R: Differences by group and adding

Question

I would like to know how to do this operation simpler.
Imagine I have a data.frame like this one:

set.seed(1)
ID <- rep(1:3,each=4)
XX <- round(run         


        

Answer 1

I think you will need to use lapply() across the relevant columns, as ave() will not take a list in its first argument. Try this:

df[-1] <- lapply(
    df[-1], 
    function(x) ave(x, df$ID, FUN = function(x) c(x[1], diff(x)))
)

which gives the updated df

   ID     XX    ZZ
1   1  0.266 0.266
2   1  0.106 0.744
3   1  0.201 1.719
4   1  0.335 3.632
5   2  0.202 0.202
6   2  0.696 1.796
7   2  0.047 2.835
8   2 -0.284 2.644
9   3  0.629 0.629
10  3 -0.567 0.124
11  3  0.144 0.618
12  3 -0.029 0.708

Data:

df <- structure(list(ID = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 
3L, 3L), XX = c(0.266, 0.372, 0.573, 0.908, 0.202, 0.898, 0.945, 
0.661, 0.629, 0.062, 0.206, 0.177), ZZ = c(0.266, 1.01, 2.729, 
6.361, 0.202, 1.998, 4.833, 7.477, 0.629, 0.753, 1.371, 2.079
)), .Names = c("ID", "XX", "ZZ"), class = "data.frame", row.names = c(NA, 
-12L))

Answer 2

Here is a simple way to do it in a data.table

set.seed(1)
ID <- rep(1:3, each=4)
XX <- round(runif(12), 3)
##ZZ <- ave(XX*TT,ID, FUN = cumsum) #we don't have TT
DF <- data.table(ID, XX)

DF[,XX_dif:=XX-c(0,head(XX,length(XX)-1)),by=ID]
# or alternatively using shift()
# DF[, XX_dif := XX-shift(XX, fill=0L), by=ID]

    ID    XX XX_dif
 1:  1 0.266  0.266
 2:  1 0.372  0.106
 3:  1 0.573  0.201
 4:  1 0.908  0.335
 5:  2 0.202  0.202
 6:  2 0.898  0.696
 7:  2 0.945  0.047
 8:  2 0.661 -0.284
 9:  3 0.629  0.629
10:  3 0.062 -0.567
11:  3 0.206  0.144
12:  3 0.177 -0.029

Answer 3

Here is an option using dplyr

library(dplyr)
DF %>% 
   group_by(ID) %>%
   mutate(ZZ = c(ZZ[1], diff(ZZ)))
#      ID    XX    ZZ
#    <int> <dbl> <dbl>
#1      1 0.266 0.266
#2      1 0.372 0.744
#3      1 0.573 1.719
#4      1 0.908 3.632
#5      2 0.202 0.202
#6      2 0.898 1.796
#7      2 0.945 2.835
#8      2 0.661 2.644
#9      3 0.629 0.629
#10     3 0.062 0.124
#11     3 0.206 0.618
#12     3 0.177 0.708