Appropriate way to store data in R

Question

I have data, which looks like this:

These data are only for one subject. I will have a lot more.
These data will be analyzed in R.

Now I'm storing them like this:

subject <- rep(1, times = 24)
measurement <- factor(x = rep(x = 1:3, each = 8), 
                      labels = c("Distance", "Frequency", "Energy"))
speed <- factor(x = rep(x = 1:2, each = 4, times = 3), 
                labels = c("speed1", "speed2"))
condition <- factor(x = rep(x = 1:2, each = 2, times = 6), 
                    labels = c("Control", "Experm"))
Try <- factor(x = rep(x = 1:2, times = 12), 
              labels = c("Try1", "Try2"))
result <- c(1:8, 
            11:18, 
            21:28)

dt <- data.frame(subject, measurement, speed, condition, Try, result)

What is the appropriate way to store these data in R (in a data frame)?

Answer 1

Without more information all I can say is that:

1. the say you're storing it is fine in general
2. you can further transform/store your data depending on your use case

To expand on #2, if I want to study Distance vs Energy across all subjects, then I would format my data like this:

> library(reshape2)
> dt2 <- dt[dt$measurement %in% c('Distance','Energy'),]
> dt_cast <- dcast(dt2, subject+Try~measurement+speed+condition, value.var='result')

The transformed data (dt_cast) would then look like:

  subject  Try Distance_speed1_Control Distance_speed1_Experm Distance_speed2_Control
1       1 Try1                       1                      3                       5
2       1 Try2                       2                      4                       6
  Distance_speed2_Experm Energy_speed1_Control Energy_speed1_Experm Energy_speed2_Control
1                      7                    21                   23                    25
2                      8                    22                   24                    26
  Energy_speed2_Experm
1                   27
2                   28

Allowing me to, for example, look at the relationship between the Distance_speed1_Control vs Energy_speed1_Control columns.

Basically subset/aggregate your data and then use the dcast to get the rows and columns the computer needs.

Answer 2

This looks a well structured dataset. You can read more about database design in this section of wikipedia. Your data are well structured so querying is easy. As Jake C says, you'll want to transform it for specific tasks. Packages like dplr and reshape2 are excellent for this. You could also consider writing your data to a specific database. This is particularly useful if your dataset is so large that R runs out of RAM. I've written an example with SQLite here: https://scottishsnow.wordpress.com/2014/08/14/writing-to-a-database-r-and-sqlite/