Passing a variable for a column name?
For example, suppose you had a function that used some of the DPLYR functions, but you couldn't expect the datasets passed to that function to have the same column names.
For a simplified example of what I mean, let's say you have a data frame arizona.trees
:
arizona.trees
group arizona.redwoods arizona.oaks
A 23 11
A 24 12
B 9 8
B 10 7
C 88 22
and another very similar to the data frame california.trees
:
california.trees
group california.redwoods california.oaks
A 25 50
A 11 33
B 90 5
B 77 3
C 90 35
And you wanted to implement a function that returns the average of the group data (A, B, ... Z) for a given type of tree, which will work for both of these data frames.
foo <- function(dataset, group1, group2, tree.type) {
column.name <- colnames(dataset[2])
result <- filter(dataset, group %in% c(group1, group2) %>%
select(group, contains(tree.type)) %>%
group_by(group) %>%
summarize("mean" = mean(column.name))
return(result)
}
The desired output for the call foo(california.trees, A, B, redwoods)
would be:
result
mean
A 18
B 83.5
For some reason, doing something like the implementation foo()
just doesn't work. This is probably due to some kind of error with indexing the dataframe - it seems like the function is trying to get the average of the row column.name
, instead of fetching the column and passing the column to mean()
. I don't know how to avoid this. There arises the problem of implicitly passing the modified data frame that cannot be directly linked to the pipe operator, which can cause the problem.
Why is this? Is there some alternative implementation?
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We can use a solution based on quosure
from devel version dplyr
(will be released soon 0.6.0
)
foo <- function(dataset, group1, group2, tree.type){
group1 <- quo_name(enquo(group1))
group2 <- quo_name(enquo(group2))
colN <- rlang::parse_quosure(names(dataset)[2])
tree.type <- quo_name(enquo(tree.type))
dataset %>%
filter(group %in% c(group1, group2)) %>%
select(group, contains(tree.type)) %>%
group_by(group) %>%
summarise(mean = mean(UQ(colN)))
}
foo(california.trees, A, B, redwoods)
# A tibble: 2 × 2
# group mean
# <chr> <dbl>
#1 A 18.0
#2 B 83.5
foo(arizona.trees, A, B, redwoods)
# A tibble: 2 × 2
# group mean
# <chr> <dbl>
#1 A 23.5
#2 B 9.5
enquo
takes input arguments and converts them to quosure
, with quo_name
, it converts to a string for use with %in%
, converts the second column name to quosure
from string using parse_quosure
, and then it incorrectly ( UQ
or !!
) evaluates withinsummarise
NOTE. This is based on the OP's feature about choosing the second column
The above solution was based on position based column selection (as per OP's code) and this may not work for other columns. So we can match "tree.type" and get the "mean" of the columns based on that
foo1 <- function(dataset, group1, group2, tree.type){
group1 <- quo_name(enquo(group1))
group2 <- quo_name(enquo(group2))
tree.type <- quo_name(enquo(tree.type))
dataset %>%
filter(group %in% c(group1, group2)) %>%
select(group, contains(tree.type)) %>%
group_by(group) %>%
summarise_at(vars(contains(tree.type)), funs(mean = mean(.)))
}
The function can be tested for different columns in two datasets
foo1(arizona.trees, A, B, oaks)
# A tibble: 2 × 2
# group mean
# <chr> <dbl>
#1 A 11.5
#2 B 7.5
foo1(arizona.trees, A, B, redwood)
# A tibble: 2 × 2
# group mean
# <chr> <dbl>
#1 A 23.5
#2 B 9.5
foo1(california.trees, A, B, redwood)
# A tibble: 2 × 2
# group mean
# <chr> <dbl>
#1 A 18.0
#2 B 83.5
foo1(california.trees, A, B, oaks)
# A tibble: 2 × 2
# group mean
# <chr> <dbl>
#1 A 41.5
#2 B 4.0
data
arizona.trees <- structure(list(group = c("A", "A", "B", "B", "C"),
arizona.redwoods = c(23L,
24L, 9L, 10L, 88L), arizona.oaks = c(11L, 12L, 8L, 7L, 22L)),
.Names = c("group",
"arizona.redwoods", "arizona.oaks"), class = "data.frame",
row.names = c(NA, -5L))
california.trees <- structure(list(group = c("A", "A", "B", "B", "C"),
california.redwoods = c(25L,
11L, 90L, 77L, 90L), california.oaks = c(50L, 33L, 5L, 3L, 35L
)), .Names = c("group", "california.redwoods", "california.oaks"
), class = "data.frame", row.names = c(NA, -5L))
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