I'd like to generate some simple calculations for each combination of two factor variables and store the results in a data frame. Here are the data:
df <- data.frame(SPECIES = as.factor(c(rep("SWAN",10), rep("DUCK",4), rep("GOOS",12),
rep("PASS",9), rep("FALC",10))),
DRAINAGE = as.factor(c(rep(c("Central", "Upper", "West"),15))),
CATCH_QTY = c(1,1,2,5,6,1,2,1,1,1,1,1,3,1,1,2,1,1,2,2,
rep(1,25)),
TAGGED = c(rep("T",6),NA,"T","T",NA,rep("T",10),NA,NA,
rep("T",9),NA,"T","T","T","T",NA,rep("T",7),NA),
RECAP = c(rep(NA,6),"RC",NA,NA,"RC",rep(NA,10),"RC","RC",
rep(NA,9),"RC",NA,NA,NA,NA,"RC",rep(NA,7),"RC"))
And here is the function:
myfunction <- function(dat, yr, spp, drain){
dat <- dat %>% filter(SPECIES == spp, DRAINAGE == drain)
estimatea <<-
dat %>%
summarise(NumCaught = sum(CATCH_QTY, na.rm = T),
NewTags = sum(!is.na(TAGGED)),
Recaps = sum(!is.na(RECAP)),
TotTags = sum(NewTags+Recaps))
dataTest1 <- cbind(yr, spp, drain, estimatea$NumCaught, estimatea$NewTags,
estimatea$Recaps, estimatea$TotTags)
}
I've mostly been experimenting with nested for loops and have been struggling to store the output in a dataframe given that the variables over which I am iterating are factors, rather than numeric, therefore a lot of the existing answers on stack exchange aren't relevant. The answer for iterating over factors here does not show how to store the output.
Some examples of my attempts:
out <- list()
for (i in seq_along(levels(df$SPECIES))) {
for (j in seq_along(levels(df$DRAINAGE))) {
out[i,j] <- myfunction(df, "2023", i, j)
}
}
Error in out[i, j] <- myfunction(df, "2023", i, j) :
incorrect number of subscripts on matrix
for (i in seq_along(levels(df$SPECIES))) {
for (j in seq_along(levels(df$DRAINAGE))) {
out[i+1,j+1] <- myfunction(df, "2023", i, j)
}
}
Error in out[i, j] <- myfunction(df, "2023", i, j) :
incorrect number of subscripts on matrix
I've also considered some non-for loop options, e.g.,
combos <- expand.grid(df$SPECIES, df$DRAINAGE) %>% distinct() %>%
drop_na() %>% rename(spp = Var1, drain = Var2)
test <- myfunction(df, "2023", combos$spp, combos$drain) #generates incorrect results
sapply(combos$spp, function(x) mapply(myfunction,x,combos$drain))
apply(combos, 2, FUN = myfunction)
Error in UseMethod("filter") :
no applicable method for 'filter' applied to an object of class "character"
Ideally, the output dataframe would look something like this:
desired_out <- data.frame(yr = rep("2023",3),
spp = c("DUCK", "DUCK", "GOOS"),
drain = c("West", "Central", "Upper"),
V4 = c(1,3,4),
V5 = c(1,1,3),
v6 = c(0,0,1),
V7 = c(1,1,4))
To get your desired output, dplyr functions do what you need without getting into loops or *apply functions:
df %>%
group_by(SPECIES, DRAINAGE) %>%
summarise(
NumCaught = sum(CATCH_QTY, na.rm = T),
NewTags = sum(!is.na(TAGGED)),
Recaps = sum(!is.na(RECAP)),
TotTags = NewTags+Recaps
)
#> SPECIES DRAINAGE NumCaught NewTags Recaps TotTags
#> 1 SWAN Central 9 2 2 4
#> 2 SWAN Upper 8 3 0 3
#> 3 SWAN West 4 3 0 3
#> 4 DUCK Upper 2 2 0 2
#> 5 DUCK West 1 1 0 1
#> 6 DUCK Central 3 1 0 1
#> 7 GOOS West 4 3 1 4
#> 8 GOOS Central 6 3 1 4
#> 9 GOOS Upper 5 4 0 4
#> 10 PASS West 3 3 0 3
#> 11 PASS Central 3 3 0 3
#> 12 PASS Upper 3 2 1 3
#> 13 FALC West 4 3 1 4
#> 14 FALC Central 3 2 1 3
#> 15 FALC Upper 3 3 0 3
If your real-life requirements are more complicated and you really need to use some kind of looping I would recommend th *apply family of functions. In your example, nested lapply()s will get to the same result:
# the function can just return the summarised 1-row data frame,
# no need to update estimatea
myfunction <- function(dat, yr, spp, drain){
dat %>% filter(SPECIES == spp, DRAINAGE == drain) %>%
summarise(NumCaught = sum(CATCH_QTY, na.rm = T),
NewTags = sum(!is.na(TAGGED)),
Recaps = sum(!is.na(RECAP)),
TotTags = sum(NewTags+Recaps),
.by = c(SPECIES, DRAINAGE))
}
# nested lapply() to create lists of 1-row data frames
# (use levels(df$SPECIES) not seq_along() because we want
# the character strings, not the numeric index)
outputs <- lapply(levels(df$SPECIES),
function(x) {
lapply(levels(df$DRAINAGE),
function(y) myfunction(df, "2023", x, y)
)
})
# bind them together into 1 data frame
do.call(bind_rows, outputs)
#> SPECIES DRAINAGE NumCaught NewTags Recaps TotTags
#> 1 DUCK Central 3 1 0 1
#> 2 DUCK Upper 2 2 0 2
#> 3 DUCK West 1 1 0 1
#> 4 FALC Central 3 2 1 3
#> 5 FALC Upper 3 3 0 3
#> 6 FALC West 4 3 1 4
#> 7 GOOS Central 6 3 1 4
#> 8 GOOS Upper 5 4 0 4
#> 9 GOOS West 4 3 1 4
#> 10 PASS Central 3 3 0 3
#> 11 PASS Upper 3 2 1 3
#> 12 PASS West 3 3 0 3
#> 13 SWAN Central 9 2 2 4
#> 14 SWAN Upper 8 3 0 3
#> 15 SWAN West 4 3 0 3
Generally caution is advised when using the <<- operator as it affects global variables outside of the function's scope. *apply functions are normally cleaner to use in R.
Edit:
As @Parfait points out in the comments by() is the more appropriate function to use here. It subsets the data frame and does the summarisation without the need for nested lapply() calls.
# the function to return the summarised 1-row data frame
# it doesn't need to filter the data frame as `by()` does that for us
myfunction <- function(dat, yr){
dat %>%
summarise(NumCaught = sum(CATCH_QTY, na.rm = T),
NewTags = sum(!is.na(TAGGED)),
Recaps = sum(!is.na(RECAP)),
TotTags = sum(NewTags+Recaps),
.by = c(SPECIES, DRAINAGE))
}
# by() to create lists of 1-row data frames
outputs <- by(df, df[, c("SPECIES", "DRAINAGE")], myfunction, yr = "2023")
# bind them together into 1 data frame
do.call(bind_rows, outputs)
#> SPECIES DRAINAGE NumCaught NewTags Recaps TotTags
#> 1 DUCK Central 3 1 0 1
#> 2 FALC Central 3 2 1 3
#> 3 GOOS Central 6 3 1 4
#> 4 PASS Central 3 3 0 3
#> 5 SWAN Central 9 2 2 4
#> 6 DUCK Upper 2 2 0 2
#> 7 FALC Upper 3 3 0 3
#> 8 GOOS Upper 5 4 0 4
#> 9 PASS Upper 3 2 1 3
#> 10 SWAN Upper 8 3 0 3
#> 11 DUCK West 1 1 0 1
#> 12 FALC West 4 3 1 4
#> 13 GOOS West 4 3 1 4
#> 14 PASS West 3 3 0 3
#> 15 SWAN West 4 3 0 3