¿Qué es?

Tidyverse es una colección de paquetes disponibles en R y orientados a la manipulación, importación, exploración y visualización de datos y que se utiliza exhaustivamente en ciencia de datos. El uso de Tidyverse permite facilitar el trabajo estadístico y la generación de trabajos reproducibles. Está compuesto de los siguientes paquetes (Araneda 2021a).

Instalar paquetes

install.packages("palmerpenguins")
install.packages("tidyverse")

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(palmerpenguins)

Datos

Datos colectados en las Islas Palmer de 2007 - 2009 por Dr. Kristen Gorman (Horst, Hill, and Gorman 2020)

data(package = 'palmerpenguins')
head(penguins)

# A tibble: 6 × 8
  species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
1 Adelie  Torgersen           39.1          18.7               181        3750
2 Adelie  Torgersen           39.5          17.4               186        3800
3 Adelie  Torgersen           40.3          18                 195        3250
4 Adelie  Torgersen           NA            NA                  NA          NA
5 Adelie  Torgersen           36.7          19.3               193        3450
6 Adelie  Torgersen           39.3          20.6               190        3650
# ℹ 2 more variables: sex <fct>, year <int>

df <- penguins

Algunas funciones

Renombrar

df2 <- rename(df,
  peso= "body_mass_g",
  largo_pico = "bill_length_mm",
  prof_pico = "bill_depth_mm"
)
names(df2)

[1] "species"           "island"            "largo_pico"       
[4] "prof_pico"         "flipper_length_mm" "peso"             
[7] "sex"               "year"

Seleccionar y filtrar datos

df_fil1 <- filter(df2, peso> 4000)
df2[df2$peso > 4000,]

# A tibble: 174 × 8
   species island    largo_pico prof_pico flipper_length_mm  peso sex    year
   <fct>   <fct>          <dbl>     <dbl>             <int> <int> <fct> <int>
 1 <NA>    <NA>            NA        NA                  NA    NA <NA>     NA
 2 Adelie  Torgersen       39.2      19.6               195  4675 male   2007
 3 Adelie  Torgersen       42        20.2               190  4250 <NA>   2007
 4 Adelie  Torgersen       34.6      21.1               198  4400 male   2007
 5 Adelie  Torgersen       42.5      20.7               197  4500 male   2007
 6 Adelie  Torgersen       46        21.5               194  4200 male   2007
 7 Adelie  Dream           39.2      21.1               196  4150 male   2007
 8 Adelie  Dream           39.8      19.1               184  4650 male   2007
 9 Adelie  Dream           44.1      19.7               196  4400 male   2007
10 Adelie  Dream           39.6      18.8               190  4600 male   2007
# ℹ 164 more rows

min(df_fil1$peso)

[1] 4050

df_fil2 <- filter(df2, peso>4000, 
                  sex != "male", 
                  largo_pico < 47, 
                  species %in% c("Gentoo", "Adelie"))

df2[df2$peso > 4000 & df2$species == c("Adelie","Gentoo"),]

# A tibble: 64 × 8
   species island    largo_pico prof_pico flipper_length_mm  peso sex     year
   <fct>   <fct>          <dbl>     <dbl>             <int> <int> <fct>  <int>
 1 Adelie  Torgersen       34.6      21.1               198  4400 male    2007
 2 Adelie  Dream           39.2      18.6               190  4250 male    2009
 3 Gentoo  Biscoe          50        16.3               230  5700 male    2007
 4 Gentoo  Biscoe          50        15.2               218  5700 male    2007
 5 Gentoo  Biscoe          46.5      13.5               210  4550 female  2007
 6 Gentoo  Biscoe          46.7      15.3               219  5200 male    2007
 7 Gentoo  Biscoe          46.8      15.4               215  5150 male    2007
 8 Gentoo  Biscoe          49        16.1               216  5550 male    2007
 9 Gentoo  Biscoe          48.4      14.6               213  5850 male    2007
10 Gentoo  Biscoe          49.3      15.7               217  5850 male    2007
# ℹ 54 more rows

Seleccionar columnas

df_sel <- dplyr::select(df_fil1, species, island)

dplyr::select(df_fil1, -species, -island)

# A tibble: 172 × 6
   largo_pico prof_pico flipper_length_mm  peso sex    year
        <dbl>     <dbl>             <int> <int> <fct> <int>
 1       39.2      19.6               195  4675 male   2007
 2       42        20.2               190  4250 <NA>   2007
 3       34.6      21.1               198  4400 male   2007
 4       42.5      20.7               197  4500 male   2007
 5       46        21.5               194  4200 male   2007
 6       39.2      21.1               196  4150 male   2007
 7       39.8      19.1               184  4650 male   2007
 8       44.1      19.7               196  4400 male   2007
 9       39.6      18.8               190  4600 male   2007
10       42.3      21.2               191  4150 male   2007
# ℹ 162 more rows

incluso permite re ordenar y renombrar

dplyr::select(df_fil1, 
              isla=island, 
              aleta=flipper_length_mm, 
              species)

# A tibble: 172 × 3
   isla      aleta species
   <fct>     <int> <fct>  
 1 Torgersen   195 Adelie 
 2 Torgersen   190 Adelie 
 3 Torgersen   198 Adelie 
 4 Torgersen   197 Adelie 
 5 Torgersen   194 Adelie 
 6 Dream       196 Adelie 
 7 Dream       184 Adelie 
 8 Dream       196 Adelie 
 9 Dream       190 Adelie 
10 Dream       191 Adelie 
# ℹ 162 more rows

o seleccionar dependiendo de la clase

dplyr::select(df, where(is.factor))

# A tibble: 344 × 3
   species island    sex   
   <fct>   <fct>     <fct> 
 1 Adelie  Torgersen male  
 2 Adelie  Torgersen female
 3 Adelie  Torgersen female
 4 Adelie  Torgersen <NA>  
 5 Adelie  Torgersen female
 6 Adelie  Torgersen male  
 7 Adelie  Torgersen female
 8 Adelie  Torgersen male  
 9 Adelie  Torgersen <NA>  
10 Adelie  Torgersen <NA>  
# ℹ 334 more rows

Nuevas columnas y operaciones

mutate() función que nos permite crear, modificar y eliminar columnas

df_mut <- mutate(df_fil1, 
                 porportion= largo_pico/length(df_fil1$species))

Conteos

count(df_mut, species)

# A tibble: 3 × 2
  species       n
  <fct>     <int>
1 Adelie       35
2 Chinstrap    15
3 Gentoo      122

count(df_mut, porportion)

# A tibble: 102 × 2
   porportion     n
        <dbl> <int>
 1      0.201     1
 2      0.204     1
 3      0.218     1
 4      0.220     1
 5      0.228     3
 6      0.230     2
 7      0.231     1
 8      0.231     1
 9      0.233     1
10      0.234     1
# ℹ 92 more rows

Agrupar

pero es más divertido agrupando los datos y poder realizar más de una operación sobre los datos

df_group <- group_by(df_mut, island)

resum <- summarise(df_group,
                   total_peso= sum(peso),
                   promedio_peso= mean(peso),
                   sd_peso= sd(peso))

Uso de tuberías o pipes

“%>%” es una herramienta para la ejecución de pipeline o tuberías de comandos que permite una encadenamiento de funciones de la forma (Araneda 2021b).

Forma “normal”

df_pipe <- rename(df, peso= "body_mass_g",
         largo_pico = "bill_length_mm",
         prof_pico = "bill_depth_mm")
df_pipe <- select(df_pipe, -island)
df_pipe <- filter(df_pipe, peso>4000)
df_pipe <- mutate(df_pipe, relacion= largo_pico/prof_pico)

Forma “pipe”

df_pipe <- df %>%
  rename(peso= "body_mass_g",
         largo_pico = "bill_length_mm",
         prof_pico = "bill_depth_mm"
  ) %>%
  dplyr::select(-island) %>% 
  filter(peso>4000) %>%
  mutate(relacion= largo_pico/prof_pico)

Especificando ggplot

Gramática para hacer gráficos. La filosofía de ggplot va en capas

1. Capa es para los datos y la estética de los datos

ggplot(data= df2, 
       aes(x=largo_pico, y=peso))

Ahora la geometría de los datos que queremos

ggplot(data= df2, 
       aes(x=largo_pico, y=peso))+
  geom_point()

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

la geometria puede tener estéticas

ggplot(data= df2, 
       aes(x=largo_pico, y=peso))+
  geom_point(aes(color=species, shape= species))

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

Podemos tener varias geometrías

ggplot(data= df2, 
       aes(x=largo_pico, y=peso))+
  geom_point(aes(color=species, shape= species))+
  geom_smooth(aes(color= species),method = "lm", se= T)

`geom_smooth()` using formula = 'y ~ x'

Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_smooth()`).

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

ggplot(data= df2, 
       aes(x=largo_pico, y=peso))+
  geom_smooth(method = "lm", se= T)+
  geom_point()

`geom_smooth()` using formula = 'y ~ x'

Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_smooth()`).

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

Hay varios temas por defecto en ggplot que controlan las características del gráfico

ggplot(data= df2, 
       aes(x=largo_pico, y=peso))+
  geom_point(aes(color=species, shape= species))+
  geom_smooth(aes(color= species),method = "lm", se= T)+
  theme_void()

`geom_smooth()` using formula = 'y ~ x'

Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_smooth()`).

Warning: Removed 2 rows containing missing values or values outside the scale range
(`geom_point()`).

probemos a hacer histogramas

ggplot(data = df2, aes(x=flipper_length_mm))+
  geom_histogram()

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_bin()`).

histograma por especie

ggplot(data = df2, aes(x=flipper_length_mm))+
  geom_histogram(aes(fill= species), alpha= 0.5)

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_bin()`).

Podemos controlar los colores

ggplot(data = df2, aes(x=flipper_length_mm))+
  geom_histogram(aes(fill= species), alpha= 0.5)+
  scale_fill_manual(values = c("#812C30","purple","black"))

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_bin()`).

Cambiar el tema

ggplot(data = df2, aes(x=flipper_length_mm))+
  geom_histogram(aes(fill= species), alpha= 0.5)+
  scale_fill_manual(values = c("darkorange","purple","cyan4"))+
  theme_gray()

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_bin()`).

Cambiar la leyenda

ggplot(data = df2, aes(x=flipper_length_mm))+
  geom_histogram(aes(fill= species), alpha= 0.5)+
  scale_fill_manual(name=" Especies", values = c("darkorange","purple","cyan4"))+
  theme_minimal()+
  labs(x= "Largo de la aleta (mm)",
       y= " Frecuencia",
       title = " Histograma de aletas")

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_bin()`).

Por últim también podemos hacer marcos del mismo gráfico agrupado por determinada variable

ggplot(data = df2, aes(x=flipper_length_mm))+
  geom_histogram(aes(fill= species), alpha= 0.5)+
  scale_fill_manual(name=" Especies", values = c("darkorange","purple","cyan4"))+
  theme_minimal()+
  labs(x= "Largo de la aleta (mm)",
       y= " Frecuencia",
       title = " Histograma de aletas")+
  facet_grid(~year)

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Warning: Removed 2 rows containing non-finite outside the scale range
(`stat_bin()`).

References

Araneda, Patricio. 2021a. “RPubs - Tidyverse Para Análisis de Datos.” https://rpubs.com/paraneda/tidyverse.

———. 2021b. “RPubs - Tidyverse Para Análisis de Datos.” https://rpubs.com/paraneda/tidyverse.

Horst, AM, AP Hill, and KB Gorman. 2020. “Palmerpenguins: Palmer Archipelago (Antarctica) Penguin Data. R Package Version 0.1.0.” https://allisonhorst.github.io/palmerpenguins/.