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" )) false is what is supposed to happen when the condition is false.true is what is supposed to happen when the condition is true.For example, weĬould do x > median(x) to test if each individual point of x is condition is something that can be true or false.The following general syntax: if_else ( condition, true, false ) Work the same way, but if_else() and fifelse() are more carefulĪbout variable types and fiflese() is super fast. base::ifelse(), dplyr::if_else(), and data.table::fiflese()īoth base::ifelse(), dplyr::if_else(), and data.table::fiflese() Below we walk through each approach to doing this. Let’s say we want to create a new variable that is categorizing our x dt <- data.table ( grp = factor ( sample ( 1L : 3L, 1e6, replace = TRUE )), x = rnorm ( 1e6 ), y = rnorm ( 1e6 ), z = sample ( c ( 1 : 10, NA ), 1e6, replace = TRUE ) ) dt We’ll also create a ficticious data set with four variables: grp, x, This is often done using base::ifelse(),ĭplyr::if_else(), dplyr::case_when(), or data.table::fiflese().īut it turns out there is another way to do this in data.table that isįor this post, we will use: # Core library ( dplyr ) library ( data.table ) library ( bench ) # Helpers library ( tidyr ) library ( ggbeeswarm ) Why do this? Well, we are often making or adjusting a variable based on If something meets a condition, do this else, do that. This very short post is presenting how one can perform Creating markdown and R scripts in RStudioīio: Jeffrey M Li is a Data Scientist at Dataquest.As I’ve spent time learning about different approaches to working withĭata, I’ve seen several subtle, but important, differences in how to do.Using string/date manipulation to write functions.Extracting years, months, days from a date object.Concatenate, extract characters in a string.How to manipulate strings and dates by writing your own headline generator.An simple method of iterating over multiple values.Writing functions with control structures.How to build your own soccer match prediction function:.Building upon the concepts in this tutorial, you'll learn: If you'd like to learn more, this tutorial is based on our R Intermediate course, which is part of our Data Analyst in R track. The if-else conditional will go between the brackets of the while loop. In our previous example, where 15 wins allowed us to make the playoffs, let's add an if-else conditional. Adding an if-else statement into our while loop is the same as adding it to our for loop. To do this, we'll need to add an if-else statement into our while loop. Now that we've printed the status of the team when they don't have enough wins, we'll add a feature tthat indicates when they do make the playoffs. Using an if-else statement within a while loop Let's write our first while loop, counting red dragon wins! Notice, that we continuously add 1 to the win total, so eventually, the win < 10 condition will return FALSE. Our loop will stop running when wins hits 10. If we were to write a loop for the following code: code block: This is the expression that's evaluated.See variables naming conventions in the first course for valid variable names. value: This is an iterator variable you use to refer to each value in the sequence.For example, this could be a vector of numbers c(1,2,3,4,5). However, if there are no more values left in the sequence, this will return FALSE and exit the loop. In this diagram, for each value in the sequence, the loop will execute the code block. A for loop repeats a chunk of code, multiple times for each element within an object: Instead, we can perform the same action using a for loop. Writing the results using if-else statements can work, but if our matches list contains 100 matches, it would be extremely cumbersome to write out each statement. Indexing with will return a list object, not the value. Keep in mind-we're using ] when indexing, since we want to return the single value, not the value with the list object.
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