knit_by_pkgdown <- !is.null(knitr::opts_chunk$get("fig.retina"))
knitr::opts_chunk$set(warning = FALSE, message = FALSE, error = FALSE, echo = TRUE)
ggplot2::theme_set(ggplot2::theme_bw())
library(codebook)
library(dplyr)

Here, I try to demonstrate that we can also use the metadata generated by Qualtrics. Because I am not a Qualtrics user, the developer of the qualtRics R package sent me two example files, one of data and one for metadata, which I use here. Usually, you’d import these files directly via his package.

Load

# library(qualtRics) # currently not on CRAN, so commented out
results <- readRDS(system.file("extdata", "ryan.rds", package = "codebook"))
metadata_ex <- readRDS(system.file("extdata", "metadata.rds", package = "codebook"))

Now, we want the metadata not to be just independent of the data frame. It’s easier to change metadata if it is in the form of a list at the dataset level, so let’s use rio::gather_attrs for that.

results <- results %>% rio::gather_attrs()
attributes(results)$label$Q7

##                                                                                  Q7 
## "How reasonable or unreasonable is the cost of courses/tuition at this university?"

Now, we can loop over the metadata list and put it in the right shape to become attributes. This means given each element the name of the variable it refers to.

names(metadata_ex$questions) <- lapply(metadata_ex$questions, function(x) {
  x$questionName
})

We only want the questions that we have data for.

qs <- names(metadata_ex$questions)
qs <- qs[qs %in% names(attributes(results)$label)]

Now, we assign our metadata list to the attributes of the data.frame.

init <- vector("list", ncol(results)) 
names(init) <- names(results)
attributes(results)$item <- init
attributes(results)$item[qs] <- metadata_ex$questions[qs]

And use rio to put the attributes on the question level again.

results <- results %>% rio::spread_attrs()

To keep this example, we select a subset of variables.

results <- results %>% select(ResponseSet, Q7, Q10)
if (!knit_by_pkgdown) knitr::opts_chunk$set(echo = FALSE)

Please note, that we just reuse the metadata column names from Qualtrics. Standardising this across multiple survey providers would be great but requires more knowledge of Qualtrics than I have.

metadata(results)$name <- "MOCK Qualtrics dataset"
metadata(results)$description <- "a MOCK dataset used to show how to import Qualtrics metadata into the codebook R package"
metadata(results)$identifier <- "doi:10.5281/zenodo.1326520"
metadata(results)$datePublished <- "2018-08-01"
metadata(results)$creator <- list(
      "@type" = "Person",
      givenName = "Ruben", familyName = "Arslan",
      email = "ruben.arslan@gmail.com", 
      affiliation = list("@type" = "Organization",
        name = "MPI Human Development, Berlin"))
metadata(results)$url <- "https://rubenarslan.github.io/codebook/articles/codebook_qualtrics.html"
metadata(results)$temporalCoverage <- "2018" 
metadata(results)$spatialCoverage <- "Nowhere"

# We don't want to look at the code in the codebook.
knitr::opts_chunk$set(warning = TRUE, message = TRUE, echo = FALSE)

## No missing values.

Metadata

Description

Dataset name: MOCK Qualtrics dataset

a MOCK dataset used to show how to import Qualtrics metadata into the codebook R package

Metadata for search engines

Temporal Coverage: 2018
Spatial Coverage: Nowhere
URL: https://rubenarslan.github.io/codebook/articles/codebook_qualtrics.html
Identifier: doi:10.5281/zenodo.1326520
Date published: 2018-08-01
Creator:

name	value
@type	Person
givenName	Ruben
familyName	Arslan
email	ruben.arslan@gmail.com
affiliation	Organization , MPI Human Development, Berlin

x
ResponseSet
Q7
Q10

Variables

ResponseSet

Distribution of values for ResponseSet

0 missing values.

name	label	data_type	n_missing	complete_rate	n_unique	empty	min	max	whitespace
ResponseSet	ResponseSet	character	0	1	1	0	20	20	0

Q7

How reasonable or unreasonable is the cost of courses/tuition at this university?

Distribution of values for Q7

0 missing values.

name	label	type	data_type	ordered	value_labels	n_missing	complete_rate	n_unique	top_counts	selector	subSelector	questionText	doesForceResponse	questionName
Q7	How reasonable or unreasonable is the cost of courses/tuition at this university?	MC	factor	TRUE	1. Extremely reasonable, 2. Moderately reasonable, 3. Slightly reasonable, 4. Neither reasonable nor unreasonable, 5. Slightly unreasonable, 6. Moderately unreasonable, 7. Extremely unreasonable	0	1	2	Mod: 1, Sli: 1, Ext: 0, Nei: 0	SAVR	TX	How reasonable or unreasonable is the cost of courses/tuition at this university?	FALSE	Q7

Item options
type	selector	subSelector	questionText	doesForceResponse	questionName
MC	SAVR	TX	How reasonable or unreasonable is the cost of courses/tuition at this university?	FALSE	Q7

Response choices
name	value
1	1 , Extremely reasonable, Extremely reasonable, TRUE
2	2 , Moderately reasonable, Moderately reasonable, TRUE
3	3 , Slightly reasonable, Slightly reasonable, TRUE
4	4 , Neither reasonable nor unreasonable, Neither reasonable nor unreasonable, TRUE
5	5 , Slightly unreasonable, Slightly unreasonable, TRUE
6	6 , Moderately unreasonable, Moderately unreasonable, TRUE
7	7 , Extremely unreasonable, Extremely unreasonable, TRUE

Q10

How helpful or unhelpful is the staff at the on-campus health center?

Distribution of values for Q10

0 missing values.

name	label	type	data_type	ordered	value_labels	n_missing	complete_rate	n_unique	top_counts	selector	subSelector	questionText	doesForceResponse	questionName
Q10	How helpful or unhelpful is the staff at the on-campus health center?	MC	factor	TRUE	1. Extremely helpful, 2. Moderately helpful, 3. Slightly helpful, 4. Neither helpful nor unhelpful, 5. Slightly unhelpful, 6. Moderately unhelpful, 7. Extremely unhelpful	0	1	2	Mod: 1, Ext: 1, Ext: 0, Sli: 0	SAVR	TX	How helpful or unhelpful is the staff at the on-campus health center?	FALSE	Q10

Item options
type	selector	subSelector	questionText	doesForceResponse	questionName
MC	SAVR	TX	How helpful or unhelpful is the staff at the on-campus health center?	FALSE	Q10

Response choices
name	value
1	1 , Extremely helpful, Extremely helpful, TRUE
2	2 , Moderately helpful, Moderately helpful, TRUE
3	3 , Slightly helpful, Slightly helpful, TRUE
4	4 , Neither helpful nor unhelpful, Neither helpful nor unhelpful, TRUE
5	5 , Slightly unhelpful, Slightly unhelpful, TRUE
6	6 , Moderately unhelpful, Moderately unhelpful, TRUE
7	7 , Extremely unhelpful, Extremely unhelpful, TRUE

Missingness report

Codebook table

JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "MOCK Qualtrics dataset",
  "description": "a MOCK dataset used to show how to import Qualtrics metadata into the codebook R package\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name        |label | n_missing|\n|:-----------|:-----|---------:|\n|ResponseSet |NA    |         0|\n|Q7          |NA    |         0|\n|Q10         |NA    |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.5).",
  "identifier": "doi:10.5281/zenodo.1326520",
  "datePublished": "2018-08-01",
  "creator": {
    "@type": "Person",
    "givenName": "Ruben",
    "familyName": "Arslan",
    "email": "ruben.arslan@gmail.com",
    "affiliation": {
      "@type": "Organization",
      "name": "MPI Human Development, Berlin"
    }
  },
  "url": "https://rubenarslan.github.io/codebook/articles/codebook_qualtrics.html",
  "temporalCoverage": "2018",
  "spatialCoverage": "Nowhere",
  "keywords": ["ResponseSet", "Q7", "Q10"],
  "@context": "https://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "ResponseSet",
      "description": "ResponseSet",
      "@type": "propertyValue"
    },
    {
      "name": "Q7",
      "description": "How reasonable or unreasonable is the cost of courses/tuition at this university?",
      "value": "1. Extremely reasonable,\n2. Moderately reasonable,\n3. Slightly reasonable,\n4. Neither reasonable nor unreasonable,\n5. Slightly unreasonable,\n6. Moderately unreasonable,\n7. Extremely unreasonable",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    },
    {
      "name": "Q10",
      "description": "How helpful or unhelpful is the staff at the on-campus health center?",
      "value": "1. Extremely helpful,\n2. Moderately helpful,\n3. Slightly helpful,\n4. Neither helpful nor unhelpful,\n5. Slightly unhelpful,\n6. Moderately unhelpful,\n7. Extremely unhelpful",
      "measurementTechnique": "self-report",
      "@type": "propertyValue"
    }
  ]
}`

Codebook example with Qualtrics dataset

Ruben Arslan

2024-10-14

Load

Metadata

Description

Variables

ResponseSet

Q7

Q10

Missingness report

Codebook table