First, the libraries
library(tidyr)
library(leaps)
library(ggplots2)
library(ggdark)
The value of the model
set.seed(1)
X = rnorm(100)
e = rnorm(100)
Y = 8 + 7*X + 2.5*X^2 - 9*X^3 + e
Fitting
data.all = data.frame(Y,X)
regfit.full = regsubsets(Y~poly(X,10,raw=T), data=data.all, nvmax=10)
(reg.summary = summary(regfit.full))
Then I get the minimum value for each variables
(reg.min.cp = which.min(reg.summary$cp))
(reg.min.bic = which.min(reg.summary$bic))
(reg.min.adjr2 = which.min(reg.summary$adjr2))
Creating the data frame for plot
df = data.frame(reg.summary$cp, reg.summary$bic, reg.summary$adjr2)
df$rownum = 1:nrow(df)
Reshaping the data frame
molten = df %>% gather(variable, value, reg.summary.cp:reg.summary.adjr2 )
Plotting with facets
(lp = molten %>% ggplot(data=.) +
aes(x=rownum, y=value) +
geom_line(col="black") +
geom_point(data=molten, aes(xint=reg.min.adjr2, z="reg.summary.adjr2", col="red")) + # this is where I got the wrong plot
facet_wrap(~variable, scales="free_y")
)
And it shows wrong. What I expect is that the geom_point(data=molten, aes(xint=reg.min.adjr2, z="reg.summary.adjr2", col="red")) will just add the reg.min.adjr2 to the facet reg.summary.adjr2 and only one point.
How to make it in that way?
I got some idea here from these two SO:
How to add different lines for facets
Add a segment only to one facet using ggplot2
What I did is to create first a new data frame for the min values for cp , bic, and adjr2. And then add the points to the main plot.
I make sure that the value for x will be the rownum and the y are the min values. I also added a parameter variable to min_plot to make sure that it will be added to the right facet.
min_plot = data.frame(
rownum=c(reg.min.cp, reg.min.bic, reg.min.adjr2),
y = c(reg.summary$cp[reg.min.cp], reg.summary$bic[reg.min.bic], reg.summary$adjr2[reg.min.adjr2]),
variable=c("reg.summary.cp", "reg.summary.bic", "reg.summary.adjr2"))
(lp = molten %>% ggplot(data=.)
+ aes(x=rownum, y=value)
+ geom_line(col="black")
+ facet_wrap(~variable, scales="free_y")
+ geom_point(data = min_plot, aes(x=rownum, y=y), col="red")
)
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 am attempting to plot multiple time series variables on a single line chart using ggplot. I am using a data.frame which contains n time series variables, and a column of time periods. Essentially, I want to loop through the data.frame, and add exactly n goem_lines to a single chart.
Initially I tried using the following code, where;
df = data.frame containing n time series variables, and 1 column of time periods
wid = n (number of time series variables)
p <- ggplot() +
scale_color_manual(values=c(colours[1:wid]))
for (i in 1:wid) {
p <- p + geom_line(aes(x=df$Time, y=df[,i], color=var.lab[i]))
}
ggplotly(p)
However, this only produces a plot of the final time series variable in the data.frame. I then investigated further, and found that following sets of code produce completely different results:
p <- ggplot() +
scale_color_manual(values=c(colours[1:wid]))
i = 1
p = p + geom_line(aes(x=df$Time, y=df[,i], color=var.lab[i]))
i = 2
p = p + geom_line(aes(x=df$Time, y=df[,i], color=var.lab[i]))
i = 3
p = p + geom_line(aes(x=df$Time, y=df[,i], color=var.lab[i]))
ggplotly(p)
Plot produced by code above
p <- ggplot() +
scale_color_manual(values=c(colours[1:wid]))
p = p + geom_line(aes(x=df$Time, y=df[,1], color=var.lab[1]))
p = p + geom_line(aes(x=df$Time, y=df[,2], color=var.lab[2]))
p = p + geom_line(aes(x=df$Time, y=df[,3], color=var.lab[3]))
ggplotly(p)
Plot produced by code above
In my mind, these two sets of code are identical, so could anyone explain why they produce such different results?
I know this could probably be done quite easily using autoplot, but I am more interested in the behavior of these two snipits of code.
What you're trying to do is a 'hack' way by plotting multiple lines, but it's not ideal in ggplot terms. To do it successfully, I'd use aes_string. But it's a hack.
df <- data.frame(Time = 1:20,
Var1 = rnorm(20),
Var2 = rnorm(20, mean = 0.5),
Var3 = rnorm(20, mean = 0.8))
vars <- paste0("Var", 1:3)
col_vec <- RColorBrewer::brewer.pal(3, "Accent")
library(ggplot2)
p <- ggplot(df, aes(Time))
for (i in 1:length(vars)) {
p <- p + geom_line(aes_string(y = vars[i]), color = col_vec[i], lwd = 1)
}
p + labs(y = "value")
How to do it properly
To make this plot more properly, you need to pivot the data first, so that each aesthetic (aes) is mapped to a variable in your data frame. That means we need a single variable to be color in our data frame. Hence, we pivot_longer and plot again:
library(tidyr)
df_melt <- pivot_longer(df, cols = Var1:Var3, names_to = "var")
ggplot(df_melt, aes(Time, value, color = var)) +
geom_line(lwd = 1) +
scale_color_manual(values = col_vec)
I'm hoping to recreate the gridExtra output below with ggplot's facet_grid, but I'm unsure of what variable ggplot identifies with the layers in the plot. In this example, there are two geoms...
require(tidyverse)
a <- ggplot(mpg)
b <- geom_point(aes(displ, cyl, color = drv))
c <- geom_smooth(aes(displ, cyl, color = drv))
d <- a + b + c
# output below
gridExtra::grid.arrange(
a + b,
a + c,
ncol = 2
)
# Equivalent with gg's facet_grid
# needs a categorical var to iter over...
d$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've poked around, but been unable to find an answer. I want to do a weighted geom_bar plot overlaid with a vertical line that shows the overall weighted average per facet. I'm unable to make this happen. The vertical line seems to a single value applied to all facets.
require('ggplot2')
require('plyr')
# data vectors
panel <- c("A","A","A","A","A","A","B","B","B","B","B","B","B","B","B","B")
instrument <-c("V1","V2","V1","V1","V1","V2","V1","V1","V2","V1","V1","V2","V1","V1","V2","V1")
cost <- c(1,4,1.5,1,4,4,1,2,1.5,1,2,1.5,2,1.5,1,2)
sensitivity <- c(3,5,2,5,5,1,1,2,3,4,3,2,1,3,1,2)
# put an initial data frame together
mydata <- data.frame(panel, instrument, cost, sensitivity)
# add a "contribution to" vector to the data frame: contribution of each instrument
# to the panel's weighted average sensitivity.
myfunc <- function(cost, sensitivity) {
return(cost*sensitivity/sum(cost))
}
mydata <- ddply(mydata, .(panel), transform, contrib=myfunc(cost, sensitivity))
# two views of each panels weighted average; should be the same numbers either way
ddply(mydata, c("panel"), summarize, wavg=weighted.mean(sensitivity, cost))
ddply(mydata, c("panel"), summarize, wavg2=sum(contrib))
# plot where each panel is getting its overall cost-weighted sensitivity from. Also
# put each panel's weighted average on the plot as a simple vertical line.
#
# PROBLEM! I don't know how to get geom_vline to honor the facet breakdown. It
# seems to be computing it overall the data and showing the resulting
# value identically in each facet plot.
ggplot(mydata, aes(x=sensitivity, weight=contrib)) +
geom_bar(binwidth=1) +
geom_vline(xintercept=sum(contrib)) +
facet_wrap(~ panel) +
ylab("contrib")
If you pass in the presumarized data, it seems to work:
ggplot(mydata, aes(x=sensitivity, weight=contrib)) +
geom_bar(binwidth=1) +
geom_vline(data = ddply(mydata, "panel", summarize, wavg = sum(contrib)), aes(xintercept=wavg)) +
facet_wrap(~ panel) +
ylab("contrib") +
theme_bw()
Example using dplyr and facet_wrap incase anyone wants it.
library(dplyr)
library(ggplot2)
df1 <- mutate(iris, Big.Petal = Petal.Length > 4)
df2 <- df1 %>%
group_by(Species, Big.Petal) %>%
summarise(Mean.SL = mean(Sepal.Length))
ggplot() +
geom_histogram(data = df1, aes(x = Sepal.Length, y = ..density..)) +
geom_vline(data = df2, mapping = aes(xintercept = Mean.SL)) +
facet_wrap(Species ~ Big.Petal)
vlines <- ddply(mydata, .(panel), summarize, sumc = sum(contrib))
ggplot(merge(mydata, vlines), aes(sensitivity, weight = contrib)) +
geom_bar(binwidth = 1) + geom_vline(aes(xintercept = sumc)) +
facet_wrap(~panel) + ylab("contrib")
I do a series of histograms with facet_grid and I want every histogram in the grid to have the same number of classes, in the example below e.g. 6 classes. The problem in this example below is that binwidth = diff(range(x$data))/6) defines the classes according to the overall range of a, b and c, i.e. defines one binwidth for all three facets.
How do I define binwidth individually for the facets a, b and c?
require("ggplot2")
a <- c(1.21,1.57,1.21,0.29,0.36,0.29,0.93,0.26,0.28,0.48,
0.12,0.38,0.83,0.82,0.41,0.69,0.25,0.98,0.52,0.11)
b <- c(0.42,0.65,0.17,0.38,0.44,0.01,0.01,0.03,0.15,0.01)
c <- c(1.09,3.55,1.07,4.55,0.55,0.11,0.72,0.66,1.22,3.04,
2.01,0.64,0.47,1.33,3.44)
x <- data.frame(data = c(a,b,c), variable = c(rep("a",20),rep("b",10),rep("c",15)),area="random")
qplot(data, data = x, geom = "histogram", binwidth = diff(range(x$data))/6) +
facet_grid(area~variable, scales = "free")
This is not optimal but you can do the histogram in different layers:
ggplot(x, aes(x=data)) +
geom_histogram(data=subset(x, variable=="a"), binwidth=.1) +
geom_histogram(data=subset(x, variable=="b"), binwidth=.2) +
geom_histogram(data=subset(x, variable=="c"), binwidth=.5) +
facet_grid(area~variable, scales="free")
One way is to pre-summarize your data in the way you want it, then to create the plot.
In your case, you need to bin your variables using the function cut(). The package dplyr is convenient for this, because it allows you to specify a mutate function for each group of your data:
library(dplyr)
zz <- x %>%
group_by(variable) %>%
mutate(
bins = cut(data, breaks=6)
)
qplot(bins, data = zz, geom = "histogram", fill=I("blue")) +
facet_grid(area~variable, scales = "free") +
theme(axis.text.x = element_text(angle=90))