HELP: one scale multiple column recode

[y.concordia]

Hello community,

A fellow researcher and I are trying to figure out a way to make our dataframe cleaner, and less cluttered.
Here is a reprex:

> head(Dummy1)
# A tibble: 6 x 18
     A0    A1    A2    A3    A4    A5    B0    B1    B2    B3    B4    B5    C0    C1    C2    C3    C4
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1     0     0     0     0     0     1     0     0     0     0     0     1     0     0     0     0     0
2     0     0     0     0     1     0     0     0     0     0     1     0     0     0     0     0     1
3     0     0     0     1     0     0     0     0     0     1     0     0     0     0     0     1     0
4     0     0     1     0     0     0     0     0     1     0     0     0     0     0     1     0     0
5     0     1     0     0     0     0     0     1     0     0     0     0     0     1     0     0     0
6     1     0     0     0     0     0     1     0     0     0     0     0     1     0     0     0     0
# … with 1 more variable: C5 <dbl>
> 

Due to the way our software registered answers, we got A0 through A5, B0 through B5, etc instead of this:

> head(Dummy2)
# A tibble: 6 x 3
      A     B     C
  <dbl> <dbl> <dbl>
1     5     5     5
2     4     4     4
3     3     3     3
4     2     2     2
5     1     1     1
6     0     0     0
> 

Is there a code that would allow us to transform the first version, each possible answer as a column with a binary 0 NO 1 YES into a single item column with the numeric result? The scale we are trying to analyze has well over 50 items, each ranging from 0 to 8.

Thank you for your help!

[nirgrahamuk]

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.…

---

Short Version

You can share your data in a forum friendly way by passing the data to share to the dput() function.
If your data is too large you can use standard methods to reduce it before sending to dput().
When you come to share the dput() text that represents your data, please be sure to format your post with triple backticks on the line before your code begins to format it appropriately.

```
( example_df <- structure(list(Sepal.Length = c(5.1, 4.9, 4.7, 4.6, 5, 5.4, 4.6, 
5, 4.4, 4.9), Sepal.Width = c(3.5, 3, 3.2, 3.1, 3.6, 3.9, 3.4, 
3.4, 2.9, 3.1), Petal.Length = c(1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 
1.4, 1.5, 1.4, 1.5), Petal.Width = c(0.2, 0.2, 0.2, 0.2, 0.2, 
0.4, 0.3, 0.2, 0.2, 0.1), Species = structure(c(1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L), .Label = c("setosa", "versicolor", "virginica"
), class = "factor")), row.names = c(NA, -10L), class = c("tbl_df", 
"tbl", "data.frame")))
```