R: Dynamically referencing and operating on variables in data frame - r

Question

I am trying to dynamically reference and perform operations on a vector in a data frame. I've tried various forms of eval, parse, and so on, but they either return the string I provide or throw errors. Someone has a solution? As I propose In the psuedo-code below, the solution is presumably to replace DO_SOMETHING() with some other functions.
# Example data
mydat <- data.frame(x = rnorm(10))
# Function to add 5 to specified variable in a data frame
add5 <- function(data, var){
var_ref <- paste0("data$", var)
out <- DO_SOMETHING(var_ref) + 5
return out
}
add5(mydat,x) // returns a numeric vector value of 5
class(add5(mydat,x)) // numeric

Answer 1

If you want to pass unquoted column names, you could use deparse substitute like :
add5 <- function(data, var){
out <- data[deparse(substitute(var))] + 5
return(out)
}
add5(mydat,x)
Using dplyr and some non-standard evaluation with curly-curly we can do :
library(dplyr)
library(rlang)
add5 <- function(data, var){
data %>% mutate(out = {{var}} + 5)
}
add5(mydat, x)
# x out
#1 1.1604 6.16
#2 0.7002 5.70
#3 1.5868 6.59
#4 0.5585 5.56
#5 -1.2766 3.72
#6 -0.5733 4.43
#7 -1.2246 3.78
#8 -0.4734 4.53
#9 -0.6204 4.38
#10 0.0421 5.04

Answer 2

With data.table, unquoting argument names is easy. If you start writing functions using variable names, I recommend you to use data.table (see a blog post I wrote on the subject).
With one variable you will use get to unquote variable name
library(data.table)
data <- data.table(x = rnorm(10))
myvar <- "x"
data[, out := get(myvar) + 5]
data
x out
1: -0.30229987 4.697700
2: 0.51658585 5.516586
3: 0.12180432 5.121804
4: 1.53438805 6.534388
5: 0.06213513 5.062135
6: 0.17935070 5.179351
7: 0.70002065 5.700021
8: 0.12067590 5.120676
9: -0.41002931 4.589971
10: 0.45385072 5.453851
Note that I don't need to reassign result because := updates by reference.
With several variables, you will use .SD + lapply. This syntax means apply something over the Subset of Data (.SD). .SDcols argument is used to control what are the columns considered in the subset of data.
This is a very general approach that works in many situations.
data <- data.table(x = rnorm(10), y = rnorm(10))
data[, c('out1','out2') := lapply(.SD, function(x) return(x + 5)), .SDcols = c("x","y")]
data
x y out1 out2
1: 0.91187875 -0.2010539 5.911879 4.798946
2: -0.70906903 0.2074829 4.290931 5.207483
3: -0.52517961 0.2027444 4.474820 5.202744
4: 0.09967933 -1.2315601 5.099679 3.768440
5: -0.40392510 -0.1777705 4.596075 4.822229
6: 0.65891623 0.2394889 5.658916 5.239489
7: 0.76275090 1.5695957 5.762751 6.569596
8: -0.52395704 -0.7083462 4.476043 4.291654
9: 0.52728890 -1.1308284 5.527289 3.869172
10: -1.00418691 -0.5569468 3.995813 4.443053
I could have used this approach with one column (.SDcols = 'x').