Calculate row sums exclude the first n columns-CodePudding

I need to calculate row sums for a data frame except for the first 5 columns. The output will consist of these first 5 columns and the row sums.

I tried this:

df1$rowsums <- rowSums(df1[,-c(1:5)], na.rm= T)

But I get this error message:

Error in rowSums(df1[, c(1:5)], na.rm = T) : 'x' must be numeric

CodePudding user response：

without data my guess is, that the columns you are using are not numeric. Then it will be hard to calculate the rowsum. Make sure, that columns you use for summing (except 1:5) are indeed numeric, then the following code should work:

library(tidyverse)
df2 <- df1[,-c(1:5)] %>% 
  rowwise() %>% 
    mutate(rowsum = sum(c_across(everything()), na.rm = T))

df_result <- cbind(df1[,c(1:5)], df2$rowsum)

EDIT: I added na.rm = T (dont know if necessary). And you might want to rename the resulting "df2$rowsum" column of the resulting df_result dataframe this can be done using

df_result <- df_result %>% rename(rowsum_name = "df2$rowsum")

CodePudding user response：

You could select the columns except the first 5 by -c(1:5) and use rowSums like this (I use mtcars as an example):

library(dplyr)
mtcars %>%
  mutate(rowsums = select(., -c(1:5)) %>% 
           rowSums(na.rm = TRUE))
#>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb rowsums
#> Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4  28.080
#> Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4  28.895
#> Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1  27.930
#> Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1  27.655
#> Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2  25.460
#> Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1  28.680
#> Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4  26.410
#> Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2  30.190
#> Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2  33.050
#> Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4  30.740
#> Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4  31.340
#> Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3  27.470
#> Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3  27.330
#> Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3  27.780
#> Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4  30.230
#> Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4  30.244
#> Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4  29.765
#> Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1  28.670
#> Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2  28.135
#> Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1  28.735
#> Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1  27.475
#> Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2  25.390
#> AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2  25.735
#> Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4  26.250
#> Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2  25.895
#> Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1  27.835
#> Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2  26.840
#> Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2  27.413
#> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4  27.670
#> Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6  30.270
#> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8  32.170
#> Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2  29.380

^{Created on 2022-07-09 by the reprex package (v2.0.1)}