Only Load Data If Not Already Open in R

I often find it beneficial to check to see whether or not a dataset is already loaded into R at the beginning of a file. This is particularly helpful when I'm dealing with a large file that I don't want to load repeatedly, and when I might be using the same dataset with multiple R scripts or re-running the same script while making changes to the code.

To check to see if an object with that name is already loaded, we can use the exists function from the base package. We can then wrap our read.csv command with an if statement to cause the file to only load if an object with that name is not already loaded.

if(!exists("largeData")) {
  largeData <- read.csv("huge-file.csv",
    header = TRUE)
}

You will probably also find it useful to use the "colClasses" option of read.csv or read.table to help the file load faster and make sure your data are in the right format. For example:

if(!exists("largeData")) {
  largeData <- read.csv("huge-file.csv",
    header = TRUE,
    colClasses = c("factor", "integer", "character", "integer", 
      "integer", "character"))
}

--
This post is one part of my series on dealing with large datasets.

Using colClasses to Load Data More Quickly in R

Specifying a colClasses argument to read.table or read.csv can save time on importing data, while also saving steps to specify classes for each variable later.

For example, loading a 893 MB took 441 seconds to load when not using colClasses, but only 268 seconds to load when using colClasses. The system.time function in base can help you check your own times.

Without specifying colClasses:

   user  system elapsed 
441.224   8.200 454.155 

When specifying colClasses:

   user  system elapsed 
268.036   6.096 284.099 

The classes you can specify are: factor, character, integer, numeric, logical, complex, and Date. Dates that are in the form %Y-%m-%d or Y/%m/%d will import correctly. This tip allows you to import dates properly for dates in other formats.

system.time(largeData <- read.csv("huge-file.csv",
  header = TRUE,
  colClasses = c("character", "character", "complex", 
    "factor", "factor", "character", "integer", 
    "integer", "numeric", "character", "character",
    "Date", "integer", "logical")))

If there aren't any classes that you want to change from their defaults, you can read in the first few rows, determine the classes from that, and then import the rest of the file:

sampleData <- read.csv("huge-file.csv", header = TRUE, nrows = 5)
classes <- sapply(sampleData, class)
largeData <- read.csv("huge-file.csv", header = TRUE, colClasses = classes)
str(largeData)

If you aren't concerned about the time it takes to read the data file, but instead just want the classes to be correct on import, you have the option of only specifying certain classes:

smallData <- read.csv("small-file.csv", 
 header = TRUE,
 colClasses=c("variableName"="character"))

> class(smallData$variableName)
[1] "character"

Citations and Further Reading

• http://stackoverflow.com/questions/2805357/specifying-colclasses-in-the-read-csv
• http://www.biostat.jhsph.edu/~rpeng/docs/R-large-tables.html
• http://www.mollietaylor.com/2013/08/date-formats-in-r.html
• http://stackoverflow.com/a/13022441

--
This post is one part of my series on dealing with large datasets.