Ytukyg

I feel there's smarter/more efficient way than this code:

df <- mtcars



df$somename <- as.array(rep(c(0), 32))



for (i in 1:32){

  df$somename[i] <- sd(c(df$wt[i], df$qsec[i]))

}

maybe with %>%? but how?

An option using purrr::map2

library(tidyverse)

mtcars %>% mutate(somename = map2(wt, qsec, ~sd(c(.x, .y))))

#    mpg cyl  disp  hp drat    wt  qsec vs am gear carb somename

#1  21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4 9.786358

#2  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4 10.00203

#3  22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1 11.51877

#4  21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1 11.47281

#5  18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2  9.60251

#6  18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1 11.85111

#7  14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4   8.6762

#8  24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2 11.88646

#9  22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2 13.96536

#10 19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4 10.50761

#11 17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4 10.93187

#12 16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3 9.425733

#13 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3 9.807571

#14 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3 10.05506

#15 10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4 9.001469

#16 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4 8.765296

#17 14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4 8.538314

#18 32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1 12.21173

#19 30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2 11.95364

#20 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1 12.77388

#21 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1 12.40619

#22 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2 9.439876

#23 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2 9.804036

#24 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4 8.181225

#25 19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2 9.337345

#26 27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1 11.99607

#27 26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2 10.29547

#28 30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2 10.88025

#29 15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4  8.01152

#30 19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6 9.001469

#31 15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8 7.799388

#32 21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2 11.18643

Update

I re-ran @42-'s microbenchmark analysis using a larger dataset

library(microbenchmark)

df <- do.call(rbind, lapply(1:100, function(x) mtcars))

res <- microbenchmark(

    orig = {

        df$somename <- as.array(rep(c(0), nrow(df)))

        for (i in 1:nrow(df)) {

            df$somename[i] <- sd(c(df$wt[i], df$qsec[i]))}},

    tidy = {

        df <- df %>% mutate(somename = map2(wt, qsec, ~sd(c(.x, .y))))},

    mapply = {

        df$somename <- mapply(function(x, y) sd(c(x, y)), df$wt, df$qsec)},

    rowMeans = {

        df$rm <- rowMeans(df[,c("wt","qsec")])

        df$sd2col <- sqrt( (df$wt - df$rm)^2 + (df$qsec - df$rm)^2 )})

res

#Unit: microseconds

#     expr       min         lq        mean      median          uq        max

#     orig 331092.86 349754.808 360716.6501 357229.3920 366635.2820 446581.924

#     tidy 168701.28 181079.910 189710.1927 187026.6290 194392.5190 273725.354

#   mapply 161711.77 172457.395 179326.5484 177263.3045 183688.5365 266102.901

# rowMeans    228.08    315.854    343.9151    334.8975    358.5915    807.847



library(ggplot2)

autoplot(res)

More of a comment than an answer:

> library(microbenchmark)

> microbenchmark(  orig = {df <- mtcars

+ 

+ df$somename <- as.array(rep(c(0), 32))

+ 

+ for (i in 1:32){

+     df$somename[i] <- sd(c(df$wt[i], df$qsec[i]))

+ }},  tidy = {

+     mtcars %>% mutate(somename = map2(wt, qsec, ~sd(c(.x, .y))))}, mapply = { mapply(function(x, y) sd(c(x, y)), df$wt, df$qsec)})



#------------------------------------

Unit: microseconds

   expr      min        lq      mean   median        uq       max neval cld

   orig 5069.391 5161.9270 5555.5886 5236.769 5490.7365 12400.502   100   b

   tidy  910.071  943.9685  986.4419  970.541  998.8075  1241.711   100  a 

 mapply  744.639  761.1875  805.6328  773.426  807.2545  2206.393   100  a 

Code:

df$somename <- apply(matrix(c(df$wt, df$qsec), ncol=2), MARGIN = 1, FUN=sd)

Output:

> head(df$somename)



   somename

1   9.786358

2  10.002025

3  11.518769

4  11.472808

5   9.602510

6  11.851110

7   8.676200

8  11.886465

9  13.965359

10 10.507607