How to use opencis

Usage

Searching for studies, questions and series

search_cis() searches the CIS catalogue and returns a tibble with matching results. The catalogo argument controls what type of item is searched: "estudio" (default), "pregunta" or "serie". You can restrict results to a date range with from and to, and change the sort order with sort ("relevance", "publishDate-", "publishDate+").

library(opencis)

# Search for survey studies
search_cis(q = "preelectoral", from = "2020-01-01", to = "2023-11-17")

# Search for survey questions
search_cis(q = "feminismo", catalogo = "pregunta")

# Search for data series
search_cis(q = "situación económica", catalogo = "serie")

Advanced search

The q argument accepts the advanced Lucene syntax supported by the CIS catalog. An advanced expression must start with *. Pass the expression as plain text; opencis handles URL encoding.

# Match any of several study codes
search_cis(q = "*surveyCode:(2610 OR 2829 OR 2956)")

# Require barometro and 2024 in the title, but exclude sanitario
search_cis(q = "*title_es_ES:(+barometro +2024 -sanitario)")

# Search question text and retrieve every result page
search_all_cis(
  q = "*question_es_ES:(divorcio)",
  catalogo = "pregunta"
)

# Combine fields: questions about divorce from studies after number 3540
search_all_cis(
  q = "*question_es_ES:(divorcio) AND surveyCodeNumber:{3540 TO *]",
  catalogo = "pregunta"
)

# Search for an exact phrase
search_cis(q = '*title_es_ES:"barómetro de la vivienda"')

# Search a numeric study-code range (used in the CIS documentation)
search_cis(q = "*(surveyCodeNumber:[3000 TO 3002])")

The CIS documents the following searchable fields. Catalog data are currently indexed only in Spanish (es_ES).

Field	Indexing	Contents
`surveyCode`	keyword	Study code
`questionCode`	keyword	Question code
`serieCode`	keyword	Series code
`title_es_ES`	text	Study, question, or series title
`description_es_ES`	text	Study or series description
`publishDate`	text	Study/question date, or latest series date
`question_es_ES`	text	Study question wording
`questionText_es_ES`	text	Study question text
`descriptors_es_ES`	text	Question descriptors
`collections_es_ES`	text	Study collection names
`publications_es_ES`	text	Study publication names
`subjectIndexes_es_ES`	text	Study subject indexes
`orders_es_ES`	text	Study commissioners
`authors_es_ES`	text	Study authors

The CIS also uses surveyCodeNumber in its numeric-range example, although it is not included in the site’s field table. Treat that field as server-specific and potentially less stable than the documented list.

Lucene expressions can combine AND, OR, required terms (+), excluded terms (-), quoted phrases, ranges, and term boosting with ^. Invalid syntax may return no results. Advanced searches also use a different relevance order from simple searches and do not highlight matches.

By default search_cis() returns only the first page of results. Use search_all_cis() to automatically paginate through all pages and get every matching result in a single tibble:

# Retrieve all postelectoral studies (all pages)
all_studies <- search_all_cis(q = "postelectoral")
print(nrow(all_studies))

# Filter by date range across all pages
studies <- search_all_cis(
  q    = "ideologia",
  from = "2010-01-01",
  to   = "2020-12-31"
)

search_all_cis() accepts the same arguments as search_cis().

Reading study data into R

read_cis() downloads the SPSS data file for a study and imports it directly into R as a labelled data frame (via haven):

df <- read_cis(3411)
print(df)

Exploring variables: the data dictionary

After loading a study with read_cis(), use get_data_dictionary() to obtain a tidy tibble with every variable name, its label and its value labels:

df   <- read_cis(3328)
dict <- get_data_dictionary(df)
print(dict)

# Find variables whose label contains a keyword
dict[grepl("sexo", dict$label, ignore.case = TRUE), ]

# Inspect value labels for a specific variable
dict$value_labels[[which(dict$variable == "SEXO")]]

Getting study metadata

get_metadata() retrieves the technical information sheet of a study from the CIS website — field dates, study type, country, authorship, thematic indices, etc. — and returns it as a two-column tibble (field, value):

meta <- get_metadata(3328)
print(meta)

Downloading the ZIP file to disk

If you want to keep the raw data files instead of reading them into a temporary directory, use download_study(). It saves the ZIP archive to any local folder:

# Save to the current working directory
path <- download_study(3328)
cat("Saved to:", path, "\n")

# Save to a specific folder
path <- download_study(3328, destdir = "data/raw")
cat("Saved to:", path, "\n")

Browsing the questionnaire and technical sheet

browse_pdf() extracts the PDF documents bundled inside the study ZIP and opens them in your default browser. CIS ZIPs typically include two PDFs:

Questionnaire (wanted_file = "cues", default)
Technical sheet (wanted_file = "ft")

# Open the questionnaire for study 3328
browse_pdf(3328)

# Open the technical sheet
browse_pdf(3328, wanted_file = "ft")