Ordenar datos tabulares (alargar filas, ensanchar columnas, valores faltantes, unir, separar)

ordenar.R

if(!require(tidyverse)) install.packages("tidyverse")
library(tidyverse)

# Alargar filas del data.frame reuniendo/fundiendo las columnas en nueva filas.
relig_income
longer <- relig_income %>% pivot_longer(-religion, names_to = "income", values_to = "count") %>% print()

# Ensanchar columnas del data.frame esparciendo/proyectando las filas en nuevas columnas.
us_rent_income
wider <- us_rent_income %>% pivot_wider(names_from = variable, values_from = c(estimate, moe)) %>% print()

# Hacer explícita la ausencia de valores faltantes implícitos
revenue <- tibble(
  year = c(rep(2019, 12), rep(2020, 3)),
  month = c(1:12, 1:2, 6),
  eur = c("4.420,2€", "3.502,3€","4.701,4€", "3.602,3€", "4.420,2€", "3.502,3€","4.701,4€", "3.602,3€", "4.420,2€", "3.502,3€","4.701,4€", "3.602,3€", "3.502,3€","NA", "3.602,3€")
)
revenue2 <- revenue %>% complete(year, month) %>% filter(!(year == 2020 & month > 6)) %>% print(n = Inf)
  
# Unir variables multicolumna
options(pillar.sigfig = 7)
revenue2 %>%
  unite(date, year, month, sep = "-") %>%
  mutate(
    date = parse_date(date, format = "%Y-%m"),
    eur = parse_number(eur, locale = locale(decimal_mark = ",", grouping_mark = "."))
  ) %>% print()

# Separar columnas multivariables
ciudades <- tribble(
  ~ciudad,     ~pob,    ~latlang,
  "Madrid",	   3167000, "40.420,-3.710",
  "Barcelona", 1549400,	"41.400,2.170"
)
ciudades %>% separate(latlang, into = c("latitude", "longitude"),  sep = ",", convert = TRUE)