በአብዛኛዎቹ አጋጣሚዎች ከኤፒአይ ከደረሰው ምላሽ ወይም ከማንኛውም ሌላ ውስብስብ የዛፍ መዋቅር ካለው መረጃ ጋር ሲሰሩ የJSON እና የኤክስኤምኤል ቅርጸቶች ያጋጥሙዎታል።
እነዚህ ቅርጸቶች ብዙ ጥቅሞች አሏቸው፡ መረጃን በጣም በተጨናነቀ ያከማቻሉ እና አላስፈላጊ የመረጃ ድግግሞሽን ለማስወገድ ያስችሉዎታል።
የእነዚህ ቅርፀቶች ጉዳታቸው የሂደታቸው እና የመተንተን ውስብስብነት ነው. ያልተዋቀረ መረጃ በስሌቶች ውስጥ መጠቀም አይቻልም እና ምስላዊነት በእሱ ላይ መገንባት አይቻልም.
ይህ ጽሑፍ የኅትመቱ ምክንያታዊ ቀጣይ ነው። . ጥቅሉን በመጠቀም ያልተዋቀሩ የመረጃ አወቃቀሮችን ወደ ሚታወቅ እና ለመተንተን ተስማሚ በሆነ የሰንጠረዥ ቅጽ ለማምጣት ይረዳዎታል tidyrበቤተ መፃህፍቱ ዋና ክፍል ውስጥ ተካትቷል። tidyverse፣ እና የተግባር ቤተሰቡ unnest_*().
ይዘቶች
የውሂብ ትንተና ላይ ፍላጎት ካሎት የእኔን ሊፈልጉ ይችላሉ и ቻናሎች. አብዛኛው ይዘቱ ለ R ቋንቋ የተወሰነ ነው።
መግቢያ
አራት ማዕዘን (የአስተርጓሚ ማስታወሻ፣ ለዚህ ቃል በቂ የትርጉም አማራጮች አላገኘሁም፣ ስለዚህ እንዳለ እንተወዋለን።) የታወቁ ረድፎችን እና አምዶችን ባካተተ ባለ ሁለት አቅጣጫዊ ሰንጠረዥ ያልተዋቀረ መረጃን ከጎጆ ድርድር ጋር የማምጣት ሂደት ነው። ውስጥ tidyr የጎጆ ዝርዝር ዓምዶችን ለማስፋት እና ውሂቡን ወደ አራት ማዕዘን ቅርጽ ያለው ሠንጠረዥ እንዲቀንሱ የሚያግዙዎት ብዙ ተግባራት አሉ።
unnest_longer()እያንዳንዱን የአምድ ዝርዝር አባል ይወስዳል እና አዲስ ረድፍ ይፈጥራል።unnest_wider()የአምዱ ዝርዝር እያንዳንዱን አካል ይወስዳል እና አዲስ አምድ ይፈጥራል።unnest_auto()የትኛውን ተግባር መጠቀም የተሻለ እንደሆነ በራስ-ሰር ይወስናል
unnest_longer()ወይምunnest_wider().hoist()ጋር ይመሳሰላል።unnest_wider()ነገር ግን የተገለጹትን ክፍሎች ብቻ ይመርጣል እና ከበርካታ የጎጆ ደረጃዎች ጋር እንዲሰሩ ያስችልዎታል.
ያልተዋቀረ መረጃን ከበርካታ ደረጃ ጎጆዎች ጋር ወደ ባለ ሁለት አቅጣጫዊ ጠረጴዛ ከማምጣት ጋር የተያያዙ አብዛኛዎቹ ችግሮች የተዘረዘሩትን ተግባራት ከዲፕሊየር ጋር በማጣመር መፍታት ይቻላል.
እነዚህን ዘዴዎች ለማሳየት, ጥቅሉን እንጠቀማለን repurrrsiveከድር ኤፒአይ የተውጣጡ በርካታ ውስብስብ፣ ባለብዙ ደረጃ ዝርዝሮችን የሚሰጥ።
library(tidyr)
library(dplyr)
library(repurrrsive)GitHub ተጠቃሚዎች
እንጀምር gh_ተጠቃሚዎችስለ ስድስት GitHub ተጠቃሚዎች መረጃ የያዘ ዝርዝር። መጀመሪያ ዝርዝሩን እንለውጥ gh_ተጠቃሚዎች в ቲብል ፍሬም
users <- tibble( user = gh_users ) ይህ ትንሽ ተቃራኒ ይመስላል፡ ለምን ዝርዝር አቅርቡ gh_ተጠቃሚዎችወደ ውስብስብ የውሂብ መዋቅር? ነገር ግን የውሂብ ፍሬም ትልቅ ጥቅም አለው፡ ብዙ ቬክተሮችን በማጣመር ሁሉም ነገር በአንድ ነገር ውስጥ እንዲከታተል ያደርጋል።
እያንዳንዱ የቁስ አካል users እያንዳንዱ አካል አምድ የሚወክልበት የተሰየመ ዝርዝር ነው።
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"የዝርዝር ክፍሎችን ወደ አምዶች ለመቀየር ሁለት መንገዶች አሉ። unnest_wider() እያንዳንዱን ክፍል ይወስዳል እና አዲስ አምድ ይፈጥራል፡-
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>በዚህ ሁኔታ, 30 አምዶችን ያካተተ ጠረጴዛ አለን, እና አብዛኛዎቹን አንፈልግም, ስለዚህ በምትኩ እንችላለን. unnest_wider() መጠቀም hoist(). hoist() ተመሳሳይ አገባብ በመጠቀም የተመረጡ ክፍሎችን ለማውጣት ያስችለናል 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() የተገለጹትን የተሰየሙ ክፍሎችን ከአምድ ዝርዝር ያስወግዳል ተጠቃሚስለዚህ ግምት ውስጥ ማስገባት ይችላሉ hoist() ልክ እንደ የቀን ክፈፉ ውስጣዊ ዝርዝር ውስጥ ክፍሎችን ወደ ከፍተኛ ደረጃ ማንቀሳቀስ።
የ Github ማከማቻዎች
የዝርዝር አሰላለፍ gh_repos ወደ በመቀየር በተመሳሳይ መንገድ እንጀምራለን 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]>በዚህ ጊዜ ንጥረ ነገሮች ተጠቃሚ በዚህ ተጠቃሚ ባለቤትነት የተያዙ ማከማቻዎች ዝርዝርን ይወክላሉ። እያንዳንዱ ማከማቻ የተለየ ምልከታ ነው, ስለዚህ በንጹህ መረጃ ጽንሰ-ሐሳብ መሰረት (በግምት. የተጣራ መረጃ) አዲስ መስመሮች መሆን አለባቸው, ለዚህም ነው የምንጠቀመው unnest_longer() ግን አይደለም 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 rowsአሁን መጠቀም እንችላለን unnest_wider() ወይም 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 rowsለአጠቃቀም ትኩረት ይስጡ c("owner", "login"): ይህ ከተቀማጭ ዝርዝር ውስጥ ሁለተኛውን ደረጃ ዋጋ እንድናገኝ ያስችለናል owner. አማራጭ አቀራረብ ሙሉውን ዝርዝር ማግኘት ነው owner እና ከዚያ ተግባሩን በመጠቀም unnest_wider() እያንዳንዱን ንጥረ ነገር በአንድ አምድ ውስጥ ያስገቡ-
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>ትክክለኛውን ተግባር ለመምረጥ ከማሰብ ይልቅ unnest_longer() ወይም unnest_wider() መጠቀም ትችላለህ unnest_auto(). ይህ ተግባር ውሂቡን ለመለወጥ በጣም ተስማሚ የሆነውን ተግባር ለመምረጥ ብዙ ሂዩሪስቲክ ዘዴዎችን ይጠቀማል እና ስለተመረጠው ዘዴ መልእክት ያሳያል።
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>የዙፋኖች ጨዋታ ቁምፊዎች
got_chars ተመሳሳይ መዋቅር አለው gh_users: ይህ የተሰየሙ ዝርዝሮች ስብስብ ነው፣ የውስጣዊው ዝርዝር እያንዳንዱ አካል አንዳንድ የዙፋኖች ጨዋታ ባህሪን የሚገልጽበት ነው። በማምጣት ላይ got_chars ለሠንጠረዡ እይታ ልክ እንደቀደሙት ምሳሌዎች የቀን ፍሬም በመፍጠር እንጀምራለን ከዚያም እያንዳንዱን አካል ወደ የተለየ አምድ እንለውጣለን።
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>መዋቅር got_chars በተወሰነ ደረጃ ከባድ gh_users, ምክንያቱም አንዳንድ ክፍሎች ዝርዝር char እራሳቸው ዝርዝር ናቸው, በዚህም ምክንያት ምሰሶዎችን እናገኛለን - ዝርዝሮች:
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 rowsየእርስዎ ተጨማሪ ድርጊቶች በትንተናው ግቦች ላይ ይወሰናሉ. ገጸ ባህሪው በሚታይበት ለእያንዳንዱ መጽሐፍ እና ተከታታዮች መስመሮች ላይ መረጃ ማስቀመጥ ያስፈልግዎ ይሆናል፡-
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 rowsወይም ደግሞ ባህሪውን እና ስራውን ለማዛመድ የሚያስችል ሰንጠረዥ መፍጠር ትፈልጋለህ፡-
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( ባዶ እሴቶቹን ልብ ይበሉ "" በመስክ ውስጥ title, ይህ ውሂብን በሚያስገቡበት ጊዜ በተደረጉ ስህተቶች ምክንያት ነው got_chars: በእውነቱ ፣ በሜዳው ውስጥ ተዛማጅ መጽሐፍ እና የቲቪ ተከታታይ ርዕሶች የሌሉባቸው ገፀ-ባህሪያት title ባዶውን ሕብረቁምፊ የያዘው ቬክተር ሳይሆን 0 ርዝመት ያለው ቬክተር ሊኖረው ይገባል።)
ተግባሩን በመጠቀም ከላይ ያለውን ምሳሌ እንደገና መፃፍ እንችላለን unnest_auto(). ይህ አቀራረብ ለአንድ ጊዜ ትንታኔ ምቹ ነው, ነገር ግን በእሱ ላይ መተማመን የለብዎትም unnest_auto() በመደበኛነት ለመጠቀም. ነጥቡ የውሂብዎ መዋቅር ከተቀየረ ነው unnest_auto() መጀመሪያ ላይ የዝርዝር ዓምዶችን ተጠቅሞ ወደ ረድፎች ካሰፋ የተመረጠውን የመረጃ ሽግግር ዘዴ መለወጥ ይችላል። unnest_longer(), ከዚያም የገቢው ውሂብ መዋቅር ሲቀየር, አመክንዮው ሞገስ ሊለወጥ ይችላል unnest_wider()እና ይህን አካሄድ ቀጣይነት ባለው መልኩ መጠቀም ወደ ያልተጠበቁ ስህተቶች ሊያመራ ይችላል።
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 rowsከ Google ጋር ጂኦኮዲንግ
በመቀጠል፣ ከGoogle ጂኦኮዲንግ አገልግሎት የተገኘውን መረጃ የበለጠ ውስብስብ አወቃቀር እንመለከታለን። መሸጎጫ ምስክርነቶች ከGoogle ካርታዎች ኤፒአይ ጋር የመስራትን ህግ ይቃረናል፣ ስለዚህ በመጀመሪያ በኤፒአይ ዙሪያ ቀለል ያለ ጥቅል እጽፋለሁ። የጉግል ካርታዎች ኤፒአይ ቁልፍን በአካባቢ ተለዋዋጭ ውስጥ በማከማቸት ላይ የተመሰረተ ነው; በአካባቢዎ ተለዋዋጮች ውስጥ ከ Google ካርታዎች ኤፒአይ ጋር አብሮ ለመስራት ቁልፉ ከሌለዎት በዚህ ክፍል ውስጥ የቀረቡት የኮድ ቁርጥራጮች አይተገበሩም።
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)
}ይህ ተግባር የሚመለሰው ዝርዝር በጣም ውስብስብ ነው፡-
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"እንደ እድል ሆኖ፣ ይህንን ውሂብ ወደ ሠንጠረዥ መልክ የመቀየር ችግርን ደረጃ በደረጃ ተግባራትን ልንፈታው እንችላለን tidyr. ተግባሩን ትንሽ ፈታኝ እና ተጨባጭ ለማድረግ፣ ጥቂት ከተሞችን ጂኦኮድ በማድረግ እጀምራለሁ፡-
city <- c ( "Houston" , "LA" , "New York" , "Chicago" , "Springfield" ) city_geo <- purrr::map (city, geocode) ውጤቱን ወደ ውስጥ እቀይራለሁ tibble, ለመመቻቸት, ተዛማጅ የከተማ ስም ያለው አምድ እጨምራለሁ.
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]>የመጀመሪያው ደረጃ ክፍሎችን ይይዛል status и result, ልንሰፋው የምንችለው 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]> OKአስታውስ አትርሳ results ባለብዙ ደረጃ ዝርዝር ነው. አብዛኛዎቹ ከተሞች 1 ኤለመንት አላቸው (ከጂኦኮዲንግ ኤፒአይ ጋር የሚዛመድ ልዩ እሴት የሚወክል)፣ ነገር ግን ስፕሪንግፊልድ ሁለት አለው። በተለዩ መስመሮች ውስጥ ልንጎትታቸው እንችላለን 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]> OKአሁን ሁሉም ተመሳሳይ ክፍሎች አሏቸው, ይህም በመጠቀም ሊረጋገጥ ይችላል 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… OKዝርዝሩን በማስፋፋት የእያንዳንዱን ከተማ ኬክሮስ እና ኬንትሮስ መጋጠሚያዎችን ማግኘት እንችላለን 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>እና ከዚያ ማስፋፋት የሚያስፈልግዎት ቦታ 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>አሁንም እንደገና፣ unnest_auto() የገቢውን ውሂብ አወቃቀር በመቀየር ሊከሰቱ ከሚችሉ አንዳንድ አደጋዎች ጋር የተገለጸውን ክዋኔ ያቃልላል፡
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>ለእያንዳንዱ ከተማ የመጀመሪያውን አድራሻ ማየት እንችላለን፡-
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>ወይም ተጠቀም hoist() ለባለብዙ ደረጃ ዳይቭ በቀጥታ ወደ መሄድ 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]>የሻርላ ጌልፋንድ ዲስኮግራፊ
በመጨረሻም, በጣም ውስብስብ የሆነውን መዋቅር እንመለከታለን - የሻርላ ጌልፋንድ ዲስኮግራፊ. ከላይ እንደተገለጹት ምሳሌዎች ዝርዝሩን ወደ ነጠላ-አምድ የውሂብ ፍሬም በመቀየር እንጀምራለን እና እያንዳንዱ አካል የተለየ አምድ እንዲሆን እናራዝመዋለን። እንዲሁም ዓምዱን እቀይራለሁ date_added ወደ ትክክለኛው የቀን እና የሰዓት ቅርጸት በ 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 rowsበዚህ ደረጃ፣ እያንዳንዱ ዲስክ መቼ ወደ ሻርላ ዲስኮግራፊ እንደታከለ መረጃ እናገኛለን፣ ነገር ግን ስለ እነዚያ ዲስኮች ምንም አይነት መረጃ አናይም። ይህንን ለማድረግ ዓምዱን ማስፋፋት ያስፈልገናል basic_information:
discs %>% unnest_wider(basic_information)
#> Column name `id` must not be duplicated.
#> Use .name_repair to specify repair.እንደ አለመታደል ሆኖ ስህተት ይደርስብናል፣ ምክንያቱም... በዝርዝሩ ውስጥ basic_information ተመሳሳይ ስም ያለው አምድ አለ basic_information. እንደዚህ አይነት ስህተት ከተከሰተ, መንስኤውን በፍጥነት ለመወሰን, መጠቀም ይችላሉ 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>ችግሩ ያ ነው basic_information በላይኛው ደረጃ ላይ የተቀመጠውን የመታወቂያ አምድ ይደግማል፣ ስለዚህ በቀላሉ ልናስወግደው እንችላለን፡-
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>በአማራጭ, ልንጠቀም እንችላለን 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>እዚህ ወደ ጎጆው ዝርዝር ውስጥ በመግባት የመጀመሪያውን መለያ እና የአርቲስት ስም በመረጃ ጠቋሚ በፍጥነት አመጣለሁ።
የበለጠ ስልታዊ አቀራረብ ለአርቲስት እና መለያ ሰንጠረዦችን መፍጠር ነው፡-
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 rowsእንደአስፈላጊነቱ ወደ መጀመሪያው የውሂብ ስብስብ መልሰው መቀላቀል ይችላሉ።
መደምደሚያ
ወደ ቤተ መፃህፍቱ እምብርት tidyverse በአንድ የጋራ የውሂብ ሂደት ፍልስፍና የተዋሃዱ ብዙ ጠቃሚ ፓኬጆችን ያካትታል።
በዚህ ጽሑፍ ውስጥ የተግባር ቤተሰብን መርምረናል unnest_*()ከጎጆ ዝርዝሮች አባሎችን በማውጣት ለመስራት ያለመ። ይህ እሽግ በፅንሰ-ሃሳቡ መሰረት መረጃን ለመለወጥ ቀላል የሚያደርጉ ሌሎች ብዙ ጠቃሚ ባህሪያትን ይዟል የተጣራ ውሂብ.
ምንጭ: hab.com