CODEWITHSUNDEEP
rdplyrtidyverse
AJMA
AJMA

Reputation: 1194

How can I mutate multiple variables using dplyr?

Given a tbl_df object df containing multiple variables (i.e. Var.50, Var.100, Var.150 and Var.200), measured twice (i.e. P1 and P2), I want to mutate a new set of the same variables from repeated measurements (for example, average P1 and P2, creating P3 for each corresponding variable).

Similar questions have been asked before, but there does not seem to have clear answers using dplyr.

Example data: 

df <- structure(list(P1.Var.50 = c(134.242050170898, 52.375, 177.126017252604
), P1.Var.100 = c(395.202219645182, 161.636606852214, 538.408426920573
), P1.Var.150 = c(544.40028889974, 266.439168294271, 718.998555501302
), P1.Var.200 = c(620.076151529948, 333.218780517578, 837.109700520833
), P2.Var.50 = c(106.133892059326, 113.252154032389, 172.384114583333
), P2.Var.100 = c(355.226725260417, 277.197153727214, 502.086781819661
), P2.Var.150 = c(481.993103027344, 329.575764973958, 709.315409342448
), P2.Var.200 = c(541.859161376953, 372.05473836263, 829.299621582031
)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
-3L), .Names = c("P1.Var.50", "P1.Var.100", "P1.Var.150", "P1.Var.200", 
"P2.Var.50", "P2.Var.100", "P2.Var.150", "P2.Var.200"))

Upvotes: 1

Views: 1832

Answers (3)

moodymudskipper
moodymudskipper

Reputation: 47350

This is less general than Akrun's solution but if you don't have missing columns and that you know your categories P and Vars it should be faster (and shorter).

It uses only base R + the pipes:

np = 2
vars <- seq(50,200,by = 50)
df %>%
  unlist %>%
  matrix(ncol=np) %>%
  cbind(rowMeans(.)) %>%
  matrix(nrow=nrow(df)) %>%
  `colnames<-`(c(names(df),paste0("P",np+1,".Var.",vars))) %>%
  as.data.frame(stringsAsFactors=FALSE)

#   P1.Var.50 P1.Var.100 P1.Var.150 P1.Var.200 P2.Var.50 P2.Var.100 P2.Var.150 P2.Var.200 P3.Var.50 P3.Var.100 P3.Var.150 P3.Var.200
# 1  134.2421   395.2022   544.4003   620.0762  106.1339   355.2267   481.9931   541.8592 120.18797   375.2145   513.1967   580.9677
# 2   52.3750   161.6366   266.4392   333.2188  113.2522   277.1972   329.5758   372.0547  82.81358   219.4169   298.0075   352.6368
# 3  177.1260   538.4084   718.9986   837.1097  172.3841   502.0868   709.3154   829.2996 174.75507   520.2476   714.1570   833.2047

Upvotes: 1

CPak
CPak

Reputation: 13591

Using dplyr:

library(dplyr)
df1 <- df %>%
          rowwise() %>%
          mutate(P3.Var.50 = mean(c(P1.Var.50,P2.Var.50)),
                 P3.Var.100 = mean(c(P1.Var.100,P2.Var.100)),
                 P3.Var.150 = mean(c(P1.Var.150,P2.Var.150)),
                 P3.Var.200 = mean(c(P1.Var.200,P2.Var.200)))

-----------programmatically--------------

newcols <- sapply(seq(50,200,50), function(i) paste0("P3.Var.",i))

[1] "P3.Var.50"  "P3.Var.100" "P3.Var.150" "P3.Var.200"

df1 <- df %>%
          rowwise() %>%
          mutate_(.dots = setNames(paste0("mean(c(",gsub("P3","P1",newcols),",",gsub("P3","P2",newcols),"))"), newcols))

Upvotes: 2

akrun
akrun

Reputation: 887901

Here is an option by gather approach

library(tidyverse)
rownames_to_column(df, 'rn') %>% 
    gather( key, value, -rn) %>%
    separate(key, into = c('key1', 'key2'), extra = 'merge', remove = FALSE) %>% 
    group_by(rn, key2) %>%
    summarise(key3 = 'P3', value = mean(value)) %>% 
    unite(key, key3, key2)  %>%
    spread(key, value) %>%
    ungroup() %>% 
    select(-rn) %>% 
    select(order(as.numeric(sub(".*\\.(\\d+)$", "\\1", names(.))))) %>%
    bind_cols(df, .)
# A tibble: 3 x 12
#  P1.Var.50 P1.Var.100 P1.Var.150 P1.Var.200 P2.Var.50 P2.Var.100 P2.Var.150 P2.Var.200 P3_Var.50 P3_Var.100 P3_Var.150 P3_Var.200
#      <dbl>      <dbl>      <dbl>      <dbl>     <dbl>      <dbl>      <dbl>      <dbl>     <dbl>      <dbl>      <dbl>      <dbl>
#1  134.2421   395.2022   544.4003   620.0762  106.1339   355.2267   481.9931   541.8592 120.18797   375.2145   513.1967   580.9677
#2   52.3750   161.6366   266.4392   333.2188  113.2522   277.1972   329.5758   372.0547  82.81358   219.4169   298.0075   352.6368
#3  177.1260   538.4084   718.9986   837.1097  172.3841   502.0868   709.3154   829.2996 174.75507   520.2476   714.1570   833.2047

Upvotes: 3

Related Questions