including libraries

creating a dataset having 100 rows with columns

data=tibble(
  category =sample (c('A','B','C'),
                    num_rows,replace=TRUE),
  numeric_variable=rnorm(num_rows,mean=50,sd=10)
)
str(data)
## tibble [100 × 2] (S3: tbl_df/tbl/data.frame)
##  $ category        : chr [1:100] "B" "A" "A" "A" ...
##  $ numeric_variable: num [1:100] 62.6 51.9 35.7 64.3 44.6 ...
summary(data)
##    category         numeric_variable
##  Length:100         Min.   :22.73   
##  Class :character   1st Qu.:45.13   
##  Mode  :character   Median :50.26   
##                     Mean   :51.16   
##                     3rd Qu.:59.80   
##                     Max.   :75.00
data_summary =data %>%
  group_by(category) %>%
  summarise(
    count =n(),
    mean_variable =mean(numeric_variable),
    median_variable =median(numeric_variable)
  )

printing the summary .Using ggplot we plot graphs using diffrent columns filled with diffrent colours and labs

## # A tibble: 3 × 4
##   category count mean_variable median_variable
##   <chr>    <int>         <dbl>           <dbl>
## 1 A           35          51.8            50.3
## 2 B           41          50.0            49.1
## 3 C           24          52.2            50.7