## Learning Objectives

• understand the concept of a data.frame
• use sequences
• know how to access any element of a data.frame

## 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

### Challenge

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

author_book <- data.frame(author_first=c("Charles", "Ernst", "Theodosius"),
author_last=c(Darwin, Mayr, Dobzhansky),
year=c(1942, 1970))
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?
• What would have been different if we had added stringsAsFactors = FALSE to this call?
• What would you need to change to ensure that each column had the accurate data type?
country_climate <- data.frame(country=c("Canada", "Panama", "South Africa", "Australia"),
climate=c("cold", "hot", "temperate", "hot/temperate"),
temperature=c(10, 30, 18, "15"),
northern_hemisphere=c(TRUE, TRUE, FALSE, "FALSE"),
has_kangaroo=c(FALSE, FALSE, FALSE, 1))
3. We introduced you to the data.frame() function and read.csv(), but what if we are starting with some vectors? The best way to do this is to pass those vectors to the data.frame() function, similar to the above.

color <- c("red", "green", "blue", "yellow")
counts <- c(50, 60, 65, 82)
new_datarame <- data.frame(colors = color, counts = counts)

Try making your own new data frame from some vectors. You can check the data type of the new object using class().

The automatic conversion of data type is sometimes a blessing, sometimes an annoyance. Be aware that it exists, learn the rules, and double check that data you import in R are of the correct type within your data frame. If not, use it to your advantage to detect mistakes that might have been introduced during data entry (a letter in a column that should only contain numbers for instance.).

## 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.

### Challenge

1. The function nrow() on a data.frame returns the number of rows. Use it, in conjunction 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, …)

2. Create a data.frame containing only the observations from row 1999 of the surveys dataset.

3. Notice how nrow() gave you the number of rows in a data.frame? Use nrow() instead of a row number to make a data.frame with observations from only the last row of the surveys dataset.

4. Now that you’ve seen how nrow() can be used to stand in for a row index, let’s combine that behavior with the - notation above to reproduce the behavior of head(surveys) excluding the 7th through final row of the surveys dataset.