Bar Plot for Count Data by Group in R

Bar plot for count data by group in R

Before you can use functions like barplot, you need to format the data properly. Look at ?table. If mydata is your dataframe:

tbl <- with(mydata, table(Species, Depth))

barplot(tbl, beside = TRUE, legend = TRUE)

Or using ggplot:

library(ggplot2)
ggplot(as.data.frame(tbl), aes(factor(Depth), Freq, fill = Species)) +     
  geom_col(position = 'dodge')

How to display number of cases per group in a stacked bar plot?

Use position_fill(vjust = 0.5) and label with after_stat(count):

ggplot(mpg, aes(manufacturer, fill = drv)) +
  geom_bar(position = "fill", stat = "count")+
  geom_text(aes(label = paste0("n=", after_stat(count))), stat='count', position = position_fill(vjust = 0.5)) +
  theme_classic()

Barplot for count data with multiple columns

Here is one with base R. Get the count of 'Yes' with rowSums on a logical matrix selecting only the 'Var' columns, then do a group by 'Sex' to summarise the count by Sex with rowsum) and use barplot

barplot(t(rowsum(rowSums(df1[-1] == 'Yes'), df1$Sex)))

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Or if we need a group by barplot, change it to

barplot(t(rowsum(+(df1[-1] == 'Yes'), df1$Sex)), beside = TRUE,
    legend = TRUE, col = c('red', 'blue', 'green'))

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Or if we prefer ggplot, reshape to 'long' format with pivot_longer (from tidyr), get a group_by, summarise to return the count of 'Yes' and use ggplot

library(dplyr)
library(tidyr)
library(ggplot2)
df1 %>%
    pivot_longer(cols = -Sex) %>%
    group_by(Sex) %>%
    summarise(n = sum(value == 'Yes')) %>%
    ggplot(aes(x = Sex, y = n)) +
       geom_col()

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For a bar for each 'Var'

df1 %>%
   pivot_longer(cols = -Sex) %>%
   group_by(Sex, name) %>%
   summarise(n = sum(value == 'Yes'), .groups = 'drop') %>% 
   ggplot(aes(x = Sex, y = n, fill = name)) + 
      geom_col(position = 'dodge') 

-output

data

df1 <- structure(list(Sex = c("Male", "Female", "Male", "Female"), 
Var1 = c("Yes", 
"No", "No", "Yes"), Var2 = c("No", "Yes", "No", "Yes"), Var3 = c("Yes", 
"No", "Yes", "No")), class = "data.frame", row.names = c(NA, 
-4L))

Make a grouped barplot from count value in ggplot?

Was able to answer my question thanks to @Jon Spring, closing the aes sooner made the difference!

bike_rides %>%  
  group_by(member_casual, month_of_use) %>%  
  summarize(Count = n()) %>% 
  ggplot(aes(x=month_of_use, y=Count, fill=member_casual)) + 
  geom_bar(stat='identity', position= "dodge")

New Graph

Practice makes perfect!

Bar plot with count on yaxis and year on x axis with 2 color groups

df1 <-df %>% 
  count(Year, Type) %>%
  mutate(Freq = n/sum(n))

ggplot(df1, aes(x=Year, y=Freq, fill=Type))+  
  geom_bar(stat="identity") + 
  geom_text(aes(label=scales::percent(Freq)), position = position_stack(vjust = .5))+
  theme_classic() + 
  labs(title = "", x = "Year", y = "%", fill="Type")+  
  scale_fill_discrete(name= "Type")

or you can use count(Year, Type)

df1 <-df %>% 
  count(Year, Type) 

ggplot(df1, aes(x=Year, y=n, fill=Type))+  
  geom_bar(stat="identity") + 
  geom_text(aes(label=n), position = position_stack(vjust = .5))+
  theme_classic() + 
  labs(title = "", x = "Year", y = "Count", fill="Type")+  
  scale_fill_discrete(name= "Type")

ggplot produces is a bar plot of the unique counts rather than count of each unique variable

It looks like when you try to rename the columns in the examples you change all the variables.

If you do this it works fine:

library(ggplot2)
library(dplyr)

Community<- c("LIMA","LIMA","SUCRE","SUCRE","SUCRE","SANTIAGO","SANTIAGO","SANTIAGO","SANTIAGO")

df<-data.frame(Communitycol=Community)

ggplot(data=df, aes(Communitycol)) + geom_bar()

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