R Notebook - Violência contra a mulher (2019)

── Attaching core tidyverse packages ────────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
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✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.0     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     ── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errorsLinking to GEOS 3.11.2, GDAL 3.8.2, PROJ 9.3.1; sf_use_s2() is TRUE

Attaching package: ‘rnaturalearthdata’

The following object is masked from ‘package:rnaturalearth’:

    countries110

df = read_csv('violencia.csv')

Rows: 27 Columns: 4── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): nome
dbl (3): cod, periodo, valor
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(df)

summary (df$valor)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   10.0    40.0    67.0   132.7   168.5   817.0 

ggplot(data=df) +
  geom_col(mapping = aes(x=nome, y=valor)) +
  xlab(label="Estados") +
  ylab(label="Violência Física (Mil pessoas)") +
  labs(title = "Crimes contra a a mulher em 2019 por estado brasileiro.") +
  theme_minimal()

df["regiao"] <- c("Nordeste", "Norte", "Norte", "Nordeste", "Nordeste", "Centro-Oeste", "Sudeste", "Centro-Oeste", "Nordeste","Centro-Oeste", "Centro-Oeste", "Sudeste", "Sul", "Nordeste", "Sul", "Nordeste", "Nordeste", "Sudeste", "Nordeste", "Sul", "Norte", "Sul", "Sudeste", "Nordeste", "Norte", "Norte", "Norte")

df_grp_regiao = df %>% group_by(regiao) %>%
  summarise(crimes=sum(valor))

ggplot(data=df_grp_regiao, aes(x=regiao, y=crimes)) + 
  geom_col() + 
  xlab("Região") +
  ylab("Violência Física (Mil pessoas)") +
  labs(
    title = "Crimes contra a mulher em 2019 agrupado por região."
  ) + 
  theme_minimal()

# Carregar os dados novamente
df = read_csv('violencia.csv')

Rows: 27 Columns: 4── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): nome
dbl (3): cod, periodo, valor
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Carregar o shapefile do Brasil
brasil <- ne_countries(country = "brazil", returnclass = "sf")

# Carregar o shapefile das regiões do Brasil
regioes_brasil <- ne_states(country = "brazil", returnclass = "sf")

# Criar dados fictícios
dados <- data.frame(regiao = regioes_brasil$region, df)

# Unir os dados das regiões com os dados fictícios
regioes_dados <- merge(regioes_brasil, dados, by.x = "postal", by.y = "nome")

# Plotar o gráfico
ggplot() +
  geom_sf(data = brasil, fill = "lightgray", color = "black") +
  geom_sf(data = regioes_dados, aes(fill = valor), color = "black") +
  scale_fill_viridis_c(name = "Crimes") +
  labs(
    title = "Crimes contra a mulher em 2019 agrupado por região."
  ) + 
  theme_minimal()

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