I am learning geom_bar on section 3.7 of r4ds.had.co.nz. I run a code like this:
library(ggplot2)
ggplot(data = diamonds) + geom_bar(mapping = aes(x = cut, y = ..prop.., group = 1))
Then I have this plot:
The point is, if I exclude the "group = 1" part:
library(ggplot2)
ggplot(data = diamonds) + geom_bar(mapping = aes(x = cut, y = ..prop..))
The plot will be wrong,
But if I replace group = 1 by group = 2 or group = "x", the plot still looks correct. So I don't quite understand the meaning of group = 1 here and how to use it.
group="whatever" is a "dummy" grouping to override the default behavior, which (here) is to group by cut and in general is to group by the x variable. The default for geom_bar is to group by the x variable in order to separately count the number of rows in each level of the x variable. For example, here, the default would be for geom_bar to return the number of rows with cut equal to "Fair", "Good", etc.
However, if we want proportions, then we need to consider all levels of cut together. In the second plot, the data are first grouped by cut, so each level of cut is considered separately. The proportion of Fair in Fair is 100%, as is the proportion of Good in Good, etc. group=1 (or group="x", etc.) prevents this, so that the proportions of each level of cut will be relative to all levels of cut.
Group will help the plot to look at the specific rows that contain the specific cut and the proportion is found with respect to the whole database as in proportion of an ideal cut in the whole dataset.
If group is not used, the proportion is calculated with respect to the data that contains that field and is ultimately going to be 100% in any case. For instance, The proportion of an ideal cut in the ideal cut specific data will be 1.
Related
I have a data frame containing 5 probes which are my variables in a dataframe, cg02823866, cg13474877, cg14305799, cg15837913 and cg19724470. I want to create a boxplot that will group cg02823866 and cg14305799 into a group called 'GeneBody' and then cg13474877, cg14305799 and cg19724470 into a group called 'Promoter'. I then want to colour code the boxplots to represent the probe names. I can't figure out how to group those variables into groups to plot the graph.
I created an ungrouped boxplot of the five probes and it looked like this.
I want there to be the titles 'Promoter' and 'GeneBody' on the x axis. Above the 'GeneBody' title there are the 2 boxplots for the cg02823866 and cg14305799 probes. Then a 'Promoter' label with the boxplots for cg13474877, cg14305799 and cg19724470. I then want each boxplots colour coded to represent each different probe.
My data frame that I imported into RStudio looks like this: https://i.stack.imgur.com/r4gEC.png
Assuming you have some data with variable names Beta (your y axis), Probe (your current x axis), and group (either "GeneBody" or "Promoter"), you can do something like the following:
library(ggplot2)
ggplot(data, aes(x = group, y = Beta, fill = Probe)) +
geom_boxplot()
If you provide a reproducible set of data, I can probably do better.
Adding to Ben's answer the traditional iris-data.frame example,which you can easily load by data(iris):
ggplot(iris) +
aes(x = "", y = Sepal.Length, group = Species) +
geom_boxplot(shape = "circle", fill = "#112446") +
theme_minimal()
So you just need a column which indicates the group dependency.
It gets of course more difficult with uncleand data, where you might need to transpond the data first etc. But those are follow up questions i guess.
Also if you want to make your life easier, use esquisse R-Studio add-on
Boxplot
how in R, should I have a histogram with a categorical variable in x-axis and
the frequency of a continuous variable on the y axis?
is this correct?
There are a couple of ways one could interpret "one graph" in the title of the question. That said, using the ggplot2 package, there are at least a couple of ways to render histograms with by groups on a single page of results.
First, we'll create data frame that contains a normally distributed random variable with a mean of 100 and a standard deviation of 20. We also include a group variable that has one of four values, A, B, C, or D.
set.seed(950141237) # for reproducibility of results
df <- data.frame(group = rep(c("A","B","C","D"),200),
y_value = rnorm(800,mean=100,sd = 20))
The resulting data frame has 800 rows of randomly generated values from a normal distribution, assigned into 4 groups of 200 observations.
Next, we will render this in ggplot2::ggplot() as a histogram, where the color of the bars is based on the value of group.
ggplot(data = df,aes(x = y_value, fill = group)) + geom_histogram()
...and the resulting chart looks like this:
In this style of histogram the values from each group are stacked atop each other(i.e. the frequency of group A is added to B, etc. before rendering the chart), which might not be what the original poster intended.
We can verify the "stacking" behavior by removing the fill = group argument from aes().
# verify the stacking behavior
ggplot(data = df,aes(x = y_value)) + geom_histogram()
...and the output, which looks just like the first chart, but drawn in a single color.
Another way to render the data is to use group with facet_wrap(), where each distribution appears in a different facet on one chart.
ggplot(data = df,aes(x = y_value)) + geom_histogram() + facet_wrap(~group)
The resulting chart looks like this:
The facet approach makes it easier to see differences in frequency of y values between the groups.
I am learning geom_bar on section 3.7 of r4ds.had.co.nz. I run a code like this:
library(ggplot2)
ggplot(data = diamonds) + geom_bar(mapping = aes(x = cut, y = ..prop.., group = 1))
Then I have this plot:
The point is, if I exclude the "group = 1" part:
library(ggplot2)
ggplot(data = diamonds) + geom_bar(mapping = aes(x = cut, y = ..prop..))
The plot will be wrong,
But if I replace group = 1 by group = 2 or group = "x", the plot still looks correct. So I don't quite understand the meaning of group = 1 here and how to use it.
group="whatever" is a "dummy" grouping to override the default behavior, which (here) is to group by cut and in general is to group by the x variable. The default for geom_bar is to group by the x variable in order to separately count the number of rows in each level of the x variable. For example, here, the default would be for geom_bar to return the number of rows with cut equal to "Fair", "Good", etc.
However, if we want proportions, then we need to consider all levels of cut together. In the second plot, the data are first grouped by cut, so each level of cut is considered separately. The proportion of Fair in Fair is 100%, as is the proportion of Good in Good, etc. group=1 (or group="x", etc.) prevents this, so that the proportions of each level of cut will be relative to all levels of cut.
Group will help the plot to look at the specific rows that contain the specific cut and the proportion is found with respect to the whole database as in proportion of an ideal cut in the whole dataset.
If group is not used, the proportion is calculated with respect to the data that contains that field and is ultimately going to be 100% in any case. For instance, The proportion of an ideal cut in the ideal cut specific data will be 1.
I'm trying to make barplot
Data are in dataframe. In those dataframes I have several column, one named ID and another count.
First I'm trying to make group of this count. In the barplot we should see,count=0,count=1,count=2,count>=3
Some exemple data
data1 <- data.frame(ID="ID_1", count=(rep(seq(0,10,by=1),each=4)))
data2 <- data.frame(ID="ID_2", count=(rep(seq(0,10,by=1),each=4)))
data3 <- data.frame(ID="ID_3", count=(rep(seq(0,10,by=1),each=4)))
Obviously here, barplots of the dataframes will look same
I tried to make this in ggplot (it's not nice at all)
ggplot(data1)+
geom_bar(aes(x = ID, fill = count),position = "fill")+
geom_bar(data=data2,aes(x = ID, fill = count),position = "fill")+
geom_bar(data=data3,aes(x = ID, fill = count),position = "fill")
I got something like that
What I'm trying to do is to have different groups within a barplot, like the proportion of counts 0, proportion of counts 1,2 and proportion of counts greater (and equal) to 3.
I expect something like that
But of course in my example barplots will look same.
Also if you have some suggestion to change Y axis from 1.00 to 100%.
Also One of my problem is that length of my real dataframes are not equal but it should doesn't matter because I try to get the percentage of count group
You need to put all the data in 1 dataframe, in long format. Then cast your counts to factors, and it works.
ggplot(bind_rows(data1, data2, data3)) +
geom_bar(aes(x = ID, fill = as.factor(count)), position = "fill") +
scale_y_continuous(labels=scales::percent) # To get the Y axis in percentage
So I did something to try to create my barplot
data1$var="first"
data2$var="second"
data3$var="third"
data4$var="fourth"
data5$var="fifth"
full_data=rbind(data1,data2,data3,data4,data5)
ggplot(ppgk) +
geom_bar(aes(x = var, fill = as.factor(Count)), position = "fill")+
scale_y_continuous(labels=scales::percent)
So I got something like that :
If Someone have the solution to make different group of counts : count=0,count=1,count=2,count>=3
How can I plot the relative proportions of two groups using a fill aesthetic in ggplot2?
I am asking this question here because several other answers on this topic seem incorrect (ex1, ex2, and ex3), but Cross Validated seems to have functionally banned R specific questions (CV meta). ..density.. is conceptually related to, but distinct from proportions (ex4 and ex5). So the correct answer does not seem to involve density.
Example:
set.seed(1200)
test <- data.frame(
test1 = factor(sample(letters[1:2], 100, replace = TRUE,prob=c(.25,.75)),ordered=TRUE,levels=letters[1:2]),
test2 = factor(sample(letters[3:8], 100, replace = TRUE),ordered=TRUE,levels=letters[3:8])
)
ggplot(test, aes(test2)) + geom_bar(aes(y = ..density.., group=test1, fill=test1) ,position="dodge")
#For example, the plotted data shows level a x c as being slightly in excess of .15, but a manual calculation shows a value of .138
counts <- with(test,table(test1,test2))
counts/matrix(rowSums(counts),nrow=2,ncol=6)
The answer that seems to yield an output that is correct resorts to a solution that doesn't use ggplot2 (calculating it outside of ggplot2) or requires that a panel be used rather than a fill aesthetic.
Edit: Digging into stat_bin yields that the function ultimately called is bin, but bin only gets passed the values in the x aes. Without rewriting stat_bin (or making another stat_) the hack that was applied in the above referenced answer can be generalized to the fill aes in the absence of the group aes with the following code for the y aes: y = ..count../sapply(fill, FUN=function(x) sum(count[fill == x])). This just replaces PANEL (the hidden column that is present at the end of StatBin) with fill). Presumably other hidden variables could get the same treatment.
This is an aweful hack, but it seems to do what you want...
ggplot(test, aes(test2)) + geom_bar(aes(y = ..count../rep(c(sum(..count..[1:6]), sum(..count..[7:12])), each=6),
group=test1, fill=test1) ,position="dodge") +
scale_y_continuous(name="proportion")