Creating a Categorical Variable from a quantitative one

[jkraw]

I am having trouble with where to start with organizing and analyzing my data. I have a variable called stai_score which is the level of anxiety for subjects. This score is numerical ranging from 20-80 and is recorded at 3 time points (baseline, week 4 and week 8). I am trying to create a new variable called change. This variable is a categorical variable that measures a success as "a change in stai_score greater than or equal to 3 from baseline to week 8" and a failure as "a change in stai_score less than 3 from baseline to week 8".

I am struggling in how to put in code that will create this new vector "change" because of the two timepoints. Any ideas?

[andresrcs]

Hi!

To help us help you, could you please prepare a reproducible example (reprex) illustrating your issue? Please have a look at this guide, to see how to create one:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[technocrat]

Hi, and welcome!

Please see the FAQ: What's a reproducible example (`reprex`) and how do I do one? Using a reprex, complete with representative data will attract quicker and more answers. This question is not complex enough to require one, though.

suppressPackageStartupMessages(library(dplyr))
dat <- structure(list(
  recid =
    c(1, 2, 3),
  obs =
    c(0, 4, 8),
  subj =
    c(1, 1, 1),
  score =
    c(20, 40, 60)
),
class =
  c("spec_tbl_df", "tbl_df", "tbl", "data.frame"),
row.names =
  c(NA, -3L), spec = structure(list(cols = list(recid = structure(list(),
  class =
    c("collector_double", "collector")
), obs = structure(list(),
  class =
    c("collector_double", "collector")
), subj = structure(list(),
  class =
    c("collector_double", "collector")
), score = structure(list(),
  class =
    c("collector_double", "collector")
)), default = structure(list(),
  class =
    c("collector_guess", "collector")
), skip = 1),
class = "col_spec"
)
)

dat
#> # A tibble: 3 x 4
#>   recid   obs  subj score
#>   <dbl> <dbl> <dbl> <dbl>
#> 1     1     0     1    20
#> 2     2     4     1    40
#> 3     3     8     1    60

dat %>%
  dplyr::mutate(change = ifelse(score > lag(score, 2), "success", "failure")) %>%
  filter(obs == 8)
#> # A tibble: 1 x 5
#>   recid   obs  subj score change 
#>   <dbl> <dbl> <dbl> <dbl> <chr>  
#> 1     3     8     1    60 success

^{Created on 2020-03-28 by the reprex package (v0.3.0)}