Using lapply in group_by() of two factors in R - r

Question

I have this data frame (named as OEM_final). This is the structure:
str(OEM_final)
'data.frame': 2265 obs. of 17 variables:
$ dia_hora_OEM : POSIXct, format: "2019-12-31 06:40:13" "2019-12-31 06:43:00" "2019-12-31 07:11:30" "2019-12-31 07:18:30" ...
$ coche_OEM : Factor w/ 6 levels "356232050832996",..: 3 3 3 3 3 3 3 3 6 6 ...
$ DTC_OEM_dec64: chr "[{\"code\":\"B1182\",\"description\":\"Tire pressure monitor module\",\"faultInformations\":[{\"description\":\"| __truncated__ "[{\"code\":\"B1182\",\"description\":\"Tire pressure monitor module\",\"faultInformations\":[{\"description\":\"| __truncated__ "[{\"code\":\"B1182\",\"description\":\"Tire pressure monitor module\",\"faultInformations\":[{\"description\":\"| __truncated__ "[{\"code\":\"B1182\",\"description\":\"Tire pressure monitor module\",\"faultInformations\":[{\"description\":\"| __truncated__ ...
$ rowname : Factor w/ 2265 levels "1","10","100",..: 1 1112 1489 1600 1711 1822 1933 2044 2155 2 ...
$ B1182 : Factor w/ 2 levels "B1182","NULL": 1 1 1 1 1 1 1 1 2 2 ...
$ B124D : Factor w/ 2 levels "B124D","NULL": 1 1 1 1 1 1 1 1 2 2 ...
$ NA. : Factor w/ 6 levels "c(NA, NA, NA, NA, NA, NA, NA, NA)",..: 3 3 3 3 3 3 3 3 1 1 ...
$ P2000 : Factor w/ 2 levels "c(\"P2000\", \"P2000\", \"P2000\")",..: 2 2 2 2 2 2 2 2 2 2 ...
$ U3003 : Factor w/ 2 levels "NULL","U3003": 1 1 1 1 1 1 1 1 1 1 ...
$ B1D01 : Factor w/ 3 levels "B1D01","c(\"B1D01\", \"B1D01\")",..: 3 3 3 3 3 3 3 3 3 3 ...
$ U0155 : Factor w/ 2 levels "NULL","U0155": 1 1 1 1 1 1 1 1 1 1 ...
$ C1B00 : Factor w/ 2 levels "C1B00","NULL": 2 2 2 2 2 2 2 2 2 2 ...
$ P037D : Factor w/ 2 levels "NULL","P037D": 1 1 1 1 1 1 1 1 1 1 ...
$ P0616 : Factor w/ 2 levels "NULL","P0616": 1 1 1 1 1 1 1 1 1 1 ...
$ P0562 : Factor w/ 2 levels "NULL","P0562": 1 1 1 1 1 1 1 1 1 1 ...
$ U0073 : Factor w/ 2 levels "NULL","U0073": 1 1 1 1 1 1 1 1 1 1 ...
$ P0138 : Factor w/ 2 levels "c(\"P0138\", \"P0138\", \"P0138\")",..: 2 2 2 2 2 2 2 2 2 2 ...
I would like to calculate the earlier date (dia_hora_OEM) that appears when grouping by two factors. The two factors are:
One of this factor, which is common in all the possible combinations, is coche_OEM.
The other one is one from column 8 (P2000) to the last one (P0138), one at a time.
So, the group_by() would be:
group_by(coche_OEM, P2000)
group_by(coche_OEM, U3003)
group_by(coche_OEM, B1D01)
group_by(coche_OEM, U0155)
...
I tried different ways to accomplish this:
Using for loops:
for (DTC in c(U3003, P2000)) {
OEM_final %>%
group_by(DTC, coche_OEM) %>%
filter(dia_hora_OEM == min(dia_hora_OEM))
}
But I get an error saying:
Error in c(U3003, P2000) : object 'U3003' not found
Using lapply
In this case, I created a function:
groupCombDTC <- function(x) {
OEM_final %>%
group_by(coche_OEM, x) %>%
filter(dia_hora_OEM == min(dia_hora_OEM))
}
And then I ran lapply():
lapply(colnames(OEM_final)[8:17], groupCombDTC)
I get this error:
Error: Column `x` is unknown
Can anybody help me iterating in different combinations using group_by()?

Answer 1

That's a standard problem of standard evaluation with dplyr. dplyr is based on non standard evaluation so quoted arguments need to be unquoted.
Several solutions exist. This one works well
groupCombDTC <- function(x) {
OEM_final %>%
group_by(coche_OEM, !!rlang::sym(x)) %>%
filter(dia_hora_OEM == min(dia_hora_OEM))
}
It requires to use together !! and rlang::sym to unquote and evaluate your variable name.
Column names as arguments are easier to handle with data.table. If you want more elements regarding SE/NSE in dplyr and data.table, you can have a look at a blog post I wrote a few days ago