Installation des packages et de la bdd

library(readr)
library(questionr)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(forcats)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ ggplot2   3.5.2     ✔ stringr   1.5.1
## ✔ lubridate 1.9.4     ✔ tibble    3.2.1
## ✔ purrr     1.0.4     ✔ tidyr     1.3.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(corrplot)
## corrplot 0.95 loaded
library(ggalluvial)
library(readr)
bdd <- read.csv("/Users/bastiandelauney/Desktop/Faculté/L3/S6/Socio/ENQ/ENQ S6 R, ESS, 1/R project and ESS copy/ESS 11 FR.csv")




### VI ####
# B43 = imbgeco = eco threat
# B44 = imueclt = cultural threat
# B45 = imwbcnt = neighborhood safety


#### VD ####
## Niveau d'étude de l'enquête = F15 = edlvdfr
0-4 5 6-10 Total
1re génération 12.7 20.4 66.9 100.0
2e génération 21.9 25.0 53.1 100.0
Natif 31.3 25.4 43.4 100.0
Ensemble 28.3 24.8 46.9 100.0 
table(bdd$imbgeco)
## 
##   0   1   2   3   4   5   6   7   8   9  10  77  88 
## 101  38  88 128 132 427 202 263 194  70  82   6  40
bdd$imbgeco_rec_mean <- case_when(
  bdd$imbgeco %in% c(77, 88) ~ NA_integer_,
  TRUE ~ bdd$imbgeco
) 
table(bdd$imbgeco_rec_mean)
## 
##   0   1   2   3   4   5   6   7   8   9  10 
## 101  38  88 128 132 427 202 263 194  70  82
bdd$imbgeco_lm <- bdd$imbgeco_rec_mean
mean(bdd$imbgeco_rec_mean, na.rm = TRUE) 
## [1] 5.40058
### Pour réduire le nombre de m.d.r., pour lisibilité du tableau
bdd$imbgeco_chr <- as.character(bdd$imbgeco)
bdd$imbgeco_rec_tri <- case_when(
  bdd$imbgeco %in% c(0,1,2,3,4) ~ "0-4",
  bdd$imbgeco == 5 ~ "5",
  bdd$imbgeco %in% c(6,7,8,9,10) ~ "6-10",
  TRUE ~ NA_character_
) 
bdd$imbgeco_rec_tri <- factor(bdd$imbgeco_rec_tri,
                              levels = c("0-4", "5","6-10"))
freq(bdd$imbgeco_rec_tri)
##        n    % val%
## 0-4  487 27.5 28.2
## 5    427 24.1 24.8
## 6-10 811 45.8 47.0
## NA    46  2.6   NA
#### imueclt ####

### idem.

table(bdd$imueclt)
## 
##   0   1   2   3   4   5   6   7   8   9  10  77  88 
## 108  57 107 133 149 305 162 238 244  88 156   4  20
bdd$imueclt_rec_mean <- case_when(
  bdd$imueclt %in% c(77, 88) ~ NA_integer_,
  TRUE ~ bdd$imueclt
) 
mean(bdd$imueclt_rec_mean, na.rm = TRUE)
## [1] 5.571265
### Pour réduire le nombre de mdr, pour lisibilité du tableau
bdd$imueclt_chr <- as.character(bdd$imueclt)
bdd$imueclt_rec_tri <- case_when(
  bdd$imueclt %in% c(0,1,2,3,4) ~ "0-4",
  bdd$imueclt == 5 ~ "5",
  bdd$imueclt %in% c(6,7,8,9,10) ~ "6-10",
  TRUE ~ NA_character_
) 
bdd$imueclt_rec_tri <- factor(bdd$imueclt_rec_tri,
                              levels = c("0-4", "5","6-10"))
freq(bdd$imueclt_rec_tri)
##        n    % val%
## 0-4  554 31.3 31.7
## 5    305 17.2 17.5
## 6-10 888 50.1 50.8
## NA    24  1.4   NA
bdd$imueclt_lm <- bdd$imueclt_rec_mean

#### imwbcnt  ####

### idem.
table(bdd$imwbcnt)
## 
##   0   1   2   3   4   5   6   7   8   9  10  77  88 
##  86  57  85 129 130 650 175 175 139  38  65   7  35
bdd$imwbcnt_rec_mean <- case_when(
  bdd$imwbcnt %in% c(77, 88) ~ NA_integer_,
  TRUE ~ bdd$imwbcnt
) 
mean(bdd$imwbcnt_rec_mean, na.rm = TRUE)
## [1] 5.068248
bdd$imwbcnt_lm <- bdd$imwbcnt_rec_mean
### Pour réduire le nombre de mdr, pour lisibilité du tableau
bdd$imwbcnt_chr <- as.character(bdd$imwbcnt)
bdd$imwbcnt_rec_tri <- case_when(
  bdd$imwbcnt %in% c(0,1,2,3,4) ~ "0-4",
  bdd$imwbcnt == 5 ~ "5",
  bdd$imwbcnt %in% c(6,7,8,9,10) ~ "6-10",
  TRUE ~ NA_character_
) 
bdd$imwbcnt_rec_tri <- factor(bdd$imwbcnt_rec_tri,
                              levels = c("0-4", "5","6-10"))
freq(bdd$imwbcnt_rec_tri)
##        n    % val%
## 0-4  487 27.5 28.2
## 5    650 36.7 37.6
## 6-10 592 33.4 34.2
## NA    42  2.4   NA
round(prop.table(table(bdd$imbgeco_rec_mean))*100,2)
## 
##     0     1     2     3     4     5     6     7     8     9    10 
##  5.86  2.20  5.10  7.42  7.65 24.75 11.71 15.25 11.25  4.06  4.75
round(prop.table(table(bdd$imueclt_rec_mean))*100,2)
## 
##     0     1     2     3     4     5     6     7     8     9    10 
##  6.18  3.26  6.12  7.61  8.53 17.46  9.27 13.62 13.97  5.04  8.93
round(prop.table(table(bdd$imwbcnt_rec_mean))*100,2)
## 
##     0     1     2     3     4     5     6     7     8     9    10 
##  4.97  3.30  4.92  7.46  7.52 37.59 10.12 10.12  8.04  2.20  3.76
0 1 2 3 4 5 6 7 8 9 10 Total
Dimension économique 5.86 2.20 5.10 7.42 7.65 24.75 11.71 15.25 11.25 4.06 4.75 100
Dimension culturelle 6.18 3.26 6.12 7.61 8.53 17.46 9.27 13.62 13.93 5.04 8.93 100
Dimension relative au milieu de vie 4.97 3.30 4.92 7.46 7.52 37.59 10.12 10.12 8.04 2.20 3.76 100

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