I have a dataset with 4 columns, 1st col is date, the other 3 are numeric. I am trying to get the % diff from previous row for those 3 numeric columns. I know there already have some posts about this kind of questions df %>% mutate_each(funs(. - lag(.))) %>% na.omit(), but most of them can not take care about the date, since I want the date to be unchange, and need % different.
here is the dataset
date=c('2018-01-01', '2018-02-01', '2018-03-01')
a=c(1,3,2)
b=c(89,56,47)
c=c(1872,7222,2930)
x=data.frame(date,a,b,c)
I wish to have the final dataset like this
x=data.frame(date,a,b,c)
a=c(NA, 2, -0.333)
b=c(NA, -0.371, -0.161)
c=c(NA,2.857, -0.594)
x=data.frame(date,a,b,c)
which means for col A, 2=3/1-1, -0.333=2/3-1 for col B, -0.371=56/89-1 etc
Thank you so much for your help!
A solution using package data.table:
x = as.data.table(x)
cols = c("a", "b", "c")
x[,(paste0(cols, "_pctChange")) := lapply(.SD, function(col){
(col-shift(col,1,type = "lag"))/shift(col,1,type = "lag")
}), .SDcols=cols]