Q:

How can this normal table be modified to work properly with R?

I have a large table with different columns and some rows.
The columns represent different characteristics of each row. The row are my different items i would say.
The overall buildup or relevant columns are like so:

ColumnID     Classification       will_use_b       Region 
1            A                    TRUE             A
2            A                    FALSE            X
3            B                    TRUE             X
4            C                    TRUE             A
5            D                    FALSE            A
6            A                    TRUE             A

The aim for me is for example to print barchart havin the column Classification at the x-axis and show the number of occurences on the y-axis. More, beforehand I wanted to filter that only items are used with the parameter will_use_b are TRUE.
WIth the current table format I don't get this to work for me, more I am not sure how to define these conditions with column will_ues_b
My first try was to make use of tibble from tidyverse:
df <- read.csv2("file.csv", header = TRUE)
data <- tibble(
  colID= df$ColumnID,
  class= df$Classification,
  willUse = df$will_use_b,
  reg= df$Region,
)
##and then
grouped <- data %>% 
  group_by(class) %>% mutate(classsum=sum(class))

But that does not work and I am not sure how to filter beforehand.
I was reading about the gather() function, could this help in my case?
The overall aim is just to have some kind of Barplot with the amount of each Classification.

A:

Do you need something like this? 
library(dplyr)
library(ggplot2)

df %>%
  group_by(Classification) %>%
  summarise(sum = sum(will_use_b)) %>%
  ggplot() + aes(y=sum, x=Classification) + geom_col()

