How to make distinct variables their own columns in ggplot2?-CodePudding

So I am attempting to replicate the following plot:

But instead of the class as listed here, I have 5 True or False Boolean variables and I would like each to populate each column, based on count of ID.

DATA

ID   var1  var2  var3  var4  var5
001  TRUE  FALSE TRUE  FALSE TRUE
002  FALSE TRUE  FALSE FALSE FALSE
003  TRUE  FALSE TRUE  TRUE  TRUE
004  TRUE  TRUE  TRUE  FALSE FALSE
005  TRUE  FALSE TRUE  TRUE  TRUE
006  FALSE FALSE TRUE  FALSE FALSE

The end goal should be:

x-axis: Varibles, with each of the variables being a column
y-axis: Count, with frequency values based on the count of the ID, aka number of rows.
group: True or False.

This is what I think the resulting df may look like but not confident...

variables   t_f  count
     var1  TRUE      4
     var1 FALSE      2
     var2  TRUE      2
     var2 FALSE      4
     var3  TRUE      5
     var3 FALSE      1
     var4  TRUE      2
     var4 FALSE      4
     var5  TRUE      3
     var5 FALSE      3

EDIT: Great answer to this from @Ekoam. I have one more level to this that I assumed would be easy but not exactly.

ID   var1  var2  var3  var4  var5   group
001  TRUE  FALSE TRUE  FALSE TRUE       A
002  FALSE TRUE  FALSE FALSE FALSE      B
003  TRUE  FALSE TRUE  TRUE  TRUE       B
004  TRUE  TRUE  TRUE  FALSE FALSE      A
005  TRUE  FALSE TRUE  TRUE  TRUE       A
006  FALSE FALSE TRUE  FALSE FALSE      B

So let's say there's a group variable. How could I facet_wrap() this data and produce two distinct bar charts for each group?

CodePudding user response：

Here is one way to do it. You do not have to manually compute the ys if it is just counting the rows. You can use geom_bar. Something like this

library(dplyr)
library(tidyr)
library(ggplot2)

df %>% 
  pivot_longer(-ID, names_to = "variables", values_to = "count") %>% 
  ggplot()   
  geom_bar(aes(x = variables, fill = count), position = "stack")

Output