Create different excel tabs in fonction of a classification

[laplanca]

Hello! So I have a dataframe and at the end a column who classify my row and I want to export that in xlsxand obtain a file with one tab for each of my classification level. For example my level are A,B,C,D and I want in my xlsx tabs with the rows corresponding to A, B,C,D :

Thank you for your time

[nirgrahamuk]

Hello,
I'm sure you shared this image with the best intentions, but perhaps you didnt realise what it implies.
If someone wished to use example data to test code against, they would type it out from your screenshot...

This is very unlikely to happen, and so it reduces the likelihood you will receive the help you desire.
Therefore please see this guide on how to reprex data. Key to this is use of either datapasta, or dput() to share your data as code

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[jrmuirhead]

Writexl will write to separate tabs in the workbook if the data.frames are split into a named list

library("tidyverse")
library("lubridate")
#> 
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#> 
#>     date, intersect, setdiff, union
library("writexl")

# Using a toy data set

set.seed(123)

dat <- tibble(
  id = rpois(7, 15),
  name = letters[1:7],
  test = sample(1:2, size = 7, replace = TRUE),
  date = dmy(c("02/04/2012", "15/07/2008", "23/11/2016", "14/12/2017",
    "16/05/2015", "26/08/2018", "15/01/2012")),
  class = sample(LETTERS[1:4], size = 7, replace = TRUE)
)

dat_split <- dat %>%
  split(dat, f = dat$class)

dat_split
#> $A
#> # A tibble: 2 × 5
#>      id name   test date       class
#>   <int> <chr> <int> <date>     <chr>
#> 1    12 a         1 2012-04-02 A    
#> 2    10 g         1 2012-01-15 A    
#> 
#> $B
#> # A tibble: 2 × 5
#>      id name   test date       class
#>   <int> <chr> <int> <date>     <chr>
#> 1    15 d         1 2017-12-14 B    
#> 2    16 f         1 2018-08-26 B    
#> 
#> $C
#> # A tibble: 2 × 5
#>      id name   test date       class
#>   <int> <chr> <int> <date>     <chr>
#> 1    19 b         1 2008-07-15 C    
#> 2    21 e         2 2015-05-16 C    
#> 
#> $D
#> # A tibble: 1 × 5
#>      id name   test date       class
#>   <int> <chr> <int> <date>     <chr>
#> 1     8 c         1 2016-11-23 D

write_xlsx(dat_split, path = "~/Desktop/dat.xlsx",
  format_headers = FALSE)

^{Created on 2022-04-23 by the reprex package (v2.0.1)}