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How to create a subset of a data frame in R without using column names?
The subsetting of a data frame can be done by using column names as well as column number. Also, we can subset by subsequent as well as non-subsequent column numbers. For example, if we have a data frame df that contains column x, y, z then we can make a subset of x and z by using df[,c(1,3)].
Example
Consider the below data frame:
> set.seed(191) > x1<-rnorm(20,1) > x2<-rnorm(20,5) > x3<-rnorm(20,2) > x4<-rnorm(20,4) > df1<-data.frame(x1,x2,x3,x4) > df1
Output
x1 x2 x3 x4 1 0.8464828 5.517463 1.3510192 3.879824 2 1.7157414 4.902044 1.7288418 4.915879 3 2.0612258 5.343704 3.4476224 3.198662 4 0.9817547 5.310376 0.7360361 4.191265 5 1.3137032 4.690344 1.8930611 3.195032 6 3.2946391 5.356714 0.7507614 2.762971 7 1.1292996 3.956172 1.3893677 3.472453 8 0.5938585 3.524826 2.4999638 3.442268 9 2.5721891 3.986746 2.1758887 3.065743 10 0.3154647 2.602883 2.2014771 4.111108 11 0.6326024 6.630669 2.4982478 2.310966 12 1.9772099 4.863338 3.0983665 3.976421 13 2.4442273 3.390198 3.7922736 3.743440 14 1.1505010 4.512891 2.7232374 3.528800 15 2.2532166 4.969238 2.1687148 3.691669 16 0.5104193 4.440487 1.9766220 4.120722 17 0.9377628 2.559686 3.1919780 2.755742 18 -0.3147257 4.919251 3.0462375 2.625914 19 0.3678290 4.088426 3.3926200 3.797904 20 2.0272953 4.151505 3.1796609 2.771270
Subsetting columns of data frame df1 by using column number:
Example
> df1[,1]
Output
[1] 0.8464828 1.7157414 2.0612258 0.9817547 1.3137032 3.2946391 [7] 1.1292996 0.5938585 2.5721891 0.3154647 0.6326024 1.9772099 [13] 2.4442273 1.1505010 2.2532166 0.5104193 0.9377628 -0.3147257 [19] 0.3678290 2.0272953
Example
> df1[,1:2]
Output
x1 x2 1 0.8464828 5.517463 2 1.7157414 4.902044 3 2.0612258 5.343704 4 0.9817547 5.310376 5 1.3137032 4.690344 6 3.2946391 5.356714 7 1.1292996 3.956172 8 0.5938585 3.524826 9 2.5721891 3.986746 10 0.3154647 2.602883 11 0.6326024 6.630669 12 1.9772099 4.863338 13 2.4442273 3.390198 14 1.1505010 4.512891 15 2.2532166 4.969238 16 0.5104193 4.440487 17 0.9377628 2.559686 18 -0.3147257 4.919251 19 0.3678290 4.088426 20 2.0272953 4.151505
Example
> df1[,1:3]
Output
x1 x2 x3 1 0.8464828 5.517463 1.3510192 2 1.7157414 4.902044 1.7288418 3 2.0612258 5.343704 3.4476224 4 0.9817547 5.310376 0.7360361 5 1.3137032 4.690344 1.8930611 6 3.2946391 5.356714 0.7507614 7 1.1292996 3.956172 1.3893677 8 0.5938585 3.524826 2.4999638 9 2.5721891 3.986746 2.1758887 10 0.3154647 2.602883 2.2014771 11 0.6326024 6.630669 2.4982478 12 1.9772099 4.863338 3.0983665 13 2.4442273 3.390198 3.7922736 14 1.1505010 4.512891 2.7232374 15 2.2532166 4.969238 2.1687148 16 0.5104193 4.440487 1.9766220 17 0.9377628 2.559686 3.1919780 18 -0.3147257 4.919251 3.0462375 19 0.3678290 4.088426 3.3926200 20 2.0272953 4.151505 3.1796609
Example
> df1[,2:4]
Output
x2 x3 x4 1 5.517463 1.3510192 3.879824 2 4.902044 1.7288418 4.915879 3 5.343704 3.4476224 3.198662 4 5.310376 0.7360361 4.191265 5 4.690344 1.8930611 3.195032 6 5.356714 0.7507614 2.762971 7 3.956172 1.3893677 3.472453 8 3.524826 2.4999638 3.442268 9 3.986746 2.1758887 3.065743 10 2.602883 2.2014771 4.111108 11 6.630669 2.4982478 2.310966 12 4.863338 3.0983665 3.976421 13 3.390198 3.7922736 3.743440 14 4.512891 2.7232374 3.528800 15 4.969238 2.1687148 3.691669 16 4.440487 1.9766220 4.120722 17 2.559686 3.1919780 2.755742 18 4.919251 3.0462375 2.625914 19 4.088426 3.3926200 3.797904 20 4.151505 3.1796609 2.771270
Example
> df1[,c(1,3)]
Output
x1 x3 1 0.8464828 1.3510192 2 1.7157414 1.7288418 3 2.0612258 3.4476224 4 0.9817547 0.7360361 5 1.3137032 1.8930611 6 3.2946391 0.7507614 7 1.1292996 1.3893677 8 0.5938585 2.4999638 9 2.5721891 2.1758887 10 0.3154647 2.2014771 11 0.6326024 2.4982478 12 1.9772099 3.0983665 13 2.4442273 3.7922736 14 1.1505010 2.7232374 15 2.2532166 2.1687148 16 0.5104193 1.9766220 17 0.9377628 3.1919780 18 -0.3147257 3.0462375 19 0.3678290 3.3926200 20 2.0272953 3.1796609
Example
> df1[,c(2,4,1)]
Output
x2 x4 x1 1 5.517463 3.879824 0.8464828 2 4.902044 4.915879 1.7157414 3 5.343704 3.198662 2.0612258 4 5.310376 4.191265 0.9817547 5 4.690344 3.195032 1.3137032 6 5.356714 2.762971 3.2946391 7 3.956172 3.472453 1.1292996 8 3.524826 3.442268 0.5938585 9 3.986746 3.065743 2.5721891 10 2.602883 4.111108 0.3154647 11 6.630669 2.310966 0.6326024 12 4.863338 3.976421 1.9772099 13 3.390198 3.743440 2.4442273 14 4.512891 3.528800 1.1505010 15 4.969238 3.691669 2.2532166 16 4.440487 4.120722 0.5104193 17 2.559686 2.755742 0.9377628 18 4.919251 2.625914 -0.3147257 19 4.088426 3.797904 0.3678290 20 4.151505 2.771270 2.0272953
Updated on: 21-Nov-2020
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