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soy_maps.R
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soy_maps.R
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rm(list=ls())
gc()
library(tidyverse)
library(brazilmaps)
library(plotly)
library(RColorBrewer)
library(GADMTools)
setwd("~/[email protected]/UFSM/Dashboards/Soybean")
soy_data <- read_csv("soy_data.csv", col_types = cols(CODIGO = col_double(),
PT2017 = col_double(), PT2018 = col_double(),
QT2017 = col_double(), QT2018 = col_double()))
map_rs<- get_brmap("City") %>% filter(State==43) %>% inner_join(soy_data, c("City" = "CODIGO")) %>%
mutate(rQT2017 = cut(QT2017, breaks = quantile(na.omit(QT2017)))
)
my_blue = brewer.pal(n = 9, "Oranges")
PRODUCTION2017a<- map_rs %>%
ggplot(aes(map_id = MUNICIPIOS)) +
geom_sf(aes(fill = QT2017), size = 0.05) #+ xlim(c(52,50)) +
scale_fill_gradient(name = "% Votos PSOL",low = my_blue[3], high = my_blue[7], na.value = "grey90")
PRODUCTION2017a
PRODUCTION2017b<- map_rs %>%
ggplot(aes(map_id = MUNICIPIOS)) +
geom_sf(aes(fill = QT2017), size = 0.05) +
scale_fill_gradient(low = my_blue[3], high = my_blue[7], na.value = "grey90")
# scale_fill_manual(values = c(my_blue))
my_blue = brewer.pal(n = 9, "Blues")
PRODUCTION2017c<- map_rs %>%
ggplot(aes(map_id = MUNICIPIOS)) +
geom_sf(aes(fill = QT2017), size = 0.05) +
scale_fill_gradient(name = "% Votos PSOL",low = my_blue[3], high = my_blue[7], na.value = "grey90")
# save.image("dados.RData")