R/visualise_variables_xx.R
In oislen/BuenaVista: Functions for Everyday Data Science Tasks
#' @title Visualise each Variable Pair in a given Dataset
#'
#' @description Plots an appropriate visualisations for two attributes in a given dataset.
#' This function utilises ggplot2 to design and plot the Visualisations.
#' Plots a scatterplot for two numeric variables.
#' Plots a bar chart for two factor variables.
#' Plots a boxplot for one numeric variable and one categorical variable.
#' The plots can be standardised, whereby the values lie on the interval 0 to 1.
#' By default, regular plots are visualised.
#' There is also an option to click through the points one by one, or print them all simultaneously.
#' The plots are outputed as a list.
#' The plots can also be saved to a specified directory as pdfs.
#' Note, that it is crucial that the variables are correctly defined as either numeric or categorical.
#'
#' @param dataset The dataset to be visualised.
#' Note, appropriate data types must be defined for the columns.
#'
#' @param y_index An integer value, indicating the column index of the response variable, the default is NULL.
#'
#' @param y_name A character value, indicating the column name of the response variable, the default is NULL.
#'
#' @param click A boolean value, indicating whether to click through the plots one by one, default is True,
#'
#' @param standardise A logical value, indicating whether the axises should be standardised to the interval 0 to 1, the default is False.
#'
#' @param directory A character object specifying the directory where the data frame is to be saved as a .pdf file.
#'
#' @return Outputs a variety of bar charts, histograms and bar charts.
#'
#' @import ggplot2
#'
#' @export
#'
#' @seealso \code{\link{visualise_qqplot}}, \code{\link{visualise_residuals}}, \code{\link{visualise_variables_x}}
#'
#' @examples
#' #-- Example 1: LungCap Data --#
#'
#' # Regular Visualisations of the data frame
#' visualise_variables_xx(dataset = lungcap, click = TRUE)
#'
#' Regular visualistions of the data using y_name
#' visualise_variables_xx(dataset = lungcap, y_name = 'Gender')
#'
#' # Standardised Visualtions of the dataset
#' visualise_variables_xx(dataset = lungcap, standardise = TRUE, click = FALSE)
#'
#' # Standardised Visualisations of the data frame using y_index
#' visualise_variables_xx(dataset = lungcap, standardise = TRUE, y_index = 1)
#'
#' #-- Example 2: Titanic Data --#
#'
#' # Regular Visualisations with click option
#' visualise_variables_xx(dataset = titanic, click = TRUE)
#'
#' Regular visualistions of the data using y_name
#' visualise_variables_xx(dataset = titanic, y_name = 'Gender', click = TRUE)
#'
#' # Standardised Visualtions of the dataset
#' visualise_variables_xx(dataset = titanic, standardise = TRUE, click = FALSE)
#'
# Standardised Visualisations of the data frame using y_index
#' visualise_variables_xx(dataset = titanic, standardise = TRUE, y_index = 1)
#'
visualise_variables_xx <- function(dataset,
y_index = NULL,
y_name = NULL,
click = TRUE,
standardise = FALSE,
directory = NULL)
{
# set whether to click through the plots
op = par(ask = click)
#-------------------------------------------------------------------------------#
# When y_index = NULL and y_name = NULL #
#-------------------------------------------------------------------------------#
if(is.null(y_index) & is.null(y_name)){
# NOTE: ggplot2 automatically removes missing observations
# Make sure the datset is converted to a data frame
dataset <- as.data.frame(x = dataset)
# create a plot list to hold the pictures
plot_list <- list()
# p is the picture index - tracks the number of pictures created
p = 1
#---------------------------------------------------------------------------#
# Unstandardised Visualisations #
#---------------------------------------------------------------------------#
# plot regular visualisations
if(standardise == FALSE) {
# use a for loop to loop through the columns of the dataset
for (i in 1:(ncol(dataset))) {
# create the jth column index
j = i + 1
# use a while loop to loop through the remaining columns of the dataset
while (j <= ncol(dataset)) {
# store the resonse variable names
xiname <- colnames(dataset)[i]
# store the predictor variable names
xjname <- colnames(dataset)[j]
#---------------------------------------------------------------------#
# If the variable is categorical, plot a bar chart #
#---------------------------------------------------------------------#
if ((is.factor(dataset[,i]) & (is.factor(dataset[,j])))){
# First it is neccesary to aggregate the raw data
agg_data <- as.data.frame(x = table(dataset[,c(i,j)]),
col.names = c(xiname, xjname, "Count"))
# Plot the aggregated data
double_bar_chart <- ggplot(data = agg_data,
mapping = aes(x = agg_data[,1],
y = agg_data[,3])) +
geom_bar(mapping = aes(fill = agg_data[,2]),
position = "dodge",
stat = "identity") +
# Give the labels to the plots
labs(title = paste("Double Bar Chart of", xjname, "and", xiname, sep = " "),
x = xiname,
y = "Count") +
# set the title of the legend
guides(fill = guide_legend(title = yname)) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20),
legend.title = element_text(size = 20),
legend.text = element_text(size = 30),
legend.position = "top")
# save the plot to the list
plot_list[[p]] <- double_bar_chart
# print the plot
print(double_bar_chart)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Double_Bar_Chart_of_", xiname, "_and_", xjname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#-------------------------------------------------------------------#
# If both variables are numeric, plot a scatterplot #
#-------------------------------------------------------------------#
} else if ((is.numeric(dataset[,i]) & (is.numeric(dataset[,j])))){
# create the scatterplot
scatterplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,i],
y = dataset[,j])) +
geom_point() +
# Add a regression line in blue
geom_smooth(method = "lm",
colour = "blue") +
# Add a loess line in red
geom_smooth(method = "loess",
colour = "red",
se = FALSE) +
# Add labels to the plot
labs(title = paste("Scatterplot of", xjname,
"and", xiname, sep = " "),
x = xiname,
y = xjname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- scatterplot
# print the plot
print(scatterplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Scatterplot_of_", xjname, "_and_", xiname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#--------------------------------------------------------------------------------#
# If the x variables is numeric and the y variable is categorical plot a boxplot #
#--------------------------------------------------------------------------------#
} else if((is.numeric(dataset[,i]) & (is.factor(dataset[,j])))){
# create the boxplot
boxplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,j],
y = dataset[,i])) +
geom_boxplot(outlier.colour = 'red') +
# Add labels to the plot
labs(title = paste("Boxplot of", xiname,
"and", xjname, sep = " "),
x = xjname,
y = xiname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- boxplot
# print the plot
print(boxplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Boxplot_of_", xiname, "_and_", xjname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#--------------------------------------------------------------------------------#
# If the y variables is numeric and the x variable is categorical plot a boxplot #
#--------------------------------------------------------------------------------#
} else if ((is.factor(dataset[,i]) & (is.numeric(dataset[,j])))){
# create the box plot
boxplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,i],
y = dataset[,j])) +
geom_boxplot(outlier.colour = 'red') +
coord_flip() +
# Add labels to the plot
labs(title = paste("Boxplot of", xjname,
"and", xiname, sep = " "),
x = xiname,
y = xjname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- boxplot
# print the plot
print(boxplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Boxplot_of_", xjname, "_and_", xiname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
}
# update the jth column index
j = j + 1
}
}
#---------------------------------------------------------------------------#
# Standardised Visualisations #
#---------------------------------------------------------------------------#
# plot standardised visualisations
} else if(standardise == TRUE) {
# use a for loop to loop through the columns of the dataset
for (i in 1:(ncol(dataset))) {
# create jth columnindex
j = i + 1
# use a while loop to loop through the remaining columns of the dataset
while (j <= ncol(dataset)) {
# store the predictor variable names
xiname <- colnames(dataset)[i]
# store the response variable name
xjname <- colnames(dataset)[j]
#---------------------------------------------------------------------#
# If the variable is categorical, plot a bar chart #
#---------------------------------------------------------------------#
if ((is.factor(dataset[,i]) & (is.factor(dataset[,j])))){
# First it is neccesary to aggregate the raw data
agg_data <- as.data.frame(x = table(dataset[,c(i,j)]),
col.names = c(xiname, xjname, "Count"))
# create the proportions
Proportion <- agg_data[,3] / sum(agg_data[,3])
# add the proportions to the aggragated dataset
agg_data[,4] <- Proportion
# Plot the aggregated data
double_bar_chart<- ggplot(data = agg_data,
mapping = aes(x = agg_data[,1],
y = agg_data[,4])) +
geom_bar(mapping = aes(fill = agg_data[,2]),
position = "dodge",
stat = "identity") +
# Give the labels to the plots
labs(title = paste("Standardised Double Bar Chart of", xjname,
"and", xiname, sep = " "),
x = xiname,
y = "Proportion") +
# set the title of the legend
guides(fill = guide_legend(title = yname)) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20),
legend.title = element_text(size = 20),
legend.text = element_text(size = 30),
legend.position = "top")
# save the plot to the list
plot_list[[p]] <- double_bar_chart
# print the plot
print(double_bar_chart)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Standardised_Double_Bar_Chart_of_", xiname, "_and_", xjname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#-------------------------------------------------------------------#
# If both variables are numeric, plot a scatterplot #
#-------------------------------------------------------------------#
} else if ((is.numeric(dataset[,i]) & (is.numeric(dataset[,j])))){
# standardise the numeric variables
dataset <- standardise_variables(dataset = dataset, method = "range", lower_bound = 0, upper_bound = 1)
# create the scatterplot
scatterplot<- ggplot(data = dataset,
mapping = aes(x = dataset[,i],
y = dataset[,j])) +
geom_point() +
# Add a regression line in blue
geom_smooth(method = "lm",
colour = "blue") +
# Add a loess line in red
geom_smooth(method = "loess",
colour = "red",
se = FALSE) +
# Add labels to the plot
labs(title = paste("Standardised Scatterplot of", xjname,
"and", xiname, sep = " "),
x = xiname,
y = xjname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- scatterplot
# print the plot
print(scatterplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Standardised_Scatterplot_of_", xjname,
"_and_", xiname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#--------------------------------------------------------------------------------#
# If the x variables is numeric and the y variable is categorical plot a boxplot #
#--------------------------------------------------------------------------------#
} else if((is.numeric(dataset[,i]) & (is.factor(dataset[,j])))){
# standardise the numeric variables
dataset <- standardise_variables(dataset = dataset, method = "range", lower_bound = 0, upper_bound = 1)
# create the boxplot
boxplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,j],
y = dataset[,i])) +
geom_boxplot(outlier.colour = 'red') +
# Add labels to the plot
labs(title = paste("Standardised Boxplot of", xiname,
"and", xjname, sep = " "),
x = xjname,
y = xiname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- boxplot
# print the plot
print(boxplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Standardised_Boxplot_of_", xiname, "_and_", xjname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#--------------------------------------------------------------------------------#
# If the y variables is numeric and the x variable is categorical plot a boxplot #
#--------------------------------------------------------------------------------#
} else if ((is.factor(dataset[,i]) & (is.numeric(dataset[,j])))){
# standardise the numeric variables
dataset <- standardise_variables(dataset = dataset, method = "range", lower_bound = 0, upper_bound = 1)
# create the boxplot
boxplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,i],
y = dataset[,j])) +
geom_boxplot(outlier.colour = 'red') +
coord_flip() +
# Add labels to the plot
labs(title = paste("Standardise Boxplot of", xjname,
"and", xiname, sep = " "),
x = xiname,
y = xjname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- boxplot
# print the plot
print(boxplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Standardised_Boxplot_of_", xjname, "_and_", xiname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
}
j = j + 1
}
}
}
#-------------------------------------------------------------------------------#
# When y_index or y_name != NULL #
#-------------------------------------------------------------------------------#
} else if(!is.null(y_index) | !is.null(y_name)){
if(!is.null(y_name)){
y_index = which(colnames(dataset) == y_name)
}
# NOTE: ggplot2 automatically removes missing observations
# Make sure the datset is converted to a data frame
dataset <- as.data.frame(x = dataset)
# create a plot list to hold the pictures
plot_list <- list()
# p is the picture index - tracks the number of pictures created
p = 1
#---------------------------------------------------------------------------#
# Unstandardised Visualisations #
#---------------------------------------------------------------------------#
# plot regular visualisations
if(standardise == FALSE) {
# use a for loop to loop through the columns of the dataset
for (i in 1:(ncol(dataset))) {
# store the predictor variable names
xiname <- colnames(dataset)[i]
# store the response variable name
yname <- colnames(dataset)[y_index]
#-----------------------------------------------------------------------#
# If the variable is categorical, plot a bar chart #
#-----------------------------------------------------------------------#
if ((is.factor(dataset[,i]) & (is.factor(dataset[,y_index])))){
# First it is neccesary to aggregate the raw data
agg_data <- as.data.frame(x = table(dataset[,c(i,y_index)]),
col.names = c(xiname, yname, "Count"))
# Plot the aggregated data
double_bar_chart <- ggplot(data = agg_data,
mapping = aes(x = agg_data[,1],
y = agg_data[,3])) +
geom_bar(mapping = aes(fill = agg_data[,2]),
position = "dodge",
stat = "identity") +
# Give the labels to the plots
labs(title = paste("Double Bar Chart of", yname,
"and", xiname, sep = " "),
x = xiname,
y = "Count") +
# set the title of the legend
guides(fill = guide_legend(title = yname)) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20),
legend.title = element_text(size = 20),
legend.text = element_text(size = 30),
legend.position = "top")
# save the plot to the list
plot_list[[p]] <- double_bar_chart
# print the plot
print(double_bar_chart)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Double_Bar_Chart_of_", xiname,
"_and_", yname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#---------------------------------------------------------------------#
# If both variables are numeric, plot a scatterplot #
#---------------------------------------------------------------------#
} else if ((is.numeric(dataset[,i]) & (is.numeric(dataset[,y_index])))){
# create the scatterplot
scatterplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,i],
y = dataset[,y_index])) +
geom_point() +
# Add a regression line in blue
geom_smooth(method = "lm",
colour = "blue") +
# Add a loess line in red
geom_smooth(method = "loess",
colour = "red",
se = FALSE) +
# Add labels to the plot
labs(title = paste("Scatterplot of", yname,
"and", xiname, sep = " "),
x = xiname,
y = yname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- scatterplot
# print the plot
print(scatterplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Scatterplot_of_", yname,
"_and_", xiname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#--------------------------------------------------------------------------------#
# If the x variables is numeric and the y variable is categorical plot a boxplot #
#--------------------------------------------------------------------------------#
} else if((is.numeric(dataset[,i]) & (is.factor(dataset[,y_index])))){
# create the boxplot
boxplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,y_index],
y = dataset[,i])) +
geom_boxplot(outlier.colour = 'red') +
# Add labels to the plot
labs(title = paste("Boxplot of", yname,
"and", xiname, sep = " "),
x = yname,
y = xiname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- boxplot
# print the plot
print(boxplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Boxplot_of_", yname, "_and_", xiname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#--------------------------------------------------------------------------------#
# If the y variables is numeric and the x variable is categorical plot a boxplot #
#--------------------------------------------------------------------------------#
} else if((is.factor(dataset[,i]) & (is.numeric(dataset[,y_index])))){
# create the boxplot
boxplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,i],
y = dataset[,y_index])) +
geom_boxplot(outlier.colour = 'red') +
coord_flip() +
# Add labels to the plot
labs(title = paste("Boxplot of", xiname,
"and", yname, sep = " "),
x = xiname,
y = yname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- boxplot
# print the plot
print(boxplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Boxplot_of_", xiname, "_and_", yname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
}
}
#---------------------------------------------------------------------------#
# Standardised Visualisations #
#---------------------------------------------------------------------------#
# create standardised plots
} else if(standardise == TRUE) {
# usea for loop to loop through the columns of the dataset
for (i in 1:(ncol(dataset))) {
# save the name of the predictor variable
xiname <- colnames(dataset)[i]
# save the name of the response variable
yname <- colnames(dataset)[y_index]
#-----------------------------------------------------------------------#
# If the variable is categorical, plot a bar chart #
#-----------------------------------------------------------------------#
if ((is.factor(dataset[,i]) & (is.factor(dataset[,y_index])))){
# First it is neccesary to aggregate the raw data
agg_data <- as.data.frame(x = table(dataset[,c(i,y_index)]),
col.names = c(xiname, yname, "Count"))
# create the proportions
Proportion <- agg_data[,3] / sum(agg_data[,3])
# add the proportions to the aggregated data
agg_data[,4] <- Proportion
# Plot the aggregated data
double_bar_chart<- ggplot(data = agg_data,
mapping = aes(x = agg_data[,1],
y = agg_data[,4])) +
geom_bar(mapping = aes(fill = agg_data[,2]),
position = "dodge",
stat = "identity") +
# Give the labels to the plots
labs(title = paste("Standardised Double Bar Chart of", yname,
"and", xiname, sep = " "),
x = xiname,
y = "Proportion") +
# set the title of the legend
guides(fill = guide_legend(title = yname)) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20),
legend.title = element_text(size = 20),
legend.text = element_text(size = 30),
legend.position = "top")
# save the plot to the list
plot_list[[p]] <- double_bar_chart
# print the plot
print(double_bar_chart)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Standardised_Double_Bar_Chart_of_", xiname,
"_and_", yname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#---------------------------------------------------------------------#
# If both variables are numeric, plot a scatterplot #
#---------------------------------------------------------------------#
} else if ((is.numeric(dataset[,i]) & (is.numeric(dataset[,y_index])))){
# standardise the numeric variables
dataset <- standardise_variables(dataset = dataset, method = "range", lower_bound = 0, upper_bound = 1)
# create the scaterplot
scatterplot<- ggplot(data = dataset,
mapping = aes(x = dataset[,i],
y = dataset[,y_index])) +
geom_point() +
# Add a regression line in blue
geom_smooth(method = "lm",
colour = "blue") +
# Add a loess line in red
geom_smooth(method = "loess",
colour = "red",
se = FALSE) +
# Add labels to the plot
labs(title = paste("Standardised Scatterplot of", yname,
"and", xiname, sep = " "),
x = xiname,
y = yname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- scatterplot
# print the plot
print(scatterplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Standardised_Scatterplot_of_", yname,
"_and_", xiname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#--------------------------------------------------------------------------------#
# If the x variables is numeric and the y variable is categorical plot a boxplot #
#--------------------------------------------------------------------------------#
} else if((is.numeric(dataset[,i]) & (is.factor(dataset[,y_index])))){
# standardise the numeric variables
dataset <- standardise_variables(dataset = dataset, method = "range", lower_bound = 0, upper_bound = 1)
# create the box plot
boxplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,y_index],
y = dataset[,i])) +
geom_boxplot(outlier.colour = 'red') +
# Add labels to the plot
labs(title = paste("Standardised Boxplot of", yname,
"and", xiname, sep = " "),
x = yname,
y = xiname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- boxplot
# print the plot
print(boxplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Standardised_Boxplot_of_", yname, "_and_", xiname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#--------------------------------------------------------------------------------#
# If the y variables is numeric and the x variable is categorical plot a boxplot #
#--------------------------------------------------------------------------------#
} else if((is.factor(dataset[,i]) & (is.numeric(dataset[,y_index])))){
# standardise the numeric variables
dataset <- standardise_variables(dataset = dataset, method = "range", lower_bound = 0, upper_bound = 1)
# create the boxplot
boxplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,i],
y = dataset[,y_index])) +
geom_boxplot(outlier.colour = 'red') +
coord_flip() +
# Add labels to the plot
labs(title = paste("Standardised Boxplot of", xiname,
"and", yname, sep = " "),
x = xiname,
y = yname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- boxplot
# print the plot
print(boxplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Standardised_Boxplot_of_", xiname, "_and_", yname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
}
}
}
}
# click through the plots
par(op)
}
oislen/BuenaVista documentation built on May 16, 2019, 8:12 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.
#' @title Visualise each Variable Pair in a given Dataset
#'
#' @description Plots an appropriate visualisations for two attributes in a given dataset.
#' This function utilises ggplot2 to design and plot the Visualisations.
#' Plots a scatterplot for two numeric variables.
#' Plots a bar chart for two factor variables.
#' Plots a boxplot for one numeric variable and one categorical variable.
#' The plots can be standardised, whereby the values lie on the interval 0 to 1.
#' By default, regular plots are visualised.
#' There is also an option to click through the points one by one, or print them all simultaneously.
#' The plots are outputed as a list.
#' The plots can also be saved to a specified directory as pdfs.
#' Note, that it is crucial that the variables are correctly defined as either numeric or categorical.
#'
#' @param dataset The dataset to be visualised.
#' Note, appropriate data types must be defined for the columns.
#'
#' @param y_index An integer value, indicating the column index of the response variable, the default is NULL.
#'
#' @param y_name A character value, indicating the column name of the response variable, the default is NULL.
#'
#' @param click A boolean value, indicating whether to click through the plots one by one, default is True,
#'
#' @param standardise A logical value, indicating whether the axises should be standardised to the interval 0 to 1, the default is False.
#'
#' @param directory A character object specifying the directory where the data frame is to be saved as a .pdf file.
#'
#' @return Outputs a variety of bar charts, histograms and bar charts.
#'
#' @import ggplot2
#'
#' @export
#'
#' @seealso \code{\link{visualise_qqplot}}, \code{\link{visualise_residuals}}, \code{\link{visualise_variables_x}}
#'
#' @examples
#' #-- Example 1: LungCap Data --#
#'
#' # Regular Visualisations of the data frame
#' visualise_variables_xx(dataset = lungcap, click = TRUE)
#'
#' Regular visualistions of the data using y_name
#' visualise_variables_xx(dataset = lungcap, y_name = 'Gender')
#'
#' # Standardised Visualtions of the dataset
#' visualise_variables_xx(dataset = lungcap, standardise = TRUE, click = FALSE)
#'
#' # Standardised Visualisations of the data frame using y_index
#' visualise_variables_xx(dataset = lungcap, standardise = TRUE, y_index = 1)
#'
#' #-- Example 2: Titanic Data --#
#'
#' # Regular Visualisations with click option
#' visualise_variables_xx(dataset = titanic, click = TRUE)
#'
#' Regular visualistions of the data using y_name
#' visualise_variables_xx(dataset = titanic, y_name = 'Gender', click = TRUE)
#'
#' # Standardised Visualtions of the dataset
#' visualise_variables_xx(dataset = titanic, standardise = TRUE, click = FALSE)
#'
# Standardised Visualisations of the data frame using y_index
#' visualise_variables_xx(dataset = titanic, standardise = TRUE, y_index = 1)
#'
visualise_variables_xx <- function(dataset,
y_index = NULL,
y_name = NULL,
click = TRUE,
standardise = FALSE,
directory = NULL)
{
# set whether to click through the plots
op = par(ask = click)
#-------------------------------------------------------------------------------#
# When y_index = NULL and y_name = NULL #
#-------------------------------------------------------------------------------#
if(is.null(y_index) & is.null(y_name)){
# NOTE: ggplot2 automatically removes missing observations
# Make sure the datset is converted to a data frame
dataset <- as.data.frame(x = dataset)
# create a plot list to hold the pictures
plot_list <- list()
# p is the picture index - tracks the number of pictures created
p = 1
#---------------------------------------------------------------------------#
# Unstandardised Visualisations #
#---------------------------------------------------------------------------#
# plot regular visualisations
if(standardise == FALSE) {
# use a for loop to loop through the columns of the dataset
for (i in 1:(ncol(dataset))) {
# create the jth column index
j = i + 1
# use a while loop to loop through the remaining columns of the dataset
while (j <= ncol(dataset)) {
# store the resonse variable names
xiname <- colnames(dataset)[i]
# store the predictor variable names
xjname <- colnames(dataset)[j]
#---------------------------------------------------------------------#
# If the variable is categorical, plot a bar chart #
#---------------------------------------------------------------------#
if ((is.factor(dataset[,i]) & (is.factor(dataset[,j])))){
# First it is neccesary to aggregate the raw data
agg_data <- as.data.frame(x = table(dataset[,c(i,j)]),
col.names = c(xiname, xjname, "Count"))
# Plot the aggregated data
double_bar_chart <- ggplot(data = agg_data,
mapping = aes(x = agg_data[,1],
y = agg_data[,3])) +
geom_bar(mapping = aes(fill = agg_data[,2]),
position = "dodge",
stat = "identity") +
# Give the labels to the plots
labs(title = paste("Double Bar Chart of", xjname, "and", xiname, sep = " "),
x = xiname,
y = "Count") +
# set the title of the legend
guides(fill = guide_legend(title = yname)) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20),
legend.title = element_text(size = 20),
legend.text = element_text(size = 30),
legend.position = "top")
# save the plot to the list
plot_list[[p]] <- double_bar_chart
# print the plot
print(double_bar_chart)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Double_Bar_Chart_of_", xiname, "_and_", xjname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#-------------------------------------------------------------------#
# If both variables are numeric, plot a scatterplot #
#-------------------------------------------------------------------#
} else if ((is.numeric(dataset[,i]) & (is.numeric(dataset[,j])))){
# create the scatterplot
scatterplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,i],
y = dataset[,j])) +
geom_point() +
# Add a regression line in blue
geom_smooth(method = "lm",
colour = "blue") +
# Add a loess line in red
geom_smooth(method = "loess",
colour = "red",
se = FALSE) +
# Add labels to the plot
labs(title = paste("Scatterplot of", xjname,
"and", xiname, sep = " "),
x = xiname,
y = xjname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- scatterplot
# print the plot
print(scatterplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Scatterplot_of_", xjname, "_and_", xiname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#--------------------------------------------------------------------------------#
# If the x variables is numeric and the y variable is categorical plot a boxplot #
#--------------------------------------------------------------------------------#
} else if((is.numeric(dataset[,i]) & (is.factor(dataset[,j])))){
# create the boxplot
boxplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,j],
y = dataset[,i])) +
geom_boxplot(outlier.colour = 'red') +
# Add labels to the plot
labs(title = paste("Boxplot of", xiname,
"and", xjname, sep = " "),
x = xjname,
y = xiname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- boxplot
# print the plot
print(boxplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Boxplot_of_", xiname, "_and_", xjname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#--------------------------------------------------------------------------------#
# If the y variables is numeric and the x variable is categorical plot a boxplot #
#--------------------------------------------------------------------------------#
} else if ((is.factor(dataset[,i]) & (is.numeric(dataset[,j])))){
# create the box plot
boxplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,i],
y = dataset[,j])) +
geom_boxplot(outlier.colour = 'red') +
coord_flip() +
# Add labels to the plot
labs(title = paste("Boxplot of", xjname,
"and", xiname, sep = " "),
x = xiname,
y = xjname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- boxplot
# print the plot
print(boxplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Boxplot_of_", xjname, "_and_", xiname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
}
# update the jth column index
j = j + 1
}
}
#---------------------------------------------------------------------------#
# Standardised Visualisations #
#---------------------------------------------------------------------------#
# plot standardised visualisations
} else if(standardise == TRUE) {
# use a for loop to loop through the columns of the dataset
for (i in 1:(ncol(dataset))) {
# create jth columnindex
j = i + 1
# use a while loop to loop through the remaining columns of the dataset
while (j <= ncol(dataset)) {
# store the predictor variable names
xiname <- colnames(dataset)[i]
# store the response variable name
xjname <- colnames(dataset)[j]
#---------------------------------------------------------------------#
# If the variable is categorical, plot a bar chart #
#---------------------------------------------------------------------#
if ((is.factor(dataset[,i]) & (is.factor(dataset[,j])))){
# First it is neccesary to aggregate the raw data
agg_data <- as.data.frame(x = table(dataset[,c(i,j)]),
col.names = c(xiname, xjname, "Count"))
# create the proportions
Proportion <- agg_data[,3] / sum(agg_data[,3])
# add the proportions to the aggragated dataset
agg_data[,4] <- Proportion
# Plot the aggregated data
double_bar_chart<- ggplot(data = agg_data,
mapping = aes(x = agg_data[,1],
y = agg_data[,4])) +
geom_bar(mapping = aes(fill = agg_data[,2]),
position = "dodge",
stat = "identity") +
# Give the labels to the plots
labs(title = paste("Standardised Double Bar Chart of", xjname,
"and", xiname, sep = " "),
x = xiname,
y = "Proportion") +
# set the title of the legend
guides(fill = guide_legend(title = yname)) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20),
legend.title = element_text(size = 20),
legend.text = element_text(size = 30),
legend.position = "top")
# save the plot to the list
plot_list[[p]] <- double_bar_chart
# print the plot
print(double_bar_chart)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Standardised_Double_Bar_Chart_of_", xiname, "_and_", xjname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#-------------------------------------------------------------------#
# If both variables are numeric, plot a scatterplot #
#-------------------------------------------------------------------#
} else if ((is.numeric(dataset[,i]) & (is.numeric(dataset[,j])))){
# standardise the numeric variables
dataset <- standardise_variables(dataset = dataset, method = "range", lower_bound = 0, upper_bound = 1)
# create the scatterplot
scatterplot<- ggplot(data = dataset,
mapping = aes(x = dataset[,i],
y = dataset[,j])) +
geom_point() +
# Add a regression line in blue
geom_smooth(method = "lm",
colour = "blue") +
# Add a loess line in red
geom_smooth(method = "loess",
colour = "red",
se = FALSE) +
# Add labels to the plot
labs(title = paste("Standardised Scatterplot of", xjname,
"and", xiname, sep = " "),
x = xiname,
y = xjname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- scatterplot
# print the plot
print(scatterplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Standardised_Scatterplot_of_", xjname,
"_and_", xiname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#--------------------------------------------------------------------------------#
# If the x variables is numeric and the y variable is categorical plot a boxplot #
#--------------------------------------------------------------------------------#
} else if((is.numeric(dataset[,i]) & (is.factor(dataset[,j])))){
# standardise the numeric variables
dataset <- standardise_variables(dataset = dataset, method = "range", lower_bound = 0, upper_bound = 1)
# create the boxplot
boxplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,j],
y = dataset[,i])) +
geom_boxplot(outlier.colour = 'red') +
# Add labels to the plot
labs(title = paste("Standardised Boxplot of", xiname,
"and", xjname, sep = " "),
x = xjname,
y = xiname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- boxplot
# print the plot
print(boxplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Standardised_Boxplot_of_", xiname, "_and_", xjname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#--------------------------------------------------------------------------------#
# If the y variables is numeric and the x variable is categorical plot a boxplot #
#--------------------------------------------------------------------------------#
} else if ((is.factor(dataset[,i]) & (is.numeric(dataset[,j])))){
# standardise the numeric variables
dataset <- standardise_variables(dataset = dataset, method = "range", lower_bound = 0, upper_bound = 1)
# create the boxplot
boxplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,i],
y = dataset[,j])) +
geom_boxplot(outlier.colour = 'red') +
coord_flip() +
# Add labels to the plot
labs(title = paste("Standardise Boxplot of", xjname,
"and", xiname, sep = " "),
x = xiname,
y = xjname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- boxplot
# print the plot
print(boxplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Standardised_Boxplot_of_", xjname, "_and_", xiname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
}
j = j + 1
}
}
}
#-------------------------------------------------------------------------------#
# When y_index or y_name != NULL #
#-------------------------------------------------------------------------------#
} else if(!is.null(y_index) | !is.null(y_name)){
if(!is.null(y_name)){
y_index = which(colnames(dataset) == y_name)
}
# NOTE: ggplot2 automatically removes missing observations
# Make sure the datset is converted to a data frame
dataset <- as.data.frame(x = dataset)
# create a plot list to hold the pictures
plot_list <- list()
# p is the picture index - tracks the number of pictures created
p = 1
#---------------------------------------------------------------------------#
# Unstandardised Visualisations #
#---------------------------------------------------------------------------#
# plot regular visualisations
if(standardise == FALSE) {
# use a for loop to loop through the columns of the dataset
for (i in 1:(ncol(dataset))) {
# store the predictor variable names
xiname <- colnames(dataset)[i]
# store the response variable name
yname <- colnames(dataset)[y_index]
#-----------------------------------------------------------------------#
# If the variable is categorical, plot a bar chart #
#-----------------------------------------------------------------------#
if ((is.factor(dataset[,i]) & (is.factor(dataset[,y_index])))){
# First it is neccesary to aggregate the raw data
agg_data <- as.data.frame(x = table(dataset[,c(i,y_index)]),
col.names = c(xiname, yname, "Count"))
# Plot the aggregated data
double_bar_chart <- ggplot(data = agg_data,
mapping = aes(x = agg_data[,1],
y = agg_data[,3])) +
geom_bar(mapping = aes(fill = agg_data[,2]),
position = "dodge",
stat = "identity") +
# Give the labels to the plots
labs(title = paste("Double Bar Chart of", yname,
"and", xiname, sep = " "),
x = xiname,
y = "Count") +
# set the title of the legend
guides(fill = guide_legend(title = yname)) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20),
legend.title = element_text(size = 20),
legend.text = element_text(size = 30),
legend.position = "top")
# save the plot to the list
plot_list[[p]] <- double_bar_chart
# print the plot
print(double_bar_chart)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Double_Bar_Chart_of_", xiname,
"_and_", yname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#---------------------------------------------------------------------#
# If both variables are numeric, plot a scatterplot #
#---------------------------------------------------------------------#
} else if ((is.numeric(dataset[,i]) & (is.numeric(dataset[,y_index])))){
# create the scatterplot
scatterplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,i],
y = dataset[,y_index])) +
geom_point() +
# Add a regression line in blue
geom_smooth(method = "lm",
colour = "blue") +
# Add a loess line in red
geom_smooth(method = "loess",
colour = "red",
se = FALSE) +
# Add labels to the plot
labs(title = paste("Scatterplot of", yname,
"and", xiname, sep = " "),
x = xiname,
y = yname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- scatterplot
# print the plot
print(scatterplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Scatterplot_of_", yname,
"_and_", xiname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#--------------------------------------------------------------------------------#
# If the x variables is numeric and the y variable is categorical plot a boxplot #
#--------------------------------------------------------------------------------#
} else if((is.numeric(dataset[,i]) & (is.factor(dataset[,y_index])))){
# create the boxplot
boxplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,y_index],
y = dataset[,i])) +
geom_boxplot(outlier.colour = 'red') +
# Add labels to the plot
labs(title = paste("Boxplot of", yname,
"and", xiname, sep = " "),
x = yname,
y = xiname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- boxplot
# print the plot
print(boxplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Boxplot_of_", yname, "_and_", xiname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#--------------------------------------------------------------------------------#
# If the y variables is numeric and the x variable is categorical plot a boxplot #
#--------------------------------------------------------------------------------#
} else if((is.factor(dataset[,i]) & (is.numeric(dataset[,y_index])))){
# create the boxplot
boxplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,i],
y = dataset[,y_index])) +
geom_boxplot(outlier.colour = 'red') +
coord_flip() +
# Add labels to the plot
labs(title = paste("Boxplot of", xiname,
"and", yname, sep = " "),
x = xiname,
y = yname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- boxplot
# print the plot
print(boxplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Boxplot_of_", xiname, "_and_", yname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
}
}
#---------------------------------------------------------------------------#
# Standardised Visualisations #
#---------------------------------------------------------------------------#
# create standardised plots
} else if(standardise == TRUE) {
# usea for loop to loop through the columns of the dataset
for (i in 1:(ncol(dataset))) {
# save the name of the predictor variable
xiname <- colnames(dataset)[i]
# save the name of the response variable
yname <- colnames(dataset)[y_index]
#-----------------------------------------------------------------------#
# If the variable is categorical, plot a bar chart #
#-----------------------------------------------------------------------#
if ((is.factor(dataset[,i]) & (is.factor(dataset[,y_index])))){
# First it is neccesary to aggregate the raw data
agg_data <- as.data.frame(x = table(dataset[,c(i,y_index)]),
col.names = c(xiname, yname, "Count"))
# create the proportions
Proportion <- agg_data[,3] / sum(agg_data[,3])
# add the proportions to the aggregated data
agg_data[,4] <- Proportion
# Plot the aggregated data
double_bar_chart<- ggplot(data = agg_data,
mapping = aes(x = agg_data[,1],
y = agg_data[,4])) +
geom_bar(mapping = aes(fill = agg_data[,2]),
position = "dodge",
stat = "identity") +
# Give the labels to the plots
labs(title = paste("Standardised Double Bar Chart of", yname,
"and", xiname, sep = " "),
x = xiname,
y = "Proportion") +
# set the title of the legend
guides(fill = guide_legend(title = yname)) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20),
legend.title = element_text(size = 20),
legend.text = element_text(size = 30),
legend.position = "top")
# save the plot to the list
plot_list[[p]] <- double_bar_chart
# print the plot
print(double_bar_chart)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Standardised_Double_Bar_Chart_of_", xiname,
"_and_", yname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#---------------------------------------------------------------------#
# If both variables are numeric, plot a scatterplot #
#---------------------------------------------------------------------#
} else if ((is.numeric(dataset[,i]) & (is.numeric(dataset[,y_index])))){
# standardise the numeric variables
dataset <- standardise_variables(dataset = dataset, method = "range", lower_bound = 0, upper_bound = 1)
# create the scaterplot
scatterplot<- ggplot(data = dataset,
mapping = aes(x = dataset[,i],
y = dataset[,y_index])) +
geom_point() +
# Add a regression line in blue
geom_smooth(method = "lm",
colour = "blue") +
# Add a loess line in red
geom_smooth(method = "loess",
colour = "red",
se = FALSE) +
# Add labels to the plot
labs(title = paste("Standardised Scatterplot of", yname,
"and", xiname, sep = " "),
x = xiname,
y = yname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- scatterplot
# print the plot
print(scatterplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Standardised_Scatterplot_of_", yname,
"_and_", xiname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#--------------------------------------------------------------------------------#
# If the x variables is numeric and the y variable is categorical plot a boxplot #
#--------------------------------------------------------------------------------#
} else if((is.numeric(dataset[,i]) & (is.factor(dataset[,y_index])))){
# standardise the numeric variables
dataset <- standardise_variables(dataset = dataset, method = "range", lower_bound = 0, upper_bound = 1)
# create the box plot
boxplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,y_index],
y = dataset[,i])) +
geom_boxplot(outlier.colour = 'red') +
# Add labels to the plot
labs(title = paste("Standardised Boxplot of", yname,
"and", xiname, sep = " "),
x = yname,
y = xiname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- boxplot
# print the plot
print(boxplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Standardised_Boxplot_of_", yname, "_and_", xiname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
#--------------------------------------------------------------------------------#
# If the y variables is numeric and the x variable is categorical plot a boxplot #
#--------------------------------------------------------------------------------#
} else if((is.factor(dataset[,i]) & (is.numeric(dataset[,y_index])))){
# standardise the numeric variables
dataset <- standardise_variables(dataset = dataset, method = "range", lower_bound = 0, upper_bound = 1)
# create the boxplot
boxplot <- ggplot(data = dataset,
mapping = aes(x = dataset[,i],
y = dataset[,y_index])) +
geom_boxplot(outlier.colour = 'red') +
coord_flip() +
# Add labels to the plot
labs(title = paste("Standardised Boxplot of", xiname,
"and", yname, sep = " "),
x = xiname,
y = yname) +
# Format the text of the plots
theme(axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
plot.title = element_text(hjust = 0.5, size = 35),
axis.text.x = element_text(size = 20, face = "bold"),
axis.text.y = element_text(size = 20))
# save the plot to the list
plot_list[[p]] <- boxplot
# print the plot
print(boxplot)
# save the plot to the sepecifed directory
if(!is.null(directory)) {
# save the plot
ggsave(filename = paste("Standardised_Boxplot_of_", xiname, "_and_", yname, ".pdf", sep = ""),
path = directory,
device = "pdf",
width = 8,
height = 6,
units = c("in"))
}
# print an update of the number of plots
print(paste("Image", as.character(p), "Completed", sep = " "))
# update plot index
p = p + 1
}
}
}
}
# click through the plots
par(op)
}
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.