Q:

convert data frame of counts to proportions by conditions in R

I would need to expand on this question: convert data frame of counts to proportions in R
I need to calculate proportion by one condition and retain the information of the dataset.
Reproducible example:
ID <- rep(c(1,2,3), each=3)
trial <- rep("a", 9)
variable1 <- sample(1:10, 9)
variable2 <- sample(1:10, 9)
variable3 <- sample(1:10, 9)
condition <- rep(c("i","j","k"), 3)
dat <- data.frame(cbind(ID, trial,variable1,variable2,variable3,condition))

For each variable I would like to have the proportion by the ID (i.e. 3 times)
Ideally the new variables would be stored in the same database as dat$variable1_p
I know how to do the trick by a series of for loops but I would like to learn how to use the apply function. Also to be able to expand it to more conditions if necessary.

A:

We can use adply from the plyr package:
library(plyr)
adply(dat, 1, function(x)
    c('variable1_p' = x$variable1 / sum(dat[x$ID == dat$ID,]$variable1)))

#   ID trial variable1 variable2 variable3 condition variable1_p
# 1  1     a         3         5         4         i  0.20000000
# 2  1     a         8         9         9         j  0.53333333
# 3  1     a         4         4         8         k  0.26666667
# 4  2     a         7        10         5         i  0.50000000
# 5  2     a         6         8        10         j  0.42857143
# 6  2     a         1         1         7         k  0.07142857
# 7  3     a        10         6         3         i  0.47619048
# 8  3     a         9         7         6         j  0.42857143
# 9  3     a         2         3         2         k  0.09523810

Another option is to use dplyr, which would handle cases where there is more than one row per condition per ID:
library(dplyr)
dat %>%
    group_by(ID, condition) %>%
    mutate(sum_v1_cond = sum(variable1)) %>%
    ungroup() %>%
    group_by(ID) %>%
    mutate(variable1_p = sum_v1_cond / sum(variable1)) %>%
    select(-sum_v1_cond)

Edit - here's a full solution for variable1, variable2, and variable3:
adply(dat, 1, function(x)
    c('variable1_p' = x$variable1 / sum(dat[x$ID == dat$ID,]$variable1),
      'variable2_p' = x$variable2 / sum(dat[x$ID == dat$ID,]$variable2),
      'variable3_p' = x$variable3 / sum(dat[x$ID == dat$ID,]$variable3)))

Data:
set.seed(123)
ID <- rep(c(1,2,3), each=3)
trial <- rep("a", 9)
variable1 <- sample(1:10, 9)
variable2 <- sample(1:10, 9)
variable3 <- sample(1:10, 9)
condition <- rep(c("i","j","k"), 3)
dat <- data.frame(ID, trial,variable1,variable2,variable3,condition,
                  stringsAsFactors = FALSE)

