I have a survey called 'pms_raw'. I import that to R from online, and then immediately make a copy, into 'survey' so I have pms_raw as raw data, and survey which I start cleaning.
I have cleaned up 'survey' which involved changing the names of all the columns, from things like 'Q1' to 'age'. Now I need to make some sort of index/table where I can check old Q names from pms_raw and compare to new Q names in 'survey' so others can see what I've done.
The way I was planning on doing this, was by making a table manually, which will be quite a long route. Does anyone have any suggestions of how to do this in a better way? It would probably involve some kind of matching of column names?
An added dimension is that some columns have been totally removed in the 'survey' so the table needs to have N/A at those spots.
In a dream world it would look like:
pms_raw survey
Q1 age
Q2 height
Q3 weight
Thanks so much
I agree with IanCampbell completely. Until then, I'll demonstrate a "lookup" (named vector):
mt <- mtcars[1:3,]
newnames <- c(cyl="newcyl", mpg="newmpg", disp="newdisp")
ifelse(names(mt) %in% names(newnames), newnames[ names(mt) ], names(mt) )
# [1] "newmpg" "newcyl" "newdisp" "hp" "drat" "wt" "qsec"
# [8] "vs" "am" "gear" "carb"
names(mt) <- ifelse(names(mt) %in% names(newnames), newnames[ names(mt) ], names(mt) )
mt
# newmpg newcyl newdisp hp drat wt qsec vs am gear carb
# Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
# Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
# Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Alternatives, all using the same premise:
# same, but if_else can be safer in general to base ifelse
dplyr::if_else(names(mt) %in% names(newnames), newnames[ names(mt) ],
# slightly different logic
dplyr::coalesce(newnames[ names(mt) ], names(mt) )
Another alternative, but first reverse the layout of the lookup.
newnames <- c("newcyl"="cyl", "newmpg"="mpg", "newdisp"="disp")
dplyr::rename(mt, !!newnames)