I am struggling with using dplyrfunctions in own functions. I am closer to understand but still missing full understanding.
Here I have df containing type and D10 variables.
df <- data.frame(type = c("KL", "KL", "A", "A", "B", "B", "9999", "-1"),
D10 = rnorm(8, 3, 4))
I want to write a function that in a new column will return M if the type == "KL"; "-1" if the type %in% c(9999, -1) and that will return K for all the other cases. I want the values of 9999, -1, KL to be possible to change when the function is initiated.
My tries i ended with the function that look like this:
klme <- function(dat, met, minusy = c(-1, 9999), Sortnr, type){
mutate_call <- lazyeval::interp(~ifelse(a %in% met, "M", ifelse(a %in% minusy, "-1", "K")), a = as.name(Sortnr))
dat %>% mutate_(.dots = setNames(list(mutate_call), type))
}
klme(df, c("KL"), minusy = c(-1, 9999), "Sortnr", "typ")
that return only K in the typ column while I'd like to obtain output like this:
type D10 type.1
1 KL -5.3210620 M
2 KL 4.4832414 M
3 A -5.3979886 K
4 A 2.7933964 K
5 B -0.9602293 K
6 B 4.5097305 K
7 9999 -3.9650796 -1
8 -1 5.2700609 -1
I believe you are looking for this, remember that you need to interp all values that are variable (also @wici was right that your call to klme should not have Sortnr since that is not a column in df):
df <- data.frame(type = c("KL", "KL", "A", "A", "B", "B", "9999", "-1"),
D10 = rnorm(8, 3, 4))
klme <- function(dat, met, minusy = c(-1, 9999), Sortnr, type){
mutate_call <- lazyeval::interp(~ifelse(a %in% y, "M",
ifelse(a %in% z, "-1", "K")),
a = as.name(Sortnr),
y = met,
z = minusy)
dat %>% mutate_(.dots = setNames(list(mutate_call), type))
}
klme(df, c("KL"), minusy = c('-1', '9999'), "type", "typ")
type D10 typ 1 KL 6.4760905 M 2 KL 7.5196368 M 3 A 2.2588101 K 4 A 1.4910878 K 5 B -0.3357310 K 6 B 1.9693856 K 7 9999 -0.3820483 -1 8 -1 4.5595150 -1