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How to find the mean for multiple columns in an R data frame using mean function not colMeans?
The easiest way to find the column means of an R data frame is to use colMeans function but if we do not want to use it then it can be done with the help of sapply. While using sapply, we need to make sure that we are selecting only numerical columns of the data frame. Check out the below examples to understand how it works.
Example1
Consider the CO2 data frame in base R −
> head(CO2,20)
Output
Plant Type Treatment conc uptake 1 Qn1 Quebec nonchilled 95 16.0 2 Qn1 Quebec nonchilled 175 30.4 3 Qn1 Quebec nonchilled 250 34.8 4 Qn1 Quebec nonchilled 350 37.2 5 Qn1 Quebec nonchilled 500 35.3 6 Qn1 Quebec nonchilled 675 39.2 7 Qn1 Quebec nonchilled 1000 39.7 8 Qn2 Quebec nonchilled 95 13.6 9 Qn2 Quebec nonchilled 175 27.3 10 Qn2 Quebec nonchilled 250 37.1 11 Qn2 Quebec nonchilled 350 41.8 12 Qn2 Quebec nonchilled 500 40.6 13 Qn2 Quebec nonchilled 675 41.4 14 Qn2 Quebec nonchilled 1000 44.3 15 Qn3 Quebec nonchilled 95 16.2 16 Qn3 Quebec nonchilled 175 32.4 17 Qn3 Quebec nonchilled 250 40.3 18 Qn3 Quebec nonchilled 350 42.1 19 Qn3 Quebec nonchilled 500 42.9 20 Qn3 Quebec nonchilled 675 43.9
Finding mean of numerical columns of CO2 −
> sapply(Filter(is.numeric,CO2),mean)
Output
conc uptake 435.0000 27.2131
Example2
Consider the mtcars data frame in base R −
> head(mtcars,20)
Output
mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Finding mean of numerical columns of mtcars −
> sapply(Filter(is.numeric,mtcars),mean)
Output
mpg cyl disp hp drat wt qsec 20.090625 6.187500 230.721875 146.687500 3.596563 3.217250 17.848750 vs am gear carb 0.437500 0.406250 3.687500 2.812500
Example3
Consider the Orange data in base R −
> head(Orange,20)
Output
Tree age circumference 1 1 118 30 2 1 484 58 3 1 664 87 4 1 1004 115 5 1 1231 120 6 1 1372 142 7 1 1582 145 8 2 118 33 9 2 484 69 10 2 664 111 11 2 1004 156 12 2 1231 172 13 2 1372 203 14 2 1582 203 15 3 118 30 16 3 484 51 17 3 664 75 18 3 1004 108 19 3 1231 115 20 3 1372 139
Finding mean of numerical columns of Orange −
> sapply(Filter(is.numeric,Orange),mean)
Output
age circumference 922.1429 115.8571
Example4
Consider ToothGrowth data in base R −
> head(ToothGrowth,20)
Output
len supp dose 1 4.2 VC 0.5 2 11.5 VC 0.5 3 7.3 VC 0.5 4 5.8 VC 0.5 5 6.4 VC 0.5 6 10.0 VC 0.5 7 11.2 VC 0.5 8 11.2 VC 0.5 9 5.2 VC 0.5 10 7.0 VC 0.5 11 16.5 VC 1.0 12 16.5 VC 1.0 13 15.2 VC 1.0 14 17.3 VC 1.0 15 22.5 VC 1.0 16 17.3 VC 1.0 17 13.6 VC 1.0 18 14.5 VC 1.0 19 18.8 VC 1.0 20 15.5 VC 1.0
Finding mean of numerical columns of ToothGrowth −
> sapply(Filter(is.numeric,ToothGrowth),mean)
Output
len dose 18.813333 1.166667
Example5
Consider USArrests data in base R −
> head(USArrests,20)
Output
Murder Assault UrbanPop Rape Alabama 13.2 236 58 21.2 Alaska 10.0 263 48 44.5 Arizona 8.1 294 80 31.0 Arkansas 8.8 190 50 19.5 California 9.0 276 91 40.6 Colorado 7.9 204 78 38.7 Connecticut 3.3 110 77 11.1 Delaware 5.9 238 72 15.8 Florida 15.4 335 80 31.9 Georgia 17.4 211 60 25.8 Hawaii 5.3 46 83 20.2 Idaho 2.6 120 54 14.2 Illinois 10.4 249 83 24.0 Indiana 7.2 113 65 21.0 Iowa 2.2 56 57 11.3 Kansas 6.0 115 66 18.0 Kentucky 9.7 109 52 16.3 Louisiana 15.4 249 66 22.2 Maine 2.1 83 51 7.8 Maryland 11.3 300 67 27.8
Finding mean of numerical columns of USArrests −
> sapply(Filter(is.numeric,USArrests),mean)
Output
Murder Assault UrbanPop Rape 7.788 170.760 65.540 21.232
Updated on: 04-Mar-2021
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