02-add-extra-data.R

library(tidyverse)
library(ISOcodes)

# fuente: https://github.com/CONCYTEC/ubigeo-peru
# ubigeos <- read_csv("datos/equivalencia-ubigeos-oti-concytec.csv.gz")

pe_iso_3166_2 <- ISO_3166_2 %>%
  filter(str_detect(Code, "PE-")) %>%
  mutate(
    Name = ifelse(Name == "Cusco [Cuzco]", "Cusco", Name), # fix weirdness with Cusco
    Name = ifelse(Name == "El Callao", "Callao", Name), # and weirdness with Callao
    Name = str_to_upper(iconv(Name, to='ASCII//TRANSLIT'))
  )

# fuente: https://www.ceplan.gob.pe/informacion-sobre-zonas-y-departamentos-del-peru/
ceplan_datos <- read_csv(
  "datos/regiones-ceplan-19may2020.csv",
  col_types = cols(
    .default = col_double(),
    Nivel = col_character(),
    zona = col_character(),
    Ubigeo = col_character(),
    Lugar = col_character(),
    `Capital legal` = col_character(),
    Tipología = col_character(),
    País_fronterizo = col_character()
  )
) %>% janitor::clean_names() %>%
  select(
    zona,
    ubigeo,
    lugar,
    superficie,
    capital_legal,
    altitud,
    latitud,
    longitud
  ) %>%
  mutate(
    lugar = str_replace_all(
      lugar,
      pattern = c(
        "Á" = "A",
        "É" = "E",
        "Í" = "I",
        "Ó" = "O",
        "Ú" = "U"
      )
    )
  )

# MINSA/INEI población al 2020, fuente: https://www.minsa.gob.pe/reunis/data/poblacion_estimada.asp

pob2020 <- readRDS("datos/peru-pob2020-departamentos.rds") %>%
  filter(departamento != "PERU") %>%
  select(
    departamento,
    total
  ) %>%
  rename(
    region = departamento,
    pop2020 = total
  )

pe_totpop2020 <- readRDS("datos/peru-pob2020-departamentos.rds") %>%
  filter(departamento == "PERU") %>%
  pull(total)

pe <- read_csv(
  "datos/covid-19-peru-data.csv",
  col_types = cols(
    country = col_character(),
    iso3c = col_character(),
    region = col_character(),
    date = col_date(format = ""),
    confirmed = col_double(),
    deaths = col_double(),
    recovered = col_double(),
    total_tests = col_double(),
    negative_tests = col_double(),
    pcr_test_positive = col_double(),
    serological_test_positive = col_double(),
    ag_test_positive = col_double(),
    pcr_serological_test_positive = col_double()
  )
) %>%
  mutate(
    region = str_to_upper(iconv(region, to='ASCII//TRANSLIT')),
    region_orig = region, # para preservar Lima Metropolitana y Lima Región - 2020-07-05
    region = if_else(str_detect(region, "LIMA"), "LIMA", region)
  ) %>%
  left_join(
    ceplan_datos,
    by = c("region" = "lugar")
  ) %>%
  rename(
    zone = zona,
    surface = superficie,
    capital = capital_legal,
    altitude = altitud,
    lat = latitud,
    lon = longitud
  ) %>%
  left_join(
    pe_iso_3166_2 %>%
      select(Code, Name) %>%
      rename(
        iso_3166_2_code = Code
      ),
    by = c("region" = "Name")
  ) %>%
  left_join(
    pob2020,
    by = "region"
  ) %>%
  mutate(
    pop2020 = if_else(
      is.na(pop2020),
      pe_totpop2020,
      pop2020
    )
  ) %>%
  select(
    country,
    iso3c,
    region,
    region_orig,
    ubigeo,
    iso_3166_2_code,
    date,
    confirmed,
    deaths,
    recovered,
    total_tests,
    negative_tests,
    pcr_test_positive,
    serological_test_positive,
    ag_test_positive,
    pcr_serological_test_positive,
    zone,
    pop2020,
    surface,
    capital,
    altitude,
    lat,
    lon
  )

write_csv(
  pe,
  file = "datos/covid-19-peru-data-augmented.csv"
)