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if (getRversion() >= "2.15.1") {
utils::globalVariables(c("xvalue", "yvalue"))
}
#' lowertriangle - rearrange dataset as the preparation of ggscatmat function
#'
#' function for making the melted dataset used to plot the lowertriangle scatterplots.
#'
#' @export
#' @param data a data matrix. Should contain numerical (continuous) data.
#' @param columns an option to choose the column to be used in the raw dataset. Defaults to \code{1:ncol(data)}
#' @param color an option to choose a factor variable to be grouped with. Defaults to \code{(NULL)}
#' @author Mengjia Ni, Di Cook \email{dicook@@monash.edu}
#' @examples
#' data(flea)
#' head(lowertriangle(flea, columns= 2:4))
#' head(lowertriangle(flea))
#' head(lowertriangle(flea, color="species"))
lowertriangle <- function(data, columns=1:ncol(data), color=NULL) {
data <- upgrade_scatmat_data(data)
data.choose <- data[columns]
dn <- data.choose[sapply(data.choose, is.numeric)]
factor <- data[sapply(data, is.factor)]
p <- ncol(dn)
newdata <- NULL
for (i in 1:p) {
for (j in 1:p) {
newdata <- rbind(newdata,
cbind(dn[[i]], dn[[j]], i, j, colnames(dn)[i], colnames(dn)[j], factor)
)
}
}
colnames(newdata) <- c("xvalue", "yvalue", "xslot", "yslot", "xlab", "ylab", colnames(factor))
rp <- data.frame(newdata)
rp[[2]][rp[[3]] >= rp[[4]]] <- "NA"
rp[[1]][rp[[3]] > rp[[4]]] <- "NA"
rp$xvalue <- suppressWarnings(as.numeric(as.character(rp$xvalue)))
rp$yvalue <- suppressWarnings(as.numeric(as.character(rp$yvalue)))
if (is.null(color)){
rp.new <- rp[1:6]
} else {
colorcolumn <- rp[[which(colnames(rp) == color)]]
rp.new <- cbind(rp[1:6], colorcolumn)
}
return(rp.new)
}
#' uppertriangle - rearrange dataset as the preparation of ggscatmat function
#'
#' function for making the dataset used to plot the uppertriangle plots.
#'
#' @export
#' @param data a data matrix. Should contain numerical (continuous) data.
#' @param columns an option to choose the column to be used in the raw dataset. Defaults to \code{1:ncol(data)}
#' @param color an option to choose a factor variable to be grouped with. Defaults to \code{(NULL)}
#' @param corMethod method argument supplied to \code{\link[stats]{cor}}
#' @author Mengjia Ni, Di Cook \email{dicook@@monash.edu}
#' @importFrom stats cor
#' @examples
#' data(flea)
#' head(uppertriangle(flea, columns=2:4))
#' head(uppertriangle(flea))
#' head(uppertriangle(flea, color="species"))
uppertriangle <- function(data, columns=1:ncol(data), color=NULL, corMethod = "pearson") {
data <- upgrade_scatmat_data(data)
data.choose <- data[columns]
dn <- data.choose[sapply(data.choose, is.numeric)]
factor <- data[sapply(data, is.factor)]
p <- ncol(dn)
newdata <- NULL
for (i in 1:p) {
for (j in 1:p) {
newdata <- rbind(newdata,
cbind(dn[, i], dn[, j], i, j, colnames(dn)[i], colnames(dn)[j],
min(dn[, i]) + 0.5 * (max(dn[, i]) - min(dn[, i])),
min(dn[, j]) + 0.5 * (max(dn[, j]) - min(dn[, j])), factor)
)
}
}
colnames(newdata) <- c(
"xvalue", "yvalue",
"xslot", "yslot",
"xlab", "ylab",
"xcenter", "ycenter",
colnames(factor)
)
rp <- data.frame(newdata)
rp[[2]][rp[[3]] <= rp[[4]]] <- "NA"
rp[[1]][rp[[3]] < rp[[4]]] <- "NA"
rp$xvalue <- suppressWarnings(as.numeric(as.character(rp$xvalue)))
rp$yvalue <- suppressWarnings(as.numeric(as.character(rp$yvalue)))
if (is.null(color)){
rp.new <- rp[1:8]
}else{
colorcolumn <- rp[[which(colnames(rp) == color)]]
rp.new <- cbind(rp[1:8], colorcolumn)
}
a <- rp.new
b <- subset(a, (a$yvalue != "NA") & (a$xvalue != "NA"))
if (is.null(color)){
data.cor <- ddply(
b, .(ylab, xlab),
function(subsetDt) {
xlab <- subsetDt$xlab
ylab <- subsetDt$ylab
xvalue <- subsetDt$xvalue
yvalue <- subsetDt$yvalue
if (identical(corMethod, "rsquare")) {
r <- cor(
xvalue, yvalue,
use = "pairwise.complete.obs",
method = "pearson"
)
r <- r ^ 2
} else {
r <- cor(
xvalue, yvalue,
use = "pairwise.complete.obs",
method = corMethod
)
}
r <- paste(round(r, digits = 2))
data.frame(
xlab = unique(xlab), ylab = unique(ylab),
r = r,
xvalue = min(xvalue) + 0.5 * (max(xvalue) - min(xvalue)),
yvalue = min(yvalue) + 0.5 * (max(yvalue) - min(yvalue))
)
}
)
return(data.cor)
}else{
c <- b
data.cor1 <- ddply(
c, .(ylab, xlab, colorcolumn),
function(subsetDt) {
xlab <- subsetDt$xlab
ylab <- subsetDt$ylab
colorcolumn <- subsetDt$colorcolumn
xvalue <- subsetDt$xvalue
yvalue <- subsetDt$yvalue
if (identical(corMethod, "rsquare")) {
r <- cor(
xvalue, yvalue,
use = "pairwise.complete.obs",
method = "pearson"
)
r <- r ^ 2
} else {
r <- cor(
xvalue, yvalue,
use = "pairwise.complete.obs",
method = corMethod
)
}
r <- paste(round(r, digits = 2))
data.frame(
ylab = unique(ylab), xlab = unique(xlab), colorcolumn = unique(colorcolumn),
r = r
)
}
)
n <- nrow(data.frame(unique(b$colorcolumn)))
position <- ddply(b, .(ylab, xlab), summarise,
xvalue = min(xvalue) + 0.5 * (max(xvalue) - min(xvalue)),
ymin = min(yvalue),
ymax = max(yvalue),
range = max(yvalue) - min(yvalue))
df <- data.frame()
for (i in 1:nrow(position)) {
for (j in 1:n){
row <- position[i, ]
df <- rbind(df, cbind(row[, 3], (row[, 4] + row[, 6] * j / (n + 1))))
}
}
data.cor <- cbind(data.cor1, df)
colnames(data.cor) <- c("ylab", "xlab", "colorcolumn", "r", "xvalue", "yvalue")
return(data.cor)
}
}
#' scatmat - plot the lowertriangle plots and density plots of the scatter plot matrix.
#'
#' function for making scatterplots in the lower triangle and diagonal density plots.
#'
#' @export
#' @param data a data matrix. Should contain numerical (continuous) data.
#' @param columns an option to choose the column to be used in the raw dataset. Defaults to \code{1:ncol(data)}
#' @param color an option to group the dataset by the factor variable and color them by different colors. Defaults to \code{NULL}
#' @param alpha an option to set the transparency in scatterplots for large data. Defaults to \code{1}.
#' @author Mengjia Ni, Di Cook \email{dicook@@monash.edu}
#' @examples
#' data(flea)
#' scatmat(flea, columns=2:4)
#' scatmat(flea, columns= 2:4, color="species")
scatmat <- function(data, columns=1:ncol(data), color=NULL, alpha=1) {
data <- upgrade_scatmat_data(data)
data.choose <- data[columns]
dn <- data.choose[sapply(data.choose, is.numeric)]
if (ncol(dn) == 0) {
stop("All of your variables are factors. Need numeric variables to make scatterplot matrix.")
} else {
ltdata.new <- lowertriangle(data, columns = columns, color = color)
r <- ggplot(ltdata.new, mapping = aes_string(x = "xvalue", y = "yvalue")) +
theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
facet_grid(ylab ~ xlab, scales = "free") +
theme(aspect.ratio = 1)
if (is.null(color)) {
densities <- do.call("rbind", lapply(1:ncol(dn), function(i) {
data.frame(xlab = names(dn)[i], ylab = names(dn)[i],
x = dn[, i])
}))
for (m in 1:ncol(dn)) {
j <- subset(densities, xlab == names(dn)[m])
r <- r + stat_density(
aes(
x = x,
y = ..scaled.. * diff(range(x)) + min(x) # nolint
),
data = j, position = "identity", geom = "line", color = "black")
}
r <- r + geom_point(alpha = alpha, na.rm = TRUE)
return(r)
} else {
densities <- do.call("rbind", lapply(1:ncol(dn), function(i) {
data.frame(xlab = names(dn)[i], ylab = names(dn)[i],
x = dn[, i], colorcolumn = data[, which(colnames(data) == color)])
}))
for (m in 1:ncol(dn)) {
j <- subset(densities, xlab == names(dn)[m])
r <- r +
stat_density(
aes_string(
x = "x", y = "..scaled.. * diff(range(x)) + min(x)",
colour = "colorcolumn"
),
data = j,
position = "identity",
geom = "line"
)
}
r <- r +
geom_point(
data = ltdata.new,
aes_string(colour = "colorcolumn"),
alpha = alpha,
na.rm = TRUE
)
return(r)
}
}
}
#' ggscatmat - a traditional scatterplot matrix for purely quantitative variables
#'
#' This function makes a scatterplot matrix for quantitative variables with density plots on the diagonal
#' and correlation printed in the upper triangle.
#'
#' @export
#' @param data a data matrix. Should contain numerical (continuous) data.
#' @param columns an option to choose the column to be used in the raw dataset. Defaults to \code{1:ncol(data)}.
#' @param color an option to group the dataset by the factor variable and color them by different colors. Defaults to \code{NULL}.
#' @param alpha an option to set the transparency in scatterplots for large data. Defaults to \code{1}.
#' @param corMethod method argument supplied to \code{\link[stats]{cor}}
#' @author Mengjia Ni, Di Cook \email{dicook@@monash.edu}
#' @examples
#' data(flea)
#' ggscatmat(flea, columns = 2:4)
#' ggscatmat(flea, columns = 2:4, color = "species")
ggscatmat <- function(data, columns = 1:ncol(data), color = NULL, alpha = 1, corMethod = "pearson"){
data <- upgrade_scatmat_data(data)
data.choose <- data[columns]
dn <- data.choose[sapply(data.choose, is.numeric)]
if (ncol(dn) == 0) {
stop("All of your variables are factors. Need numeric variables to make scatterplot matrix.")
}
if (ncol(dn) < 2){
stop ("Not enough numeric variables to make a scatter plot matrix")
}
a <- uppertriangle(data, columns = columns, color = color, corMethod = corMethod)
if (is.null(color)){
plot <- scatmat(data, columns = columns, alpha = alpha) +
geom_text(data = a, aes_string(label = "r"), colour = "black")
} else {
plot <- scatmat(data, columns = columns, color = color, alpha = alpha) +
geom_text(data = a, aes_string(label = "r", color = "colorcolumn")) + labs(color = color)
}
factor <- data.choose[sapply(data.choose, is.factor)]
if (ncol(factor) == 0){
return(plot)
} else {
warning("Factor variables are omitted in plot")
return(plot)
}
}
upgrade_scatmat_data <- function(data) {
data <- as.data.frame(data)
dataIsCharacter <- sapply(data, is.character)
if (any(dataIsCharacter)) {
dataCharacterColumns <- names(dataIsCharacter[dataIsCharacter])
for (dataCol in dataCharacterColumns) {
data[[dataCol]] <- as.factor(data[[dataCol]])
}
}
data
}
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