Learning Objectives

What are data frames?

data.frame is 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 the functions read.csv() or read.table(), in other words, when importing spreadsheets from your hard drive (or the web).

By default, data.frame converts (= coerces) columns that contain characters (i.e., text) 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 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 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

Challenge

  1. There are a few mistakes in this hand crafted data.frame, can you spot and fix them? Don’t hesitate to experiment!

  2. Can you predict the class for each of the columns in the following example?

Check your guesses using str(country_climate). Are they what you expected? Why? Why not? Check your gueses using str(country_climate). Are they what you expected? Why? Why not?

R coerces (when possible) to the data type that is the least common denominator and the easiest to coerce to.

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.

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

Indexing and sequences

If we want to extract one or several values from a vector, we must provide one or several indices in square brackets, just as we do in math. For instance:

animals <- c("mouse", "rat", "dog", "cat")
animals[2]
#> [1] "rat"
animals[c(3, 2)]
#> [1] "dog" "rat"
animals[2:4]
#> [1] "rat" "dog" "cat"
more_animals <- animals[c(1:3, 2:4)]
more_animals
#> [1] "mouse" "rat"   "dog"   "rat"   "dog"   "cat"

R indexes start at 1. Programming languages like Fortran, MATLAB, and R start counting at 1, because that’s what human beings typically do. Languages in the C family (including C++, Java, Perl, and Python) count from 0 because that’s simpler for computers to do.

: is a special function that creates numeric vectors of integer in increasing or decreasing order, test 1:10 and 10:1 for instance. The function seq() (for __seq__uence) 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.

surveys[1, 1]   # first element in the first column of the data frame
surveys[1, 6]   # first element in the 6th column
surveys[1:3, 7] # first three elements in the 7th column
surveys[3, ]    # the 3rd element for all columns
surveys[, 8]    # the entire 8th column
head_surveys <- surveys[1:6, ] # surveys[1:6, ] is equivalent to head(surveys)

Challenge

  1. The function nrow() on a data.frame returns the number of rows. Use it, in conjuction with seq() to create a new data.frame called surveys_by_10 that includes every 10th row of the survey data frame starting at row 10 (10, 20, 30, …)

For larger datasets, it can be tricky to remember the column number that corresponds to a particular variable. (Are species names in column 5 or 7? oh, right… they are in column 6). In some cases, in which column the variable will be can change if the script you are using adds or removes columns. It’s therefore often better to use column names to refer to a particular variable, and it makes your code easier to read and your intentions clearer.

You can do operations on a particular column, by selecting it using the $ sign. In this case, the entire column is a vector. For instance, to extract all the weights from our datasets, we can use: surveys$wgt. You can use names(surveys) or colnames(surveys) to remind yourself of the column names.

In some cases, you may way to select more than one column. You can do this using the square brackets: surveys[, c("wgt", "sex")].