tidywikidatar

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The goal of tidywikidatar is to facilitate interaction with Wikidata:

If you want to benefit of the wealth of information stored by Wikidata, but you do not like SPARQL queries and nested lists, then you may find tidywikidatar useful. If you prefer working with nested lists and SPARQL queries, or if you plan to build more complex queries, then you should probably use WikidataR or Wikimedia’s own WikidataQueryServiceR (under the hood, tidywikidatar is largely based on those packages).

Installation

You can install the released version of tidywikidatar from CRAN with:

install.packages("tidywikidatar")

For the latest fixes and improvements, you can install the development version from Github with:

# install.packages("remotes")
remotes::install_github("EDJNet/tidywikidatar")

Limitations and known issues

tidywikidatar strives to strike a balance between ease of use and full access to information available on Wikidata. This means that, for examples, dates are returned as simple text strings, without accompanying details such as calendar (e.g. Julian or Gregorian) and precision (e.g. precise just to the level of century). Some amounts are returned as numeric strings, without the accompanying unit of measurement. The user should be aware of such issues in their own use cases, and consider using other packages if such matters are determinant for them. Recent versions of tidywikidatar include a dedicated function to get such details, tw_get_property_with_details(), but it does not currently cache results.

tidywikidatar is most useful in particular for the exploratory analysis of relatively small numbers of wikidata items (dozens or hundreds), but becomes quickly less efficient when asking for many properties or thousands of items. Functions will take their time, but will eventually complete. Some performance improvements may come with future versions of tidywikidatar, but for larger batches of data (large number of items/many properties), well formed queries will remain more efficient.

Known issues

Use cases and publicly available examples

These articles or repository demonstrate some use cases for tidywikidatar:

While the code used there may not be fully compatible or be the most efficient with the latest version of Wikidata, they still provide a useful term of reference.

See the vignette vignette("wikipedia_start") for an example of a possible workflow.

Before you start

This package assumes some familiarity with basic Wikidata concepts. For reference, see the introduction on the official website.

At the most basic, you should know that every item in Wikidata has an id (it always starts with a Q, something like Q123456). Each item is described by properties (they always start with a P, something like P1234).

So for example, if I am interested in the anthropologist Margaret Mead, I will search her name on Wikidata and discover that she is Q180099. She is described by many properties. For example, she is “an instance of” (P31) “Q5”, which means “human”. Her “sex or gender” (P21) is “Q180099”, which means, female. By “occupation” (P106), she was “Q36180”, “Q4773904”, and “Q674426”, which means, a writer, an anthropologist, and a curator. And so forth.

As you’ll see, many queries return just another wikidata id, and if you want to know what that means, you’ll need to ask for what that id stands for.

How to use

tidywikidatar makes it easy to cache locally responses (both searches and details about specific items) in a sqlite database to reduce load on Wikidata’s servers and increase processing speed. These sqlite databases are by default stored in the current working directory under a tw_data folder. It may be useful to store them in a folder where they can be retrieved easily even when working on different projects, but this is obviously a matter of personal taste. You can enable caching for the current session with tw_enable_cache(), set the cache folder to be used throughout a session with tw_set_cache_folder(), and set the language used by all functions (if not set, it defaults to English). The first lines of a script using tidywikidatar would often look like this:

library("tidywikidatar")
tw_enable_cache()
tw_set_cache_folder(path = fs::path(fs::path_home_r(), "R", "tw_data"))
tw_set_language(language = "en")
tw_create_cache_folder(ask = FALSE)

This also means that you can re-run code when offline, as data are downloaded from Wikidata’s server only at first run (that is, unless you set cache = FALSE or overwrite_cache = TRUE when calling the respective functions, or disable caching for the current session with tw_disable_cache()).

Finding details about something

Most tidywikidatar functions are built around the idea that you know what you are looking for, and just want to get what Wikidata knows about it, assuming the preferred choice would be among the top results.

Let’s take this again from the beginning. As I mentioned, I am interested in Margaret Mead, the famous pioneer anthropologist author of “Coming of Age in Samoa”. This seems quite straightforward but there are actually a number of things that are returned by searching for “Margaret Mead” that are not the woman herself.

tw_search(search = "Margaret Mead")
#> # A tibble: 10 × 3
#>    id        label                               description                    
#>    <chr>     <chr>                               <chr>                          
#>  1 Q180099   Margaret Mead                       American anthropologist        
#>  2 Q81015029 Margaret mead                       scientific article published o…
#>  3 Q66701460 Margaret Mead                       scientific article published o…
#>  4 Q85724626 Mead & Bateson                      business organisation          
#>  5 Q75281958 Lady Margaret Meade-Fetherstonhaugh British author (1888–1977)     
#>  6 Q96077616 Margaret Meadows                    (1718-1781)                    
#>  7 Q76238541 Margaret Meadowe                    Peerage person ID=628312       
#>  8 Q75506638 Margaret Meadows                    Peerage person ID=183057       
#>  9 Q75812372 Margaret Meade-Waldo                (died 1954)                    
#> 10 Q6759717  Margaret Mead Film Festival         annual film festival held in N…

If I am running through a list of strings, and, for example, I am actually interested in the most famous person by that name, I can filter result by property, using the standard form. If, for example, I want only the first result that is associated with “an instance of” (P31) - “human” (Q5), I can run:

tw_search(search = "Margaret Mead") %>%
  tw_filter_first(p = "P31", q = "Q5")
#> # A tibble: 1 × 3
#>   id      label         description            
#>   <chr>   <chr>         <chr>                  
#> 1 Q180099 Margaret Mead American anthropologist

and, as expected, I get a single output: my beloved Margaret Mead.

Where was she born? I can ask directly for P19, place of birth:

tw_get_property(id = "Q180099", p = "P19")
#> # A tibble: 1 × 4
#>   id      property value rank  
#>   <chr>   <chr>    <chr> <chr> 
#> 1 Q180099 P19      Q1345 normal

which, as expected, will give me another wikidata id. But what does, “Q1345” stand for? I should ask for its label.

tw_get_label(id = "Q1345")
#> [1] "Philadelphia"

Alright, I know where Philadelphia, but if it was a smaller place, perhaps I’d need to ask in which country it is located. So I would ask for the correspondent property, P17.

tw_get_property(id = "Q1345", p = "P17")
#> # A tibble: 1 × 4
#>   id    property value rank  
#>   <chr> <chr>    <chr> <chr> 
#> 1 Q1345 P17      Q30   normal

Oh, no, another Wikidata id! That’s the way it works… let’s ask for its label:

tw_get_label(id = "Q30")
#> [1] "United States of America"

It takes some time to get used, but I suppose you get the gist of it.

You can also pipe all of the above, like this:

tw_search(search = "Margaret Mead") %>% # search for Margeret Mead
  tw_filter_first(p = "P31", q = "Q5") %>% # keep only the first result that is of a human
  tw_get_property(p = "P19") %>% # ask for the place of birth
  dplyr::pull(value) %>% # take its result and
  tw_get_property(p = "P17") %>% # ask for the country where that place of birth is located
  tw_get_label() # ask what that id stands for
#> [1] "Philadelphia"

And here we are, we know in which country Margaret Mead was born.

The procedure above may seem a bit convoluted, but it is actually quite representative of how Wikidata stores information.

As you would expect, such functions can also be combined, for example, like this:

get_bio <- function(id, language = "en") {
  tibble::tibble(
    label = tw_get_label(id = id, language = language),
    description = tw_get_description(id = id, language = language),
    year_of_birth = tw_get_property(id = id, p = "P569") %>%
      dplyr::pull(value) %>%
      head(1) %>%
      lubridate::ymd_hms() %>%
      lubridate::year(),
    year_of_death = tw_get_property(id = id, p = "P570") %>%
      dplyr::pull(value) %>%
      head(1) %>%
      lubridate::ymd_hms() %>%
      lubridate::year()
  )
}

tw_search(search = "Margaret Mead") %>%
  tw_filter_first(p = "P31", q = "Q5") %>%
  get_bio()
#> # A tibble: 1 × 4
#>   label         description             year_of_birth year_of_death
#>   <chr>         <chr>                           <dbl>         <dbl>
#> 1 Margaret Mead American anthropologist          1901          1978

I can of course get the response in languages other than English, as long as those are available on Wikidata.

tw_search(search = "Margaret Mead") %>%
  tw_filter_first(p = "P31", q = "Q5") %>%
  get_bio(language = "it")
#> # A tibble: 1 × 4
#>   label         description              year_of_birth year_of_death
#>   <chr>         <chr>                            <dbl>         <dbl>
#> 1 Margaret Mead antropologa statunitense          1901          1978

Serial operations

More examples regarding serial operations, and streamlined queries over long lists of ids will be available in a dedicated vignette in a future version.

In the meantime, let us just say that if we wanted to have a list of all the “awards received” (P166) by Margaret Mead, and fellow anthropologists and folklorists Ruth Benedict and Zora Neale Hurston, we can achieve that in a single call:

tw_get_property(
  id = c("Q180099", "Q228822", "Q220480"),
  p = "P166",
  language = "en"
) 
#> # A tibble: 14 × 4
#>    id      property value     rank  
#>    <chr>   <chr>    <chr>     <chr> 
#>  1 Q180099 P166     Q17144    normal
#>  2 Q180099 P166     Q782022   normal
#>  3 Q180099 P166     Q8017107  normal
#>  4 Q180099 P166     Q1967852  normal
#>  5 Q180099 P166     Q52382875 normal
#>  6 Q228822 P166     Q1967852  normal
#>  7 Q228822 P166     Q52382875 normal
#>  8 Q228822 P166     Q752297   normal
#>  9 Q220480 P166     Q1316544  normal
#> 10 Q220480 P166     Q1967852  normal
#> 11 Q220480 P166     Q5461701  normal
#> 12 Q220480 P166     Q5461189  normal
#> 13 Q220480 P166     Q4765305  normal
#> 14 Q220480 P166     Q1316544  normal

Again, Wikidata ids. We can of course get their relative labels using the functions outlined above, but tidywikidatar has a convenience function - tw_label() that will achieve what you want in most such cases.

tw_get_property(
  id = c("Q180099", "Q228822", "Q220480"),
  p = "P166",
  language = "en"
) %>% 
  tw_label()
#> # A tibble: 14 × 4
#>    id                 property       value                                 rank 
#>    <chr>              <chr>          <chr>                                 <chr>
#>  1 Margaret Mead      award received Presidential Medal of Freedom         norm…
#>  2 Margaret Mead      award received Kalinga Prize                         norm…
#>  3 Margaret Mead      award received William Procter Prize for Scientific… norm…
#>  4 Margaret Mead      award received National Women's Hall of Fame         norm…
#>  5 Margaret Mead      award received AAAS Fellow                           norm…
#>  6 Ruth Benedict      award received National Women's Hall of Fame         norm…
#>  7 Ruth Benedict      award received AAAS Fellow                           norm…
#>  8 Ruth Benedict      award received Doctor of Philosophy                  norm…
#>  9 Zora Neale Hurston award received Guggenheim Fellowship                 norm…
#> 10 Zora Neale Hurston award received National Women's Hall of Fame         norm…
#> 11 Zora Neale Hurston award received Florida Women's Hall of Fame          norm…
#> 12 Zora Neale Hurston award received Florida Artists Hall of Fame          norm…
#> 13 Zora Neale Hurston award received Anisfield-Wolf Book Awards            norm…
#> 14 Zora Neale Hurston award received Guggenheim Fellowship                 norm…

Piped operations

Using the pipe (%>%) when working with Wikidata is often not straightforward, due to the fact that a given property may have an unspecified number of values. tidywikidatar offers dedicated functions to work with the pipe more consistently, in particular tw_get_property_same_length() (or its shorter alias tw_get_p()).

One main distinction to keep in mind in this context is that for some properties we really just expect to have a single value, and we are happy to dismiss other values that may be present, while in other cases we expect and want to retain more values.

For example, some Wikidata items have two reported dates of birth for a single individual, possibly due to disagreements among historians about the actual date of birth of the given person. If this is not specifically the issue we are interested it, we may well be want just to keep the first reported date of birth and dismiss the others. In other cases, we probably want to retain all properties, and process them further in subsequent steps of the pipe.

Let’s look at some of these issues with an example.

The anthropologist Franz Boas (Q76857) had many influential doctoral students (P185), including the above-mentioned Margaret Mead. Who where the others? And when and where were they born? We expect the answer to this latter questions to be unique, and we may be fine with discarding other values that may be recorded in Wikidata.

library("dplyr", warn.conflicts = FALSE)
library("tidyr")
students <-
  tw_get_property(id = "Q76857", p = "P185") %>%  # who were Boas' doctoral students?
  transmute(student_label = tw_get_label(value), # get their name
            student_id = value) # and keep their id


students %>%  
  mutate(date_of_birth = tw_get_p(id = student_id,
                                  p = "P569", # property for date of birth
                                  only_first = TRUE)) %>%
  # we don't care about possible multiple values on when they were born
  mutate(place_of_birth = tw_get_p(id = student_id,
                                   p = "P19", # property for place of birth
                                   only_first = TRUE) %>% 
           tw_get_label())
#> # A tibble: 20 × 4
#>    student_label                 student_id date_of_birth         place_of_birth
#>    <chr>                         <chr>      <chr>                 <chr>         
#>  1 Ruth Benedict                 Q228822    +1887-06-05T00:00:00Z New York City 
#>  2 Edward Sapir                  Q191095    +1884-01-26T00:00:00Z Lębork        
#>  3 Alexander Francis Chamberlain Q32178     +1865-01-01T00:00:00Z Kenninghall   
#>  4 Manuel Gamio                  Q2602445   +1883-01-01T00:00:00Z Mexico City   
#>  5 Alexander Goldenweiser        Q1396805   +1880-01-29T00:00:00Z Kyiv          
#>  6 Irving Goldman                Q6074597   +1911-09-02T00:00:00Z <NA>          
#>  7 Melville J. Herskovits        Q711288    +1895-09-10T00:00:00Z Bellefontaine 
#>  8 George Herzog                 Q15454430  +1901-12-11T00:00:00Z Budapest      
#>  9 E. Adamson Hoebel             Q5321710   +1906-01-01T00:00:00Z Madison       
#> 10 Melville Jacobs               Q6813885   +1902-07-03T00:00:00Z New York City 
#> 11 William Jones                 Q8013732   +1871-00-00T00:00:00Z <NA>          
#> 12 Alfred L. Kroeber             Q311538    +1876-06-11T00:00:00Z Hoboken       
#> 13 Alexander Lesser              Q4719396   +1902-01-01T00:00:00Z <NA>          
#> 14 Robert Lowie                  Q44968     +1883-06-12T00:00:00Z Vienna        
#> 15 Margaret Mead                 Q180099    +1901-12-16T00:00:00Z Philadelphia  
#> 16 Paul Radin                    Q557443    +1883-04-02T00:00:00Z Łódź          
#> 17 Gladys Reichard               Q15998733  +1893-07-17T00:00:00Z Bangor        
#> 18 Leslie Spier                  Q6531152   +1893-12-13T00:00:00Z <NA>          
#> 19 Ruth Sawtell Wallis           Q7383203   +1895-03-15T00:00:00Z Springfield   
#> 20 Edward A. Kennard             Q58050409  +1907-10-24T00:00:00Z <NA>

In other cases, however, we do expect multiple valid values. For example, we expect them to have a single place and date of birth, but quite possibly to have worked in different locations at different points in their career.

Here is how we may want to go if we want, for example, to create a map of all the universities where one of Franz Boas’ doctoral students has worked. We get the id of all the places where they have worked, check if they are universities or not, and then get the coordinates for the given institutions.

students %>% 
  mutate(worked_at_id = tw_get_p(id = student_id,
                                 p = "P108", # property for employer
                                 only_first = FALSE)) %>% # not only the first result
  unnest(worked_at_id) %>%
  filter(is.na(worked_at_id)==FALSE) %>% # remove those for which we have no employer
  mutate(worked_at_label = tw_get_label(worked_at_id)) %>% 
  # but keep in mind we are only interested in the employer if they are a university
  # so we ask what `instance of` the employer is. 
  mutate(employer_instance_of = tw_get_p(id = worked_at_id,
                                         p = "P31",
                                         only_first = FALSE)) %>%  
  unnest(employer_instance_of) %>% 
  mutate(employer_instance_of_label = tw_get_label(employer_instance_of)) %>% 
  # some institutions may be e.g. "instance of" -> "private university", not of "university"
  # so whe check what "subclass of" that id
  mutate(employer_instance_of2 = tw_get_p(id = worked_at_id,
                                          p = "P31",
                                          only_first = FALSE)) %>% 
  unnest(employer_instance_of2) %>% 
  mutate(employer_instance_of2_subclass_of = tw_get_p(id = employer_instance_of2,
                                                      p = "P279",
                                                      only_first = FALSE)) %>% 
  unnest(employer_instance_of2_subclass_of) %>% 
  # keep only if employer is a university (or something which is a subclass of university)
  filter(employer_instance_of == "Q3918" | employer_instance_of2_subclass_of == "Q3918") %>% 
  distinct(student_label, worked_at_id, worked_at_label) %>% 
  mutate(worked_at_coordinates = tw_get_p(worked_at_id,
                                          p = "P625",
                                          only_first = TRUE)) %>% 
  select(-worked_at_id) %>% 
  separate(worked_at_coordinates, into = c("lat", "lon"), sep = ",")
#> # A tibble: 19 × 4
#>    student_label                 worked_at_label            lat        lon      
#>    <chr>                         <chr>                      <chr>      <chr>    
#>  1 Ruth Benedict                 Columbia University        40.8075    -73.9619…
#>  2 Edward Sapir                  Yale University            41.311111… -72.9266…
#>  3 Edward Sapir                  University of Chicago      41.789722… -87.5997…
#>  4 Alexander Francis Chamberlain Clark University           42.250977  -71.8231…
#>  5 Alexander Goldenweiser        Columbia University        40.8075    -73.9619…
#>  6 Alexander Goldenweiser        University of Washington   47.654166… -122.308…
#>  7 Melville J. Herskovits        Northwestern University    42.054853  -87.6739…
#>  8 Melville J. Herskovits        Columbia University        40.8075    -73.9619…
#>  9 Melville J. Herskovits        Howard University          38.921666… -77.02   
#> 10 E. Adamson Hoebel             New York University        40.73      -73.995  
#> 11 Melville Jacobs               University of Washington   47.654166… -122.308…
#> 12 Alexander Lesser              Columbia University        40.8075    -73.9619…
#> 13 Alexander Lesser              Brandeis University        42.36566   -71.25974
#> 14 Alexander Lesser              Hofstra University         40.714605… -73.6004…
#> 15 Margaret Mead                 Columbia University        40.8075    -73.9619…
#> 16 Margaret Mead                 University of Rhode Island 41.4807    -71.5258 
#> 17 Paul Radin                    University of Chicago      41.789722… -87.5997…
#> 18 Paul Radin                    Fisk University            36.1688    -86.8047 
#> 19 Paul Radin                    Brandeis University        42.36566   -71.25974

Starting with version 0.5, to reduce typing, tw_get_p() can be used instead of the more verbose tw_get_property_same_length().

Qualifiers

In most cases, things are quite straightforward: each item has one or more values for a given property.

However, some properties have additional qualifiers.

As an example, let’s look at someone whose life is seemingly less adventurous than that of Margaret Mead, but whose Wikidata page has properties with a more interesting combination of qualifiers: the former president of the European Parliament David Sassoli (Q2391857). (this example based on David Sassoli was included in this document before his premature death in early 2022)

If we look at his “positions held” (P39), we find the following:

purrr::map_chr(
  .x = tw_get_property(id = "Q2391857", p = "P39") %>% dplyr::pull(value),
  .f = tw_get_label
)
#> [1] "President of the European Parliament"
#> [2] "member of the European Parliament"   
#> [3] "member of the European Parliament"   
#> [4] "member of the European Parliament"

He has been more than once “member of the European Parliament”, and once “President of the European Parliament”. But this is not all that Wikidata knows about it: each of these properties comes with qualifiers.

qualifiers_df <- tw_get_qualifiers(id = "Q2391857", p = "P39")
qualifiers_df
#> # A tibble: 26 × 8
#>    id    property qualifier_id qualifier_prope… qualifier_value qualifier_value…
#>    <chr> <chr>    <chr>        <chr>            <chr>           <chr>           
#>  1 Q239… P39      Q27169       P2937            Q17315694       wikibase-entity…
#>  2 Q239… P39      Q27169       P580             +2014-07-01T00… time            
#>  3 Q239… P39      Q27169       P4100            Q507343         wikibase-entity…
#>  4 Q239… P39      Q27169       P768             Q3677909        wikibase-entity…
#>  5 Q239… P39      Q27169       P1268            Q47729          wikibase-entity…
#>  6 Q239… P39      Q27169       P2715            Q1376095        wikibase-entity…
#>  7 Q239… P39      Q740126      P580             +2019-07-03T00… time            
#>  8 Q239… P39      Q740126      P1365            Q440710         wikibase-entity…
#>  9 Q239… P39      Q740126      P582             +2022-01-11T00… time            
#> 10 Q239… P39      Q740126      P1534            Q5247364        wikibase-entity…
#> # … with 16 more rows, and 2 more variables: rank <chr>, set <dbl>

As usual, Wikidata presents everything as combinations of properties and values. Let’s translate each of these to their respective label, and separate each set of information we have about the “positions held” by Mr. Sassoli:

qualifiers_labelled_df <- qualifiers_df %>%
  dplyr::transmute(
    who = tw_get_label(id = id, language = "en"),
    did = tw_get_property_label(property = property, language = "en"),
    what = tw_get_label(id = qualifier_id, language = "en"),
    how = tw_get_property_label(property = qualifier_property, language = "en"),
    value = purrr::map_chr(
      .x = qualifier_value,
      .f = function(x) {
        if (stringr::str_starts(
          string = x,
          pattern = "Q"
        )) {
          tw_get_label(
            id = x,
            language = "en"
          )
        } else {
          stringr::str_extract(
            string = x,
            pattern = "[[:digit:]]{4}-[[:digit:]]{2}-[[:digit:]]{2}"
          )
        }
      }
    ),
    set = set
  )

qualifiers_labelled_df %>%
  dplyr::group_by(set) %>%
  knitr::kable()
who did what how value set
David Sassoli position held member of the European Parliament parliamentary term Eighth European Parliament 1
David Sassoli position held member of the European Parliament start time 2014-07-01 1
David Sassoli position held member of the European Parliament parliamentary group Progressive Alliance of Socialists and Democrats 1
David Sassoli position held member of the European Parliament electoral district Central Italy 1
David Sassoli position held member of the European Parliament represents Democratic Party 1
David Sassoli position held member of the European Parliament elected in 2014 European Parliament election 1
David Sassoli position held President of the European Parliament start time 2019-07-03 2
David Sassoli position held President of the European Parliament replaces Antonio Tajani 2
David Sassoli position held President of the European Parliament end time 2022-01-11 2
David Sassoli position held President of the European Parliament end cause death in office 2
David Sassoli position held President of the European Parliament replaced by Roberta Metsola Tedesco Triccas 2
David Sassoli position held member of the European Parliament parliamentary term Seventh European Parliament 3
David Sassoli position held member of the European Parliament start time 2009-07-14 3
David Sassoli position held member of the European Parliament parliamentary group Progressive Alliance of Socialists and Democrats 3
David Sassoli position held member of the European Parliament electoral district Central Italy 3
David Sassoli position held member of the European Parliament represents Democratic Party 3
David Sassoli position held member of the European Parliament elected in 2009 European Parliament election 3
David Sassoli position held member of the European Parliament end time 2014-06-30 3
David Sassoli position held member of the European Parliament parliamentary term Ninth European Parliament 4
David Sassoli position held member of the European Parliament start time 2019-07-02 4
David Sassoli position held member of the European Parliament parliamentary group Progressive Alliance of Socialists and Democrats 4
David Sassoli position held member of the European Parliament electoral district Italy 4
David Sassoli position held member of the European Parliament represents Democratic Party 4
David Sassoli position held member of the European Parliament elected in 2019 European Parliament election 4
David Sassoli position held member of the European Parliament end time 2022-01-11 4
David Sassoli position held member of the European Parliament end cause death in office 4

That’s quite a lot of useful detail. The construction of the request can be quite complicated, but keep in mind that if you do this programmatically you will likely use this for filtering a specific piece of information based on a combination of properties, and you will only less frequently need to extract all available information.

Fundamentally, you won’t be touching anything that is not a vector or a tidy data frame, which is ultimately a key goal of tidywikidatar: make use of the wealth of information stored by Wikidata from R without having to deal with either nested lists or SPARQL queries.

Getting the right property when more than one is available

In Wikidata, the order in which statements for a property are shown depends on a number of factors. Consistent with the API behaviour, tidywikidatar returns them in the same order as they appear on the online on Wikidata dot org. Depending on the use case and subsequent processing operations this may be either completely irrelevant or very important, with a big impact even on the most basic of queries.

For example, let’s compare results when we are trying to find out in which country (P17) London (Q84) and Rome (Q220) are located.

If we ask Wikidata in which country London is located, this is the response we get:

tw_get_property(id = "Q84", p = "P17") %>%
  dplyr::mutate(value = tw_get_label(value))
#> # A tibble: 8 × 4
#>   id    property value                                       rank     
#>   <chr> <chr>    <chr>                                       <chr>    
#> 1 Q84   P17      Roman Empire                                normal   
#> 2 Q84   P17      Kingdom of Essex                            normal   
#> 3 Q84   P17      Kingdom of Mercia                           normal   
#> 4 Q84   P17      Kingdom of Wessex                           normal   
#> 5 Q84   P17      Kingdom of England                          normal   
#> 6 Q84   P17      Great Britain                               normal   
#> 7 Q84   P17      United Kingdom of Great Britain and Ireland normal   
#> 8 Q84   P17      United Kingdom                              preferred

These statements may all be fairly accurate at different points in time, as we would see if we looked at the qualifiers of each of these statements (see above) or check the respective section on Wikidata’s website. The order, however, is determined by a number of factors and this may lead to inconsistent results. If we are interested in having just one result, as is often the case when processing large amounts of items, can we safely pick the first (or last) and be sure it’s the more recent? As it emerges looking at same for property for Rome, this is not the case.

tw_get_property(id = "Q220", p = "P17") %>%
  dplyr::mutate(value = tw_get_label(value))
#> # A tibble: 10 × 4
#>    id    property value                             rank     
#>    <chr> <chr>    <chr>                             <chr>    
#>  1 Q220  P17      Italy                             preferred
#>  2 Q220  P17      Papal States                      normal   
#>  3 Q220  P17      Kingdom of Italy                  normal   
#>  4 Q220  P17      Ostrogothic Kingdom               normal   
#>  5 Q220  P17      Byzantine Empire                  normal   
#>  6 Q220  P17      Kingdom of Italy                  normal   
#>  7 Q220  P17      Roman Kingdom                     normal   
#>  8 Q220  P17      Roman Republic                    normal   
#>  9 Q220  P17      Roman Empire                      normal   
#> 10 Q220  P17      Western Roman Empire (395-476 AD) normal

So while we may be tempted to just keep the first statement returned by Wikidata for the given property, this is probably not what we want.

tibble::tibble(city_qid = c("Q84", "Q220")) %>% 
  dplyr::mutate(city_label = tw_get_label(city_qid), 
                country_qid = tw_get_p(id = city_qid,
                                       p = "P17",
                                       only_first = TRUE)) %>% 
  dplyr::mutate(country_label = tw_get_label(country_qid))
#> # A tibble: 2 × 4
#>   city_qid city_label country_qid country_label
#>   <chr>    <chr>      <chr>       <chr>        
#> 1 Q84      London     Q2277       Roman Empire 
#> 2 Q220     Rome       Q38         Italy

Besides looking at the qualifiers, the standard way for Wikidata to choose which is the “preferred” statement is the dedicated ranking element (in the online interface, a small dot with arrows next to the label), which can either be “preferred”, “normal”, or “deprecated”. In piped operations, we get the “preferred” property by setting preferred to TRUE in tw_get_p().

tibble::tibble(city_qid = c("Q84", "Q220")) %>% 
  dplyr::mutate(city_label = tw_get_label(city_qid), 
                country_qid = tw_get_p(id = city_qid,
                                       p = "P17",
                                       preferred = TRUE,
                                       only_first = TRUE)) %>% 
  dplyr::mutate(country_label = tw_get_label(country_qid))
#> # A tibble: 2 × 4
#>   city_qid city_label country_qid country_label 
#>   <chr>    <chr>      <chr>       <chr>         
#> 1 Q84      London     Q145        United Kingdom
#> 2 Q220     Rome       Q38         Italy

Keep in mind that there may be more than one “preferred” statement, so setting preferred to TRUE is no guarantee of having a single result: for example, London is both “capital of” (P1376) the United Kingdom and England, and both statements are “preferred”. Rome is capital of Italy and Lazio (the region where it is located), and both are “preferred”.

When the “preferred” option does not give the desired result or gives more than one, in some cases it may be useful to use instead the parameter latest_start_time, to pick the statement that has the most recent “start time” (P580) qualifier (this can also be used in combination with preferred). This option slows a bit the process as it depends on a call to tw_get_qualifiers() to retrieve and cache relevant details.

tibble::tibble(city_qid = c("Q84", "Q220")) %>% 
  dplyr::mutate(city_label = tw_get_label(city_qid), 
                country_qid = tw_get_p(id = city_qid,
                                       p = "P17",
                                       latest_start_time = TRUE, 
                                       only_first = TRUE)) %>% 
  dplyr::mutate(country_label = tw_get_label(country_qid))
#> # A tibble: 2 × 4
#>   city_qid city_label country_qid country_label 
#>   <chr>    <chr>      <chr>       <chr>         
#> 1 Q84      London     Q145        United Kingdom
#> 2 Q220     Rome       Q38         Italy

If none of the above works, then you may still be able to get consistent results through customs solutions based on tw_get_qualifiers(), or by checking the validity of alternative results based on their properties (for example, many of the properties of “Roman empire” (Q2277) could be used to determine that it is not, in fact, a contemporary country).

Queries

All of the above works similarly to how we often use websites such as Wikipedia, or search engines: we search for something specific to find information about it. Wikidata, however, has powerful tools for complex queries. Think something like “give me all of these fields for all items that have this value for this property, but not that other value for that other property”.

To achieve this, you can run queries, following instructions on Wikidata.org. From R, you would run those using WikidataQueryServiceR::query_wikidata(). This is powerful, but perhaps somewhat intimidating for those who are less familiar with database queries, SPARQL, and the likes.

tidiwikidatar does not currently plan to deal with complex queries. However, at this stage it has a basic function, tw_query, which should instantly make sense for R users.

Say, for example, you are interested in all women (P21 == Q6581072) who are resistance fighters (P106 == Q6581072).

You can then make a data frame with two columns (p and q), and some requirements, like this:

query_df <- tibble::tribble(
  ~p, ~q,
  "P106", "Q1397808",
  "P21", "Q6581072"
)

# if you prefer, you can input the same as a list, like this:
# query_l <- list(c(p = "P106", q = "Q1397808"),
#                c(p = "P21", q = "Q6581072"))

query_df
#> # A tibble: 2 × 2
#>   p     q       
#>   <chr> <chr>   
#> 1 P106  Q1397808
#> 2 P21   Q6581072

You can then pass it to tw_query(), and get a nicely formatted dataframe with all women who are resistance fighters on Wikidata.

tw_query(query = query_df)
#> Rows: 767 Columns: 3
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (3): item, itemLabel, itemDescription
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 767 × 3
#>    id      label                  description                                   
#>    <chr>   <chr>                  <chr>                                         
#>  1 Q274041 Nanny of the Maroons   leader of Windward Maroons in Jamaica         
#>  2 Q276410 Marga Klompé           Dutch politician (1912-1986)                  
#>  3 Q283654 Maria Skobtsova        Russian saint                                 
#>  4 Q285995 Maria Restituta Kafka  Franciscan nun and nurse; Nazi critic; victim…
#>  5 Q304262 Hannie van Leeuwen     Dutch politician (1926-2018)                  
#>  6 Q324718 Martha Dodd            American spy for the Soviet Union             
#>  7 Q354512 Adele Stürzl           Austrian politician, member of the Austrian r…
#>  8 Q394661 Agnes Wendland         <NA>                                          
#>  9 Q441439 Henriette Roland Holst Dutch politician, editor (1869-1952)          
#> 10 Q443262 Lozen                  Apache prophetess and warrior                 
#> # … with 757 more rows

Or perhaps, you are interested only in women who are resistance fighters who have “France” (Q142) as “country of citizenship” (P27)? And perhaps you want the description in Italian, and if not available in French, and only then look for other fallback options?

tibble::tribble(
  ~p, ~q,
  "P106", "Q1397808", # Occupation: resistance fighter
  "P21", "Q6581072", # Sex or gender: female
  "P27", "Q142"
) %>% # Country of citizenship: France
  tw_query(language = c("it", "fr"))
#> Rows: 127 Columns: 3
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (3): item, itemLabel, itemDescription
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 127 × 3
#>    id        label                           description                        
#>    <chr>     <chr>                           <chr>                              
#>  1 Q270319   Christiane Desroches Noblecourt egittologa e archeologa francese   
#>  2 Q6837011  Michelle Dubois                 <NA>                               
#>  3 Q10289954 Giselle Cossard                 résistante française, femme de let…
#>  4 Q5257705  Denise Laroque                  <NA>                               
#>  5 Q15970412 Raymonde Tillon                 femme politique française          
#>  6 Q16262713 Simone Schloss                  résistante communiste française    
#>  7 Q2696536  Yolande Beekman                 espionne et agente secret des Spec…
#>  8 Q3009723  Cécile Cerf                     résistante française               
#>  9 Q3081207  Francine Fromond                <NA>                               
#> 10 Q3132483  Henriette Moriamé               <NA>                               
#> # … with 117 more rows

You can also ask other fields, beyond label and description, using the field parameter of tw_query(). But for this readme, I’ll keep things simple. Do you want more information about these results without learning yet another set of Wikidata terminology? You can still use the same commands described above, e.g.

tibble::tribble(
  ~p, ~q,
  "P106", "Q1397808",
  "P21", "Q6581072",
  "P27", "Q142"
) %>%
  tw_query() %>%
  dplyr::slice(1) %>%
  get_bio()
#> Rows: 127 Columns: 3
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (3): item, itemLabel, itemDescription
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 1 × 4
#>   label                           description        year_of_birth year_of_death
#>   <chr>                           <chr>                      <dbl>         <dbl>
#> 1 Christiane Desroches Noblecourt French egyptologi…          1913          2011

Keep in mind that Wikidata queries are not cached locally.

Getting Wikidata identifiers from a Wikipedia page

Besides querying Wikidata and using the basic tw_search() function described above, tidywikidatar includes function that facilitate retrieving Wikidata identifiers based on Wikipedia pages, as well as the Wikidata identifiers corresponding to all the Wikipedia pages included in a given Wikipedia page. This may be useful in particular on Wikipedia pages that are lists of other pages, or as an alternative approach for finding relations between various Wikidata items.

In this case, the starting point is usually the full URL or the title of a Wikipedia page, which give the same result (the user, however, should be mindful of redirection if using the title).

tw_get_wikipedia_page_qid(title = "Margaret Mead")
#> # A tibble: 1 × 7
#>   title_url     wikipedia_title wikipedia_id qid     description  disambiguation
#>   <chr>         <chr>                  <int> <chr>   <chr>        <lgl>         
#> 1 Margaret Mead Margaret Mead          19617 Q180099 American cu… FALSE         
#> # … with 1 more variable: language <chr>
tw_get_wikipedia_page_qid(url = "https://en.wikipedia.org/wiki/Margaret_Mead")
#> # A tibble: 1 × 7
#>   title_url     wikipedia_title wikipedia_id qid     description  disambiguation
#>   <chr>         <chr>                  <int> <chr>   <chr>        <lgl>         
#> 1 Margaret_Mead Margaret Mead          19617 Q180099 American cu… FALSE         
#> # … with 1 more variable: language <chr>

Depending on the workflow, it is also possible to get the full link to the Wikipedia page starting from a given Wikidata identifier.

tw_get_wikipedia(id = "Q180099")
#> [1] "https://en.wikipedia.org/wiki/Margaret Mead"

Who and what is mentioned in Margaret Mead’s Wikipedia page? As it turns out, hundreds of pages, including a variety of people, places, concepts, etc.

wikipedia_df <- tw_get_wikipedia(id = "Q180099") %>% 
  tw_get_wikipedia_page_links()

wikipedia_df
#> # A tibble: 893 × 8
#>    source_title_url source_wikipedia… source_qid wikipedia_title    wikipedia_id
#>    <chr>            <chr>             <chr>      <chr>                     <int>
#>  1 Margaret Mead    Margaret Mead     Q180099    Alex Barker                  NA
#>  2 Margaret Mead    Margaret Mead     Q180099    Alfred S. Hayes              NA
#>  3 Margaret Mead    Margaret Mead     Q180099    Blackberry Winter…           NA
#>  4 Margaret Mead    Margaret Mead     Q180099    Continuities in C…           NA
#>  5 Margaret Mead    Margaret Mead     Q180099    Culture and Commi…           NA
#>  6 Margaret Mead    Margaret Mead     Q180099    John P. Gillin               NA
#>  7 Margaret Mead    Margaret Mead     Q180099    A Darwinian Left        3890352
#>  8 Margaret Mead    Margaret Mead     Q180099    A Rap on Race          14527943
#>  9 Margaret Mead    Margaret Mead     Q180099    Abby Kelley             4056835
#> 10 Margaret Mead    Margaret Mead     Q180099    Abigail Adams            102745
#> # … with 883 more rows, and 3 more variables: qid <chr>, description <chr>,
#> #   language <chr>

What if we are potentially interested only in the people mentioned in this page? We proceed as usual, checking which of these are “instance of” (“P19”) “human” (“Q5”), and take it from there.

wikipedia_df %>% 
  dplyr::pull(wikidata_id) %>% 
  tw_get_property(p = "P31") %>% 
  dplyr::filter(value == "Q5")

All functions that interact with Wikipedia and the related MediaWiki API are not cached locally at this stage.

For a more extended example of exploring Wikidata starting from Wikipedia, consult the dedicated vignette with vignette("wikipedia_start")

Getting images, including credits

Many Wikidata items have an image that can be used for illustrative purposes. tw_get_image() facilitate getting the link to the WikiMedia Commons page where more details about the image can be found, as well as a direct link to the image at the desired resolution for direct embeds.

tw_get_image(id = "Q180099", format = "commons") %>% 
  dplyr::pull(image)
#> [1] "https://commons.wikimedia.org/wiki/File:Margaret Mead (1901-1978).jpg"
tw_get_image(id = "Q180099", format = "embed", width = 300) %>% 
  dplyr::pull(image)
#> [1] "https://commons.wikimedia.org/w/index.php?title=Special:Redirect/file/Margaret Mead (1901-1978).jpg&width=300"

The user should be mindful that these links depend on the filename of the image, and (unlike Wikidata Q identifiers) may be changed without offering a redirect from the previous file to the most recent one. If there are no relevant copyright limitations, depending on the use case, it may be wise to store images locally.

Wikidata itself does not include details about copyright of the image, nor an easy way to get information about the author, or a suggested way to credit the image. All of these are available through the Wikimedia commons API. tidywikidatar includes a convenience function to get access to all such details:

tw_get_image_metadata(id = "Q180099") %>% 
  tidyr::pivot_longer(cols = dplyr::everything(),
                      names_to = "property",
                      values_to = "values",
                      values_transform = list(values = as.character))
#> # A tibble: 19 × 2
#>    property                   values                                            
#>    <chr>                      <chr>                                             
#>  1 id                         "Q180099"                                         
#>  2 image_filename             "Margaret Mead (1901-1978).jpg"                   
#>  3 object_name                "Margaret Mead (1901-1978)"                       
#>  4 image_description          "<b>Subject</b>: Mead, Margaret\n<p>       Intern…
#>  5 categories                 "Black and white photographs of female heads|Blac…
#>  6 assessments                ""                                                
#>  7 credit                     "<p><a rel=\"nofollow\" class=\"external text\" h…
#>  8 artist                     "<a rel=\"nofollow\" class=\"external text\" href…
#>  9 permission                 "<a rel=\"nofollow\" class=\"external text\" href…
#> 10 license_short_name         "No restrictions"                                 
#> 11 license_url                "https://www.flickr.com/commons/usage/"           
#> 12 license                     <NA>                                             
#> 13 usage_terms                "No known copyright restrictions"                 
#> 14 attribution_required       "0"                                               
#> 15 copyrighted                "1"                                               
#> 16 restrictions               ""                                                
#> 17 date_time                  "2019-07-02 03:33:00"                             
#> 18 date_time_original         "18 July 2011, 16:02"                             
#> 19 commons_metadata_extension "1.2"

This function does not currently cache data.

How caching works

tidywikidatar tries to reduce load on Wikidata’s server and speeding up re-processing of scripts by caching data locally in sqlite databases. They are stored locally in the folder defined by tw_set_cache_folder() - by default, in the current working directory - when cache is enabled (typically, with tw_enable_cache() at the beginning of a session).

To reduce the size of local files, if data are requested in a specific language, then only data in that language are stored locally.

The easiest way to reset the cache is simply to delete the cache folder.

Results are stored in different databases by language, and function used; tw_search(), tw_get(), and tw_get_qualifiers(), for example, store data in different files.

tw_query() is never cached.

See the the dedicated vignette for more details on caching: vignette("caching").

Requirements and installation issues

Fedora users may need to install the package libjpeg-turbo-devel, which is required by one of the packages that tidywikidatar relies on, as well as some of the database drivers that can be used for caching, such as unixODBC-devel, and mysql-devel, mysql-connector-odbc.

This package has been created by Giorgio Comai, data analyst and researcher at OBCT/CCI, within the scope of EDJNet, the European Data Journalism Network.

It is distributed under the MIT license.