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How to subset nth row from an R data frame?
We can find subsets using many ways in R and the easiest way is to use single-square brackets. If we want to subset a row or a number of consecutive or non-consecutive rows then it can be directly done with the data frame name and the single-square brackets. For example, if we have a data frame called df and we want to subset 1st row of df then we can use df[1,] and that’s it.
Example
Consider the below data frame:
> set.seed(214) > x<-rnorm(20) > y<-rnorm(20,1,0.5) > z<-rnorm(20,2,0.57) > a<-rnorm(20,1,0.27) > b<-rpois(20,2) > c<-rpois(20,8) > q<-rpois(20,5) > w<-rpois(20,1) > df1<-data.frame(x,y,z,a,b,c,q,w) > df1
Output
x y z a b c q w 1 -0.46774980 1.1546101 2.3342540 0.9143609 2 8 6 0 2 0.04088223 0.7590773 2.2095770 0.9712316 1 5 6 2 3 1.00335193 1.7272210 1.7318417 1.1871876 2 8 7 0 4 2.02522505 0.8515016 1.9366870 0.4658958 4 4 3 2 5 0.30640096 1.2055142 2.5719530 0.8469379 4 9 5 1 6 0.42577748 1.6967249 1.5668833 0.9602888 5 7 5 1 7 0.74889267 2.0073967 2.4715450 0.7116510 3 5 5 1 8 0.44645148 1.0209466 1.1198797 1.3250236 2 10 6 1 9 -2.20514180 1.6927716 2.1447475 1.1950635 0 7 5 2 10 1.98181366 1.3930763 2.3038074 1.1096453 2 6 5 1 11 -2.62555247 1.2849028 1.7522339 1.1864803 2 5 2 1 12 -0.72301789 1.0450742 0.2930952 1.1930435 2 8 4 2 13 -0.88306915 0.8239228 2.5604929 0.9686630 1 8 2 0 14 -0.52517037 1.3413851 2.0189895 0.8643248 2 9 4 0 15 -0.94756990 0.2507953 1.1719018 1.0294649 5 10 3 1 16 -0.51916173 1.1889573 2.1277015 0.6870978 3 8 5 0 17 -1.12071138 1.4807661 1.9248328 1.0950342 1 12 4 2 18 0.42359496 1.5472942 1.7000941 0.8440301 1 12 2 2 19 0.46975875 1.4835207 1.2282101 1.0651645 2 6 7 0 20 -0.21269994 0.8056228 1.7694949 0.9686047 5 11 6 0
Subsetting different rows of df1:
Example
> df1[1,]
Output
x y z a b c q w 1 -0.4677498 1.15461 2.334254 0.9143609 2 8 6 0
Example
> df1[2,]
Output
x y z a b c q w 2 0.04088223 0.7590773 2.209577 0.9712316 1 5 6 2
Example
> df1[3,]
Output
x y z a b c q w 3 1.003352 1.727221 1.731842 1.187188 2 8 7 0
Example
> df1[5,]
Output
x y z a b c q w 5 0.306401 1.205514 2.571953 0.8469379 4 9 5 1
Example
> df1[8,]
Output
x y z a b c q w 8 0.4464515 1.020947 1.11988 1.325024 2 10 6 1
Example
> df1[9,]
Output
x y z a b c q w 9 -2.205142 1.692772 2.144747 1.195064 0 7 5 2
Example
> df1[12,]
Output
x y z a b c q w 12 -0.7230179 1.045074 0.2930952 1.193043 2 8 4 2
Example
> df1[15,]
Output
x y z a b c q w 15 -0.9475699 0.2507953 1.171902 1.029465 5 10 3 1
Example
> df1[18,]
Output
x y z a b c q w 18 0.423595 1.547294 1.700094 0.8440301 1 12 2 2
Example
> df1[20,]
Output
x y z a b c q w 20 -0.2126999 0.8056228 1.769495 0.9686047 5 11 6 0
Let’s have a look at another example:
Example
> v1<-rexp(20,1.24) > v2<-rexp(20,3.7) > v3<-runif(20,2,8) > df2<-data.frame(v1,v2,v3) > df2
Output
v1 v2 v3 1 0.20602134 0.06916392 6.107286 2 0.29568560 0.36353986 3.529261 3 0.12250478 0.18168857 4.524547 4 2.37228009 0.20580564 6.795179 5 0.51194665 0.03005732 3.208580 6 0.25267457 0.12722097 2.184198 7 0.03742423 0.01711751 4.135536 8 0.45572624 0.29921997 6.046839 9 0.63617201 0.55386034 7.812157 10 0.81699828 0.56160708 4.071993 11 0.26570318 0.06759301 3.625271 12 0.63101790 0.10742853 2.573730 13 0.60664724 0.28611242 4.053965 14 0.79000859 0.09818221 6.257031 15 0.44555943 0.01828257 3.953676 16 1.87292479 0.20373389 3.407394 17 0.17258681 0.20278572 5.874761 18 0.09658603 0.09844967 5.382432 19 0.04970458 0.46433382 7.007515 20 0.31233081 0.06999427 4.855714
Subsetting different rows of df2:
Example
> df2[3,]
Output
v1 v2 v3 3 0.1225048 0.1816886 4.524547
Example
> df2[5,]
Output
v1 v2 v3 5 0.5119466 0.03005732 3.20858
Example
> df2[7,]
Output
v1 v2 v3 7 0.03742423 0.01711751 4.135536
Example
> df2[9,]
Output
v1 v2 v3 9 0.636172 0.5538603 7.812157
Example
> df2[10,]
Output
v1 v2 v3 10 0.8169983 0.5616071 4.071993
Example
> df2[12,]
Output
v1 v2 v3 12 0.6310179 0.1074285 2.57373
Example
> df2[15,]
Output
v1 v2 v3 15 0.4455594 0.01828257 3.953676
Example
> df2[17,]
Output
v1 v2 v3 17 0.1725868 0.2027857 5.874761
Example
> df2[20,]
Output
v1 v2 v3 20 0.3123308 0.06999427 4.855714
Updated on: 21-Nov-2020
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