I need help on my code

[Felipe-Basilio]

I have a trouble with R because i have a data base with a few cateories and i want to do a thing like these excel table on R

[jlooper]

@R-icntay a wee boost here?

[R-icntay]

Hello @SKAIWO , @jlooper ,

Firstly, apologies for seeing this late.

@SKAIWO, you would simply need the count verb from dplyr 🙂. See the example below:

# Load dplyr and palmerpenguins dataset
library(tidyverse)
library(palmerpenguins)

penguins %>% 
  slice_head(n = 5)
#> # A tibble: 5 x 8
#>   species island bill_length_mm bill_depth_mm flipper_length_~ body_mass_g sex  
#>   <fct>   <fct>           <dbl>         <dbl>            <int>       <int> <fct>
#> 1 Adelie  Torge~           39.1          18.7              181        3750 male 
#> 2 Adelie  Torge~           39.5          17.4              186        3800 fema~
#> 3 Adelie  Torge~           40.3          18                195        3250 fema~
#> 4 Adelie  Torge~           NA            NA                 NA          NA <NA> 
#> 5 Adelie  Torge~           36.7          19.3              193        3450 fema~
#> # ... with 1 more variable: year <int>

# Count the number of male and female penguins for each species
penguins %>% 
  # Drop missing values aka NAs
  drop_na() %>% 
  count(sex, species)
#> # A tibble: 6 x 3
#>   sex    species       n
#>   <fct>  <fct>     <int>
#> 1 female Adelie       73
#> 2 female Chinstrap    34
#> 3 female Gentoo       58
#> 4 male   Adelie       73
#> 5 male   Chinstrap    34
#> 6 male   Gentoo       61

^{Created on 2022-06-11 by the reprex package (v2.0.1)}

There you go! This is what we would call tidy data since each variable (sex, species, n) has its own column. With this, you can do other downstream analysis much easily. If you still want your tibble to look that the excel screenshot, then you would need to restructure your results using pivot_wider:

# Load tidyverse and palmerpenguins dataset
library(tidyverse)
library(palmerpenguins)


# Count the number of male and female penguins for each species
penguins %>% 
  # Drop missing values aka NAs
  drop_na() %>% 
  count(sex, species) %>% 
  pivot_wider(names_from = species, values_from = n)
#> # A tibble: 2 x 4
#>   sex    Adelie Chinstrap Gentoo
#>   <fct>   <int>     <int>  <int>
#> 1 female     73        34     58
#> 2 male       73        34     61

^{Created on 2022-06-11 by the reprex package (v2.0.1)}

We have covered such tasks here: https://docs.microsoft.com/en-us/learn/modules/explore-analyze-data-with-r/?WT.mc_id=academic-59300-cacaste

Happy learning 🙂.