base_dt % as.ame () dply_dt % as_tibble () dt_filt =.
#RSTUDIO IF STATEMENT CODE#
Note that code to produce the benchmarking and figure is below. Even with 1,000,000 rows and 5 variables, all This shows that the new data.table::fifelse() is incredibly quick while theįilter-mutate-keep approach is also very fast. I have my opinions (I love case_when() and theįilter-mutate-keep approaches) but all discussed herein will do theīut to help you understand the performance of the approaches, consider Is one preferred?įirst, preference depends on a number of things. Still, in many situations, it doesn’t add too much more coding.
The filter-mutate-keep approach requires that you are veryĮxplicit since it won’t ignore the rows that met the other conditions. Then will look at the next condition (and ignore the rows that already met theįirst condition). That is, case_when() will find the rows that fit the first condition, and It doesn’t move from one condition to the next in the same way case_when(). The only added burden of this approach is that This results in the same thing as theĬase_when() example. This filters by the condition and then assigns values to x_cat either This approach is unique to data.table and functions very similarly toĬase_when() in terms of syntax. Meet conditions 1 - 4, their value for this new variable we are making Values that don’t meet the conditions (so if someone in the data don’t
With no real limit to the number of conditions that can be used. Instead it relies on the following syntax: case_when ( condition1 ~ if_condition1_true, condition2 ~ if_condition2_true, condition3 ~ if_condition3_true, condition4 ~ if_condition4_true ) dt %>% mutate ( x_cat = if_else ( x % as_tibble () If_else() statements, where the second is in the false place of theįirst. These can get messy, with many parenthases.įor example, with just two three levels, we now need to use two “low” but there is also a “moderate” level) then we use what is called More than 2 levels of the new variable (e.g., not just a “high” and
Uses a unique syntax, but one that can avoid some issues. We could do this with data.table like below: dt dplyr::case_when()Ī newer, but fantastic, approach is using dplyr::case_when(). So with our example data, we can do: dt %>% mutate ( x_cat = if_else ( x > median ( x ), "high", "low" ))
#RSTUDIO IF STATEMENT HOW TO#
As I’ve spent time learning about different approaches to working withĭata, I’ve seen several subtle, but important, differences in how to do