[R傳播VS聚集在tidyr

我有以下形式的數據幀：[R傳播VS聚集在tidyr

  person currentTest beforeValue afterValue 
    1  1   A 1.284297055 2.671763513 
    2  2   A -0.618359548 -2.354926905 
    3  3   A 0.039457430 -0.091709968 
    4  4   A -0.448608324 -0.362851832 
    5  5   A -0.961777124 -1.416284339 
    6  6   A 0.702471895 2.052181444 
    7  7   A -0.455222045 -2.125684279 
    8  8   A -1.231549132 -2.777425148 
    9  9   A -0.797234990 -0.558306183 
    10  10   A -0.709734963 -1.244159550 
    11  1   B -0.472799377 -0.869472343 
    12  2   B 0.059720737 1.444855389 
    13  3   B 0.924201532 2.731049485 
    14  4   B 0.658884183 1.017542475 
    15  5   B -1.989807256 -4.712671740 
    16  6   B 0.660241305 1.971232718 
    17  7   B 0.089636952 -0.564457911 
    18  8   B -0.828399941 0.507659171 
    19  9   B -0.838074237 -0.316996942 
    20  10   B -1.659197101 -3.317623686 
    ...

我想什麼是獲得的數據幀：我已經試過收集

person A_Before  A_After  B_Before,  B_After, ... 
    1  1.284297055 2.671763513 -0.472799377 -0.869472343 
    2  -0.618359548 -2.354926905 0.059720737 1.444855389 
...

並傳播，但這不是我所需要的，因爲有新列的創建。有什麼建議麼？

的dput版本爲方便低於：

resultsData <- dput(resultsData) 
structure(list(person = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 
5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 
5L, 6L, 7L, 8L, 9L, 10L), currentTest = structure(c(1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L), .Label = c("A", "B", "C", 
"D", "E", "F"), class = "factor"), beforeValue = c(1.28429705541541, 
-0.618359548370402, 0.039457429902531, -0.448608324038257, -0.961777123997687, 
0.702471895259405, -0.455222044740939, -1.23154913153736, -0.797234989892673, 
-0.709734963076803, -0.47279937661921, 0.0597207367403981, 0.924201531911827, 
0.658884182599422, -1.98980725637449, 0.660241304554785, 0.0896369516528346, 
-0.828399941497236, -0.838074236572976, -1.65919710134782, 0.577469369909437, 
1.92748171699512, -0.245593641496638, 0.126104785456265, -0.559338325961641, 
1.29802115505785, 0.719406692531958, 0.969414499181256, -0.814697072724845, 
0.86465983690719, -0.709539159817187, 1.02775240926492, -0.50490096148732, 
0.40769259465753, -0.868531009656408, 0.949518511358715, 2.32458579520932, 
-0.257578702370506, -0.789761851618986, 0.0979274657020477, -0.00803566278013502, 
1.42984177159549, 1.45485678109231, -0.956556613290905, 0.443323691839299, 
-0.261951072972966, -1.30990441429799, 0.0921741874883992, -1.02612779569131, 
0.81550719514697, -0.403037731404182, -0.384422139459082, 0.417074857491798, 
-1.37128032791855, -0.0796160137501127, 1.35302483988882, -0.752751140138746, 
0.812453275384099, -1.32443072805549, -1.66986584340583), afterValue = c(2.67176351335094, 
-2.35492690509713, -0.0917099675669388, -0.362851831626841, -1.4162843393352, 
2.05218144382074, -2.12568427901904, -2.77742514848958, -0.558306182843248, 
-1.24415954975022, -0.869472343362331, 1.44485538931333, 2.73104948477609, 
1.01754247530805, -4.71267174035743, 1.9712327179732, -0.564457911016569, 
0.507659170771878, -0.31699694238194, -3.31762368638082, 1.09068172988414, 
4.37537723545199, -0.116850493406969, 1.9533832597394, -1.69003563933244, 
2.62250581307257, -0.00837379068728961, 1.84192937988371, -0.675899868505659, 
2.08506660046288, -0.583526785879512, 0.699298693972492, -1.26172199141024, 
1.23589313451783, -1.56008919968504, 0.436686458587792, 0.11699090169902, 
-1.07206510594109, 1.21204947218164, -0.812406581646911, 0.50373332256566, 
-0.084945367568491, -0.236015748624917, -0.479606239480476, -0.596799139055039, 
-0.562575023441403, -0.339935276865152, -0.213813544612318, -0.265296303857373, 
-1.12545083569158, 0.0105156062602101, 0.635695183644557, 0.767433440961415, 
0.16648012185356, 0.544633089427927, -0.904001384160196, -0.429299134808951, 
0.764224744168297, -0.166062348771635, -0.101892580202475)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -60L), .Names = c("person", 
"currentTest", "beforeValue", "afterValue"))

來源

2017-10-14 user1357015

你可以請人列一行做到這一點，每一行是一個獨特的價值？ –

@Hardikgupta：道歉，第一列只是行號。 – user1357015

您可以使用組合gather + spread方法;收集*值的列，並與currentTest結合起來，形成新的頭，然後蔓延到寬幅：

resultsData %>% 
    gather(key, value, -person, -currentTest) %>% 
    unite(header, c('currentTest', 'key'), sep = "_") %>% 
    spread(header, value) 

# A tibble: 10 x 13 
# person A_afterValue A_beforeValue B_afterValue B_beforeValue C_afterValue C_beforeValue 
# * <int>  <dbl>   <dbl>  <dbl>   <dbl>  <dbl>   <dbl> 
# 1  1 2.67176351 1.28429706 -0.8694723 -0.47279938 1.090681730  0.5774694 
# 2  2 -2.35492691 -0.61835955 1.4448554 0.05972074 4.375377235  1.9274817 
# 3  3 -0.09170997 0.03945743 2.7310495 0.92420153 -0.116850493 -0.2455936 
# 4  4 -0.36285183 -0.44860832 1.0175425 0.65888418 1.953383260  0.1261048 
# 5  5 -1.41628434 -0.96177712 -4.7126717 -1.98980726 -1.690035639 -0.5593383 
# 6  6 2.05218144 0.70247190 1.9712327 0.66024130 2.622505813  1.2980212 
# 7  7 -2.12568428 -0.45522204 -0.5644579 0.08963695 -0.008373791  0.7194067 
# 8  8 -2.77742515 -1.23154913 0.5076592 -0.82839994 1.841929380  0.9694145 
# 9  9 -0.55830618 -0.79723499 -0.3169969 -0.83807424 -0.675899869 -0.8146971 
#10  10 -1.24415955 -0.70973496 -3.3176237 -1.65919710 2.085066600  0.8646598 
# ... with 6 more variables: D_afterValue <dbl>, D_beforeValue <dbl>, E_afterValue <dbl>, 
# E_beforeValue <dbl>, F_afterValue <dbl>, F_beforeValue <dbl>

如果您需要重命名列：

resultsData %>% 
    gather(key, value, -person, -currentTest) %>% 
    unite(header, c('currentTest', 'key'), sep = "_") %>% 
    spread(header, value) %>% 
    rename_at(vars(matches("Value$")), funs(gsub("Value$", "", .)))

來源

2017-10-14 05:47:57 Psidom

@這是燦爛的。不知道團結。非常感謝！ – user1357015

我們可以使用dcast從reshape2

library(reshape2) 
meltdf <- melt(resultsData, id.vars=1:2) 
dcast(meltdf, person ~ currentTest + variable) 

> dcast(meltdf, person ~ currentTest + variable) 
    person A_beforeValue A_afterValue B_beforeValue B_afterValue C_beforeValue C_afterValue D_beforeValue D_afterValue E_beforeValue 
1  1 1.28429706 2.67176351 -0.47279938 -0.8694723  0.5774694 1.090681730 -0.70953916 -0.5835268 -0.008035663 
2  2 -0.61835955 -2.35492691 0.05972074 1.4448554  1.9274817 4.375377235 1.02775241 0.6992987 1.429841772 
3  3 0.03945743 -0.09170997 0.92420153 2.7310495 -0.2455936 -0.116850493 -0.50490096 -1.2617220 1.454856781 
4  4 -0.44860832 -0.36285183 0.65888418 1.0175425  0.1261048 1.953383260 0.40769259 1.2358931 -0.956556613 
5  5 -0.96177712 -1.41628434 -1.98980726 -4.7126717 -0.5593383 -1.690035639 -0.86853101 -1.5600892 0.443323692 
6  6 0.70247190 2.05218144 0.66024130 1.9712327  1.2980212 2.622505813 0.94951851 0.4366865 -0.261951073 
7  7 -0.45522204 -2.12568428 0.08963695 -0.5644579  0.7194067 -0.008373791 2.32458580 0.1169909 -1.309904414 
8  8 -1.23154913 -2.77742515 -0.82839994 0.5076592  0.9694145 1.841929380 -0.25757870 -1.0720651 0.092174187 
9  9 -0.79723499 -0.55830618 -0.83807424 -0.3169969 -0.8146971 -0.675899869 -0.78976185 1.2120495 -1.026127796 
10  10 -0.70973496 -1.24415955 -1.65919710 -3.3176237  0.8646598 2.085066600 0.09792747 -0.8124066 0.815507195 

E_afterValue F_beforeValue F_afterValue 
1 0.50373332 -0.40303773 0.01051561 
2 -0.08494537 -0.38442214 0.63569518 
3 -0.23601575 0.41707486 0.76743344 
4 -0.47960624 -1.37128033 0.16648012 
5 -0.59679914 -0.07961601 0.54463309 
6 -0.56257502 1.35302484 -0.90400138 
7 -0.33993528 -0.75275114 -0.42929913 
8 -0.21381354 0.81245328 0.76422474 
9 -0.26529630 -1.32443073 -0.16606235 
10 -1.12545084 -1.66986584 -0.10189258

來源

2017-10-14 05:54:58

我們可以使用recast

reshape2::recast(resultsData, person ~currentTest + variable, id.var = 1:2) 
#person A_beforeValue A_afterValue B_beforeValue B_afterValue C_beforeValue C_afterValue D_beforeValue D_afterValue 
#1  1 1.28429706 2.67176351 -0.47279938 -0.8694723  0.5774694 1.090681730 -0.70953916 -0.5835268 
#2  2 -0.61835955 -2.35492691 0.05972074 1.4448554  1.9274817 4.375377235 1.02775241 0.6992987 
#3  3 0.03945743 -0.09170997 0.92420153 2.7310495 -0.2455936 -0.116850493 -0.50490096 -1.2617220 
#4  4 -0.44860832 -0.36285183 0.65888418 1.0175425  0.1261048 1.953383260 0.40769259 1.2358931 
#5  5 -0.96177712 -1.41628434 -1.98980726 -4.7126717 -0.5593383 -1.690035639 -0.86853101 -1.5600892 
#6  6 0.70247190 2.05218144 0.66024130 1.9712327  1.2980212 2.622505813 0.94951851 0.4366865 
#7  7 -0.45522204 -2.12568428 0.08963695 -0.5644579  0.7194067 -0.008373791 2.32458580 0.1169909 
#8  8 -1.23154913 -2.77742515 -0.82839994 0.5076592  0.9694145 1.841929380 -0.25757870 -1.0720651 
#9  9 -0.79723499 -0.55830618 -0.83807424 -0.3169969 -0.8146971 -0.675899869 -0.78976185 1.2120495 
#10  10 -0.70973496 -1.24415955 -1.65919710 -3.3176237  0.8646598 2.085066600 0.09792747 -0.8124066 
# E_beforeValue E_afterValue F_beforeValue F_afterValue 
#1 -0.008035663 0.50373332 -0.40303773 0.01051561 
#2 1.429841772 -0.08494537 -0.38442214 0.63569518 
#3 1.454856781 -0.23601575 0.41707486 0.76743344 
#4 -0.956556613 -0.47960624 -1.37128033 0.16648012 
#5 0.443323692 -0.59679914 -0.07961601 0.54463309 
#6 -0.261951073 -0.56257502 1.35302484 -0.90400138 
#7 -1.309904414 -0.33993528 -0.75275114 -0.42929913 
#8 0.092174187 -0.21381354 0.81245328 0.76422474 
#9 -1.026127796 -0.26529630 -1.32443073 -0.16606235 
#10 0.815507195 -1.12545084 -1.66986584 -0.10189258

來源

2017-10-14 05:59:39 akrun

你有一行答案的一切akrun - 輝煌！ –

[R傳播VS聚集在tidyr

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