Explore 'Wikidata' Through Tidy Data Frames.
tidywikidatar
The goal of tidywikidatar is to facilitate interaction with Wikidata:
- all responses are transformed into data frames or simple character vectors
- it is easy to enable efficient caching in a local sqlite database (integration with other databases is also available)
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.
Use cases and publicly available examples
These articles or repository demonstrate some use cases for tidywikidatar:
- Finding out more about Members of the European Parliament with Wikidata
- Retrieve details about Olympics 2020 medalists via Wikipedia and Wikidata / see also this interactive map based on Wikidata
- Which among the busiest air routes in Europe could actually be travelled by land?
- Finding gendered street names
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 Q66701460 Margaret Mead scientific article published o…
#> 3 Q81015029 Margaret mead scientific article published o…
#> 4 Q85724626 Mead & Bateson business organisation
#> 5 Q96077616 Margaret Meadows (1718-1781)
#> 6 Q75281958 Lady Margaret Meade-Fetherstonhaugh British author (1888–1977)
#> 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 is, 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
dplyr::pull(value) %>% # take its result and
tw_get_label() # ask what that id stands for
#> [1] "United States of America"
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: 15 × 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 Q180099 P166 Q110471679 normal
#> 7 Q180099 P166 Q110939855 normal
#> 8 Q228822 P166 Q1967852 normal
#> 9 Q228822 P166 Q52382875 normal
#> 10 Q220480 P166 Q1316544 normal
#> 11 Q220480 P166 Q1967852 normal
#> 12 Q220480 P166 Q5461701 normal
#> 13 Q220480 P166 Q5461189 normal
#> 14 Q220480 P166 Q4765305 normal
#> 15 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: 15 × 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 Fellow of the American Academy of Ar… norm…
#> 6 Margaret Mead award received honorary doctor of the University of… norm…
#> 7 Margaret Mead award received Gold Medal of the Society of Woman G… norm…
#> 8 Ruth Benedict award received National Women's Hall of Fame norm…
#> 9 Ruth Benedict award received Fellow of the American Academy of Ar… norm…
#> 10 Zora Neale Hurston award received Guggenheim Fellowship norm…
#> 11 Zora Neale Hurston award received National Women's Hall of Fame norm…
#> 12 Zora Neale Hurston award received Florida Women's Hall of Fame norm…
#> 13 Zora Neale Hurston award received Florida Artists Hall of Fame norm…
#> 14 Zora Neale Hurston award received Anisfield-Wolf Book Awards norm…
#> 15 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(), or tw_get_p1() to always get a character vector with the first property in response).
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: 24 × 4
#> student_label worked_at_label lat lon
#> <chr> <chr> <chr> <chr>
#> 1 Ruth Benedict Columbia University 40.8075 -73.…
#> 2 Edward Sapir Yale University 41.311111111111 -72.…
#> 3 Edward Sapir University of Chicago 41.789722222222 -87.…
#> 4 Alexander Francis Chamberlain Clark University 42.250977 -71.…
#> 5 Alexander Goldenweiser Columbia University 40.8075 -73.…
#> 6 Alexander Goldenweiser University of Washington 47.6541666666667 -122…
#> 7 Melville J. Herskovits Northwestern University 42.054853 -87.…
#> 8 Melville J. Herskovits Columbia University 40.8075 -73.…
#> 9 Melville J. Herskovits Howard University 38.921666666667 -77.…
#> 10 E. Adamson Hoebel New York University 40.73 -73.…
#> # ℹ 14 more rows
Starting with version 0.5, to reduce typing, tw_get_p() can be used instead of the more verbose tw_get_property_same_length(). By default, tw_get_p() returns a vector of lists, as it is common for a property to have multiple values. If one is interested only in the first preferred value associated with a given property, tw_get_p1(), which consistently returns a character vector of the same length as the input, can be used instead.
Starting with version 0.5.2, a more efficient tw_get_p_wide() has been introduced to replicate a common use pattern, i.e. getting a number of property of a given set of identifiers, and retrieving their labels.
tw_get_p_wide(
id = c("Q180099", "Q228822", "Q191095"),
p = c("P27", "P19", "P20"),
only_first = TRUE,
preferred = TRUE,
label = TRUE
)
#> # A tibble: 3 × 5
#> id label P27 P19 P20
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Q180099 Margaret Mead United States of America Philadelphia New York City
#> 2 Q228822 Ruth Benedict United States of America New York City New York City
#> 3 Q191095 Edward Sapir United States of America Lębork New Haven
It is however common for properties to have more than one meaningful value. By default, tw_get_p_wide() would get these as lists columns, but there is also an additional parameter to facilitate sharing the result, for example, as a csv file.
tw_get_p_wide(
id = c("Q180099", "Q228822", "Q191095"),
p = c("P108", "P26", "P451"),
only_first = TRUE,
preferred = TRUE,
label = TRUE,
unlist = TRUE,
collapse = ";"
)
#> # A tibble: 3 × 5
#> id label P108 P26 P451
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Q180099 Margaret Mead Columbia University Gregory Bateson Ruth Bene…
#> 2 Q228822 Ruth Benedict Columbia University Stanley Rossiter Benedict Margaret …
#> 3 Q191095 Edward Sapir Yale University <NA> <NA>
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: 27 × 8
#> id property qualifier_id qualifier_property qualifier_value
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Q2391857 P39 Q740126 P580 +2019-07-03T00:00:00Z
#> 2 Q2391857 P39 Q740126 P1365 Q440710
#> 3 Q2391857 P39 Q740126 P582 +2022-01-11T00:00:00Z
#> 4 Q2391857 P39 Q740126 P1534 Q5247364
#> 5 Q2391857 P39 Q740126 P1366 Q7351526
#> 6 Q2391857 P39 Q27169 P580 +2019-07-02T00:00:00Z
#> 7 Q2391857 P39 Q27169 P582 +2022-01-11T00:00:00Z
#> 8 Q2391857 P39 Q27169 P1534 Q5247364
#> 9 Q2391857 P39 Q27169 P1366 Q110513292
#> 10 Q2391857 P39 Q27169 P2937 Q64038205
#> # ℹ 17 more rows
#> # ℹ 3 more variables: qualifier_value_type <chr>, 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 | President of the European Parliament | start time | 2019-07-03 | 1 |
| David Sassoli | position held | President of the European Parliament | replaces | Antonio Tajani | 1 |
| David Sassoli | position held | President of the European Parliament | end time | 2022-01-11 | 1 |
| David Sassoli | position held | President of the European Parliament | end cause | death in office | 1 |
| David Sassoli | position held | President of the European Parliament | replaced by | Roberta Metsola | 1 |
| David Sassoli | position held | member of the European Parliament | start time | 2019-07-02 | 2 |
| David Sassoli | position held | member of the European Parliament | end time | 2022-01-11 | 2 |
| David Sassoli | position held | member of the European Parliament | end cause | death in office | 2 |
| David Sassoli | position held | member of the European Parliament | replaced by | Camilla Laureti | 2 |
| David Sassoli | position held | member of the European Parliament | parliamentary term | Ninth European Parliament | 2 |
| David Sassoli | position held | member of the European Parliament | parliamentary group | Progressive Alliance of Socialists and Democrats | 2 |
| David Sassoli | position held | member of the European Parliament | electoral district | Italy | 2 |
| David Sassoli | position held | member of the European Parliament | elected in | 2019 European Parliament election | 2 |
| David Sassoli | position held | member of the European Parliament | represents | Democratic Party | 2 |
| David Sassoli | position held | member of the European Parliament | start time | 2014-07-01 | 3 |
| David Sassoli | position held | member of the European Parliament | parliamentary term | Eighth European Parliament | 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 | elected in | 2014 European Parliament election | 3 |
| David Sassoli | position held | member of the European Parliament | represents | Democratic Party | 3 |
| David Sassoli | position held | member of the European Parliament | start time | 2009-07-14 | 4 |
| David Sassoli | position held | member of the European Parliament | end time | 2014-06-30 | 4 |
| David Sassoli | position held | member of the European Parliament | parliamentary term | Seventh European Parliament | 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 | Central Italy | 4 |
| David Sassoli | position held | member of the European Parliament | elected in | 2009 European Parliament election | 4 |
| David Sassoli | position held | member of the European Parliament | represents | Democratic Party | 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 Kingdom of 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 most recent? As it emerges looking at the 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: 11 × 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 normal
#> 11 Q220 P17 Vatican City 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 Q201038 Roman Kingdom
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: 1083 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: 1,083 × 3
#> id label description
#> <chr> <chr> <chr>
#> 1 Q241097 Corrie ten Boom Dutch resistance hero and writer
#> 2 Q246835 Trinity fictional character from the films o…
#> 3 Q255016 Violette Szabo French-British SOE spy
#> 4 Q256628 Xenia Stad-de Jong Dutch sprinter
#> 5 Q270319 Christiane Desroches Noblecourt French egyptologist (1913-2011)
#> 6 Q272017 Bep Voskuijl Dutch person who hid Anne Frank
#> 7 Q274040 Alida Bosshardt Dutch Righteous Among the Nations (1…
#> 8 Q274041 Nanny of the Maroons leader of Windward Maroons in Jamaica
#> 9 Q275274 Geneviève de Gaulle-Anthonioz French resistance member (1920-2002)
#> 10 Q276410 Marga Klompé Dutch politician (1912-1986)
#> # ℹ 1,073 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?
fr_resistance_fighters_df <- 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: 181 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.
fr_resistance_fighters_df
#> # A tibble: 181 × 3
#> id label description
#> <chr> <chr> <chr>
#> 1 Q109251934 Jeanne Bleton-Barraud résistante française
#> 2 Q109252587 Louise Losserand résistante et déportée française
#> 3 Q109406143 Esther Poggio résistante française
#> 4 Q109515952 Marie-Antoinette Gout personnalité française reconnue Juste parmi…
#> 5 Q110357401 Christiane Cabalé une des plus jeunes déportées au camp de co…
#> 6 Q110842679 Henriette de Mornac résistante française
#> 7 Q111016944 Simone Hirschler l'épouse et la collaboratrice de René Hirsc…
#> 8 Q111033181 Frantxia Haltzuet résistante basque de la Seconde Guerre mond…
#> 9 Q111235729 Régine Lemberger résistante française
#> 10 Q111272415 Marcelle Dorr résistante française pendant la Seconde Gue…
#> # ℹ 171 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.
fr_resistance_fighters_df %>%
dplyr::slice(1) %>%
get_bio()
#> # A tibble: 1 × 4
#> label description year_of_birth year_of_death
#> <chr> <chr> <dbl> <dbl>
#> 1 Jeanne Bleton-Barraud <NA> 1924 2016
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> <dbl> <chr> <chr> <lgl>
#> 1 Margaret Mead Margaret Mead 19617 Q180099 American cu… FALSE
#> # ℹ 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> <dbl> <chr> <chr> <lgl>
#> 1 Margaret_Mead Margaret Mead 19617 Q180099 American cu… FALSE
#> # ℹ 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: 800 × 8
#> source_title_url source_wikipedia_title source_qid wikipedia_title
#> <chr> <chr> <chr> <chr>
#> 1 Margaret Mead Margaret Mead Q180099 Alex Barker
#> 2 Margaret Mead Margaret Mead Q180099 Alfred S. Hayes
#> 3 Margaret Mead Margaret Mead Q180099 Martin Orans
#> 4 Margaret Mead Margaret Mead Q180099 A Rap on Race
#> 5 Margaret Mead Margaret Mead Q180099 Abby Kelley
#> 6 Margaret Mead Margaret Mead Q180099 Abigail Adams
#> 7 Margaret Mead Margaret Mead Q180099 Affinity (law)
#> 8 Margaret Mead Margaret Mead Q180099 Aimee Mullins
#> 9 Margaret Mead Margaret Mead Q180099 Akhil Gupta
#> 10 Margaret Mead Margaret Mead Q180099 Alan H. Goodman
#> # ℹ 790 more rows
#> # ℹ 4 more variables: wikipedia_id <dbl>, 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 "!Mais Teoria da História na Wiki (LGBTQIA+)|!Mai…
#> 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 "2011-07-18 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.
Copyright and credits
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.