In this vignette we’ll show some examples how crosstabs can be generated. First, a labelled data set is modified with datadaptor to prepare the data for the crosstabs. The latter are then calculated with the crosstabser functionalities. Here you can find an interactive version of this vignette that will also automatically generate an interactive table_charter app of the crosstabs in the browser.
First, we’ll load the library.
In order to use crosstabser, we need a dataset and a mapping file. The latter defines the commands to modify the data (optional; using datadaptor functionalities), and those to calculate crosstabs.
We’ll use the datadaptor::fruit_survey toy data set
together with the example mapping file for this data set in the package. If you install crosstabser on your machine, this file is also on your computer:
We can apply the data modifications and calculate the crosstabs in one command by defining an R6 object of class “Tabula”:
and then print the crosstabs defined in the mapping:
(by passing a high value of the width
parameter, we can make sure that all headers are printed)
m |> print(width = 1e4)
#> $`3`
#> $`3`[[1]]
#> # Q1. Do you like fruits?
#> TOTAL Likes ----- ----- Favo… ----- ----- ----- Frui… ----- -----
#> Appl… Bana… Oran… Apple Bana… Oran… Othe… 3 or… 4 or… 6 an…
#> TOTAL abs 100 57 54 54 27 12 19 9 18 22 27
#> Yes abs 75 57 54 54 27 12 19 9 18 22 27
#> in % 75 100 100 100 100 100 100 100 100 100 100
#> No abs 19 0 0 0 0 0 0 0 0 0 0
#> in % 19 0 0 0 0 0 0 0 0 0 0
#> No answer abs 6 0 0 0 0 0 0 0 0 0 0
#> in % 6 0 0 0 0 0 0 0 0 0 0
#> VALID CASES abs 100 57 54 54 27 12 19 9 18 22 27
#> in % 100 100 100 100 100 100 100 100 100 100 100
#>
#>
#> $`4`
#> $`4`[[1]]
#> # Q2. Which fruits do you like?
#> TOTAL Likes ----- ----- Favo… ----- ----- ----- Frui… ----- -----
#> Appl… Bana… Oran… Apple Bana… Oran… Othe… 3 or… 4 or… 6 an…
#> TOTAL abs 100 57 54 54 27 12 19 9 18 22 27
#> Sum of valid answe… abs 222 182 180 178 81 40 59 21 56 66 79
#> Apple abs 57 57 42 40 27 8 13 4 14 18 20
#> in % 77.0 100 77.8 74.1 100 66.7 68.4 44.4 77.8 81.8 74.1
#> Banana abs 54 42 54 42 21 12 13 3 16 16 17
#> in % 73.0 73.7 100 77.8 77.8 100 68.4 33.3 88.9 72.7 63.0
#> Orange abs 54 40 42 54 15 9 19 5 13 15 20
#> in % 73.0 70.2 77.8 100 55.6 75 100 55.6 72.2 68.2 74.1
#> Others abs 57 43 42 42 18 11 14 9 13 17 22
#> in % 77.0 75.4 77.8 77.8 66.7 91.7 73.7 100 72.2 77.3 81.5
#> VALID CASES abs 74 57 54 54 27 12 19 9 18 22 27
#> in % 74 100 100 100 100 100 100 100 100 100 100
#> No answer abs 1 0 0 0 0 0 0 0 0 0 0
#> in % 1 0 0 0 0 0 0 0 0 0 0
#> FILTER abs 25 0 0 0 0 0 0 0 0 0 0
#> in % 25 0 0 0 0 0 0 0 0 0 0
#>
#>
#> $`5`
#> $`5`[[1]]
#> # Q3. Please rate on a scale from 1 to 5 how much you like this fruit TOP2 overview
#> TOTAL Likes ----- ----- Favo… ----- ----- ----- Frui… ----- -----
#> Appl… Bana… Oran… Apple Bana… Oran… Othe… 3 or… 4 or… 6 an…
#> VALID CASES (at le… abs 74 57 54 54 27 12 19 9 18 22 27
#> Apple TOP2 40.4 40.4 47.6 35 81.5 0 7.69 0 50 50 35
#> VALI… 57 57 42 40 27 8 13 4 14 18 20
#> Banana TOP2 31.5 35.7 31.5 28.6 33.3 58.3 15.4 0 43.8 37.5 17.6
#> VALI… 54 42 54 42 21 12 13 3 16 16 17
#> Orange TOP2 50 50 57.1 50 40 22.2 78.9 0 61.5 60 30
#> VALI… 54 40 42 54 15 9 19 5 13 15 20
#> Others TOP2 31.6 32.6 28.6 31.0 22.2 18.2 28.6 66.7 38.5 47.1 13.6
#> VALI… 57 43 42 42 18 11 14 9 13 17 22
#>
#> $`5`[[2]]
#> # Q3. Please rate on a scale from 1 to 5 how much you like this fruit Summary of means
#> TOTAL Likes ----- ----- Favo… ----- ----- ----- Frui… ----- -----
#> Appl… Bana… Oran… Apple Bana… Oran… Othe… 3 or… 4 or… 6 an…
#> VALID CASES (at le… abs 74 57 54 54 27 12 19 9 18 22 27
#> Apple Mean 2.95 2.95 3.26 2.78 4.04 2 1.77 2.25 3.29 3 2.85
#> VALI… 57 57 42 40 27 8 13 4 14 18 20
#> Banana Mean 2.80 2.86 2.80 2.74 2.86 3.83 2.15 1.67 3.06 2.88 2.59
#> VALI… 54 42 54 42 21 12 13 3 16 16 17
#> Orange Mean 3.43 3.42 3.64 3.43 3.13 2.67 4.32 1.6 3.69 3.6 2.95
#> VALI… 54 40 42 54 15 9 19 5 13 15 20
#> Others Mean 2.79 2.74 2.76 2.69 2.56 2.91 2.29 3.89 3.15 3.29 2.18
#> VALI… 57 43 42 42 18 11 14 9 13 17 22
#>
#> $`5`[[3]]
#> # Q3. Please rate on a scale from 1 to 5 how much you like this fruit Apple
#> TOTAL Likes ------ ------ Favori… ------ ------ ------ Fruit… ------ ------
#> Apples Banan… Orang… Apple Banana Orange Others 3 or … 4 or 5 6 and…
#> TOTAL abs 100 57 54 54 27 12 19 9 18 22 27
#> 5 = I love it abs 11 11 10 7 11 0 0 0 4 5 2
#> in % 19.3 19.3 23.8 17.5 40.7 0 0 0 28.6 27.8 10
#> 4 abs 12 12 10 7 11 0 1 0 3 4 5
#> in % 21.1 21.1 23.8 17.5 40.7 0 7.69 0 21.4 22.2 25
#> 3 abs 9 9 8 6 2 2 2 2 2 1 5
#> in % 15.8 15.8 19.0 15 7.41 25 15.4 50 14.3 5.56 25
#> 2 abs 13 13 9 10 1 4 3 1 3 2 4
#> in % 22.8 22.8 21.4 25 3.70 50 23.1 25 21.4 11.1 20
#> 1 = It's not bad abs 12 12 5 10 2 2 7 1 2 6 4
#> in % 21.1 21.1 11.9 25 7.41 25 53.8 25 14.3 33.3 20
#> SUMMARY TOP-2 abs 23 23 20 14 22 0 1 0 7 9 7
#> TOP-2 in % 40.4 40.4 47.6 35 81.5 0 7.69 0 50 50 35
#> MID-1 abs 9 9 8 6 2 2 2 2 2 1 5
#> MID-1 in % 15.8 15.8 19.0 15 7.41 25 15.4 50 14.3 5.56 25
#> LOW-2 abs 25 25 14 20 3 6 10 2 5 8 8
#> LOW-2 in % 43.9 43.9 33.3 50 11.1 75 76.9 50 35.7 44.4 40
#> Mean 2.95 2.95 3.26 2.78 4.04 2 1.77 2.25 3.29 3 2.85
#> Std.Err. Of Mean 0.191 0.191 0.210 0.231 0.223 0.267 0.281 0.479 0.398 0.404 0.293
#> VALID CASES abs 57 57 42 40 27 8 13 4 14 18 20
#> in % 57 100 77.8 74.1 100 66.7 68.4 44.4 77.8 81.8 74.1
#> FILTER abs 43 0 12 14 0 4 6 5 4 4 7
#> in % 43 0 22.2 25.9 0 33.3 31.6 55.6 22.2 18.2 25.9
#>
#> $`5`[[4]]
#> # Q3. Please rate on a scale from 1 to 5 how much you like this fruit Banana
#> TOTAL Likes ------- ------ Favor… ------- ------ ------- Fruit… ------ ------
#> Apples Bananas Orang… Apple Banana Orange Others 3 or … 4 or 5 6 and…
#> TOTAL abs 100 57 54 54 27 12 19 9 18 22 27
#> 5 = I love it abs 7 6 7 5 3 4 0 0 4 3 0
#> in % 13.0 14.3 13.0 11.9 14.3 33.3 0 0 25 18.8 0
#> 4 abs 10 9 10 7 4 3 2 0 3 3 3
#> in % 18.5 21.4 18.5 16.7 19.0 25 15.4 0 18.8 18.8 17.6
#> 3 abs 10 7 10 9 4 4 1 0 2 1 6
#> in % 18.5 16.7 18.5 21.4 19.0 33.3 7.69 0 12.5 6.25 35.3
#> 2 abs 19 13 19 14 7 1 7 2 4 7 6
#> in % 35.2 31.0 35.2 33.3 33.3 8.33 53.8 66.7 25 43.8 35.3
#> 1 = It's not bad abs 8 7 8 7 3 0 3 1 3 2 2
#> in % 14.8 16.7 14.8 16.7 14.3 0 23.1 33.3 18.8 12.5 11.8
#> SUMMARY TOP-2 abs 17 15 17 12 7 7 2 0 7 6 3
#> TOP-2 in % 31.5 35.7 31.5 28.6 33.3 58.3 15.4 0 43.8 37.5 17.6
#> MID-1 abs 10 7 10 9 4 4 1 0 2 1 6
#> MID-1 in % 18.5 16.7 18.5 21.4 19.0 33.3 7.69 0 12.5 6.25 35.3
#> LOW-2 abs 27 20 27 21 10 1 10 3 7 9 8
#> LOW-2 in % 50 47.6 50 50 47.6 8.33 76.9 100 43.8 56.2 47.1
#> Mean 2.80 2.86 2.80 2.74 2.86 3.83 2.15 1.67 3.06 2.88 2.59
#> Std.Err. Of Mean 0.174 0.206 0.174 0.196 0.287 0.297 0.274 0.333 0.382 0.352 0.228
#> VALID CASES abs 54 42 54 42 21 12 13 3 16 16 17
#> in % 54 73.7 100 77.8 77.8 100 68.4 33.3 88.9 72.7 63.0
#> FILTER abs 46 15 0 12 6 0 6 6 2 6 10
#> in % 46 26.3 0 22.2 22.2 0 31.6 66.7 11.1 27.3 37.0
#>
#> $`5`[[5]]
#> # Q3. Please rate on a scale from 1 to 5 how much you like this fruit Orange
#> TOTAL Likes ------ ------- Favor… ------ ------- ----- Fruit… ----- ------
#> Apples Banan… Oranges Apple Banana Orange Othe… 3 or … 4 or… 6 and…
#> TOTAL abs 100 57 54 54 27 12 19 9 18 22 27
#> 5 = I love it abs 18 13 17 18 4 0 11 0 4 7 4
#> in % 33.3 32.5 40.5 33.3 26.7 0 57.9 0 30.8 46.7 20
#> 4 abs 9 7 7 9 2 2 4 0 4 2 2
#> in % 16.7 17.5 16.7 16.7 13.3 22.2 21.1 0 30.8 13.3 10
#> 3 abs 12 9 8 12 4 3 3 1 3 0 8
#> in % 22.2 22.5 19.0 22.2 26.7 33.3 15.8 20 23.1 0 40
#> 2 abs 8 6 6 8 2 3 1 1 1 5 1
#> in % 14.8 15 14.3 14.8 13.3 33.3 5.26 20 7.69 33.3 5
#> 1 = It's not bad abs 7 5 4 7 3 1 0 3 1 1 5
#> in % 13.0 12.5 9.52 13.0 20 11.1 0 60 7.69 6.67 25
#> SUMMARY TOP-2 abs 27 20 24 27 6 2 15 0 8 9 6
#> TOP-2 in % 50 50 57.1 50 40 22.2 78.9 0 61.5 60 30
#> MID-1 abs 12 9 8 12 4 3 3 1 3 0 8
#> MID-1 in % 22.2 22.5 19.0 22.2 26.7 33.3 15.8 20 23.1 0 40
#> LOW-2 abs 15 11 10 15 5 4 1 4 2 6 6
#> LOW-2 in % 27.8 27.5 23.8 27.8 33.3 44.4 5.26 80 15.4 40 30
#> Mean 3.43 3.42 3.64 3.43 3.13 2.67 4.32 1.6 3.69 3.6 2.95
#> Std.Err. Of Mean 0.194 0.223 0.215 0.194 0.389 0.333 0.217 0.4 0.347 0.4 0.320
#> VALID CASES abs 54 40 42 54 15 9 19 5 13 15 20
#> in % 54 70.2 77.8 100 55.6 75 100 55.6 72.2 68.2 74.1
#> FILTER abs 46 17 12 0 12 3 0 4 5 7 7
#> in % 46 29.8 22.2 0 44.4 25 0 44.4 27.8 31.8 25.9
#>
#> $`5`[[6]]
#> # Q3. Please rate on a scale from 1 to 5 how much you like this fruit Others
#> TOTAL Likes ------ ------ Favor… ------ ------ ------- Fruit… ------ ------
#> Apples Banan… Orang… Apple Banana Orange Others 3 or … 4 or 5 6 and…
#> TOTAL abs 100 57 54 54 27 12 19 9 18 22 27
#> 5 = I love it abs 8 6 7 4 2 2 1 2 1 5 1
#> in % 14.0 14.0 16.7 9.52 11.1 18.2 7.14 22.2 7.69 29.4 4.55
#> 4 abs 10 8 5 9 2 0 3 4 4 3 2
#> in % 17.5 18.6 11.9 21.4 11.1 0 21.4 44.4 30.8 17.6 9.09
#> 3 abs 14 10 11 10 5 5 1 3 5 4 5
#> in % 24.6 23.3 26.2 23.8 27.8 45.5 7.14 33.3 38.5 23.5 22.7
#> 2 abs 12 7 9 8 4 3 3 0 2 2 6
#> in % 21.1 16.3 21.4 19.0 22.2 27.3 21.4 0 15.4 11.8 27.3
#> 1 = It's not bad abs 13 12 10 11 5 1 6 0 1 3 8
#> in % 22.8 27.9 23.8 26.2 27.8 9.09 42.9 0 7.69 17.6 36.4
#> SUMMARY TOP-2 abs 18 14 12 13 4 2 4 6 5 8 3
#> TOP-2 in % 31.6 32.6 28.6 31.0 22.2 18.2 28.6 66.7 38.5 47.1 13.6
#> MID-1 abs 14 10 11 10 5 5 1 3 5 4 5
#> MID-1 in % 24.6 23.3 26.2 23.8 27.8 45.5 7.14 33.3 38.5 23.5 22.7
#> LOW-2 abs 25 19 19 19 9 4 9 0 3 5 14
#> LOW-2 in % 43.9 44.2 45.2 45.2 50 36.4 64.3 0 23.1 29.4 63.6
#> Mean 2.79 2.74 2.76 2.69 2.56 2.91 2.29 3.89 3.15 3.29 2.18
#> Std.Err. Of Mean 0.180 0.216 0.215 0.206 0.315 0.368 0.384 0.261 0.296 0.361 0.252
#> VALID CASES abs 57 43 42 42 18 11 14 9 13 17 22
#> in % 57 75.4 77.8 77.8 66.7 91.7 73.7 100 72.2 77.3 81.5
#> FILTER abs 43 14 12 12 9 1 5 0 5 5 5
#> in % 43 24.6 22.2 22.2 33.3 8.33 26.3 0 27.8 22.7 18.5
#>
#>
#> $`6`
#> $`6`[[1]]
#> # Q4. What's your favorite fruit?
#> TOTAL Likes ----- ----- Favo… ----- ----- ----- Frui… ----- -----
#> Appl… Bana… Oran… Apple Bana… Oran… Othe… 3 or… 4 or… 6 an…
#> TOTAL abs 100 57 54 54 27 12 19 9 18 22 27
#> Apple abs 27 27 21 15 27 0 0 0 8 8 11
#> in % 40.3 51.9 42.9 31.2 100 0 0 0 44.4 36.4 40.7
#> Banana abs 12 8 12 9 0 12 0 0 2 5 5
#> in % 17.9 15.4 24.5 18.8 0 100 0 0 11.1 22.7 18.5
#> Orange abs 19 13 13 19 0 0 19 0 5 6 8
#> in % 28.4 25 26.5 39.6 0 0 100 0 27.8 27.3 29.6
#> Others abs 9 4 3 5 0 0 0 9 3 3 3
#> in % 13.4 7.69 6.12 10.4 0 0 0 100 16.7 13.6 11.1
#> VALID CASES abs 67 52 49 48 27 12 19 9 18 22 27
#> in % 67 91.2 90.7 88.9 100 100 100 100 100 100 100
#> No answer abs 7 5 5 6 0 0 0 0 0 0 0
#> in % 7 8.77 9.26 11.1 0 0 0 0 0 0 0
#> FILTER abs 26 0 0 0 0 0 0 0 0 0 0
#> in % 26 0 0 0 0 0 0 0 0 0 0
#>
#>
#> $`7`
#> $`7`[[1]]
#> # Q5. How many of your favorite fruit do you like to eat?
#> TOTAL Likes ------ ------ Favori… ------- ------- ------- Fruit … ------- -------
#> Apples Banan… Orang… Apple Banana Orange Others 3 or l… 4 or 5 6 and …
#> TOTAL abs 100 57 54 54 27 12 19 9 18 22 27
#> 1 abs 1 0 0 1 0 0 0 1 1 0 0
#> in % 1.49 0 0 2.08 0 0 0 11.1 5.56 0 0
#> 2 abs 12 9 11 7 6 1 3 2 12 0 0
#> in % 17.9 17.3 22.4 14.6 22.2 8.33 15.8 22.2 66.7 0 0
#> 3 abs 5 5 5 5 2 1 2 0 5 0 0
#> in % 7.46 9.62 10.2 10.4 7.41 8.33 10.5 0 27.8 0 0
#> 4 abs 15 12 11 10 7 3 2 3 0 15 0
#> in % 22.4 23.1 22.4 20.8 25.9 25 10.5 33.3 0 68.2 0
#> 5 abs 7 6 5 5 1 2 4 0 0 7 0
#> in % 10.4 11.5 10.2 10.4 3.70 16.7 21.1 0 0 31.8 0
#> 6 abs 13 11 8 8 6 3 1 3 0 0 13
#> in % 19.4 21.2 16.3 16.7 22.2 25 5.26 33.3 0 0 48.1
#> 7 abs 5 3 1 5 1 0 4 0 0 0 5
#> in % 7.46 5.77 2.04 10.4 3.70 0 21.1 0 0 0 18.5
#> 8 abs 6 3 5 5 2 2 2 0 0 0 6
#> in % 8.96 5.77 10.2 10.4 7.41 16.7 10.5 0 0 0 22.2
#> 9 abs 1 1 1 1 1 0 0 0 0 0 1
#> in % 1.49 1.92 2.04 2.08 3.70 0 0 0 0 0 3.70
#> 10 abs 2 2 2 1 1 0 1 0 0 0 2
#> in % 2.99 3.85 4.08 2.08 3.70 0 5.26 0 0 0 7.41
#> SUMMARY Up t… abs 18 14 16 13 8 2 5 3 18 0 0
#> Up t… in % 26.9 26.9 32.7 27.1 29.6 16.7 26.3 33.3 100 0 0
#> 4 or… abs 22 18 16 15 8 5 6 3 0 22 0
#> 4 or… in % 32.8 34.6 32.7 31.2 29.6 41.7 31.6 33.3 0 100 0
#> More… abs 27 20 17 20 11 5 8 3 0 0 27
#> More… in % 40.3 38.5 34.7 41.7 40.7 41.7 42.1 33.3 0 0 100
#> Mean 4.85 4.83 4.69 4.94 4.78 5.08 5.26 3.89 2.22 4.32 7.04
#> Median 5 4.5 4 5 4 5 5 4 2 4 7
#> Std.Err. Of Mean 0.265 0.293 0.325 0.314 0.441 0.529 0.529 0.633 0.129 0.102 0.242
#> VALID CASES abs 67 52 49 48 27 12 19 9 18 22 27
#> in % 67 91.2 90.7 88.9 100 100 100 100 100 100 100
#> FILTER abs 33 5 5 6 0 0 0 0 0 0 0
#> in % 33 8.77 9.26 11.1 0 0 0 0 0 0 0In the following, we’ll have a closer look, how this works.
crosstabser’s example file mapping_file to generate
crosstabs was built by editing the empty template of an Excel mapping
file. For every data set df, this is how you can generate
the empty mapping file (named "mapping.xlsx" here) from the
template:
You can also see the relevant parts in the sheets of
mapping_file here, in the following 5 data.frames:
Variables
#> # A tibble: 1 × 6
#> var type varlab new_label new_name op
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 q4 double What's your favorite fruit? Favorite fruit <NA> <NA>The variable label of q4 is changed to
"Favorite fruit".
Label
#> # A tibble: 3 × 7
#> var nv vallab new_label sum_var_label sum_var_value sum_var_vallab
#> <chr> <dbl> <chr> <chr> <chr> <dbl> <chr>
#> 1 q2_1 1 Selected <NA> Likes 1 Apples
#> 2 q2_2 1 Selected <NA> Likes 1 Bananas
#> 3 q2_3 1 Selected <NA> Likes 1 OrangesThis creates the 3 variables kq2_1, kq2_2
& kq2_3 from q2_1, q2_2 &
q2_3 with the variable label “Likes” and the respective
value labels “Apples”, “Bananas” & “Oranges”. The created variables
are NA if the respective fruits weren’t selected, and serve
as the basis to calculate and label the corresponding headers in the
crosstabs (see Macro sheet).
Free1
#> # A tibble: 15 × 6
#> row X1 X2 X3 X4 X5
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1 #COMP n_fruits ((dat_mod |> dplyr::select(q2_1:q2_97)) == 1) |> rowSums() <NA> <NA>
#> 2 2 #IF n_fruits == 0 & q1 == 1 q2_99 = 1 <NA> <NA>
#> 3 3 #VARL q2_99 No answer <NA> <NA>
#> 4 5 #VARL q{2 3}_1 Apple <NA> <NA>
#> 5 6 #VARL q{2 3}_2 Banana <NA> <NA>
#> 6 7 #VARL q{2 3}_3 Orange <NA> <NA>
#> 7 8 #VARL q{2 3}_97 Others <NA> <NA>
#> 8 10 #IF n_fruits == 1 & q2_{1 2 3 97} == 1 q4 = {1 2 3 97} <NA> <NA>
#> 9 12 #RMVAL q4 kq4 <NA> <NA>
#> 10 13 <NA> -2 <NA> <NA> <NA>
#> 11 14 . 99 <NA> <NA> <NA>
#> 12 16 #REC q5 kq5 Fruit number category <NA>
#> 13 17 <NA> 1 3 1 3 or less
#> 14 18 <NA> 4 5 2 4 or 5
#> 15 19 . 6 Inf 3 6 and moreThe following calculations are done:
n_fruits: The number of fruits selected in
Q2.Q2, but it was asked
according to Q1, Q2 is coded as “No
answer”.Q2, Q4
wasn’t asked and we set Q4 to this fruit.Q2 & Q3 (which
provides the row labels in the crosstabs) are changed to “Apple”,
“Banana” & “Orange” & “Other”, respectively.kq4 / kq5 are
generated (also for use in the Macro sheet):
kq4: with a subset of values (NA
otherwise)kq5: labelled values of summarized intervalsMacro
#> # A tibble: 4 × 2
#> A E
#> <chr> <chr>
#> 1 Scenario 1
#> 2 ColVar kq2_1 kq2_2 kq2_3 kq4 kq5
#> 3 Invalid -1,-3,-2
#> 4 Gesamtstichprobe 1=1We only need the column where Scenario is equal to the
value "V_Scenario" specified in the mapping.
(This allows to store varying parameter sets for different scenarios. And by using this named region in Excel formulas, you can also enter scenario-specific commands in the Excel file.)
The only relevant argument in this case is the value in the
"ColVar" row. The variable names are entered in a space
separated list, corresponding to this character vector:
ColVar <- c("kq2_1", "kq2_2", "kq2_3", "kq4", "kq5")Questions
#> # A tibble: 5 × 11
#> Title RowVar Type Unguelt CatRec CatLab MetrMac Fussnote Sort MdgMissLab RepOV
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Q1. Do you like fruits? q1 cat <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 Q2. Which fruits do you like? q2_1 q2_2 q2_3 q2_97 mdg q2_99 <NA> <NA> <NA> FILTER: Q1 ≠ 'Yes'. <NA> FILTER <NA>
#> 3 Q3. Please rate on a scale from 1 to 5 how much you like this fruit q3_1 q3_2 q3_3 q3_97 mw <NA> (1 THRU 2=3) (3=2) (4 THRU 5=1) 1 'TOP-2' 2 'MID-1' 3 'LOW-2' S1E2 FILTER: Fruit selected in Q2. ORDER=D <NA> TOP2:(4 THRU 5=100) (1 THRU 5=0)
#> 4 Q4. What's your favorite fruit? q4 cat 99, -2 <NA> <NA> <NA> FILTER: Fruits selected in Q2. <NA> <NA> <NA>
#> 5 Q5. How many of your favorite fruit do you like to eat? q5 cat <NA> (1 THRU 3 = 1) (4,5 = 2) (6 THRU HI = 3) 1 'Up to 3' 2 '4 or 5' 3 'More than 5' S1M0E2 FILTER: Q1 ≠ 'Yes'. <NA> <NA> <NA>Only the columns RowVar & Type are
mandatory for every table. The other options allow for further
customization of the tables. For the various options how to modify the
crosstabs, please have a look at
vignette("questions-parameters").
The following 5 are tabulated:
Q1: Single answer questionQ2: Multiple answer question, “No answer” is invalid,
and we label as “FILTER” if no variable is selected (also invalid).Q3: Item battery:
Q4: Single choice question, “No answer” is set to
invalid.Q5: Like the single items in Q3 this
variable is tabulated with additional rows for summarized intervals, as
well as the mean & its standard error.Next, we’ll show how we can apply the data modifications and calculate the crosstabs in the mapping.
The data is modified by applying the commands in the following sheets
(using datadaptor::Mapping$modify_data()
under the hood):
Variables: This modifies the data set’s variables and
their labels.Label: This modifies the data set’s variables’ value
labels and allows to generate summary variables.Free1: This allows to define additional commands
modifying the data. All the modifications of the Variables
& Label sheets could also be done there.See here for the documentation of the commands used in these sheets.
The following sheets control, which and how crosstabs are calculated:
Macro: This allows to define the additional header
variables for the x-axis of the crosstabs.Questions: The rows in this table define the crosstabs
that are calculated.Instead of using an Excel mapping file, we can also define the mapping entirely in R without the need of an Excel file by passing the relevant data as a list: