Calculate rowMeans on a range of column (Variable number) - r

Question

I want to calculate rowMeans of a range of column but I cannot give the hard-coded value for colnames (e.g c(C1,C3)) or range (e.g. C1:C3) as both names and range are variable. My df looks like:
> df
chr name age MGW.1 MGW.2 MGW.3 HEL.1 HEL.2 HEL.3
1 123 abc 12 10.00 19 18.00 12 13.00 -14
2 234 bvf 24 -13.29 13 -3.02 12 -0.12 24
3 376 bxc 17 -6.95 10 -18.00 15 4.00 -4
This is just a sample, in reality I have columns ranging in MGW.1 ... MGW.196 and so. Here Instead of giving the exact colnames or an exact range I want to pass initial of colnames and want to get average of all columns having that initials. Something like: MGW=rowMeans(df[,MGW.*]), HEL=rowMeans(df[,HEL.*])
So my final output should look like:
> df
chr name age MGW Hel
1 123 abc 12 10.00 19
2 234 bvf 24 13.29 13
3 376 bxc 17 -6.95 10
I know these values are not correct but it is just to give you and idea. Secondly I want to remove all those rows from data frame which contains NA in the entire row except the first 3 values.
Here is the dput for sample example:
> dput(df)
structure(list(chr = c(123L, 234L, 376L), name = structure(1:3, .Label = c("abc",
"bvf", "bxc"), class = "factor"), age = c(12L, 24L, 17L), MGW.1 = c(10,
-13.29, -6.95), MGW.2 = c(19L, 13L, 10L), MGW.3 = c(18, -3.02,
-18), HEL.1 = c(12L, 12L, 15L), HEL.2 = c(13, -0.12, 4), HEL.3 = c(-14L,
24L, -4L)), .Names = c("chr", "name", "age", "MGW.1", "MGW.2",
"MGW.3", "HEL.1", "HEL.2", "HEL.3"), class = "data.frame", row.names = c(NA,
-3L))

Answer 1

Firstly
I think you are looking for this to get mean of rows:
df$mean.Hel <- rowMeans(df[, grep("^HEL.", names(df))])
And to delete the columns afterwards:
df[, grep("^HEL.", names(df))] <- NULL
Secondly
To delete rows which have only NA after the first three elements.
rows.delete <- which(rowSums(!is.na(df)[,4:ncol(df)]) == 0)
df <- df[!(1:nrow(df) %in% rows.delete),]

Answer 2

Here's an idea achieving your desired output without hardcoding variable names:
library(dplyr)
library(tidyr)
df %>%
# remove rows where all values are NA except the first 3 columns
filter(rowSums(is.na(.[4:length(.)])) != length(.) - 3) %>%
# gather the data in a tidy format
gather(key, value, -(chr:age)) %>%
# separate the key column into label and num allowing
# to regroup by variables without hardcoding them
separate(key, into = c("label", "num")) %>%
group_by(chr, name, age, label) %>%
# calculate the mean
summarise(mean = mean(value, na.rm = TRUE)) %>%
spread(label, mean)
I took the liberty to modify your initial data to show how the logic would fit special cases. For example, here we have a row (#4) where all values but the first 3 columns are NAs (according to your requirements, this row should be removed) and one where there is a mix of NAs and values (#5). In this case, I assumed we would like to have a result for MGW since there is a value at MGW.1:
# chr name age MGW.1 MGW.2 MGW.3 HEL.1 HEL.2 HEL.3
#1 123 abc 12 10.00 19 18.00 12 13.00 -14
#2 234 bvf 24 -13.29 13 -3.02 12 -0.12 24
#3 376 bxc 17 -6.95 10 -18.00 15 4.00 -4
#4 999 zzz 21 NA NA NA NA NA NA
#5 888 aaa 12 10.00 NA NA NA NA NA
Which gives:
#Source: local data frame [4 x 5]
#Groups: chr, name, age [4]
#
# chr name age HEL MGW
#* <int> <fctr> <int> <dbl> <dbl>
#1 123 abc 12 3.666667 15.666667
#2 234 bvf 24 11.960000 -1.103333
#3 376 bxc 17 5.000000 -4.983333
#4 888 aaa 12 NaN 10.000000
Data
df <- structure(list(chr = c(123L, 234L, 376L, 999L, 888L), name = structure(c(2L,
3L, 4L, 5L, 1L), .Label = c("aaa", "abc", "bvf", "bxc", "zzz"
), class = "factor"), age = c(12L, 24L, 17L, 21L, 12L), MGW.1 = c(10,
-13.29, -6.95, NA, 10), MGW.2 = c(19L, 13L, 10L, NA, NA), MGW.3 = c(18,
-3.02, -18, NA, NA), HEL.1 = c(12L, 12L, 15L, NA, NA), HEL.2 = c(13,
-0.12, 4, NA, NA), HEL.3 = c(-14L, 24L, -4L, NA, NA)), .Names = c("chr",
"name", "age", "MGW.1", "MGW.2", "MGW.3", "HEL.1", "HEL.2", "HEL.3"
), class = "data.frame", row.names = c("1", "2", "3", "4", "5"))