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GeoViz2018_R_DML_2_Tables.R
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GeoViz2018_R_DML_2_Tables.R
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## ---- eval = FALSE-------------------------------------------------------
## library(readr)
## ---- eval = TRUE, echo = FALSE------------------------------------------
library(readr)
## ------------------------------------------------------------------------
library(readr)
df_dmr <- read_csv2("data/dans-ma-rue.zip")
## ------------------------------------------------------------------------
class(df_dmr)
## ------------------------------------------------------------------------
df_dmr
## ------------------------------------------------------------------------
library(dplyr)
## ------------------------------------------------------------------------
print(df_dmr)
## ------------------------------------------------------------------------
select(df_dmr, TYPE, SOUSTYPE, CODE_POSTAL)
## ------------------------------------------------------------------------
select(df_dmr, 3:7)
## ------------------------------------------------------------------------
select(df_dmr, -(4:7), - SOUSTYPE)
## ------------------------------------------------------------------------
colnames(df_dmr)
select(df_dmr, starts_with("A"))
## ------------------------------------------------------------------------
colnames(df_dmr)
select(df_dmr, ends_with("DECLARATION"))
## ------------------------------------------------------------------------
colnames(df_dmr)
select(df_dmr, contains("TYPE"))
## ------------------------------------------------------------------------
nrow(df_dmr)
df_dmr_paris6 <- filter(df_dmr, CODE_POSTAL == 75006)
nrow(df_dmr_paris6)
head(df_dmr_paris6)
## ------------------------------------------------------------------------
nrow(df_dmr)
filter(df_dmr, CODE_POSTAL == 75006 & `ANNEE DECLARATION` == 2018)
nrow(filter(df_dmr, CODE_POSTAL == 75006, `ANNEE DECLARATION` == 2018))
## ------------------------------------------------------------------------
nrow(df_dmr)
filter(df_dmr, CODE_POSTAL == 75006 | CODE_POSTAL == 75007)
## ------------------------------------------------------------------------
nrow(df_dmr)
filter(df_dmr, !(CODE_POSTAL == 75006))
## ------------------------------------------------------------------------
nrow(df_dmr)
# L'opérateur %in% permet de chercher parmi les valeurs d'un vecteur
filter(df_dmr, CODE_POSTAL %in% c(75006, 75008))
## ------------------------------------------------------------------------
df_dmr
## ------------------------------------------------------------------------
arrange(df_dmr, CODE_POSTAL)
## ------------------------------------------------------------------------
arrange(df_dmr, CODE_POSTAL)
## ------------------------------------------------------------------------
arrange(df_dmr, CODE_POSTAL, TYPE)
## ------------------------------------------------------------------------
arrange(df_dmr, CODE_POSTAL, TYPE)
## ------------------------------------------------------------------------
arrange(df_dmr, desc(CODE_POSTAL), TYPE)
## ------------------------------------------------------------------------
tableau_reorganise <- select(df_dmr, TYPE, CODE_POSTAL, `ANNEE DECLARATION`)
tableau_reorganise <- filter(tableau_reorganise, CODE_POSTAL %in% c(75006, 75008))
tableau_reorganise <- arrange(tableau_reorganise, desc(`ANNEE DECLARATION`), TYPE)
tableau_reorganise
## ------------------------------------------------------------------------
arrange(
filter(
select(df_dmr,
TYPE, CODE_POSTAL, `ANNEE DECLARATION`),
CODE_POSTAL %in% c(75006, 75008)),
desc(`ANNEE DECLARATION`, TYPE)
)
## ------------------------------------------------------------------------
arrange(
filter(
select(df_dmr,
TYPE, CODE_POSTAL, `ANNEE DECLARATION`),
CODE_POSTAL %in% c(75006, 75008)),
desc(`ANNEE DECLARATION`, TYPE)
)
## ------------------------------------------------------------------------
df_dmr$CODE_POSTAL %>% mean() %>% log()
log(mean(df_dmr$CODE_POSTAL))
## ---- eval = FALSE-------------------------------------------------------
## tableau_reorganise <- select(df_dmr, TYPE, CODE_POSTAL, `ANNEE DECLARATION`)
## tableau_reorganise <- filter(tableau_reorganise, CODE_POSTAL %in% c(75006, 75008))
## tableau_reorganise <- arrange(tableau_reorganise, desc(`ANNEE DECLARATION`), TYPE)
## ---- eval = FALSE-------------------------------------------------------
## tableau_reorganise <- df_dmr %>%
## select(TYPE, CODE_POSTAL, `ANNEE DECLARATION`) %>%
## filter(CODE_POSTAL %in% c(75006, 75008)) %>%
## arrange(desc(`ANNEE DECLARATION`), TYPE)
## ------------------------------------------------------------------------
colnames(df_dmr)
df_dmr_renamed <- rename(df_dmr, ANNEE_DECLARATION = `ANNEE DECLARATION`)
colnames(df_dmr_renamed)
## ------------------------------------------------------------------------
df_dmr_renamed <- rename(df_dmr,
ANNEE_DECLARATION = `ANNEE DECLARATION`,
MOIS_DECLARATION = `MOIS DECLARATION`)
colnames(df_dmr_renamed)
## ---- eval = FALSE-------------------------------------------------------
## library(stringr)
## df_dmr_renamed <- rename_at(.tbl = df_dmr,
## .vars = vars(contains(" ")),
## .funs = funs(str_replace_all(string = .,
## pattern = " ",
## replacement = "_")))
## ---- echo=FALSE---------------------------------------------------------
library(stringr)
## ---- eval = TRUE--------------------------------------------------------
df_dmr_renamed <- rename_at(.tbl = df_dmr,
.vars = vars(contains(" ")),
.funs = funs(str_replace_all(string = .,
pattern = " ",
replacement = "_")))
colnames(df_dmr_renamed)
## ---- eval = TRUE--------------------------------------------------------
df_dmr_renamed <- df_dmr %>%
rename_at(.vars = vars(contains(" ")),
.funs = funs(str_replace_all(string = ., pattern = " ", replacement = "_"))) %>%
rename_all(funs(toupper(.)))
colnames(df_dmr_renamed)
## ------------------------------------------------------------------------
df_dmr_renamed_muted <- df_dmr_renamed %>%
mutate(DATE_DECLARATION = paste(ANNEE_DECLARATION, MOIS_DECLARATION, sep = "-"))
head(df_dmr_renamed_muted$DATE_DECLARATION)
## ------------------------------------------------------------------------
df_dmr_renamed_muted <- df_dmr_renamed %>%
mutate(CODE_POSTAL = as.character(CODE_POSTAL))
## ------------------------------------------------------------------------
df_dmr_renamed %>% select(3:5)
## ------------------------------------------------------------------------
df_dmr_renamed_muted %>% select(3:5)
## ------------------------------------------------------------------------
df_dmr_renamed_muted <- df_dmr_renamed_muted %>%
mutate(TRIMESTRE_DECLARATION = cut(MOIS_DECLARATION,
breaks = c(0, 3, 6, 9, 12),
labels = c("Q1", "Q2", "Q3", "Q4")))
df_dmr_renamed_muted %>%
select(MOIS_DECLARATION, TRIMESTRE_DECLARATION)
## ------------------------------------------------------------------------
df_dmr_renamed_muted %>%
group_by(ANNEE_DECLARATION) %>%
summarise(NbDeclarations = n()) %>%
arrange(ANNEE_DECLARATION)
## ------------------------------------------------------------------------
df_dmr_renamed_muted %>%
group_by(ANNEE_DECLARATION, TRIMESTRE_DECLARATION) %>%
summarise(NbDeclarations = n()) %>%
arrange(ANNEE_DECLARATION, TRIMESTRE_DECLARATION)
## ------------------------------------------------------------------------
df_dmr_renamed_muted %>%
group_by(ANNEE_DECLARATION, ARRONDISSEMENT) %>%
summarise(NbDeclarations = n()) %>%
group_by(ARRONDISSEMENT) %>%
summarise(NbDeclarationTotal = sum(NbDeclarations),
NbDeclarationAnnuel = mean(NbDeclarations)) %>%
head()
## ------------------------------------------------------------------------
total_arrondissement <- df_dmr_renamed_muted %>%
group_by(CODE_POSTAL) %>%
summarise(NbArrondissement = n())
df_dmr_renamed_muted %>%
group_by(ANNEE_DECLARATION, CODE_POSTAL) %>%
summarise(NbDeclarations = n()) %>%
left_join(y = total_arrondissement,
by = "CODE_POSTAL") %>%
mutate(Pct_Declaration_Annee =
NbDeclarations / NbArrondissement * 100
) %>%
select(CODE_POSTAL,
ANNEE_DECLARATION,
Pct_Declaration_Annee)
## ------------------------------------------------------------------------
library(tidyr)
## ------------------------------------------------------------------------
resume_annuel <- df_dmr_renamed_muted %>%
group_by(ANNEE_DECLARATION, CODE_POSTAL) %>%
summarise(NbDeclarations = n())
resume_annuel
## ------------------------------------------------------------------------
head(resume_annuel)
resume_annuel_large <- resume_annuel %>%
spread(key = ANNEE_DECLARATION, value = NbDeclarations)
head(resume_annuel_large)
## ------------------------------------------------------------------------
head(resume_annuel_large)
resume_annuel_large %>% gather(key = ANNEE, NbIncidents, -CODE_POSTAL) %>% head()
## ------------------------------------------------------------------------
df_dmr_renamed_muted %>% select(GEO_POINT_2D)
## ------------------------------------------------------------------------
df_dmr_spatialise <- df_dmr_renamed_muted %>%
separate(col = GEO_POINT_2D,
into = c("Lat", "Long"), sep = ", ")
df_dmr_spatialise %>% select(Lat, Long) %>% head()
## ------------------------------------------------------------------------
df_dmr_spatialise <- df_dmr_spatialise %>%
mutate(Lat = as.numeric(Lat)) %>%
mutate(Long = as.numeric(Long))
df_dmr_spatialise %>% select(Lat, Long) %>% head()
## ---- eval=FALSE---------------------------------------------------------
## library(readr)
## write_csv(df_dmr_spatialise,
## path = "data/dans_ma_rue_clean.csv")
## ---- eval = FALSE-------------------------------------------------------
## saveRDS(object = df_dmr_spatialise,
## file = "dans_ma_rue_clean.RDS")
## ---- eval = FALSE-------------------------------------------------------
## df_dmr_spatialise <- readRDS(file = "dans_ma_rue_clean.RDS")
## ------------------------------------------------------------------------
library(tidyverse)
tidyverse_packages()
## ---- eval = FALSE-------------------------------------------------------
## # On commence par supprimer l'ensemble des données créées jusque là :
## rm(list = ls())
## # On charge le package tidyverse
## library(tidyverse)
## ------------------------------------------------------------------------
# Lecture du jeu de données
df_dmr <- read_csv2(file = "data/dans-ma-rue.zip")
## ------------------------------------------------------------------------
df_dmr_clean <- df_dmr %>%
rename_all(.funs = funs(str_replace_all(
string = ., pattern = " ", replacement = "_"
))) %>%
rename_all(.funs = funs(toupper)) %>%
mutate(CODE_POSTAL = as.character(CODE_POSTAL)) %>%
select(-VILLE, -DATEDECL, -(NUMERO:GEO_SHAPE)) %>%
mutate(TRIMESTRE = cut(MOIS_DECLARATION,
breaks = seq(from = 0, to = 12, by = 3),
labels = paste("Q", 1:4, sep=""))) %>%
mutate(TRIMESTRE = as.character(TRIMESTRE)) %>%
mutate(TRIMESTRE_DECLARATION =paste(ANNEE_DECLARATION,
TRIMESTRE,
sep="-")) %>%
arrange(TRIMESTRE_DECLARATION, CODE_POSTAL)
df_dmr_clean
## ------------------------------------------------------------------------
total_declaration <- df_dmr_clean %>%
group_by(CODE_POSTAL) %>%
summarise(NbTotal = n())
total_declaration
## ------------------------------------------------------------------------
donnees_resumees <- df_dmr_clean %>%
group_by(TRIMESTRE_DECLARATION, CODE_POSTAL) %>%
summarise(NbLocal = n())
donnees_resumees
# Le tableau fait mention de "Groups" restant suite au summarise :
# On enlève cet artefact avec la fonction ungroup()
donnees_resumees <- donnees_resumees %>%
ungroup()
## ------------------------------------------------------------------------
donnees_finales <- donnees_resumees %>%
full_join(y = total_declaration, by = "CODE_POSTAL") %>%
mutate(TAUX_DECLARATION = NbLocal / NbTotal) %>%
arrange(TRIMESTRE_DECLARATION, CODE_POSTAL)
donnees_finales
## ------------------------------------------------------------------------
donnees_finales_completes <- donnees_finales %>%
complete(TRIMESTRE_DECLARATION, CODE_POSTAL)
donnees_finales_completes
## ------------------------------------------------------------------------
donnees_finales_completes <- donnees_finales_completes %>%
mutate(TAUX_DECLARATION = if_else(condition = is.na(TAUX_DECLARATION),
true = 0,
false = TAUX_DECLARATION))
donnees_finales_completes
## ---- fig.height = 3.5, fig.width = 8------------------------------------
ggplot(donnees_finales_completes) +
geom_line(mapping = aes(x = TRIMESTRE_DECLARATION,
y = TAUX_DECLARATION,
group = CODE_POSTAL)) +
facet_wrap(~CODE_POSTAL, nrow = 4) +
scale_y_continuous(labels = scales::percent)