Reshape multiple value columns to wide format
Solution 1:
Your best option is to reshape your data to long format, using melt, and then to dcast:
library(reshape2)
meltExpensesByMonth <- melt(expensesByMonth, id.vars=1:2)
dcast(meltExpensesByMonth, expense_type ~ month + variable, fun.aggregate = sum)
The first few lines of output:
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
Solution 2:
data.table can cast on multiple value.var variables. This is quite direct (and efficient).
Therefore:
library(data.table) # v1.9.5+
dcast(setDT(expensesByMonth), expense_type ~ month, value.var = c("value", "percent"))
Solution 3:
Reshaping from long to wide format with multiple value/measure columns is now possible with the new function pivot_wider() introduced in tidyr 1.0.0.
This is superior to the previous tidyr strategy of gather() than spread(), because the attributes are no longer dropped (e.g., dates remain dates, strings remain strings).
pivot_wider() (counterpart: pivot_longer()) works similar to spread().
However, it offers additional functionality such as multiple value columns.
To this end, the argument values_from—that indicates from which column(s) the values are taken—may take more than one column name.
NAs may be filled using the 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>
Alternatively, the reshape may be done using a pivot spec that offers finer control (see link below):
# 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)
Created on 2019-03-26 by the reprex package (v0.2.1)
See also: https://tidyr.tidyverse.org/dev/articles/pivot.html
Solution 4:
As this question is often visited, it deserves a complete base R answer too in my opinion. The reshape-function from base R is quite versatile and can easily be applied to this problem as well:
expenses <- reshape(expensesByMonth, idvar = 'expense_type', direction = 'wide',
timevar = 'month', sep = '_')
The cells with NA-values can be replaced with 0 with:
expenses[is.na(expenses)] <- 0
which gives (ordered by expense_type to make it easier to compare with the desired output):
> 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
You could also achieve this with the tidyverse:
library(dplyr)
library(tidyr)
expensesByMonth %>%
gather(k, v, 3:4) %>%
unite(km, k, month) %>%
spread(km, v, fill = 0)
Solution 5:
I prefer the tabulate function in package tables for this. It requires factors, but this is anyway a good idea with the type of data you have.
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)
Partial output:
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