I'm looking for a way to plot a bar chart containing two different series, hide the bars for one of the series and instead have a line (smooth if possible) go through the top of where bars for the hidden series would have been (similar to how one might overlay a freq polynomial on a histogram). I've tried the example below but appear to be running into two problems.
First, I need to summarize (total) the data by group, and second, I'd like to convert one of the series (df2) to a line.
df <- data.frame(grp=c("A","A","B","B","C","C"),val=c(1,1,2,2,3,3))
df2 <- data.frame(grp=c("A","A","B","B","C","C"),val=c(1,4,3,5,1,2))
ggplot(df, aes(x=grp, y=val)) +
geom_bar(stat="identity", alpha=0.75) +
geom_bar(data=df2, aes(x=grp, y=val), stat="identity", position="dodge")
You can get group totals in many ways. One of them is
with(df, tapply(val, grp, sum))
For simplicity, you can combine bar and line data into a single dataset.
df_all <- data.frame(grp = factor(levels(df$grp)))
df_all$bar_heights <- with(df, tapply(val, grp, sum))
df_all$line_y <- with(df2, tapply(val, grp, sum))
Bar charts use a categorical x-axis. To overlay a line you will need to convert the axis to be numeric.
ggplot(df_all) +
geom_bar(aes(x = grp, weight = bar_heights)) +
geom_line(aes(x = as.numeric(grp), y = line_y))
Perhaps your sample data aren't representative of the real data you are working with, but there are no lines to be drawn for df2. There is only one value for each x and y value. Here's a modifed version of your df2 with enough data points to construct lines:
df <- data.frame(grp=c("A","A","B","B","C","C"),val=c(1,2,3,1,2,3))
df2 <- data.frame(grp=c("A","A","B","B","C","C"),val=c(1,4,3,5,0,2))
p <- ggplot(df, aes(x=grp, y=val))
p <- p + geom_bar(stat="identity", alpha=0.75)
p + geom_line(data=df2, aes(x=grp, y=val), colour="blue")
Alternatively, if your example data above is correct, you can plot this information as a point with geom_point(data = df2, aes(x = grp, y = val), colour = "red", size = 6). You can obviously change the color and size to your liking.
EDIT: In response to comment
I'm not entirely sure what the visual for a freq polynomial over a histogram is supposed to look like. Are the x-values supposed to be connected to one another? Secondly, you keep referring to wanting lines but your code shows geom_bar() which I assume isn't what you want? If you want lines, use geom_lines(). If the two assumptions above are correct, then here's an approach to do that:
#First let's summarise df2 by group
df3 <- ddply(df2, .(grp), summarise, total = sum(val))
> df3
grp total
1 A 5
2 B 8
3 C 3
#Second, let's plot df3 as a line while treating the grp variable as numeric
p <- ggplot(df, aes(x=grp, y=val))
p <- p + geom_bar(alpha=0.75, stat = "identity")
p + geom_line(data=df3, aes(x=as.numeric(grp), y=total), colour = "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$Name, "_plot.pdf"),
myPlot)
}
library(plyr)
d_ply(yourMelt, .variables = "Name", .fun = bp)
How do I avoid ggplot to make an emtpy boxplot in the case when I have only three groups? ggplot(df, aes(group, value, fill=group)) + geom_boxplot()
It is hard to know for sure without seeing the data but it seems like you have four groups as follows:
# Make 3 repetative groups
group <- rep(c("group_1","group_2","group_3"),n)
# Generate values for defined groups
value <- rnorm(length(group), mean = 5, sd = 1)
# Data frame with 1 more group with value
df <- data.frame(c("group_01", group), c(5, value))
colnames(df) <- c("group", "value")
ggplot(df, aes(group, value, fill = group)) + geom_boxplot()
From this simulated dataset we obtain boxplot as follows in this graph, which seems to be your case.
You should check for levels in your data frame and remove the ones that are not necessary:
# Check for levels
levels(df$group)
# Remove unwanted group
df <- df[df$group != "group_01",]
# Plot the cleaned df
ggplot(df, aes(group, value, fill = group)) + geom_boxplot()
Now you acquire a graph with three groups.
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")
This question's theme is simple but drives me crazy:
1. how to use melt()
2. how to deal with multi-lines in single one image?
Here is my raw data:
a 4.17125 41.33875 29.674375 8.551875 5.5
b 4.101875 29.49875 50.191875 13.780625 4.90375
c 3.1575 29.621875 78.411875 25.174375 7.8012
Q1:
I've learn from this post Plotting two variables as lines using ggplot2 on the same graph to know how to draw the multi-lines for multi-variables, just like this:
The following codes can get the above plot. However, the x-axis is indeed time-series.
df <- read.delim("~/Desktop/df.b", header=F)
colnames(df)<-c("sample",0,15,30,60,120)
df2<-melt(df,id="sample")
ggplot(data = df2, aes(x=variable, y= value, group = sample, colour=sample)) + geom_line() + geom_point()
I wish it could treat 0 15 30 60 120 as real number to show the time series, rather than name_characteristics. Even having tried this, I failed.
row.names(df)<-df$sample
df<-df[,-1]
df<-as.matrix(df)
df2 <- data.frame(sample = factor(rep(row.names(df),each=5)), Time = factor(rep(c(0,15,30,60,120),3)),Values = c(df[1,],df[2,],df[3,]))
ggplot(data = df2, aes(x=Time, y= Values, group = sample, colour=sample))
+ geom_line()
+ geom_point()
Loooooooooking forward to your help.
Q2:
I've learnt that the following script can add the spline() function for single one line, what about I wish to apply spline() for all the three lines in single one image?
n <-10
d <- data.frame(x =1:n, y = rnorm(n))
ggplot(d,aes(x,y))+ geom_point()+geom_line(data=data.frame(spline(d, n=n*10)))
Your variable column is a factor (you can verify by calling str(df2)). Just convert it back to numeric:
df2$variable <- as.numeric(as.character(df2$variable))
For your other question, you might want to stick with using geom_smooth or stat_smooth, something like this:
p <- ggplot(data = df2, aes(x=variable, y= value, group = sample, colour=sample)) +
geom_line() +
geom_point()
library(splines)
p + geom_smooth(aes(group = sample),method = "lm",formula = y~bs(x),se = FALSE)
which gives me something like this:
I have two graphs with the same x axis - the range of x is 0-5 in both of them.
I would like to combine both of them to one graph and I didn't find a previous example.
Here is what I got:
c <- ggplot(survey, aes(often_post,often_privacy)) + stat_smooth(method="loess")
c <- ggplot(survey, aes(frequent_read,often_privacy)) + stat_smooth(method="loess")
How can I combine them?
The y axis is "often privacy" and in each graph the x axis is "often post" or "frequent read".
I thought I can combine them easily (somehow) because the range is 0-5 in both of them.
Many thanks!
Example code for Ben's solution.
#Sample data
survey <- data.frame(
often_post = runif(10, 0, 5),
frequent_read = 5 * rbeta(10, 1, 1),
often_privacy = sample(10, replace = TRUE)
)
#Reshape the data frame
survey2 <- melt(survey, measure.vars = c("often_post", "frequent_read"))
#Plot using colour as an aesthetic to distinguish lines
(p <- ggplot(survey2, aes(value, often_privacy, colour = variable)) +
geom_point() +
geom_smooth()
)
You can use + to combine other plots on the same ggplot object. For example, to plot points and smoothed lines for both pairs of columns:
ggplot(survey, aes(often_post,often_privacy)) +
geom_point() +
geom_smooth() +
geom_point(aes(frequent_read,often_privacy)) +
geom_smooth(aes(frequent_read,often_privacy))
Try this:
df <- data.frame(x=x_var, y=y1_var, type='y1')
df <- rbind(df, data.frame(x=x_var, y=y2_var, type='y2'))
ggplot(df, aes(x, y, group=type, col=type)) + geom_line()