Convert data.frame wide to long while concatenating date formats - r

Question

In R (or other language), I want to transform an upper data frame to lower one.
How can I do that?
Thank you beforehand.
year month income expense
2016 07 50 15
2016 08 30 75
month income_expense
1 2016-07 50
2 2016-07 -15
3 2016-08 30
4 2016-08 -75

Answer 1

Well, it seems that you are trying to do multiple operations in the same question: combine dates columns, melt your data, some colnames transformations and sorting
This will give your expected output:
library(tidyr); library(reshape2); library(dplyr)
df %>% unite("date", c(year, month)) %>%
mutate(expense=-expense) %>% melt(value.name="income_expense") %>%
select(-variable) %>% arrange(date)
#### date income_expense
#### 1 2016_07 50
#### 2 2016_07 -15
#### 3 2016_08 30
#### 4 2016_08 -75
I'm using three different libraries here, for better readability of the code. It might be possible to do it with base R, though.

Answer 2

Here's a solution using only two packages, dplyr and tidyr
First, your dataset:
df <- dplyr::data_frame(
year =2016,
month = c("07", "08"),
income = c(50,30),
expense = c(15, 75)
)
The mutate() function in dplyr creates/edits individual variables. The gather() function in tidyr will bring multiple variables/columns together in the way that you specify.
df <- df %>%
dplyr::mutate(
month = paste0(year, "-", month)
) %>%
tidyr::gather(
key = direction, #your name for the new column containing classification 'key'
value = income_expense, #your name for the new column containing values
income:expense #which columns you're acting on
) %>%
dplyr::mutate(income_expense =
ifelse(direction=='expense', -income_expense, income_expense)
)
The output has all the information you'd need (but we will clean it up in the last step)
> df
# A tibble: 4 × 4
year month direction income_expense
<dbl> <chr> <chr> <dbl>
1 2016 2016-07 income 50
2 2016 2016-08 income 30
3 2016 2016-07 expense -15
4 2016 2016-08 expense -75
Finally, we select() to drop columns we don't want, and then arrange it so that df shows the rows in the same order as you described in the question.
df <- df %>%
dplyr::select(-year, -direction) %>%
dplyr::arrange(month)
> df
# A tibble: 4 × 2
month income_expense
<chr> <dbl>
1 2016-07 50
2 2016-07 -15
3 2016-08 30
4 2016-08 -75
NB: I guess that I'm using three libraries, including magrittr for the pipe operator %>%. But, since the pipe operator is the best thing ever, I often forget to count magrittr.