Conditionals

Conditional functions are a useful tool to update or create new columns conditional on the values of a column of data. When continuous variables are converted to categories, this is sometimes referred to as "recoding" a column.

TidierData.jl provides two functions to recode data: if_else() and case_when().

if_else()

Why do we need another if_else() function if base Julia already comes with an ifelse() function. Similar to R, the base Julia implementation of if_else() does not include a way to designate what value to return if the enclosed vector contains a missing value. Additionally, the base Julia implementation of ifelse() produces an error if presented with a missing value in the condition. The TidierData.jl if_else() can handle missing values and includes an optional 4th argument that is used to designate what to return in the event of a `missing`` value for the condition. Let's take a look at some examples.

using TidierData

df = DataFrame(a = [1, 2, missing, 4, 5])

5×1 DataFrame

Row	a
	Int64?
1	1
2	2
3	missing
4	4
5	5

Here, we have created a DataFrame containing a single column a with 5 values, for which the 3rd value is missing.

Now, let's create a new column b that contains a "yes" if a is greater than or equal to 3, and a "no" otherwise. Notice that when we do this, the missing values remains as missing.

@chain df begin
  @mutate(b = if_else(a >= 3, "yes", "no"))
end

5×2 DataFrame

Row	a	b
	Int64?	String?
1	1	no
2	2	no
3	missing	missing
4	4	yes
5	5	yes

What if we wanted to fill in the missing value with "unknown"? All we need to do is provide an optional 4th argument containing the value to return in the event of a missing condition. When we run this version, missing values in a are converted to "unknown" in b.

@chain df begin
  @mutate(b = if_else(a >= 3, "yes", "no", "unknown"))
end

5×2 DataFrame

Row	a	b
	Int64?	String
1	1	no
2	2	no
3	missing	unknown
4	4	yes
5	5	yes

Although both of these examples showed how to return a single value (like "yes" and "no"), you can also return a vector of values, which is useful for updating only a subset of the values of a column. For example, if we wanted to create a column b that contains a 3 when a is greater than or equal to 3 but otherwise remains unchanged, we could provide a 3 for the yes condition and a vector (column) a in the no condition. If we do not provide the optional 4th argument, missing values remain missing.

@chain df begin
  @mutate(b = if_else(a >= 3, 3, a))
end

5×2 DataFrame

Row	a	b
	Int64?	Int64?
1	1	1
2	2	2
3	missing	missing
4	4	3
5	5	3

case_when()

Although if_else() is convenient when evaluating a single condition, it can be cumbersome when evaluating multiple conditions because subsequent conditions need to be nested within the no condition for the preceding argument. For situations where multiple conditions need to be evaluated, case_when() is more convenient.

Let's first consider a similar example from above and recreate it using case_when(). The following code creates a column b that assigns a value of 3 if a >= 3 and otherwise leaves the value unchanged.

@chain df begin
  @mutate(b = case_when(a >= 3  =>  3,
                        true    =>  a))
end

5×2 DataFrame

Row	a	b
	Int64?	Int64?
1	1	1
2	2	2
3	missing	missing
4	4	3
5	5	3

What is going on here? case_when() uses a condition => return_value syntax, which are encoded as pairs in Julia. You can provide a single pair, or multiple pairs separated by commas. Because the pairs operator (=>) might be confused with a greater than or equal to sign (>=), we have padded two spaces on either side of the => to make sure that the pair remains visually distinct. We do not use a ~ operator in case_when() (as is used in R) because the ~ operator is used to denote de-vectorized functions in TidierData.jl.

There are 2 other things to note above. First, the true condition evaluates to true for all remaining values of a. The only reason that the b contains a missing value here is that the true condition was met, leading to the value of a (in this case, missing) to be assigned to b. Second, we were able to return a single value (3) in the first condition, and a vector (column) of data (a) in the second condition.

What if we wanted to fill in the missing values with something else? In this case, we would need to create an explicit condition that checks for missing values and assigns a return value to that condition.

@chain df begin
  @mutate(b = case_when(a >= 3        =>  3,
                        ismissing(a)  =>  0,
                        true          =>  a))
end

5×2 DataFrame

Row	a	b
	Int64?	Int64
1	1	1
2	2	2
3	missing	0
4	4	3
5	5	3

Do our conditions have to be mutually exclusive? No. The return value for the first matching condition is assigned to b because the conditions are evaluated sequentially from first to last.

@chain df begin
  @mutate(b = case_when(a > 4  =>  "hi",
                        a > 2  =>  "medium",
                        a > 0  =>  "low"))
end

5×2 DataFrame

Row	a	b
	Int64?	String?
1	1	low
2	2	low
3	missing	missing
4	4	medium
5	5	hi

Again, if we want to fill in remaining values (which in this case are the missing ones), we can map the final condition true to the value of "unknown". Because the ordering of the conditions matters, the true condition should always be listed last if it is included.

@chain df begin
  @mutate(b = case_when(a > 4  =>  "hi",
                        a > 2  =>  "medium",
                        a > 0  =>  "low",
                        true   =>  "unknown"))
end

5×2 DataFrame

Row	a	b
	Int64?	String
1	1	low
2	2	low
3	missing	unknown
4	4	medium
5	5	hi

Do these functions work outside of TidierData.jl?

Yes, both if_else() and case_when() work outside of TidierData.jl. However, you'll need to remember that if working with vectors, both the functions and conditions will need to be vectorized, and in the case of case_when(), the => will need to be written as .=>. The reason this is not needed when using these functions inside of TidierData.jl is because they are auto-vectorized.

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