Reshape multiple value columns to wide format

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"))

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

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