I would like to create a plot with
Using part of the data to create a base plot with facet_grid of two columns.
Use remaining part of the data and plot on top of the existing facets but using only a single column.
The sample code:
library(ggplot2)
library(gridExtra)
df2 <- data.frame(Class=rep(c('A','B','C'),each=20),
Type=rep(rep(c('T1','T2'),each=10), 3),
X=rep(rep(1:10,each=2), 3),
Y=c(rep(seq(3,-3, length.out = 10),2),
rep(seq(1,-4, length.out = 10),2),
rep(seq(-2,-8, length.out = 10),2)))
g2 <- ggplot() + geom_line(data = df2 %>% filter(Class %in% c('B','C')),
aes(X,Y,color=Class, linetype=Type)) +
facet_grid(Type~Class)
g3 <- ggplot() + geom_line(data = df2 %>% filter(Class == 'A'),
aes(X,Y,color=Class, linetype=Type)) +
facet_wrap(~Type)
grid.arrange(g2, g3)
The output plots:
How to include g3 plot on g2 plot? The resulting plot should include the g3 two lines twice on two facets.
I assume the plot below is what you were looking for.
library(dplyr)
library(ggplot2)
df_1 <- filter(df2, Class %in% c('B','C')) %>%
dplyr::rename(Class_1 = Class)
df_2 <- filter(df2, Class == 'A')
g2 <- ggplot() +
geom_line(data = df_1,
aes(X, Y, color = Class_1, linetype = Type)) +
geom_line(data = df_2,
aes(X, Y, color = Class, linetype = Type)) +
facet_grid(Type ~ Class_1)
g2
explaination
For tasks like this I found it better to work with two datasets. Since the variable df2$class has three unique values: A, B and C, faceting Class~Type does not give you desired plot, since you want the data for df2$Class == "A" to be displayed in the respective facets.
That's why I renamed variable Class in df_1 to Class_1 because this variable only contains two unique values: B and C.
Faceting Class_1 ~ Type allows you to plot the data for df2$Class == "A" on top without being faceted by Class.
edit
Based on the comment below here is a solution using only one dataset
g2 + geom_line(data = filter(df2, Class == 'A')[, -1],
aes(X, Y, linetype = Type, col = "A"))
Similar / same question: ggplot2:: Facetting plot with the same reference plot in all panels
Related
I have a csv file which looks like the following:
Name,Count1,Count2,Count3
application_name1,x1,x2,x3
application_name2,x4,x5,x6
The x variables represent numbers and the applications_name variables represent names of different applications.
Now I would like to make a barplot for each row by using ggplot2. The barplot should have the application_name as title. The x axis should show Count1, Count2, Count3 and the y axis should show the corresponding values (x1, x2, x3).
I would like to have a single barplot for each row, because I have to store the different plots in different files. So I guess I cannot use "melt".
I would like to have something like:
for each row in rows {
print barplot in file
}
Thanks for your help.
You can use melt to rearrange your data and then use either facet_wrap or facet_grid to get a separate plot for each application name
library(ggplot2)
library(reshape2)
# example data
mydf <- data.frame(name = paste0("name",1:4), replicate(5,rpois(4,30)))
names(mydf)[2:6] <- paste0("count",1:5)
# rearrange data
m <- melt(mydf)
# if you are wanting to export each plot separately
# I used facet_wrap as a quick way to add the application name as a plot title
for(i in levels(m$name)) {
p <- ggplot(subset(m, name==i), aes(variable, value, fill = variable)) +
facet_wrap(~ name) +
geom_bar(stat="identity", show_guide=FALSE)
ggsave(paste0("figure_",i,".pdf"), p)
}
# or all plots in one window
ggplot(m, aes(variable, value, fill = variable)) +
facet_wrap(~ name) +
geom_bar(stat="identity", show_guide=FALSE)
I didn't see #user20650's nice answer before preparing this. It's almost identical, except that I use plyr::d_ply to save things instead of a loop. I believe dplyr::do() is another good option (you'd group_by(Name) first).
yourData <- data.frame(Name = sample(letters, 10),
Count1 = rpois(10, 20),
Count2 = rpois(10, 10),
Count3 = rpois(10, 8))
library(reshape2)
yourMelt <- melt(yourData, id.vars = "Name")
library(ggplot2)
# Test a function on one piece to develope graph
ggplot(subset(yourMelt, Name == "a"), aes(x = variable, y = value)) +
geom_bar(stat = "identity") +
labs(title = subset(yourMelt, Name == 'a')$Name)
# Wrap it up, with saving to file
bp <- function(dat) {
myPlot <- ggplot(dat, aes(x = variable, y = value)) +
geom_bar(stat = "identity") +
labs(title = dat$Name)
ggsave(filname = paste0("path/to/save/", dat$Name, "_plot.pdf"),
myPlot)
}
library(plyr)
d_ply(yourMelt, .variables = "Name", .fun = bp)
I want to plot the exact same variable names (ses & math) from 2 separate data.frames (dat1 & dat2) but side by side so I can visually compare them.
I have tried the following but it places both data.frames on top of each other.
Is there a function within ggplot2 to plot ses vs. math from dat1 and the same from dat2 side by side and placed on the same axes scales?
library(ggplot2)
dat1 <- read.csv('https://raw.githubusercontent.com/rnorouzian/e/master/hsb.csv')
dat2 <- read.csv('https://raw.githubusercontent.com/rnorouzian/e/master/sm.csv')
ggplot(dat1, aes(x = ses, y = math, colour = factor(sector))) +
geom_point() +
geom_point(data = dat2, aes(x = ses, y = math, colour = factor(sector)))
You can try faceting combining the two datasets :
library(dplyr)
library(ggplot2)
list(dat1 = dat1 %>%
select(sector,ses, math) %>%
mutate(sector = as.character(sector)) ,
dat2 = dat2 %>% select(sector,ses, math)) %>%
bind_rows(.id = 'name') %>%
ggplot() +
aes(x = ses, y = math, colour = factor(sector)) +
geom_point() +
facet_wrap(.~name)
Another option is to create list of plots and arrange them with grid.arrange :
list_plots <- lapply(list(dat1, dat2), function(df) {
ggplot(df, aes(x = ses, y = math, colour = factor(sector))) + geom_point()
})
do.call(gridExtra::grid.arrange, c(list_plots, ncol = 2))
I'm hoping to recreate the gridExtra output below with ggplot's facet_grid, but I'm unsure of what variable ggplot identifies with the layers in the plot. In this example, there are two geoms...
require(tidyverse)
a <- ggplot(mpg)
b <- geom_point(aes(displ, cyl, color = drv))
c <- geom_smooth(aes(displ, cyl, color = drv))
d <- a + b + c
# output below
gridExtra::grid.arrange(
a + b,
a + c,
ncol = 2
)
# Equivalent with gg's facet_grid
# needs a categorical var to iter over...
d$layers
#d + facet_grid(. ~ d$layers??)
The gridExtra output that I'm hoping to recreate is:
A hacky way of doing this is to take the existing data frame and create two, three, as many copies of the data frame you need with a value linked to it to be used for the facet and filtering later on. Union (or rbind) the data frames together into one data frame. Then set up the ggplot and geoms and filter each geom for the desired attribute. Also for the facet use the existing attribute to split the plots.
This can be seen below:
df1 <- data.frame(
graph = "point_plot",
mpg
)
df2 <- data.frame(
graph = "spline_plot",
mpg
)
df <- rbind(df1, df2)
ggplot(df, mapping = aes(x = displ, y = hwy, color = class)) +
geom_point(data = filter(df, graph == "point_plot")) +
geom_smooth(data = filter(df, graph == "spline_plot"), se=FALSE) +
facet_grid(. ~ graph)
If you really want to show different plots on different facets, one hacky way would be to make separate copies of the data and subset those...
mpg2 <- mpg %>% mutate(facet = 1) %>%
bind_rows(mpg %>% mutate(facet = 2))
ggplot(mpg2, aes(displ, cyl, color = drv)) +
geom_point(data = subset(mpg2, facet == 1)) +
geom_smooth(data = subset(mpg2, facet == 2)) +
facet_wrap(~facet)
i have a dataframe structured like this
Elem. Category. SEZa SEZb SEZc
A. ONE. 1. 3. 4
B. TWO. 4. 5. 6
i want to plot three histograms in three different facets (SEZa, SEZb, SEZc) with ggplot where the x values are the category values (ONE. e TWO.) and the y values are the number present in columns SEZa, SEZb, SEZc.
something like this:
how can I do? thank you for your suggestions!
Assume df is your data.frame, I would first convert from wide format to a long format:
new_df <- reshape2::melt(df, id.vars = c("Elem", "Category"))
And then make the plot using geom_col() instead of geom_histogram() because it seems you've precomputed the y-values and wouldn't need ggplot to calculate these values for you.
ggplot(new_df, aes(x = Category, y = value, fill = Elem)) +
geom_col() +
facet_grid(variable ~ .)
I think that what you are looking for is something like this :
library(ggplot2)
library(reshape2)
df <- data.frame(Category = c("One", "Two"),
SEZa = c(1, 4),
SEZb = c(3, 5),
SEZc = c(4, 6))
df <- melt(df)
ggplot(df, aes(x = Category, y = value)) +
geom_col(aes(fill = variable)) +
facet_grid(variable ~ .)
My inspiration is :
http://felixfan.github.io/stacking-plots-same-x/
I have a csv file which looks like the following:
Name,Count1,Count2,Count3
application_name1,x1,x2,x3
application_name2,x4,x5,x6
The x variables represent numbers and the applications_name variables represent names of different applications.
Now I would like to make a barplot for each row by using ggplot2. The barplot should have the application_name as title. The x axis should show Count1, Count2, Count3 and the y axis should show the corresponding values (x1, x2, x3).
I would like to have a single barplot for each row, because I have to store the different plots in different files. So I guess I cannot use "melt".
I would like to have something like:
for each row in rows {
print barplot in file
}
Thanks for your help.
You can use melt to rearrange your data and then use either facet_wrap or facet_grid to get a separate plot for each application name
library(ggplot2)
library(reshape2)
# example data
mydf <- data.frame(name = paste0("name",1:4), replicate(5,rpois(4,30)))
names(mydf)[2:6] <- paste0("count",1:5)
# rearrange data
m <- melt(mydf)
# if you are wanting to export each plot separately
# I used facet_wrap as a quick way to add the application name as a plot title
for(i in levels(m$name)) {
p <- ggplot(subset(m, name==i), aes(variable, value, fill = variable)) +
facet_wrap(~ name) +
geom_bar(stat="identity", show_guide=FALSE)
ggsave(paste0("figure_",i,".pdf"), p)
}
# or all plots in one window
ggplot(m, aes(variable, value, fill = variable)) +
facet_wrap(~ name) +
geom_bar(stat="identity", show_guide=FALSE)
I didn't see #user20650's nice answer before preparing this. It's almost identical, except that I use plyr::d_ply to save things instead of a loop. I believe dplyr::do() is another good option (you'd group_by(Name) first).
yourData <- data.frame(Name = sample(letters, 10),
Count1 = rpois(10, 20),
Count2 = rpois(10, 10),
Count3 = rpois(10, 8))
library(reshape2)
yourMelt <- melt(yourData, id.vars = "Name")
library(ggplot2)
# Test a function on one piece to develope graph
ggplot(subset(yourMelt, Name == "a"), aes(x = variable, y = value)) +
geom_bar(stat = "identity") +
labs(title = subset(yourMelt, Name == 'a')$Name)
# Wrap it up, with saving to file
bp <- function(dat) {
myPlot <- ggplot(dat, aes(x = variable, y = value)) +
geom_bar(stat = "identity") +
labs(title = dat$Name)
ggsave(filname = paste0("path/to/save/", dat$Name, "_plot.pdf"),
myPlot)
}
library(plyr)
d_ply(yourMelt, .variables = "Name", .fun = bp)