Learning Objectives

  • Describe what a data frame is.
  • Load external data from a .csv file into a data frame.
  • Summarize the contents of a data frame.
  • Describe what a factor is.
  • Convert between strings and factors.
  • Reorder and rename factors.
  • Change how character strings are handled in a data frame.
  • Format dates.

Presentation of the Survey Data

We are studying the species repartition and weight of animals caught in plots in our study area. The dataset is stored as a comma separated value (CSV) file. Each row holds information for a single animal, and the columns represent:

Column Description
record_id Unique id for the observation
month month of observation
day day of observation
year year of observation
plot_id ID of a particular plot
species_id 2-letter code
sex sex of animal (“M”, “F”)
hindfoot_length length of the hindfoot in mm
weight weight of the animal in grams
genus genus of animal
species species of animal
taxon e.g. Rodent, Reptile, Bird, Rabbit
plot_type type of plot

We are going to use the R function download.file() to download the CSV file that contains the survey data from figshare, and we will use read.csv() to load into memory the content of the CSV file as an object of class data.frame.

You are now ready to load the data:

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: surveys.

Wow… that was a lot of output. At least it means the data loaded properly. Let’s check the top (the first 6 lines) of this data frame using the function head():

#>   record_id month day year plot_id species_id sex hindfoot_length weight
#> 1         1     7  16 1977       2         NL   M              32     NA
#> 2        72     8  19 1977       2         NL   M              31     NA
#> 3       224     9  13 1977       2         NL                  NA     NA
#> 4       266    10  16 1977       2         NL                  NA     NA
#> 5       349    11  12 1977       2         NL                  NA     NA
#> 6       363    11  12 1977       2         NL                  NA     NA
#>     genus  species   taxa plot_type
#> 1 Neotoma albigula Rodent   Control
#> 2 Neotoma albigula Rodent   Control
#> 3 Neotoma albigula Rodent   Control
#> 4 Neotoma albigula Rodent   Control
#> 5 Neotoma albigula Rodent   Control
#> 6 Neotoma albigula Rodent   Control

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 surveys data above could have also been loaded by using read.table() with the separation argument as ,. The code is as follows: surveys <- read.table(file="data/portal_data_joined.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():

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

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

  • What is the class of the object surveys?
  • How many rows and how many columns are in this object?
  • How many species have been recorded during these surveys?

Answer

#> 'data.frame':  34786 obs. of  13 variables:
#>  $ record_id      : int  1 72 224 266 349 363 435 506 588 661 ...
#>  $ month          : int  7 8 9 10 11 11 12 1 2 3 ...
#>  $ day            : int  16 19 13 16 12 12 10 8 18 11 ...
#>  $ year           : int  1977 1977 1977 1977 1977 1977 1977 1978 1978 1978 ...
#>  $ plot_id        : int  2 2 2 2 2 2 2 2 2 2 ...
#>  $ species_id     : Factor w/ 48 levels "AB","AH","AS",..: 16 16 16 16 16 16 16 16 16 16 ...
#>  $ sex            : Factor w/ 3 levels "","F","M": 3 3 1 1 1 1 1 1 3 1 ...
#>  $ hindfoot_length: int  32 31 NA NA NA NA NA NA NA NA ...
#>  $ weight         : int  NA NA NA NA NA NA NA NA 218 NA ...
#>  $ genus          : Factor w/ 26 levels "Ammodramus","Ammospermophilus",..: 13 13 13 13 13 13 13 13 13 13 ...
#>  $ species        : Factor w/ 40 levels "albigula","audubonii",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ taxa           : Factor w/ 4 levels "Bird","Rabbit",..: 4 4 4 4 4 4 4 4 4 4 ...
#>  $ plot_type      : Factor w/ 5 levels "Control","Long-term Krat Exclosure",..: 1 1 1 1 1 1 1 1 1 1 ...

Indexing and subsetting data frames

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.

: 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:

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

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

Challenge

  1. Create a data.frame (surveys_200) containing only the data in row 200 of the surveys dataset.

  2. 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 (surveys_last) from that last row.
  3. 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 surveys_middle.

  4. Combine nrow() with the - notation above to reproduce the behavior of head(surveys), keeping just the first through 6th rows of the surveys dataset.

Factors

When we did str(surveys) we saw that several of the columns consist of integers. The columns genus, species, sex, plot_type, … however, are 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 with 2 levels:

R will assign 1 to the level "female" and 2 to the level "male" (because f comes before m, even though the first element in this vector is "male"). You can see this by using the function levels() and you can find the number of levels using nlevels():

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 sex vector would be:

#> [1] male   female female male  
#> Levels: female male
#> [1] male   female female male  
#> Levels: male female

In R’s memory, these factors are represented by integers (1, 2, 3), but are more informative than integers because factors are self describing: "female", "male" is more descriptive than 1, 2. Which one is “male”? 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 (like the species names in our example dataset).

Converting factors

If you need to convert a factor to a character vector, you use as.character(x).

Converting factors where the levels appear as numbers (such as concentration levels, or years) to a numeric vector 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:

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

Renaming factors

When your data is stored as a factor, you can use the plot() function to get a quick glance at the number of observations represented by each factor level. Let’s look at the number of males and females captured over the course of the experiment:

In addition to males and females, there are about 1700 individuals for which the sex information hasn’t been recorded. Additionally, for these individuals, there is no label to indicate that the information is missing or undetermined. Let’s rename this label to something more meaningful. Before doing that, we’re going to pull out the data on sex and work with that data, so we’re not modifying the working copy of the data frame:

#> [1] M M        
#> Levels:  F M
#> [1] ""  "F" "M"
#> [1] "undetermined" "F"            "M"
#> [1] M            M            undetermined undetermined undetermined
#> [6] undetermined
#> Levels: undetermined F M

Challenge

  • Rename “F” and “M” to “female” and “male” respectively.
  • Now that we have renamed the factor level to “undetermined”, can you recreate the barplot such that “undetermined” is last (after “male”)?

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.

Challenge

  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!

  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?

    Answer

    • 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
    • country, climate, temperature, and northern_hemisphere are factors; has_kangaroo is numeric
    • using stringsAsFactors = FALSE would have made them character 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 (a letter in a column that should only contain numbers for instance).

Learn more in this RStudio tutorial

Formatting Dates

One of the most common issues that new (and experienced!) R users have is converting date and time information into a variable that is appropriate and usable during analyses. As a reminder from earlier in this lesson, the best practice for dealing with date data is to ensure that each component of your date is stored as a separate variable. Using str(), We can confirm that our data frame has a separate column for day, month, and year, and that each contains integer values.

We are going to use the ymd() function from the package lubridate (which belongs to the tidyverse; learn more here). . lubridate gets installed as part as the tidyverse installation. When you load the tidyverse (library(tidyverse)), the core packages (the packages used in most data analyses) get loaded. lubridate however does not belong to the core tidyverse, so you have to load it explicitly with library(lubridate)

Start by loading the required package:

ymd() takes a vector representing year, month, and day, and converts it to a Date vector. Date is a class of data recognized by R as being a date and can be manipulated as such. The argument that the function requires is flexible, but, as a best practice, is a character vector formatted as “YYYY-MM-DD”.

Let’s create a date object and inspect the structure:

Now let’s paste the year, month, and day separately - we get the same result:

Now we apply this function to the surveys dataset. Create a character vector from the year, month, and day columns of surveys using paste():

This character vector can be used as the argument for ymd():

#> Warning: 129 failed to parse.

The resulting Date vector can be added to surveys as a new column called date:

#> Warning: 129 failed to parse.

Let’s make sure everything worked correctly. One way to inspect the new column is to use summary():

#>         Min.      1st Qu.       Median         Mean      3rd Qu. 
#> "1977-07-16" "1984-03-12" "1990-07-22" "1990-12-15" "1997-07-29" 
#>         Max.         NA's 
#> "2002-12-31"        "129"

Something went wrong: some dates have missing values. Let’s investigate where they are coming from.

We can use the functions we saw previously to deal with missing data to identify the rows in our data frame that are failing. If we combine them with what we learned about subsetting data frames earlier, we can extract the columns “year,”month“,”day" from the records that have NA in our new column date. We will also use head() so we don’t clutter the output:

#>      year month day
#> 3144 2000     9  31
#> 3817 2000     4  31
#> 3818 2000     4  31
#> 3819 2000     4  31
#> 3820 2000     4  31
#> 3856 2000     9  31

Why did these dates fail to parse? If you had to use these data for your analyses, how would you deal with this situation?

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