How to change a data frame with comma separated values in columns to multiple columns in R?

Mostly, we need to import the data from an outside source in R environment for analysis and these data can be recorded as comma separated values that represent rows. If we want to create the columns for the comma separated values then cSplit function of splitstackshape package can be used. In the below example, we have created a data frame with comma separated values then splitting those values as single value in each column.
Consider the below data frame −
 Live Demo
df=data.frame(x=apply(matrix(rpois(200,10),20,10),1,paste,collapse=","))
df
x
1 8,12,7,12,10,8,11,6,8,7
2 9,13,14,12,9,10,11,15,7,8
3 6,15,12,11,15,6,9,8,12,7
4 13,9,18,10,9,7,7,7,4,9
5 8,5,14,7,8,9,11,7,7,6
6 10,9,7,11,12,14,11,9,8,7
7 12,9,11,15,13,8,10,11,17,6
8 10,9,9,10,8,12,5,7,10,13
9 10,14,8,8,11,6,4,11,12,8
10 13,10,15,13,8,7,11,8,11,12
11 9,13,17,6,6,9,12,16,13,11
12 14,12,11,7,15,8,14,14,9,8
13 12,23,10,7,8,9,7,14,11,7
14 8,10,12,9,10,10,7,9,13,12
15 7,14,9,10,10,11,10,9,11,6
16 7,11,8,8,6,9,5,11,4,7
17 13,9,14,13,9,11,7,9,4,12
18 6,7,7,10,4,9,9,16,4,9
19 13,11,6,8,9,11,7,14,11,10
20 10,7,13,10,11,7,10,11,13,10
Loading splitstackshape package and splitting the comma separated values −
library(splitstackshape) cSplit(df,"x",",")
x_01 x_02 x_03 x_04 x_05 x_06 x_07 x_08 x_09 x_10
1: 9  15   16   11    9    8    7    12    7    11
2: 17 13    7   20    8   12   10    17    5    13
3: 8  13   12    7    9   10    7     7    7     5
4: 11 15   13   15   10    6    8    11    9     9
5: 6  11    8   10   14   11    8    15   10     9
6: 8  5    10    9   10   12    7     9    6    10
7: 7  11   10   11   11   12   17    13   11    11
8: 9  7     9    3   11    7   10    10   12     9
9: 9  10   15    9   16   13   14    12    4    10
10: 7 18   10    8   11   10    7     8   17    13
11: 8 11    7   11   15   10   12     7   13     9
12: 10 5   15    8    9    7    5     7   15    11
13: 11 11  13    4   13   11    6     7    3    11
14: 12 7   14   13   12    9    7     9   11    10
15: 7 11   15   14    8    9    3    10   14     9
16: 13 13   5    9    8   12    9    13    9     5
17: 10 12  16   10    7   10    7    17   15    11
18: 14 10  13   11    8   12   12     6    9     6
19: 4   3   5    8   11   11   13     9   10     4
20: 10 10  10   12   10    4   18    12    8     6
Let’s have a look at another example −
 Live Demo
Rate=data.frame(x=apply(matrix(rexp(120,3.5),20,3),1,paste,collapse=","))
Rate
x
1 0.0159381624045117,0.0144605446992187,0.12841108095433
2 0.54651167305141,0.167623156548611,0.242777586503458
3 0.178547318625663,0.291459725891611,0.00192235474549493
4 0.0913145632616111,0.964466593047224,0.101487736882908
5 0.0518861563344087,0.0108454372741601,0.0576572126598852
6 0.0378260237297841,0.736165504866593,0.314890729401991
7 0.0135630246784006,0.0276414981900331,0.0129506557381579
8 0.066366289742291,0.234642320167511,0.845350959423283
9 0.192795767315796,0.203176257587675,0.339430415874007
10 0.143820086227996,0.823251408256266,0.839730198087015
11 0.154881650581956,0.035932720736067,0.384848597314446
12 0.141309956620846,0.0586299393326044,0.191098268676017
13 0.224339416612165,0.166978088340589,0.244316381757197
14 0.00643399850066219,0.225754089641489,0.0545826112585408
15 0.159973739912467,0.0245804649550754,0.0734476327725455
16 0.047296607414733,0.68713491991819,0.230530212222171
17 0.0110702448125396,0.823835745405824,0.159328434749373
18 0.0129699476861528,0.215176185314914,0.132213854183943
19 0.288309599660042,0.408196979669972,0.234206797809179
20 0.201607588932688,0.0768561341932842,1.41488279179445
Splitting the comma separated values −
cSplit(Rate,"x",",")
x_1 x_2 x_3
1: 0.715195454 0.13364483 0.23434234
2: 0.287033157 0.15786313 1.25176852
3: 0.058617612 0.20123608 0.04697261
4: 0.017659639 0.59218378 0.12339740
5: 0.140093290 0.01579469 0.60002405
6: 0.056465154 0.24717511 0.81812581
7: 0.388322346 0.00329370 0.23367574
8: 0.140153797 0.07190267 0.12958013
9: 0.651111966 0.17373439 0.17571569
10: 0.452951349 0.33638535 0.34905380
11: 0.008617901 0.34899487 0.33835583
12: 0.107828330 0.12259176 0.12120932
13: 0.396324511 0.11236437 0.03790748
14: 0.063644511 0.38064627 0.20201243
15: 0.024197156 0.23522396 0.29972512
16: 0.013202143 1.31472758 0.40429690
17: 0.017637878 0.11423790 0.36857345
18: 0.113290019 0.18345270 0.34162356
19: 0.063014545 0.36422309 0.05693683
20: 0.075592492 0.18737919 0.04674416