Figuras_Espanhol_IPEA_CEPAL
Leonardo Cardoso
2024-03-19
Contexto
Vou refazer todas as figuras do artigo em espanhol. Aí já vou aproveitar e colocar este arquivo no site para o caso de alguma pessoa interessada em replicar o artigo.
Estou falando do artigo INFLAÇÃO DE ALIMENTOS NO BRASIL: O PAPEL DOS PREÇOS INTERNACIONAIS (com José Eustáquio Ribeiro Vieira Filho).
Os dados para gerar as figuras estão aqui.
Packages
Nem todos os pacotes são usados. Então, se algum der muito trabalho para carregar, tira ele e olha se o código continua funcionando sem problemas.
library(loadinstall)
packages <- c(
"zoo", "readr", "ggplot2", "methods", "ggthemes", "directlabels", "ggrepel",
"readxl", "haven", "dplyr", "knitr", "owidR", "readxl", "sidrar", "reshape",
"scales", "basedosdados", "DBI", "bigrquery", "tidyr", "corrplot",
"modelsummary", "wesanderson", "urca", "vars", "mFilter", "tseries",
"TSstudio", "forecast", "tidyverse", "data.table", "stats"
)
lapply(packages, dynamic_require)##
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## [46] "base"Bases de dados
Esta parte é para que eu me ache no futuro, para saber de onde saiu cada base de dados
Carregando as bases
load("figuras_cepal_ipea.RData")Figura 1
p3 <- ggplot(data = pub, aes(x = pub$Ano, y =100*pub$Food_Prices/pub$Economia)) +
geom_line() +
geom_point()+
labs(title = "",
y = "Publicaciones con el término (% Economía)",
x = "")+
theme_clean()+
theme(legend.position = "none")+
annotate(geom="text",x=2019,
y=11.3,label="Food Prices", colour = "black")
ggsave(file = "espanhol_f1.png", plot = p3, width = 4.8, height = 3.6, dpi = 300)## Warning: Use of `pub$Ano` is discouraged.
## ℹ Use `Ano` instead.## Warning: Use of `pub$Food_Prices` is discouraged.
## ℹ Use `Food_Prices` instead.## Warning: Use of `pub$Economia` is discouraged.
## ℹ Use `Economia` instead.## Warning: Use of `pub$Ano` is discouraged.
## ℹ Use `Ano` instead.## Warning: Use of `pub$Food_Prices` is discouraged.
## ℹ Use `Food_Prices` instead.## Warning: Use of `pub$Economia` is discouraged.
## ℹ Use `Economia` instead.p3 ## Warning: Use of `pub$Ano` is discouraged.
## ℹ Use `Ano` instead.## Warning: Use of `pub$Food_Prices` is discouraged.
## ℹ Use `Food_Prices` instead.## Warning: Use of `pub$Economia` is discouraged.
## ℹ Use `Economia` instead.## Warning: Use of `pub$Ano` is discouraged.
## ℹ Use `Ano` instead.## Warning: Use of `pub$Food_Prices` is discouraged.
## ℹ Use `Food_Prices` instead.## Warning: Use of `pub$Economia` is discouraged.
## ℹ Use `Economia` instead.
Figura 2
f2 <- ggplot(data = fao, aes(x = fao$Year, y =fao$`Food Price Index`)) +
geom_line() +
geom_point()+
labs(title = "",
y = "Precio mundial de los alimentos (index, FAO, año)",
x = "")+
theme_clean()+
theme(legend.position = "none")+
annotate("rect", xmin = 2006, xmax = 2008, ymin = -Inf, ymax = 140,
alpha = .2)+
annotate(geom="text",x=2007,
y=143,label="+41%", colour = "black")+
annotate("rect", xmin = 2020, xmax = 2022, ymin = -Inf, ymax = 140,
alpha = .2)+
annotate(geom="text",x=2021,
y=143,label="+41%", colour = "black")
print(f2)## Warning: Use of `fao$Year` is discouraged.
## ℹ Use `Year` instead.## Warning: Use of `` fao$`Food Price Index` `` is discouraged.
## ℹ Use `Food Price Index` instead.## Warning: Use of `fao$Year` is discouraged.
## ℹ Use `Year` instead.## Warning: Use of `` fao$`Food Price Index` `` is discouraged.
## ℹ Use `Food Price Index` instead.
ggsave(file="espanholf2.png", plot=f2, width = 4.8, height = 3.6, dpi = 300)## Warning: Use of `fao$Year` is discouraged.
## ℹ Use `Year` instead.
## Use of `` fao$`Food Price Index` `` is discouraged.
## ℹ Use `Food Price Index` instead.## Warning: Use of `fao$Year` is discouraged.
## ℹ Use `Year` instead.## Warning: Use of `` fao$`Food Price Index` `` is discouraged.
## ℹ Use `Food Price Index` instead.Figura 3
p12 <- ggplot() +
geom_line(data = merge12, aes(x = time, y = alimentos), color="darkred") +
geom_line(data = merge12, aes(x = time, y = geral), color="black") +
geom_line(data = merge12, aes(x = time, y = fao_nominal), color="blue") +
labs(title = "",
y = "Índice de precios (base=2014-2016)",
x = "") +
theme_clean()+
annotate("rect", xmin = as.Date('2020-01-01'), xmax = as.Date('2023-02-01'), ymin = -Inf, ymax = 170, alpha = .1, fill = "red")+
annotate(geom="text",x= as.Date("2006-06-01"),y=100,label="FAO", colour = "blue")+
annotate(geom="text",x= as.Date("2000-01-01"),y=45,label="General", colour = "black")+
annotate(geom="text",x= as.Date("2003-01-01"),y=30,label="Alimentos", colour = "darkred")+
annotate(geom="text",x= as.Date("2020-03-01"),y=175,label="Alimentos(BRA)=+38%", colour = "darkred")+
annotate(geom="text",x= as.Date("2021-06-01"),y=182,label="IPCA=+22% ", colour = "black")+
annotate(geom="text",x= as.Date("2021-06-01"),y=189,label="FAO=+29%", colour = "blue")+
scale_x_date(date_breaks = "2 year", date_labels = "%Y")+
scale_x_date(date_breaks = "2 year", date_labels = "%Y")+
scale_y_continuous(breaks = scales::pretty_breaks(n = 7))## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.p12
ggsave(file="espanholf3p12.png", plot=p12, width = 4.8, height = 3.6, dpi = 300)Figura 4
g24 <- ggplot() +
geom_line(data=acu24, aes(x = time, y = alimentosacu24), color="darkred") +
geom_line(data=acu24, aes(x = time, y = faoacu24), color="blue", linetype=1) +
labs(title = "",
y = "Variación acumulada, alimentos, 24 meses (%)",
x = "") +
theme_clean()+
scale_x_date(date_breaks = "2 year", date_labels = "%Y")+
scale_y_continuous(breaks = scales::pretty_breaks(n = 7))+
annotate(geom="text",x= as.Date("1998-01-01"),y=20,label="Brasil (IPCA-IBGE)", colour = "darkred")+
annotate(geom="text",x= as.Date("2015-01-01"),y=50,label="Internacional (FAO)", colour = "blue")
g24## Warning: Removed 24 rows containing missing values (`geom_line()`).
## Removed 24 rows containing missing values (`geom_line()`).
ggsave(file="espanholf4.png", plot=g24, width=6.72, height=5.04, dpi = 300)## Warning: Removed 24 rows containing missing values (`geom_line()`).
## Removed 24 rows containing missing values (`geom_line()`).Figura 5
colnames(br5)<-c("FAO(real)", "FAO(nominal)", "Cereales", "Harinas", "Tubérculos", "Azúcares", "Hortalizas", "Frutas", "Carnes", "Pescado", "Carnes y pescados (ind)", "Aves y huevos", "Leches y derivados", "Productos de panadería", "Aceites", "Bebidas", "Enlatado", "Especias", "Ali. Fuera de casa", "Ali. Hechos")
M<-cor(na.omit(br5))
head(round(M,2))## FAO(real) FAO(nominal) Cereales Harinas Tubérculos Azúcares
## FAO(real) 1.00 0.92 0.72 0.79 0.07 0.74
## FAO(nominal) 0.92 1.00 0.63 0.76 0.13 0.76
## Cereales 0.72 0.63 1.00 0.97 0.17 0.79
## Harinas 0.79 0.76 0.97 1.00 0.20 0.87
## Tubérculos 0.07 0.13 0.17 0.20 1.00 0.24
## Azúcares 0.74 0.76 0.79 0.87 0.24 1.00
## Hortalizas Frutas Carnes Pescado Carnes y pescados (ind)
## FAO(real) 0.57 -0.82 0.86 0.86 0.86
## FAO(nominal) 0.67 -0.83 0.86 0.90 0.90
## Cereales 0.61 -0.61 0.88 0.80 0.86
## Harinas 0.70 -0.72 0.95 0.89 0.94
## Tubérculos 0.60 -0.08 0.16 0.27 0.20
## Azúcares 0.77 -0.75 0.89 0.91 0.89
## Aves y huevos Leches y derivados Productos de panadería Aceites
## FAO(real) 0.82 0.84 0.82 0.72
## FAO(nominal) 0.79 0.87 0.81 0.66
## Cereales 0.91 0.84 0.92 0.97
## Harinas 0.96 0.93 0.98 0.98
## Tubérculos 0.22 0.26 0.20 0.21
## Azúcares 0.86 0.89 0.92 0.79
## Bebidas Enlatado Especias Ali. Fuera de casa Ali. Hechos
## FAO(real) 0.86 0.86 0.84 0.88 0.83
## FAO(nominal) 0.94 0.91 0.89 0.93 0.89
## Cereales 0.76 0.82 0.84 0.77 0.85
## Harinas 0.89 0.92 0.93 0.88 0.94
## Tubérculos 0.18 0.20 0.20 0.17 0.19
## Azúcares 0.90 0.90 0.90 0.90 0.90corrplot(M, method = "color", tl.cex = 1, tl.col = "black", diag = TRUE, type = "upper")
png("espanholfig5.png", width = 6.72, height = 5.04, units = "in", res = 300)
corrplot(M, method = "color", tl.cex = 1, tl.col = "black", diag = TRUE, type = "upper")
dev.off()## png
## 2Figura 6
Para gerar este gráfico é necessário fazer as estimativas. Elas estão no final deste documento como “Estimativas para a figura 6”.
fig_artigo_1 <- ggplot(data=city_results) +
geom_line(aes(y=irf_dol_y1, x=time, colour = "c1", linetype = "c1")) +
geom_line(aes(y=irf_fao_y1, x=time, colour = "c2", linetype = "c2")) +
geom_line(aes(y=irf_pib_y1, x=time, colour = "c3", linetype = "c3")) +
geom_line(aes(y=irf_oil_y1, x=time, colour = "c4", linetype = "c4")) +
labs(title = "",
y = "Precios domésticos de alimentos",
x = "Meses",
colour = "") +
theme_minimal() +
scale_color_manual(values = c("c1" = "red", "c2" = "darkgreen", "c3" = "blue", "c4" = "black")) +
scale_linetype_manual(values = c("c1" = "solid", "c2" = "dashed", "c3" = "solid", "c4" = "solid")) +
geom_ribbon(aes(ymin=lower_dol_y1, ymax=upper_dol_y1, x=time, fill = "b1"), alpha = 0.1) +
geom_ribbon(aes(ymin=lower_fao_y1, ymax=upper_fao_y1, x=time, fill = "b2"), alpha = 0.1) +
geom_ribbon(aes(ymin=lower_pib_y1, ymax=upper_pib_y1, x=time, fill = "b3"), alpha = 0.07) +
geom_ribbon(aes(ymin=lower_oil_y1, ymax=upper_oil_y1, x=time, fill = "b4"), alpha = 0.15) +
scale_fill_manual(values = c("b1" = "red", "b2" = "lightgreen", "b3" = "blue", "b4" = "gray")) +
xlim(1, 13) + guides(fill = FALSE, linetype = FALSE, colour = FALSE) +
scale_y_continuous(labels = function(x) format(x, big.mark = ".", decimal.mark = ","))+
annotate("text", x = 12.4, y = max(city_results$irf_dol_y1), label = "Dólar", color = "red") +
annotate("text", x = 12.4, y = max(city_results$irf_fao_y1), label = "FAO", color = "darkgreen") +
annotate("text", x = 12.4, y = -0.08, label = "PIB", color = "blue") +
annotate("text", x = 12.6, y = 0.06, label = "Petróleo", color = "black")+
theme(text = element_text(size = 12)) ## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.fig_artigo_1## Warning: Removed 1 row containing missing values (`geom_line()`).## Warning: Removed 1 row containing missing values (`geom_line()`).
## Removed 1 row containing missing values (`geom_line()`).
## Removed 1 row containing missing values (`geom_line()`).
ggsave(file="espanholf5.png", plot=fig_artigo_1, width=6.72, height=5.04, dpi = 300)## Warning: Removed 1 row containing missing values (`geom_line()`).
## Removed 1 row containing missing values (`geom_line()`).
## Removed 1 row containing missing values (`geom_line()`).
## Removed 1 row containing missing values (`geom_line()`).Figura 7
time_mapping <- c(
y2 = "Belém",
y3 = "Fortaleza",
y4 = "Recife",
y5 = "Salvador",
y6 = "Belo Horizonte",
y7 = "Rio de Janeiro",
y8 = "São Paulo",
y9 = "Curitiba",
y10 = "Porto Alegre"
)
serie_labels <- c(
dol = "Dólar",
fao = "FAO",
oil = "Petróleo",
pib = "PIB"
)
# Criar o gráfico ggplot com rótulos do eixo x girados em 90 graus
g20<-ggplot(test_collapsed, aes(x = factor(time, levels = names(time_mapping)), y = irf, group = serie, color = serie)) +
geom_point(position = position_dodge(width = 0.4)) +
geom_errorbar(
aes(ymin = lower, ymax = upper),
position = position_dodge(width = 0.4),
width = 0.1
) +
labs(title = "",
x = "",
y = "Precios domésticos de alimentos", colour="") +
scale_x_discrete(labels = time_mapping) + # Mapear rótulos de "time"
scale_color_manual(values = c("dol" = "blue", "fao" = "darkorange", "oil" = "black", "pib" = "gray"), labels = serie_labels) + # Rótulos personalizados para "serie"
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
scale_y_continuous(labels = function(x) format(x, big.mark = ".", decimal.mark = ","))
ggsave(file="espanholf6.png", plot=g20, width=6.72, height=5.04, dpi = 300)
g20
Figura 8
## mudando os nomes
load("r6.Rda")
traducoes_espanhol <- c("Cereales", "Harinas + Pastas", "Tub. + Raíces + Leg.", "Azúcares y derivados",
"Hortalizas y verduras", "Frutas", "Carnes", "Pescado", "Carnes y pescados (ind)",
"Aves y huevos", "Leches y derivados", "Productos de panadería", "Aceites y grasas",
"Bebidas", "Enlatado", "Sal y especias", "Ali. Fuera de casa")
# Adicionando a variável 'traducao_espanhol' ao dataframe 'r6'
r6 <- cbind(r6, traducoes_espanhol)
g21 <- ggplot(r6, aes(x = traducoes_espanhol, group = traducoes_espanhol)) +
geom_pointrange(aes(y = dol_irf, ymin = dol_lower, ymax = dol_upper, color = "Dólar"), position = position_dodge(width = 0.5)) +
geom_pointrange(aes(y = fao_irf, ymin = fao_lower, ymax = fao_upper, color = "FAO"), position = position_dodge(width = 0.5)) +
geom_pointrange(aes(y = oil_irf, ymin = oil_lower, ymax = oil_upper, color = "Petróleo"), position = position_dodge(width = 0.5)) +
geom_pointrange(aes(y = pib_irf, ymin = pib_lower, ymax = pib_upper, color = "PIB"), position = position_dodge(width = 0.5)) +
labs(
title = "",
x = "",
y = "Precios domésticos de alimentos",
color = ""
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 90, hjust = 1),
legend.position = "bottom", # Move legend to the bottom
legend.direction = "horizontal", # Display legend items horizontally
legend.text = element_text(angle = 0) # Set legend text angle to 0 (horizontal)
) +
scale_color_manual(
values = c("blue", "darkorange", "black", "gray"),
labels = c("Dólar", "FAO", "Petróleo", "PIB")
)
ggsave(file="espanholf8.png", plot=g21, width=6.72, height=5.04, dpi=300)
g21
Figura A1
dados <- data.frame(
nomeportugues = c("Alimentação e bebidas", "Habitação", "Artigos de residência", "Vestuário",
"Transportes", "Saúde e cuidados pessoais", "Despesas pessoais", "Educação",
"Comunicação"),
valor = c(35.45, 23.19, 25.43, 28.05, 26.42, 23.91, 19.35, 24.89, 8.13),
nomespanhol = c("1 – Alimentos y bebidas", "2 – Habitación", "3 – Artículos de residencia",
"4 – Vestuario", "5 – Transporte", "6 – Salud y cuidados personales",
"7 – Gastos personales", "8 – Educación", "9 – Comunicación"),
stringsAsFactors = FALSE
)
# Visualizar o dataframe
print(dados)## nomeportugues valor nomespanhol
## 1 Alimentação e bebidas 35.45 1 – Alimentos y bebidas
## 2 Habitação 23.19 2 – Habitación
## 3 Artigos de residência 25.43 3 – Artículos de residencia
## 4 Vestuário 28.05 4 – Vestuario
## 5 Transportes 26.42 5 – Transporte
## 6 Saúde e cuidados pessoais 23.91 6 – Salud y cuidados personales
## 7 Despesas pessoais 19.35 7 – Gastos personales
## 8 Educação 24.89 8 – Educación
## 9 Comunicação 8.13 9 – Comunicaciónali3<-ggplot(dados, aes(valor, nomespanhol, fill=nomespanhol)) +
geom_col()+ labs(title = "",
y = "",
x = "Inflación acumulada entre Ene-2020 y Feb-2023, %")+
theme_clean()+
scale_fill_manual(values=c("steelblue", "grey50", "grey50", "grey50", "grey50","grey50","grey50","grey50","grey50"))+
theme(legend.position = "none")
ggsave(file="espanholf9.png", plot=ali3, width=6.72, height=5.04, dpi=300)
ali3