Apoti tidyr to wa ninu koko ọkan ninu awọn ile-ikawe olokiki julọ ni ede R - tidyverse.
Idi akọkọ ti package ni lati mu data wa sinu fọọmu deede.
Tẹlẹ wa lori Habré igbẹhin si package yii, ṣugbọn o wa pada si ọdun 2015. Ati pe Mo fẹ lati sọ fun ọ nipa awọn iyipada lọwọlọwọ julọ, eyiti a kede ni ọjọ diẹ sẹhin nipasẹ onkọwe rẹ, Hedley Wickham.
S.J.K.: Yoo ṣajọ () ati tan kaakiri () jẹ idinku?
Hadley Wickham: Si diẹ ninu awọn iye. A kii yoo ṣeduro lilo awọn iṣẹ wọnyi mọ ati ṣatunṣe awọn idun ninu wọn, ṣugbọn wọn yoo tẹsiwaju lati wa ninu package ni ipo lọwọlọwọ wọn.
Awọn akoonu
Ti o ba nifẹ si itupalẹ data, o le nifẹ ninu mi и awọn ikanni. Pupọ julọ akoonu jẹ igbẹhin si ede R.
TidyData Erongba
Ero tidyr - ṣe iranlọwọ fun ọ lati mu data wa si eyiti a pe ni fọọmu afinju. Data afinju jẹ data nibiti:
- Oniyipada kọọkan wa ninu iwe kan.
- Akiyesi kọọkan jẹ okun.
- Iye kọọkan jẹ sẹẹli kan.
O rọrun pupọ ati irọrun diẹ sii lati ṣiṣẹ pẹlu data ti o ṣafihan ni data mimọ nigbati o nṣe itupalẹ.
Awọn iṣẹ akọkọ ti o wa ninu package tidyr
tidyr ni akojọpọ awọn iṣẹ ti a ṣe apẹrẹ lati yi awọn tabili pada:
fill()- kikun awọn iye ti o padanu ninu iwe kan pẹlu awọn iye iṣaaju;separate()- pin aaye kan si ọpọlọpọ nipa lilo oluyapa;unite()- ṣe iṣẹ ṣiṣe ti apapọ awọn aaye pupọ sinu ọkan, iṣẹ onidakeji ti iṣẹ naaseparate();pivot_longer()- iṣẹ kan ti o ṣe iyipada data lati ọna kika jakejado si ọna kika gigun;pivot_wider()- iṣẹ kan ti o ṣe iyipada data lati ọna kika gigun si ọna kika jakejado. Iṣiṣẹ iyipada ti ọkan ti o ṣe nipasẹ iṣẹ naapivot_longer().gather()igba atijọ - iṣẹ kan ti o ṣe iyipada data lati ọna kika jakejado si ọna kika gigun;spread()igba atijọ - iṣẹ kan ti o ṣe iyipada data lati ọna kika gigun si ọna kika jakejado. Iṣiṣẹ iyipada ti ọkan ti o ṣe nipasẹ iṣẹ naagather().
Agbekale tuntun fun iyipada data lati jakejado si ọna kika gigun ati idakeji
Ni iṣaaju, awọn iṣẹ ni a lo fun iru iyipada yii gather() и spread(). Ni awọn ọdun ti aye ti awọn iṣẹ wọnyi, o han gbangba pe fun ọpọlọpọ awọn olumulo, pẹlu onkọwe ti package, awọn orukọ awọn iṣẹ wọnyi ati awọn ariyanjiyan wọn ko han gbangba, o fa awọn iṣoro ni wiwa wọn ati oye eyiti ninu awọn iṣẹ wọnyi ṣe iyipada. fireemu ọjọ kan lati jakejado si ọna kika gigun, ati ni idakeji.
Ni idi eyi, in tidyr Tuntun meji, awọn iṣẹ pataki ti jẹ apẹrẹ lati yi awọn fireemu ọjọ pada.
Awọn ẹya tuntun pivot_longer() и pivot_wider() ni atilẹyin nipasẹ diẹ ninu awọn ẹya ti o wa ninu package cdata, ti a ṣẹda nipasẹ John Mount ati Nina Zumel.
Fifi awọn julọ lọwọlọwọ version of tidyr 0.8.3.9000
Lati fi sori ẹrọ tuntun, ẹya lọwọlọwọ julọ ti package tidyr 0.8.3.9000, nibiti awọn ẹya tuntun wa, lo koodu atẹle.
devtools::install_github("tidyverse/tidyr")
Ni akoko kikọ, awọn iṣẹ wọnyi wa nikan ni ẹya dev ti package lori GitHub.
Iyipada si awọn ẹya tuntun
Ni otitọ, ko nira lati gbe awọn iwe afọwọkọ atijọ lati ṣiṣẹ pẹlu awọn iṣẹ tuntun; fun oye ti o dara julọ, Emi yoo gba apẹẹrẹ lati inu iwe ti awọn iṣẹ atijọ ati ṣafihan bii awọn iṣẹ ṣiṣe kanna ṣe ni lilo awọn tuntun. pivot_*() awọn iṣẹ.
Iyipada ọna kika jakejado si ọna kika gigun.
Apeere koodu lati akojo iṣẹ iwe
# 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")
Yiyipada ọna kika gigun si ọna kika jakejado.
Apẹẹrẹ koodu lati iwe iṣẹ itankale
# old
stocks_spread <- stocks_gather %>% spread(key = stock,
value = price)
# new
stock_wide <- stocks_long %>% pivot_wider(names_from = "stock",
values_from = "price")
Nitori ninu awọn loke apeere ti ṣiṣẹ pẹlu awọn pivot_longer() и pivot_wider(), ninu atilẹba tabili akojopo ko si ọwọn akojọ si ni awọn ariyanjiyan awọn orukọ_si и iye_to orukọ wọn gbọdọ wa ni awọn ami ifọrọhan.
Tabili ti yoo ṣe iranlọwọ fun ọ julọ ni irọrun ro bi o ṣe le yipada si ṣiṣẹ pẹlu imọran tuntun tidyr.
Akiyesi lati onkowe
Gbogbo ọrọ ti o wa ni isalẹ jẹ adaṣe, Emi yoo paapaa sọ itumọ ọfẹ lati awọn osise tidyverse ìkàwé aaye ayelujara.
Apeere ti o rọrun ti iyipada data lati fife si ọna kika gigun
pivot_longer () - jẹ ki awọn eto data gun gun nipasẹ idinku nọmba awọn ọwọn ati jijẹ nọmba awọn ori ila.
Lati ṣiṣẹ awọn apẹẹrẹ ti a gbekalẹ ninu nkan naa, o nilo akọkọ lati sopọ awọn idii pataki:
library(tidyr)
library(dplyr)
library(readr)Jẹ ki a sọ pe a ni tabili pẹlu awọn abajade iwadi kan ti (pẹlu awọn ohun miiran) beere lọwọ eniyan nipa ẹsin wọn ati owo-ori ọdọọdun:
#> # 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>Tabili yii ni awọn data ẹsin ti awọn oludahun ninu awọn ori ila, ati awọn ipele owo-wiwọle ti tuka kaakiri awọn orukọ ọwọn. Nọmba awọn idahun lati ẹka kọọkan ti wa ni ipamọ ninu awọn iye sẹẹli ni ikorita ti ẹsin ati ipele owo-wiwọle. Lati mu tabili wa sinu afinju, ọna kika ti o tọ, o to lati lo 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 rowsAwọn ariyanjiyan iṣẹ pivot_longer()
- Akọkọ ariyanjiyan kola, ṣe apejuwe awọn ọwọn wo ni o nilo lati dapọ. Ni idi eyi, gbogbo awọn ọwọn ayafi akoko.
- Ariyanjiyan awọn orukọ_si yoo fun awọn orukọ ti awọn oniyipada ti yoo wa ni da lati awọn orukọ ti awọn ọwọn ti a concatenated.
- iye_to n fun orukọ oniyipada kan ti yoo ṣẹda lati data ti o fipamọ sinu awọn iye ti awọn sẹẹli ti awọn ọwọn ti a dapọ.
Awọn alaye pato (satunkọ)
Eyi jẹ iṣẹ-ṣiṣe tuntun ti package tidyr, eyiti ko si tẹlẹ nigbati o ba n ṣiṣẹ pẹlu awọn iṣẹ iṣe.
Sipesifikesonu jẹ fireemu data kan, ila kọọkan eyiti o baamu iwe kan ninu fireemu ọjọ iṣelọpọ tuntun, ati awọn ọwọn pataki meji ti o bẹrẹ pẹlu:
- .name ni awọn atilẹba iwe orukọ.
- .iye ni orukọ ti ọwọn ti yoo ni awọn iye sẹẹli ninu.
Awọn ọwọn ti o ku ti sipesifikesonu ṣe afihan bi iwe tuntun yoo ṣe han orukọ awọn ọwọn fisinuirindigbindigbin lati .name.
Sipesifikesonu ṣe apejuwe metadata ti a fipamọ sinu orukọ ọwọn, pẹlu ila kan fun iwe kọọkan ati iwe kan fun oniyipada kọọkan, ni idapo pẹlu orukọ iwe, itumọ yii le dabi airoju ni akoko, ṣugbọn lẹhin wiwo awọn apẹẹrẹ diẹ yoo di pupọ. kedere.
Ojuami ti sipesifikesonu ni pe o le gba pada, yipada, ati ṣalaye metadata tuntun fun iyipada dataframe naa.
Lati ṣiṣẹ pẹlu awọn pato nigba iyipada tabili lati ọna kika jakejado si ọna kika gigun, lo iṣẹ naa pivot_longer_spec().
Bii iṣẹ yii ṣe n ṣiṣẹ ni pe o gba fireemu ọjọ eyikeyi ati ṣe ipilẹṣẹ metadata rẹ ni ọna ti a ṣalaye loke.
Gẹgẹbi apẹẹrẹ, jẹ ki a mu data data tani ti o pese pẹlu package tidyr. Ipilẹ data yii ni alaye ti a pese nipasẹ ajọ ilera agbaye lori iṣẹlẹ ti iko.
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 variablesJẹ ká kọ awọn oniwe-sipesifikesonu.
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 rowsAwọn aaye orilẹ-ede, isoxnumx, isoxnumx jẹ tẹlẹ oniyipada. Iṣẹ-ṣiṣe wa ni lati yi awọn ọwọn pada pẹlu titun_sp_m014 on tuntun_f65.
Awọn orukọ ti awọn ọwọn wọnyi tọju alaye wọnyi:
- Ìpele
new_tọkasi wipe awọn iwe ni awọn data lori titun igba ti iko, awọn ti isiyi fireemu ọjọ ni alaye nikan lori titun arun, ki yi ìpele ninu awọn ti isiyi o tọ ko ni gbe eyikeyi itumo. sp/rel/sp/epṣe apejuwe ọna kan fun ṣiṣe ayẹwo aisan kan.m/fiwa alaisan.014/1524/2535/3544/4554/65alaisan ori ibiti o.
A le pin awọn ọwọn wọnyi nipa lilo iṣẹ naa extract()lilo deede ikosile.
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 rowsJọwọ ṣe akiyesi ọwọn naa .name yẹ ki o wa ko yipada nitori eyi ni atọka wa sinu awọn orukọ ọwọn ti ipilẹ data atilẹba.
Iwa ati ọjọ ori (awọn ọwọn abo и ori) ni awọn iye ti o wa titi ati ti a mọ, nitorinaa o gba ọ niyanju lati yi awọn ọwọn wọnyi pada si awọn okunfa:
spec <- spec %>%
mutate(
gender = factor(gender, levels = c("f", "m")),
age = factor(age, levels = unique(age), ordered = TRUE)
) Nikẹhin, lati le lo sipesifikesonu ti a ṣẹda si fireemu ọjọ atilẹba ti o a nilo lati lo ariyanjiyan Ami ni iṣẹ 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 rowsOhun gbogbo ti a kan ṣe ni a le ṣe afihan ni ọna kika bi atẹle:
Sipesifikesonu nipa lilo awọn iye pupọ (.iye)
Ni awọn apẹẹrẹ loke, awọn sipesifikesonu iwe .iye iye kan ṣoṣo ti o wa ninu, ni ọpọlọpọ igba eyi jẹ ọran naa.
Ṣugbọn lẹẹkọọkan ipo kan le dide nigbati o nilo lati gba data lati awọn ọwọn pẹlu awọn oriṣi data oriṣiriṣi ni awọn iye. Lilo iṣẹ-iṣajulọ spread() eyi yoo nira pupọ lati ṣe.
Awọn apẹẹrẹ ni isalẹ wa ni ya lati si package tabili.
Jẹ ki ká ṣẹda ikẹkọ dataframe.
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 1Férémù ọjọ́ tí a ṣẹ̀dá ní dátà nínú àwọn ọmọ ẹbí kan nínú ìlà kọ̀ọ̀kan. Awọn idile le ni ọmọ kan tabi meji. Fun ọmọ kọọkan, a pese data ni ọjọ ibi ati abo, ati pe data fun ọmọ kọọkan wa ni awọn ọwọn lọtọ; iṣẹ-ṣiṣe wa ni lati mu data yii wa si ọna kika to pe fun itupalẹ.
Jọwọ ṣe akiyesi pe a ni awọn oniyipada meji pẹlu alaye nipa ọmọ kọọkan: akọ-abo ati ọjọ ibi wọn (awọn ọwọn pẹlu ìpele iṣaaju dop ni ọjọ ibi ninu, awọn ọwọn pẹlu ìpele abo ni ibalopo ti awọn ọmọ). Abajade ti o nireti ni pe wọn yẹ ki o han ni awọn ọwọn lọtọ. A le se eyi nipa ti o npese kan sipesifikesonu ninu eyi ti awọn iwe .value yoo ni meji ti o yatọ itumo.
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 2Nitorinaa, jẹ ki a wo igbese-nipasẹ-igbesẹ awọn iṣe ti o ṣe nipasẹ koodu ti o wa loke.
pivot_longer_spec(-family)- ṣẹda kan sipesifikesonu ti o compress gbogbo awọn ti wa tẹlẹ ọwọn ayafi awọn ebi iwe.separate(col = name, into = c(".value", "child"))- pin awọn ọwọn .name, eyiti o ni awọn orukọ ti awọn aaye orisun, ni lilo abẹlẹ ati titẹ awọn iye abajade sinu awọn ọwọn .iye и ọmọ.mutate(child = parse_number(child))- yipada awọn iye aaye ọmọ lati ọrọ to nomba data iru.
Bayi a le lo sipesifikesonu abajade si dataframe atilẹba ati mu tabili wa si fọọmu ti o fẹ.
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 1A lo ariyanjiyan na.rm = TRUE, nitori awọn ti isiyi fọọmu ti awọn data fi agbara mu awọn ẹda ti afikun awọn ori ila fun ti kii-existent akiyesi. Nitori idile 2 ni ọmọ kan ṣoṣo, na.rm = TRUE onigbọwọ wipe ebi 2 yoo ni ọna kan ninu awọn wu.
Yiyipada awọn fireemu ọjọ lati gun si ọna kika fife
pivot_wider() - jẹ iyipada onidakeji, ati ni idakeji mu nọmba awọn ọwọn ti fireemu ọjọ pọ si nipa idinku nọmba awọn ori ila.
Iru iyipada yii jẹ ṣọwọn lilo pupọ lati mu data wa sinu fọọmu deede, sibẹsibẹ, ilana yii le wulo fun ṣiṣẹda awọn tabili pivot ti a lo ninu awọn igbejade, tabi fun iṣọpọ pẹlu awọn irinṣẹ miiran.
Ni otitọ awọn iṣẹ pivot_longer() и pivot_wider() jẹ onírẹlẹ̀, wọ́n sì ń gbé àwọn ìgbésẹ̀ tí ó yàtọ̀ síra wọn, ie. df %>% pivot_longer(spec = spec) %>% pivot_wider(spec = spec) и df %>% pivot_wider(spec = spec) %>% pivot_longer(spec = spec) yoo da df atilẹba pada.
Apẹẹrẹ ti o rọrun julọ ti yiyipada tabili kan si ọna kika jakejado
Lati ṣe afihan bi iṣẹ naa ṣe n ṣiṣẹ pivot_wider() a yoo lo dataset fish_incounters, eyi ti o tọju alaye nipa bi awọn ibudo oriṣiriṣi ṣe ṣe igbasilẹ iṣipopada ẹja lẹba odo.
#> # 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 rowsNi ọpọlọpọ igba, tabili yii yoo jẹ alaye diẹ sii ati rọrun lati lo ti o ba ṣafihan alaye fun ibudo kọọkan ni iwe lọtọ.
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>Eto data yii n ṣe igbasilẹ alaye nikan nigbati o ti rii ẹja nipasẹ ibudo, i.e. ti eyikeyi ẹja ko ba gba silẹ nipasẹ diẹ ninu awọn ibudo, lẹhinna data yii kii yoo wa ninu tabili. Eyi tumọ si abajade yoo kun pẹlu NA.
Sibẹsibẹ, ninu ọran yii a mọ pe isansa igbasilẹ tumọ si pe a ko rii ẹja naa, nitorinaa a le lo ariyanjiyan naa iye_fill ni iṣẹ pivot_wider() ki o si kun awọn iye ti o padanu pẹlu awọn odo:
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>Ṣiṣẹda orukọ ọwọn lati awọn oniyipada orisun pupọ
Fojuinu pe a ni tabili ti o ni apapo ọja, orilẹ-ede ati ọdun. Lati ṣe agbekalẹ fireemu ọjọ idanwo kan, o le ṣiṣẹ koodu atẹle:
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 rowsIṣẹ-ṣiṣe wa ni lati faagun fireemu data ki iwe kan ni data ninu fun akojọpọ ọja ati orilẹ-ede kọọkan. Lati ṣe eyi, o kan kọja ni ariyanjiyan awọn orukọ_lati a fekito ti o ni awọn orukọ ti awọn aaye lati wa ni dapọ.
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 rowsO tun le lo awọn pato si iṣẹ kan pivot_wider(). Sugbon nigba ti silẹ si pivot_wider() sipesifikesonu wo ni idakeji iyipada pivot_longer(): Awọn ọwọn pato ninu .name, lilo awọn iye lati .iye ati awọn miiran ọwọn.
Fun datasetiti yii, o le ṣe agbekalẹ sipesifikesonu aṣa ti o ba fẹ ki gbogbo orilẹ-ede ti o ṣeeṣe ati akojọpọ ọja lati ni ọwọn tirẹ, kii ṣe awọn ti o wa ninu data nikan:
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 0.594Awọn apẹẹrẹ to ti ni ilọsiwaju pupọ ti ṣiṣẹ pẹlu imọran tidyr tuntun
Nsọ data di mimọ nipa lilo owo-wiwọle ikaniyan AMẸRIKA ati iwe data iyalo gẹgẹbi apẹẹrẹ.
Eto data owo oya_iyalo ni owo-wiwọle agbedemeji ati alaye iyalo fun gbogbo ipinlẹ ni AMẸRIKA fun ọdun 2017 (ṣeto data ti o wa ninu package 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 rowsNi fọọmu ti o ti fipamọ data sinu ibi ipamọ data owo oya_iyalo Ṣiṣẹ pẹlu wọn ko ni irọrun pupọ, nitorinaa a yoo fẹ lati ṣẹda ṣeto data pẹlu awọn ọwọn: iyalo, iyalo_moe, wá, owo_moe. Awọn ọna pupọ lo wa lati ṣẹda sipesifikesonu yii, ṣugbọn aaye akọkọ ni pe a nilo lati ṣe agbekalẹ gbogbo apapọ ti awọn iye oniyipada ati ifoju / moeati lẹhinna ṣe ina orukọ ọwọn.
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_moePese yi sipesifikesonu pivot_wider() fun wa ni abajade ti a n wa:
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 rowsBanki Agbaye
Nigba miiran mimu data ṣeto sinu fọọmu ti o fẹ nilo awọn igbesẹ pupọ.
Eto data aye_bank_pop ni data Banki Agbaye lori iye eniyan ti orilẹ-ede kọọkan laarin ọdun 2000 ati 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>Ibi-afẹde wa ni lati ṣẹda ṣeto data afinju pẹlu oniyipada kọọkan ninu iwe tirẹ. Koyewa ni pato kini awọn igbesẹ ti o nilo, ṣugbọn a yoo bẹrẹ pẹlu iṣoro ti o han julọ: ọdun ti tan kaakiri awọn ọwọn pupọ.
Lati ṣe atunṣe eyi o nilo lati lo iṣẹ naa 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 rowsIgbesẹ t’okan ni lati wo oniyipada atọka.
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 4752Nibiti SP.POP.GROW ti wa ni idagbasoke olugbe, SP.POP.TOTL ni lapapọ olugbe, ati SP.URB. * Ohun kanna, ṣugbọn fun awọn agbegbe ilu nikan. Jẹ ki a pin awọn iye wọnyi si awọn oniyipada meji: agbegbe - agbegbe (lapapọ tabi ilu) ati oniyipada kan ti o ni data gangan (olugbe tabi idagbasoke):
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 rowsBayi gbogbo ohun ti a ni lati ṣe ni pin oniyipada si awọn ọwọn meji:
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 rowsAkojọ ti awọn olubasọrọ
Apeere kan ti o kẹhin, fojuinu pe o ni atokọ olubasọrọ ti o daakọ ati lẹẹmọ lati oju opo wẹẹbu kan:
contacts <- tribble(
~field, ~value,
"name", "Jiena McLellan",
"company", "Toyota",
"name", "John Smith",
"company", "google",
"email", "[email protected]",
"name", "Huxley Ratcliffe"
)Ṣiṣeto atokọ yii nira pupọ nitori pe ko si oniyipada ti o ṣe idanimọ iru data ti o jẹ ti olubasọrọ wo. A le ṣe atunṣe eyi nipa ṣiṣe akiyesi pe data fun olubasọrọ tuntun kọọkan bẹrẹ pẹlu orukọ kan ("orukọ"), nitorinaa a le ṣẹda idanimọ alailẹgbẹ kan ki o pọsi nipasẹ ẹyọkan ni akoko kọọkan iye “orukọ” ba pade ni aaye aaye:
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 3Ni bayi pe a ni ID alailẹgbẹ fun olubasọrọ kọọkan, a le yi aaye ati iye si awọn ọwọn:
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>ipari
Mi ti ara ẹni ero ni wipe titun Erongba tidyr lotitọ ni oye diẹ sii, ati pe o ga julọ ni iṣẹ ṣiṣe si awọn iṣẹ-ijogunba spread() и gather(). Mo nireti pe nkan yii ṣe iranlọwọ fun ọ lati koju pivot_longer() и pivot_wider().
orisun: www.habr.com