Ezimweni eziningi, lapho usebenza ngempendulo etholwe ku-API, noma nganoma iyiphi enye idatha enesakhiwo sesihlahla esiyinkimbinkimbi, ubhekene namafomethi we-JSON kanye ne-XML.
Lawa mafomethi anezinzuzo eziningi: agcina idatha ngokuhlangene futhi akuvumela ukuthi ugweme ukuphindaphinda okungadingekile kolwazi.
Ububi bala mafomethi ubunkimbinkimbi bokucubungula nokuhlaziywa kwawo. Idatha engahlelekile ayikwazi ukusetshenziswa ekubalweni futhi ukubona ngeso akukwazi ukwakhiwa kuyo.
Lesi sihloko siwukuqhutshwa okunengqondo kokushicilelwa . Kuzokusiza ukuthi ulethe izakhiwo zedatha ezingahlelekile zibe yifomu elijwayelekile nelifanele ukuhlaziywa kwethebula usebenzisa iphakheji tidyr, kufakwe phakathi komtapo wolwazi tidyverse, kanye nomndeni wayo wemisebenzi unnest_*().
Okuqukethwe
Uma uthanda ukuhlaziya idatha, ungase ube nentshisekelo yami и iziteshi. Iningi lokuqukethwe linikezelwe olimini lwe-R.
Isingeniso
Unxande (isaziso somhumushi, angitholanga izinketho ezanele zokuhumusha zaleli gama, ngakho-ke sizokushiya njengoba sinjalo.) inqubo yokuletha idatha engahlelekile enezinhlu ezibekwe kusidleke kuthebula elinezinhlangothi ezimbili elihlanganisa imigqa namakholomu ajwayelekile. IN tidyr Kunemisebenzi embalwa ezokusiza ukuthi unwebe amakholomu ohlu afakwe esidlekeni futhi wehlise idatha ibe unxande, ifomu lethebula:
unnest_longer()ithatha ingxenye ngayinye yohlu lwekholomu bese idala umugqa omusha.unnest_wider()ithatha ingxenye ngayinye yohlu lwekholomu bese idala ikholomu entsha.unnest_auto()inquma ngokuzenzakalelayo ukuthi yimuphi umsebenzi ongcono kakhulu ongawusebenzisa
unnest_longer()nomaunnest_wider().hoist()fana nounnest_wider()kodwa ikhetha kuphela izingxenye ezishiwo futhi ikuvumela ukuthi usebenze ngamaleveli amaningana wokuzalela.
Iningi lezinkinga ezihlobene nokuletha idatha engahlelekile enamazinga amaningana okuzalela kuthebula lezinhlangothi ezimbili zingaxazululwa ngokuhlanganisa imisebenzi esohlwini ne-dplyr.
Ukuze sibonise lezi zindlela, sizosebenzisa iphakheji repurrrsive, ehlinzeka ngohlu oluyinkimbinkimbi, olunamazinga amaningi olususelwa ku-API yewebhu.
library(tidyr)
library(dplyr)
library(repurrrsive)Abasebenzisi be-GitHub
Qala ngokuthi gh_abasebenzisi, uhlu oluqukethe ulwazi mayelana nabasebenzisi abayisithupha be-GitHub. Ake siqale siguqule uhlu gh_abasebenzisi в tibble ifreyimu:
users <- tibble( user = gh_users ) Lokhu kubonakala kungenangqondo: kungani unikeza uhlu gh_abasebenzisi, kusakhiwo sedatha esiyinkimbinkimbi? Kodwa uhlaka lwedatha lunenzuzo enkulu: luhlanganisa ama-vector amaningi ukuze yonke into ilandelelwe entweni eyodwa.
Into ngayinye users iwuhlu olunegama lapho ingxenye ngayinye imele ikholomu.
names(users$user[[1]])
#> [1] "login" "id" "avatar_url"
#> [4] "gravatar_id" "url" "html_url"
#> [7] "followers_url" "following_url" "gists_url"
#> [10] "starred_url" "subscriptions_url" "organizations_url"
#> [13] "repos_url" "events_url" "received_events_url"
#> [16] "type" "site_admin" "name"
#> [19] "company" "blog" "location"
#> [22] "email" "hireable" "bio"
#> [25] "public_repos" "public_gists" "followers"
#> [28] "following" "created_at" "updated_at"Kunezindlela ezimbili zokuguqula izingxenye zohlu zibe amakholomu. unnest_wider() ithatha ingxenye ngayinye bese idala ikholomu entsha:
users %>% unnest_wider(user)
#> # A tibble: 6 x 30
#> login id avatar_url gravatar_id url html_url followers_url
#> <chr> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 gabo… 6.60e5 https://a… "" http… https:/… https://api.…
#> 2 jenn… 5.99e5 https://a… "" http… https:/… https://api.…
#> 3 jtle… 1.57e6 https://a… "" http… https:/… https://api.…
#> 4 juli… 1.25e7 https://a… "" http… https:/… https://api.…
#> 5 leep… 3.51e6 https://a… "" http… https:/… https://api.…
#> 6 masa… 8.36e6 https://a… "" http… https:/… https://api.…
#> # … with 23 more variables: following_url <chr>, gists_url <chr>,
#> # starred_url <chr>, subscriptions_url <chr>, organizations_url <chr>,
#> # repos_url <chr>, events_url <chr>, received_events_url <chr>,
#> # type <chr>, site_admin <lgl>, name <chr>, company <chr>, blog <chr>,
#> # location <chr>, email <chr>, public_repos <int>, public_gists <int>,
#> # followers <int>, following <int>, created_at <chr>, updated_at <chr>,
#> # bio <chr>, hireable <lgl>Kulokhu, sinetafula eliqukethe amakholomu angu-30, futhi ngeke siwadinga amaningi, ngakho-ke singakwazi esikhundleni salokho. unnest_wider() sebenzisa hoist(). hoist() kusivumela ukuthi sikhiphe izingxenye ezikhethiwe sisebenzisa i-syntax efanayo ne purrr::pluck():
users %>% hoist(user,
followers = "followers",
login = "login",
url = "html_url"
)
#> # A tibble: 6 x 4
#> followers login url user
#> <int> <chr> <chr> <list>
#> 1 303 gaborcsardi https://github.com/gaborcsardi <named list [27]>
#> 2 780 jennybc https://github.com/jennybc <named list [27]>
#> 3 3958 jtleek https://github.com/jtleek <named list [27]>
#> 4 115 juliasilge https://github.com/juliasilge <named list [27]>
#> 5 213 leeper https://github.com/leeper <named list [27]>
#> 6 34 masalmon https://github.com/masalmon <named list [27]>hoist() ikhipha izingxenye ezishiwo ohlwini lwekholomu umsebenzisingakho ungacabangela hoist() njengokuhambisa izingxenye kusuka kuhlu lwangaphakathi lohlaka lwedethi kuya kuleveli yalo ephezulu.
I-Github yokugcina
Ukuqondanisa uhlu gh_repos siqala ngokufanayo ngokuyiguqulela ku tibble:
repos <- tibble(repo = gh_repos)
repos
#> # A tibble: 6 x 1
#> repo
#> <list>
#> 1 <list [30]>
#> 2 <list [30]>
#> 3 <list [30]>
#> 4 <list [26]>
#> 5 <list [30]>
#> 6 <list [30]>Lesi sikhathi izakhi umsebenzisi umele uhlu lwamaqoqo alo msebenzisi. Inqolobane ngayinye iwukubhekwa okuhlukile, ngakho ngokomqondo wedatha ehlanzekile (cishe idatha ehlelekile) kufanele abe imigqa emisha, yingakho sisebenzisa unnest_longer() kodwa hhayi unnest_wider():
repos <- repos %>% unnest_longer(repo)
repos
#> # A tibble: 176 x 1
#> repo
#> <list>
#> 1 <named list [68]>
#> 2 <named list [68]>
#> 3 <named list [68]>
#> 4 <named list [68]>
#> 5 <named list [68]>
#> 6 <named list [68]>
#> 7 <named list [68]>
#> 8 <named list [68]>
#> 9 <named list [68]>
#> 10 <named list [68]>
#> # … with 166 more rowsManje singasebenzisa unnest_wider() noma hoist() :
repos %>% hoist(repo,
login = c("owner", "login"),
name = "name",
homepage = "homepage",
watchers = "watchers_count"
)
#> # A tibble: 176 x 5
#> login name homepage watchers repo
#> <chr> <chr> <chr> <int> <list>
#> 1 gaborcsardi after <NA> 5 <named list [65]>
#> 2 gaborcsardi argufy <NA> 19 <named list [65]>
#> 3 gaborcsardi ask <NA> 5 <named list [65]>
#> 4 gaborcsardi baseimports <NA> 0 <named list [65]>
#> 5 gaborcsardi citest <NA> 0 <named list [65]>
#> 6 gaborcsardi clisymbols "" 18 <named list [65]>
#> 7 gaborcsardi cmaker <NA> 0 <named list [65]>
#> 8 gaborcsardi cmark <NA> 0 <named list [65]>
#> 9 gaborcsardi conditions <NA> 0 <named list [65]>
#> 10 gaborcsardi crayon <NA> 52 <named list [65]>
#> # … with 166 more rowsNaka ukusetshenziswa c("owner", "login"): Lokhu kusivumela ukuthi sithole inani leleveli yesibili ohlwini olufakwe esidlekeni owner. Enye indlela yokuthola uhlu lonke owner bese usebenzisa umsebenzi unnest_wider() faka izakhi zayo ngayinye ibe yikholomu:
repos %>%
hoist(repo, owner = "owner") %>%
unnest_wider(owner)
#> # A tibble: 176 x 18
#> login id avatar_url gravatar_id url html_url followers_url
#> <chr> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 gabo… 660288 https://a… "" http… https:/… https://api.…
#> 2 gabo… 660288 https://a… "" http… https:/… https://api.…
#> 3 gabo… 660288 https://a… "" http… https:/… https://api.…
#> 4 gabo… 660288 https://a… "" http… https:/… https://api.…
#> 5 gabo… 660288 https://a… "" http… https:/… https://api.…
#> 6 gabo… 660288 https://a… "" http… https:/… https://api.…
#> 7 gabo… 660288 https://a… "" http… https:/… https://api.…
#> 8 gabo… 660288 https://a… "" http… https:/… https://api.…
#> 9 gabo… 660288 https://a… "" http… https:/… https://api.…
#> 10 gabo… 660288 https://a… "" http… https:/… https://api.…
#> # … with 166 more rows, and 11 more variables: following_url <chr>,
#> # gists_url <chr>, starred_url <chr>, subscriptions_url <chr>,
#> # organizations_url <chr>, repos_url <chr>, events_url <chr>,
#> # received_events_url <chr>, type <chr>, site_admin <lgl>, repo <list>Esikhundleni sokucabanga ngokukhetha umsebenzi ofanele unnest_longer() noma unnest_wider() ungasebenzisa unnest_auto(). Lo msebenzi usebenzisa izindlela ezimbalwa ze-heuristic ukukhetha umsebenzi ofaneleka kakhulu wokuguqula idatha, futhi ubonisa umlayezo omayelana nendlela ekhethiwe.
tibble(repo = gh_repos) %>%
unnest_auto(repo) %>%
unnest_auto(repo)
#> Using `unnest_longer(repo)`; no element has names
#> Using `unnest_wider(repo)`; elements have 68 names in common
#> # A tibble: 176 x 67
#> id name full_name owner private html_url description fork url
#> <int> <chr> <chr> <lis> <lgl> <chr> <chr> <lgl> <chr>
#> 1 6.12e7 after gaborcsa… <nam… FALSE https:/… Run Code i… FALSE http…
#> 2 4.05e7 argu… gaborcsa… <nam… FALSE https:/… Declarativ… FALSE http…
#> 3 3.64e7 ask gaborcsa… <nam… FALSE https:/… Friendly C… FALSE http…
#> 4 3.49e7 base… gaborcsa… <nam… FALSE https:/… Do we get … FALSE http…
#> 5 6.16e7 cite… gaborcsa… <nam… FALSE https:/… Test R pac… TRUE http…
#> 6 3.39e7 clis… gaborcsa… <nam… FALSE https:/… Unicode sy… FALSE http…
#> 7 3.72e7 cmak… gaborcsa… <nam… FALSE https:/… port of cm… TRUE http…
#> 8 6.80e7 cmark gaborcsa… <nam… FALSE https:/… CommonMark… TRUE http…
#> 9 6.32e7 cond… gaborcsa… <nam… FALSE https:/… <NA> TRUE http…
#> 10 2.43e7 cray… gaborcsa… <nam… FALSE https:/… R package … FALSE http…
#> # … with 166 more rows, and 58 more variables: forks_url <chr>,
#> # keys_url <chr>, collaborators_url <chr>, teams_url <chr>,
#> # hooks_url <chr>, issue_events_url <chr>, events_url <chr>,
#> # assignees_url <chr>, branches_url <chr>, tags_url <chr>,
#> # blobs_url <chr>, git_tags_url <chr>, git_refs_url <chr>,
#> # trees_url <chr>, statuses_url <chr>, languages_url <chr>,
#> # stargazers_url <chr>, contributors_url <chr>, subscribers_url <chr>,
#> # subscription_url <chr>, commits_url <chr>, git_commits_url <chr>,
#> # comments_url <chr>, issue_comment_url <chr>, contents_url <chr>,
#> # compare_url <chr>, merges_url <chr>, archive_url <chr>,
#> # downloads_url <chr>, issues_url <chr>, pulls_url <chr>,
#> # milestones_url <chr>, notifications_url <chr>, labels_url <chr>,
#> # releases_url <chr>, deployments_url <chr>, created_at <chr>,
#> # updated_at <chr>, pushed_at <chr>, git_url <chr>, ssh_url <chr>,
#> # clone_url <chr>, svn_url <chr>, size <int>, stargazers_count <int>,
#> # watchers_count <int>, language <chr>, has_issues <lgl>,
#> # has_downloads <lgl>, has_wiki <lgl>, has_pages <lgl>,
#> # forks_count <int>, open_issues_count <int>, forks <int>,
#> # open_issues <int>, watchers <int>, default_branch <chr>,
#> # homepage <chr>Abalingiswa beGame of Thrones
got_chars inesakhiwo esifanayo neso gh_users: Lena isethi yohlu oluqanjwe igama, lapho ingxenye ngayinye yohlu lwangaphakathi ichaza isibaluli esithile sohlamvu lwe-Game of Thrones. Ukuletha got_chars Ngokubuka kwethebula, siqala ngokwakha uhlaka lwedethi, njengasezibonelweni ezedlule, bese siguqula isici ngasinye sibe ikholomu ehlukile:
chars <- tibble(char = got_chars)
chars
#> # A tibble: 30 x 1
#> char
#> <list>
#> 1 <named list [18]>
#> 2 <named list [18]>
#> 3 <named list [18]>
#> 4 <named list [18]>
#> 5 <named list [18]>
#> 6 <named list [18]>
#> 7 <named list [18]>
#> 8 <named list [18]>
#> 9 <named list [18]>
#> 10 <named list [18]>
#> # … with 20 more rows
chars2 <- chars %>% unnest_wider(char)
chars2
#> # A tibble: 30 x 18
#> url id name gender culture born died alive titles aliases father
#> <chr> <int> <chr> <chr> <chr> <chr> <chr> <lgl> <list> <list> <chr>
#> 1 http… 1022 Theo… Male Ironbo… In 2… "" TRUE <chr … <chr [… ""
#> 2 http… 1052 Tyri… Male "" In 2… "" TRUE <chr … <chr [… ""
#> 3 http… 1074 Vict… Male Ironbo… In 2… "" TRUE <chr … <chr [… ""
#> 4 http… 1109 Will Male "" "" In 2… FALSE <chr … <chr [… ""
#> 5 http… 1166 Areo… Male Norvos… In 2… "" TRUE <chr … <chr [… ""
#> 6 http… 1267 Chett Male "" At H… In 2… FALSE <chr … <chr [… ""
#> 7 http… 1295 Cres… Male "" In 2… In 2… FALSE <chr … <chr [… ""
#> 8 http… 130 Aria… Female Dornish In 2… "" TRUE <chr … <chr [… ""
#> 9 http… 1303 Daen… Female Valyri… In 2… "" TRUE <chr … <chr [… ""
#> 10 http… 1319 Davo… Male Wester… In 2… "" TRUE <chr … <chr [… ""
#> # … with 20 more rows, and 7 more variables: mother <chr>, spouse <chr>,
#> # allegiances <list>, books <list>, povBooks <list>, tvSeries <list>,
#> # playedBy <list>isakhiwo got_chars kunzima kakhulu kunalokho gh_users, ngoba ezinye izingxenye zohlu char ngokwabo bawuhlu, ngenxa yalokho sithola izinsika - uhlu:
chars2 %>% select_if(is.list)
#> # A tibble: 30 x 7
#> titles aliases allegiances books povBooks tvSeries playedBy
#> <list> <list> <list> <list> <list> <list> <list>
#> 1 <chr [3]> <chr [4]> <chr [1]> <chr [3]> <chr [2]> <chr [6]> <chr [1]>
#> 2 <chr [2]> <chr [11]> <chr [1]> <chr [2]> <chr [4]> <chr [6]> <chr [1]>
#> 3 <chr [2]> <chr [1]> <chr [1]> <chr [3]> <chr [2]> <chr [1]> <chr [1]>
#> 4 <chr [1]> <chr [1]> <???> <chr [1]> <chr [1]> <chr [1]> <chr [1]>
#> 5 <chr [1]> <chr [1]> <chr [1]> <chr [3]> <chr [2]> <chr [2]> <chr [1]>
#> 6 <chr [1]> <chr [1]> <???> <chr [2]> <chr [1]> <chr [1]> <chr [1]>
#> 7 <chr [1]> <chr [1]> <???> <chr [2]> <chr [1]> <chr [1]> <chr [1]>
#> 8 <chr [1]> <chr [1]> <chr [1]> <chr [4]> <chr [1]> <chr [1]> <chr [1]>
#> 9 <chr [5]> <chr [11]> <chr [1]> <chr [1]> <chr [4]> <chr [6]> <chr [1]>
#> 10 <chr [4]> <chr [5]> <chr [2]> <chr [1]> <chr [3]> <chr [5]> <chr [1]>
#> # … with 20 more rowsIzenzo zakho ezengeziwe zincike ezinhlosweni zokuhlaziya. Mhlawumbe udinga ukubeka ulwazi emigqeni yencwadi ngayinye nochungechunge lapho umlingisi avela khona:
chars2 %>%
select(name, books, tvSeries) %>%
pivot_longer(c(books, tvSeries), names_to = "media", values_to = "value") %>%
unnest_longer(value)
#> # A tibble: 180 x 3
#> name media value
#> <chr> <chr> <chr>
#> 1 Theon Greyjoy books A Game of Thrones
#> 2 Theon Greyjoy books A Storm of Swords
#> 3 Theon Greyjoy books A Feast for Crows
#> 4 Theon Greyjoy tvSeries Season 1
#> 5 Theon Greyjoy tvSeries Season 2
#> 6 Theon Greyjoy tvSeries Season 3
#> 7 Theon Greyjoy tvSeries Season 4
#> 8 Theon Greyjoy tvSeries Season 5
#> 9 Theon Greyjoy tvSeries Season 6
#> 10 Tyrion Lannister books A Feast for Crows
#> # … with 170 more rowsNoma mhlawumbe ufuna ukwakha ithebula elikuvumela ukuthi ufanise umlingiswa nomsebenzi:
chars2 %>%
select(name, title = titles) %>%
unnest_longer(title)
#> # A tibble: 60 x 2
#> name title
#> <chr> <chr>
#> 1 Theon Greyjoy Prince of Winterfell
#> 2 Theon Greyjoy Captain of Sea Bitch
#> 3 Theon Greyjoy Lord of the Iron Islands (by law of the green lands)
#> 4 Tyrion Lannister Acting Hand of the King (former)
#> 5 Tyrion Lannister Master of Coin (former)
#> 6 Victarion Greyjoy Lord Captain of the Iron Fleet
#> 7 Victarion Greyjoy Master of the Iron Victory
#> 8 Will ""
#> 9 Areo Hotah Captain of the Guard at Sunspear
#> 10 Chett ""
#> # … with 50 more rows(Qaphela amanani angenalutho "" ensimini title, lokhu kungenxa yamaphutha enziwe lapho kufakwa idatha kukho got_chars: empeleni, izinhlamvu ezingenayo incwadi ehambisanayo nezihloko zochungechunge lwe-TV endaweni title kufanele ibe nevekhtha yobude obungu-0, hhayi ivekhtha yobude obu-1 equkethe iyunithi yezinhlamvu engenalutho.)
Singabhala kabusha isibonelo esingenhla sisebenzisa umsebenzi unnest_auto(). Le ndlela ilungele ukuhlaziywa kwesikhathi esisodwa, kodwa akufanele uthembele kuyo unnest_auto() ukusetshenziswa njalo. Iphuzu liwukuthi uma isakhiwo sedatha yakho sishintsha unnest_auto() ingashintsha indlela yokuguqula idatha ekhethiwe uma ekuqaleni inwebe amakholomu ohlu ibe imigqa isebenzisa unnest_longer(), lapho-ke isakhiwo sedatha engenayo sishintsha, ingqondo ingashintshwa ukuze ivune unnest_wider(), futhi ukusebenzisa le ndlela ngokuqhubekayo kungaholela emaphutheni angalindelekile.
tibble(char = got_chars) %>%
unnest_auto(char) %>%
select(name, title = titles) %>%
unnest_auto(title)
#> Using `unnest_wider(char)`; elements have 18 names in common
#> Using `unnest_longer(title)`; no element has names
#> # A tibble: 60 x 2
#> name title
#> <chr> <chr>
#> 1 Theon Greyjoy Prince of Winterfell
#> 2 Theon Greyjoy Captain of Sea Bitch
#> 3 Theon Greyjoy Lord of the Iron Islands (by law of the green lands)
#> 4 Tyrion Lannister Acting Hand of the King (former)
#> 5 Tyrion Lannister Master of Coin (former)
#> 6 Victarion Greyjoy Lord Captain of the Iron Fleet
#> 7 Victarion Greyjoy Master of the Iron Victory
#> 8 Will ""
#> 9 Areo Hotah Captain of the Guard at Sunspear
#> 10 Chett ""
#> # … with 50 more rowsI-Geocoding nge-Google
Okulandelayo, sizobheka isakhiwo esiyinkimbinkimbi yedatha etholwe kusevisi ye-geocoding ye-Google. Ukufaka kunqolobane kungqubuzana nemithetho yokusebenza ne-Google Maps API, ngakho ngizoqale ngibhale isembozo esilula esizungeze i-API. Okususelwe ekugcineni ukhiye we-Google Maps API ku-variable yemvelo; Uma ungenawo ukhiye wokusebenza nge-Google Maps API egcinwe ezindaweni eziguquguqukayo, izingcezu zekhodi ezethulwe kulesi sigaba ngeke zisetshenziswe.
has_key <- !identical(Sys.getenv("GOOGLE_MAPS_API_KEY"), "")
if (!has_key) {
message("No Google Maps API key found; code chunks will not be run")
}
# https://developers.google.com/maps/documentation/geocoding
geocode <- function(address, api_key = Sys.getenv("GOOGLE_MAPS_API_KEY")) {
url <- "https://maps.googleapis.com/maps/api/geocode/json"
url <- paste0(url, "?address=", URLencode(address), "&key=", api_key)
jsonlite::read_json(url)
}Uhlu olubuya yilo msebenzi luyinkimbinkimbi:
houston <- geocode("Houston TX")
str(houston)
#> List of 2
#> $ results:List of 1
#> ..$ :List of 5
#> .. ..$ address_components:List of 4
#> .. .. ..$ :List of 3
#> .. .. .. ..$ long_name : chr "Houston"
#> .. .. .. ..$ short_name: chr "Houston"
#> .. .. .. ..$ types :List of 2
#> .. .. .. .. ..$ : chr "locality"
#> .. .. .. .. ..$ : chr "political"
#> .. .. ..$ :List of 3
#> .. .. .. ..$ long_name : chr "Harris County"
#> .. .. .. ..$ short_name: chr "Harris County"
#> .. .. .. ..$ types :List of 2
#> .. .. .. .. ..$ : chr "administrative_area_level_2"
#> .. .. .. .. ..$ : chr "political"
#> .. .. ..$ :List of 3
#> .. .. .. ..$ long_name : chr "Texas"
#> .. .. .. ..$ short_name: chr "TX"
#> .. .. .. ..$ types :List of 2
#> .. .. .. .. ..$ : chr "administrative_area_level_1"
#> .. .. .. .. ..$ : chr "political"
#> .. .. ..$ :List of 3
#> .. .. .. ..$ long_name : chr "United States"
#> .. .. .. ..$ short_name: chr "US"
#> .. .. .. ..$ types :List of 2
#> .. .. .. .. ..$ : chr "country"
#> .. .. .. .. ..$ : chr "political"
#> .. ..$ formatted_address : chr "Houston, TX, USA"
#> .. ..$ geometry :List of 4
#> .. .. ..$ bounds :List of 2
#> .. .. .. ..$ northeast:List of 2
#> .. .. .. .. ..$ lat: num 30.1
#> .. .. .. .. ..$ lng: num -95
#> .. .. .. ..$ southwest:List of 2
#> .. .. .. .. ..$ lat: num 29.5
#> .. .. .. .. ..$ lng: num -95.8
#> .. .. ..$ location :List of 2
#> .. .. .. ..$ lat: num 29.8
#> .. .. .. ..$ lng: num -95.4
#> .. .. ..$ location_type: chr "APPROXIMATE"
#> .. .. ..$ viewport :List of 2
#> .. .. .. ..$ northeast:List of 2
#> .. .. .. .. ..$ lat: num 30.1
#> .. .. .. .. ..$ lng: num -95
#> .. .. .. ..$ southwest:List of 2
#> .. .. .. .. ..$ lat: num 29.5
#> .. .. .. .. ..$ lng: num -95.8
#> .. ..$ place_id : chr "ChIJAYWNSLS4QIYROwVl894CDco"
#> .. ..$ types :List of 2
#> .. .. ..$ : chr "locality"
#> .. .. ..$ : chr "political"
#> $ status : chr "OK"Ngenhlanhla, singakwazi ukuxazulula inkinga yokuguqula le datha ibe ifomu lethebula isinyathelo ngesinyathelo sisebenzisa imisebenzi tidyr. Ukuze ngenze umsebenzi ube inselele futhi ube ngokoqobo, ngizoqala ngokwenza amadolobha ambalwa:
city <- c ( "Houston" , "LA" , "New York" , "Chicago" , "Springfield" ) city_geo <- purrr::map (city, geocode) Ngizoguqula umphumela ube tibble, ukuze kube lula, ngizofaka ikholomu enegama ledolobha elihambisanayo.
loc <- tibble(city = city, json = city_geo)
loc
#> # A tibble: 5 x 2
#> city json
#> <chr> <list>
#> 1 Houston <named list [2]>
#> 2 LA <named list [2]>
#> 3 New York <named list [2]>
#> 4 Chicago <named list [2]>
#> 5 Springfield <named list [2]>Izinga lokuqala liqukethe izingxenye status и result, esinganweba ngayo unnest_wider() :
loc %>%
unnest_wider(json)
#> # A tibble: 5 x 3
#> city results status
#> <chr> <list> <chr>
#> 1 Houston <list [1]> OK
#> 2 LA <list [1]> OK
#> 3 New York <list [1]> OK
#> 4 Chicago <list [1]> OK
#> 5 Springfield <list [1]> OKUyacelwa ukuthi uqaphele lokho results iwuhlu olunamazinga amaningi. Amadolobha amaningi anesici esingu-1 (esimele inani eliyingqayizivele elihambisana ne-geocoding API), kodwa i-Springfield inezimbili. Singazidonsa zibe imigqa ehlukene nge unnest_longer() :
loc %>%
unnest_wider(json) %>%
unnest_longer(results)
#> # A tibble: 5 x 3
#> city results status
#> <chr> <list> <chr>
#> 1 Houston <named list [5]> OK
#> 2 LA <named list [5]> OK
#> 3 New York <named list [5]> OK
#> 4 Chicago <named list [5]> OK
#> 5 Springfield <named list [5]> OKManje zonke zinezingxenye ezifanayo, ezingaqinisekiswa kusetshenziswa unnest_wider():
loc %>%
unnest_wider(json) %>%
unnest_longer(results) %>%
unnest_wider(results)
#> # A tibble: 5 x 7
#> city address_componen… formatted_addre… geometry place_id types status
#> <chr> <list> <chr> <list> <chr> <lis> <chr>
#> 1 Houst… <list [4]> Houston, TX, USA <named … ChIJAYWN… <lis… OK
#> 2 LA <list [4]> Los Angeles, CA… <named … ChIJE9on… <lis… OK
#> 3 New Y… <list [3]> New York, NY, U… <named … ChIJOwg_… <lis… OK
#> 4 Chica… <list [4]> Chicago, IL, USA <named … ChIJ7cv0… <lis… OK
#> 5 Sprin… <list [5]> Springfield, MO… <named … ChIJP5jI… <lis… OKSingathola izixhumanisi ze-latitude ne-longitude zedolobha ngalinye ngokwandisa uhlu geometry:
loc %>%
unnest_wider(json) %>%
unnest_longer(results) %>%
unnest_wider(results) %>%
unnest_wider(geometry)
#> # A tibble: 5 x 10
#> city address_compone… formatted_addre… bounds location location_type
#> <chr> <list> <chr> <list> <list> <chr>
#> 1 Hous… <list [4]> Houston, TX, USA <name… <named … APPROXIMATE
#> 2 LA <list [4]> Los Angeles, CA… <name… <named … APPROXIMATE
#> 3 New … <list [3]> New York, NY, U… <name… <named … APPROXIMATE
#> 4 Chic… <list [4]> Chicago, IL, USA <name… <named … APPROXIMATE
#> 5 Spri… <list [5]> Springfield, MO… <name… <named … APPROXIMATE
#> # … with 4 more variables: viewport <list>, place_id <chr>, types <list>,
#> # status <chr>Bese kuba indawo lapho udinga ukwandisa location:
loc %>%
unnest_wider(json) %>%
unnest_longer(results) %>%
unnest_wider(results) %>%
unnest_wider(geometry) %>%
unnest_wider(location)
#> # A tibble: 5 x 11
#> city address_compone… formatted_addre… bounds lat lng location_type
#> <chr> <list> <chr> <list> <dbl> <dbl> <chr>
#> 1 Hous… <list [4]> Houston, TX, USA <name… 29.8 -95.4 APPROXIMATE
#> 2 LA <list [4]> Los Angeles, CA… <name… 34.1 -118. APPROXIMATE
#> 3 New … <list [3]> New York, NY, U… <name… 40.7 -74.0 APPROXIMATE
#> 4 Chic… <list [4]> Chicago, IL, USA <name… 41.9 -87.6 APPROXIMATE
#> 5 Spri… <list [5]> Springfield, MO… <name… 37.2 -93.3 APPROXIMATE
#> # … with 4 more variables: viewport <list>, place_id <chr>, types <list>,
#> # status <chr>Futhi futhi, unnest_auto() yenza umsebenzi ochaziwe ube lula ngezingozi ezithile ezingase zidalwe ukushintsha ukwakheka kwedatha engenayo:
loc %>%
unnest_auto(json) %>%
unnest_auto(results) %>%
unnest_auto(results) %>%
unnest_auto(geometry) %>%
unnest_auto(location)
#> Using `unnest_wider(json)`; elements have 2 names in common
#> Using `unnest_longer(results)`; no element has names
#> Using `unnest_wider(results)`; elements have 5 names in common
#> Using `unnest_wider(geometry)`; elements have 4 names in common
#> Using `unnest_wider(location)`; elements have 2 names in common
#> # A tibble: 5 x 11
#> city address_compone… formatted_addre… bounds lat lng location_type
#> <chr> <list> <chr> <list> <dbl> <dbl> <chr>
#> 1 Hous… <list [4]> Houston, TX, USA <name… 29.8 -95.4 APPROXIMATE
#> 2 LA <list [4]> Los Angeles, CA… <name… 34.1 -118. APPROXIMATE
#> 3 New … <list [3]> New York, NY, U… <name… 40.7 -74.0 APPROXIMATE
#> 4 Chic… <list [4]> Chicago, IL, USA <name… 41.9 -87.6 APPROXIMATE
#> 5 Spri… <list [5]> Springfield, MO… <name… 37.2 -93.3 APPROXIMATE
#> # … with 4 more variables: viewport <list>, place_id <chr>, types <list>,
#> # status <chr>Futhi singabheka nje ikheli lokuqala ledolobha ngalinye:
loc %>%
unnest_wider(json) %>%
hoist(results, first_result = 1) %>%
unnest_wider(first_result) %>%
unnest_wider(geometry) %>%
unnest_wider(location)
#> # A tibble: 5 x 11
#> city address_compone… formatted_addre… bounds lat lng location_type
#> <chr> <list> <chr> <list> <dbl> <dbl> <chr>
#> 1 Hous… <list [4]> Houston, TX, USA <name… 29.8 -95.4 APPROXIMATE
#> 2 LA <list [4]> Los Angeles, CA… <name… 34.1 -118. APPROXIMATE
#> 3 New … <list [3]> New York, NY, U… <name… 40.7 -74.0 APPROXIMATE
#> 4 Chic… <list [4]> Chicago, IL, USA <name… 41.9 -87.6 APPROXIMATE
#> 5 Spri… <list [5]> Springfield, MO… <name… 37.2 -93.3 APPROXIMATE
#> # … with 4 more variables: viewport <list>, place_id <chr>, types <list>,
#> # status <chr>Noma sebenzisa hoist() ukuze ungene ku-multilevel dive ukuya khona ngqo lat и lng.
loc %>%
hoist(json,
lat = list("results", 1, "geometry", "location", "lat"),
lng = list("results", 1, "geometry", "location", "lng")
)
#> # A tibble: 5 x 4
#> city lat lng json
#> <chr> <dbl> <dbl> <list>
#> 1 Houston 29.8 -95.4 <named list [2]>
#> 2 LA 34.1 -118. <named list [2]>
#> 3 New York 40.7 -74.0 <named list [2]>
#> 4 Chicago 41.9 -87.6 <named list [2]>
#> 5 Springfield 37.2 -93.3 <named list [2]>I-Discography ka-Sharla Gelfand
Ekugcineni, sizobheka isakhiwo esiyinkimbinkimbi kakhulu - i-discography kaSharla Gelfand. Njengakuzibonelo ezingenhla, siqala ngokuguqula uhlu lube luhlaka lwedatha yekholomu eyodwa, bese siyalunweba ukuze ingxenye ngayinye ibe ikholomu ehlukile. Futhi ngiguqula ikholomu date_added osukwini olufanele kanye nefomethi yesikhathi ku-R.
discs <- tibble(disc = discog) %>%
unnest_wider(disc) %>%
mutate(date_added = as.POSIXct(strptime(date_added, "%Y-%m-%dT%H:%M:%S")))
discs
#> # A tibble: 155 x 5
#> instance_id date_added basic_information id rating
#> <int> <dttm> <list> <int> <int>
#> 1 354823933 2019-02-16 17:48:59 <named list [11]> 7496378 0
#> 2 354092601 2019-02-13 14:13:11 <named list [11]> 4490852 0
#> 3 354091476 2019-02-13 14:07:23 <named list [11]> 9827276 0
#> 4 351244906 2019-02-02 11:39:58 <named list [11]> 9769203 0
#> 5 351244801 2019-02-02 11:39:37 <named list [11]> 7237138 0
#> 6 351052065 2019-02-01 20:40:53 <named list [11]> 13117042 0
#> 7 350315345 2019-01-29 15:48:37 <named list [11]> 7113575 0
#> 8 350315103 2019-01-29 15:47:22 <named list [11]> 10540713 0
#> 9 350314507 2019-01-29 15:44:08 <named list [11]> 11260950 0
#> 10 350314047 2019-01-29 15:41:35 <named list [11]> 11726853 0
#> # … with 145 more rowsKuleli zinga, sithola ulwazi mayelana nokuthi idiski ngayinye yengezwe nini ku-discography ka-Sharla, kodwa ayikho idatha esiyibonayo mayelana nalawo madiski. Ukuze senze lokhu sidinga ukwandisa ikholomu basic_information:
discs %>% unnest_wider(basic_information)
#> Column name `id` must not be duplicated.
#> Use .name_repair to specify repair.Ngeshwa, sizothola iphutha, ngoba... ngaphakathi ohlwini basic_information kukhona ikholomu yegama elifanayo basic_information. Uma iphutha elinjalo lenzeka, ukuze unqume ngokushesha imbangela yalo, ungasebenzisa names_repair = "unique":
discs %>% unnest_wider(basic_information, names_repair = "unique")
#> New names:
#> * id -> id...6
#> * id -> id...14
#> # A tibble: 155 x 15
#> instance_id date_added labels year artists id...6 thumb title
#> <int> <dttm> <list> <int> <list> <int> <chr> <chr>
#> 1 354823933 2019-02-16 17:48:59 <list… 2015 <list … 7.50e6 http… Demo
#> 2 354092601 2019-02-13 14:13:11 <list… 2013 <list … 4.49e6 http… Obse…
#> 3 354091476 2019-02-13 14:07:23 <list… 2017 <list … 9.83e6 http… I
#> 4 351244906 2019-02-02 11:39:58 <list… 2017 <list … 9.77e6 http… Oído…
#> 5 351244801 2019-02-02 11:39:37 <list… 2015 <list … 7.24e6 http… A Ca…
#> 6 351052065 2019-02-01 20:40:53 <list… 2019 <list … 1.31e7 http… Tash…
#> 7 350315345 2019-01-29 15:48:37 <list… 2014 <list … 7.11e6 http… Demo
#> 8 350315103 2019-01-29 15:47:22 <list… 2015 <list … 1.05e7 http… Let …
#> 9 350314507 2019-01-29 15:44:08 <list… 2017 <list … 1.13e7 "" Sub …
#> 10 350314047 2019-01-29 15:41:35 <list… 2017 <list … 1.17e7 http… Demo
#> # … with 145 more rows, and 7 more variables: formats <list>,
#> # cover_image <chr>, resource_url <chr>, master_id <int>,
#> # master_url <chr>, id...14 <int>, rating <int>Inkinga ukuthi basic_information iphinda ikholomu ye-id nayo egcinwe ezingeni eliphezulu, ukuze sikwazi ukuvele siyisuse:
discs %>%
select(-id) %>%
unnest_wider(basic_information)
#> # A tibble: 155 x 14
#> instance_id date_added labels year artists id thumb title
#> <int> <dttm> <list> <int> <list> <int> <chr> <chr>
#> 1 354823933 2019-02-16 17:48:59 <list… 2015 <list … 7.50e6 http… Demo
#> 2 354092601 2019-02-13 14:13:11 <list… 2013 <list … 4.49e6 http… Obse…
#> 3 354091476 2019-02-13 14:07:23 <list… 2017 <list … 9.83e6 http… I
#> 4 351244906 2019-02-02 11:39:58 <list… 2017 <list … 9.77e6 http… Oído…
#> 5 351244801 2019-02-02 11:39:37 <list… 2015 <list … 7.24e6 http… A Ca…
#> 6 351052065 2019-02-01 20:40:53 <list… 2019 <list … 1.31e7 http… Tash…
#> 7 350315345 2019-01-29 15:48:37 <list… 2014 <list … 7.11e6 http… Demo
#> 8 350315103 2019-01-29 15:47:22 <list… 2015 <list … 1.05e7 http… Let …
#> 9 350314507 2019-01-29 15:44:08 <list… 2017 <list … 1.13e7 "" Sub …
#> 10 350314047 2019-01-29 15:41:35 <list… 2017 <list … 1.17e7 http… Demo
#> # … with 145 more rows, and 6 more variables: formats <list>,
#> # cover_image <chr>, resource_url <chr>, master_id <int>,
#> # master_url <chr>, rating <int>Kungenjalo, singasebenzisa hoist():
discs %>%
hoist(basic_information,
title = "title",
year = "year",
label = list("labels", 1, "name"),
artist = list("artists", 1, "name")
)
#> # A tibble: 155 x 9
#> instance_id date_added title year label artist
#> <int> <dttm> <chr> <int> <chr> <chr>
#> 1 354823933 2019-02-16 17:48:59 Demo 2015 Tobi… Mollot
#> 2 354092601 2019-02-13 14:13:11 Obse… 2013 La V… Una B…
#> 3 354091476 2019-02-13 14:07:23 I 2017 La V… S.H.I…
#> 4 351244906 2019-02-02 11:39:58 Oído… 2017 La V… Rata …
#> 5 351244801 2019-02-02 11:39:37 A Ca… 2015 Kato… Ivy (…
#> 6 351052065 2019-02-01 20:40:53 Tash… 2019 High… Tashme
#> 7 350315345 2019-01-29 15:48:37 Demo 2014 Mind… Desgr…
#> 8 350315103 2019-01-29 15:47:22 Let … 2015 Not … Phant…
#> 9 350314507 2019-01-29 15:44:08 Sub … 2017 Not … Sub S…
#> 10 350314047 2019-01-29 15:41:35 Demo 2017 Pres… Small…
#> # … with 145 more rows, and 3 more variables: basic_information <list>,
#> # id <int>, rating <int>Lapha ngithola ngokushesha ilebula yokuqala negama lomculi ngenkomba ngokungena ohlwini olubekwe esidlekeni.
Indlela ehleleke kakhulu iwukwenza amathebula ahlukene eciko namalebula:
discs %>%
hoist(basic_information, artist = "artists") %>%
select(disc_id = id, artist) %>%
unnest_longer(artist) %>%
unnest_wider(artist)
#> # A tibble: 167 x 8
#> disc_id join name anv tracks role resource_url id
#> <int> <chr> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 7496378 "" Mollot "" "" "" https://api.discog… 4.62e6
#> 2 4490852 "" Una Bèstia… "" "" "" https://api.discog… 3.19e6
#> 3 9827276 "" S.H.I.T. (… "" "" "" https://api.discog… 2.77e6
#> 4 9769203 "" Rata Negra "" "" "" https://api.discog… 4.28e6
#> 5 7237138 "" Ivy (18) "" "" "" https://api.discog… 3.60e6
#> 6 13117042 "" Tashme "" "" "" https://api.discog… 5.21e6
#> 7 7113575 "" Desgraciad… "" "" "" https://api.discog… 4.45e6
#> 8 10540713 "" Phantom He… "" "" "" https://api.discog… 4.27e6
#> 9 11260950 "" Sub Space … "" "" "" https://api.discog… 5.69e6
#> 10 11726853 "" Small Man … "" "" "" https://api.discog… 6.37e6
#> # … with 157 more rows
discs %>%
hoist(basic_information, format = "formats") %>%
select(disc_id = id, format) %>%
unnest_longer(format) %>%
unnest_wider(format) %>%
unnest_longer(descriptions)
#> # A tibble: 280 x 5
#> disc_id descriptions text name qty
#> <int> <chr> <chr> <chr> <chr>
#> 1 7496378 Numbered Black Cassette 1
#> 2 4490852 LP <NA> Vinyl 1
#> 3 9827276 "7"" <NA> Vinyl 1
#> 4 9827276 45 RPM <NA> Vinyl 1
#> 5 9827276 EP <NA> Vinyl 1
#> 6 9769203 LP <NA> Vinyl 1
#> 7 9769203 Album <NA> Vinyl 1
#> 8 7237138 "7"" <NA> Vinyl 1
#> 9 7237138 45 RPM <NA> Vinyl 1
#> 10 13117042 "7"" <NA> Vinyl 1
#> # … with 270 more rowsUngabe usuwajoyina ubuyele kudathasethi yoqobo njengoba kudingeka.
isiphetho
Emnyombweni womtapo wolwazi tidyverse ihlanganisa amaphakheji amaningi awusizo ahlanganiswe ifilosofi evamile yokucubungula idatha.
Kulesi sihloko sihlole umndeni wemisebenzi unnest_*(), okuhloswe ngayo ukusebenza ngokukhipha izakhi ohlwini olufakwe ezidlekeni. Le phakheji iqukethe ezinye izici eziningi eziwusizo ezenza kube lula ukuguqula idatha ngokuya ngomqondo Idatha Ehlelekile.
Source: www.habr.com