I have a dataset with a named column. The name of the column is also in a variable. I would like to be able to select all rows that satisfy a condition on the column "col".
Here I would like to select all rows whose value in the "col" column matches the condition "< 2".
name = "col"
dataset = data.frame(col = 1:3)
I tried to use "eval" in subset, or the "select" function of the dplyr package, but it does not do what I want (or I misused it).
Is there a simple way to do this?
If you're new(ish) to R, I'm going to recommend using the tidyverse set of packages, including the ever-useful dplyr for problems like this so you can have more immediately readable and understandable code. You can get this package using install.packages('tidyverse'). With that installed, to answer your question:
library(dplyr)
df <- data.frame(
col = c(0:10),
another_col = c(10:20),
third_col = c(25:35)
)
dynamic_name <- "col"
filter_at(df, dynamic_name, ~ .x < 2)
Note: The tidyverse family of packages typically accept the formula syntax (that ~ expression) as a way to introduce anonymous (lambda) functions, so ~ .x < 2 is a function that returns TRUE if the value passed in is less than 2).
across() is very new and I wouldn't expect the *_at, *_if, *_all functions to go away any time soon. If you're looking for learning materials, there's some really good (mostly free) stuff here: rstudio.com/resources/booksplease see below.
a <- 1:5
b <- 6:10
namevar <- "a"
df <- data.frame(a,b)
df[df[,namevar] %in% c(1:3),]
a b
1 1 6
2 2 7
3 3 8
What happens here is that df[,namevar] %in% c(1:3) gives a vector of TRUE, FALSE depending on whether the condition is met.
Then passing this boolean vector as indices to df yields all the rows where the condition is TRUE.
For more details about %in% see help(is.element())
dataset[ dataset[[ name ]] < 2, ]