Create 4 categories variables

Question

I used a survey that has 20 questions and I calculated the mean of the 20 questions as "Total" for 400 participants. Now I need to categorise the Total into 4 groups: Total < 2 is limited, Total >= 2 is basic, Total < 3 is Good, and Total >= 3 is Full

I was able to create three but not four variables as follow:

level <- ifelse (df$Total <2, "Limited", ifelse((df$Total>= 2) & (df$Total<3), "Basic","Good"))

Then I want to see what's the percentage of each category either on numbers or graphs.

Take a look at `?cut`, something like `cut(1:5, breaks=c(-Inf,2,3,+Inf), right=FALSE)` I think. You can add `labels=` to the output too. — thelatemail, Jul 06 '21 at 01:48
This post might help - https://stackoverflow.com/questions/12979456/categorize-numeric-variable-into-group-bins-breaks — Ronak Shah, Jul 06 '21 at 01:57

score 0 · Accepted Answer · answered Jul 06 '21 at 01:50

I may be misunderstanding something, but you appear to have overlapping categories- Total >= 2 is basic, but Total < 3 is good. You may want to confirm the bounds for your groupings. Once that's sorted, you were actually pretty close to a working solution- you can nest ifelse statements and consider that they are evaluated in order. So, if a condition evaluates to TRUE "early" in the chain, it will return whatever is the output for a TRUE response at that point. Otherwise, it will move to the next ifelse to evaluate. Note here that I've used 1, 2, and 3 as the 'breaks' for the categories, so that the logic evaluates to: "If it's less than 1, it's Limited. If it's less than 2, it's Basic. If it's less than 3, it's good. Otherwise, it's Full."

set.seed(123)
df <- data.frame(total = runif(n = 15, min = 0, max = 4))
df


df$level = ifelse(df$total < 1, "Limited", 
                  ifelse(df$total < 2, "Basic", 
                         ifelse(df$total < 3, "Good", "Full")))
> df
       total   level
1  0.5691772 Limited
2  2.1971386    Good
3  3.8163650    Full
4  2.3419334    Good
5  1.6180411   Basic
6  2.5915739    Good
7  1.2792825   Basic
8  1.2308800   Basic
9  0.8790705 Limited
10 1.4779555   Basic
11 3.9368768    Full
12 0.6168092 Limited
13 0.3641760 Limited
14 0.5676276 Limited
15 2.7600284    Good

With just four categories an ifelse block is probably fine- if I were using many more bounds I'd likely use a different approach Edit: like thelatemail's- it's far cleaner.

if it requires more then 2 nested ifelses, i usually go with `dplyr::case_when()` — GuedesBF, Jul 06 '21 at 01:57
@GuedesBF - Agreed, especially for complex statements rather than simpler numerical bounds. Good shout! — Luke C, Jul 06 '21 at 01:58

Create 4 categories variables

1 Answers1