r - Vectorizing or for loop or apply function? -

i have dataframe 6 columns. column 1 holds dates, column 2 individuals , column 3 6 used calcuation.

date <- c(1, 1, 2, 2, 2, 3) ind <- c("a","a","a","b","c","c") c <- c(5, 6, 5, 7, 8, 8) d <- c(8, 8, 9, 9, 9, 9) e <- c(8, 9, 11, 10, 9, 7) f <- c(5, 6, 8, 5, 7, 4)  df <- data.frame(date, ind, c, d, e, f) 

i want perform calculation (like (c-e)+(d-f) (in real life coordinates , i'm calculating distances, that's not problem right now).

i want perform calculations, stored in new column (g), 1 day difference between use value of column c , e day 1, , values of column e , f day+1 same individual.

i'm not sure if should use loop or apply function. i've tried far, vectorized operation , subsetting, based on thread: loop on rows of dataframe applying function if-statement

df$g <- na  df[!(df$date ==(df$date+1)), "g"] <- ((c-e)+(d-f)) 

this works, calculations on coordinates same row (c, d, e, f same row). realize why this, don't state row take coordinates. c , d need taken row date = date, , e , f row date = (date+1). realize it, can't head around how that.

continue route? in loop? using apply function?

the dplyr package provides nice lag , lead functions.

> library(dplyr) > df %>% mutate(g = c + d + lead(e,1) + lead(f,1))   date ind c d  e f  g 1    1   5 8  8 5 28 2    1   6 8  9 6 33 3    2   5 9 11 8 29 4    2   b 7 9 10 5 32 5    2   c 8 9  9 7 28 6    3   c 8 9  7 4 na 

g na last row because there no next date value.

edit:

as others have mentioned, looks example data has 2 dates ind==a. may want careful doing lead/lag in situation.

if makes sense so, aggregate them first, before doing lead/lag.

df %>% group_by(date,ind) %>%    summarise(c=mean(c),d=mean(d),e=mean(e),f=mean(f)) %>%   ungroup %>%   mutate(g = c + d + lead(e,1) + lead(f,1)) 

which produces:

  date ind   c d    e   f    g 1    1   5.5 8  8.5 5.5 32.5 2    2   5.0 9 11.0 8.0 29.0 3    2   b 7.0 9 10.0 5.0 32.0 4    2   c 8.0 9  9.0 7.0 28.0 5    3   c 8.0 9  7.0 4.0   na