Loop through list of data frames and take every n columns to create new data frame-CodePudding

I currently have a list of data frames and want to loop through each list to grab every ten columns and put them together in a new data frame. To clarify, I want to go through the first data frame, grab 10 columns, make those columns 1:10 in the new data frame, then go to the next, grab 10 columns, and make them 11:20 in the new data frame. I want to keep doing this process until I go through each list of data frames and complete the list.

I've tried the below:

a1<-rnorm(100)
a2<-rnorm(100)
b1<-rnorm(100)
b2<-rnorm(100)
c1<-rnorm(100)
c2<-rnorm(100)

dataframe_list <- vector("list", 5)
for (i in seq_along(dataframe_list)) {
  dataframe_list[[i]] <- rbind(a1,a2,b1,b2,c1,c2)
}

new_df_list <- list()
j = 1:10
while (max(j) < 101) {
  for (i in seq_along(dataframe_list)) {
    new_df[,j] <- dataframe_list[[i]][,j]
    new_df_list[[i]]<- new_df
  }
  j = j   10
}

But I think this is just replacing the ten columns with the next ten in the list.

Thanks in advance.

CodePudding user response：

We could use map function from purrr package for this. Afterwards we use bind_cols from dplyr package:

library(purrr)
library(dplyr)

my_df <- map(dataframe_list,~(.x %>% as_tibble() %>% 
                       select(1:10))) %>% 
  bind_cols() %>% 
  rename_with(., ~paste0("col", 1:100))

# A tibble: 6 × 50
    col1   col2   col3   col4   col5    col6   col7    col8   col9  col10  col11  col12  col13  col14  col15   col16
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>   <dbl>  <dbl>   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>   <dbl>
1 -1.21   1.27   0.702  1.32  -1.14  -0.194  -0.706  0.916   0.450  0.767 -1.21   1.27   0.702  1.32  -1.14  -0.194 
2 -0.473  1.67   1.26  -0.177 -0.757 -0.308  -0.678 -0.844   0.705  0.301 -0.473  1.67   1.26  -0.177 -0.757 -0.308 
3  0.443  2.02  -2.62   1.24  -1.13  -0.312  -0.190  1.05   -0.552  1.53   0.443  2.02  -2.62   1.24  -1.13  -0.312 
4  1.28  -0.525 -1.09  -1.64   0.326 -0.570   0.986 -0.620   0.616 -0.168  1.28  -0.525 -1.09  -1.64   0.326 -0.570 
5  0.609 -0.102 -2.40  -1.32   0.818 -0.0271 -0.804  0.0921  0.759  0.211  0.609 -0.102 -2.40  -1.32   0.818 -0.0271
6  0.274 -0.830  0.381  0.343  0.330  0.278   0.726  1.67   -0.318  1.26   0.274 -0.830  0.381  0.343  0.330  0.278 
# … with 34 more variables: col17 <dbl>, col18 <dbl>, col19 <dbl>, col20 <dbl>, col21 <dbl>, col22 <dbl>, col23 <dbl>,
#   col24 <dbl>, col25 <dbl>, col26 <dbl>, col27 <dbl>, col28 <dbl>, col29 <dbl>, col30 <dbl>, col31 <dbl>,
#   col32 <dbl>, col33 <dbl>, col34 <dbl>, col35 <dbl>, col36 <dbl>, col37 <dbl>, col38 <dbl>, col39 <dbl>,
#   col40 <dbl>, col41 <dbl>, col42 <dbl>, col43 <dbl>, col44 <dbl>, col45 <dbl>, col46 <dbl>, col47 <dbl>,
#   col48 <dbl>, col49 <dbl>, col50 <dbl>
# ℹ Use `colnames()` to see all variable names

CodePudding user response：

An option could be by first using lapply to select the first ten columns of each dataframe in your list saved as a list. After that, you can use bind_cols from dplyr to combine the dataframes into one dataframe. Here is some reproducible code:

library(dplyr)
set.seed(7) # reproducibility
df1 <- data.frame(replicate(20,sample(0:1,5,rep=TRUE)))
df2 <- data.frame(replicate(20,sample(0:1,5,rep=TRUE)))
df3 <- data.frame(replicate(20,sample(0:1,5,rep=TRUE)))
l <- list(df1, df2, df3)

df_filtered <- lapply(l, \(x) x[,1:10])
df_combined <- bind_cols(df_filtered)

df_combined  
#>   X1...1 X2...2 X3...3 X4...4 X5...5 X6...6 X7...7 X8...8 X9...9 X10...10
#> 1      1      1      1      0      0      0      0      0      0        1
#> 2      0      0      0      1      1      1      1      1      1        1
#> 3      0      1      1      0      1      1      0      1      1        0
#> 4      1      1      1      1      1      1      1      1      0        0
#> 5      0      1      1      1      0      0      1      1      0        0
#>   X1...11 X2...12 X3...13 X4...14 X5...15 X6...16 X7...17 X8...18 X9...19
#> 1       0       1       0       1       0       1       0       1       1
#> 2       0       1       1       0       1       0       1       1       1
#> 3       1       1       0       0       1       1       1       0       0
#> 4       1       0       1       0       1       0       1       1       1
#> 5       1       0       0       1       0       0       0       1       0
#>   X10...20 X1...21 X2...22 X3...23 X4...24 X5...25 X6...26 X7...27 X8...28
#> 1        1       1       1       1       0       0       0       0       1
#> 2        0       0       0       0       1       0       1       1       0
#> 3        0       1       0       1       0       0       1       0       0
#> 4        1       0       1       1       1       1       1       1       1
#> 5        0       0       1       0       1       0       1       0       1
#>   X9...29 X10...30
#> 1       0        1
#> 2       1        0
#> 3       0        0
#> 4       0        0
#> 5       1        1

^{Created on 2023-02-04 with reprex v2.0.2}

CodePudding user response：

Using base R

setNames(do.call(rbind.data.frame, lapply(dataframe_list, head, 10)), 
   paste0("col", 1:100))

-output

    col1       col2       col3        col4       col5       col6       col7        col8       col9      col10       col11
a1  0.7793030  0.8841912  0.7256730 -0.08524323 -1.5596393 -0.7890424  1.5697219 -0.33654621 -0.3918948 -1.2193419 -0.03365252
a2 -1.5690657 -0.6538868 -0.7165864  1.81906352  0.2562943 -0.7977065  0.3269243  1.39661425 -1.1106302 -0.2516798 -0.08253067
b1 -0.6683287  1.2149600 -0.7072091  1.52961386 -1.4461004 -1.2166032 -0.6197978  0.14605286 -0.7379117  0.4066479  1.43679626
b2 -0.8429860  1.8727278 -0.3365289  1.06346899 -0.1593293 -1.0063262  0.7495746  0.10276405  1.0159325  0.8899335 -0.49866402
c1 -1.2564704  0.8536332  1.0737972 -1.38818780 -0.7225646 -0.8556486  2.7553238 -0.71280895  0.8959037 -1.4323856 -1.20640743
c2  0.6077386  0.5187255 -0.3555744 -0.07142831 -0.6076046 -0.2820873 -0.7573153 -0.04613757 -0.7928139  0.8464911  0.28639421
        col12      col13      col14       col15      col16       col17       col18     col19      col20      col21      col22
a1  0.5120785  0.6062648  0.7347130 -0.64246070 -1.7933904  0.11657383  1.96598080 1.4335286  0.2800078 -1.7016504  0.2535456
a2  0.4469310 -0.4348510  0.6337614  1.71715804 -0.2211258  0.61107952  0.05411653 0.4805574 -2.3078178 -0.2359667 -0.3897207
b1 -0.8733858 -1.2229974 -1.9789484 -0.06424681  2.2867944  0.08829771  0.69457271 0.6012572 -0.0406423  1.1822902 -0.6147050
b2 -0.8920748  0.5908152 -2.8652760  0.13183740 -0.9894885  0.28331050 -0.01529145 0.9484549  1.8160632 -0.5762984  0.1633331
c1  0.3514350 -0.6183382  0.9893045  1.19786700  0.3534182  1.41276696  0.22865727 0.4086079  0.8739275 -0.2749598  1.4794709
c2  0.1105736 -0.7947728 -0.1775345 -0.77035363 -1.1157140 -0.92719964 -0.74202720 1.6377775  0.6354976  0.6321249  1.6836422
        col23      col24      col25      col26      col27      col28      col29      col30      col31       col32      col33
a1  1.0240128  0.9835438  1.3677794  0.3082867 -1.0201232  0.3524333  0.6861180  1.8573144 -0.2299341 -0.72236322  0.8495473
a2 -0.5814792 -0.3148247  0.7141669  0.4010274 -0.5416061 -0.3104302  0.4687586 -0.3554200  0.9837601 -0.09010772 -0.5555971
b1 -1.2437221  0.7634912 -1.7936421 -1.6906611  0.3346778  0.4209662 -0.2519508 -2.7232369  0.9999341 -0.70299050 -0.0815552
b2 -0.4736396  0.6648603  0.5343397 -0.4054694 -0.3835265  0.8999484  1.1167929  1.4498313 -0.3473225  1.35935249  0.3236380
c1 -1.9960265  0.0776958 -1.6706888  1.1083987  0.1922520  0.3470949  2.3068525  1.6248107 -0.2760143  0.02223134 -2.0775715
c2 -0.0622619 -1.1820780  0.7475911 -1.3150508 -0.5920544 -1.2691909  0.7650461  0.3850958  0.5682972 -0.23255386 -0.3600243
        col34       col35      col36      col37      col38       col39       col40      col41      col42       col43      col44
a1 -1.0747343 -0.64127056  0.8406057 -0.4190465 -0.5190223 -0.62741844  0.09140228  0.1786809  1.0984214 -0.04853677  1.0179664
a2  0.6006356  0.74402923  0.3310838  1.8294160  1.5150618 -0.24280953  1.40826323 -0.9409830 -0.3820763 -0.03339920 -1.1755142
b1 -0.8873179  0.90903430 -0.9687017  0.7019327 -2.2772416  1.52807461 -0.54425360  0.3429737 -0.7505054  0.87557955 -1.1314788
b2  0.2031488 -0.93172680  0.5154006  0.2351499  1.3748313  1.07133405 -0.20361958  0.1362986  0.7740150 -0.85283613  0.9490901
c1  0.6894595 -0.02527927 -0.7486883  0.4070326  1.0156088  0.08941385  0.67335643 -0.3762824 -0.9780800  1.58938237 -0.8515895
c2  0.0325183  1.36884157 -0.4717803  0.5702121  0.8678536 -0.38389090  0.88268795  0.7532073  0.8429715 -0.70129253  0.4116780
        col45      col46     col47       col48       col49      col50      col51       col52      col53      col54      col55
a1 -0.4750872  0.9090170 0.6962839  0.09606974  1.00928085 -0.6617571 -1.7900352 -0.99124732  0.0717143  0.4148560 -1.1094484
a2 -1.0049608 -1.9616289 0.4113797 -0.49320198 -1.48715169 -0.7788449  1.2420158  1.34560478 -0.3127484 -1.5625836  0.9082922
b1 -0.2188074  0.2900230 1.5989995  0.19797974  2.00511740 -0.6318765 -0.6236234  0.68407922  0.7795980 -2.2538488  0.3939866
b2 -0.9787701 -0.3719629 0.2192201  0.90962619  0.02344414 -1.2228794  0.8293247  0.53628789 -0.8330837 -1.1463084  1.3088559
c1 -0.2965836  1.9758497 0.6588472 -1.94731886  0.10122057 -0.5536944 -0.1089527  0.08571981 -2.3571389  0.4387805 -0.4665783
c2 -1.1574806 -1.4127067 1.0171311 -1.71410232  0.16672603 -1.0914208  1.2950789  0.21185310 -0.3298243 -0.2845049  0.7687018
         col56      col57      col58      col59      col60      col61      col62         col63      col64      col65      col66
a1  0.01272115 -1.9844373  0.8520790  0.3279583 -0.2973611 -1.5521428  0.7643032  0.3921617392  0.9975850 -0.5645073  0.2214905
a2  0.96750744  1.1573404  0.6708822 -0.6946397  1.2315285  0.9887806  1.1335573  0.0001820987 -1.1563536  1.1548800  1.2597795
b1 -0.10738226 -0.6717304 -1.0758664 -0.5596440  0.8081687  2.3387244 -0.3766570  0.0812219759 -0.9799805 -3.2497680 -0.3252068
b2 -0.57600457  1.6004682 -1.4968619 -0.6195628 -0.6603415 -0.2201152  0.9212431 -0.6013577876 -0.9261861  1.5440999  0.4555838
c1  0.93322027 -0.6555662 -1.5485457 -0.3815470 -0.5487509 -0.1088095  1.1534634 -0.6924598008  0.8439085 -0.8973990 -0.1131937
c2 -0.96779003  0.5922429 -0.8897607 -0.4686718 -0.4415431 -0.8195054 -0.1596652 -0.7574782476 -0.3527167  0.8046849  0.3996969
         col67      col68      col69       col70       col71      col72      col73      col74      col75      col76      col77
a1 -0.68269511 0.03893115 -0.6436089  0.07773332 -0.28587683 -0.3028721 -0.1789556 -0.1632689 -0.1294269 -1.2202864  0.2151611
a2  0.59346701 0.69919256 -0.1317558  0.45761080  0.02450006 -1.7228764  0.4853908 -2.0343850 -0.9498310 -1.1288737  1.9986529
b1 -0.09814586 0.34259238  1.0246251 -0.16067195  0.67969015 -0.9472902 -1.4628413 -0.6212548  0.8266192  0.8142735  1.0774274
b2 -0.05629432 1.06592461  0.6577438 -0.90820974 -0.62916847  0.7231349  0.9737884 -1.1389942 -0.1312306 -0.5720297 -0.2506232
c1  0.18813132 1.16102101 -0.6344364 -0.55548641 -0.21728376 -0.7594900  0.4166999 -0.4967771  0.3892739 -0.5434174 -1.2368652
c2  1.25342296 1.53746759 -1.2278828 -0.30934653  0.34064661 -1.9134106  0.2497647 -0.9949310  0.4075976 -0.2902088 -1.4894533
         col78       col79       col80      col81      col82      col83       col84      col85      col86      col87      col88
a1  0.05863123  0.29188503  1.83927119 -0.1062726  1.8072097  0.3741005 -1.23424170 -0.9692847 -0.6721848  1.6429834  0.6449804
a2 -0.27791924  0.34388559  0.02434897  0.4015362  1.6221964 -1.9400274 -0.17843231  0.5275579  0.3619368  1.1351464  0.1219594
b1  0.28265044  1.36831797  1.93560633 -1.8268550  0.8097726 -0.7856248  0.64148703  1.1686414  0.5482865 -0.5876068  0.3335761
b2  0.76897643  0.01052551 -1.30880502  0.7691328 -0.6175673  0.5842387  0.22371124 -0.2220520  0.1437221 -0.7462069 -0.3082803
c1  0.69765562  2.81249004 -0.69575956  0.6537419 -1.2000512 -0.1885172 -0.16253778 -0.4880369  1.1737648  2.5936935  1.3076305
c2  1.49474609 -0.14955373  1.39893339 -0.5869510  1.2872034 -3.0537851  0.01186699 -0.1217406  0.2246990 -0.3786971 -1.2225998
        col89      col90      col91      col92      col93      col94      col95      col96       col97      col98      col99
a1  0.2131518 -0.1706756  1.0407849 -0.4877381  1.9409925 -0.4862350 0.40368228 -1.0189452  1.60333870 -0.2465299  0.6331086
a2 -0.8078525 -1.5610299 -0.5374766 -1.3210438  1.5579983  1.6835180 0.66376940  0.2311369 -0.01016803  1.2000827  0.3989912
b1  0.4425652  0.6929049  0.3789561 -0.6110659 -0.2744571  1.2633267 0.70118432  0.3623799 -0.37786629 -0.6579411 -1.2802693
b2 -0.6773242 -0.2979941 -0.6329421 -0.7203474  1.8497844  0.1000457 0.67462673 -2.1607909 -0.25726335  1.1928355 -1.5531827
c1 -1.2816743 -0.8374141  2.1771791  1.1570421 -1.6065999 -0.6932305 0.09213165 -0.3763484 -0.52642540  0.9988159  1.9761539
c2 -0.5157842 -0.1329479 -1.7129097 -0.6319982 -0.2939219  0.6110354 1.53061870 -1.1742069 -0.28214885 -1.7897660 -0.7202226
       col100
a1 -0.1994077
a2 -0.6941029
b1  1.5086469
b2 -0.4975319
c1  0.7100122
c2  0.3809441
...