouped into two groups: categories correlated with the positive ('pole +') or negative ('pole -') pole of the selected dimension.
#' At the right-hand side, a legend (which is enabled/disabled using the 'leg' parameter) indicates the column categories' contribution (in permills) to the selected dimension (value enclosed within round brackets), and a symbol (+ or -) indicating whether they are actually contributing to the definition of the positive or negative side of the dimension, respectively.
#' Further, an asterisk (*) flags the categories which can be considered major contributors to the definition of the dimension.
#' @param data: name of the dataset (must be in dataframe format).
#' @param x: dimension for which the row categories correlation is returned (1st dimension by default).
#' @param sort: logical value (TRUE/FALSE) which allows to sort the categories in descending order of correlation with the selected dimension. TRUE is set by default.
#' @param filter: filter the column categories listed in the top-right legend, only showing those who have a major contribution to the definition of the selected dimension.
#' @param leg: enable (TRUE; default) or disable (FALSE) the legend at the right-hand side of the dotplot.
#' @param dotprightm: increases the empty space between the right margin of the dotplot and the left margin of the legend box.
#' @param cex.leg: adjust the size of the legend's characters.
#' @param cex.labls: adjust the size of the dotplot's labels.
#' @param leg.x.spc: adjust the horizontal space of the chart's legend. See more info from the 'legend' function's help (?legend).
#' @param leg.y.spc: adjust the y interspace of the chart's legend. See more info from the 'legend' function's help (?legend).
#' @keywords rows correlation
#' @export
#' @examples
#' data(greenacre_data)
#' rows.corr(greenacre_data, 1, sort=TRUE) #Plots the correlation of the row categories with the 1st CA dimension.
#'
rows.corr <- function (data, x = 1, sort = TRUE, filter= FALSE, leg=TRUE, dotprightm=5, cex.leg=0.6, cex.labls=0.75, leg.x.spc=1, leg.y.spc=1) {
cadataframe <- CA(data, graph = FALSE)
df <- data.frame(corr = round(sqrt((cadataframe$row$cos2[, x])), digits = 3), coord=cadataframe$row$coord[,x])
df$labels <- ifelse(df$coord < 0,
paste(rownames(df), " - ", sep = ""),
paste(rownames(df), " + ", sep = ""))
df.col.cntr <- data.frame(coord=cadataframe$col$coord[,x], cntr=(cadataframe$col$contrib[,x]*10))
df.col.cntr$labels <- ifelse(df.col.cntr$coord < 0,
paste(rownames(df.col.cntr), " - ", sep = ""),
paste(rownames(df.col.cntr), " + ", sep = ""))
df.col.cntr$specif <- ifelse(df.col.cntr$cntr > (100/ncol(data)) * 10,
"*",
"")
df.col.cntr$specif2 <- paste0(df.col.cntr$specif, df.col.cntr$labels, "(", round(df.col.cntr$cntr,2), ")")
ifelse(sort == TRUE,
df.to.use <- df[order(-df$corr), ],
df.to.use <- df)
df.to.use$pole <- ifelse(df.to.use$coord > 0,
"pole +",
"pole -")
ifelse(filter== FALSE,
df.col.cntr <- df.col.cntr,
df.col.cntr <- subset(df.col.cntr, cntr>(100/ncol(data))*10))
if(leg==TRUE){
par(oma=c(0,0,0,dotprightm))
} else {}
dotchart2(df.to.use$corr,
labels = df.to.use$labels,
groups=df.to.use$pole,
sort = FALSE,
lty = 2,
xlim = c(0, 1),
cex.labels=cex.labls,
xlab = paste("Row categories' correlation with Dim. ", x))
par(oma=c(0,0,0,0))
if(leg==TRUE){
legend(x="topright",
legend=df.col.cntr[order(-df.col.cntr$cntr),]$specif2,
xpd=TRUE,
cex=cex.leg,
x.intersp = leg.x.spc,
y.intersp = leg.y.spc)
} else {}
}
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