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How to find the sum of variables by row in an R data frame?
To find the sum of variables by row we mean the sum of row values in the data frame. This can be easily done with the help of rowSums function. For example, if we have a data frame called df then the sum of variables by row can be found by using the command −
rowSums(df)
Example1
Consider the below data frame −
> x1<-rpois(20,2) > x2<-rpois(20,2) > x3<-rpois(20,2) > df1<-data.frame(x1,x2,x3) > df1
Output
x1 x2 x3 1 0 2 3 2 1 0 1 3 1 0 2 4 3 3 2 5 4 2 2 6 3 1 5 7 2 2 1 8 4 2 2 9 2 4 0 10 3 1 5 11 3 1 2 12 0 3 1 13 2 3 0 14 1 1 0 15 1 1 1 16 3 1 0 17 1 2 1 18 1 0 2 19 4 2 1 20 0 2 3
Finding row sums for data in df1 −
> df1$x_total<-rowSums(df1) > df1
Output
x1 x2 x3 x_total 1 0 2 3 5 2 1 0 1 2 3 1 0 2 3 4 3 3 2 8 5 4 2 2 8 6 3 1 5 9 7 2 2 1 5 8 4 2 2 8 9 2 4 0 6 10 3 1 5 9 11 3 1 2 6 12 0 3 1 4 13 2 3 0 5 14 1 1 0 2 15 1 1 1 3 16 3 1 0 4 17 1 2 1 4 18 1 0 2 3 19 4 2 1 7 20 0 2 3 5
Example2
> y1<-rnorm(20) > y2<-rnorm(20) > y3<-rnorm(20) > df2<-data.frame(y1,y2,y3) > df2
Output
y1 y2 y3 1 0.59017486 -0.1196278 0.822573265 2 -1.12831016 0.7454030 0.173110462 3 0.65838766 -0.4798106 0.700891619 4 -3.09790550 -0.4401321 0.343264756 5 -0.27170928 -0.8106993 -0.658724220 6 1.51823786 0.1162130 -0.145487501 7 1.66852069 -0.4254530 -0.372181307 8 0.39011721 1.7260926 -0.253079767 9 0.12034466 0.4142485 -0.547526473 10 0.73264687 0.3537576 -0.248502362 11 -1.19683499 1.0923578 0.139092087 12 -0.02475713 -0.7436116 1.154820901 13 -1.68760189 -1.1306767 -0.002655384 14 -0.56164347 0.6580410 1.831938297 15 0.26013745 -0.6617675 -1.398981829 16 0.68685167 -1.2256218 0.435335557 17 -0.01735620 1.2882963 -0.378027363 18 -0.86458207 1.6013430 0.567320925 19 0.15593632 0.7086373 -0.231115639 20 0.54771681 1.8800467 0.235589089
Finding row sums for data in df2 −
> df2$y_total<-rowSums(df2) > df2
Output
y1 y2 y3 y_total 1 0.59017486 -0.1196278 0.822573265 1.29312029 2 -1.12831016 0.7454030 0.173110462 -0.20979674 3 0.65838766 -0.4798106 0.700891619 0.87946873 4 -3.09790550 -0.4401321 0.343264756 -3.19477280 5 -0.27170928 -0.8106993 -0.658724220 -1.74113281 6 1.51823786 0.1162130 -0.145487501 1.48896334 7 1.66852069 -0.4254530 -0.372181307 0.87088643 8 0.39011721 1.7260926 -0.253079767 1.86313004 9 0.12034466 0.4142485 -0.547526473 -0.01293328 10 0.73264687 0.3537576 -0.248502362 0.83790209 11 -1.19683499 1.0923578 0.139092087 0.03461487 12 -0.02475713 -0.7436116 1.154820901 0.38645212 13 -1.68760189 -1.1306767 -0.002655384 -2.82093399 14 -0.56164347 0.6580410 1.831938297 1.92833585 15 0.26013745 -0.6617675 -1.398981829 -1.80061190 16 0.68685167 -1.2256218 0.435335557 -0.10343459 17 -0.01735620 1.2882963 -0.378027363 0.89291278 18 -0.86458207 1.6013430 0.567320925 1.30408187 19 0.15593632 0.7086373 -0.231115639 0.63345799 20 0.54771681 1.8800467 0.235589089 2.66335255
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