In remymoine/Lupa: Lupa
Informations Générales
Avertissements:
Les données présentées dans ce document ont été compilées le r lubridate::day(Sys.Date())
r lubridate::month(Sys.Date(), label = TRUE, abbr = FALSE) r lubridate::year(Sys.Date()) à l'aide du package LUPA sous le logiciel r R.version$version.string.
Les données utilisées ont été collectées par le r params$Parc sur la période du
r lubridate::day(as.character(params$d_sel_min))
r lubridate::month(as.character(params$d_sel_min), label = TRUE, abbr = FALSE)
r lubridate::year(as.character(params$d_sel_min)) au
r lubridate::day(as.character(params$d_sel_max))
r lubridate::month(as.character(params$d_sel_max), label = TRUE, abbr = FALSE)
r lubridate::year(as.character(params$d_sel_max)).
Seules les données incluses à l'emprise géographique ont été prises en compte.
Les données sont sytématiquement comparées avec la même période de l'année précédente
Attaques
Chiffres clés et évolution
ggplot(
data = params$constetref,
aes(
x = as.character(annee),
y = attaque,
fill = type,
group = type)
) +
geom_bar(position = "stack", stat = "identity") +
xlab("Année") +
ylab("Nombre d'attaques") +
scale_fill_manual(
name = "Localisation des attaques",
values = c("red","forestgreen","grey"),
labels = c("Zone coeur", "Aire d'adhésion", "Département(s)"))+
theme(
panel.background = element_blank(),
panel.grid.major = element_line(linetype = "dashed", colour = "black"),
legend.position = "top",
legend.direction = "horizontal",
legend.text = element_text(size = 15),
legend.title = element_text(size = 18),
axis.text = element_text(size = 15),
axis.title = element_text(size = 18),
axis.text.x = element_text(angle = 45, hjust = 1)
)
tmp <- aggregate(data = params$constetref, attaque ~ annee + type,sum)
tmp2 <- dplyr::filter(tmp, type == "1_ZC")
test <- 1-(tmp2[nrow(tmp2)-1,3]/tmp2[nrow(tmp2),3])
r paste(tmp2[nrow(tmp2),3]," attaques en zone coeur en ",tmp2[nrow(tmp2),1],sep = "")
(soit une r ifelse(test = test>0,yes = paste("augmentation de ",abs(round(test*100,2)),"%", sep = ""),no = paste("diminution de ",abs(round(test*100,2)),"%", sep = "")))
tmp <- aggregate(data = params$constetref, attaque ~ annee + type,sum)
tmp2 <- dplyr::filter(tmp, type == "2_AA")
test <- 1-(tmp2[nrow(tmp2)-1,3]/tmp2[nrow(tmp2),3])
r paste(tmp2[nrow(tmp2),3]," attaques en aire d'adhésion en ",tmp2[nrow(tmp2),1],sep = "")
(soit une r ifelse(test = test>0,yes = paste("augmentation de ",abs(round(test*100,2)),"%", sep = ""),no = paste("diminution de ",abs(round(test*100,2)),"%", sep = "")))
(par rapport à la même période l'année précédente)
Répartition communale - chiffres
data <- params$comp_intercom
tmp <- aggregate(data = data, attaque ~ commune + type + annee, sum)
ggplot(
data = tmp,
aes(x = commune, y = attaque, fill = type )) +
geom_col(position = "stack") +
xlab("Communes") +
ylab("Nombre d'attaques") +
scale_fill_manual(
name = "Localisation des attaques",
values = c("red","forestgreen","grey"),
labels = c("Zone coeur", "Aire d'adhésion", "Département(s)"))+
facet_grid(annee ~ .) +
theme(
panel.background = element_blank(),
panel.grid.major = element_line(linetype = "dashed", colour = "black"),
legend.position = "top",
legend.direction = "horizontal",
legend.text = element_text(size = 15),
legend.title = element_text(size = 18),
axis.text = element_text(size = 15),
axis.title = element_text(size = 18),
axis.text.x = element_text(angle = 75, hjust = 1, size = 13),
strip.text.y = element_text(size = 15)
)
Répartition communale - carte
Parc_area <- params$Parc_area
tmp <- aggregate(
data = dplyr::filter(
params$constat,
type != "3_DPT"),
attaque ~ insee,
sum)
tmp2 <- dplyr::left_join(tmp, area_com, by = "insee")
tmp2 <- sf::st_as_sf(tmp2)
ggplot() +
geom_sf(
data = tmp2,
aes(fill = attaque)
) +
geom_sf(
data = Parc_area[2,],
col = data_parc$col[data_parc$nom == params$Parc],
fill = NA,
size = 0.8) +
geom_sf(
data = Parc_area[1,],
col = data_parc$col[data_parc$nom == params$Parc],
fill = NA,
size = 0.6,
linetype = "dashed") +
scale_fill_gradient(name = "Nombre d'attaques\npar communes" ,low = "lightgrey", high = "red") +
theme(
panel.background = element_blank(),
panel.grid.major = element_line(linetype = "dashed", colour = "black"),
legend.position = "right",
legend.direction = "vertical",
legend.text = element_text(size = 13),
legend.title = element_text(size = 16),
axis.text = element_text(size = 13),
axis.title = element_text(size = 16),
axis.text.x = element_text(angle = 60, hjust = 1)
)
Nombre de victimes
Chiffres clés et évolution
ggplot(
data = params$constetref,
aes(
x = as.character(annee),
y = nb_vict,
fill = type,
group = type)
) +
geom_bar(position = "stack", stat = "identity") +
xlab("Année") +
ylab("Nombre d'nb_victs") +
scale_fill_manual(
name = "Localisation des victimes",
values = c("red","forestgreen","grey"),
labels = c("Zone coeur", "Aire d'adhésion", "Département(s)"))+
theme(
panel.background = element_blank(),
panel.grid.major = element_line(linetype = "dashed", colour = "black"),
legend.position = "top",
legend.direction = "horizontal",
legend.text = element_text(size = 15),
legend.title = element_text(size = 18),
axis.text = element_text(size = 15),
axis.title = element_text(size = 18),
axis.text.x = element_text(angle = 45, hjust = 1)
)
tmp <- aggregate(data = params$constetref, nb_vict ~ annee + type,sum)
tmp2 <- dplyr::filter(tmp, type == "1_ZC")
test <- 1-(tmp2[nrow(tmp2)-1,3]/tmp2[nrow(tmp2),3])
r paste(tmp2[nrow(tmp2),3]," victimes en zone coeur en ",tmp2[nrow(tmp2),1],sep = "")
(soit une r ifelse(test = test>0,yes = paste("augmentation de ",abs(round(test*100,2)),"%", sep = ""),no = paste("diminution de ",abs(round(test*100,2)),"%", sep = "")))
tmp <- aggregate(data = params$constetref, nb_vict ~ annee + type,sum)
tmp2 <- dplyr::filter(tmp, type == "2_AA")
test <- 1-(tmp2[nrow(tmp2)-1,3]/tmp2[nrow(tmp2),3])
r paste(tmp2[nrow(tmp2),3]," victimes en aire d'adhésion en ",tmp2[nrow(tmp2),1],sep = "")
(soit une r ifelse(test = test>0,yes = paste("augmentation de ",abs(round(test*100,2)),"%", sep = ""),no = paste("diminution de ",abs(round(test*100,2)),"%", sep = "")))
(par rapport à la même période l'année précédente)
Répartition communale - chiffres
data <- params$comp_intercom
tmp <- aggregate(data = data, nb_vict ~ commune + type + annee, sum)
ggplot(
data = tmp,
aes(x = commune, y = nb_vict, fill = type )) +
geom_col(position = "stack") +
xlab("Communes") +
ylab("Nombre de victimes") +
scale_fill_manual(
name = "Localisation des victimes",
values = c("red","forestgreen","grey"),
labels = c("Zone coeur", "Aire d'adhésion", "Département(s)"))+
facet_grid(annee ~ .) +
theme(
panel.background = element_blank(),
panel.grid.major = element_line(linetype = "dashed", colour = "black"),
legend.position = "top",
legend.direction = "horizontal",
legend.text = element_text(size = 15),
legend.title = element_text(size = 18),
axis.text = element_text(size = 15),
axis.title = element_text(size = 18),
axis.text.x = element_text(angle = 75, hjust = 1, size = 13),
strip.text.y = element_text(size = 15)
)
Répartition communale - carte
Parc_area <- params$Parc_area
tmp <- aggregate(
data = dplyr::filter(
params$constat,
type != "3_DPT"),
nb_vict ~ insee,
sum)
tmp2 <- dplyr::left_join(tmp, area_com, by = "insee")
tmp2 <- sf::st_as_sf(tmp2)
ggplot() +
geom_sf(
data = tmp2,
aes(fill = nb_vict)
) +
geom_sf(
data = Parc_area[2,],
col = data_parc$col[data_parc$nom == params$Parc],
fill = NA,
size = 0.8) +
geom_sf(
data = Parc_area[1,],
col = data_parc$col[data_parc$nom == params$Parc],
fill = NA,
size = 0.6,
linetype = "dashed") +
scale_fill_gradient(name = "Nombre de victimes\npar communes" ,low = "lightgrey", high = "red") +
theme(
panel.background = element_blank(),
panel.grid.major = element_line(linetype = "dashed", colour = "black"),
legend.position = "right",
legend.direction = "vertical",
legend.text = element_text(size = 13),
legend.title = element_text(size = 16),
axis.text = element_text(size = 13),
axis.title = element_text(size = 16),
axis.text.x = element_text(angle = 60, hjust = 1)
)
remymoine/Lupa documentation built on Aug. 5, 2020, 12:07 a.m.
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Informations Générales
Avertissements:
Les données présentées dans ce document ont été compilées le r lubridate::day(Sys.Date())
r lubridate::month(Sys.Date(), label = TRUE, abbr = FALSE) r lubridate::year(Sys.Date()) à l'aide du package LUPA sous le logiciel r R.version$version.string.
Les données utilisées ont été collectées par le r params$Parc sur la période du
r lubridate::day(as.character(params$d_sel_min))
r lubridate::month(as.character(params$d_sel_min), label = TRUE, abbr = FALSE)
r lubridate::year(as.character(params$d_sel_min)) au
r lubridate::day(as.character(params$d_sel_max))
r lubridate::month(as.character(params$d_sel_max), label = TRUE, abbr = FALSE)
r lubridate::year(as.character(params$d_sel_max)).
Seules les données incluses à l'emprise géographique ont été prises en compte.
Les données sont sytématiquement comparées avec la même période de l'année précédente
Attaques
Chiffres clés et évolution
ggplot( data = params$constetref, aes( x = as.character(annee), y = attaque, fill = type, group = type) ) + geom_bar(position = "stack", stat = "identity") + xlab("Année") + ylab("Nombre d'attaques") + scale_fill_manual( name = "Localisation des attaques", values = c("red","forestgreen","grey"), labels = c("Zone coeur", "Aire d'adhésion", "Département(s)"))+ theme( panel.background = element_blank(), panel.grid.major = element_line(linetype = "dashed", colour = "black"), legend.position = "top", legend.direction = "horizontal", legend.text = element_text(size = 15), legend.title = element_text(size = 18), axis.text = element_text(size = 15), axis.title = element_text(size = 18), axis.text.x = element_text(angle = 45, hjust = 1) )
tmp <- aggregate(data = params$constetref, attaque ~ annee + type,sum) tmp2 <- dplyr::filter(tmp, type == "1_ZC") test <- 1-(tmp2[nrow(tmp2)-1,3]/tmp2[nrow(tmp2),3])
r paste(tmp2[nrow(tmp2),3]," attaques en zone coeur en ",tmp2[nrow(tmp2),1],sep = "")
(soit une r ifelse(test = test>0,yes = paste("augmentation de ",abs(round(test*100,2)),"%", sep = ""),no = paste("diminution de ",abs(round(test*100,2)),"%", sep = "")))
tmp <- aggregate(data = params$constetref, attaque ~ annee + type,sum) tmp2 <- dplyr::filter(tmp, type == "2_AA") test <- 1-(tmp2[nrow(tmp2)-1,3]/tmp2[nrow(tmp2),3])
r paste(tmp2[nrow(tmp2),3]," attaques en aire d'adhésion en ",tmp2[nrow(tmp2),1],sep = "")
(soit une r ifelse(test = test>0,yes = paste("augmentation de ",abs(round(test*100,2)),"%", sep = ""),no = paste("diminution de ",abs(round(test*100,2)),"%", sep = "")))
(par rapport à la même période l'année précédente)
Répartition communale - chiffres
data <- params$comp_intercom tmp <- aggregate(data = data, attaque ~ commune + type + annee, sum) ggplot( data = tmp, aes(x = commune, y = attaque, fill = type )) + geom_col(position = "stack") + xlab("Communes") + ylab("Nombre d'attaques") + scale_fill_manual( name = "Localisation des attaques", values = c("red","forestgreen","grey"), labels = c("Zone coeur", "Aire d'adhésion", "Département(s)"))+ facet_grid(annee ~ .) + theme( panel.background = element_blank(), panel.grid.major = element_line(linetype = "dashed", colour = "black"), legend.position = "top", legend.direction = "horizontal", legend.text = element_text(size = 15), legend.title = element_text(size = 18), axis.text = element_text(size = 15), axis.title = element_text(size = 18), axis.text.x = element_text(angle = 75, hjust = 1, size = 13), strip.text.y = element_text(size = 15) )
Répartition communale - carte
Parc_area <- params$Parc_area tmp <- aggregate( data = dplyr::filter( params$constat, type != "3_DPT"), attaque ~ insee, sum) tmp2 <- dplyr::left_join(tmp, area_com, by = "insee") tmp2 <- sf::st_as_sf(tmp2) ggplot() + geom_sf( data = tmp2, aes(fill = attaque) ) + geom_sf( data = Parc_area[2,], col = data_parc$col[data_parc$nom == params$Parc], fill = NA, size = 0.8) + geom_sf( data = Parc_area[1,], col = data_parc$col[data_parc$nom == params$Parc], fill = NA, size = 0.6, linetype = "dashed") + scale_fill_gradient(name = "Nombre d'attaques\npar communes" ,low = "lightgrey", high = "red") + theme( panel.background = element_blank(), panel.grid.major = element_line(linetype = "dashed", colour = "black"), legend.position = "right", legend.direction = "vertical", legend.text = element_text(size = 13), legend.title = element_text(size = 16), axis.text = element_text(size = 13), axis.title = element_text(size = 16), axis.text.x = element_text(angle = 60, hjust = 1) )
Nombre de victimes
Chiffres clés et évolution
ggplot( data = params$constetref, aes( x = as.character(annee), y = nb_vict, fill = type, group = type) ) + geom_bar(position = "stack", stat = "identity") + xlab("Année") + ylab("Nombre d'nb_victs") + scale_fill_manual( name = "Localisation des victimes", values = c("red","forestgreen","grey"), labels = c("Zone coeur", "Aire d'adhésion", "Département(s)"))+ theme( panel.background = element_blank(), panel.grid.major = element_line(linetype = "dashed", colour = "black"), legend.position = "top", legend.direction = "horizontal", legend.text = element_text(size = 15), legend.title = element_text(size = 18), axis.text = element_text(size = 15), axis.title = element_text(size = 18), axis.text.x = element_text(angle = 45, hjust = 1) )
tmp <- aggregate(data = params$constetref, nb_vict ~ annee + type,sum) tmp2 <- dplyr::filter(tmp, type == "1_ZC") test <- 1-(tmp2[nrow(tmp2)-1,3]/tmp2[nrow(tmp2),3])
r paste(tmp2[nrow(tmp2),3]," victimes en zone coeur en ",tmp2[nrow(tmp2),1],sep = "")
(soit une r ifelse(test = test>0,yes = paste("augmentation de ",abs(round(test*100,2)),"%", sep = ""),no = paste("diminution de ",abs(round(test*100,2)),"%", sep = "")))
tmp <- aggregate(data = params$constetref, nb_vict ~ annee + type,sum) tmp2 <- dplyr::filter(tmp, type == "2_AA") test <- 1-(tmp2[nrow(tmp2)-1,3]/tmp2[nrow(tmp2),3])
r paste(tmp2[nrow(tmp2),3]," victimes en aire d'adhésion en ",tmp2[nrow(tmp2),1],sep = "")
(soit une r ifelse(test = test>0,yes = paste("augmentation de ",abs(round(test*100,2)),"%", sep = ""),no = paste("diminution de ",abs(round(test*100,2)),"%", sep = "")))
(par rapport à la même période l'année précédente)
Répartition communale - chiffres
data <- params$comp_intercom tmp <- aggregate(data = data, nb_vict ~ commune + type + annee, sum) ggplot( data = tmp, aes(x = commune, y = nb_vict, fill = type )) + geom_col(position = "stack") + xlab("Communes") + ylab("Nombre de victimes") + scale_fill_manual( name = "Localisation des victimes", values = c("red","forestgreen","grey"), labels = c("Zone coeur", "Aire d'adhésion", "Département(s)"))+ facet_grid(annee ~ .) + theme( panel.background = element_blank(), panel.grid.major = element_line(linetype = "dashed", colour = "black"), legend.position = "top", legend.direction = "horizontal", legend.text = element_text(size = 15), legend.title = element_text(size = 18), axis.text = element_text(size = 15), axis.title = element_text(size = 18), axis.text.x = element_text(angle = 75, hjust = 1, size = 13), strip.text.y = element_text(size = 15) )
Répartition communale - carte
Parc_area <- params$Parc_area tmp <- aggregate( data = dplyr::filter( params$constat, type != "3_DPT"), nb_vict ~ insee, sum) tmp2 <- dplyr::left_join(tmp, area_com, by = "insee") tmp2 <- sf::st_as_sf(tmp2) ggplot() + geom_sf( data = tmp2, aes(fill = nb_vict) ) + geom_sf( data = Parc_area[2,], col = data_parc$col[data_parc$nom == params$Parc], fill = NA, size = 0.8) + geom_sf( data = Parc_area[1,], col = data_parc$col[data_parc$nom == params$Parc], fill = NA, size = 0.6, linetype = "dashed") + scale_fill_gradient(name = "Nombre de victimes\npar communes" ,low = "lightgrey", high = "red") + theme( panel.background = element_blank(), panel.grid.major = element_line(linetype = "dashed", colour = "black"), legend.position = "right", legend.direction = "vertical", legend.text = element_text(size = 13), legend.title = element_text(size = 16), axis.text = element_text(size = 13), axis.title = element_text(size = 16), axis.text.x = element_text(angle = 60, hjust = 1) )
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