Reputation: 1015
I want to identify which row matches the information in a vector. As an example, I'll use the iris
dataset (in tibble
format to better approximate my situation): iris %>% as_tibble()
. Then I have a tibble with a single row, which came directly from the original dataset:
choice <– structure(list(Sepal.Length = 4.5, Sepal.Width = 2.3, Petal.Length = 1.3,
Petal.Width = 0.3, Species = structure(1L, .Label = c("setosa",
"versicolor", "virginica"), class = "factor")), row.names = c(NA,
-1L), class = c("tbl_df", "tbl", "data.frame"))
# A tibble: 1 x 5
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 4.5 2.3 1.3 0.3 setosa
I want to identify which row matches this exactly. I think it's better if it's a vector, but that's going to depend on what the function is; if that's the case, than you just have to add an as.numeric()
to that choice
.
The correct row is 42.
Upvotes: 5
Views: 327
Reputation: 102359
You can try this
> which(do.call(paste, iris) == do.call(paste, choice))
[1] 42
or
> match(data.frame(t(choice)), data.frame(t(iris)))
[1] 42
Upvotes: 0
Reputation: 1925
This is similar to akrun's first solution, I offer the tidyverse version of it:
map2(iris, choice, `==`) %>%
reduce(`&`) %>%
which()
[1] 42
Upvotes: 5
Reputation: 887621
One option is Map
. With Map
, we compare (==
) the corresponding elements (here the unit is a column) of 'iris' and 'choice' (as choice have only a single row, that element is recycled), returning a list
of logical
vectors which is then Reduce
d to a single logical vector
with &
i.e. it checks for the elementwise corresponding elements of the list
(columns of iris converted to logical), returns TRUE if all the elements are TRUE), then wrap with which
to get the position index of that logical vector
which(Reduce(`&`, Map(`==`, iris, choice)))
#[1] 42
Or another option is to replicate the rows of 'choice' to make the dim same as 'iris', do a ==
, use rowSums
and check if it is equal to number of columns
library(tidyr)
which(rowSums(iris == uncount(choice, nrow(iris))) == ncol(iris))
#[1] 42
Or this can be done in tidyverse
. Create a row number column (row_number()
), use filter
with if_all
to loop across the column names except the 'rn', compare with the extracted corresponding column of 'choice', so that it returns the row only when all the columns for that row are TRUE (if_all
, if_any
- is either one of them), pull
the column 'rn' as a vector
library(dplyr)
iris %>%
mutate(rn = row_number()) %>%
filter(if_all(all_of(names(choice)),
~ . == choice[[cur_column()]])) %>%
pull(rn)
#[1] 42
Upvotes: 7