use of pivot_wider to plot the evolution of variables in R - r

Question

I would like to plot the evolution of the number of workers per category ("A", "D", "F", "I"), from 2017 to 2021, with a stacked bar chart (with the labels in the middle of each bar, for each category), one bar per year. Yet my dataset isn't in the right way to do this, I think I need to use pivot_wider() or pivot_longer() from what I have seen here, but I don't really know how to manipulate these functions. Could anyone help ?
Here is the structure of my dataset, for reproducibility :
structure(list(A = c("10", "7", "8", "8", "9", "Total"), D = c(23,
14, 29, 35, 16, 117), F = c(8, 7, 11, 6, 6, 38), I = c(449, 498,
415, 470, 531, 2363), annee = c("2017", "2018", "2019", "2020",
"2021", NA)), core = structure(list(A = c("10", "7", "8", "8",
"9"), D = c(23, 14, 29, 35, 16), F = c(8, 7, 11, 6, 6), I = c(449,
498, 415, 470, 531)), class = "data.frame", row.names = c(NA,
-5L)), tabyl_type = "two_way", totals = "row", row.names = c(NA,
6L), class = c("tabyl", "data.frame"))

Answer 1

library(tidyverse)
library(ggrepel)
df <- structure(list(A = c("10", "7", "8", "8", "9", "Total"), D = c(
23,
14, 29, 35, 16, 117
), F = c(8, 7, 11, 6, 6, 38), I = c(
449, 498,
415, 470, 531, 2363
), annee = c(
"2017", "2018", "2019", "2020",
"2021", NA
)), core = structure(list(A = c(
"10", "7", "8", "8",
"9"
), D = c(23, 14, 29, 35, 16), F = c(8, 7, 11, 6, 6), I = c(
449,
498, 415, 470, 531
)), class = "data.frame", row.names = c(
NA,
-5L
)), tabyl_type = "two_way", totals = "row", row.names = c(
NA,
6L
), class = c("tabyl", "data.frame"))
df |>
filter(!is.na(annee)) |>
mutate(A = as.double(A)) |>
pivot_longer(-annee, names_to = "category") |>
ggplot(aes(annee, value, fill = category, label = value)) +
geom_col() +
geom_label_repel(position = position_stack(), max.overlaps = 20)
Created on 2022-08-08 by the reprex package (v2.0.1)

Answer 2

Once you remove the total row, and ensuring that A through I are numeric, you can pivot_longer and pass to ggplot() like this:
data %>%
filter(A!="Total") %>%
mutate(across(A:I, as.numeric)) %>%
pivot_longer(cols = -annee, names_to = "group", values_to = "ct") %>%
ggplot(aes(annee,ct,fill=group)) +
geom_col()
I did not add the category labels, since group I dominates each year; you might want to reconsider that visualization