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.

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:

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.

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():

#> '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 ...

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?
  • How many taxa have been recorded during these surveys?

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.

: 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:


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.

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.

In case you are wondering what a Neotoma albigula is: