Dataframe to tidy format in R - r

Question

I've this dataframe
x <- data.frame("date" = c("03-01-2005","04-01-2005","05-01-2005","06-01-2005"),
"pricemax.0" = c(50,20,25,56),
"pricemax.200" = c(25,67,89,30),
"pricemax.1000" = c(45,60,40,30),
"pricemax.1400" = c(60,57,32,44),
"pricemin.0" = c(22,15,23,43),
"pricemin.200" = c(21,40,59,21),
"pricemin.1000" = c(32,12,20,24),
"pricemin.1400" = c(30,20,14,20))
The numbers after the dot represents hours, e.g pricemax.200 would be 02:00. I need to gather the date and time information in one column of class POSIXct with the other two columns being pricemax and pricemin.
So, what I want is something like this:
And what I've done so far:
tidy_x <- x %>%
pivot_longer(
cols = contains("pricemax"),
names_to = c(NA,"hour"),
names_sep = "\\.",
values_to = "pricemax"
) %>%
pivot_longer(
cols = contains("pricemin"),
names_to = c(NA,"hour_2"),
names_sep = "\\.",
values_to = "pricemin"
)
I'm not sure how I can combine the date and time columns and keep the variables pricemin and pricemax organized.

Answer 1

Using dplyr and tidyr, you can do :
library(dplyr)
library(tidyr)
x %>%
pivot_longer(cols = -date,
names_to = c('.value', 'time'),
names_sep = '\\.') %>%
mutate(time = sprintf('%04s', time)) %>%
unite(datetime, date, time, sep = " ") %>%
mutate(datetime = lubridate::dmy_hm(datetime))
# A tibble: 16 x 3
# datetime pricemax pricemin
# <dttm> <dbl> <dbl>
# 1 2005-01-03 00:00:00 50 22
# 2 2005-01-03 02:00:00 25 21
# 3 2005-01-03 10:00:00 45 32
# 4 2005-01-03 14:00:00 60 30
# 5 2005-01-04 00:00:00 20 15
# 6 2005-01-04 02:00:00 67 40
# 7 2005-01-04 10:00:00 60 12
# 8 2005-01-04 14:00:00 57 20
# 9 2005-01-05 00:00:00 25 23
#10 2005-01-05 02:00:00 89 59
#11 2005-01-05 10:00:00 40 20
#12 2005-01-05 14:00:00 32 14
#13 2005-01-06 00:00:00 56 43
#14 2005-01-06 02:00:00 30 21
#15 2005-01-06 10:00:00 30 24
#16 2005-01-06 14:00:00 44 20
Get the data in long format with max and min in different column and hour information in different column. We make hour information consistent (of 4 digits) using sprintf and combine them into one column and convert it into datetime value.

Answer 2

Maybe you can try reshape like below to make a long data frame
y <- transform(
reshape(x, direction = "long", varying = -1),
date = strptime(paste(date, time / 100), "%d-%m-%Y %H")
)[c("date", "pricemax", "pricemin")]
y <- `row.names<-`(y[order(y$date),],NULL)
which gives
> y
date pricemax pricemin
1 2005-01-03 00:00:00 50 22
2 2005-01-03 02:00:00 25 21
3 2005-01-03 10:00:00 45 32
4 2005-01-03 14:00:00 60 30
5 2005-01-04 00:00:00 20 15
6 2005-01-04 02:00:00 67 40
7 2005-01-04 10:00:00 60 12
8 2005-01-04 14:00:00 57 20
9 2005-01-05 00:00:00 25 23
10 2005-01-05 02:00:00 89 59
11 2005-01-05 10:00:00 40 20
12 2005-01-05 14:00:00 32 14
13 2005-01-06 00:00:00 56 43
14 2005-01-06 02:00:00 30 21
15 2005-01-06 10:00:00 30 24
16 2005-01-06 14:00:00 44 20

Answer 3

Here is a data.table approach:
setDT(x)
DT <- melt.data.table(x, id.vars = "date")
DT[, c("var", "time") := tstrsplit(variable , ".", fixed=TRUE)
][, datetime := as.POSIXct(paste(date, as.integer(time) / 100), format = "%d-%m-%Y %H")
][, setdiff(names(DT), c("datetime", "var", "value")) := NULL]
DT <- dcast.data.table(DT, datetime ~ var, value.var = "value")
> DT
datetime pricemax pricemin
1: 2005-01-03 00:00:00 50 22
2: 2005-01-03 02:00:00 25 21
3: 2005-01-03 10:00:00 45 32
4: 2005-01-03 14:00:00 60 30
5: 2005-01-04 00:00:00 20 15
6: 2005-01-04 02:00:00 67 40
7: 2005-01-04 10:00:00 60 12
8: 2005-01-04 14:00:00 57 20
9: 2005-01-05 00:00:00 25 23
10: 2005-01-05 02:00:00 89 59
11: 2005-01-05 10:00:00 40 20
12: 2005-01-05 14:00:00 32 14
13: 2005-01-06 00:00:00 56 43
14: 2005-01-06 02:00:00 30 21
15: 2005-01-06 10:00:00 30 24
16: 2005-01-06 14:00:00 44 20