Starting with data

Overview

Teaching: min
Exercises: min

Questions

How do I load data into R?

How can I subset data?

What are factors and how are they used?

Objectives

Load external data from a .csv file into a data frame.

Describe what a data frame is.

Summarize the contents of a data frame.

Use indexing to subset specific portions of data frames.

Describe what a factor is.

Convert between strings and factors.

Reorder and rename factors.

Change how character strings are handled in a data frame.

Presentation of the Workshop Data

In this workshop, we will be working with two sets of data: one is the iris data from the classic paper by Fisher “The use of multiple measurements in taxonomic problems” (1936) and the other has a set of physiological observations from a recent paper by O’Keefe and Nippert (ref). Each dataset is stored as comma separated value (CSV) file. We will work with the iris data throughout this workshop; the physiological data will be introduced in the next episode.

The iris data have morphological measures from more than 100 samples of 3 species of irises. plot of chunk Teaser

Each row holds information for an individual plant observation, and the columns represent the species of the specimen and the lengths and widths of its sepal and petal (in centimeters):

Column
Sepal.Length
Sepal.Width
Petal.Length
Petal.Width
Species

You are now ready to load the data:

iris <- read.csv("data/iris.csv")

This statement doesn’t produce any output because, as you might recall, assignments don’t display anything. If we want to check that our data has been loaded, we can see the contents of the data frame by typing its name: iris.

Wow… that was a more than a screenful of output. Let’s check the top (the first 6 lines) of this data frame using the function head():

head(iris)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
        5.1         3.5          1.4         0.2  setosa
        4.9         3.0          1.4         0.2  setosa
        4.7         3.2          1.3         0.2  setosa
        4.6         3.1          1.5         0.2  setosa
        5.0         3.6          1.4         0.2  setosa
        5.4         3.9          1.7         0.4  setosa

## Try also
View(iris)

Note

read.csv assumes that fields are delineated by commas, however, in several countries, the comma is used as a decimal separator and the semicolon (;) is used as a field delineator. If you want to read in this type of files in R, you can use the read.csv2 function. It behaves exactly like read.csv but uses different parameters for the decimal and the field separators. If you are working with another format, they can be both specified by the user. Check out the help for read.csv() by typing ?read.csv to learn more. There is also the read.delim() for in tab separated data files. It is important to note that all of these functions are actually wrapper functions for the main read.table() function with different arguments. As such, the iris data above could have also been loaded by using read.table() with the separation argument as ,. The code is as follows: iris <- read.table(file="data/iris.csv", sep=",", header=TRUE). The header argument has to be set to TRUE to be able to read the headers as by default read.table() has the header argument set to FALSE.

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 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).

A data frame is the representation of data in the format of a table where the columns are vectors that all have the same length. Because columns are vectors, each column must contain a single type of data (e.g., characters, integers, factors). For example, here is a figure depicting a data frame comprising a numeric, a character, and a logical vector.

We can see this when inspecting the structure of a data frame with the function str():

str(iris)

'data.frame':	150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

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. Let’s try them out!

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

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

Challenge 1

Based on the output of str(iris), can you answer the following questions?

What is the class of the object iris?

How many rows and how many columns are in this object?

How many species of iris are represented in the data?
Solution to Challenge 1
str(iris)
'data.frame':	150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
## * class: data frame
## * how many rows: 150,  how many columns: 5
## * how many species: 3

Indexing and subsetting data frames

Our iris 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.

# first element in the first column of the data frame (as a vector)
iris[1, 1]   
# first element in the 3rd column (as a vector)
iris[1, 3]   
# first column of the data frame (as a vector)
iris[, 1]    
# first column of the data frame (as a data.frame)
iris[1]      
# first three elements in the 4th column (as a vector)
iris[1:3, 4] 
# the 3rd row of the data frame (as a data.frame)
iris[3, ]    
# equivalent to head_iris <- head(iris)
head_iris <- iris[1:6, ] 

: is a special function that creates numeric vectors of integers in increasing or decreasing order, test 1:10 and 10:1 for instance.

You can also exclude certain indices of a data frame using the “-” sign:

iris[, -1]          # The whole data frame, except the first column
iris[-c(7:150), ] # Equivalent to head(iris)

Data frames can be subset by calling indices (as shown previously), but also by calling their column names directly:

iris["Species"]       # Result is a data.frame
iris[, "Species"]     # Result is a vector
iris[["Species"]]     # Result is a vector
iris$Species          # Result is a vector

In RStudio, you can use the autocompletion feature to get the full and correct names of the columns.

Challenge 2

Create a data.frame (iris_100) containing only the data in row 100 of the iris dataset.

Notice how nrow() gave you the number of rows in a data.frame?

Use that number to pull out just that last row in the data frame.

Compare that with what you see as the last row using tail() to make sure it’s meeting expectations.

Pull out that last row using nrow() instead of the row number.

Create a new data frame (iris_last) from that last row.

Use nrow() to extract the row that is in the middle of the data frame. Store the content of this row in an object named iris_middle.

Combine nrow() with the - notation above to reproduce the behavior of head(iris), keeping just the first through 6th rows of the iris dataset.
Solution for challenge 2
## 1.
iris_100 <- iris[100, ]
## 2.
# Saving `n_rows` to improve readability and reduce duplication
n_rows <- nrow(iris)
iris_last <- iris[n_rows, ]
## 3.
iris_middle <- iris[n_rows / 2, ]
## 4.
iris_head <- iris[-(7:n_rows), ]

Factors

When we did str(iris) we saw that four of the columns consist of numbers. The column Species, … however, is of a special class called factor. Factors are very useful and actually contribute to making R particularly well suited to working with data. So we are going to spend a little time introducing them. Factors represent categorical data. They are stored as integers associated with labels and they can be ordered or unordered. While factors look (and often behave) like character vectors, they are actually treated as integer vectors by R. So you need to be very careful when treating them as strings. Once created, factors can only contain a pre-defined set of values, known as levels. By default, R always sorts levels in alphabetical order. For instance, if you have a factor that represents your experiment locations with 3 levels:

locations <- factor(c("Madison", "Hancock", "Madison", "Arlington", "Hancock"))

R will assign 1 to the level "Arlington" and 2 to the level "Hancock" and 3 to the level "Madison"( due to alphabetical order, even though the first element in this vector is "Madison"). You can see this by using the function levels() and you can find the number of levels using nlevels():

levels(locations)

[1] "Arlington" "Hancock"   "Madison"

nlevels(locations)

[1] 3

Sometimes, the order of the factors does not matter, other times you might want to specify the order because it is meaningful (e.g., “low”, “medium”, “high”), it improves your visualization, or it is required by a particular type of analysis. Here, one way to reorder our levels in the locations vector would be:

locations # current order

[1] Madison   Hancock   Madison   Arlington Hancock  
Levels: Arlington Hancock Madison

locations <- factor(locations, levels = c("Madison", "Arlington", "Hancock"))
locations # after re-ordering

[1] Madison   Hancock   Madison   Arlington Hancock  
Levels: Madison Arlington Hancock

In R’s memory, these factors are represented by integers (1, 2, 3) but are more informative than integers because factors are self describing: "Madison", "Arlington" is more descriptive than 1, 2. Which one is “Arlington”? You wouldn’t be able to tell just from the integer data. Factors, on the other hand, have this information built in. It is particularly helpful when there are many levels.

In some cases, you may have to convert factors where the levels appear as numbers (such as concentration levels or years) to a numeric vector. For instance, in one part of your analysis the years might need to be encoded as factors (e.g., comparing average lengths across years) but in another part of your analysis they may need to be stored as numeric values (e.g., doing math operations on the years). This conversion from factor to numeric is a little trickier. The as.numeric() function returns the index values of the factor, not its levels, so it will result in an entirely new (and unwanted in this case) set of numbers. One method to avoid this is to convert factors to characters, and then to numbers.

Another method is to use the levels() function. Compare:

year_fct <- factor(c(1990, 1983, 1977, 1998, 1990))
as.numeric(year_fct)               # Wrong! And there is no warning...

[1] 3 2 1 4 3

as.numeric(as.character(year_fct)) # Works...

[1] 1990 1983 1977 1998 1990

as.numeric(levels(year_fct))[year_fct]    # The recommended way.

[1] 1990 1983 1977 1998 1990

Notice that in the levels() approach, three important steps occur:

We obtain all the factor levels using levels(year_fct)
We convert these levels to numeric values using as.numeric(levels(year_fct))
We then access these numeric values using the underlying integers of the vector year_fct inside the square brackets

Using `stringsAsFactors=FALSE`

By default, when building or importing a data frame, the columns that contain characters (i.e. text) are coerced (= converted) into factors. 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.

In most cases, it is preferable to set stringsAsFactors = FALSE when importing data and to convert as a factor only the columns that require this data type.

## Compare the difference between our data read as `factor` vs `character`.
iris <- read.csv("data/iris.csv", stringsAsFactors = TRUE)
str(iris)

'data.frame':	150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

iris <- read.csv("data/iris.csv", stringsAsFactors = FALSE)
str(iris)

'data.frame':	150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : chr  "setosa" "setosa" "setosa" "setosa" ...

## Convert the column "species" into a factor
iris$Species <- factor(iris$Species)
str(iris)

'data.frame':	150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

Challenge 3

Part 1. We have seen how data frames are created when using read.csv(), but they can also be created by hand with the data.frame() function. There are a few mistakes in this hand-crafted data.frame. Can you spot and fix them? Don’t hesitate to experiment!
    animal_data <- data.frame(
              animal = c(dog, cat, sea cucumber, sea urchin),
              feel = c("furry", "squishy", "spiny"),
              weight = c(45, 8 1.1, 0.8)
              )
Part 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 when creating the data frame? * 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)
           )
Solutions to Challenge # 3

Part 1

missing quotations around the names of the animals

missing one entry in the feel column (probably for one of the furry animals)

missing one comma in the weight column

Part 2

country, climate, temperature, and northern_hemisphere are factors; has_kangaroo is numeric

using stringsAsFactors = FALSE would have made character vectors instead of factors

removing the quotes in temperature and northern_hemisphere and replacing 1 by TRUE in the has_kangaroo column would give what was probably intended

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 (for instance, a letter in a column that should only contain numbers).

Learn more in this RStudio tutorial

Key Points

R stores data tables in a structure called a data frame.

Columns in a data frame must contain values of the same data type.

Subsetting by position is similar as in vectors.

Data frames can also be subset by names.

Factors store characters as integers with text labels.

strings.factors = FALSE can prevent R from reading strings as factors.

previous episode

R for plant science

next episode

Starting with data

Overview

Presentation of the Workshop Data

Note

What are data frames?

Inspecting `data.frame` Objects

Challenge 1

Solution to Challenge 1

Indexing and subsetting data frames

Challenge 2

Solution for challenge 2

Factors

Using `stringsAsFactors=FALSE`

Challenge 3

Solutions to Challenge # 3

Part 1

Part 2

Key Points

previous episode

next episode

previous episode

R for plant science

next episode

Starting with data

Overview

Presentation of the Workshop Data

Note

What are data frames?

Inspecting data.frame Objects

Challenge 1

Solution to Challenge 1

Indexing and subsetting data frames

Challenge 2

Solution for challenge 2

Factors

Using stringsAsFactors=FALSE

Challenge 3

Solutions to Challenge # 3

Part 1

Part 2

Key Points

previous episode

next episode

Inspecting `data.frame` Objects

Using `stringsAsFactors=FALSE`