Cargar paquete
library(readr)
Cargar CSV
read_csv("data/gapminder_comas.csv")
##
## -- Column specification --------------------------------------------------------
## cols(
## country = col_character(),
## continent = col_character(),
## year = col_double(),
## lifeExp = col_double(),
## pop = col_double(),
## gdpPercap = col_double()
## )
## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # ... with 1,694 more rows
Asignar nombre a datos
datos_obtenidos_csv <- read_csv("data/gapminder_comas.csv")
##
## -- Column specification --------------------------------------------------------
## cols(
## country = col_character(),
## continent = col_character(),
## year = col_double(),
## lifeExp = col_double(),
## pop = col_double(),
## gdpPercap = col_double()
## )
datos_obtenidos_csv
## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # ... with 1,694 more rows
Leer TSV
datos_obtenidos_tsv <- read_tsv("data/gapminder_tabs.tsv")
##
## -- Column specification --------------------------------------------------------
## cols(
## country = col_character(),
## continent = col_character(),
## year = col_double(),
## lifeExp = col_double(),
## pop = col_double(),
## gdpPercap = col_double()
## )
datos_obtenidos_tsv
## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # ... with 1,694 more rows
Leer delimitar arbitrario
datos_obtenidos_punto_y_coma <- read_delim("data/gapminder_michi.txt", delim = "#")
##
## -- Column specification --------------------------------------------------------
## cols(
## country = col_character(),
## continent = col_character(),
## year = col_double(),
## lifeExp = col_double(),
## pop = col_double(),
## gdpPercap = col_double()
## )
datos_obtenidos_michi <- read_delim("data/gapminder_slash.txt", delim = "#")
##
## -- Column specification --------------------------------------------------------
## cols(
## `country/continent/year/lifeExp/pop/gdpPercap` = col_character()
## )