Learning 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.
  • 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. Inside the download.file command, the first entry is a character string with the source URL (“https://ndownloader.figshare.com/files/2292169”). This source URL downloads a CSV file from figshare. The text after the comma (“data_raw/portal_data_joined.csv”) is the destination of the file on your local machine. You’ll need to have a folder on your machine called “data_raw” where you’ll download the file. So this command downloads a file from Figshare, names it “portal_data_joined.csv” and adds it to a preexisting folder named “data_raw”.

download.file(url = "https://ndownloader.figshare.com/files/2292169",
              destfile = "data_raw/portal_data_joined.csv")

You can put the arguments to a function on different lines. As long as they are within the round parentheses and each line ends with a comma, R will understand. (As above)

You are now ready to load the data:

surveys <- read.csv("data_raw/portal_data_joined.csv", 
                    stringsAsFactors = TRUE)

This statement doesn’t produce any output because, as you might recall, assignments don’t display anything.

We have used the argument stringsAsFactors = TRUE here. We will explain why in the ‘factors’ section below.

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():

head(surveys)
#>   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
## Try also
View(surveys)

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_raw/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.

In addition to the above versions of the csv format, you should develop the habits of looking at and record some parameters of your csv files. For instance, the character encoding, control characters used for line ending, date format (if the date is not splitted into three variables), and the presence of unexpected newlines are important characteristics of your data files. Those parameters will ease up the import step of your data in R.

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(surveys)

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

2.1 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 

str(surveys)
#> '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 ...
## * class: data frame
## * how many rows: 34786,  how many columns: 13
## * how many species: 40 (the text "Factor w/ 40 levels" from str() tells us this. More on factors soon.

Time for a git commit

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.

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

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

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

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

In RStudio, you can use the autocompletion feature to get the full and correct names of the columns. Try typing “surveys$” then pause for RStudio to show you the column names (you may need to press the ‘tab’ key). (Sometimes this doesn’t work or is slow if R is busy.)

Challenge

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

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

 * Use that number to pull out just the 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.

2.4 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.

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

Answer 

## 
surveys_200 <- surveys[200, ]
## 
# Saving `n_rows` to improve readability and reduce duplication
n_rows <- nrow(surveys)
surveys_last <- surveys[n_rows, ]
tail(surveys)
surveys[nrow(surveys), ]
surveys_last <- surveys[nrow(surveys), ]
## 
surveys_middle <- surveys[round(n_rows / 2), ]
## 
surveys_head <- surveys[-(7:n_rows), ]

Time for a git commit

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 (a “string” is a series of characters, like a word or a phrase).

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:

sex <- factor(c("male", "female", "female", "male"))

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():

levels(sex)
nlevels(sex)

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:

sex # current order
#> [1] male   female female male  
#> Levels: female male
sex <- factor(sex, levels = c("male", "female"))
sex # after re-ordering
#> [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).

as.character(sex)

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 weights 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...
as.numeric(as.character(year_fct)) # Works
as.numeric(as.vector(year_fct)) # Also works. 
# as.vector() changes the factor to a vector of character objects. 
# It uses character objects (instead of e.g. numerics) since these 
# are the lowest common denominator for atomic data types so conversion
# is safe and won't lose any data.

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:

## bar plot of the number of females and males captured during the experiment:
plot(surveys$sex)

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:

sex <- surveys$sex
head(sex)
#> [1] M M        
#> Levels:  F M
levels(sex)
#> [1] ""  "F" "M"
levels(sex)[1] <- "undetermined"
levels(sex)
#> [1] "undetermined" "F"            "M"
head(sex)
#> [1] M            M            undetermined undetermined undetermined
#> [6] undetermined
#> Levels: undetermined F M

Challenge

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

Answer 

levels(sex)[2:3] <- c("female", "male")
sex <- factor(sex, levels = c("female", "male", "undetermined"))
plot(sex)

Using stringsAsFactors=FALSE

In the read.csv command at the begining of this section, we used the argument stringsAsFactors=TRUE. This means that when building/importing the 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() you can set stringsAsFactors 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.

On 2020-04-24, version 4 of R was released. Some of you have version 4 and some of you probably have version 3. (Try running sessionInfo() to see which you have). The only difference between these versions that matters to us is that the default value of stringsAsFactors has been changed from TRUE in v3 to FALSE in v4. Changing the default behaviour of a function is rare but can occur. For this reason, it is good practice to print out the versions of R and all your packages by calling sessionInfo(). This way your code will be reproducible. In the case of stringsAsFactors, since it is a recent change, it is a good idea to be explicit about this argument value in your code.

## Compare the difference between our data read as `factor` vs `character`.
surveys <- read.csv("data_raw/portal_data_joined.csv", stringsAsFactors = TRUE)
str(surveys)
surveys <- read.csv("data_raw/portal_data_joined.csv", stringsAsFactors = FALSE)
str(surveys)
## Convert the column "plot_type" into a factor
surveys$plot_type <- factor(surveys$plot_type)

Challenge

2.8 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)
              )

2.9 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?

  • Can you gues the best data type for each column? What would you need to change to ensure that each column had the most appropriate data types?

    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)
       )

Answer

Data frame 1 errors: * 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 (between 8 and 1.1) # * 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).

After the workshop, you can learn more in this RStudio tutorial

Time for a git commit

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.

Often the best way to handle dates is by storing each component of your date (i.e. day, month, year) as a separate variable. This means that dates like ‘01/03/02’ will never be misinterpreted (January 3 2002? March 2 2001?) and Excel won’t create problems by incorrectly converting your dates.

Using str(), we can confirm that our data frame has a separate column for day, month, and year, and that each contains integer values.

str(surveys)

We are going to use the ymd() function from the package lubridate.

“Packages” in R are sets of functions written by other R users that let you do more stuff more easily. There are packages made to handle different biological data types (e.g. RNASeq data, microbiome data), do fancy plotting tasks, run machine learning, etc. etc. The functions we’ve been using so far, like str() or data.frame(), come built into R. Loading additional packages gives you access to more functions.

Before you use a package for the first time you need to install it on your machine, and then you should import it in every subsequent R session when you need it. It’s a good idea to do this at the top of your R script.

Advanced discussion: lubridate 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:

# you already installed this package when you installed the package called 'tidyverse'
library(lubridate)

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:

my_date <- ymd("2015-01-01")
str(my_date)

We can use the paste() function to combine character objects into one character object. The sep argument allows you to specify what to put between the character objects.

# sep indicates the character(s) to use to separate each component
paste("2015", "1", "1", sep = "-")

Now let’s use paste() to combine the year, month, and day. We get the same result:

my_date <- ymd(paste("2015", "1", "1", sep = "-")) 
str(my_date)

Now we apply this function to the surveys dataset. The paste() function can also take vectors and will combine the first element from each vector into one character object, then do the second element of each vector, etc. Run this example:

letters_lower <- c("a","b","c")
letters_upper <- c("A","B","C")
paste(letters_lower , letters_upper, sep="_")

Now we can create a character vector from the year, month, and day columns of surveys using paste():

paste(surveys$year, surveys$month, surveys$day, sep = "-")

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

ymd(paste(surveys$year, surveys$month, surveys$day, sep = "-"))
#> Warning: 129 failed to parse.

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

surveys$date <- ymd(paste(surveys$year, surveys$month, surveys$day, sep = "-"))
#> Warning: 129 failed to parse.

You may have gotten a warning here. Let’s make sure everything worked correctly. One way to inspect the new column is to use summary():

str(surveys) # notice the new column, with 'date' as the class
#> 'data.frame':    34786 obs. of  14 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 ...
#>  $ date           : Date, format: "1977-07-16" "1977-08-19" ...
summary(surveys$date)
#>         Min.      1st Qu.       Median         Mean      3rd Qu.         Max. 
#> "1977-07-16" "1984-03-12" "1990-07-22" "1990-12-15" "1997-07-29" "2002-12-31" 
#>         NA's 
#>        "129"

See the NA’s? This means 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:

# get the rows where date is NA and the columns called year, month, and day
missing_dates <- surveys[is.na(surveys$date), c("year", "month", "day")]
# show the first 6 rows
head(missing_dates)
#>      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?

Hint: Which months have 31 days?

Time for a git commit

Summary of functions in this lesson

  • download.file() # download files from the internet to your computer
  • read.csv() # load CSV file into R memory (be careful with stringsAsFactors)
  • head() # shows the first 6 rows
  • View() # invoke a spreadsheet-style data viewer
  • read.table() # load a file in table format into R memory (be careful with stringsAsFactors)
  • str() # check structure of the object and information about the class, length and content of each column
  • dim() # check dimension of data frame
  • nrow() # returns the number of rows
  • ncol() # returns the number of columns
  • tail() # shows the last 6 rows of a dataframe or elements in a vector
  • colnames() # returns the column names of a dataframe (synonym of names() for data frame objects)
  • rownames() # returns the row names of a dataframe
  • summary() # summary statistics for a vector or each column in a dataframe
  • factor() # create factors
  • levels() # check levels of a factor
  • nlevels() # get number of levels of a factor
  • as.character() # convert an object to a character vector
  • as.numeric() # convert an object to a numeric vector
  • as.numeric(as.character(x)) # convert factors where the levels appear as characters to a numeric vector
  • data.frame() # create a data.frame object
  • ymd() # convert a vector representing year, month, and day to a Date vector
  • paste() # convert arguments to character objects and stick them together (concatenate)

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