Dplyr - Mean for multiple columns - r

Question

I want to calculate the mean for several columns and thus create a new column for the mean using dplyr and without melting + merging.
> head(growth2)
CODE_COUNTRY CODE_PLOT IV12_ha_yr IV23_ha_yr IV34_ha_yr IV14_ha_yr IV24_ha_yr IV13_ha_yr
1 1 6 4.10 6.97 NA NA NA 4.58
2 1 17 9.88 8.75 NA NA NA 8.25
3 1 30 NA NA NA NA NA NA
4 1 37 15.43 15.07 11.89 10.00 12.09 14.33
5 1 41 20.21 15.01 14.72 11.31 13.27 17.09
6 1 46 12.64 14.36 13.65 9.07 12.47 12.36
>
I need a new column within the dataset with the mean of all the IV columns.
I tried this:
growth2 %>%
group_by(CODE_COUNTRY, CODE_PLOT) %>%
summarise(IVmean=mean(IV12_ha_yr:IV13_ha_yr, na.rm=TRUE))
And returned several errors depending on the example used, such as:
Error in NA_real_:NA_real_ : NA/NaN argument
or
Error in if (trim > 0 && n) { : missing value where TRUE/FALSE needed

Answer 1

You don't need to group, just select() and then mutate()
library(dplyr)
mutate(df, IVMean = rowMeans(select(df, starts_with("IV")), na.rm = TRUE))

Answer 2

Use . in dplyr.
library(dplyr)
mutate(df, IVMean = rowMeans(select(., starts_with("IV")), na.rm = TRUE))

Answer 3

Here is a dplyr solution using c_across which is designed for row-wise aggregations. This makes it easy to refer to columns by name, type or position and to apply any function to the selected columns.
library("tidyverse")
df <-
tibble::tribble(
~CODE_COUNTRY, ~CODE_PLOT, ~IV12_ha_yr, ~IV23_ha_yr, ~IV34_ha_yr, ~IV14_ha_yr, ~IV24_ha_yr, ~IV13_ha_yr,
1L, 6L, 4.1, 6.97, NA, NA, NA, 4.58,
1L, 17L, 9.88, 8.75, NA, NA, NA, 8.25,
1L, 30L, NA, NA, NA, NA, NA, NA,
1L, 37L, 15.43, 15.07, 11.89, 10, 12.09, 14.33,
1L, 41L, 20.21, 15.01, 14.72, 11.31, 13.27, 17.09,
1L, 46L, 12.64, 14.36, 13.65, 9.07, 12.47, 12.36
)
df %>%
rowwise() %>%
mutate(
IV_mean = mean(c_across(starts_with("IV")), na.rm = TRUE),
IV_sd = sd(c_across(starts_with("IV")), na.rm = TRUE)
)
#> # A tibble: 6 × 10
#> # Rowwise:
#> CODE_COUNTRY CODE_PLOT IV12_ha_yr IV23_ha_yr IV34_ha_yr IV14_ha_yr IV24_ha_yr
#> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 6 4.1 6.97 NA NA NA
#> 2 1 17 9.88 8.75 NA NA NA
#> 3 1 30 NA NA NA NA NA
#> 4 1 37 15.4 15.1 11.9 10 12.1
#> 5 1 41 20.2 15.0 14.7 11.3 13.3
#> 6 1 46 12.6 14.4 13.6 9.07 12.5
#> # … with 3 more variables: IV13_ha_yr <dbl>, IV_mean <dbl>, IV_sd <dbl>
Created on 2022-06-25 by the reprex package (v2.0.1)

Answer 4

I tried to comment on Rick Scriven's answer but don't have the experience points for it. Anyway, wanted to contribute. His answer said to do this:
library(dplyr)
mutate(df, IVMean = rowMeans(select(df, starts_with("IV")), na.rm = TRUE))
That works, but if all columns don't start with "IV", which was my case, how do you do it? Turns out, that select does not want a logical vector, so you can't use AND or OR. For example, you cannot say "starts_with('X') | starts_with('Y')". You have to build a numeric vector. Here is how it is done.
mutate(df, IVMean = rowMeans(select(df, c(starts_with("IV"), starts_with("IX"))), na.rm = TRUE))

Answer 5

you can use as follows:
your data
data<- structure(list(CODE_COUNTRY = c(1L, 1L, 1L, 1L, 1L, 1L), CODE_PLOT = c(6L,
17L, 30L, 37L, 41L, 46L), IV12_ha_yr = c(4.1, 9.88, NA, 15.43,
20.21, 12.64), IV23_ha_yr = c(6.97, 8.75, NA, 15.07, 15.01, 14.36
), IV34_ha_yr = c(NA, NA, NA, 11.89, 14.72, 13.65), IV14_ha_yr = c(NA,
NA, NA, 10, 11.31, 9.07), IV24_ha_yr = c(NA, NA, NA, 12.09, 13.27,
12.47), IV13_ha_yr = c(4.58, 8.25, NA, 14.33, 17.09, 12.36)), .Names = c("CODE_COUNTRY",
"CODE_PLOT", "IV12_ha_yr", "IV23_ha_yr", "IV34_ha_yr", "IV14_ha_yr",
"IV24_ha_yr", "IV13_ha_yr"), class = "data.frame", row.names = c("1",
"2", "3", "4", "5", "6"))
mydata <- cbind(data,IVMean=apply(data[,3:8],1,mean, na.rm=TRUE))
you can also do this
mydata <- cbind(data,IVMean=rowMeans(data[3:8], na.rm=TRUE))