How to round each value in columns if some columns are categorical in R data frame?

To round each value in columns if some columns are categorical in R data frame, we can follow the below steps −

• First of all, create a data frame.

• Then, use numcolwise function from plyr package to round each value in columns if some columns are categorical.

Example

Create the data frame

Let’s create a data frame as shown below −

Level<-sample(c("low","medium","high"),25,replace=TRUE)
Group<-sample(c("first","second"),25,replace=TRUE)
DV1<-rnorm(25)
DV2<-rnorm(25)
df<-data.frame(Level,Group,DV1,DV2)
df

Output

On executing, the above script generates the below output(this output will vary on your system due to randomization) −

   Level   Group    DV1         DV2
1  low    first   0.3113086 -0.560410192
2  high   second  1.2462502 -0.839353354
3  high   second -0.1069003  0.876603377
4  low    second  0.7132453  0.444083125
5  high   second -0.9718300  0.719396980
6  medium first  -1.1686395  0.692503766
7  high   first   0.2996904  1.549099366
8  medium first   0.1843041 -0.623725508
9  medium first  -0.2806531  0.265089090
10 medium second -0.1707617  0.783538873
11 medium second -0.3580571  0.367009599
12 low    second -0.7904927 -0.363554432
13 high   second  1.2020259 -0.990149369
14 medium first   0.1223149 -0.096059087
15 high   second -0.2941084 -0.584607516
16 medium first   0.8305360 -0.521546585
17 high   first   1.3346562 -0.234318326
18 medium first  -0.6467870 -0.002709633
19 medium second  0.2400778 -0.969352846
20 low    first   0.4702012  0.168694928
21 medium first  -0.5836102  0.145309996
22 low    second  1.0728229  0.641423060
23 low    first  -0.2079424 -0.603290667
24 medium first   0.6148493 -0.919263451
25 high   second -0.1642258  0.065443836

Round each value in columns if some columns are categorica

Using numcolwise function from plyr package to round each value in numerical columns if some columns are categorical in the data frame df −

Level<-sample(c("low","medium","high"),25,replace=TRUE)
Group<-sample(c("first","second"),25,replace=TRUE)
DV1<-rnorm(25)
DV2<-rnorm(25)
df<-data.frame(Level,Group,DV1,DV2)
library(plyr)
numcolwise(round)(df)

Output

   DV1 DV2
1   0 -1
2   1 -1
3   0  1
4   1  0
5  -1  1
6  -1  1
7   0  2
8   0 -1
9   0  0
10  0  1
11  0  0
12 -1  0
13  1 -1
14  0  0
15  0 -1
16  1 -1
17  1  0
18 -1  0
19  0 -1
20  0  0
21 -1  0
22  1  1
23  0 -1
24  1 -1
25  0  0