麻煩不平衡DF從廣角到長

我有一種不平衡，寬數據幀，看起來是這樣的：麻煩不平衡DF從廣角到長

set.seed(1) 
df <- data.frame(id1=seq(1:10), 
       id2=runif(10), 
       v1.a=runif(10), 
       v1.b=runif(10), 
       v1.c=runif(10), 
       v2.a=runif(10), 
       v2.b=runif(10), 
       v2.c=runif(10), 
       v3.a=runif(10), 
       #v3.b=runif(10), 
       v3.c=runif(10), 
       v4.a=runif(10), 
       v4.b=runif(10), 
       v4.c=runif(10), 
       #v5.a=runif(10), 
       #v5.b=runif(10), 
       v5.c=runif(10), 
       v6.a=runif(10), 
       v6.b=runif(10), 
       v6.c=runif(10), 
       v7.a=rep(NA, 10), 
       v7.b=rep(NA, 10), 
       v7.c=rep(NA, 10), 
       v8.d=runif(10))

而且我試圖把它變成一個長格式。 reshape失敗，因爲並非所有變化列都出現在每次，所以我轉向Reshapesplitstackshape。

library(splitstackshape) 
vary <- grep("\\.a$|\\.b$|\\.c$|\\.d$", names(df)) 
stubs <- unique(sub("\\..*$", "", names(df[vary]))) 
df2 <- Reshape(df, 
       id.vars=c("id1", "id2"), 
       var.stubs=stubs, 
       sep=".")

但是，最終結果似乎不太正確。例如，v3缺少投入爲「B」，我會認爲是時候2.在df2，也有1次和2個長v3值，但不能3.

id1  id2 time   v1   v2  v3 
1 1 0.26550866 1 0.20597457 0.82094629 0.3390729 
2 2 0.37212390 1 0.17655675 0.64706019 0.8394404 
3 3 0.57285336 1 0.68702285 0.78293276 0.3466835 
4 4 0.90820779 1 0.38410372 0.55303631 0.3337749 
5 5 0.20168193 1 0.76984142 0.52971958 0.4763512 
6 6 0.89838968 1 0.49769924 0.78935623 0.8921983 
7 7 0.94467527 1 0.71761851 0.02333120 0.8643395 
8 8 0.66079779 1 0.99190609 0.47723007 0.3899895 
9 9 0.62911404 1 0.38003518 0.73231374 0.7773207 
10 10 0.06178627 1 0.77744522 0.69273156 0.9606180 
11 1 0.26550866 2 0.93470523 0.47761962 0.4346595 
12 2 0.37212390 2 0.21214252 0.86120948 0.7125147 
13 3 0.57285336 2 0.65167377 0.43809711 0.3999944 
14 4 0.90820779 2 0.12555510 0.24479728 0.3253522 
15 5 0.20168193 2 0.26722067 0.07067905 0.7570871 
16 6 0.89838968 2 0.38611409 0.09946616 0.2026923 
17 7 0.94467527 2 0.01339033 0.31627171 0.7111212 
18 8 0.66079779 2 0.38238796 0.51863426 0.1216919 
19 9 0.62911404 2 0.86969085 0.66200508 0.2454885 
20 10 0.06178627 2 0.34034900 0.40683019 0.1433044 
21 1 0.26550866 3 0.48208012 0.91287592  NA 
22 2 0.37212390 3 0.59956583 0.29360337  NA 
23 3 0.57285336 3 0.49354131 0.45906573  NA 
24 4 0.90820779 3 0.18621760 0.33239467  NA 
25 5 0.20168193 3 0.82737332 0.65087047  NA 
26 6 0.89838968 3 0.66846674 0.25801678  NA 
27 7 0.94467527 3 0.79423986 0.47854525  NA 
28 8 0.66079779 3 0.10794363 0.76631067  NA 
29 9 0.62911404 3 0.72371095 0.08424691  NA 
30 10 0.06178627 3 0.41127443 0.87532133  NA

我是不是製作錯誤？

是否有更好的選擇使用melt或gather？我嘗試了一些方法，但我沒有太多的運氣。我的實際使用案例包括我稱之爲vary列的1302，3個時間段（a，b，c）和821個獨特的stubs（非常明顯不平衡）。

來源

2016-07-14 Eric Green

您的預期產出是多少？ – akrun

當你沒有變量的所有組合時相關 - http://stackoverflow.com/questions/34713846/reshape-messy-longitudinal-survey-data-containing-multiple-different-variables/34714134 – thelatemail

試試這個，從其他鏈接的答案適應：

spl <- strsplit(names(df)[-(1:2)],"\\.") 
allvars <- c(outer(unique(sapply(spl,`[`,1)), unique(sapply(spl,`[`,2)),paste,sep=".")) 
df[setdiff(allvars, names(df))] <- NA 

reshape(df, direction="long", sep=".", varying=allvars) 

#  id1  id2 time   v1   v2  v3   v4  v5   v6 v7   v8 id 
#1.a 1 0.26550866 a 0.20597457 0.82094629 0.3390729 0.23962942  NA 0.57487220 NA   NA 1 
#2.a 2 0.37212390 a 0.17655675 0.64706019 0.8394404 0.05893438  NA 0.07706438 NA   NA 2 
#...

來源

2016-07-14 03:55:16 thelatemail

謝謝，@thelatemail。當我運行這個時，看起來'v3'具有時間'a'和'b'的值，但不是'c'或'd'。給定輸入'df'，我只希望時間'a'和'c'的值。 –

沒錯，有趣的事情正在發生。讓我看看 – thelatemail

@EricGreen - 修正它，只需顯式引用「vary ='的名稱。 – thelatemail

也許，我們可以使用中的melt，它可以在measure中使用多個patterns。它與data.table容易，因爲它需要多種模式

library(data.table) 
setDT(df) 
d1 <- read.table(text=names(df)[-(1:2)], sep=".") 
df[, (setdiff(outer(d1$V1, d1$V2, FUN = paste, sep="."), names(df)[-(1:2)])) := NA] 

melt(df[, order(names(df)), with = FALSE], measure = patterns(paste0("v", 
       1:8)), value.name = paste0("v", 1:8))

或者它可能是melt/dcast

res <- dcast(melt(df, id.var = c("id1", "id2"))[, c("var1", "var2") := 
    tstrsplit(variable, "[.]")], id1 + id2 + var2 ~ var1, value.var = "value") 
res[order(var2, id1)] 
# id1  id2 var2   v1   v2  v3   v4  v5   v6 v7   v8 
# 1: 1 0.26550866 a 0.20597457 0.82094629 0.3390729 0.23962942  NA 0.57487220 NA   NA 
# 2: 2 0.37212390 a 0.17655675 0.64706019 0.8394404 0.05893438  NA 0.07706438 NA   NA 
# 3: 3 0.57285336 a 0.68702285 0.78293276 0.3466835 0.64228826  NA 0.03554058 NA   NA 
# 4: 4 0.90820779 a 0.38410372 0.55303631 0.3337749 0.87626921  NA 0.64279549 NA   NA 
# 5: 5 0.20168193 a 0.76984142 0.52971958 0.4763512 0.77891468  NA 0.92861520 NA   NA 
# 6: 6 0.89838968 a 0.49769924 0.78935623 0.8921983 0.79730883  NA 0.59809242 NA   NA 
# 7: 7 0.94467527 a 0.71761851 0.02333120 0.8643395 0.45527445  NA 0.56090075 NA   NA 
# 8: 8 0.66079779 a 0.99190609 0.47723007 0.3899895 0.41008408  NA 0.52602772 NA   NA 
# 9: 9 0.62911404 a 0.38003518 0.73231374 0.7773207 0.81087024  NA 0.98509522 NA   NA 
#10: 10 0.06178627 a 0.77744522 0.69273156 0.9606180 0.60493329  NA 0.50764182 NA   NA 
#11: 1 0.26550866 b 0.93470523 0.47761962  NA 0.65472393  NA 0.68278808 NA   NA 
#12: 2 0.37212390 b 0.21214252 0.86120948  NA 0.35319727  NA 0.60154122 NA   NA 
#13: 3 0.57285336 b 0.65167377 0.43809711  NA 0.27026015  NA 0.23886868 NA   NA 
#14: 4 0.90820779 b 0.12555510 0.24479728  NA 0.99268406  NA 0.25816593 NA   NA 
#15: 5 0.20168193 b 0.26722067 0.07067905  NA 0.63349326  NA 0.72930962 NA   NA 
#16: 6 0.89838968 b 0.38611409 0.09946616  NA 0.21320814  NA 0.45257083 NA   NA 
#17: 7 0.94467527 b 0.01339033 0.31627171  NA 0.12937235  NA 0.17512677 NA   NA 
#18: 8 0.66079779 b 0.38238796 0.51863426  NA 0.47811803  NA 0.74669827 NA   NA 
#19: 9 0.62911404 b 0.86969085 0.66200508  NA 0.92407447  NA 0.10498764 NA   NA 
#20: 10 0.06178627 b 0.34034900 0.40683019  NA 0.59876097  NA 0.86454495 NA   NA 
#21: 1 0.26550866 c 0.48208012 0.91287592 0.4346595 0.97617069 0.9918386 0.61464497 NA   NA 
#22: 2 0.37212390 c 0.59956583 0.29360337 0.7125147 0.73179251 0.4955936 0.55715954 NA   NA 
#23: 3 0.57285336 c 0.49354131 0.45906573 0.3999944 0.35672691 0.4843495 0.32877732 NA   NA 
#24: 4 0.90820779 c 0.18621760 0.33239467 0.3253522 0.43147369 0.1734423 0.45313145 NA   NA 
#25: 5 0.20168193 c 0.82737332 0.65087047 0.7570871 0.14821156 0.7548209 0.50044097 NA   NA 
#26: 6 0.89838968 c 0.66846674 0.25801678 0.2026923 0.01307758 0.4538955 0.18086636 NA   NA 
#27: 7 0.94467527 c 0.79423986 0.47854525 0.7111212 0.71556607 0.5111698 0.52963060 NA   NA 
#28: 8 0.66079779 c 0.10794363 0.76631067 0.1216919 0.10318424 0.2075451 0.07527575 NA   NA 
#29: 9 0.62911404 c 0.72371095 0.08424691 0.2454885 0.44628435 0.2286581 0.27775593 NA   NA 
#30: 10 0.06178627 c 0.41127443 0.87532133 0.1433044 0.64010105 0.5957120 0.21269952 NA   NA 
#31: 1 0.26550866 d   NA   NA  NA   NA  NA   NA NA 0.28479048 
#32: 2 0.37212390 d   NA   NA  NA   NA  NA   NA NA 0.89509410 
#33: 3 0.57285336 d   NA   NA  NA   NA  NA   NA NA 0.44623532 
#34: 4 0.90820779 d   NA   NA  NA   NA  NA   NA NA 0.77998489 
#35: 5 0.20168193 d   NA   NA  NA   NA  NA   NA NA 0.88061903 
#36: 6 0.89838968 d   NA   NA  NA   NA  NA   NA NA 0.41312421 
#37: 7 0.94467527 d   NA   NA  NA   NA  NA   NA NA 0.06380848 
#38: 8 0.66079779 d   NA   NA  NA   NA  NA   NA NA 0.33548749 
#39: 9 0.62911404 d   NA   NA  NA   NA  NA   NA NA 0.72372595 
#40: 10 0.06178627 d   NA   NA  NA   NA  NA   NA NA 0.33761533

來源

2016-07-14 03:46:58 akrun

謝謝，@akrun 。我相信這些方法會給出不同的解'dcast'看起來像我所期望的。 –

@EricGreen我修正了第一種方法。 – akrun

在我的實際例子中，我有很多字符變量。看起來像使用'data.table'的'melt/dcast'方法正在轉換爲整數。這超出了原始問題的範圍，而且您的解決方案解決了玩具示例 - 但也許您會認識到我的問題。 –

什麼我認爲你會喜歡tidyr：

library(tidyr) 

     # gather non-ID columns to long form 
df %>% gather(var, val, -id1:-id2) %>% 
    # split former column names into variable name and time variables 
    separate(var, c('var', 'time')) %>% 
    # spread back to wide form 
    spread(var, val) %>% 
    head() 

## id1  id2 time  v1  v2  v3   v4  v5   v6 v7  v8 
## 1 1 0.2655087 a 0.2059746 0.8209463 0.3390729 0.23962942  NA 0.57487220 NA  NA 
## 2 1 0.2655087 b 0.9347052 0.4776196  NA 0.65472393  NA 0.68278808 NA  NA 
## 3 1 0.2655087 c 0.4820801 0.9128759 0.4346595 0.97617069 0.9918386 0.61464497 NA  NA 
## 4 1 0.2655087 d  NA  NA  NA   NA  NA   NA NA 0.2847905 
## 5 2 0.3721239 a 0.1765568 0.6470602 0.8394404 0.05893438  NA 0.07706438 NA  NA 
## 6 2 0.3721239 b 0.2121425 0.8612095  NA 0.35319727  NA 0.60154122 NA  NA

來源

2016-07-14 04:02:46 alistaire

謝謝，@alistaire。這看起來很優雅。 –

我不能完全理解我的實際用例，但我喜歡這種方法。 –

麻煩不平衡DF從廣角到長

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