在R中生成“ 2D”直方图

Question

I am new to R and I would like to know how to generate histograms for the following situation :

I initially have a regular frequency table with 2 columns : Column A is the category (or bin) and Column B is the number of cases that fall in that category

Col A    Col B
1-10       7
11-20      4
21-30      5

From this initial frequency table, I create a table with 3 columns : Col A is again the category (or bin), but now Col B is the "fraction of total cases", so for the category 1-10, column B will have the value 7/(7+4+5) = 7/16 . Now there is also a third column, Col C which is "fraction of total cases falling between the categories 1-20", so for 1-10, the value for Col C would be 7/(7+4) = 7/11. The complete table would look like below :

Col A    Col B    Col C
1-10      7/16     7/11
11-20     4/16     4/11
21-30     5/16      0

How do I generate a histogram from this 3-column table above ? My X axis should be the bin (1-10, 11-20 etc.) and my Y axis should be the fraction, however for every bin I have two fractions (Col B and Col C), so there will be two fraction "bars" for every bin in the histogram.

Any help would be greatly appreciated.

Answer 1

The data:

dat <- data.frame(A = c("1-10", "11-20", "21-30"), B = c(7, 4, 5))

Now, calculate the proportions and create a new object:

dat2 <- rbind(B = dat$B/sum(dat$B), C = c(dat$B[1:2]/sum(dat$B[1:2]), 0))
colnames(dat2) <- dat$A

Plot:

barplot(dat2, beside = TRUE, legend = rownames(dat2))

Answer 2

Your title should be changed to "Dodged Bar Chart" instead of 2D histogram, because histograms have continuous scale on x axis unlike bar chart and they are basically used for comparing the distributions of univariate data or the distributions of univariate data modeled on the dependent factor. You are trying to compare colB vs colC which can be effectively visualized using a 2D scatter plot but not with bar chart. The better way to compare the distributions of colB and colC using histograms would be plotting two histograms separately and check the change in location of the data points.

If you want to compare distributions of colB and colC, try the following code: I did round up the values for getting a reasonable data per your data description. Notice a random sampling by permutation is happening and everytime, you run the same code, there will be slight change in the distribution, but that will not affect the inference of distribution between colB and colC.

library("ggplot2")
# 44 datapoints between 1-10
a <- rep(1:10, 4)
a <- c(a, sample(a, size=4, replace=FALSE))
# 25 datapoints between 11-20
b <- rep(11:20, 2)
b <- c(b, sample(b, size=5, replace=FALSE))
# 31 datapoints between 21-30
c <- rep(21:30, 3)
c <- c(c, sample(c, size=1, replace=FALSE))
colB <- c(a, b, c)
# 64 datapoints between 1-10
a <- rep(1:10, 6)
a <- c(a, sample(a, size=4, replace=FALSE))
# 36 datapoints between 11-20
b <- rep(11:20, 3)
b <- c(b, sample(b, size=6, replace=FALSE))
colC <- c(a, b)
df <- data.frame(cbind(colB, colC=colC))
write.table(df, file = "data")
data <- read.table("data", header=TRUE)
data
ggplot(data=data, aes(x=colB, xmin=1, xmax=30)) + stat_bin(binwidth = 1)
ggplot(data=data, aes(x=colC, xmin=1, xmax=30)) + stat_bin(binwidth = 1)

# if you want density distribution, then you can try something like this:
ggplot(data=data, aes(x=colB, y = ..density.., xmin=1, xmax=30)) + stat_bin(binwidth = 1)
ggplot(data=data, aes(x=colC, y = ..density.., xmin=1, xmax=30)) + stat_bin(binwidth = 1)

HTH -Sathish