split dataframe with recurring columnames-CodePudding

I have imported an excel sheet in R which is a compilation of several dataframes with identical columnnames. To illustrate it looks like this:

df <- tibble(    empty   = c(runif(3), NA, NA, NA, NA), 
                 A = c(runif(3), NA, NA, NA, NA), 
                 B = c(runif(3), NA, NA, NA, NA), 
                 C = c(runif(3), NA, NA, NA, NA), 
                 empty = c(runif(6), NA), 
                 A = c(runif(6), NA), 
                 B = c(runif(6), NA), 
                 C = c(runif(6), NA), 
                 empty = c(runif(5), NA, NA), 
                 A = c(runif(5), NA, NA), 
                 B = c(runif(5), NA, NA), 
                 C = c(runif(5), NA, NA), 
                 .name_repair = "minimal")

How can I transform this dataframe in this result:

> df1
# A tibble: 3 x 4
  empty      A     B     C
  <dbl>  <dbl> <dbl> <dbl>
1 0.200 0.0665 0.723 0.487
2 0.576 0.990  0.969 0.289
3 0.727 0.192  0.780 0.243

> df2
# A tibble: 6 x 4
  empty     A     B      C
  <dbl> <dbl> <dbl>  <dbl>
1 0.556 0.698 0.796 0.357 
2 0.308 0.542 0.867 0.103 
3 0.643 0.792 0.385 0.882 
4 0.675 0.504 0.489 0.0515
5 0.426 0.775 0.410 0.748 
6 0.343 0.752 0.185 0.542 

> df3
# A tibble: 5 x 4
   empty      A      B      C
   <dbl>  <dbl>  <dbl>  <dbl>
1 0.229  0.0508 0.0880 0.486 
2 0.146  0.295  0.562  0.731 
3 0.292  0.804  0.133  0.0480
4 0.0404 0.399  0.366  0.152 
5 0.226  0.702  0.476  0.416

The column with name empty has actually no name although I don't know how to assign this in this example.

The reason I ask this question is because I have several other sheets with a different number of similar columns per sheet (D, E etc).

I found a nice post here: split data frame with recurring column names although this post looks the same, it is quit different.

Thanks!

CodePudding user response：

This puts the results in a list which should be more convenient than sequentially named data frames.

first_col = "empty"
name_groups = cumsum(names(df) == "empty")

result = split.default(df, name_groups)
# omit rows that have only missing values
result = lapply(result, \(x) x[rowSums(is.na(x)) < ncol(x), ])
result
# $`1`
# # A tibble: 3 × 4
#    empty     A     B     C
#    <dbl> <dbl> <dbl> <dbl>
# 1 0.590  0.602 0.527 0.900
# 2 0.0450 0.713 0.936 0.911
# 3 0.567  0.781 0.349 0.686
# 
# $`2`
# # A tibble: 6 × 4
#    empty      A     B      C
#    <dbl>  <dbl> <dbl>  <dbl>
# 1 0.480  0.543  0.744 0.0684
# 2 0.0423 0.799  0.927 0.537 
# 3 0.962  0.0745 0.851 0.0639
# 4 0.615  0.546  0.390 0.0985
# 5 0.258  0.857  0.139 0.172 
# 6 0.944  0.375  0.356 0.715 
# 
# $`3`
# # A tibble: 5 × 4
#   empty     A      B      C
#   <dbl> <dbl>  <dbl>  <dbl>
# 1 0.790 0.572 0.600  0.701 
# 2 0.732 0.610 0.0395 0.283 
# 3 0.130 0.168 0.120  0.0682
# 4 0.112 0.682 0.586  0.640 
# 5 0.211 0.267 0.0189 0.606

If you really want df1, df2, ... in your global environment, add these lines:

names(result) = paste0("df", names(result))
list2env(result, envir = .GlobalEnv)

CodePudding user response：

df1 <- df[,1:4][1:3,]

df2 <- df[,5:8][1:6,]

df3 <- df[,9:12][1:5,]

CodePudding user response：

When the number of repetition is constant (here 4) then we could do something likes this:

base R:

df1 <- df[,1:4]
df2 <- df[,5:8]
df3 <- df[,9:12]


> df1
# A tibble: 7 x 4
   empty      A       B      C
   <dbl>  <dbl>   <dbl>  <dbl>
1  0.120  0.448  0.0453  0.315
2  0.337  0.296  0.757   0.448
3  0.533  0.574  0.681   0.324
4 NA     NA     NA      NA    
5 NA     NA     NA      NA    
6 NA     NA     NA      NA    
7 NA     NA     NA      NA    
> df2
# A tibble: 7 x 4
     empty        A      B      C
     <dbl>    <dbl>  <dbl>  <dbl>
1  0.420    0.306    0.472  0.107
2  0.639    0.666    0.349  0.768
3  0.469    0.311    0.100  0.744
4  0.00122  0.586    0.437  0.796
5  0.122    0.00989  0.289  0.408
6  0.570    0.253    0.877  0.197
7 NA       NA       NA     NA    
> df3
# A tibble: 7 x 4
    empty       A      B      C
    <dbl>   <dbl>  <dbl>  <dbl>
1  0.812   0.0464  0.473  0.638
2  0.340   0.482   0.269  0.164
3  0.0323  0.952   0.842  0.282
4  0.511   0.263   0.934  0.183
5  0.0711  0.483   0.763  0.639
6 NA      NA      NA     NA    
7 NA      NA      NA     NA

CodePudding user response：

Another possible solution, based on tidyverse:

library(tidyverse)

stack(df) %>% 
  filter(!is.na(values)) %>% 
  group_by(aux = cumsum(ind == "empty" & lag(ind, default = "") != "empty")) %>% 
  group_split() %>% 
  map(~ pivot_wider(.x %>% select(-aux), names_from = "ind", 
    values_from = "values", values_fn = list) %>% unnest(everything()))

#> [[1]]
#> # A tibble: 3 × 4
#>   empty      A     B     C
#>   <dbl>  <dbl> <dbl> <dbl>
#> 1 0.865 0.0634 0.127 0.136
#> 2 0.343 0.431  0.943 0.985
#> 3 0.482 0.635  0.150 0.263
#> 
#> [[2]]
#> # A tibble: 6 × 4
#>    empty      A     B      C
#>    <dbl>  <dbl> <dbl>  <dbl>
#> 1 0.0656 0.514  0.834 0.662 
#> 2 0.977  0.657  0.878 0.427 
#> 3 0.670  0.641  0.910 0.175 
#> 4 0.402  0.0494 0.433 0.0241
#> 5 0.211  0.388  0.971 0.273 
#> 6 0.681  0.355  0.749 0.0536
#> 
#> [[3]]
#> # A tibble: 5 × 4
#>    empty     A       B      C
#>    <dbl> <dbl>   <dbl>  <dbl>
#> 1 0.440  0.856 0.00734 0.0474
#> 2 0.0347 0.328 0.471   0.845 
#> 3 0.106  0.393 0.303   0.811 
#> 4 0.385  0.184 0.540   0.180 
#> 5 0.564  0.579 0.414   0.0110