use dplyr mutate() in programming

问题:

I am trying to assign a column name to a variable using mutate.

df <-data.frame(x = sample(1:100, 50), y = rnorm(50))

new <- function(name){
     df%>%mutate(name = ifelse(x <50, "small", "big"))
}

When I run

new(name = "newVar")

it doesn't work. I know mutate_() could help but I'm struggling in using it together with ifelse.

Any help would be appreciated.

回答1:

Using dplyr 0.7.1 and its advances in NSE, you have to UQ the argument to mutate and then use := when assigning. There is lots of info on programming with dplyr and NSE here: https://cran.r-project.org/web/packages/dplyr/vignettes/programming.html

I've changed the name of the function argument to myvar to avoid confusion. You could also use case_when from dplyr instead of ifelse if you have more categories to recode.

df <- data.frame(x = sample(1:100, 50), y = rnorm(50))

new <- function(myvar){
    df %>% mutate(UQ(myvar) := ifelse(x < 50, "small", "big"))
}

new(myvar = "newVar")

This returns

     x        y newVar
1   37  1.82669  small
2   63 -0.04333    big
3   46  0.20748  small
4   93  0.94169    big
5   83 -0.15678    big
6   14 -1.43567  small
7   61  0.35173    big
8   26 -0.71826  small
9   21  1.09237  small
10  90  1.99185    big
11  60 -1.01408    big
12  70  0.87534    big
13  55  0.85325    big
14  38  1.70972  small
15   6  0.74836  small
16  23 -0.08528  small
17  27  2.02613  small
18  76 -0.45648    big
19  97  1.20124    big
20  99 -0.34930    big
21  74  1.77341    big
22  72 -0.32862    big
23  64 -0.07994    big
24  53 -0.40116    big
25  16 -0.70226  small
26   8  0.78965  small
27  34  0.01871  small
28  24  1.95154  small
29  82 -0.70616    big
30  77 -0.40387    big
31  43 -0.88383  small
32  88 -0.21862    big
33  45  0.53409  small
34  29 -2.29234  small
35  54  1.00730    big
36  22 -0.62636  small
37 100  0.75193    big
38  52 -0.41389    big
39  36  0.19817  small
40  89 -0.49224    big
41  81 -1.51998    big
42  18  0.57047  small
43  78 -0.44445    big
44  49 -0.08845  small
45  20  0.14014  small
46  32  0.48094  small
47   1 -0.12224  small
48  66  0.48769    big
49  11 -0.49005  small
50  87 -0.25517    big

回答2:

Following the dlyr programming vignette, define your function as follows:

new <- function(name)
{
    nn <- enquo(name) %>% quo_name()
    df %>% mutate( !!nn := ifelse(x <50, "small", "big"))
}

enquo takes its expression argument and quotes it, followed by quo_name converting it into a string. Since nn is now quoted, we need to tell mutate not to quote it a second time. That's what !! is for. Finally, := is a helper operator to make it valid R code. Note that with this definition, you can simply pass newVar instead of "newVar" to your function, maintaining dplyr style.

> new( newVar ) %>% head
    x           y newVar
 1 94 -1.07642088    big
 2 85  0.68746266    big
 3 80  0.02630903    big
 4 74  0.18323506    big
 5 86  0.85086915    big
 6 38  0.41882858  small

回答3:

Base R solution

df <-data.frame(x = sample(1:100, 50), y = rnorm(50))

new <- function(name){
    df[,name]='s'
    df[,name][df$x>50]='b'
    return(df)
}

I am using dplyr 0.5 so i just combine base R with mutate

new <- function(Name){

    df=mutate(df,ifelse(x <50, "small", "big"))
    names(df)[3]=Name
    return(df)
}

new("newVar")