#' Factor observed clod percentages.
#'
#' @param clmn A vector.
#' @return A vector of length clmn factored according to standard clod percentages
#' @examples
#' factorClods(data$f1.small)
factorClods <- function(clmn){
clmn[clmn=="100"] <- "90-100"
clmn <- factor(clmn, levels=c("0", "1-10", "10-20", "20-30", "30-40", "40-50",
"50-60", "60-70", "70-80", "80-90", "90-100"),
labels=c("0", "10", "20", "30", "40", "50", "60", "70", "80",
"90", "100"))
clmn
}
#' Create a list of plots for densities of tillage roughness characteristics.
#'
#' @param data A melted data frame for tillage roughness data with standard "variable"
#' and "value" column names.
#' @param compare A character string for the identity variable for which the data
#' should be plotted separately in each panel.
#' @return A ggplot2 object with density plots for the 4 roughness characteristics.
#' @examples
#' factorClods(rough_melt, observer)
compareDensity <- function(data, compare){
plots <- list()
for (i in 1:length(unique(data$variable))){
plt <- data %>% filter(variable==unique(data$variable)[i]) %>%
ggplot(aes(x=value)) +
geom_density(aes_string(color=compare)) +
xlab(unique(data$variable)[i])
plots <- c(plots, list(plt))
}
grid.arrange(plots[[1]], plots[[2]], plots[[3]], plots[[4]], ncol=2)
}
makePNG <- function(plt, filename, ht=6, wt=6, ppi=300){
png(filename, width=wt*ppi, height=ht*ppi, res=ppi)
print(plt)
dev.off()
}
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