Understanding data

To be able to do proper data analyses, it is crucial to understand your data before you can analyse it. So before we start doing any form of analyses we will first understand the dataset that we will be using throughout this course. Let us first download the file and have a look at the data.

We are going to use the R function download.file() to download the CSV file that contains the data.

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

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/portal_data_joined.csv”) is the destination of the file on your local machine.

If you go in the Files section in RStudio, click on the portal_data_joined.csv file in the data folder and then click View File you will be able to see the content of the file.

"record_id","month","day","year","plot_id","species_id","sex","hindfoot_length","weight","genus","species","taxa","plot_type"
1,7,16,1977,2,"NL","M","32","","Neotoma","albigula","Rodent","Control"
72,8,19,1977,2,"NL","M","31","","Neotoma","albigula","Rodent","Control"  224,9,13,1977,2,"NL","","","","Neotoma","albigula","Rodent","Control"

From the first 4 lines of the portal_data_joined.csv file displayed above, we can notice that the file is in the comma separated value (CSV) format which is a very popular format where different values are separated by a comma. The first line of the file is the header of the file which provides a title for each column. In this dataset, we are studying the species repartition and weight of animals caught in plots in our study area. The dataset has the following columns, with each row holding information for a single animal:

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

Reading in data from a file

Now that we have looked at the raw format of the file (CSV format), let us load the data into R and look at how data is loaded into R. 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:

surveys <- read.csv("data/portal_data_joined.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: 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   genus  species   taxa plot_type
#> 1         1     7  16 1977       2         NL   M              32     NA Neotoma albigula Rodent   Control
#> 2        72     8  19 1977       2         NL   M              31     NA Neotoma albigula Rodent   Control
#> 3       224     9  13 1977       2         NL                  NA     NA Neotoma albigula Rodent   Control
#> 4       266    10  16 1977       2         NL                  NA     NA Neotoma albigula Rodent   Control
#> 5       349    11  12 1977       2         NL                  NA     NA Neotoma albigula Rodent   Control
#> 6       363    11  12 1977       2         NL                  NA     NA 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/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.

Data frames

Data frames are another data structure in R which is most widely used in the R programming world. It is very popular as most of the data is readily available in tabular form and it is the also the data structure used when plotting and performing most analyses in R.

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, logical). For example, here is a figure depicting a data frame comprising a numeric, a character, and a logical vector.

In R we can see this by inspecting the structure of a data frame with the function str():

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     : chr  "NL" "NL" "NL" "NL" ...
#>  $ sex            : chr  "M" "M" "" "" ...
#>  $ 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          : chr  "Neotoma" "Neotoma" "Neotoma" "Neotoma" ...
#>  $ species        : chr  "albigula" "albigula" "albigula" "albigula" ...
#>  $ taxa           : chr  "Rodent" "Rodent" "Rodent" "Rodent" ...
#>  $ plot_type      : chr  "Control" "Control" "Control" "Control" ...

Inspecting data.frame Objects

As we mentioned before, it is important to understand your data before analysing it. Furthermore we want to make sure that the data has loaded in R properly. To do that, there are several functions we can use that help us to inspect our data.frame object.

We already saw how the functions head(), view() 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 c 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?
  • Which columns are of type character?

Answer 

str(surveys)

## * class: data frame
## * how many rows: 34786,  how many columns: 13
## * columns of type `character`: species_id, sex, genus, species, taxa, plot_type

Indexing and subsetting data frames

Numeric indexing

You can think of a data frame as a table with rows and columns. Each element in the data frame can be indexed by the position of the row and the column in respect to the whole data frame. The index is specified as [R,C] where R is the position of the row (or row number) and C is the position of the column (or column number). Note that [] are used for indexing, while () are used to call a function. Indexing in a data frame starts from 1. To be able to extract specific data from the surveys data frame, we need to specify the indices or positions of the elements we want from it. In the image below we zoom into the first three columns and rows of the surveys data frame and show their indexes displayed on top of their values in skyblue.

The illustration above illustrates how numeric indexing works. Below are some examples of how we can retrieve subset of values from the surveys data frame using numeric indexing.

# get first element in the first column of the data frame
surveys[1, 1]   
# get first element in the 6th column
surveys[1, 6]   
# get first column of the data frame (as a vector)
surveys[, 1]    
# get first three elements in the 7th column (as a vector)
surveys[1:3, 7] 
# get 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 an operator in R that creates a sequence of numeric vectors of integers in increasing or decreasing order, test 1:10 and 10:1 for instance. It is equivalent to the function seq(from, to).

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

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


Name indexing

Data frames can be subset by calling indices (as shown previously), but also by calling their row names and column names directly. This is known as name indexing. Below are some example of how we retrieve data from a data frame using column names.

# get species_id column as a vector
surveys[, "species_id"]     
# same as above
surveys$species_id          
# get the record_id and species columns for the first three rows
# Note: we are mixing numeric and name indexing here
surveys[1:3, c("record_id", "species")]

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


Logical indexing

Another way to retrieve data from a data frame is by logical indexing, or in other words, by performing a logical operation on a data frame.

# get all the records that have species as "albigula"
surveys[surveys$species == "albigula",]
# save all the records that have species as "albigula" into a variable
albigula_data <- surveys[surveys$species == "albigula",]
# how many records have species as "albigula" in the surveys data frame?
nrow(albigula_data)

In case you are wondering what a Neotoma albigula is: Wikipedia


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.

Answer 

## 1.
surveys_200 <- surveys[200, ]
## 2.
# Saving `n_rows` to improve readability and reduce duplication
n_rows <- nrow(surveys)
surveys_last <- surveys[n_rows, ]
## 3.
surveys_middle <- surveys[n_rows / 2, ]
## 4.
surveys_head <- surveys[-(7:n_rows), ]

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 the class character. Arguably, these columns contain categorical data, that is, they can only take on a limited number of values.

R has a special class for working with categorical data, 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.

Once created, factors can only contain a pre-defined set of values, known as levels. Factors 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.

We can convert these columns that contain categorical data to type factor by using the factor() function:

surveys$sex <- factor(surveys$sex)

We can see that the conversion has worked by using the summary() function again. This produces a table with the counts for each factor level:

summary(surveys$sex)

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).

Challenge

  1. Change the columns taxa and genus in the surveys data frame into a factor.

  2. Using the functions you learned before, can you find out…

    • How many rabbits were observed?
    • How many different genera are in the genus column?

Answer 

surveys$genus <- factor(surveys$genus)
summary(surveys)
nlevels(surveys$genus)

## * how many genera: There are 26 unique genera in the `genus` column.
## * how many rabbits: There are 75 rabbits in the `taxa` column.

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

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


Optional sections

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)

However, as we saw when we used summary(surveys$sex), there are about 1700 individuals for which the sex information hasn’t been recorded. To show them in the plot, we can turn the missing values into a factor level. We will also have to give the new factor level a label. We are going to work with a copy of the sex column, so we’re not modifying the working copy of the data frame:

sex <- as.factor(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 undetermined
#> Levels: undetermined F M

Now we can plot the data again, using plot(sex).

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”)?

Answer 

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

Using stringsAsFactors=TRUE

Depending on what you want to do with the data, when you have a column with categorical data you may want to keep these columns as character or else you may want to change them to factor. To do so, read.csv() and read.table() have an argument called stringsAsFactors which can be set to TRUE.

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

    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)
          )

    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

  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)
       )

    Answer

    • country, climate, temperature, and northern_hemisphere are character; has_kangaroo is numeric
    • using stringsAsFactors = TRUE would have made factor vectors instead of character
    • 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).