Paket tidyr kalebu ing inti saka salah sawijining perpustakaan paling populer ing basa R - tidyverse.
Tujuan utama paket kasebut yaiku nggawa data menyang wangun sing akurat.
Wis kasedhiya ing Habré darmabakti kanggo paket iki, nanging tanggal bali menyang 2015. Lan aku pengin ngandhani babagan owah-owahan paling anyar, sing diumumake sawetara dina kepungkur dening penulis, Hedley Wickham.
S.J.K.: Bakal klumpukne () lan nyebar () bakal deprecated?
Hadley Wickham: Kanggo sawetara ombone. Kita ora bakal menehi rekomendasi maneh nggunakake fungsi kasebut lan ndandani kewan omo, nanging bakal tetep ana ing paket kasebut ing kahanan saiki.
Isi
Yen sampeyan kasengsem ing analisis data, sampeyan bisa uga kasengsem ing sandi и saluran. Sebagéyan gedhé isiné dikhususaké kanggo basa R.
Konsep TidyData
Tujuane tidyr - mbantu sampeyan nggawa data menyang wangun sing diarani rapi. Data sing rapi yaiku data ing ngendi:
- Saben variabel ana ing kolom.
- Saben pengamatan minangka senar.
- Saben nilai minangka sel.
Luwih gampang lan luwih trep kanggo nggarap data sing ditampilake ing data sing rapi nalika nindakake analisis.
Fungsi utama kalebu ing paket tidyr
tidyr ngemot sakumpulan fungsi sing dirancang kanggo ngowahi tabel:
fill()- ngisi nilai sing ilang ing kolom kanthi nilai sadurunge;separate()- pamisah siji lapangan dadi sawetara nggunakake separator;unite()- nindakake operasi nggabungake sawetara lapangan dadi siji, tumindak kuwalik saka fungsi kasebutseparate();pivot_longer()- fungsi sing ngowahi data saka format sudhut kanggo format dawa;pivot_wider()- fungsi sing ngowahi data saka format dawa kanggo format sudhut. Operasi mbalikke sing ditindakake dening fungsi kasebutpivot_longer().gather()lungse - fungsi sing ngowahi data saka format sudhut kanggo format dawa;spread()lungse - fungsi sing ngowahi data saka format dawa kanggo format sudhut. Operasi mbalikke sing ditindakake dening fungsi kasebutgather().
Konsep anyar kanggo ngowahi data saka format amba menyang dawa lan kosok balene
Sadurunge, fungsi digunakake kanggo jinis transformasi iki gather() и spread(). Sajrone pirang-pirang taun fungsi kasebut, dadi jelas manawa kanggo umume pangguna, kalebu penulis paket kasebut, jeneng fungsi kasebut lan argumene ora jelas, lan nyebabake kesulitan nemokake lan ngerteni fungsi kasebut sing diowahi. pigura tanggal saka sudhut kanggo format dawa, lan kosok balene.
Ing babagan iki, ing tidyr Loro fungsi penting anyar wis ditambahake sing dirancang kanggo ngowahi pigura tanggal.
Fitur anyar pivot_longer() и pivot_wider() padha inspirasi dening sawetara fitur ing paket cdata, digawe dening John Mount lan Nina Zumel.
Nginstal versi paling saiki tidyr 0.8.3.9000
Kanggo nginstal anyar, versi paling anyar saka paket tidyr 0.8.3.9000, ing ngendi fitur anyar kasedhiya, gunakake kode ing ngisor iki.
devtools::install_github("tidyverse/tidyr")
Nalika nulis, fungsi kasebut mung kasedhiya ing versi dev paket ing GitHub.
Transisi menyang fitur anyar
Nyatane, ora angel nransfer skrip lawas kanggo nggarap fungsi anyar; kanggo pangerten sing luwih apik, aku bakal njupuk conto saka dokumentasi fungsi lawas lan nuduhake carane operasi sing padha ditindakake nggunakake sing anyar. pivot_*() fungsi.
Ngonversi format sudhut menyang format dawa.
Kode conto saka dokumentasi fungsi kumpul
# example
library(dplyr)
stocks <- data.frame(
time = as.Date('2009-01-01') + 0:9,
X = rnorm(10, 0, 1),
Y = rnorm(10, 0, 2),
Z = rnorm(10, 0, 4)
)
# old
stocks_gather <- stocks %>% gather(key = stock,
value = price,
-time)
# new
stocks_long <- stocks %>% pivot_longer(cols = -time,
names_to = "stock",
values_to = "price")
Ngonversi format dawa menyang format lebar.
Kode conto saka dokumentasi fungsi nyebar
# old
stocks_spread <- stocks_gather %>% spread(key = stock,
value = price)
# new
stock_wide <- stocks_long %>% pivot_wider(names_from = "stock",
values_from = "price")
Amarga ing conto ing ndhuwur nggarap pivot_longer() и pivot_wider(), ing tabel asli saham ora ana kolom sing kadhaptar ing argumen jeneng_kanggo и nilai_kanggo jenenge kudu nganggo tanda petik.
Tabel sing bakal mbantu sampeyan paling gampang ngerti carane ngalih menyang karya karo konsep anyar tidyr.
Cathetan saka penulis
Kabeh teks ing ngisor iki adaptif, Aku malah bakal ngomong free terjemahan saka situs web perpustakaan tidyverse resmi.
Conto prasaja ngonversi data saka format amba menyang dawa
pivot_longer () - nggawe set data luwih dawa kanthi nyuda jumlah kolom lan nambah jumlah larik.
Kanggo mbukak conto sing ditampilake ing artikel kasebut, sampeyan kudu nyambungake paket sing dibutuhake:
library(tidyr)
library(dplyr)
library(readr)Ayo kita duwe tabel kanthi asil survey sing (antara liya) takon babagan agama lan penghasilan taunan:
#> # A tibble: 18 x 11
#> religion `<$10k` `$10-20k` `$20-30k` `$30-40k` `$40-50k` `$50-75k`
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Agnostic 27 34 60 81 76 137
#> 2 Atheist 12 27 37 52 35 70
#> 3 Buddhist 27 21 30 34 33 58
#> 4 Catholic 418 617 732 670 638 1116
#> 5 Don’t k… 15 14 15 11 10 35
#> 6 Evangel… 575 869 1064 982 881 1486
#> 7 Hindu 1 9 7 9 11 34
#> 8 Histori… 228 244 236 238 197 223
#> 9 Jehovah… 20 27 24 24 21 30
#> 10 Jewish 19 19 25 25 30 95
#> # … with 8 more rows, and 4 more variables: `$75-100k` <dbl>,
#> # `$100-150k` <dbl>, `>150k` <dbl>, `Don't know/refused` <dbl>Tabel iki ngemot data agama responden ing baris, lan tingkat income kasebar ing jeneng kolom. Jumlah responden saka saben kategori disimpen ing nilai sel ing persimpangan agama lan tingkat penghasilan. Kanggo nggawa meja menyang rapi, format bener, iku cukup kanggo nggunakake pivot_longer():
pew %>%
pivot_longer(cols = -religion, names_to = "income", values_to = "count")pew %>%
pivot_longer(cols = -religion, names_to = "income", values_to = "count")
#> # A tibble: 180 x 3
#> religion income count
#> <chr> <chr> <dbl>
#> 1 Agnostic <$10k 27
#> 2 Agnostic $10-20k 34
#> 3 Agnostic $20-30k 60
#> 4 Agnostic $30-40k 81
#> 5 Agnostic $40-50k 76
#> 6 Agnostic $50-75k 137
#> 7 Agnostic $75-100k 122
#> 8 Agnostic $100-150k 109
#> 9 Agnostic >150k 84
#> 10 Agnostic Don't know/refused 96
#> # … with 170 more rowsArgumen Fungsi pivot_longer()
- Argumentasi pisanan kerah, nerangake kolom endi sing kudu digabung. Ing kasus iki, kabeh kolom kajaba wektu.
- Argumentasi jeneng_kanggo menehi jeneng variabel sing bakal digawe saka jeneng kolom kita concatenated.
- nilai_kanggo menehi jeneng variabel sing bakal digawe saka data sing disimpen ing nilai sel saka kolom gabungan.
Spesifikasi
Iki minangka fungsi anyar saka paket kasebut tidyr, sing sadurunge ora kasedhiya nalika nggarap fungsi warisan.
Spesifikasi minangka pigura data, saben baris cocog karo siji kolom ing pigura tanggal output anyar, lan rong kolom khusus sing diwiwiti karo:
- .name ngemot jeneng kolom asli.
- .nilai ngemot jeneng kolom sing bakal ngemot nilai sel.
Kolom spesifikasi sing isih ana nggambarake carane kolom anyar bakal nampilake jeneng kolom sing dikompres saka .name.
Spesifikasi kasebut nggambarake metadata sing disimpen ing jeneng kolom, kanthi siji baris kanggo saben kolom lan siji kolom kanggo saben variabel, digabungake karo jeneng kolom, definisi iki bisa uga katon bingung, nanging sawise ndeleng sawetara conto bakal dadi akeh. luwih cetha.
Titik spesifikasi yaiku sampeyan bisa njupuk, ngowahi, lan nemtokake metadata anyar kanggo pigura data sing diowahi.
Kanggo nggarap spesifikasi nalika ngowahi tabel saka format amba menyang format dawa, gunakake fungsi kasebut pivot_longer_spec().
Cara kerjane fungsi iki yaiku njupuk pigura tanggal lan ngasilake metadata kanthi cara sing kasebut ing ndhuwur.
Minangka conto, ayo njupuk dataset sing diwenehake karo paket kasebut tidyr. Dataset iki ngemot informasi sing diwenehake dening organisasi kesehatan internasional babagan kedadeyan tuberkulosis.
who
#> # A tibble: 7,240 x 60
#> country iso2 iso3 year new_sp_m014 new_sp_m1524 new_sp_m2534
#> <chr> <chr> <chr> <int> <int> <int> <int>
#> 1 Afghan… AF AFG 1980 NA NA NA
#> 2 Afghan… AF AFG 1981 NA NA NA
#> 3 Afghan… AF AFG 1982 NA NA NA
#> 4 Afghan… AF AFG 1983 NA NA NA
#> 5 Afghan… AF AFG 1984 NA NA NA
#> 6 Afghan… AF AFG 1985 NA NA NA
#> 7 Afghan… AF AFG 1986 NA NA NA
#> 8 Afghan… AF AFG 1987 NA NA NA
#> 9 Afghan… AF AFG 1988 NA NA NA
#> 10 Afghan… AF AFG 1989 NA NA NA
#> # … with 7,230 more rows, and 53 more variablesAyo mbangun spesifikasi.
spec <- who %>%
pivot_longer_spec(new_sp_m014:newrel_f65, values_to = "count")#> # A tibble: 56 x 3
#> .name .value name
#> <chr> <chr> <chr>
#> 1 new_sp_m014 count new_sp_m014
#> 2 new_sp_m1524 count new_sp_m1524
#> 3 new_sp_m2534 count new_sp_m2534
#> 4 new_sp_m3544 count new_sp_m3544
#> 5 new_sp_m4554 count new_sp_m4554
#> 6 new_sp_m5564 count new_sp_m5564
#> 7 new_sp_m65 count new_sp_m65
#> 8 new_sp_f014 count new_sp_f014
#> 9 new_sp_f1524 count new_sp_f1524
#> 10 new_sp_f2534 count new_sp_f2534
#> # … with 46 more rowskothak negara, isoxnumx, isoxnumx wis variabel. Tugas kita kanggo flip kolom karo new_sp_m014 ing newrel_f65.
Jeneng kolom kasebut nyimpen informasi ing ngisor iki:
- Ater-ater
new_nuduhake yen kolom kasebut ngemot data babagan kasus tuberkulosis anyar, pigura tanggal saiki mung ngemot informasi babagan penyakit anyar, saengga ater-ater iki ing konteks saiki ora duwe makna. sp/rel/sp/epnjlèntrèhaké cara kanggo diagnosa penyakit.m/fgender pasien.014/1524/2535/3544/4554/65rentang umur pasien.
Kita bisa pamisah kolom iki nggunakake fungsi extract()nggunakake ekspresi reguler.
spec <- spec %>%
extract(name, c("diagnosis", "gender", "age"), "new_?(.*)_(.)(.*)")#> # A tibble: 56 x 5
#> .name .value diagnosis gender age
#> <chr> <chr> <chr> <chr> <chr>
#> 1 new_sp_m014 count sp m 014
#> 2 new_sp_m1524 count sp m 1524
#> 3 new_sp_m2534 count sp m 2534
#> 4 new_sp_m3544 count sp m 3544
#> 5 new_sp_m4554 count sp m 4554
#> 6 new_sp_m5564 count sp m 5564
#> 7 new_sp_m65 count sp m 65
#> 8 new_sp_f014 count sp f 014
#> 9 new_sp_f1524 count sp f 1524
#> 10 new_sp_f2534 count sp f 2534
#> # … with 46 more rowsWigati kolom .name kudu tetep ora diganti amarga iki indeks kita menyang jeneng kolom saka dataset asli.
Jenis kelamin lan umur (kolom jender и umur) duwe nilai tetep lan dikenal, mula disaranake ngowahi kolom kasebut dadi faktor:
spec <- spec %>%
mutate(
gender = factor(gender, levels = c("f", "m")),
age = factor(age, levels = unique(age), ordered = TRUE)
) Pungkasan, kanggo ngetrapake spesifikasi sing digawe menyang pigura tanggal asli sing kita kudu nggunakake argumentasi spec ing fungsi pivot_longer().
who %>% pivot_longer(spec = spec)
#> # A tibble: 405,440 x 8
#> country iso2 iso3 year diagnosis gender age count
#> <chr> <chr> <chr> <int> <chr> <fct> <ord> <int>
#> 1 Afghanistan AF AFG 1980 sp m 014 NA
#> 2 Afghanistan AF AFG 1980 sp m 1524 NA
#> 3 Afghanistan AF AFG 1980 sp m 2534 NA
#> 4 Afghanistan AF AFG 1980 sp m 3544 NA
#> 5 Afghanistan AF AFG 1980 sp m 4554 NA
#> 6 Afghanistan AF AFG 1980 sp m 5564 NA
#> 7 Afghanistan AF AFG 1980 sp m 65 NA
#> 8 Afghanistan AF AFG 1980 sp f 014 NA
#> 9 Afghanistan AF AFG 1980 sp f 1524 NA
#> 10 Afghanistan AF AFG 1980 sp f 2534 NA
#> # … with 405,430 more rowsKabeh sing ditindakake bisa digambarake kanthi skema kaya ing ngisor iki:
Spesifikasi nggunakake sawetara nilai (.value)
Ing conto ing ndhuwur, kolom spesifikasi .nilai isine mung siji nilai, ing paling kasus iki cilik.
Nanging sok-sok ana kahanan nalika sampeyan kudu ngumpulake data saka kolom kanthi macem-macem jinis data ing nilai. Nggunakake fungsi warisan spread() iki bakal cukup angel kanggo nindakake.
Tuladha ing ngisor iki dijupuk saka menyang paket data.tabel.
Ayo nggawe dataframe latihan.
family <- tibble::tribble(
~family, ~dob_child1, ~dob_child2, ~gender_child1, ~gender_child2,
1L, "1998-11-26", "2000-01-29", 1L, 2L,
2L, "1996-06-22", NA, 2L, NA,
3L, "2002-07-11", "2004-04-05", 2L, 2L,
4L, "2004-10-10", "2009-08-27", 1L, 1L,
5L, "2000-12-05", "2005-02-28", 2L, 1L,
)
family <- family %>% mutate_at(vars(starts_with("dob")), parse_date)#> # A tibble: 5 x 5
#> family dob_child1 dob_child2 gender_child1 gender_child2
#> <int> <date> <date> <int> <int>
#> 1 1 1998-11-26 2000-01-29 1 2
#> 2 2 1996-06-22 NA 2 NA
#> 3 3 2002-07-11 2004-04-05 2 2
#> 4 4 2004-10-10 2009-08-27 1 1
#> 5 5 2000-12-05 2005-02-28 2 1Bingkai tanggal sing digawe ngemot data babagan anak saka siji kulawarga ing saben baris. Kulawarga bisa duwe anak siji utawa loro. Kanggo saben bocah, data diwenehake ing tanggal lair lan jender, lan data kanggo saben bocah ana ing kolom sing kapisah; tugas kita yaiku nggawa data kasebut menyang format sing bener kanggo dianalisis.
Elinga yen kita duwe rong variabel kanthi informasi babagan saben bocah: jender lan tanggal lair (kolom kanthi ater-ater dop ngemot tanggal lair, kolom kanthi ater-ater jender ngemot jinising bocah). Asil samesthine iku kudu katon ing kolom kapisah. Kita bisa nindakake iki dening ngasilaken specification kang kolom .value bakal duwe rong makna sing beda.
spec <- family %>%
pivot_longer_spec(-family) %>%
separate(col = name, into = c(".value", "child"))%>%
mutate(child = parse_number(child))
#> # A tibble: 4 x 3
#> .name .value child
#> <chr> <chr> <dbl>
#> 1 dob_child1 dob 1
#> 2 dob_child2 dob 2
#> 3 gender_child1 gender 1
#> 4 gender_child2 gender 2Dadi, ayo goleki langkah-langkah langkah-langkah sing ditindakake dening kode ing ndhuwur.
pivot_longer_spec(-family)- nggawe specification sing compresses kabeh kolom ana kajaba kolom kulawarga.separate(col = name, into = c(".value", "child"))- pamisah kolom .name, sing ngemot jeneng kolom sumber, nggunakake garis ngisor lan ngetik nilai sing diasilake menyang kolom .nilai и Bocah.mutate(child = parse_number(child))- ngowahi nilai lapangan Bocah saka teks menyang jinis data numerik.
Saiki kita bisa ngetrapake spesifikasi sing diasilake menyang kerangka data asli lan nggawa tabel menyang wangun sing dikarepake.
family %>%
pivot_longer(spec = spec, na.rm = T)#> # A tibble: 9 x 4
#> family child dob gender
#> <int> <dbl> <date> <int>
#> 1 1 1 1998-11-26 1
#> 2 1 2 2000-01-29 2
#> 3 2 1 1996-06-22 2
#> 4 3 1 2002-07-11 2
#> 5 3 2 2004-04-05 2
#> 6 4 1 2004-10-10 1
#> 7 4 2 2009-08-27 1
#> 8 5 1 2000-12-05 2
#> 9 5 2 2005-02-28 1Kita nggunakake argumentasi na.rm = TRUE, amarga wangun data saiki meksa nggawe baris ekstra kanggo pengamatan sing ora ana. Amarga kulawarga 2 mung duwe anak siji, na.rm = TRUE njamin sing kulawarga 2 bakal siji baris ing output.
Ngonversi pigura tanggal saka format dawa nganti amba
pivot_wider() - punika transformasi kuwalik, lan kosok balene nambah nomer kolom pigura tanggal kanthi ngurangi jumlah larik.
Transformasi jenis iki arang banget digunakake kanggo ngowahi data dadi akurat, nanging teknik iki bisa migunani kanggo nggawe tabel pangsi sing digunakake ing presentasi, utawa kanggo integrasi karo sawetara alat liyane.
Sejatine fungsi pivot_longer() и pivot_wider() simetris, lan ngasilake tumindak sing saling berlawanan, yaiku: df %>% pivot_longer(spec = spec) %>% pivot_wider(spec = spec) и df %>% pivot_wider(spec = spec) %>% pivot_longer(spec = spec) bakal bali df asli.
Conto paling gampang kanggo ngowahi tabel dadi format sing amba
Kanggo nduduhake cara kerjane pivot_wider() kita bakal nggunakake dataset fish_encounters, sing nyimpen informasi babagan carane stasiun beda ngrekam gerakan iwak ing sadawane kali.
#> # A tibble: 114 x 3
#> fish station seen
#> <fct> <fct> <int>
#> 1 4842 Release 1
#> 2 4842 I80_1 1
#> 3 4842 Lisbon 1
#> 4 4842 Rstr 1
#> 5 4842 Base_TD 1
#> 6 4842 BCE 1
#> 7 4842 BCW 1
#> 8 4842 BCE2 1
#> 9 4842 BCW2 1
#> 10 4842 MAE 1
#> # … with 104 more rowsIng sawetara kasus, tabel iki bakal luwih informatif lan luwih gampang digunakake yen sampeyan menehi informasi kanggo saben stasiun ing kolom kapisah.
fish_encounters %>% pivot_wider(names_from = station, values_from = seen)
fish_encounters %>% pivot_wider(names_from = station, values_from = seen)
#> # A tibble: 19 x 12
#> fish Release I80_1 Lisbon Rstr Base_TD BCE BCW BCE2 BCW2 MAE
#> <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 4842 1 1 1 1 1 1 1 1 1 1
#> 2 4843 1 1 1 1 1 1 1 1 1 1
#> 3 4844 1 1 1 1 1 1 1 1 1 1
#> 4 4845 1 1 1 1 1 NA NA NA NA NA
#> 5 4847 1 1 1 NA NA NA NA NA NA NA
#> 6 4848 1 1 1 1 NA NA NA NA NA NA
#> 7 4849 1 1 NA NA NA NA NA NA NA NA
#> 8 4850 1 1 NA 1 1 1 1 NA NA NA
#> 9 4851 1 1 NA NA NA NA NA NA NA NA
#> 10 4854 1 1 NA NA NA NA NA NA NA NA
#> # … with 9 more rows, and 1 more variable: MAW <int>Set data iki mung nyathet informasi nalika iwak wis dideteksi dening stasiun, i.e. yen ana iwak ora direkam dening sawetara stasiun, banjur data iki ora bakal ing meja. Iki tegese output bakal diisi karo NA.
Nanging, ing kasus iki kita ngerti yen ora ana rekaman tegese iwak kasebut ora katon, mula kita bisa nggunakake argumen kasebut. nilai_isi ing fungsi pivot_wider() lan isi angka sing ilang iki karo nol:
fish_encounters %>% pivot_wider(
names_from = station,
values_from = seen,
values_fill = list(seen = 0)
)#> # A tibble: 19 x 12
#> fish Release I80_1 Lisbon Rstr Base_TD BCE BCW BCE2 BCW2 MAE
#> <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 4842 1 1 1 1 1 1 1 1 1 1
#> 2 4843 1 1 1 1 1 1 1 1 1 1
#> 3 4844 1 1 1 1 1 1 1 1 1 1
#> 4 4845 1 1 1 1 1 0 0 0 0 0
#> 5 4847 1 1 1 0 0 0 0 0 0 0
#> 6 4848 1 1 1 1 0 0 0 0 0 0
#> 7 4849 1 1 0 0 0 0 0 0 0 0
#> 8 4850 1 1 0 1 1 1 1 0 0 0
#> 9 4851 1 1 0 0 0 0 0 0 0 0
#> 10 4854 1 1 0 0 0 0 0 0 0 0
#> # … with 9 more rows, and 1 more variable: MAW <int>Ngasilake jeneng kolom saka macem-macem variabel sumber
Mbayangno kita duwe tabel sing ngemot kombinasi produk, negara lan taun. Kanggo nggawe pigura tanggal test, sampeyan bisa mbukak kode ing ngisor iki:
df <- expand_grid(
product = c("A", "B"),
country = c("AI", "EI"),
year = 2000:2014
) %>%
filter((product == "A" & country == "AI") | product == "B") %>%
mutate(value = rnorm(nrow(.)))#> # A tibble: 45 x 4
#> product country year value
#> <chr> <chr> <int> <dbl>
#> 1 A AI 2000 -2.05
#> 2 A AI 2001 -0.676
#> 3 A AI 2002 1.60
#> 4 A AI 2003 -0.353
#> 5 A AI 2004 -0.00530
#> 6 A AI 2005 0.442
#> 7 A AI 2006 -0.610
#> 8 A AI 2007 -2.77
#> 9 A AI 2008 0.899
#> 10 A AI 2009 -0.106
#> # … with 35 more rowsTugas kita yaiku nggedhekake pigura data supaya siji kolom ngemot data kanggo saben kombinasi produk lan negara. Kanggo nindakake iki, mung pass ing argumentasi jeneng_saka vektor sing ngemot jeneng kolom sing bakal digabung.
df %>% pivot_wider(names_from = c(product, country),
values_from = "value")#> # A tibble: 15 x 4
#> year A_AI B_AI B_EI
#> <int> <dbl> <dbl> <dbl>
#> 1 2000 -2.05 0.607 1.20
#> 2 2001 -0.676 1.65 -0.114
#> 3 2002 1.60 -0.0245 0.501
#> 4 2003 -0.353 1.30 -0.459
#> 5 2004 -0.00530 0.921 -0.0589
#> 6 2005 0.442 -1.55 0.594
#> 7 2006 -0.610 0.380 -1.28
#> 8 2007 -2.77 0.830 0.637
#> 9 2008 0.899 0.0175 -1.30
#> 10 2009 -0.106 -0.195 1.03
#> # … with 5 more rowsSampeyan uga bisa aplikasi specifications kanggo fungsi pivot_wider(). Nanging nalika diajukake kanggo pivot_wider() specification nindakake konversi ngelawan pivot_longer(): Kolom sing ditemtokake ing .name, nggunakake nilai saka .nilai lan kolom liyane.
Kanggo set data iki, sampeyan bisa nggawe spesifikasi khusus yen sampeyan pengin saben negara lan kombinasi produk duwe kolom dhewe, ora mung sing ana ing data kasebut:
spec <- df %>%
expand(product, country, .value = "value") %>%
unite(".name", product, country, remove = FALSE)#> # A tibble: 4 x 4
#> .name product country .value
#> <chr> <chr> <chr> <chr>
#> 1 A_AI A AI value
#> 2 A_EI A EI value
#> 3 B_AI B AI value
#> 4 B_EI B EI valuedf %>% pivot_wider(spec = spec) %>% head()#> # A tibble: 6 x 5
#> year A_AI A_EI B_AI B_EI
#> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 2000 -2.05 NA 0.607 1.20
#> 2 2001 -0.676 NA 1.65 -0.114
#> 3 2002 1.60 NA -0.0245 0.501
#> 4 2003 -0.353 NA 1.30 -0.459
#> 5 2004 -0.00530 NA 0.921 -0.0589
#> 6 2005 0.442 NA -1.55 0.594Sawetara conto majeng nggarap konsep tidyr anyar
Ngresiki data nggunakake US Census Income lan Rent dataset minangka conto.
kumpulan data us_rent_income ngemot penghasilan rata-rata lan informasi sewa kanggo saben negara ing AS kanggo 2017 (set data kasedhiya ing paket tidycensus).
us_rent_income
#> # A tibble: 104 x 5
#> GEOID NAME variable estimate moe
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 01 Alabama income 24476 136
#> 2 01 Alabama rent 747 3
#> 3 02 Alaska income 32940 508
#> 4 02 Alaska rent 1200 13
#> 5 04 Arizona income 27517 148
#> 6 04 Arizona rent 972 4
#> 7 05 Arkansas income 23789 165
#> 8 05 Arkansas rent 709 5
#> 9 06 California income 29454 109
#> 10 06 California rent 1358 3
#> # … with 94 more rowsIng wangun kang data disimpen ing dataset us_rent_income nggarap dheweke pancen ora trep, mula kita pengin nggawe set data kanthi kolom: rent, rent_moe, teka, income_moe. Ana akeh cara kanggo nggawe spesifikasi iki, nanging sing utama yaiku kita kudu ngasilake saben kombinasi nilai variabel lan taksiran/moebanjur generate jeneng kolom.
spec <- us_rent_income %>%
expand(variable, .value = c("estimate", "moe")) %>%
mutate(
.name = paste0(variable, ifelse(.value == "moe", "_moe", ""))
)#> # A tibble: 4 x 3
#> variable .value .name
#> <chr> <chr> <chr>
#> 1 income estimate income
#> 2 income moe income_moe
#> 3 rent estimate rent
#> 4 rent moe rent_moeNyedhiyakake spesifikasi iki pivot_wider() menehi asil sing kita goleki:
us_rent_income %>% pivot_wider(spec = spec)
#> # A tibble: 52 x 6
#> GEOID NAME income income_moe rent rent_moe
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 01 Alabama 24476 136 747 3
#> 2 02 Alaska 32940 508 1200 13
#> 3 04 Arizona 27517 148 972 4
#> 4 05 Arkansas 23789 165 709 5
#> 5 06 California 29454 109 1358 3
#> 6 08 Colorado 32401 109 1125 5
#> 7 09 Connecticut 35326 195 1123 5
#> 8 10 Delaware 31560 247 1076 10
#> 9 11 District of Columbia 43198 681 1424 17
#> 10 12 Florida 25952 70 1077 3
#> # … with 42 more rowsBank Dunia
Kadhangkala nggawa set data menyang formulir sing dikarepake mbutuhake sawetara langkah.
kumpulan data world_bank_pop ngemot data Bank Dunia babagan populasi saben negara antarane 2000 lan 2018.
#> # A tibble: 1,056 x 20
#> country indicator `2000` `2001` `2002` `2003` `2004` `2005` `2006`
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ABW SP.URB.T… 4.24e4 4.30e4 4.37e4 4.42e4 4.47e+4 4.49e+4 4.49e+4
#> 2 ABW SP.URB.G… 1.18e0 1.41e0 1.43e0 1.31e0 9.51e-1 4.91e-1 -1.78e-2
#> 3 ABW SP.POP.T… 9.09e4 9.29e4 9.50e4 9.70e4 9.87e+4 1.00e+5 1.01e+5
#> 4 ABW SP.POP.G… 2.06e0 2.23e0 2.23e0 2.11e0 1.76e+0 1.30e+0 7.98e-1
#> 5 AFG SP.URB.T… 4.44e6 4.65e6 4.89e6 5.16e6 5.43e+6 5.69e+6 5.93e+6
#> 6 AFG SP.URB.G… 3.91e0 4.66e0 5.13e0 5.23e0 5.12e+0 4.77e+0 4.12e+0
#> 7 AFG SP.POP.T… 2.01e7 2.10e7 2.20e7 2.31e7 2.41e+7 2.51e+7 2.59e+7
#> 8 AFG SP.POP.G… 3.49e0 4.25e0 4.72e0 4.82e0 4.47e+0 3.87e+0 3.23e+0
#> 9 AGO SP.URB.T… 8.23e6 8.71e6 9.22e6 9.77e6 1.03e+7 1.09e+7 1.15e+7
#> 10 AGO SP.URB.G… 5.44e0 5.59e0 5.70e0 5.76e0 5.75e+0 5.69e+0 4.92e+0
#> # … with 1,046 more rows, and 11 more variables: `2007` <dbl>,
#> # `2008` <dbl>, `2009` <dbl>, `2010` <dbl>, `2011` <dbl>, `2012` <dbl>,
#> # `2013` <dbl>, `2014` <dbl>, `2015` <dbl>, `2016` <dbl>, `2017` <dbl>Tujuan kita yaiku nggawe set data sing rapi karo saben variabel ing kolom dhewe. Ora jelas persis apa langkah sing dibutuhake, nanging kita bakal miwiti kanthi masalah sing paling jelas: taun kasebut nyebar ing pirang-pirang kolom.
Kanggo ndandani iki, sampeyan kudu nggunakake fungsi kasebut pivot_longer().
pop2 <- world_bank_pop %>%
pivot_longer(`2000`:`2017`, names_to = "year")#> # A tibble: 19,008 x 4
#> country indicator year value
#> <chr> <chr> <chr> <dbl>
#> 1 ABW SP.URB.TOTL 2000 42444
#> 2 ABW SP.URB.TOTL 2001 43048
#> 3 ABW SP.URB.TOTL 2002 43670
#> 4 ABW SP.URB.TOTL 2003 44246
#> 5 ABW SP.URB.TOTL 2004 44669
#> 6 ABW SP.URB.TOTL 2005 44889
#> 7 ABW SP.URB.TOTL 2006 44881
#> 8 ABW SP.URB.TOTL 2007 44686
#> 9 ABW SP.URB.TOTL 2008 44375
#> 10 ABW SP.URB.TOTL 2009 44052
#> # … with 18,998 more rowsLangkah sabanjure yaiku ndeleng variabel indikator.
pop2 %>% count(indicator)
#> # A tibble: 4 x 2
#> indicator n
#> <chr> <int>
#> 1 SP.POP.GROW 4752
#> 2 SP.POP.TOTL 4752
#> 3 SP.URB.GROW 4752
#> 4 SP.URB.TOTL 4752Where SP.POP.GROW punika wutah populasi, SP.POP.TOTL total populasi, lan SP.URB. * bab sing padha, nanging mung kanggo wilayah kutha. Dibagi nilai kasebut dadi rong variabel: area - area (total utawa kutha) lan variabel sing ngemot data nyata (populasi utawa pertumbuhan):
pop3 <- pop2 %>%
separate(indicator, c(NA, "area", "variable"))#> # A tibble: 19,008 x 5
#> country area variable year value
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 ABW URB TOTL 2000 42444
#> 2 ABW URB TOTL 2001 43048
#> 3 ABW URB TOTL 2002 43670
#> 4 ABW URB TOTL 2003 44246
#> 5 ABW URB TOTL 2004 44669
#> 6 ABW URB TOTL 2005 44889
#> 7 ABW URB TOTL 2006 44881
#> 8 ABW URB TOTL 2007 44686
#> 9 ABW URB TOTL 2008 44375
#> 10 ABW URB TOTL 2009 44052
#> # … with 18,998 more rowsSaiki sing kudu ditindakake yaiku pamisah variabel dadi rong kolom:
pop3 %>%
pivot_wider(names_from = variable, values_from = value)#> # A tibble: 9,504 x 5
#> country area year TOTL GROW
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 ABW URB 2000 42444 1.18
#> 2 ABW URB 2001 43048 1.41
#> 3 ABW URB 2002 43670 1.43
#> 4 ABW URB 2003 44246 1.31
#> 5 ABW URB 2004 44669 0.951
#> 6 ABW URB 2005 44889 0.491
#> 7 ABW URB 2006 44881 -0.0178
#> 8 ABW URB 2007 44686 -0.435
#> 9 ABW URB 2008 44375 -0.698
#> 10 ABW URB 2009 44052 -0.731
#> # … with 9,494 more rowsDhaptar kontak
Conto pungkasan, bayangake sampeyan duwe dhaptar kontak sing disalin lan ditempel saka situs web:
contacts <- tribble(
~field, ~value,
"name", "Jiena McLellan",
"company", "Toyota",
"name", "John Smith",
"company", "google",
"email", "[email protected]",
"name", "Huxley Ratcliffe"
)Tabulasi dhaptar iki cukup angel amarga ora ana variabel sing ngenali data sing dadi kontak. Kita bisa ndandani iki kanthi nyathet yen saben data kontak anyar diwiwiti kanthi "jeneng", supaya kita bisa nggawe pengenal unik lan nambah siji saben kolom kolom ngemot nilai "jeneng":
contacts <- contacts %>%
mutate(
person_id = cumsum(field == "name")
)
contacts#> # A tibble: 6 x 3
#> field value person_id
#> <chr> <chr> <int>
#> 1 name Jiena McLellan 1
#> 2 company Toyota 1
#> 3 name John Smith 2
#> 4 company google 2
#> 5 email [email protected] 2
#> 6 name Huxley Ratcliffe 3Saiki kita duwe ID unik kanggo saben kontak, kita bisa ngowahi kolom lan nilai dadi kolom:
contacts %>%
pivot_wider(names_from = field, values_from = value)#> # A tibble: 3 x 4
#> person_id name company email
#> <int> <chr> <chr> <chr>
#> 1 1 Jiena McLellan Toyota <NA>
#> 2 2 John Smith google [email protected]
#> 3 3 Huxley Ratcliffe <NA> <NA>kesimpulan
Pendapat pribadiku yaiku konsep anyar tidyr saestu luwih intuisi, lan pinunjul ing fungsi kanggo fungsi warisan spread() и gather(). Muga-muga artikel iki mbantu sampeyan ngatasi pivot_longer() и pivot_wider().
Source: www.habr.com