How to combine transmute with grep function? - r

Question

I'm trying to find a way to create a new table with variables using the rowSums() function from an existing dataframe. For example, my existing dataframe is called 'asn' and I want to sum up the values for each row of all variables which contain "2011" in the variable title. I want a new table consisting of just one column called asn_y2011 which contains the sum of each row using the variables containing "2011"
Data
structure(list(row = 1:3, south_2010 = c(1L, 5L, 7L), south_2011 = c(4L,
0L, 4L), south_2012 = c(5L, 8L, 6L), north_2010 = c(3L, 4L, 1L
), north_2011 = c(2L, 6L, 0L), north_2012 = c(1L, 1L, 2L)), class = "data.frame", row.names = c(NA,
-3L))
The existing 'asn' dataframe looks like this
row south_2010 south_2011 south_2012 north_2010 north_2011 north_2012
1 1 4 5 3 2 1
2 5 0 8 4 6 1
3 7 4 6 1 0 2
I'm trying to use the following function:
asn %>%
transmute(asn_y2011 = rowSums(, grep("2011")))
to get something like this
row asn_y2011
1 6
2 6
3 4

Answer 1

Continuing with your code, grep() should work like this:
library(dplyr)
asn %>%
transmute(row, asn_y2011 = rowSums(.[grep("2011", names(.))]))
# row asn_y2011
# 1 1 6
# 2 2 6
# 3 3 4
Or you can use tidy selection in c_across():
asn %>%
rowwise() %>%
transmute(row, asn_y2011 = sum(c_across(contains("2011")))) %>%
ungroup()

Answer 2

Another base R option using rowSums
cbind(asn[1],asn_y2011 = rowSums(asn[grep("2011",names(asn))]))
which gives
row asn_y2011
1 1 6
2 2 6
3 3 4

Answer 3

An option in base R with Reduce
cbind(df['row'], asn_y2011 = Reduce(`+`, df[endsWith(names(df), '2011')]))
# row asn_y2011
#1 1 6
#2 2 6
#3 3 4
data
df <- structure(list(row = 1:3, south_2010 = c(1L, 5L, 7L), south_2011 = c(4L,
0L, 4L), south_2012 = c(5L, 8L, 6L), north_2010 = c(3L, 4L, 1L
), north_2011 = c(2L, 6L, 0L), north_2012 = c(1L, 1L, 2L)),
class = "data.frame", row.names = c(NA,
-3L))

Answer 4

I think that this code will do what you want:
library(magrittr)
tibble::tibble(row = 1:3, south_2011 = c(4, 0, 4), north_2011 = c(2, 6, 0)) %>%
tidyr::gather(- row, key = "key", value = "value") %>%
dplyr::mutate(year = purrr::map_chr(.x = key, .f = function(x)stringr::str_split(x, pattern = "_")[[1]][2])) %>%
dplyr::group_by(row, year) %>%
dplyr::summarise(sum(value))
I first load the package magrittr so that I can use the pipe, %>%. I've explicitly listed the packages from which the functions are exported, but you are welcome to load the packages with library if you like.
I then create a tibble, or data frame, like what you specify.
I use gather to reorganize the data frame before creating a new variable, year. I then summarise the counts by value of row and year.

Answer 5

You can try this approach
library(tidyverse)
df2 <- df %>%
select(grep("_2011|row", names(df), value = TRUE)) %>%
rowwise() %>%
mutate(asn_y2011 = sum(c_across(south_2011:north_2011))) %>%
select(row, asn_y2011)
# row asn_y2011
# <int> <int>
# 1 1 6
# 2 2 6
# 3 3 4
Data
df <- structure(list(row = 1:3, south_2010 = c(1L, 5L, 7L), south_2011 = c(4L, 0L, 4L), south_2012 = c(5L, 8L, 6L), north_2010 = c(3L, 4L, 1L), north_2011 = c(2L, 6L, 0L), north_2012 = c(1L, 1L, 2L)), class = "data.frame", row.names = c(NA,-3L))