The data.frame class

What are data frames?

Data frames are the de facto data structure for most tabular data, and what we use for statistics and plotting.

A data frame is a collection of vectors of identical lengths. Each vector represents a column, and each vector can be of a different data type (e.g., characters, integers, factors). The str() function is useful to inspect the data types of the columns.

A data frame can be created by hand, but most commonly they are generated by the functions read.csv() or read.table(); in other words, when importing spreadsheets from your hard drive (or the web).

By default, when building or importing a data frame, the columns that contain characters (i.e., text) are coerced (=converted) into the factor data type. Depending on what you want to do with the data, you may want to keep these columns as character. To do so, read.csv() and read.table() have an argument called stringsAsFactors which can be set to FALSE:

some_data <- read.csv("data/some_file.csv", stringsAsFactors=FALSE)

You can also create a data frame manually with the function data.frame(). This function can also take the argument stringsAsFactors. Compare the output of these examples, and compare the difference between when the data are being read as character, and when they are being read as factor.

## Compare the output of these examples, and compare the difference between when
## the data are being read as `character`, and when they are being read as
## `factor`.
example_data <- data.frame(animal=c("dog", "cat", "sea cucumber", "sea urchin"),
                           feel=c("furry", "furry", "squishy", "spiny"),
                           weight=c(45, 8, 1.1, 0.8))
str(example_data)

#> 'data.frame':    4 obs. of  3 variables:
#>  $ animal: Factor w/ 4 levels "cat","dog","sea cucumber",..: 2 1 3 4
#>  $ feel  : Factor w/ 3 levels "furry","spiny",..: 1 1 3 2
#>  $ weight: num  45 8 1.1 0.8

example_data <- data.frame(animal=c("dog", "cat", "sea cucumber", "sea urchin"),
                           feel=c("furry", "furry", "squishy", "spiny"),
                           weight=c(45, 8, 1.1, 0.8), stringsAsFactors=FALSE)
str(example_data)

#> 'data.frame':    4 obs. of  3 variables:
#>  $ animal: chr  "dog" "cat" "sea cucumber" "sea urchin"
#>  $ feel  : chr  "furry" "furry" "squishy" "spiny"
#>  $ weight: num  45 8 1.1 0.8

Inspecting data.frame Objects

We already saw how the functions head() and str() can be useful to check the content and the structure of a data.frame. Here is a non-exhaustive list of functions to get a sense of the content/structure of the data.

• Size:
  • dim() - returns a vector with the number of rows in the first element, and the number of columns as the second element (the dimensions of the object)
  • nrow() - returns the number of rows
  • ncol() - returns the number of columns
• Content:
  • head() - shows the first 6 rows
  • tail() - shows the last 6 rows
• Names:
  • names() - returns the column names (synonym of colnames() for data.frame objects)
  • rownames() - returns the row names
• Summary:
  • str() - structure of the object and information about the class, length and content of each column
  • summary() - summary statistics for each column

Note: most of these functions are “generic”, they can be used on other types of objects besides data.frame.

Indexing, Sequences, and Subsetting

: is a special function that creates numeric vectors of integers in increasing or decreasing order, test 1:10 and 10:1 for instance. The function seq() (for sequence) can be used to create more complex patterns:

seq(1, 10, by=2)

#> [1] 1 3 5 7 9

seq(5, 10, length.out=3)

#> [1]  5.0  7.5 10.0

seq(50, by=5, length.out=10)

#>  [1] 50 55 60 65 70 75 80 85 90 95

seq(1, 8, by=3) # sequence stops to stay below upper limit

#> [1] 1 4 7

Our survey data frame has rows and columns (it has 2 dimensions), if we want to extract some specific data from it, we need to specify the “coordinates” we want from it. Row numbers come first, followed by column numbers. However, note that different ways of specifying these coordinates lead to results with different classes.

surveys[1]      # first column in the data frame (as a data.frame)
surveys[,1]     # first column in the data frame (as a vector)
surveys[1, 1]   # first element in the first column of the data frame (as a vector)
surveys[1, 6]   # first element in the 6th column (as a vector)
surveys[1:3, 7] # first three elements in the 7th column (as a vector)
surveys[3, ]    # the 3rd element for all columns (as a data.frame)
head_surveys <- surveys[1:6, ] # equivalent to head(surveys)

You can also exclude certain parts of a data frame

surveys[,-1]   #The whole data frame, except the first column
surveys[-c(7:34786),] #equivalent to head(surveys)

As well as using numeric values to subset a data.frame (or matrix), columns can be called by name, using one of the three following notations:

surveys["species_id"]       # Result is a data.frame
surveys[, "species_id"]     # Result is a vector
surveys[["species_id"]]     # Result is a vector
surveys$species_id          # Result is a vector

For our purposes, these three notations are equivalent. However, the last one with the $ does partial matching on the name. So you could also select the column "day" by typing surveys$d. It’s a shortcut, as with all shortcuts, they can have dangerous consequences, and are best avoided. Besides, with auto-completion in RStudio, you rarely have to type more than a few characters to get the full and correct column name.