Divide your dataset into two subsets: Subset A and Subset B. Subset B
includes the “individuals” whose missing values for the variable
“Esperance_maintien” (you can use “filter”, “!is.na” or “is.na”).
I tried this but i have an empty database ...
data <- read_csv("C:/Users/ABC/Downloads/sgl-arbres-urbains-wgs84.csv")
library(tidyverse)
data <- read_csv("https://static.data.gouv.fr/resources/arbres-urbains/20210218-172059/sgl-arbres-urbains-wgs84.csv")
data1 <- data %>%
filter(is.na(esperance_maintien)) # use is.na