To extract a data frame column values as a vector by matching a vector in R, we can use subsetting with %in% operator.
In this method, a specific column can be extracted from the dataframe using its name using $. The column returned will be in a form of a vector, but it can be converted to a dataframe explicitly using data. frame() function of R language.
Every tibble is a named list of vectors, each of the same length. These vectors form the tibble columns.
With dplyr >= 0.7.0, you can use pull
to get a vector from a tbl
.
library("dplyr")
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
db <- src_sqlite(tempfile(), create = TRUE)
iris2 <- copy_to(db, iris)
vec <- pull(iris2, Species)
head(vec)
#> [1] "setosa" "setosa" "setosa" "setosa" "setosa" "setosa"
As per the comment from @nacnudus, it looks like a pull
function was implemented in dplyr 0.6:
iris2 %>% pull(Species)
For older versions of dplyr, here's a neat function to make pulling out a column a bit nicer (easier to type, and easier to read):
pull <- function(x,y) {x[,if(is.name(substitute(y))) deparse(substitute(y)) else y, drop = FALSE][[1]]}
This lets you do either of these:
iris2 %>% pull('Species')
iris2 %>% pull(Species)
iris2 %>% pull(5)
Resulting in...
[1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 15.0 21.4
And it also works fine with data frames:
> mtcars %>% pull(5)
[1] 3.90 3.90 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 3.92 3.07 3.07 3.07 2.93 3.00 3.23 4.08 4.93 4.22 3.70 2.76 3.15 3.73 3.08 4.08 4.43
[28] 3.77 4.22 3.62 3.54 4.11
A nice way to do this in v0.2 of dplyr
:
iris2 %>% select(Species) %>% collect %>% .[[5]]
Or if you prefer:
iris2 %>% select(Species) %>% collect %>% .[["Species"]]
Or if your table isn't too big, simply...
iris2 %>% collect %>% .[["Species"]]
You can also use unlist
which I find easier to read because you do not need to repeat the name of the column or specify the index.
iris2 %>% select(Species) %>% unlist(use.names = FALSE)
I would use the extract2
convenience function from magrittr
:
library(magrittr)
library(dplyr)
iris2 %>%
select(Species) %>%
extract2(1)
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