CONTEXTE
Les données du #Tidytuesday de cette semaine proviennent de nu3.
OBJECTIFS
- Visualiser l’influence de la consommation des différentes catégories d’aliments sur les émission de C02
IMPORTER
tuesdata <- tidytuesdayR::tt_load('2020-02-18')
food <- tuesdata$food_consumption
EXPLORER
glimpse(food)
## Observations: 1,430
## Variables: 4
## $ country <chr> "Argentina", "Argentina", "Argentina", "Argentina"…
## $ food_category <chr> "Pork", "Poultry", "Beef", "Lamb & Goat", "Fish", …
## $ consumption <dbl> 10.51, 38.66, 55.48, 1.56, 4.36, 11.39, 195.08, 10…
## $ co2_emmission <dbl> 37.20, 41.53, 1712.00, 54.63, 6.96, 10.46, 277.87,…
food %>%
count(food_category) %>%
knitr::kable()
food_category | n |
---|---|
Beef | 130 |
Eggs | 130 |
Fish | 130 |
Lamb & Goat | 130 |
Milk – inc. cheese | 130 |
Nuts inc. Peanut Butter | 130 |
Pork | 130 |
Poultry | 130 |
Rice | 130 |
Soybeans | 130 |
Wheat and Wheat Products | 130 |
plt1 <-food %>%
ggplot(aes(x=" ", y = consumption)) +
geom_boxplot(fill = "#FFFFFF", color = "black") +
coord_flip() +
theme_classic() +
xlab("") +
ylab("consommation")+
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank())
plt2 <-food %>%
ggplot() +
geom_histogram(aes(x = consumption, y = (..count..)/sum(..count..)),
position = "identity", binwidth = 1,
fill = "#FFFFFF", color = "black") +
ylab("Fréquence Relative")+
xlab("")+ theme_classic()+
theme(axis.text.x = element_blank())+
theme(axis.ticks.x = element_blank())
plt2 + plt1 + plot_layout(nrow = 2, heights = c(2, 1))
plt1 <-food %>%
ggplot(aes(x=" ", y = co2_emmission)) +
geom_boxplot(fill = "#FFFFFF", color = "black") +
coord_flip() +
theme_classic() +
xlab("") +
ylab("Émissions de C02")+
theme(axis.text.y=element_blank(),
axis.ticks.y=element_blank())
plt2 <-food %>%
ggplot() +
geom_histogram(aes(x = co2_emmission, y = (..count..)/sum(..count..)),
position = "identity", binwidth = 1,
fill = "#FFFFFF", color = "black") +
ylab("Fréquence Relative")+
xlab("")+
theme_classic()+
theme(axis.text.x = element_blank())+
theme(axis.ticks.x = element_blank())
plt2 + plt1 + plot_layout(nrow = 2, heights = c(2, 1))
PRÉPARER
data_tot<-food %>%
group_by(country) %>%
summarise_if(is.numeric, sum, na.rm=TRUE) %>%
ungroup()
VISUALISER
#Graphique
gg<-ggplot()
#Dumbell
gg<-gg + geom_dumbbell(data=data_tot,
aes(x = consumption, xend = co2_emmission, y = reorder(country,consumption),group = country),
colour = "white",
size = 2,
colour_x = "#922A7D",
colour_xend = "#0F0E0E",
dot_guide_size=0)
#modifier le thème
gg <- gg + theme(plot.background = element_rect(fill = "#687169"),
panel.background = element_rect(fill = "#687169"),
panel.grid.major.y= element_blank(),
panel.grid.major.x= element_blank(),
panel.grid.minor = element_blank(),
axis.line.x = element_line(color="white"),
axis.line.y = element_line(color="white"),
axis.ticks.x = element_blank(),
axis.ticks.y = element_blank())
#ajouter les titres
gg<-gg + labs(title=
"Notre <span style='color:#0F0E0E'>émission de C02</span> ne dépend pas seulement de<br>ce qu'on mange mais de <span style='color:#922A7D'>quelle quantité</span> on mange...<br>",
subtitle = " ",
x="kg/personne/année",
y=" ",
caption="\nSOURCE: nu3 | DESIGN: Johanie Fournier, agr.")
gg<-gg + theme( plot.title = element_markdown(lineheight = 1.1,size=20, hjust=1,vjust=0.5, color="white"),
plot.subtitle = element_blank(),
plot.caption = element_text(size=8, hjust=1,vjust=0.5, family="Tw Cen MT", color="white"),
axis.title.y = element_blank(),
axis.title.x = element_text(size=8, hjust=0,vjust=0.5, family="Tw Cen MT", color="white"),
axis.text.x = element_text(size=8, hjust=0.5,vjust=0.5, family="Tw Cen MT", color="white"),
axis.text.y = element_text(size=8, hjust=1,vjust=0.5, family="Tw Cen MT", color="white"))
Voici ce que ça donne: