ghql
- a GraphQL client for R
Note: To be clear, this R package isn't just for the GitHub GraphQL API, but it is the most public GraphQL API we can think of, so is used in examples throughout here.
See https://docs.github.com/en/graphql/guides/forming-calls-with-graphql#authenticating-with-graphql for getting an OAuth token.
Store the token in a env var called GITHUB_TOKEN
CRAN version
install.packages("ghql")
Development version
remotes::install_github("ropensci/ghql")
library("ghql") library("jsonlite") library("dplyr")
token <- Sys.getenv("GITHUB_TOKEN") con <- GraphqlClient$new( url = "https://api.github.com/graphql", headers = list(Authorization = paste0("Bearer ", token)) )
Since not every GraphQL server has a schema at the base URL, have to manually load the schema in this case
con$load_schema()
Make a Query
class object
qry <- Query$new()
When you construct queries we check that they are properly formatted using the [graphql][] package that leverages the [libgraphqlparser][] C++ parser. If the query is malformed, we return a message as to why the query is malformed.
Get some stargazer counts
qry$query('mydata', '{ repositoryOwner(login:"sckott") { repositories(first: 5, orderBy: {field:PUSHED_AT,direction:DESC}, isFork:false) { edges { node { name stargazers { totalCount } } } } } }') qry #> <ghql: query> #> queries: #> mydata qry$queries$mydata #> #> { #> repositoryOwner(login:"sckott") { #> repositories(first: 5, orderBy: {field:PUSHED_AT,direction:DESC}, isFork:false) { #> edges { #> node { #> name #> stargazers { #> totalCount #> } #> } #> } #> } #> } #> }
# returns json (x <- con$exec(qry$queries$mydata)) #> [1] "{\"data\":{\"repositoryOwner\":{\"repositories\":{\"edges\":[{\"node\":{\"name\":\"cranchecksdocs\",\"stargazers\":{\"totalCount\":6}}},{\"node\":{\"name\":\"badges\",\"stargazers\":{\"totalCount\":0}}},{\"node\":{\"name\":\"mutant-proposal\",\"stargazers\":{\"totalCount\":0}}},{\"node\":{\"name\":\"extcite\",\"stargazers\":{\"totalCount\":6}}},{\"node\":{\"name\":\"Headstart\",\"stargazers\":{\"totalCount\":140}}}]}}}}\n" # parse to an R list jsonlite::fromJSON(x) #> $data #> $data$repositoryOwner #> $data$repositoryOwner$repositories #> $data$repositoryOwner$repositories$edges #> node.name node.totalCount #> 1 cranchecksdocs 6 #> 2 badges 0 #> 3 mutant-proposal 0 #> 4 extcite 6 #> 5 Headstart 140
Define a query
qry <- Query$new() qry$query('getgeninfo', 'query getGeneInfo($genId: String!){ geneInfo(geneId: $genId) { id symbol chromosome start end bioType __typename } }')
Define a variable as a named list
variables <- list(genId = 'ENSG00000137033')
Creat a clint and make a request, passing in the query and then the variables
con <- GraphqlClient$new('https://genetics-api.opentargets.io/graphql') res <- con$exec(qry$queries$getgeninfo, variables) jsonlite::fromJSON(res) #> $data #> $data$geneInfo #> $data$geneInfo$id #> [1] "ENSG00000137033" #> #> $data$geneInfo$symbol #> [1] "IL33" #> #> $data$geneInfo$chromosome #> [1] "9" #> #> $data$geneInfo$start #> [1] 6215786 #> #> $data$geneInfo$end #> [1] 6257983 #> #> $data$geneInfo$bioType #> [1] "protein_coding" #> #> $data$geneInfo$`__typename` #> [1] "Gene"
Datacite provides DOIs for research data. Check out the Datacite GraphQL docs to get started. A minimal example:
con <- GraphqlClient$new("https://api.datacite.org/graphql") qry <- Query$new() qry$query('dc', '{ publications(query: "climate") { totalCount nodes { id titles { title } descriptions { description } creators { name familyName } fundingReferences { funderIdentifier funderName awardTitle awardNumber } } } }') res <- con$exec(qry$queries$dc) head(jsonlite::fromJSON(res)$data$publications$nodes) #> id titles #> 1 https://doi.org/10.4122/1.1000000046 Single Cell Protein from Landfill Gas #> 2 https://doi.org/10.4122/1.1000000046 Single Cell Protein from Landfill Gas #> 3 https://doi.org/10.4122/1.1000000047 Single Cell Protein from Landfill Gas #> 4 https://doi.org/10.4122/1.1000000047 Single Cell Protein from Landfill Gas #> 5 https://doi.org/10.4122/1.1000000048 Single Cell Protein from Landfill Gas #> 6 https://doi.org/10.4122/1.1000000048 Single Cell Protein from Landfill Gas #> descriptions #> 1 Municipal solid waste (MSW) landfills are one of the largest human-generated sources of methane emissions in the United States and other countries globally. Methane is believed to be a very potent greenhouse gas that is a key contributor to global climate change, over 21 times stronger than CO2. Methane also has a short (10-year) atmospheric life. Because methane is both potent and short-lived, reducing methane emissions from MSW landfills is one of the best ways to achieve a near-term beneficial impact in mitigating global climate change. The United States Environmental Protection Agency estimates that a landfill gas (LFG) project will capture roughly 60-90% of the methane emitted from the landfill, depending on system design and effectiveness. The captured methane can be then purified and used for industrial applications, as in this case the production of SCP. Utilizing methane in this way decreases its demand from fossil fuels which is its traditional source. #> 2 Municipal solid waste (MSW) landfills are one of the largest human-generated sources of methane emissions in the United States and other countries globally. Methane is believed to be a very potent greenhouse gas that is a key contributor to global climate change, over 21 times stronger than CO2. Methane also has a short (10-year) atmospheric life. Because methane is both potent and short-lived, reducing methane emissions from MSW landfills is one of the best ways to achieve a near-term beneficial impact in mitigating global climate change. The United States Environmental Protection Agency estimates that a landfill gas (LFG) project will capture roughly 60-90% of the methane emitted from the landfill, depending on system design and effectiveness. The captured methane can be then purified and used for industrial applications, as in this case the production of SCP. Utilizing methane in this way decreases its demand from fossil fuels which is its traditional source. #> 3 Municipal solid waste (MSW) landfills are one of the largest human-generated sources of methane emissions in the United States and other countries globally. Methane is believed to be a very potent greenhouse gas that is a key contributor to global climate change, over 21 times stronger than CO2. Methane also has a short (10-year) atmospheric life. Because methane is both potent and short-lived, reducing methane emissions from MSW landfills is one of the best ways to achieve a near-term beneficial impact in mitigating global climate change. The United States Environmental Protection Agency estimates that a landfill gas (LFG) project will capture roughly 60-90% of the methane emitted from the landfill, depending on system design and effectiveness. The captured methane can be then purified and used for industrial applications, as in this case the production of SCP. Utilizing methane in this way decreases its demand from fossil fuels which is its traditional source. #> 4 Municipal solid waste (MSW) landfills are one of the largest human-generated sources of methane emissions in the United States and other countries globally. Methane is believed to be a very potent greenhouse gas that is a key contributor to global climate change, over 21 times stronger than CO2. Methane also has a short (10-year) atmospheric life. Because methane is both potent and short-lived, reducing methane emissions from MSW landfills is one of the best ways to achieve a near-term beneficial impact in mitigating global climate change. The United States Environmental Protection Agency estimates that a landfill gas (LFG) project will capture roughly 60-90% of the methane emitted from the landfill, depending on system design and effectiveness. The captured methane can be then purified and used for industrial applications, as in this case the production of SCP. Utilizing methane in this way decreases its demand from fossil fuels which is its traditional source. #> 5 Municipal solid waste (MSW) landfills are one of the largest human-generated sources of methane emissions in the United States and other countries globally. Methane is believed to be a very potent greenhouse gas that is a key contributor to global climate change, over 21 times stronger than CO2. Methane also has a short (10-year) atmospheric life. Because methane is both potent and short-lived, reducing methane emissions from MSW landfills is one of the best ways to achieve a near-term beneficial impact in mitigating global climate change. The United States Environmental Protection Agency estimates that a landfill gas (LFG) project will capture roughly 60-90% of the methane emitted from the landfill, depending on system design and effectiveness. The captured methane can be then purified and used for industrial applications, as in this case the production of SCP. Utilizing methane in this way decreases its demand from fossil fuels which is its traditional source. #> 6 Municipal solid waste (MSW) landfills are one of the largest human-generated sources of methane emissions in the United States and other countries globally. Methane is believed to be a very potent greenhouse gas that is a key contributor to global climate change, over 21 times stronger than CO2. Methane also has a short (10-year) atmospheric life. Because methane is both potent and short-lived, reducing methane emissions from MSW landfills is one of the best ways to achieve a near-term beneficial impact in mitigating global climate change. The United States Environmental Protection Agency estimates that a landfill gas (LFG) project will capture roughly 60-90% of the methane emitted from the landfill, depending on system design and effectiveness. The captured methane can be then purified and used for industrial applications, as in this case the production of SCP. Utilizing methane in this way decreases its demand from fossil fuels which is its traditional source. #> creators #> 1 Babi, Deenesh, Price, Jason, Woodley, Prof. John, Babi, Price, NA #> 2 Babi, Deenesh, Price, Jason, Woodley, Prof. John, Babi, Price, NA #> 3 Babi, Deenesh, Price, Jason, Woodley, Prof. John, Babi, Price, NA #> 4 Babi, Deenesh, Price, Jason, Woodley, Prof. John, Babi, Price, NA #> 5 Babi, Deenesh, Price, Jason, Woodley, Prof. John, Babi, Price, NA #> 6 Babi, Deenesh, Price, Jason, Woodley, Prof. John, Babi, Price, NA #> fundingReferences #> 1 NULL #> 2 NULL #> 3 NULL #> 4 NULL #> 5 NULL #> 6 NULL
A public GraphQL API for information about countries, continents, and languages. This project uses Countries List and provinces as data sources, so the schema follows the shape of that data, with a few exceptions:
Link to the GraphQL schema api
link <- 'https://countries.trevorblades.com/'
Create a new graphqlClient object
con <- GraphqlClient$new(url = link)
Define a Graphql Query
query <- ' query($code: ID!){ country(code: $code){ name native capital currency phone languages{ code name } } }'
The ghql
query class and define query in a character string
new <- Query$new()$query('link', query)
Inspecting the schema
new$link #> #> #> query($code: ID!){ #> country(code: $code){ #> name #> native #> capital #> currency #> phone #> languages{ #> code #> name #> } #> } #> }
define a variable as a named list
variable <- list( code = "DE" )
Making a request, passing in the query and then the variables. Then you convert the raw object to a structured json object
result <- con$exec(new$link, variables = variable) %>% fromJSON(flatten = FALSE) result #> $data #> $data$country #> $data$country$name #> [1] "Germany" #> #> $data$country$native #> [1] "Deutschland" #> #> $data$country$capital #> [1] "Berlin" #> #> $data$country$currency #> [1] "EUR" #> #> $data$country$phone #> [1] "49" #> #> $data$country$languages #> code name #> 1 de German
Convert the json data into a tibble object
country_data <- result$data$country %>% as_tibble() country_data #> # A tibble: 1 x 6 #> name native capital currency phone languages$code $name #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> #> 1 Germany Deutschland Berlin EUR 49 de German
server.js
file from this package located at inst/server.js
somewhere on your machine. Can locate it on your machine like system.file("js/server.js", package = "ghql")
. Or you can run the file from where it's at, up to you.node server.js
(con <- GraphqlClient$new("http://localhost:4000/graphql")) #> <ghql client> #> url: http://localhost:4000/graphql
xxx <- Query$new() xxx$query('query', '{ __schema { queryType { name, fields { name, description } } } }')
con$exec(xxx$queries$query) #> $data #> $data$`__schema` #> $data$`__schema`$queryType #> $data$`__schema`$queryType$name #> [1] "Query" #> #> $data$`__schema`$queryType$fields #> name description #> 1 hello #> 2 name
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