With purrr this can be done on lists and vectors, but I can't find a way of doing it with rows of a data frame / tibble. This is one of the most common patterns in other languages, such as using foreach in c#.
What would be the tidy equivalent of the following code:
df <- data.frame(code = letters[1:26], value = rnorm(26))
for (i in seq_along(df$code)) {
row <- df[i, ]
print(row) # or do something much more complex with the row
}
library(purrr)
df <- data.frame(code = letters[1:26],
value = rnorm(26),
stringsAsFactors = FALSE)
forfunc <- function(df){
for (i in seq_along(df$code)) {
row <- df[i, ]
print(row) # or do something much more complex with the row
}}
walkfor <- function(df){
walk(1:nrow(df), ~print(df[., ]))
}
pwalkfunc <- function(df){
pwalk(.l = df, .f = ~print(paste0(..1," ",..2)))
}
directfunc <- function(df){
cat(paste0(df[,1]," ",df[,2],"\n"))
}
library(microbenchmark)
microbenchmark(forfunc(df),times = 10L,unit="us")
microbenchmark(walkfor(df),times = 10L,unit="us")
microbenchmark(pwalkfunc(df),times = 10L,unit="us")
microbenchmark(print(df),times = 10L,unit="us")
microbenchmark(directfunc(df),times = 10L,unit="us")
EDITED:: to add slide
# expr min lq mean median uq max neval
# forfunc(df) 9770 10224 11303 11061 11814 13777 10
# walkfor(df) 10170 10864 11808 11416 11917 20878 10
# pwalkfunc(df) 857 1306 1472 1561 1722 1821 10
# print(df) 749 904 1248 1333 1501 1592 10
# directfunc(df) 521 621 900 657 735 2693 10
# slidefunc(df) 71 72 223 73 76 1249 10
in conclusion, since R is vectorised, its usually best to go direct if you can. You dont need a for loop or any programmer determined iterator when the base language (and many packaged extensions ) has a default to iterate for you
Lol. I think I deleted a digit next to mean for the walkfor, when I was manually editing off decimal points. obviously the mean is not less than the min!
Hi,
I was wondering about that too 
The walk solution seems almost identical to the basic for iteration, just using . instead of i. It also has this disadvantage of being more difficult to debug, one nice thing about using iterators is that if something goes wrong, all the variables are still set. I’m usually creating SQL scripts based on each row, so it is easy to just copy and run the last script to see what went wrong.
However being able to parallelize with pwalk is something I hadn’t really taken consideration of, and that is quite interesting.
Another option is the new slider package, which uses map()-like syntax to iterate over rows (instead of columns).
library(purrr)
library(slide)
df <- data.frame(code = letters[1:26],
value = rnorm(26),
stringsAsFactors = FALSE)
slide(df, ~.x)
#> [[1]]
#> code value
#> 1 a 0.6423173
#>
#> [[2]]
#> code value
#> 1 b 0.5751017
#>
#> [[3]]
#> code value
#> 1 c -0.5670547
#>
#> [[4]]
#> code value
#> 1 d 0.3301827
#>
#> [[5]]
#> code value
#> 1 e 0.01724866
#>
#> [[6]]
#> code value
#> 1 f -0.3050139
#>
#> [[7]]
#> code value
#> 1 g 1.07928
#>
#> [[8]]
#> code value
#> 1 h -1.555621
#>
#> [[9]]
#> code value
#> 1 i -0.2743272
#>
#> [[10]]
#> code value
#> 1 j -0.1304349
#>
#> [[11]]
#> code value
#> 1 k 1.023238
#>
#> [[12]]
#> code value
#> 1 l -1.149406
#>
#> [[13]]
#> code value
#> 1 m -0.7004986
#>
#> [[14]]
#> code value
#> 1 n 0.3426395
#>
#> [[15]]
#> code value
#> 1 o 0.2735956
#>
#> [[16]]
#> code value
#> 1 p -0.9509219
#>
#> [[17]]
#> code value
#> 1 q -1.576721
#>
#> [[18]]
#> code value
#> 1 r -0.9072278
#>
#> [[19]]
#> code value
#> 1 s -1.280973
#>
#> [[20]]
#> code value
#> 1 t -2.960007
#>
#> [[21]]
#> code value
#> 1 u -0.2768247
#>
#> [[22]]
#> code value
#> 1 v -0.1905839
#>
#> [[23]]
#> code value
#> 1 w 0.4464869
#>
#> [[24]]
#> code value
#> 1 x 1.871529
#>
#> [[25]]
#> code value
#> 1 y 0.1432761
#>
#> [[26]]
#> code value
#> 1 z 0.6543933
Another nice thing about this as compared to pmap() is that you can refer to the columns by name in the function you apply to each row:
slide_dbl(df, ~.x$value + 1)
#> [1] 1.64231731 1.57510169 0.43294532 1.33018271 1.01724866 0.69498611
#> [7] 2.07928021 -0.55562117 0.72567283 0.86956514 2.02323772 -0.14940556
#> [13] 0.29950139 1.34263952 1.27359560 0.04907812 -0.57672104 0.09277225
#> [19] -0.28097344 -1.96000711 0.72317534 0.80941607 1.44648691 2.87152866
#> [25] 1.14327608 1.65439329
Created on 2020-02-11 by the reprex package (v0.3.0)
nice always good to have options, it seems very fast too !!
Slider looks like quite a useful package, and does exactly what I was looking for, as well as having some other quite useful features, ie the sliding part of slide. Thanks.