Gráficos com bandeiras dos seus países
library(dplyr) library(ggplot2) library(countrycode) library(ggflags) library(cowplot) library(ggthemes) library(readxl)
setwd("C:/Users/milen/Desktop/R") data2 <- read.csv("faostat22.csv") names(data2)
data2$iso2 <- countrycode(data2$Area, "country.name", "iso2c") data2$code <- tolower(countrycode(data2$Area, origin = 'country.name', destination = 'iso2c')) data2$continent <- tolower(countrycode(data2$Area, origin = 'country.name', destination = 'continent'))
names(data2) data2$Value <- as.numeric(data2$Value)
data2$Area[data2$Area == "United States of America"] <- "USA" data2$Area[data2$Area == "China, mainland"] <- "China" data2$Area[data2$Area == "Russian Federation"] <- "Russia"
data2$continent[data2$continent == "asia"] <- "Asia" data2$continent[data2$continent == "americas"] <- "Americas" data2$continent[data2$continent == "europe"] <- "Europe" data2$continent[data2$continent == "oceania"] <- "Oceania"
my_palette <- c("Americas" = "#E15554", "Asia" = "#3BB273", "Europe" = "#7768AE", "Oceania" = "#4D9DE0")
p <- ggplot(data2, aes(x=reorder(Area, Value),y = Value/10^6, fill = continent))+ geom_bar(stat = "identity")+ geom_flag(y = -5, aes(country = code), size = 8)+ labs(fill = "Continente", title = "Commodities:Top 10 Produtores de Trigo", subtitle = "Fonte:FAO - Statistics, 2020", y = 'Produ??o em (milh?es de toneladas)', x = '')+ theme_economist(horizontal = FALSE) + theme(legend.position = c(.9,.6), axis.text.x = element_text(vjust = 3)) + coord_flip()+ expand_limits(y = 0) + scale_fill_manual(values = my_palette)
p
data3 <- read.csv("faostat_arroz.csv") names(data3)
data3$iso2 <- countrycode(data3$Area, "country.name", "iso2c") data3$code <- tolower(countrycode(data3$Area, origin = 'country.name', destination = 'iso2c')) data3$continent <- tolower(countrycode(data3$Area, origin = 'country.name', destination = 'continent'))
names(data3) data3$Value <- as.numeric(data3$Value)
data3$Area[data3$Area == "China, mainland"] <- "China" data3$Area[data3$Area == "Viet Nam"] <- "Vietnam"
data3$continent[data3$continent == "asia"] <- "Asia" data3$continent[data3$continent == "americas"] <- "Americas" data3$continent[data3$continent == "europe"] <- "Europe" data3$continent[data3$continent == "oceania"] <- "Oceania"
my_palette <- c("Americas" = "#E15554", "Asia" = "#3BB273", "Europe" = "#7768AE", "Oceania" = "#4D9DE0")
p3 <- ggplot(data3, aes(x=reorder(Area, Value),y = Value/10^6, fill = continent))+ geom_bar(stat = "identity")+ geom_flag(y = -5, aes(country = code), size = 8)+ labs(fill = "Continente", title = "Commodities:Top 10 Produtores de Arroz", subtitle = "Fonte:FAO - Statistics, 2020", y = 'Produ??o em (milh?es de toneladas)', x = '')+ theme_economist(horizontal = FALSE) + theme(legend.position = c(.9,.6), axis.text.x = element_text(vjust = 3)) + coord_flip()+ expand_limits(y = 0) + scale_fill_manual(values = my_palette)
p3
data4 <- read.csv("faostat_soja.csv") names(data4)
data4$iso2 <- countrycode(data4$Area, "country.name", "iso2c") data4$code <- tolower(countrycode(data4$Area, origin = 'country.name', destination = 'iso2c')) data4$continent <- tolower(countrycode(data4$Area, origin = 'country.name', destination = 'continent'))
names(data4) data4$Value <- as.numeric(data4$Value)
data4$Area[data4$Area == "United States of America"] <- "USA" data4$Area[data4$Area == "China, mainland"] <- "China" data4$Area[data4$Area == "Russian Federation"] <- "Russia" data4$Area[data4$Area == "Bolivia (Plurinational State of)"] <- "Bolivia"
data4$continent[data4$continent == "asia"] <- "Asia" data4$continent[data4$continent == "americas"] <- "Americas" data4$continent[data4$continent == "europe"] <- "Europe" data4$continent[data4$continent == "oceania"] <- "Oceania"
my_palette <- c("Americas" = "#E15554", "Asia" = "#3BB273", "Europe" = "#7768AE", "Oceania" = "#4D9DE0")
p4 <- ggplot(data4, aes(x=reorder(Area, Value),y = Value/10^6, fill = continent))+ geom_bar(stat = "identity")+ geom_flag(y = -5, aes(country = code), size = 8)+ labs(fill = "Continente", title = "Commodities:Top 10 Produtores de Soja", subtitle = "Fonte:FAO - Statistics, 2020", y = 'Produ??o em (milh?es de toneladas)', x = '')+ theme_economist(horizontal = FALSE) + theme(legend.position = c(.9,.6), axis.text.x = element_text(vjust = 3)) + coord_flip()+ expand_limits(y = 0) + scale_fill_manual(values = my_palette)
p4
data5 <- read.csv("faostat_cafe.csv") names(data5)
data5$iso2 <- countrycode(data5$Area, "country.name", "iso2c") data5$code <- tolower(countrycode(data5$Area, origin = 'country.name', destination = 'iso2c')) data5$continent <- tolower(countrycode(data5$Area, origin = 'country.name', destination = 'continent'))
names(data5) data5$Value <- as.numeric(data5$Value)
data5$Area[data5$Area == "Viet Nam"] <- "Vietnam"
data5$continent[data5$continent == "asia"] <- "Asia" data5$continent[data5$continent == "americas"] <- "Americas" data5$continent[data5$continent == "europe"] <- "Europe" data5$continent[data5$continent == "oceania"] <- "Oceania" data5$continent[data5$continent == "africa"] <- "Africa"
my_palette <- c("Americas" = "#E15554", "Asia" = "#3BB273", "Europe" = "#7768AE", "Oceania" = "#4D9DE0", "Africa" = "#F0E442")
p5 <- ggplot(data5, aes(x=reorder(Area, Value),y = Value/10^6, fill = continent))+ geom_bar(stat = "identity")+ geom_flag(y=-0.1, aes(country = code), size = 8)+ labs(fill = "Continente", title = "Commodities:Top 10 Produtores de Caf?", subtitle = "Fonte:FAO - Statistics, 2020", y = 'Produ??o em (milh?es de toneladas)', x = '')+ theme_economist(horizontal = FALSE) + theme(legend.position = c(.9,.6), axis.text.x = element_text(vjust = 3)) + coord_flip()+ expand_limits(y = 0) + scale_fill_manual(values = my_palette)
p5
data6 <- read.csv("faostat_laranjas.csv") names(data6)
data6$iso2 <- countrycode(data6$Area, "country.name", "iso2c") data6$code <- tolower(countrycode(data6$Area, origin = 'country.name', destination = 'iso2c')) data6$continent <- tolower(countrycode(data6$Area, origin = 'country.name', destination = 'continent'))
names(data6) data6$Value <- as.numeric(data6$Value)
data6$Area[data6$Area == "United States of America"] <- "USA" data6$Area[data6$Area == "China, mainland"] <- "China" data6$Area[data6$Area == "Iran (Islamic Republic of)"] <- "Iran"
data6$continent[data6$continent == "asia"] <- "Asia" data6$continent[data6$continent == "americas"] <- "Americas" data6$continent[data6$continent == "europe"] <- "Europe" data6$continent[data6$continent == "oceania"] <- "Oceania" data6$continent[data6$continent == "africa"] <- "Africa"
my_palette <- c("Americas" = "#E15554", "Asia" = "#3BB273", "Europe" = "#7768AE", "Oceania" = "#4D9DE0", "Africa" = "#F0E442")
p6 <- ggplot(data6, aes(x=reorder(Area, Value),y = Value/10^6, fill = continent))+ geom_bar(stat = "identity")+ geom_flag(y = -0.5, aes(country = code), size = 8)+ labs(fill = "Continente", title = "Commodities:Top 10 Produtores de Laranja", subtitle = "Fonte:FAO - Statistics, 2020", y = 'Produ??o em (milh?es de toneladas)', x = '')+ theme_economist(horizontal = FALSE) + theme(legend.position = c(.9,.6), axis.text.x = element_text(vjust = 3)) + coord_flip()+ expand_limits(y = 0) + scale_fill_manual(values = my_palette)
p