Integrantes: - George Felipe Bedoya - Jennifer Ponce - Nelly Reyes - Almendra Rodriguez
Ejercicio integrador 1
Recrea el siguiente conjunto de datos usando select(), filter() y arrange(). Recuerda que al resultado de cada operación puedes asignarle un nombre con <-.
library(gapminder)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4
## ✓ tibble 3.1.1 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()gapminder## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <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 rowssubset_gapminder <- select(gapminder, country, continent, year, gdpPercap)
subset_gapminder2 <- filter(subset_gapminder, continent == "Americas", year == 1952, gdpPercap >= 3522.)
subset_gapminder3 <-arrange(subset_gapminder2, desc(gdpPercap))subset_gapminder3## # A tibble: 9 x 4
## country continent year gdpPercap
## <fct> <fct> <int> <dbl>
## 1 United States Americas 1952 13990.
## 2 Canada Americas 1952 11367.
## 3 Venezuela Americas 1952 7690.
## 4 Argentina Americas 1952 5911.
## 5 Uruguay Americas 1952 5717.
## 6 Cuba Americas 1952 5587.
## 7 Chile Americas 1952 3940.
## 8 Peru Americas 1952 3759.
## 9 Ecuador Americas 1952 3522.