Working with variables in a function in R - r

Question

I am trying to create a function to run chi squared where I have to group by several groups. However, while the method works when it's not a function, I am having trouble turning into a function. As I'll be repeating the procedure multiple times, its seems worth doing, but I just can't get the function to recognise the "z" variable and always get the "Unknown or uninitialised column" warning.
Example is below.
library(tidyverse)
library(datasets)
#data
data(iris)
df<-iris%>%
gather(Type, value, -Species)%>%
separate(Type, c("type", "attribute"), sep="[.]")
#functions------------
frequency<-function(data, x, y, z){
x <- enquo(x)
y <- enquo(y)
z <- enquo(z)
data%>%
filter(!is.na(!!x), !is.na(!!y), !is.na(!!z))%>%
count(!!x, !!y, !!z)
}
group_chi<-function(data, x, y, z){
x <- enquo(x)
y <- enquo(y)
data %>%
group_by(!! x) %>%
nest() %>%
mutate(M = map(data, function(dat){
dat2 <- dat %>% spread(!! y, n)
M <- as.matrix(dat2[, -1])
row.names(M) <- dat2$'z' #I've done it like this becasue z <- enquo(z) and dat2$!!z doesn't work. jsut having it a z doesnt work either
return(M)
}))%>%
mutate(pvalue = map_dbl(M, ~chisq.test(.x)$p.value)) %>%
select(-data, -M) %>%
ungroup()
}
#aplying them--------------------
test<-frequency(df, type, Species, attribute)
chi_test<-group_chi(test, type, Species, attribute)#brings up warning
#> Warning: Unknown or uninitialised column: 'z'.
#> Warning: Unknown or uninitialised column: 'z'.
#test without the function=no warning.
No_function<-test %>%
group_by(type) %>%
nest() %>%
mutate(M = map(data, function(dat){
dat2 <- dat %>% spread(Species, n)
M <- as.matrix(dat2[, -1])
row.names(M) <- dat2$attribute
return(M)
}))%>%
mutate(pvalue = map_dbl(M, ~chisq.test(.x)$p.value)) %>%
select(-data, -M) %>%
ungroup()
# in the example the results are the same but....the warning message is of concern and the function doesn't output the same in a more compelx dataset.
chi_test
#> # A tibble: 2 x 2
#> type pvalue
#> <chr> <dbl>
#> 1 Petal 1
#> 2 Sepal 1
No_function
#> # A tibble: 2 x 2
#> type pvalue
#> <chr> <dbl>
#> 1 Petal 1
#> 2 Sepal 1
# what am I doing wrong?
Created on 2020-01-27 by the reprex package (v0.3.0)
What am I doing wrong here?

Answer 1

You can't use $ for an indirect column reference (as in dat2$'z'), instead use dat2[[z]]. When I replace that, there are no warnings/errors.
Try this version of your function instead:
group_chi<-function(data, x, y, z){
x <- enquo(x)
y <- enquo(y)
data %>%
group_by(!! x) %>%
nest() %>%
mutate(M = map(data, function(dat){
dat2 <- dat %>% spread(!! y, n)
M <- as.matrix(dat2[, -1])
row.names(M) <- dat2[[z]]
return(M)
}))%>%
mutate(pvalue = map_dbl(M, ~chisq.test(.x)$p.value)) %>%
select(-data, -M) %>%
ungroup()
}
and then call with the string:
chi_test <- group_chi(test, type, Species, "attribute")
Alternatively, you can first z <- enquo(z) then pull(dat2, !!z) (as in #akrun's answer).
group_chi<-function(data, x, y, z){
x <- enquo(x)
y <- enquo(y)
z <- enquo(z)
data %>%
group_by(!! x) %>%
nest() %>%
mutate(M = map(data, function(dat){
dat2 <- dat %>% spread(!! y, n)
M <- as.matrix(dat2[, -1])
row.names(M) <- pull(dat2, !!z)
return(M)
}))%>%
mutate(pvalue = map_dbl(M, ~chisq.test(.x)$p.value)) %>%
select(-data, -M) %>%
ungroup()
}
group_chi(test, type, Species, attribute)
# # A tibble: 2 x 2
# type pvalue
# <chr> <dbl>
# 1 Petal 1
# 2 Sepal 1

Answer 2

We could also use z <- enquo(z), then make use of the select and pull to extract the column as a vector
group_chi<-function(data, x, y, z){
x <- enquo(x)
y <- enquo(y)
z <- enquo(z)
data %>%
group_by(!! x) %>%
nest() %>%
mutate(M = map(data, function(dat){
dat2 <- dat %>% spread(!! y, n)
M <- as.matrix(dat2[, -1])
row.names(M) <- dat2 %>%
select(!!z) %>%
pull(1)
return(M)
}))%>%
mutate(pvalue = map_dbl(M, ~chisq.test(.x)$p.value)) %>%
select(-data, -M) %>%
ungroup()
}
-checking
chi_test <- group_chi(test, type, Species, attribute)
chi_test
# A tibble: 2 x 2
# type pvalue
# <chr> <dbl>
#1 Petal 1
#2 Sepal 1
With the newer versions of tidyverse, the curly-curly operator ({{}}) can replace the !!/enquo
group_chi<-function(data, x, y, z){
data %>%
group_by({{x}}) %>%
nest() %>%
mutate(M = map(data, function(dat){
dat2 <- dat %>% spread({{y}}, n)
M <- as.matrix(dat2[, -1])
row.names(M) <- dat2 %>%
pull({{z}})
return(M)
}))%>%
mutate(pvalue = map_dbl(M, ~chisq.test(.x)$p.value)) %>%
select(-data, -M) %>%
ungroup()
}
chi_test <- group_chi(test, type, Species, attribute)