De la carga de los datos a la visualización en R (fácilmente)
By Javier Tamayo-Leiva
- 21 minutes read - 4281 wordsIntroducción
Muchas veces la razón por la que la gente se acerca al lenguaje R es porque han visto que es una gran herramienta para lograr mejores visualizaciones. Sin embargo, dado que R sigue siendo un lenguaje de programación, puede ser frustrante que para hacerlo tengas que lidiar con unas cuantas líneas de código para cargar, y preprocesar tus datos antes de empezar a crear la más simple visualización. Así que el objetivo principal de este post es proporcionar una manera más fácil de hacer frente a los pasos de la carga de sus datos a la visualización. Así que seguirás una guía para principiantes paso a paso para manejar tus datos hasta la visualización.
Lleva tus datos a R {datapasta}
R tiene diferentes opciones para cargar sus datos en R, ya sea utilizando la base de R, con la ayuda de Tidyverse u otros paquetes de datos, y con el uso de un IDE (Integrated Development Environment) como RStudio. Sin embargo, aunque con el tiempo cargar archivos en R se convierte en algo rutinario, para un usuario con poca experiencia puede ser una de las tareas que más frustración genera, y acaba por desanimar el aprendizaje. Por eso, utilizar un proceso intuitivo como copiar y pegar o arrastrar y soltar puede ser un muy buen amigo mientras se adquiere más experiencia en la escritura de código R.
Con esta idea nació el paquete de datos {datapasta}, que facilita el proceso de importación de datos en R, reduciendo el tiempo invertido y la frustración asociada.
¿Cómo funciona {datapasta}?
Lo primero que tenemos que hacer en RStudio es instalar {datapasta}:
# Instalar el paquete de datos
install.packages("datapasta")Lo segundo que debes hacer es reiniciar tú sesión de R (muy importante) y luego cargar {datapasta} con el siguiente código:
# Cargar el paquete de datos
library(datapasta)Luego debes asignar el operador y nombrar tu paquete de datos (data o el nombre que mejor te parezca) de la siguiente forma:
# Nombrar tu set de datos
data <- Ahora nos dirigiremos a donde tenemos los datos que nos interesa copiar y realizaremos la copia con el clic derecho y copy. Luego, cuando volvamos a nuestra sesión de R, colocaremos el cursor a la derecha del “<-”, y seguiremos los siguientes pasos:
Addins -> Browse Addins -> Paste as Tribble
Paste as Tribble Permite leer tablas delimitadas con tabulador, coma, pipe o punto y coma.
Para nuestro ejemplo de hoy utilizaremos la el set de datos penguins que puedes encontrar en el siguiente link:
Ahora tu código R debería ser como este, pero con muchas más filas, porque para este ejemplo, sólo he copiado las diez primeras.
# Copiar y pegar los datos
data <- tibble::tribble(
~species, ~island, ~bill_length_mm, ~bill_depth_mm, ~flipper_length_mm, ~body_mass_g, ~sex,
"Adelie", "Torgersen", 39.1, 18.7, 181L, 3750L, "MALE",
"Adelie", "Torgersen", 39.5, 17.4, 186L, 3800L, "FEMALE",
"Adelie", "Torgersen", 40.3, 18, 195L, 3250L, "FEMALE",
"Adelie", "Torgersen", NA, NA, NA, NA, NA,
"Adelie", "Torgersen", 36.7, 19.3, 193L, 3450L, "FEMALE",
"Adelie", "Torgersen", 39.3, 20.6, 190L, 3650L, "MALE",
"Adelie", "Torgersen", 38.9, 17.8, 181L, 3625L, "FEMALE",
"Adelie", "Torgersen", 39.2, 19.6, 195L, 4675L, "MALE",
"Adelie", "Torgersen", 34.1, 18.1, 193L, 3475L, NA
)Código R con el set de datos penguins completo
# Copiar y pegar los datos
data <- tibble::tribble(
~species, ~island, ~bill_length_mm, ~bill_depth_mm, ~flipper_length_mm, ~body_mass_g, ~sex,
"Adelie", "Torgersen", 39.1, 18.7, 181L, 3750L, "MALE",
"Adelie", "Torgersen", 39.5, 17.4, 186L, 3800L, "FEMALE",
"Adelie", "Torgersen", 40.3, 18, 195L, 3250L, "FEMALE",
"Adelie", "Torgersen", NA, NA, NA, NA, NA,
"Adelie", "Torgersen", 36.7, 19.3, 193L, 3450L, "FEMALE",
"Adelie", "Torgersen", 39.3, 20.6, 190L, 3650L, "MALE",
"Adelie", "Torgersen", 38.9, 17.8, 181L, 3625L, "FEMALE",
"Adelie", "Torgersen", 39.2, 19.6, 195L, 4675L, "MALE",
"Adelie", "Torgersen", 34.1, 18.1, 193L, 3475L, NA,
"Adelie", "Torgersen", 42, 20.2, 190L, 4250L, NA,
"Adelie", "Torgersen", 37.8, 17.1, 186L, 3300L, NA,
"Adelie", "Torgersen", 37.8, 17.3, 180L, 3700L, NA,
"Adelie", "Torgersen", 41.1, 17.6, 182L, 3200L, "FEMALE",
"Adelie", "Torgersen", 38.6, 21.2, 191L, 3800L, "MALE",
"Adelie", "Torgersen", 34.6, 21.1, 198L, 4400L, "MALE",
"Adelie", "Torgersen", 36.6, 17.8, 185L, 3700L, "FEMALE",
"Adelie", "Torgersen", 38.7, 19, 195L, 3450L, "FEMALE",
"Adelie", "Torgersen", 42.5, 20.7, 197L, 4500L, "MALE",
"Adelie", "Torgersen", 34.4, 18.4, 184L, 3325L, "FEMALE",
"Adelie", "Torgersen", 46, 21.5, 194L, 4200L, "MALE",
"Adelie", "Biscoe", 37.8, 18.3, 174L, 3400L, "FEMALE",
"Adelie", "Biscoe", 37.7, 18.7, 180L, 3600L, "MALE",
"Adelie", "Biscoe", 35.9, 19.2, 189L, 3800L, "FEMALE",
"Adelie", "Biscoe", 38.2, 18.1, 185L, 3950L, "MALE",
"Adelie", "Biscoe", 38.8, 17.2, 180L, 3800L, "MALE",
"Adelie", "Biscoe", 35.3, 18.9, 187L, 3800L, "FEMALE",
"Adelie", "Biscoe", 40.6, 18.6, 183L, 3550L, "MALE",
"Adelie", "Biscoe", 40.5, 17.9, 187L, 3200L, "FEMALE",
"Adelie", "Biscoe", 37.9, 18.6, 172L, 3150L, "FEMALE",
"Adelie", "Biscoe", 40.5, 18.9, 180L, 3950L, "MALE",
"Adelie", "Dream", 39.5, 16.7, 178L, 3250L, "FEMALE",
"Adelie", "Dream", 37.2, 18.1, 178L, 3900L, "MALE",
"Adelie", "Dream", 39.5, 17.8, 188L, 3300L, "FEMALE",
"Adelie", "Dream", 40.9, 18.9, 184L, 3900L, "MALE",
"Adelie", "Dream", 36.4, 17, 195L, 3325L, "FEMALE",
"Adelie", "Dream", 39.2, 21.1, 196L, 4150L, "MALE",
"Adelie", "Dream", 38.8, 20, 190L, 3950L, "MALE",
"Adelie", "Dream", 42.2, 18.5, 180L, 3550L, "FEMALE",
"Adelie", "Dream", 37.6, 19.3, 181L, 3300L, "FEMALE",
"Adelie", "Dream", 39.8, 19.1, 184L, 4650L, "MALE",
"Adelie", "Dream", 36.5, 18, 182L, 3150L, "FEMALE",
"Adelie", "Dream", 40.8, 18.4, 195L, 3900L, "MALE",
"Adelie", "Dream", 36, 18.5, 186L, 3100L, "FEMALE",
"Adelie", "Dream", 44.1, 19.7, 196L, 4400L, "MALE",
"Adelie", "Dream", 37, 16.9, 185L, 3000L, "FEMALE",
"Adelie", "Dream", 39.6, 18.8, 190L, 4600L, "MALE",
"Adelie", "Dream", 41.1, 19, 182L, 3425L, "MALE",
"Adelie", "Dream", 37.5, 18.9, 179L, 2975L, NA,
"Adelie", "Dream", 36, 17.9, 190L, 3450L, "FEMALE",
"Adelie", "Dream", 42.3, 21.2, 191L, 4150L, "MALE",
"Adelie", "Biscoe", 39.6, 17.7, 186L, 3500L, "FEMALE",
"Adelie", "Biscoe", 40.1, 18.9, 188L, 4300L, "MALE",
"Adelie", "Biscoe", 35, 17.9, 190L, 3450L, "FEMALE",
"Adelie", "Biscoe", 42, 19.5, 200L, 4050L, "MALE",
"Adelie", "Biscoe", 34.5, 18.1, 187L, 2900L, "FEMALE",
"Adelie", "Biscoe", 41.4, 18.6, 191L, 3700L, "MALE",
"Adelie", "Biscoe", 39, 17.5, 186L, 3550L, "FEMALE",
"Adelie", "Biscoe", 40.6, 18.8, 193L, 3800L, "MALE",
"Adelie", "Biscoe", 36.5, 16.6, 181L, 2850L, "FEMALE",
"Adelie", "Biscoe", 37.6, 19.1, 194L, 3750L, "MALE",
"Adelie", "Biscoe", 35.7, 16.9, 185L, 3150L, "FEMALE",
"Adelie", "Biscoe", 41.3, 21.1, 195L, 4400L, "MALE",
"Adelie", "Biscoe", 37.6, 17, 185L, 3600L, "FEMALE",
"Adelie", "Biscoe", 41.1, 18.2, 192L, 4050L, "MALE",
"Adelie", "Biscoe", 36.4, 17.1, 184L, 2850L, "FEMALE",
"Adelie", "Biscoe", 41.6, 18, 192L, 3950L, "MALE",
"Adelie", "Biscoe", 35.5, 16.2, 195L, 3350L, "FEMALE",
"Adelie", "Biscoe", 41.1, 19.1, 188L, 4100L, "MALE",
"Adelie", "Torgersen", 35.9, 16.6, 190L, 3050L, "FEMALE",
"Adelie", "Torgersen", 41.8, 19.4, 198L, 4450L, "MALE",
"Adelie", "Torgersen", 33.5, 19, 190L, 3600L, "FEMALE",
"Adelie", "Torgersen", 39.7, 18.4, 190L, 3900L, "MALE",
"Adelie", "Torgersen", 39.6, 17.2, 196L, 3550L, "FEMALE",
"Adelie", "Torgersen", 45.8, 18.9, 197L, 4150L, "MALE",
"Adelie", "Torgersen", 35.5, 17.5, 190L, 3700L, "FEMALE",
"Adelie", "Torgersen", 42.8, 18.5, 195L, 4250L, "MALE",
"Adelie", "Torgersen", 40.9, 16.8, 191L, 3700L, "FEMALE",
"Adelie", "Torgersen", 37.2, 19.4, 184L, 3900L, "MALE",
"Adelie", "Torgersen", 36.2, 16.1, 187L, 3550L, "FEMALE",
"Adelie", "Torgersen", 42.1, 19.1, 195L, 4000L, "MALE",
"Adelie", "Torgersen", 34.6, 17.2, 189L, 3200L, "FEMALE",
"Adelie", "Torgersen", 42.9, 17.6, 196L, 4700L, "MALE",
"Adelie", "Torgersen", 36.7, 18.8, 187L, 3800L, "FEMALE",
"Adelie", "Torgersen", 35.1, 19.4, 193L, 4200L, "MALE",
"Adelie", "Dream", 37.3, 17.8, 191L, 3350L, "FEMALE",
"Adelie", "Dream", 41.3, 20.3, 194L, 3550L, "MALE",
"Adelie", "Dream", 36.3, 19.5, 190L, 3800L, "MALE",
"Adelie", "Dream", 36.9, 18.6, 189L, 3500L, "FEMALE",
"Adelie", "Dream", 38.3, 19.2, 189L, 3950L, "MALE",
"Adelie", "Dream", 38.9, 18.8, 190L, 3600L, "FEMALE",
"Adelie", "Dream", 35.7, 18, 202L, 3550L, "FEMALE",
"Adelie", "Dream", 41.1, 18.1, 205L, 4300L, "MALE",
"Adelie", "Dream", 34, 17.1, 185L, 3400L, "FEMALE",
"Adelie", "Dream", 39.6, 18.1, 186L, 4450L, "MALE",
"Adelie", "Dream", 36.2, 17.3, 187L, 3300L, "FEMALE",
"Adelie", "Dream", 40.8, 18.9, 208L, 4300L, "MALE",
"Adelie", "Dream", 38.1, 18.6, 190L, 3700L, "FEMALE",
"Adelie", "Dream", 40.3, 18.5, 196L, 4350L, "MALE",
"Adelie", "Dream", 33.1, 16.1, 178L, 2900L, "FEMALE",
"Adelie", "Dream", 43.2, 18.5, 192L, 4100L, "MALE",
"Adelie", "Biscoe", 35, 17.9, 192L, 3725L, "FEMALE",
"Adelie", "Biscoe", 41, 20, 203L, 4725L, "MALE",
"Adelie", "Biscoe", 37.7, 16, 183L, 3075L, "FEMALE",
"Adelie", "Biscoe", 37.8, 20, 190L, 4250L, "MALE",
"Adelie", "Biscoe", 37.9, 18.6, 193L, 2925L, "FEMALE",
"Adelie", "Biscoe", 39.7, 18.9, 184L, 3550L, "MALE",
"Adelie", "Biscoe", 38.6, 17.2, 199L, 3750L, "FEMALE",
"Adelie", "Biscoe", 38.2, 20, 190L, 3900L, "MALE",
"Adelie", "Biscoe", 38.1, 17, 181L, 3175L, "FEMALE",
"Adelie", "Biscoe", 43.2, 19, 197L, 4775L, "MALE",
"Adelie", "Biscoe", 38.1, 16.5, 198L, 3825L, "FEMALE",
"Adelie", "Biscoe", 45.6, 20.3, 191L, 4600L, "MALE",
"Adelie", "Biscoe", 39.7, 17.7, 193L, 3200L, "FEMALE",
"Adelie", "Biscoe", 42.2, 19.5, 197L, 4275L, "MALE",
"Adelie", "Biscoe", 39.6, 20.7, 191L, 3900L, "FEMALE",
"Adelie", "Biscoe", 42.7, 18.3, 196L, 4075L, "MALE",
"Adelie", "Torgersen", 38.6, 17, 188L, 2900L, "FEMALE",
"Adelie", "Torgersen", 37.3, 20.5, 199L, 3775L, "MALE",
"Adelie", "Torgersen", 35.7, 17, 189L, 3350L, "FEMALE",
"Adelie", "Torgersen", 41.1, 18.6, 189L, 3325L, "MALE",
"Adelie", "Torgersen", 36.2, 17.2, 187L, 3150L, "FEMALE",
"Adelie", "Torgersen", 37.7, 19.8, 198L, 3500L, "MALE",
"Adelie", "Torgersen", 40.2, 17, 176L, 3450L, "FEMALE",
"Adelie", "Torgersen", 41.4, 18.5, 202L, 3875L, "MALE",
"Adelie", "Torgersen", 35.2, 15.9, 186L, 3050L, "FEMALE",
"Adelie", "Torgersen", 40.6, 19, 199L, 4000L, "MALE",
"Adelie", "Torgersen", 38.8, 17.6, 191L, 3275L, "FEMALE",
"Adelie", "Torgersen", 41.5, 18.3, 195L, 4300L, "MALE",
"Adelie", "Torgersen", 39, 17.1, 191L, 3050L, "FEMALE",
"Adelie", "Torgersen", 44.1, 18, 210L, 4000L, "MALE",
"Adelie", "Torgersen", 38.5, 17.9, 190L, 3325L, "FEMALE",
"Adelie", "Torgersen", 43.1, 19.2, 197L, 3500L, "MALE",
"Adelie", "Dream", 36.8, 18.5, 193L, 3500L, "FEMALE",
"Adelie", "Dream", 37.5, 18.5, 199L, 4475L, "MALE",
"Adelie", "Dream", 38.1, 17.6, 187L, 3425L, "FEMALE",
"Adelie", "Dream", 41.1, 17.5, 190L, 3900L, "MALE",
"Adelie", "Dream", 35.6, 17.5, 191L, 3175L, "FEMALE",
"Adelie", "Dream", 40.2, 20.1, 200L, 3975L, "MALE",
"Adelie", "Dream", 37, 16.5, 185L, 3400L, "FEMALE",
"Adelie", "Dream", 39.7, 17.9, 193L, 4250L, "MALE",
"Adelie", "Dream", 40.2, 17.1, 193L, 3400L, "FEMALE",
"Adelie", "Dream", 40.6, 17.2, 187L, 3475L, "MALE",
"Adelie", "Dream", 32.1, 15.5, 188L, 3050L, "FEMALE",
"Adelie", "Dream", 40.7, 17, 190L, 3725L, "MALE",
"Adelie", "Dream", 37.3, 16.8, 192L, 3000L, "FEMALE",
"Adelie", "Dream", 39, 18.7, 185L, 3650L, "MALE",
"Adelie", "Dream", 39.2, 18.6, 190L, 4250L, "MALE",
"Adelie", "Dream", 36.6, 18.4, 184L, 3475L, "FEMALE",
"Adelie", "Dream", 36, 17.8, 195L, 3450L, "FEMALE",
"Adelie", "Dream", 37.8, 18.1, 193L, 3750L, "MALE",
"Adelie", "Dream", 36, 17.1, 187L, 3700L, "FEMALE",
"Adelie", "Dream", 41.5, 18.5, 201L, 4000L, "MALE",
"Chinstrap", "Dream", 46.5, 17.9, 192L, 3500L, "FEMALE",
"Chinstrap", "Dream", 50, 19.5, 196L, 3900L, "MALE",
"Chinstrap", "Dream", 51.3, 19.2, 193L, 3650L, "MALE",
"Chinstrap", "Dream", 45.4, 18.7, 188L, 3525L, "FEMALE",
"Chinstrap", "Dream", 52.7, 19.8, 197L, 3725L, "MALE",
"Chinstrap", "Dream", 45.2, 17.8, 198L, 3950L, "FEMALE",
"Chinstrap", "Dream", 46.1, 18.2, 178L, 3250L, "FEMALE",
"Chinstrap", "Dream", 51.3, 18.2, 197L, 3750L, "MALE",
"Chinstrap", "Dream", 46, 18.9, 195L, 4150L, "FEMALE",
"Chinstrap", "Dream", 51.3, 19.9, 198L, 3700L, "MALE",
"Chinstrap", "Dream", 46.6, 17.8, 193L, 3800L, "FEMALE",
"Chinstrap", "Dream", 51.7, 20.3, 194L, 3775L, "MALE",
"Chinstrap", "Dream", 47, 17.3, 185L, 3700L, "FEMALE",
"Chinstrap", "Dream", 52, 18.1, 201L, 4050L, "MALE",
"Chinstrap", "Dream", 45.9, 17.1, 190L, 3575L, "FEMALE",
"Chinstrap", "Dream", 50.5, 19.6, 201L, 4050L, "MALE",
"Chinstrap", "Dream", 50.3, 20, 197L, 3300L, "MALE",
"Chinstrap", "Dream", 58, 17.8, 181L, 3700L, "FEMALE",
"Chinstrap", "Dream", 46.4, 18.6, 190L, 3450L, "FEMALE",
"Chinstrap", "Dream", 49.2, 18.2, 195L, 4400L, "MALE",
"Chinstrap", "Dream", 42.4, 17.3, 181L, 3600L, "FEMALE",
"Chinstrap", "Dream", 48.5, 17.5, 191L, 3400L, "MALE",
"Chinstrap", "Dream", 43.2, 16.6, 187L, 2900L, "FEMALE",
"Chinstrap", "Dream", 50.6, 19.4, 193L, 3800L, "MALE",
"Chinstrap", "Dream", 46.7, 17.9, 195L, 3300L, "FEMALE",
"Chinstrap", "Dream", 52, 19, 197L, 4150L, "MALE",
"Chinstrap", "Dream", 50.5, 18.4, 200L, 3400L, "FEMALE",
"Chinstrap", "Dream", 49.5, 19, 200L, 3800L, "MALE",
"Chinstrap", "Dream", 46.4, 17.8, 191L, 3700L, "FEMALE",
"Chinstrap", "Dream", 52.8, 20, 205L, 4550L, "MALE",
"Chinstrap", "Dream", 40.9, 16.6, 187L, 3200L, "FEMALE",
"Chinstrap", "Dream", 54.2, 20.8, 201L, 4300L, "MALE",
"Chinstrap", "Dream", 42.5, 16.7, 187L, 3350L, "FEMALE",
"Chinstrap", "Dream", 51, 18.8, 203L, 4100L, "MALE",
"Chinstrap", "Dream", 49.7, 18.6, 195L, 3600L, "MALE",
"Chinstrap", "Dream", 47.5, 16.8, 199L, 3900L, "FEMALE",
"Chinstrap", "Dream", 47.6, 18.3, 195L, 3850L, "FEMALE",
"Chinstrap", "Dream", 52, 20.7, 210L, 4800L, "MALE",
"Chinstrap", "Dream", 46.9, 16.6, 192L, 2700L, "FEMALE",
"Chinstrap", "Dream", 53.5, 19.9, 205L, 4500L, "MALE",
"Chinstrap", "Dream", 49, 19.5, 210L, 3950L, "MALE",
"Chinstrap", "Dream", 46.2, 17.5, 187L, 3650L, "FEMALE",
"Chinstrap", "Dream", 50.9, 19.1, 196L, 3550L, "MALE",
"Chinstrap", "Dream", 45.5, 17, 196L, 3500L, "FEMALE",
"Chinstrap", "Dream", 50.9, 17.9, 196L, 3675L, "FEMALE",
"Chinstrap", "Dream", 50.8, 18.5, 201L, 4450L, "MALE",
"Chinstrap", "Dream", 50.1, 17.9, 190L, 3400L, "FEMALE",
"Chinstrap", "Dream", 49, 19.6, 212L, 4300L, "MALE",
"Chinstrap", "Dream", 51.5, 18.7, 187L, 3250L, "MALE",
"Chinstrap", "Dream", 49.8, 17.3, 198L, 3675L, "FEMALE",
"Chinstrap", "Dream", 48.1, 16.4, 199L, 3325L, "FEMALE",
"Chinstrap", "Dream", 51.4, 19, 201L, 3950L, "MALE",
"Chinstrap", "Dream", 45.7, 17.3, 193L, 3600L, "FEMALE",
"Chinstrap", "Dream", 50.7, 19.7, 203L, 4050L, "MALE",
"Chinstrap", "Dream", 42.5, 17.3, 187L, 3350L, "FEMALE",
"Chinstrap", "Dream", 52.2, 18.8, 197L, 3450L, "MALE",
"Chinstrap", "Dream", 45.2, 16.6, 191L, 3250L, "FEMALE",
"Chinstrap", "Dream", 49.3, 19.9, 203L, 4050L, "MALE",
"Chinstrap", "Dream", 50.2, 18.8, 202L, 3800L, "MALE",
"Chinstrap", "Dream", 45.6, 19.4, 194L, 3525L, "FEMALE",
"Chinstrap", "Dream", 51.9, 19.5, 206L, 3950L, "MALE",
"Chinstrap", "Dream", 46.8, 16.5, 189L, 3650L, "FEMALE",
"Chinstrap", "Dream", 45.7, 17, 195L, 3650L, "FEMALE",
"Chinstrap", "Dream", 55.8, 19.8, 207L, 4000L, "MALE",
"Chinstrap", "Dream", 43.5, 18.1, 202L, 3400L, "FEMALE",
"Chinstrap", "Dream", 49.6, 18.2, 193L, 3775L, "MALE",
"Chinstrap", "Dream", 50.8, 19, 210L, 4100L, "MALE",
"Chinstrap", "Dream", 50.2, 18.7, 198L, 3775L, "FEMALE",
"Gentoo", "Biscoe", 46.1, 13.2, 211L, 4500L, "FEMALE",
"Gentoo", "Biscoe", 50, 16.3, 230L, 5700L, "MALE",
"Gentoo", "Biscoe", 48.7, 14.1, 210L, 4450L, "FEMALE",
"Gentoo", "Biscoe", 50, 15.2, 218L, 5700L, "MALE",
"Gentoo", "Biscoe", 47.6, 14.5, 215L, 5400L, "MALE",
"Gentoo", "Biscoe", 46.5, 13.5, 210L, 4550L, "FEMALE",
"Gentoo", "Biscoe", 45.4, 14.6, 211L, 4800L, "FEMALE",
"Gentoo", "Biscoe", 46.7, 15.3, 219L, 5200L, "MALE",
"Gentoo", "Biscoe", 43.3, 13.4, 209L, 4400L, "FEMALE",
"Gentoo", "Biscoe", 46.8, 15.4, 215L, 5150L, "MALE",
"Gentoo", "Biscoe", 40.9, 13.7, 214L, 4650L, "FEMALE",
"Gentoo", "Biscoe", 49, 16.1, 216L, 5550L, "MALE",
"Gentoo", "Biscoe", 45.5, 13.7, 214L, 4650L, "FEMALE",
"Gentoo", "Biscoe", 48.4, 14.6, 213L, 5850L, "MALE",
"Gentoo", "Biscoe", 45.8, 14.6, 210L, 4200L, "FEMALE",
"Gentoo", "Biscoe", 49.3, 15.7, 217L, 5850L, "MALE",
"Gentoo", "Biscoe", 42, 13.5, 210L, 4150L, "FEMALE",
"Gentoo", "Biscoe", 49.2, 15.2, 221L, 6300L, "MALE",
"Gentoo", "Biscoe", 46.2, 14.5, 209L, 4800L, "FEMALE",
"Gentoo", "Biscoe", 48.7, 15.1, 222L, 5350L, "MALE",
"Gentoo", "Biscoe", 50.2, 14.3, 218L, 5700L, "MALE",
"Gentoo", "Biscoe", 45.1, 14.5, 215L, 5000L, "FEMALE",
"Gentoo", "Biscoe", 46.5, 14.5, 213L, 4400L, "FEMALE",
"Gentoo", "Biscoe", 46.3, 15.8, 215L, 5050L, "MALE",
"Gentoo", "Biscoe", 42.9, 13.1, 215L, 5000L, "FEMALE",
"Gentoo", "Biscoe", 46.1, 15.1, 215L, 5100L, "MALE",
"Gentoo", "Biscoe", 44.5, 14.3, 216L, 4100L, NA,
"Gentoo", "Biscoe", 47.8, 15, 215L, 5650L, "MALE",
"Gentoo", "Biscoe", 48.2, 14.3, 210L, 4600L, "FEMALE",
"Gentoo", "Biscoe", 50, 15.3, 220L, 5550L, "MALE",
"Gentoo", "Biscoe", 47.3, 15.3, 222L, 5250L, "MALE",
"Gentoo", "Biscoe", 42.8, 14.2, 209L, 4700L, "FEMALE",
"Gentoo", "Biscoe", 45.1, 14.5, 207L, 5050L, "FEMALE",
"Gentoo", "Biscoe", 59.6, 17, 230L, 6050L, "MALE",
"Gentoo", "Biscoe", 49.1, 14.8, 220L, 5150L, "FEMALE",
"Gentoo", "Biscoe", 48.4, 16.3, 220L, 5400L, "MALE",
"Gentoo", "Biscoe", 42.6, 13.7, 213L, 4950L, "FEMALE",
"Gentoo", "Biscoe", 44.4, 17.3, 219L, 5250L, "MALE",
"Gentoo", "Biscoe", 44, 13.6, 208L, 4350L, "FEMALE",
"Gentoo", "Biscoe", 48.7, 15.7, 208L, 5350L, "MALE",
"Gentoo", "Biscoe", 42.7, 13.7, 208L, 3950L, "FEMALE",
"Gentoo", "Biscoe", 49.6, 16, 225L, 5700L, "MALE",
"Gentoo", "Biscoe", 45.3, 13.7, 210L, 4300L, "FEMALE",
"Gentoo", "Biscoe", 49.6, 15, 216L, 4750L, "MALE",
"Gentoo", "Biscoe", 50.5, 15.9, 222L, 5550L, "MALE",
"Gentoo", "Biscoe", 43.6, 13.9, 217L, 4900L, "FEMALE",
"Gentoo", "Biscoe", 45.5, 13.9, 210L, 4200L, "FEMALE",
"Gentoo", "Biscoe", 50.5, 15.9, 225L, 5400L, "MALE",
"Gentoo", "Biscoe", 44.9, 13.3, 213L, 5100L, "FEMALE",
"Gentoo", "Biscoe", 45.2, 15.8, 215L, 5300L, "MALE",
"Gentoo", "Biscoe", 46.6, 14.2, 210L, 4850L, "FEMALE",
"Gentoo", "Biscoe", 48.5, 14.1, 220L, 5300L, "MALE",
"Gentoo", "Biscoe", 45.1, 14.4, 210L, 4400L, "FEMALE",
"Gentoo", "Biscoe", 50.1, 15, 225L, 5000L, "MALE",
"Gentoo", "Biscoe", 46.5, 14.4, 217L, 4900L, "FEMALE",
"Gentoo", "Biscoe", 45, 15.4, 220L, 5050L, "MALE",
"Gentoo", "Biscoe", 43.8, 13.9, 208L, 4300L, "FEMALE",
"Gentoo", "Biscoe", 45.5, 15, 220L, 5000L, "MALE",
"Gentoo", "Biscoe", 43.2, 14.5, 208L, 4450L, "FEMALE",
"Gentoo", "Biscoe", 50.4, 15.3, 224L, 5550L, "MALE",
"Gentoo", "Biscoe", 45.3, 13.8, 208L, 4200L, "FEMALE",
"Gentoo", "Biscoe", 46.2, 14.9, 221L, 5300L, "MALE",
"Gentoo", "Biscoe", 45.7, 13.9, 214L, 4400L, "FEMALE",
"Gentoo", "Biscoe", 54.3, 15.7, 231L, 5650L, "MALE",
"Gentoo", "Biscoe", 45.8, 14.2, 219L, 4700L, "FEMALE",
"Gentoo", "Biscoe", 49.8, 16.8, 230L, 5700L, "MALE",
"Gentoo", "Biscoe", 46.2, 14.4, 214L, 4650L, NA,
"Gentoo", "Biscoe", 49.5, 16.2, 229L, 5800L, "MALE",
"Gentoo", "Biscoe", 43.5, 14.2, 220L, 4700L, "FEMALE",
"Gentoo", "Biscoe", 50.7, 15, 223L, 5550L, "MALE",
"Gentoo", "Biscoe", 47.7, 15, 216L, 4750L, "FEMALE",
"Gentoo", "Biscoe", 46.4, 15.6, 221L, 5000L, "MALE",
"Gentoo", "Biscoe", 48.2, 15.6, 221L, 5100L, "MALE",
"Gentoo", "Biscoe", 46.5, 14.8, 217L, 5200L, "FEMALE",
"Gentoo", "Biscoe", 46.4, 15, 216L, 4700L, "FEMALE",
"Gentoo", "Biscoe", 48.6, 16, 230L, 5800L, "MALE",
"Gentoo", "Biscoe", 47.5, 14.2, 209L, 4600L, "FEMALE",
"Gentoo", "Biscoe", 51.1, 16.3, 220L, 6000L, "MALE",
"Gentoo", "Biscoe", 45.2, 13.8, 215L, 4750L, "FEMALE",
"Gentoo", "Biscoe", 45.2, 16.4, 223L, 5950L, "MALE",
"Gentoo", "Biscoe", 49.1, 14.5, 212L, 4625L, "FEMALE",
"Gentoo", "Biscoe", 52.5, 15.6, 221L, 5450L, "MALE",
"Gentoo", "Biscoe", 47.4, 14.6, 212L, 4725L, "FEMALE",
"Gentoo", "Biscoe", 50, 15.9, 224L, 5350L, "MALE",
"Gentoo", "Biscoe", 44.9, 13.8, 212L, 4750L, "FEMALE",
"Gentoo", "Biscoe", 50.8, 17.3, 228L, 5600L, "MALE",
"Gentoo", "Biscoe", 43.4, 14.4, 218L, 4600L, "FEMALE",
"Gentoo", "Biscoe", 51.3, 14.2, 218L, 5300L, "MALE",
"Gentoo", "Biscoe", 47.5, 14, 212L, 4875L, "FEMALE",
"Gentoo", "Biscoe", 52.1, 17, 230L, 5550L, "MALE",
"Gentoo", "Biscoe", 47.5, 15, 218L, 4950L, "FEMALE",
"Gentoo", "Biscoe", 52.2, 17.1, 228L, 5400L, "MALE",
"Gentoo", "Biscoe", 45.5, 14.5, 212L, 4750L, "FEMALE",
"Gentoo", "Biscoe", 49.5, 16.1, 224L, 5650L, "MALE",
"Gentoo", "Biscoe", 44.5, 14.7, 214L, 4850L, "FEMALE",
"Gentoo", "Biscoe", 50.8, 15.7, 226L, 5200L, "MALE",
"Gentoo", "Biscoe", 49.4, 15.8, 216L, 4925L, "MALE",
"Gentoo", "Biscoe", 46.9, 14.6, 222L, 4875L, "FEMALE",
"Gentoo", "Biscoe", 48.4, 14.4, 203L, 4625L, "FEMALE",
"Gentoo", "Biscoe", 51.1, 16.5, 225L, 5250L, "MALE",
"Gentoo", "Biscoe", 48.5, 15, 219L, 4850L, "FEMALE",
"Gentoo", "Biscoe", 55.9, 17, 228L, 5600L, "MALE",
"Gentoo", "Biscoe", 47.2, 15.5, 215L, 4975L, "FEMALE",
"Gentoo", "Biscoe", 49.1, 15, 228L, 5500L, "MALE",
"Gentoo", "Biscoe", 47.3, 13.8, 216L, 4725L, NA,
"Gentoo", "Biscoe", 46.8, 16.1, 215L, 5500L, "MALE",
"Gentoo", "Biscoe", 41.7, 14.7, 210L, 4700L, "FEMALE",
"Gentoo", "Biscoe", 53.4, 15.8, 219L, 5500L, "MALE",
"Gentoo", "Biscoe", 43.3, 14, 208L, 4575L, "FEMALE",
"Gentoo", "Biscoe", 48.1, 15.1, 209L, 5500L, "MALE",
"Gentoo", "Biscoe", 50.5, 15.2, 216L, 5000L, "FEMALE",
"Gentoo", "Biscoe", 49.8, 15.9, 229L, 5950L, "MALE",
"Gentoo", "Biscoe", 43.5, 15.2, 213L, 4650L, "FEMALE",
"Gentoo", "Biscoe", 51.5, 16.3, 230L, 5500L, "MALE",
"Gentoo", "Biscoe", 46.2, 14.1, 217L, 4375L, "FEMALE",
"Gentoo", "Biscoe", 55.1, 16, 230L, 5850L, "MALE",
"Gentoo", "Biscoe", 44.5, 15.7, 217L, 4875L, NA,
"Gentoo", "Biscoe", 48.8, 16.2, 222L, 6000L, "MALE",
"Gentoo", "Biscoe", 47.2, 13.7, 214L, 4925L, "FEMALE",
"Gentoo", "Biscoe", NA, NA, NA, NA, NA,
"Gentoo", "Biscoe", 46.8, 14.3, 215L, 4850L, "FEMALE",
"Gentoo", "Biscoe", 50.4, 15.7, 222L, 5750L, "MALE",
"Gentoo", "Biscoe", 45.2, 14.8, 212L, 5200L, "FEMALE",
"Gentoo", "Biscoe", 49.9, 16.1, 213L, 5400L, "MALE"
)Grafica tus datos con {ggplot2}
{ggplot2} Es un paquete de visualización escrito para el lenguaje R, creado por Hadley Wickham en 2005 y basado en Leland Wilkinson’s “Grammar of Graphics”. Un esquema general para la visualización de datos, que separa un gráfico en componentes semánticos como escalas y capas.
Lo primero que tenemos que hacer en RStudio es instalar y cargar la librería {ggplot2} con el siguiente código:
- Instalar la librería de paquetes Tidyverse completa (recomendado)
# Instalar desde CRAN (The Comprehensive R Archive Network)
install.packages("tidyverse")
# Cargar las librerías
library(tidyverse)- Instalar solo la librería {ggplot2}
# Instalar solo ggplot2
install.packages("ggplot2")
# Cargar la librería
library(ggplot2)Gráfico básico
Lo primero que haremos es revisar el conjunto de datos de pingüinos que acabamos de cargar en R, y seleccionar las variables que representaremos en nuestra primera figura.
De todas las variables, para nuestro primer gráfico seleccionaremos “bill_length_mm” (longitud del pico en milímetros) y “bill_depth_mm” (profundidad del pico en milímetros) debido a que son variables continuas.
ggplot(data = data,
mapping = aes(x = bill_length_mm,
y = bill_depth_mm)) +
geom_point()
Componentes de un gráfico en {ggplot2}
ggplot() : Es la función que crea un sistema de coordenadas -de forma general- a las que se incorporán capas. El primer argumento de la función es el set de datos (data=). Por si sola esta función no genera una capa.
data : El set de datos es una colección rectangular de datos con las variables (columnas) y sus observaciones/valores (filas) a mapear (penguins).
mapping : Es donde se especifican el conjunto de variables y observaciones que se “mapean” o asignan a las propiedades visuales a usar en el gráfico y que ejes asignar a estos valores (x= bill_length_mm,y= bill_depth_mm). Si no son especificadas -de forma general-, se deben indicar en cada capa adicionada a el gráfico.
geom_point() : Las capas del gráfico son incorporadas por las funciones o Geom. En este caso la función agrega una capa de puntos al gráfico. ggplot2 incluye más de 30 funciones geom, además de las desarrolladas por otros autores.
Elegir los Colores
El gráfico aterior solo nos permite ver la relación entre dos variables númericas continuas. Sin embargo, el set de datos tambien posee información asigada a grupos discretos como lo son la especie del pinguino y el sexo del individuo. Por esa razón ocuparemos otra propiedad visual como el color para incorporar una nueva capa de información en el gráfico. Para ello ocuparemos la opción “color” dentro de las variables mapeadas en “aes”.
ggplot(data = data,
mapping = aes(x = bill_length_mm,
y = bill_depth_mm,
color = species)) +
geom_point()
Si no especificamos una escala de colores, R trazará cada nivel de la variable discreta mapeada según la paleta de colores cargada por defecto en ggplot2. Para evitar o modificar esto, utilizaremos la opción de incorporar una escala de colores manualmente con “scale_color_manual” y una lista de colores en su código HEX que debe tener el mismo número de valores que los niveles a mapear en la variable discreta. En este caso hay 3 especies presentes en el conjunto de datos de penguins, por lo que nuestra lista tiene 3 códigos HEX.
ggplot(data = data,
mapping = aes(x = bill_length_mm,
y = bill_depth_mm,
color = species)) +
geom_point() +
scale_color_manual(values = c("#393459","#F2AB27","#D96704"))
Elija el gráfico con los objeto geom
Una capa en ggplot2, combina datos, propiedades visuales, objetos geométricos (Geoms), funciones y/o transformaciones estadísticas (Stat), y de ajuste de la posición. En nuestro gráfico anterior hemos realizado un scatterplot. Por defecto en R hay que elegir un objeto “geom_…” para generar una visualización, pues de lo contrarío obtendremos una representación sin nuestros datos en ella.
En el gráfico anterior podemos ver que hay diferencias en la distribución de datos según la especie. Ahora eligiremos otra representación para mostrar de mejor manera la distribución de las variables continuas (bill_length_mm o bill_depth_mm) en base a los grupos discretos presentes (especies) en el set de datos. Por esa razón cambiaremos nuestro geom desde geom_point() a geom_boxplot() y generaremos dos plot, uno para cada variable continua (bill_length_mm o bill_depth_mm).
Bill length
ggplot(data = data,
mapping = aes(x = species,
y = bill_length_mm,
color = species)) +
geom_boxplot() +
scale_color_manual(values = c("#393459","#F2AB27","#D96704"))
Bill depth
ggplot(data = data,
mapping = aes(x = species,
y = bill_depth_mm,
color = species)) +
geom_boxplot() +
scale_color_manual(values = c("#393459","#F2AB27","#D96704"))
Gráfico final
Ahora que hemos generado visualizaciones de los componentes que nos interesan, podemos modificar aquellos parámetros estéticos, que pueden ayudar a generar más interés y claridad en la figura. En primer lugar modificaremos la capa de color, debido a la construcción de la función geom_boxplot(), si asignamos una variable a “color”, ésta se representará en las líneas de los elementos. Sin embargo, nos interesa que la mayor superficie esté coloreada (para ayudar a la interpretación de los datos), por lo que modificaremos este parámetro de color a fill. Nótese que también hemos cambiado scale_color_manual(), por scale_fill_manual(), dejando todo dentro de la función como estaba.
ggplot(data = data,
mapping = aes(x = species,
y = bill_length_mm,
fill = species)) +
geom_boxplot() +
scale_fill_manual(values = c("#393459","#F2AB27","#D96704"))
Ahora agregaremos leyendas a cada eje mas un titulo que ayude a interpretar los datos representados en el gráfico. Para eso usaremos la función labs(), tambien parte de {ggplot2}.
ggplot(data = data,
mapping = aes(x = species,
y = bill_length_mm,
fill = species)) +
geom_boxplot() +
scale_fill_manual(values = c("#393459","#F2AB27","#D96704")) +
labs(x = "Especies",
y = "Longitud del pico (mm)",
fill = "Especies",
title = "Especies de pinguinos del\narchipelago Palmer, Antártica")
Por último, modificaremos el tema del gráfico. Al modificar el tema estamos modificando el lienzo sobre el que se han trazado nuestros ejes y datos. Para ello utilizaremos la función “theme_…”, y seleccionaremos uno de los ocho temas que se cargan por defecto en la librería {ggplot2}. En este ejemplo hemos elegido el “theme_classic()”, porque genera un trazado más limpio, manteniendo los ejes. En caso de que necesites utilizar otro tema, también puedes aplicar temas de librerías como {ggpubr}, o crear el tuyo propio con la función theme().Pero tendremos que mostrar este proceso con más detalle en un próximo post.
ggplot(data = data,
mapping = aes(x = species,
y = bill_length_mm,
fill = species)) +
geom_boxplot() +
scale_fill_manual(values = c("#393459","#F2AB27","#D96704")) +
labs(x = "Especies",
y = "Longitud del pico (mm)",
fill = "Especies",
title = "Especies de pinguinos del\narchipiélago Palmer, Antártica") +
theme_classic()
R Session Info
## R version 4.1.3 (2022-03-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur/Monterey 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4
## [5] readr_2.1.2 tidyr_1.2.0 tibble_3.1.7 ggplot2_3.3.6
## [9] tidyverse_1.3.1 DataEditR_0.1.4 datapasta_3.1.0 systemfonts_1.0.4
##
## loaded via a namespace (and not attached):
## [1] fs_1.5.2 lubridate_1.8.0 bit64_4.0.5
## [4] httr_1.4.3 tools_4.1.3 backports_1.4.1
## [7] bslib_0.3.1 utf8_1.2.2 R6_2.5.1
## [10] DT_0.23 DBI_1.1.2 colorspace_2.0-3
## [13] withr_2.5.0 tidyselect_1.1.2 bit_4.0.4
## [16] curl_4.3.2 compiler_4.1.3 cli_3.3.0
## [19] rvest_1.0.2 xml2_1.3.3 shinyjs_2.1.0
## [22] rhandsontable_0.3.8 labeling_0.4.2 bookdown_0.24
## [25] sass_0.4.1 scales_1.2.0 digest_0.6.29
## [28] shinyBS_0.61 rmarkdown_2.11 pkgconfig_2.0.3
## [31] htmltools_0.5.2 highr_0.9 dbplyr_2.1.1
## [34] fastmap_1.1.0 htmlwidgets_1.5.4 rlang_1.0.2
## [37] readxl_1.3.1 rstudioapi_0.13 shiny_1.7.1
## [40] farver_2.1.0 jquerylib_0.1.4 generics_0.1.2
## [43] jsonlite_1.8.0 crosstalk_1.2.0 vroom_1.5.7
## [46] magrittr_2.0.3 Rcpp_1.0.8.3 munsell_0.5.0
## [49] fansi_1.0.3 lifecycle_1.0.1 stringi_1.7.6
## [52] yaml_2.3.5 grid_4.1.3 parallel_4.1.3
## [55] promises_1.2.0.1 crayon_1.5.1 miniUI_0.1.1.1
## [58] haven_2.4.3 hms_1.1.1 knitr_1.37
## [61] pillar_1.7.0 reprex_2.0.1 glue_1.6.2
## [64] evaluate_0.14 blogdown_1.8 modelr_0.1.8
## [67] vctrs_0.4.1 tzdb_0.3.0 httpuv_1.6.5
## [70] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
## [73] xfun_0.29 mime_0.12 xtable_1.8-4
## [76] broom_0.7.12 later_1.3.0 shinythemes_1.2.0
## [79] ellipsis_0.3.2