Equivalent for deprecated select_() and mutate_() [duplicate] - r

Question

This question already has an answer here:
Passing variable in function to other function variables in R
(1 answer)
Closed 2 years ago.
Equivalent for deprecated select_() and mutate_()
I am trying to make a function with this data and would really appreciate your help!
Imagine I have a data.frame like this one (the fusion of control and sites).
I want to select the InitDryW and FinalDryW columns of the Treatment “Control” and then calculate the average.
Inside the function I must write select_() and then mutate_(). However, I understand that these two functions are deprecated.
control <- data.frame(Day=c(0,0,0,0,0,0),
Replica=c(1,1,1,1,1,1),
Initial_Dry_Weight=c(5.010,5.010,5.010,5.010,5.010,5.000),
Final_Dry_Weight=c(4.990,4.940,4.840,4.820,4.960,4.970),
InitiaFraction=c(1.1071,1.1964,1.0647,1.0005,1.0453,1.1212),
FinalFraction=c(0.3858,0.3504,0.4248,0.3333,0.3417,0.3467),
Treatment=c("Control","Control","Control","Control","Control","Control"))
control
sites <-data.frame(Day=c(2,4,8,16,32,44),
Replica=c(1,1,1,1,1,1,2,2,2,2,2,2,3,3,3,3,3,3),
Initial_Dry_Weight=c(5.000,5.000,5.000,5.000,5.01,5.000,5.000,5.000,
5.000,5.000,5.000,5.01,5.01,5.01,5.000,5.000,5.000,5.000),
Final_Dry_Weight=c(4.65,4.63,4.67,4.64,4.37,4.37,4.17,3.72,4.12,4,3.99,3.64,
4.26,3.3,3.47,3.7,3.75,3.3),
InitiaFraction=c(1.0081,1.0972,1.1307,1.0898,1.075,1.0295,1.0956,1.042,1.0876,
1.006,1.1052,1.0922,1.0472,1.0843,1.0177,1.0143,1.1112,1.0061),
FinalFraction=c(0.3229,0.3605,0.3304,0.3489,0.3181,0.2948,0.4098,0.3762,0.3787,
0.3345,0.3595,0.3511,0.3921,0.3908,0.3385,0.347,0.3366,0.3318),
Treatment=c("CC","CC","CC","CC","CC","CC","CC","CC","CC","CC","CC","CC","CC",
"CC","CC","CC","CC","CC"))
sites
total <- dplyr::bind_rows(control,sites)
total
My functions is:
manipulation <- function(data,
InitDryW,
FinalDryW,
Treatment,
Difference) {control <- data %>%
filter(Treatment == "Control") %>%
select_(InitDryW,FinalDryW) %>%
mutate_(Difference = lazyeval::interp (~a/b, a=as.name(FinalDryW),b=as.name(InitDryW)))
meanControl <- mean(control$Difference, na.rm = TRUE)
return (meanControl)
}
manipulation()
Then, I run the example:
control <- manipulation(data= total,
InitDryW = "Initial_Dry_Weight",
FinalDryW = "Final_Dry_Weight",
Treatment = "Treatment")
control
Now, I'm getting warnings like these (for both select_() and mutate_()):
Warning message:
mutate_() is deprecated.
Please use mutate() instead
The 'programming' vignette or the tidyeval book can help you
to program with mutate() : https://tidyeval.tidyverse.org
The result is correct, but the first time that warning appears.
My question is: what is the equivalent of select_() and mutate_() in functions in this case?
I think now select_() is solved using only select()
Thanks in advance!!!

Answer 1

You can pass unqouted column names and use {{}} to evaluate it.
library(dplyr)
library(rlang)
manipulation <- function(data,InitDryW,FinalDryW,Treatment,Difference) {
control <- data %>%
filter({{Treatment}} == "Control") %>%
select({{InitDryW}},{{FinalDryW}}) %>%
mutate(Difference = {{FinalDryW}}/{{InitDryW}})
meanControl <- mean(control$Difference, na.rm = TRUE)
return (meanControl)
}
manipulation(data= total,
InitDryW = Initial_Dry_Weight,
FinalDryW = Final_Dry_Weight,
Treatment = Treatment)
However, based on #27 ϕ 9's comment we think you might want to do :
manipulation <- function(data,InitDryW,FinalDryW,Treatment) {
control <- data %>%
filter(Treatment == Treatment) %>%
select({{InitDryW}},{{FinalDryW}}) %>%
mutate(Difference = {{FinalDryW}}/{{InitDryW}})
meanControl <- mean(control$Difference, na.rm = TRUE)
return (meanControl)
}
manipulation(data= total,
InitDryW = Initial_Dry_Weight,
FinalDryW = Final_Dry_Weight,
Treatment = "Control")

Answer 2

I think NSE (non standard evaluation) could help you. At first it might be a little bit confusing, but I think it's quite an elegant way to forget about the underscore functions :) All(?) the dplyr functions work somehow that way. So you should already be familiar with the concept (even if you didn't know about it):
... here is an example.
# some data
dat <- dplyr::tibble(A=1:5,
B=5:1)
# some function
some_function <- function(dat,
.var){
.var <- rlang::enquo(.var)
dat %>%
dplyr::select(!!.var)
}
# run function
some_function(dat,.var=B)
# output
# A tibble: 5 x 1
B
<int>
1 5
2 4
3 3
4 2
5 1