Population Projections with ColOpenData"
In ColOpenData: Download Colombian Demographic, Climate and Geospatial Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = identical(tolower(Sys.getenv("NOT_CRAN")), "true")
)

::: {style="text-align: justify;"} We can use ColOpenData to retrieve population projections and back-projections on multiple levels of spatial aggregation, including municipalities, departments and national levels. Availability of years depends on spatial levels. These projections include differentiation by gender and even ethnic groups; however, the latter is only available for municipalities.

Availability of years by spatial levels goes as follows: :::

level <- c(
  "National", "National with sex",
  "Department", "Department with Sex",
  "Municipality", "Municipality with Sex",
  "Municipaity with Sex and Ethnic Groups"
)
years <- c(
  "1950 - 2070", "1985 - 2050",
  "1985 - 2050", "1985 - 2050", "1985 - 2035", "1985 - 2035",
  "2018 - 2035"
)
dictionary_key <- c(
  "DANE_MGN_2018_DPTO", "DANE_MGN_2018_MPIO",
  "DANE_MGN_2018_MPIOCL", "DANE_MGN_2018_MZN",
  "DANE_MGN_2018_SECR", "DANE_MGN_2018_SECU",
  "DANE_MGN_2018_SETR", "DANE_MGN_2018_SETU",
  "DANE_MGN_2018_ZU"
)

mgncnpv <- data.frame(
  Level = level, Years = years,
  stringsAsFactors = FALSE
)
knitr::kable(mgncnpv)

::: {style="text-align: justify;"} For this example, we will present projections and back projections of national population by area, sex and age for the period from 1950 to 2070. We will observe the expected female population under 99 by personalized age brackets for 2034. :::

We will first load the needed libraries.

library(ColOpenData)
library(dplyr)
library(ggplot2)

Now we can download the data. We will use the function download_pop_projections(), which has five parameters:

spatial_level character with the spatial level to be consulted. Can be either "national", "department" or "municipality".
start_year numeric with the start year to be consulted.
end_year numeric with the end year to be consulted.
include_sex logical for including (or not) division by sex. Default is FALSE.
include_ethnic logical for including (or not) division by ethnic group (only available for "municipality"). Default is FALSE.

asen <- download_pop_projections(
  spatial_level = "national",
  start_year = 2034,
  end_year = 2034,
  include_sex = TRUE,
  include_ethnic = FALSE
)

We will filter the downloaded data for ages under 99.

female_2034 <- asen %>%
  filter(
    area == "total",
    sexo == "mujer",
    edad != "100_y_mas"
  ) %>%
  mutate(edad = as.numeric(edad))

Age groups will be defined by breaks and included in the original dataset.

age_groups <- cut(female_2034[["edad"]],
  breaks = c(-1, 2, 12, 19, 29, 39, 49, 59, 69, 79, 89, 99),
  labels = c(
    "0-2", "3-12", "13-19", "20-29", "30-39", "40-49",
    "50-59", "60-69", "70-79", "80-89", "90-99"
  )
)
female_groups <- female_2034 %>%
  mutate(age_group = age_groups) %>%
  group_by(age_group) %>%
  summarise(total_sum = sum(total))

Finally, we can plot the output.

ggplot(female_groups, aes(
  x = age_group,
  y = total_sum
)) +
  geom_bar(stat = "identity", fill = "#f04a4c", color = "black", width = 0.6) +
  labs(
    title = "Female population counts in Colombia by age group for 2034",
    x = "Age group",
    y = "Female population"
  ) +
  theme_minimal() +
  theme(
    plot.background = element_rect(fill = "white", colour = "white"),
    panel.background = element_rect(fill = "white", colour = "white"),
    axis.text.x = element_text(angle = 45, hjust = 1),
    plot.title = element_text(hjust = 0.5)
  )