ProHoster > Блог > Whakahaerenga > Te whakawhanui i nga pou kohanga - nga rarangi e whakamahi ana i te reo R (pkai tidyr me nga mahi a te whanau korekore)
Te whakawhanui i nga pou kohanga - nga rarangi e whakamahi ana i te reo R (pkai tidyr me nga mahi a te whanau korekore)
I te nuinga o nga wa, i te wa e mahi ana koe me te whakautu mai i te API, me etahi atu raraunga he hanganga rakau uaua, kei te anga koe ki nga whakatakotoranga JSON me XML.
He maha nga painga o enei whakatakotoranga: he pai te penapena raraunga ka taea e koe te karo i nga korero taapiri kore.
Ko te kino o enei whakatakotoranga ko te uaua o a raatau tukatuka me te tātari. Kaore e taea te whakamahi i nga raraunga kore hanga ki nga tatauranga, kaore e taea te hanga tirohanga ki runga.
Ko tenei tuhinga he haere tonu o te whakaputanga "R package tidyr me ona mahi hou pivot_longer me pivot_wide". Ka awhina i a koe ki te kawe mai i nga hanganga raraunga kore hanga ki roto i te ahua mohio me te pai mo te tātari i te puka ripanga ma te whakamahi i te kete tidyr, kei roto i te matua o te whare pukapuka tidyverse, me te whanau o nga mahi unnest_*().
Tuhinga
Mena kei te pirangi koe ki te tātari raraunga, ka aro pea koe ki taku waea и youtube hongere. Ko te nuinga o nga korero e whakatapua ana ki te reo R.
Tapawhā(te tuhipoka a te kaiwhakamaori, kaore au i kite i nga whiringa whakamaori tika mo tenei kupu, no reira ka waiho noa.) Ko te tukanga ki te kawe mai i nga raraunga kaore i hangai me nga huinga kohanga ki roto i te ripanga ahu-rua kei roto i nga rarangi me nga pou e mohio ana. IN tidyr He maha nga mahi hei awhina i a koe ki te whakawhanui i nga rarangi rarangi kohanga me te whakaiti i nga raraunga ki te ahua tapawhā, te ahua ripanga:
unnest_longer() ka mau ia huānga o te rārangi tīwae ka hanga he haupae hōu.
unnest_wider() ka tango ia huānga o te rārangi tīwae me te hanga tīwae hōu.
unnest_auto() ka whakatau aunoa ko tehea mahi e pai ana ki te whakamahi unnest_longer() ranei unnest_wider().
hoist() rite ki unnest_wider() engari ka kowhiri i nga waahanga kua tohua ka taea e koe te mahi me etahi taumata o te kohanga.
Ko te nuinga o nga raruraru e pa ana ki te kawe mai i nga raraunga kaore i hangaia me te maha o nga taumata o te kohanga ki roto i te ripanga-rua ka taea te whakatau ma te whakakotahi i nga mahi kua whakarārangihia me te dplyr.
Hei whakaatu i enei tikanga, ka whakamahia e matou te kete repurrrsive, e whakarato ana i nga rarangi matatini maha, taumata-maha i ahu mai i te API tukutuku.
Kia timata ma gh_kaiwhakamahi, he rarangi kei roto nga korero mo nga kaiwhakamahi GitHub e ono. Tuatahi ka huri tatou i te rarangi gh_kaiwhakamahi в tipa tāpare:
users <- tibble( user = gh_users )
Ko te ahua he iti nei te whakaaro: he aha te whakatakoto rarangi gh_kaiwhakamahi, ki te hanganga raraunga uaua ake? Engari he painga nui te anga raraunga: he whakakotahi i nga vectors maha kia whai waahi nga mea katoa ki te mea kotahi.
Ia huānga ahanoa users he rarangi ingoa e tohu ana ia huānga ki tetahi pou.
I tenei take, he ripanga kei roto e 30 nga pou, kaore e hiahiatia te nuinga o era, na ka taea unnest_wider() whakamahi hoist(). hoist() ka taea e maatau te tango i nga waahanga kua tohua ma te whakamahi i te wetereo rite 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() ka tango i nga waahanga kua whakaingoatia mai i te rarangi pou kaiwhakamahikia taea e koe te whakaaro hoist() penei i te neke i nga waahanga mai i te rarangi o roto o te anga ra ki tona taumata o runga.
Nga putunga Github
Whakaritenga rarangi gh_repos ka timata ano tatou ma te huri ki tibble:
Tenei wa nga huānga kaiwhakamahi e tohu ana i te rarangi o nga whare putunga na tenei kaiwhakamahi. Ko ia putunga he tirohanga motuhake, no reira i runga i te ariā o te raraunga tau (tata ki te raraunga pai) me noho hei rarangi hou, na reira ka whakamahia e matou unnest_longer() a kaore 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 rows
Inaianei ka taea e taatau te whakamahi unnest_wider() ranei 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 rows
Kia tupato ki te whakamahinga c("owner", "login"): Ma tenei ka taea e matou te tiki i te uara taumata tuarua mai i te rarangi kohanga owner. Ko tetahi huarahi rereke ko te tango i te rarangi katoa owner katahi ka whakamahi i te mahi unnest_wider() hoatu ia o ona huānga ki roto i te pou:
Engari i te whakaaro ki te whiriwhiri i te mahi tika unnest_longer() ranei unnest_wider() ka taea e koe te whakamahi unnest_auto(). He maha nga tikanga heuristic e whakamahia ana e tenei mahi hei whiriwhiri i te mahi tino pai mo te whakarereke i nga raraunga, me te whakaatu i tetahi panui mo te tikanga kua tohua.
got_chars he rite tonu te hanganga ki gh_users: He huinga rarangi whakaingoatia tenei, ko ia huānga o te rarangi o roto e whakaatu ana i etahi huanga o te ahua Game of Thrones. Te kawe got_chars Mo te tirohanga ripanga, ka timata ma te hanga i tetahi anga ra, pera i nga tauira o mua, ka huri i ia huānga ki tetahi pou motuhake:
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>
hanganga got_chars ahua uaua ake gh_users, no te mea etahi wahanga rarangi char ko ratou ano he rarangi, no reira ka whiwhi tatou i nga pou - rarangi:
Ko o mahi ano kei runga i nga whaainga o te tātaritanga. Ka hiahia pea koe ki te whakatakoto korero mo nga rarangi mo ia pukapuka me nga raupapa ka puta te ahua:
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 rows
Ka hiahia pea koe ki te hanga tepu e taea ai e koe te whakarite i te ahua me te mahi:
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
(Tuhipoka nga uara kau "" i te mara title, na nga hapa i mahia i te whakaurunga raraunga ki roto got_chars: i roto i te meka, pūāhua mo nei kahore pukapuka e hāngai ana, me te taitara raupapa TV i roto i te mara title me whai vector te roa 0, kaua ko te vector roa 1 kei roto te aho putua.)
Ka taea e tatou te tuhi ano i te tauira i runga ake nei ma te whakamahi i te mahi unnest_auto(). He watea tenei huarahi mo te tātari kotahi-wa, engari kaua koe e whakawhirinaki unnest_auto() mo te whakamahi i ia wa. Ko te tohu mena ka huri to hanganga raraunga unnest_auto() Ka taea te whakarereke i te tikanga whakarereke raraunga kua tohua mena ka whakaroahia e ia nga rarangi rarangi hei rarangi ma te whakamahi unnest_longer(), katahi ka huri te hanganga o nga raraunga taumai, ka taea te whakarereke i te arorau unnest_wider(), me te whakamahi tonu i tenei huarahi ka arahi ki nga hapa ohorere.
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 rows
Geocoding me Google
I muri mai, ka titiro tatou ki tetahi hanganga uaua ake o nga raraunga i riro mai i te ratonga geocoding a Google. Ko nga tohu keteroki kei te takahi i nga ture o te mahi me te API mapi a Google, no reira ka tuhi tuatahi ahau i tetahi takai ngawari huri noa i te API. Ko te mea e ahu mai ana i te penapena i te Mahere Google API ki roto i te taurangi taiao; Mena kaore koe i te ki mo te mahi me te Google Maps API kua rongoa i roto i o taurangi taiao, ka kore e mahia nga wahanga waehere e whakaatuhia ana i tenei waahanga.
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)
}
He tino uaua te rarangi ka whakahokia mai e tenei mahi:
Waimarie, ka taea e taatau te whakaoti i te raru o te huri i enei raraunga ki roto i te ahua ripanga ma te whakamahi i nga mahi tidyr. Kia iti ake ai te wero me te whai kiko o te mahi, ka timata ahau ma te geocoding etahi taone iti:
city <- c ( "Houston" , "LA" , "New York" , "Chicago" , "Springfield" ) city_geo <- purrr::map (city, geocode)
Ka hurihia e ahau te hua ka puta ki roto tibble, mo te watea, ka taapirihia e ahau he pou me te ingoa taone nui.
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]>
Kei te taumata tuatahi nga waahanga status и result, ka taea e tatou te whakawhānui atu 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]> OK
mōhio e results he rarangi taumata-maha. Ko te nuinga o nga taone he 1 huānga (e tohu ana i te uara ahurei e rite ana ki te API geocoding), engari e rua nga waahanga o Springfield. Ka taea e tatou te kumea ki roto i nga raina motuhake 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]> OK
Inaianei he rite nga waahanga katoa, ka taea te manatoko ma te whakamahi 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… OK
Ka kitea te ahopae me te ahopou o ia taone ma te whakawhanui i te rarangi 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>
Na ko te waahi e hiahia ana koe ki te whakawhānui ake 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>
Ano ano, unnest_auto() he whakangawari ake i te mahi kua whakaahuatia me etahi morearea ka puta mai i te whakarereke i te hanganga o nga raraunga taumai:
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>
Ka taea hoki te titiro ki te wahi noho tuatahi mo ia taone nui:
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>
Whakamahi ranei hoist() mo te ruku taumata-maha kia haere tika atu 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]>
He korero mo Sharla Gelfand
Ka mutu, ka titiro tatou ki te hanganga tino uaua - te whakaaturanga a Sharla Gelfand. Pērā i ngā tauira i runga ake nei, ka tīmata mā te huri i te rārangi ki tētahi anga raraunga tīwae-kotahi, kātahi ka whakawhānuihia kia noho motuhake ia wāhanga. Ka huri ano ahau i te pou date_added ki te whakatakotoranga ra me te wa e tika ana i roto i te 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 rows
I tenei taumata, ka whiwhi korero mo te wa i taapirihia ai ia kōpae ki te kopae a Sharla, engari kaore matou e kite i nga raraunga mo aua kōpae. Ki te mahi i tenei me whakawhānuihia te pou basic_information:
discs %>% unnest_wider(basic_information)
#> Column name `id` must not be duplicated.
#> Use .name_repair to specify repair.
Kia aroha mai, ka whiwhi hapa tatou, na te mea... i roto i te rarangi basic_information he pou rite te ingoa basic_information. Mena ka puta he hapa, kia tere ai te whakatau i tona take, ka taea e koe te whakamahi names_repair = "unique":
Ka taea e koe te hono atu ki a raatau ki te huinga raraunga taketake ina hiahiatia.
mutunga
Ki te matua o te whare pukapuka tidyverse kei roto te maha o nga kohinga whaihua e whakakotahihia ana e tetahi tikanga tukatuka raraunga noa.
I roto i tenei tuhinga i tirotirohia e matou te whanau o nga mahi unnest_*(), e whai ana ki te mahi me te tango i nga huānga mai i nga rarangi kohanga. He maha atu nga waahanga whai hua kei roto i tenei kete ka ngawari ake te huri raraunga kia rite ki te kaupapa Raraunga Tidy.