In data analysis, we often need column totals, especially in situations where we want to perform the analysis in a step by step manner. There are many analytical techniques in which we find the column totals such as ANALYSIS OF VARIANCE, CORRELATION, REGRESSION, etc. To find the column totals, we can use colSums function and use the single square brackets to put these totals as a row in the data frame.
Consider the below data frame −
> x1<-1:20 > x2<-1:20 > x3<-1:20 > df1<-data.frame(x1,x2,x3) > df1
x1 x2 x3 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 6 6 6 6 7 7 7 7 8 8 8 8 9 9 9 9 10 10 10 10 11 11 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15 15 15 16 16 16 16 17 17 17 17 18 18 18 18 19 19 19 19 20 20 20 20
> df1["Total",]<-colSums(df1) > df1
x1 x2 x3 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 6 6 6 6 7 7 7 7 8 8 8 8 9 9 9 9 10 10 10 10 11 11 11 11 12 12 12 12 13 13 13 13 14 14 14 14 15 15 15 15 16 16 16 16 17 17 17 17 18 18 18 18 19 19 19 19 20 20 20 20 Total 210 210 210
> x1<-rpois(20,1) > x2<-rpois(20,2) > x3<-rpois(20,5) > df2<-data.frame(x1,x2,x3) > df2
x1 x2 x3 1 0 4 3 2 3 2 1 3 1 4 7 4 1 3 5 5 1 3 7 6 1 0 6 7 0 0 7 8 1 2 5 9 0 1 7 10 3 3 6 11 0 1 5 12 2 1 6 13 0 0 7 14 0 2 3 15 0 4 3 16 1 1 3 17 2 3 6 18 1 2 5 19 1 3 4 20 0 2 1
> df2["Total",]<-colSums(df2) > df2
x1 x2 x3 1 0 4 3 2 3 2 1 3 1 4 7 4 1 3 5 5 1 3 7 6 1 0 6 7 0 0 7 8 1 2 5 9 0 1 7 10 3 3 6 11 0 1 5 12 2 1 6 13 0 0 7 14 0 2 3 15 0 4 3 16 1 1 3 17 2 3 6 18 1 2 5 19 1 3 4 20 0 2 1 Total 18 41 97
> x1<-rnorm(20,0.5) > x2<-rnorm(20,1.5) > x3<-rnorm(20,2.5) > df3<-data.frame(x1,x2,x3) > df3
x1 x2 x3 1 0.6164833 0.47429064 3.5166292 2 2.0596947 1.10363170 3.4169209 3 1.5354324 1.96449893 1.7139730 4 -0.3155407 0.06443867 4.0183405 5 1.0863162 1.85855640 1.8751935 6 1.4546097 2.27657919 1.6122213 7 2.0087382 1.74009432 2.5015685 8 -0.3410458 2.41762264 2.9820183 9 -0.2868343 1.13547227 4.3164365 10 -1.0235788 2.14507250 2.3995348 11 0.2634310 1.63758312 2.3744627 12 0.9245307 -1.12596690 1.5528442 13 0.6475464 3.60709659 3.4380703 14 0.6304414 0.30028737 3.5130523 15 -0.8681919 2.16587601 0.8144658 16 0.1540673 2.11388876 2.0729619 17 2.6927877 2.37447334 2.9837406 18 -0.9019373 1.60907910 3.6548412 19 -0.2584275 1.04103727 0.7283439 20 0.8461264 0.85496302 3.2411674
> df3["Total",]<-colSums(df3) > df3
x1 x2 x3 1 0.6164833 0.47429064 3.5166292 2 2.0596947 1.10363170 3.4169209 3 1.5354324 1.96449893 1.7139730 4 -0.3155407 0.06443867 4.0183405 5 1.0863162 1.85855640 1.8751935 6 1.4546097 2.27657919 1.6122213 7 2.0087382 1.74009432 2.5015685 8 -0.3410458 2.41762264 2.9820183 9 -0.2868343 1.13547227 4.3164365 10 -1.0235788 2.14507250 2.3995348 11 0.2634310 1.63758312 2.3744627 12 0.9245307 -1.12596690 1.5528442 13 0.6475464 3.60709659 3.4380703 14 0.6304414 0.30028737 3.5130523 15 -0.8681919 2.16587601 0.8144658 16 0.1540673 2.11388876 2.0729619 17 2.6927877 2.37447334 2.9837406 18 -0.9019373 1.60907910 3.6548412 19 -0.2584275 1.04103727 0.7283439 20 0.8461264 0.85496302 3.2411674 Total 10.9246490 29.75857494 52.7267868