Population Pyramid
library(tidyverse) # untuk manupulasi, merapikan, & visualisasi data
set.seed(2000)
df1 = tibble(gender = 'Male', age = '15-20',
value = sample((5:10), size = 1))
df2 = tibble(gender = 'Male', age = '21-25',
value = sample((10:15), size = 1))
df3 = tibble(gender = 'Male', age = '26-30',
value = sample((15:20), size = 1))
df4 = tibble(gender = 'Male', age = '31-35',
value = sample((20:25), size = 1))
df5 = tibble(gender = 'Male', age = '36-40',
value = sample((25:30), size = 1))
df6 = tibble(gender = 'Male', age = '41-45',
value = sample((30:35), size = 1))
df7 = tibble(gender = 'Male', age = '46-50',
value = sample((35:40), size = 1))
df8 = tibble(gender = 'Male', age = '51-55',
value = sample((40:45), size = 1))
df9 = tibble(gender = 'Male', age = '56-60',
value = sample((45:50), size = 1))
df10 = tibble(gender = 'Female', age = '15-20',
value = sample((5:10), size = 1))
df11 = tibble(gender = 'Female', age = '21-25',
value = sample((10:15), size = 1))
df12 = tibble(gender = 'Female', age = '26-30',
value = sample((15:20), size = 1))
df13 = tibble(gender = 'Female', age = '31-35',
value = sample((20:25), size = 1))
df14 = tibble(gender = 'Female', age = '36-40',
value = sample((25:30), size = 1))
df15 = tibble(gender = 'Female', age = '41-45',
value = sample((30:35), size = 1))
df16 = tibble(gender = 'Female', age = '46-50',
value = sample((35:40), size = 1))
df17 = tibble(gender = 'Female', age = '51-55',
value = sample((40:45), size = 1))
df18 = tibble(gender = 'Female', age = '56-60',
value = sample((45:50), size = 1))
df_for_pop_pyramid <- rbind(df1, df2, df3, df4, df5, df6,
df7, df8, df9, df10, df11, df12,
df13, df14, df15, df16, df17, df18) %>%
mutate(age = as.factor(age))
female_side <- df_for_pop_pyramid %>%
filter(gender == 'Female') %>%
ggplot(aes(x = value,
y = reorder(age, desc(age)),
fill = gender)) +
geom_col(fill = '#094568') +
geom_text(aes(label = value),
hjust = 2,
size = 3,
fontface = 'bold',
color = 'white') +
scale_x_continuous(limits = c(0, 50)) +
theme_minimal() +
theme(
plot.title.position = 'plot',
plot.subtitle = element_text(color = '#660d20',
face = 'bold'),
axis.title.x = element_text(color = '#660d20',
face = 'bold'),
axis.title.y = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_text(color = '#660d20',
face = 'bold'),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill = '#ffffff',
color = NA),
plot.background = element_rect(fill = '#ffffff',
color = '#ffffff')
) +
xlab('Female') +
labs(subtitle = 'Age')
male_side <- df_for_pop_pyramid %>%
filter(gender == 'Male') %>%
ggplot(aes(x = -value,
y = reorder(age, desc(age)),
fill = gender)) +
geom_col(fill = '#e59a52') +
geom_text(aes(label = value),
hjust = -1,
size = 3,
fontface = 'bold',
color = 'white') +
scale_x_continuous(limits = c(-50, 0)) +
theme_minimal() +
theme(
axis.title.x = element_text(color = '#660d20',
face = 'bold'),
axis.title.y = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill = '#ffffff',
color = NA),
plot.background = element_rect(fill = '#ffffff',
color = '#ffffff')
) +
xlab('Male')
library(patchwork)
pop_pyramid <- (male_side|female_side) +
plot_annotation(
theme = theme(
panel.background = element_rect(fill = '#FFFFFF',
color = NA),
plot.background = element_rect(fill = '#FFFFFF',
color = '#FFFFFF')
))
![](images/pop%20pyramid-1.png)