CODEWITHSUNDEEP
rdplyrtidyr
covariantmonkey
covariantmonkey

Reputation: 223

How do you compare data from two experiments

I am often trying to measure percentage changes under two distinct scenarios/test/period.

An example dataset: 

library(dplyr)
set.seed(11)
toy_dat <- data.frame(state = sample(state.name,3, replace=F), 
                 experiment=c('control','measure'), 
                 accuracy=sample(30:50, size=6, replace=T), 
                 speed=sample(21:39, size=6, replace=T)) %>% arrange(state)


     state experiment accuracy speed
1  Alabama    measure       31    24
2  Alabama    control       36    37
3  Indiana    control       30    23
4  Indiana    measure       31    38
5 Missouri    control       50    29
6 Missouri    measure       48    34

I then resort to writing something horrible like this:

result <- toy_dat %>%  group_by(state) %>% arrange(experiment) %>%
  summarise(acc_delta = (accuracy[2]-accuracy[1])/accuracy[1],
            speed_delta = (speed[2]-speed[1])/speed[1])

However, the above solution does not scale at all when the number of measurable begins to grow. In addition, the code is very fragile in terms of the ordering.

I am very new to R. I was hoping that this is a common enough pattern that there are well-known (smarter) solutions to the problem.

I would greatly appreciate any help/pointers.

Upvotes: 1

Views: 223

Answers (1)

David Arenburg
David Arenburg

Reputation: 92282

Just create your own custom function and use summarise_each in order to apply it on all the measurements at once (it doesn't matter how many measurements you have)

delta_fun <- function(x) diff(x)/x[1L]

toy_dat %>%  
  group_by(state) %>% 
  arrange(experiment) %>%
  summarise_each(funs(delta_fun), -experiment)

# Source: local data frame [3 x 3]
# 
#      state    accuracy      speed
# 1  Alabama -0.13888889 -0.3513514
# 2  Indiana  0.03333333  0.6521739
# 3 Missouri -0.04000000  0.1724138

As you mentioned that you are new to R, here's another awesome package you can use in order to achieve the same effect

library(data.table)
setDT(toy_dat)[order(experiment), 
               lapply(.SD, delta_fun), 
               .SDcols = -"experiment",
               by = state]
#       state    accuracy      speed
# 1:  Alabama -0.13888889 -0.3513514
# 2:  Indiana  0.03333333  0.6521739
# 3: Missouri -0.04000000  0.1724138

Upvotes: 1

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