I'm having trouble displaying the multiple graphs on the same page. I'm having a data frame with 18 numerical columns. For each column, I need to show its histogram and boxplot on the same page with a 4*9 grid. Following is what I tried. But I need to show it along with the boxplot as well. Through a for a loop if possible. Can someone please help me to do it.
library(gridExtra)
library(ggplot2)
p <- list()
for(i in 1:18){
x <- my_data[,i]
p[[i]] <- ggplot(gather(x), aes(value)) +
geom_histogram(bins = 10) +
facet_wrap(~key, scales = 'free_x')
}
do.call(grid.arrange,p)
I received the following graph.
When following is tried, I'm getting the graph in separate pages
library(dplyr)
dat2 <- my_data %>% mutate_all(scale)
# Boxplot from the R trees dataset
boxplot(dat2, col = rainbow(ncol(dat2)))
par(mfrow = c(2, 2)) # Set up a 2 x 2 plotting space
# Create the loop.vector (all the columns)
loop.vector <- 1:4
p <- list()
for (i in loop.vector) { # Loop over loop.vector
# store data in column.i as x
x <- my_data[,i]
# Plot histogram of x
p[[i]] <-hist(x,
main = paste("Question", i),
xlab = "Scores",
xlim = c(0, 100))
plot_grid(p, label_size = 12)
}
You can assemble the base R boxplot and the ggplot object generated with facet_wrap together using the R package patchwork:
library(ggplot2)
library(patchwork)
p <- ggplot(mtcars, aes(x = mpg)) +
geom_histogram() +
facet_wrap(~gear)
wrap_elements(~boxplot(split(mtcars$mpg, mtcars$gear))) / p
ggsave('test.png', width = 6, height = 8, units = 'in')
Related
I'm using the svars package to generate some IRF plots. The plots are rendered using ggplot2, however I need some help with changing some of the aesthetics.
Is there any way I can change the fill and alpha of the shaded confidence bands, as well as the color of the solid line? I know in ggplot2 you can pass fill and alpha arguments to geom_ribbon (and col to geom_line), just unsure of how to do the same within the plot function of this package's source code.
# Load Dataset and packages
library(tidyverse)
library(svars)
data(USA)
# Create SVAR Model
var.model <- vars::VAR(USA, lag.max = 10, ic = "AIC" )
svar.model <- id.chol(var.model)
# Wild Bootstrap
cores <- parallel::detectCores() - 1
boot.svar <- wild.boot(svar.model, n.ahead = 30, nboot = 500, nc = cores)
# Plot the IRFs
plot(boot.svar)
I'm also looking at the command for a historical decomposition plot (see below). Is there any way I could omit the first two facets and plot only the bottom three lines on the same facet?
hist.decomp <- hd(svar.model, series = 1)
plot(hist.decomp)
Your first desired result is easily achieved by resetting the aes_params after calling plot. For your second goal. There is probably an approach to manipulate the ggplot object. Instead my approach below constructs the plot from scratch. Basically I copy and pasted the data wrangling code from vars:::plot.hd and filtered the prepared dataset for the desired series:
# Plot the IRFs
p <- plot(boot.svar)
p$layers[[1]]$aes_params$fill <- "pink"
p$layers[[1]]$aes_params$alpha <- .5
p$layers[[2]]$aes_params$colour <- "green"
p
# Helper to convert to long dataframe. Source: svars:::plot.hd
hd2PlotData <- function(x) {
PlotData <- as.data.frame(x$hidec)
if (inherits(x$hidec, "ts")) {
tsStructure = attr(x$hidec, which = "tsp")
PlotData$Index <- seq(from = tsStructure[1], to = tsStructure[2],
by = 1/tsStructure[3])
PlotData$Index <- as.Date(yearmon(PlotData$Index))
}
else {
PlotData$Index <- 1:nrow(PlotData)
PlotData$V1 <- NULL
}
dat <- reshape2::melt(PlotData, id = "Index")
dat
}
hist.decomp <- hd(svar.model, series = 1)
dat <- hd2PlotData(hist.decomp)
dat %>%
filter(grepl("^Cum", variable)) %>%
ggplot(aes(x = Index, y = value, color = variable)) +
geom_line() +
xlab("Time") +
theme_bw()
EDIT One approach to change the facet labels is via a custom labeller function. For a different approach which changes the facet labels via the data see here:
myvec <- LETTERS[1:9]
mylabel <- function(labels, multi_line = TRUE) {
data.frame(variable = labels)
}
p + facet_wrap(~variable, labeller = my_labeller(my_labels))
I try to use ggplot to plot quarted circles to visualize contour plots but I get misconfigured plot using geom_area (following this tutorial on stacked area with ggplot2)
The code I tried reads
library(ggplot2)
library(dplyr)
N <- 1E2
r <- rev(c(1,2,4,7))
maxXY = max(r)+.25*max(r)
grupp <- c("0","0.25","0.5","0.75")
datalist = list()
plot(0,0,xlim=c(0,maxXY),ylim=c(0,maxXY))
for (i in 1:length(r)) {
quadX <- seq(from = 0,to = r[i],length.out = N) # calculate x coords
quadY <- sqrt(r[i]^2 - quadX^2) # calculate y coords
lines(quadX,quadY)
# data for ggplot
dat <- data.frame(X = quadX, Y = quadY)
dat$group <- grupp[i]
datalist[[i]] <- dat # add it to your list
}
DF = do.call(rbind, datalist)
# stacked area chart
p1 <- ggplot(DF, aes(x=X, y=Y, fill=group)) +
geom_area(alpha=0.6 , size=1, colour="black")
plot(p1)
and I get quarter circles plotted correctly with basic plot
but a weird one with geom_area
Any help would be very appreciated. MJS
EDIT: using Z.Lin's suggestions I get the correct plot, thanks!
I generated the histogram with the following code:
# Load Data
file <- "SharedData.csv"
data <- read.csv(file,header = TRUE,sep = ",")
## Bin Levels
data$xLevel <- cut(data$xLevel,
breaks = quantile(data$xLevel,(0:5)/5),
labels = paste("Quant",1:5,sep = "."),
include.lowest = TRUE,ordered_result = TRUE)
# Histogram
g <- ggplot(data, aes(x=xTime,color = xLevel)) +
geom_histogram(aes(y=..density..),
binwidth=100)
g
How do I create the above histogram with an x axis that goes from 0-300 and from 1500-2400, but not include 300-1500? The unit here is military time.
Data: https://www.dropbox.com/s/e5gaym7dhefs04e/SharedData.csv?dl=0
According to https://groups.google.com/forum/#!topic/ggplot2/jSrL_FnS8kc and Using ggplot2, can I insert a break in the axis? it does not seem to be possible. you can plots 2 graphics instead
first create a new column splitting the data in two
data$Block <- ifelse(data$xTime <=500, "A", "B")
and then plot the graphics
library(scales)
# Histogram
g <- ggplot(data, aes(x=xTime,color = xLevel)) +
geom_histogram(aes(y=..density..),
binwidth=100) + facet_grid(.~Block, scales = "free_x")
g
I have created a number of plots through a for loop using ggplot, and saved each plot into a list plots.
plots <- list()
for (i in 1:238)
{
gene <- row.names(geneExpression)[i]
df.sub <- df[ , c("source", gene)]
names(test.sub) <- c("source", "exp")
plots[[i]] <- ggplot() + geom_violin(data=test.sub, aes(source, exp, fill=source, color=source), alpha=.4, trim=F, environment = environment()) + coord_flip() + ggtitle(gene) + theme(legend.position="none") + labs(x="")
}
I am using the gridExtra function as suggested elsewhere, but when I do this, it prints all of the plots (240 plots) in a single page.
pdf("violinPlots.pdf")
do.call(grid.arrange, plots)
dev.off()
Is there a way I can specify that I want 24 plots per page? (i.e. 6 rows x 4 columns?)
I tried this but it returns an error...
grid.arrange(plots, ncol=4, newpage = T )
You can use gridExtra's marrangeGrob function:
pdf("violinPlots.pdf")
ml <- marrangeGrob(grobs = plots, nrow = 6, ncol = 4)
print(ml)
dev.off()
I'm running an R script generating plots of the PCA analysis using FactorMineR.
I'd like to output the coordinates for the generated PCA plots but I'm having trouble finding the right coordinates. I found results1$ind$coord and results1$var$coord but neither look like the default plot.
I found
http://www.statistik.tuwien.ac.at/public/filz/students/seminar/ws1011/hoffmann_ausarbeitung.pdf
and
http://factominer.free.fr/classical-methods/principal-components-analysis.html
but neither describe the contents of the variable created by the PCA
library(FactoMineR)
data1 <- read.table(file=args[1], sep='\t', header=T, row.names=1)
result1 <- PCA(data1,ncp = 4, graph=TRUE) # graphs generated automatically
plot(result1)
I found that $ind$coord[,1] and $ind$coord[,2] are the first two pca coords in the PCA object. Here's a worked example that includes a few other things you might want to do with the PCA output...
# Plotting the output of FactoMineR's PCA using ggplot2
#
# load libraries
library(FactoMineR)
library(ggplot2)
library(scales)
library(grid)
library(plyr)
library(gridExtra)
#
# start with a clean slate
rm(list=ls(all=TRUE))
#
# load example data
data(decathlon)
#
# compute PCA
res.pca <- PCA(decathlon, quanti.sup = 11:12, quali.sup=13, graph = FALSE)
#
# extract some parts for plotting
PC1 <- res.pca$ind$coord[,1]
PC2 <- res.pca$ind$coord[,2]
labs <- rownames(res.pca$ind$coord)
PCs <- data.frame(cbind(PC1,PC2))
rownames(PCs) <- labs
#
# Just showing the individual samples...
ggplot(PCs, aes(PC1,PC2, label=rownames(PCs))) +
geom_text()
# Now get supplementary categorical variables
cPC1 <- res.pca$quali.sup$coor[,1]
cPC2 <- res.pca$quali.sup$coor[,2]
clabs <- rownames(res.pca$quali.sup$coor)
cPCs <- data.frame(cbind(cPC1,cPC2))
rownames(cPCs) <- clabs
colnames(cPCs) <- colnames(PCs)
#
# Put samples and categorical variables (ie. grouping
# of samples) all together
p <- ggplot() + theme(aspect.ratio=1) + theme_bw(base_size = 20)
# no data so there's nothing to plot...
# add on data
p <- p + geom_text(data=PCs, aes(x=PC1,y=PC2,label=rownames(PCs)), size=4)
p <- p + geom_text(data=cPCs, aes(x=cPC1,y=cPC2,label=rownames(cPCs)),size=10)
p # show plot with both layers
# Now extract the variables
#
vPC1 <- res.pca$var$coord[,1]
vPC2 <- res.pca$var$coord[,2]
vlabs <- rownames(res.pca$var$coord)
vPCs <- data.frame(cbind(vPC1,vPC2))
rownames(vPCs) <- vlabs
colnames(vPCs) <- colnames(PCs)
#
# and plot them
#
pv <- ggplot() + theme(aspect.ratio=1) + theme_bw(base_size = 20)
# no data so there's nothing to plot
# put a faint circle there, as is customary
angle <- seq(-pi, pi, length = 50)
df <- data.frame(x = sin(angle), y = cos(angle))
pv <- pv + geom_path(aes(x, y), data = df, colour="grey70")
#
# add on arrows and variable labels
pv <- pv + geom_text(data=vPCs, aes(x=vPC1,y=vPC2,label=rownames(vPCs)), size=4) + xlab("PC1") + ylab("PC2")
pv <- pv + geom_segment(data=vPCs, aes(x = 0, y = 0, xend = vPC1*0.9, yend = vPC2*0.9), arrow = arrow(length = unit(1/2, 'picas')), color = "grey30")
pv # show plot
# Now put them side by side in a single image
#
grid.arrange(p,pv,nrow=1)
#
# Now they can be saved or exported...
Adding something extra to Ben's answer. You'll note in the first chart in Ben's response that the labels overlap somewhat. The pointLabel() function in the maptools package attempts to find locations for the labels without overlap. It's not perfect, but you can adjust the positions in the new dataframe (see below) to fine tune if you want. (Also, when you load maptools you get a note about gpclibPermit(). You can ignore it if you're concerned about the restricted licence). The first part of the script below is Ben's script.
# load libraries
library(FactoMineR)
library(ggplot2)
library(scales)
library(grid)
library(plyr)
library(gridExtra)
#
# start with a clean slate
# rm(list=ls(all=TRUE))
#
# load example data
data(decathlon)
#
# compute PCA
res.pca <- PCA(decathlon, quanti.sup = 11:12, quali.sup=13, graph = FALSE)
#
# extract some parts for plotting
PC1 <- res.pca$ind$coord[,1]
PC2 <- res.pca$ind$coord[,2]
labs <- rownames(res.pca$ind$coord)
PCs <- data.frame(cbind(PC1,PC2))
rownames(PCs) <- labs
#
# Now, the code to produce Ben's first chart but with less overlap of the labels.
library(maptools)
PCs$label=rownames(PCs)
# Base plot first for pointLabels() to get locations
plot(PCs$PC1, PCs$PC2, pch = 20, col = "red")
new = pointLabel(PCs$PC1, PCs$PC2, PCs$label, cex = .7)
new = as.data.frame(new)
new$label = PCs$label
# Then plot using ggplot2
(p = ggplot(data = PCs) +
geom_hline(yintercept = 0, linetype = 3, colour = "grey20") +
geom_vline(xintercept = 0, linetype = 3, colour = "grey20") +
geom_point(aes(PC1, PC2), shape = 20, col = "red") +
theme_bw())
(p = p + geom_text(data = new, aes(x, y, label = label), size = 3))
The result is:
An alternative is to use the biplot function from CoreR or biplot.psych from the psych package. This will put the components and the data onto the same figure.
For the decathlon data set, use principal and biplot from the psych package:
library(FactoMineR) #needed to get the example data
library(psych) #needed for principal
data(decathlon) #the data set
pc2 <- principal(decathlon[1:10],2) #just the first 10 columns
biplot(pc2,labels = rownames(decathlon),cex=.5, main="Biplot of Decathlon results")
#this is a call to biplot.psych which in turn calls biplot.
#adjust the cex parameter to change the type size of the labels.
This looks like:
!a biplot http://personality-project.org/r/images/olympic.biplot.pdf
Bill