I am wondering if there is any easy R commands or packages that will all allow me to easily add variables to data.frames which are the "difference" or change of over time of those variables.
If my data looked like this:
set.seed(1)
MyData <- data.frame(Day=0:9 %% 5+1,
Price=rpois(10,10),
Good=rep(c("apples","oranges"), each=5))
MyData
Day Price Good
1 1 8 apples
2 2 10 apples
3 3 7 apples
4 4 11 apples
5 5 14 apples
6 1 12 oranges
7 2 11 oranges
8 3 9 oranges
9 4 14 oranges
10 5 11 oranges
Then after "first differencing" the price variable, my data would look like this.
Day Price Good P1d
1 1 8 apples NA
2 2 10 apples 2
3 3 7 apples -3
4 4 11 apples 4
5 5 14 apples 3
6 1 12 oranges NA
7 2 11 oranges -1
8 3 9 oranges -2
9 4 14 oranges 5
10 5 11 oranges -3
ave
transform(MyData, P1d = ave(Price, Good, FUN = function(x) c(NA, diff(x))))
ave/gsubfn
The last solution can be shorteneed slightly using fn$ in the gsubfn package:
library(gsubfn)
transform(MyData, P1d = fn$ave(Price, Good, FUN = ~ c(NA, diff(x))))
dplyr
library(dplyr)
MyData %>%
group_by(Good) %>%
mutate(P1d = Price - lag(Price)) %>%
ungroup
data.table
library(data.table)
dt <- data.table(MyData)
dt[, P1d := c(NA, diff(Price)), by = Good]
dplyr now uses %>% instead of %.% .