Remodeler plusieurs colonnes de valeurs au format large

Question

J'ai le bloc de données suivant et je souhaite utiliser cast pour créer un "tableau croisé dynamique" avec des colonnes pour deux valeurs (valeur et pourcentage). Voici le bloc de données:

expensesByMonth <- structure(list(month = c("2012-02-01", "2012-02-01", "2012-02-01", "2012-02-01", "2012-02-01", "2012-02-01", "2012-02-01", "2012-02-01", "2012-02-01", "2012-02-01", "2012-02-01", "2012-02-01", "2012-03-01", "2012-03-01", "2012-03-01", "2012-03-01", "2012-03-01", "2012-03-01", "2012-03-01", "2012-03-01", "2012-03-01", "2012-03-01", "2012-03-01", "2012-03-01", "2012-03-01", "2012-03-01", "2012-03-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-04-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-05-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-06-01", "2012-07-01", "2012-07-01", "2012-07-01", "2012-07-01", "2012-07-01", "2012-07-01", "2012-07-01", "2012-07-01", "2012-07-01", "2012-07-01", "2012-07-01", "2012-07-01", "2012-07-01"), expense_type = c("Adjustment", "Bank Service Charge", "Cable", "Clubbing", "Dining", "Education", "Gifts", "Groceries", "Lunch", "Personal Care", "Rent", "Transportation", "Adjustment", "Bank Service Charge", "Cable", "Clubbing", "Dining", "Gifts", "Groceries", "Lunch", "Medical Expenses", "Miscellaneous", "Personal Care", "Phone", "Recreation", "Rent", "Transportation", "Adjustment", "Bank Service Charge", "Clothes", "Clubbing", "Computer", "Dining", "Gifts", "Groceries", "Lunch", "Maintenance", "Medical Expenses", "Miscellaneous", "Personal Care", "Phone", "Recreation", "Rent", "Transportation", "Travel", "Bank Service Charge", "Cable", "Clothes", "Clubbing", "Computer", "Dining", "Electric", "Gifts", "Groceries", "Lunch", "Maintenance", "Medical Expenses", "Miscellaneous", "Personal Care", "Phone", "Recreation", "Rent", "Transportation", "Adjustment", "Bank Service Charge", "Cable", "Charity", "Clothes", "Computer", "Dining", "Education", "Electric", "Gifts", "Groceries", "Lunch", "Maintenance", "Medical Expenses", "Miscellaneous", "Personal Care", "Phone", "Recreation", "Rent", "Transportation", "Computer", "Gifts", "Groceries", "Lunch", "Maintenance", "Medical Expenses", "Miscellaneous", "Personal Care", "Phone", "Recreation", "Rent", "Repair and Maintenance", "Transportation"), value = c(442.37, 200, 21.33, 75, 22.5, 1800, 10, 233.33, 154.75, 30, 545, 32.5, 2, 200, 36.33, 206.55, 74.5, 89, 372.68, 383.75, 144.19, 508.11, 30, 38.4, 81.75, 1746.7, 35, 16.37, 200, 806.9, 324.81, 756, 80.5, 100, 398.37, 326.25, 151, 29.95, 101, 90, 38.45, 61, 743.75, 129, 228.53, 200, 39.05, 237, 40, 283.83, 141.32, 32.88, 30, 424.4, 412, 142.75, 86.55, 1051.5, 30, 38.9, 51.5, 749.7, 35, 10, 200, 16, 32.59, 149.81, 100, 80, 60, 31.91, 55, 397.25, 486.4, 115.6, 47.08, 1000, 120, 41.11, 256, 761.6, 55, 10.54, 10, 342.11, 291, 76.5, 66.8, 1008, 30, 41.11, 316, 765, 65, 62), percent = c(0.124025030980324, 0.0560729845967511, 0.00598018380724351, 0.0210273692237817, 0.0063082107671345, 0.50465686137076, 0.00280364922983756, 0.0654175474797997, 0.0433864718317362, 0.00841094768951267, 0.152798883026147, 0.00911185999697206, 0.000506462461002391, 0.0506462461002391, 0.00919989060410842, 0.0523049106600219, 0.018865726672339, 0.0225375795146064, 0.0943742149831854, 0.0971774847048337, 0.0365134111259673, 0.128669320529962, 0.00759693691503586, 0.0097240792512459, 0.0207016530934727, 0.442318990316438, 0.00886309306754183, 0.00357276925628781, 0.0436502047194601, 0.176106750940662, 0.0708901149746392, 0.164997773839559, 0.0175692073995827, 0.0218251023597301, 0.0869446602704567, 0.0712043964486193, 0.0329559045631924, 0.00653661815673915, 0.0220433533833274, 0.0196425921237571, 0.00839175185731621, 0.0133133124394353, 0.162324198800492, 0.0281543820440518, 0.0498769064226911, 0.0496724104530621, 0.00969853814096037, 0.0588618063868785, 0.00993448209061241, 0.070492601294463, 0.0350985252261336, 0.0081661442784834, 0.00745086156795931, 0.105404854981398, 0.102325165533308, 0.035453682960873, 0.0214957356235626, 0.261152697956974, 0.00745086156795931, 0.00966128383312057, 0.0127906456916635, 0.186197030583303, 0.00869267182928586, 0.00249044292527426, 0.0498088585054852, 0.00398470868043882, 0.00811635349346881, 0.0373093254635337, 0.0249044292527426, 0.0199235434021941, 0.0149426575516456, 0.00794700337455016, 0.0136974360890084, 0.09893284520652, 0.12113514388534, 0.0287895202161704, 0.0117250052921912, 0.249044292527426, 0.0298853151032911, 0.0102382108658025, 0.0637553388870211, 0.189672133188888, 0.0136974360890084, 0.00341757293956667, 0.0032424790697976, 0.110928451456846, 0.0943561409311103, 0.0248049648839517, 0.021659760186248, 0.326841890235599, 0.00972743720939281, 0.013329831455938, 0.102462338605604, 0.248049648839517, 0.0210761139536844, 0.0201033702327451)), .Names = c("month", "expense_type", "value", "percent"), row.names = c(NA, -96L), class = "data.frame" )

Voici ce que je voudrais créer (bien sûr, avec différents noms d'en-tête comme: [month] _value, [month] _percent):

expenses value percent value.1 percent.1 value.2 percent.2 value.3 percent.3 value.4 percent.4 value.5 percent.5 1 Adjustment 442.37 0.124025031 2.00 0.000506462 16.37 0.003572769 0.00 0.000000000 10.00 0.002490443 0.00 0.000000000 2 Bank Service Charge 200.00 0.056072985 200.00 0.050646246 200.00 0.043650205 200.00 0.049672410 200.00 0.049808859 0.00 0.000000000 3 Cable 21.33 0.005980184 36.33 0.009199891 0.00 0.000000000 39.05 0.009698538 16.00 0.003984709 0.00 0.000000000 4 Charity 0.00 0.000000000 0.00 0.000000000 0.00 0.000000000 0.00 0.000000000 32.59 0.008116353 0.00 0.000000000 5 Clothes 0.00 0.000000000 0.00 0.000000000 806.90 0.176106751 237.00 0.058861806 149.81 0.037309325 0.00 0.000000000 6 Clubbing 75.00 0.021027369 206.55 0.052304911 324.81 0.070890115 40.00 0.009934482 0.00 0.000000000 0.00 0.000000000 7 Computer 0.00 0.000000000 0.00 0.000000000 756.00 0.164997774 283.83 0.070492601 100.00 0.024904429 10.54 0.003417573 8 Dining 22.50 0.006308211 74.50 0.018865727 80.50 0.017569207 141.32 0.035098525 80.00 0.019923543 0.00 0.000000000 9 Education 1800.00 0.504656861 0.00 0.000000000 0.00 0.000000000 0.00 0.000000000 60.00 0.014942658 0.00 0.000000000 10 Electric 0.00 0.000000000 0.00 0.000000000 0.00 0.000000000 32.88 0.008166144 31.91 0.007947003 0.00 0.000000000 11 Gifts 10.00 0.002803649 89.00 0.022537580 100.00 0.021825102 30.00 0.007450862 55.00 0.013697436 10.00 0.003242479 12 Groceries 233.33 0.065417547 372.68 0.094374215 398.37 0.086944660 424.40 0.105404855 397.25 0.098932845 342.11 0.110928451 13 Lunch 154.75 0.043386472 383.75 0.097177485 326.25 0.071204396 412.00 0.102325166 486.40 0.121135144 291.00 0.094356141 14 Maintenance 0.00 0.000000000 0.00 0.000000000 151.00 0.032955905 142.75 0.035453683 115.60 0.028789520 76.50 0.024804965 15 Medical Expenses 0.00 0.000000000 144.19 0.036513411 29.95 0.006536618 86.55 0.021495736 47.08 0.011725005 66.80 0.021659760 16 Miscellaneous 0.00 0.000000000 508.11 0.128669321 101.00 0.022043353 1051.50 0.261152698 1000.00 0.249044293 1008.00 0.326841890 17 Personal Care 30.00 0.008410948 30.00 0.007596937 90.00 0.019642592 30.00 0.007450862 120.00 0.029885315 30.00 0.009727437 18 Phone 0.00 0.000000000 38.40 0.009724079 38.45 0.008391752 38.90 0.009661284 41.11 0.010238211 41.11 0.013329831 19 Recreation 0.00 0.000000000 81.75 0.020701653 61.00 0.013313312 51.50 0.012790646 256.00 0.063755339 316.00 0.102462339 20 Rent 545.00 0.152798883 1746.70 0.442318990 743.75 0.162324199 749.70 0.186197031 761.60 0.189672133 765.00 0.248049649 21 Repair and Maintenance 0.00 0.000000000 0.00 0.000000000 0.00 0.000000000 0.00 0.000000000 0.00 0.000000000 65.00 0.021076114 22 Transportation 32.50 0.009111860 35.00 0.008863093 129.00 0.028154382 35.00 0.008692672 55.00 0.013697436 62.00 0.020103370 23 Travel 0.00 0.000000000 0.00 0.000000000 228.53 0.049876906 0.00 0.000000000 0.00 0.000000000 0.00 0.000000000

J'ai également rencontré l'erreur suivante lors de l'utilisation de cast sur une seule colonne de valeur: elle ne prend pas en compte le paramètre "value". Donc, même si je spécifie value = "percent", il affiche toujours les valeurs de la colonne "value".

cast(expensesByMonth, expense_type ~ month, fun.aggregate = sum, value = "percent")

Andrie · Accepted Answer

Votre meilleure option consiste à remodeler vos données au format long, en utilisant melt, puis à dcast:

library(reshape2) meltExpensesByMonth <- melt(expensesByMonth, id.vars=1:2) dcast(meltExpensesByMonth, expense_type ~ month + variable, fun.aggregate = sum)

Les premières lignes de sortie:

 expense_type 2012-02-01_value 2012-02-01_percent 2012-03-01_value 2012-03-01_percent 1 Adjustment 442.37 0.124025031 2.00 0.0005064625 2 Bank Service Charge 200.00 0.056072985 200.00 0.0506462461 3 Cable 21.33 0.005980184 36.33 0.0091998906 4 Charity 0.00 0.000000000 0.00 0.0000000000

Arun · Answer

data.table peut lancer plusieurs value.var variables. C'est assez direct (et efficace).

Par conséquent:

library(data.table) # v1.9.5+ dcast(setDT(expensesByMonth), expense_type ~ month, value.var = c("value", "percent"))

Jaap · Answer

Comme cette question est souvent visitée, elle mérite aussi à mon avis une réponse R de base complète. La fonction reshape- de la base R est assez polyvalente et peut également être facilement appliquée à ce problème:

expenses <- reshape(expensesByMonth, idvar = 'expense_type', direction = 'wide', timevar = 'month', sep = '_')

Les cellules avec des valeurs NA- peuvent être remplacées par 0 avec:

expenses[is.na(expenses)] <- 0

ce qui donne (ordonné par expense_type pour faciliter la comparaison avec la sortie souhaitée):

> expenses[order(expenses$expense_type),] expense_type value_2012-02-01 percent_2012-02-01 value_2012-03-01 percent_2012-03-01 value_2012-04-01 percent_2012-04-01 value_2012-05-01 percent_2012-05-01 value_2012-06-01 percent_2012-06-01 value_2012-07-01 percent_2012-07-01 1 Adjustment 442.37 0.124025031 2.00 0.0005064625 16.37 0.003572769 0.00 0.000000000 10.00 0.002490443 0.00 0.000000000 2 Bank Service Charge 200.00 0.056072985 200.00 0.0506462461 200.00 0.043650205 200.00 0.049672410 200.00 0.049808859 0.00 0.000000000 3 Cable 21.33 0.005980184 36.33 0.0091998906 0.00 0.000000000 39.05 0.009698538 16.00 0.003984709 0.00 0.000000000 67 Charity 0.00 0.000000000 0.00 0.0000000000 0.00 0.000000000 0.00 0.000000000 32.59 0.008116353 0.00 0.000000000 30 Clothes 0.00 0.000000000 0.00 0.0000000000 806.90 0.176106751 237.00 0.058861806 149.81 0.037309325 0.00 0.000000000 4 Clubbing 75.00 0.021027369 206.55 0.0523049107 324.81 0.070890115 40.00 0.009934482 0.00 0.000000000 0.00 0.000000000 32 Computer 0.00 0.000000000 0.00 0.0000000000 756.00 0.164997774 283.83 0.070492601 100.00 0.024904429 10.54 0.003417573 5 Dining 22.50 0.006308211 74.50 0.0188657267 80.50 0.017569207 141.32 0.035098525 80.00 0.019923543 0.00 0.000000000 6 Education 1800.00 0.504656861 0.00 0.0000000000 0.00 0.000000000 0.00 0.000000000 60.00 0.014942658 0.00 0.000000000 52 Electric 0.00 0.000000000 0.00 0.0000000000 0.00 0.000000000 32.88 0.008166144 31.91 0.007947003 0.00 0.000000000 7 Gifts 10.00 0.002803649 89.00 0.0225375795 100.00 0.021825102 30.00 0.007450862 55.00 0.013697436 10.00 0.003242479 8 Groceries 233.33 0.065417547 372.68 0.0943742150 398.37 0.086944660 424.40 0.105404855 397.25 0.098932845 342.11 0.110928451 9 Lunch 154.75 0.043386472 383.75 0.0971774847 326.25 0.071204396 412.00 0.102325166 486.40 0.121135144 291.00 0.094356141 37 Maintenance 0.00 0.000000000 0.00 0.0000000000 151.00 0.032955905 142.75 0.035453683 115.60 0.028789520 76.50 0.024804965 21 Medical Expenses 0.00 0.000000000 144.19 0.0365134111 29.95 0.006536618 86.55 0.021495736 47.08 0.011725005 66.80 0.021659760 22 Miscellaneous 0.00 0.000000000 508.11 0.1286693205 101.00 0.022043353 1051.50 0.261152698 1000.00 0.249044293 1008.00 0.326841890 10 Personal Care 30.00 0.008410948 30.00 0.0075969369 90.00 0.019642592 30.00 0.007450862 120.00 0.029885315 30.00 0.009727437 24 Phone 0.00 0.000000000 38.40 0.0097240793 38.45 0.008391752 38.90 0.009661284 41.11 0.010238211 41.11 0.013329831 25 Recreation 0.00 0.000000000 81.75 0.0207016531 61.00 0.013313312 51.50 0.012790646 256.00 0.063755339 316.00 0.102462339 11 Rent 545.00 0.152798883 1746.70 0.4423189903 743.75 0.162324199 749.70 0.186197031 761.60 0.189672133 765.00 0.248049649 95 Repair and Maintenance 0.00 0.000000000 0.00 0.0000000000 0.00 0.000000000 0.00 0.000000000 0.00 0.000000000 65.00 0.021076114 12 Transportation 32.50 0.009111860 35.00 0.0088630931 129.00 0.028154382 35.00 0.008692672 55.00 0.013697436 62.00 0.020103370 45 Travel 0.00 0.000000000 0.00 0.0000000000 228.53 0.049876906 0.00 0.000000000 0.00 0.000000000 0.00 0.000000000

Vous pouvez également y parvenir avec le tidyverse:

library(dplyr) library(tidyr) expensesByMonth %>% gather(k, v, 3:4) %>% unite(km, k, month) %>% spread(km, v, fill = 0)

Dieter Menne · Answer

Je préfère la fonction tabulate dans le package tables pour cela. Cela nécessite des facteurs, mais c'est de toute façon une bonne idée avec le type de données dont vous disposez.

library(tables) expensesByMonth$month= as.factor(expensesByMonth$month) expensesByMonth$expense_type= as.factor(expensesByMonth$expense_type) tabular(expense_type~(month)*(value+percent)*(sum),data=expensesByMonth) # Optional formatting tabular(expense_type~month* ((Format(digits=1))*value+(Format(digits=3))*percent)*sum, data=expensesByMonth)

Sortie partielle:

 value percent value percent value percent expense_type sum sum sum sum sum sum Adjustment 442 0.124025 2 0.000506 16 0.003573 Bank Service Charge 200 0.056073 200 0.050646 200 0.043650 Cable 21 0.005980 36 0.009200 0 0.000000

hplieninger · Answer

Le remodelage du format long vers le format large avec plusieurs colonnes valeur/mesure est désormais possible avec la nouvelle fonction pivot_wider() introduite dans tidyr 1.0.0.

Ceci est supérieur à la stratégie tidyr précédente de gather() que spread(), car les attributs ne sont plus supprimés (par exemple, les dates restent des dates, les chaînes restent des chaînes).

pivot_wider() (homologue: pivot_longer()) fonctionne de manière similaire à spread(). Cependant, il offre des fonctionnalités supplémentaires telles que plusieurs colonnes de valeurs. À cette fin, l'argument values_from— qui indique de quelle (s) colonne (s) les valeurs sont extraites - peut prendre plus d'un nom de colonne.

NAs peuvent être remplis en utilisant l'argument values_fill.

library("tidyr") library("magrittr") pivot_wider(expensesByMonth, id_cols = expense_type, names_from = month, values_from = c(value, percent)) #> # A tibble: 23 x 13 #> expense_type `value_2012-02-~ `value_2012-03-~ `value_2012-04-~ #> <chr> <dbl> <dbl> <dbl> #> 1 Adjustment 442. 2 16.4 #> 2 Bank Servic~ 200 200 200 #> 3 Cable 21.3 36.3 NA #> 4 Clubbing 75 207. 325. #> 5 Dining 22.5 74.5 80.5 #> 6 Education 1800 NA NA #> 7 Gifts 10 89 100 #> 8 Groceries 233. 373. 398. #> 9 Lunch 155. 384. 326. #> 10 Personal Ca~ 30 30 90 #> # ... with 13 more rows, and 9 more variables: `value_2012-05-01` <dbl>, #> # `value_2012-06-01` <dbl>, `value_2012-07-01` <dbl>, #> # `percent_2012-02-01` <dbl>, `percent_2012-03-01` <dbl>, #> # `percent_2012-04-01` <dbl>, `percent_2012-05-01` <dbl>, #> # `percent_2012-06-01` <dbl>, `percent_2012-07-01` <dbl>

Alternativement, la refonte peut être effectuée en utilisant une spécification de pivot qui offre un contrôle plus fin (voir le lien ci-dessous):

# see also ?build_wider_spec spec <- expensesByMonth %>% expand(month, .value = c("percent", "value")) %>% dplyr::mutate(.name = paste(.$month, .$.value, sep = "_")) pivot_wider_spec(expensesByMonth, spec = spec)

^{Créé le 2019-03-26 par le package reprex (v0.2.1)}

Voir aussi: https://tidyr.tidyverse.org/dev/articles/pivot.html