Description

Dataframes
Part 1
2. Download all the data at once, found here: https://alexd106.github.io/intro2R/ data.html
This will download a zip file to your local computer. Do not decompress it.
3. In Rstudio Workbench, under the Files tab, click the Upload button. Browse to where you downloaded the zip file (usually ”Downloads”), and select the all_data.zip file. This will upload and simultaneously unzip the file into the filesystem on the server.
4. Still in the Files tab, there should now be a data folder. Open this folder and click on the whaledata.xls, and then ”Import dataset”. Click again on the ”Import” button.
5. In the console you should now see the text:
> library(readxl)
> whaledata <- read_excel(“data/whaledata.xls”)
> View(whaledata)
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RStudio has entered the R commands needed to load the dataset into a dataframe, or a specialized kind of dataframe called a tibble. (More about this later)
6. Instead of clicking the button, you could have entered these commands in the console, or sourced them from a script. Let’s do that now. In your script for this exercise, enter the following:
library(readxl)
whaledata <- read_excel(“data/whaledata.xls”)
View(whaledata)
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Now source the script into the console.
Entering these commands into a script seems like more work. But the next time you want to deal with this script and this dataset, the commands will load it for you. You will not have to open the file browser, locate the file, and click on it. In the long run, opening the files you need from the script is much more efficient than the ”hunt-and-click” that seems convenient the first time.
Get in the habit of making your scripts self-contained. Loading the script and pressing the ”Source” button should do everything necessary.
7. The whaledata.xls is an Excel spreadsheet. A lot of data is stored in this format. However, a lot of data is stored in text files, without the overhead required by Excel.
To see this, go back to your local computer’s file browser, and navigate to the all_data.zip file, and unzip it (double-click and follow instructions).
Now navigate into the data folder (on your local computer, where you unzipped the file), and open the whaledata.xls file with spreadsheet software. If you don’t have Microsoft products, you can get excellent free versions here: https://www.libreoffice. org/discover/libreoffice/
8. In the spreadsheet software, select ”Save As”, and then save the file as a Text CSV file, called whaledata.csv
First, examine the two files in your file browser, selecting ”View→Details”, and notice that the CSV file is less than a fifth the size of the XLS file, 4 KB instead of 28 KB. Most data prepared for data analysis is in this format, not in spreadsheet format, simply because spreadsheets waste a lot of space on formatting and other matters not related to the data itself.
Now open the CSV file with a text editor, such as Notepad on Windows or TextEdit on Macs or gedit on Unix. Notice that the file is humanly readable, with simple layout.
9. For contrast, open the XLS file with a text editor. You get something like we see in Figure 1.
There are many disadvantages to the XLS format. It is bulky, not humanly readable, only readable by spreadsheet software. Storing files in text format is preferable in nearly all cases.
10. Upload the whaledata.csv file to the same folder on the server. Do not read the data by clicking on it. Instead, enter the following into your script and source it to the console:
whale <- read.csv(“data/whaledata.csv”)
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read.csvis a variation on the read.table function. Spend some time with §3.2.2 of the manual and also the help desk to learn more about this important class of functions.

Figure 1: An XLS file opened with a text editor.
11. After you load a data file, it’s a good idea to look at it and make sure it has loaded properly, and get a feel for what’s there. The RStudio command added View(whaledata) to its commands, which opens a spreadsheet-like viewer in the editor panel. You can try the same with your CSV data, with View(whale). Can you spot some subtle differences between the whaledata dataset (loaded with read_excel), and the whale dataset (loaded with read.csv)?
In what follows we will use the whale dataset.
13. The best way to learn a bit more about a dataframe enter the command
str(whale)
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into your script and source it to the console. You will see a display like this:
> str (whale)
’data . frame ’ : 100 obs . of 8 variables :
$ time . at . station : int 1344 1633 743 1050 1764 580 459 561 709 690 . . .
$ water . noise : chr ”low” ”medium” ”medium” ”medium” . . .
$ number. whales : int 7 13 12 10 12 10 5 8 11 12 . . .
$ latitude : num 60.4 60.4 60.5 60.3 60.4 . . .
$ longitude : num =4.18 =4.19 =4.62 =4.35 =5.2 =5.22 =5.08 =5 =4.64 =4.84 . . .
$ depth : int 520 559 1006 540 1000 1000 993 988 954 984 . . .
$ gradient > : int 415 405 88 409 97 173 162 162 245 161 . . .
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This gives you important information about your data, including the names of the columns and the types of each, either numeric (num) or character (chr).
14. Also try summary(whale) and explore what you get. Part 2
15. Summarising and manipulating dataframes is a key skill to acquire when learning R. Although there are many ways to do this, we will concentrate on using the square bracket [ ] notation which you used previously with vectors. The key thing to remember when using [ ] with dataframes is that dataframes have two dimensions (think rows and columns) so you always need to specify which rows and which columns you want inside the [ ] (see §3.4.1 for some additional background information and a few examples). Let’s practice.
Put commands to find the answers to each of the following into your R script and source them to the console:
Extract all the elements of the first 10 rows and the first 4 columns of the whale dataframe and assign to a new variable called whale.sub.
Next, extract all observations (remember – rows) from the whale dataframe and the columns month, water.noise and number.whales and assign to a variable called whale.num.
Finally, extract all rows except the first 10 rows and all columns except the last column. Remember, for some of these questions you can specify the columns you want either by position or by name. Practice both ways. Do you have a preference? If so why?
16. In addition to extracting rows and columns from your dataframe by position you can also use conditional statements to select particular rows based on some logical criteria. This is very useful but takes a bit of practice to get used to (see Section 3.4.2 for an introduction). Extract rows from your dataframe (all columns by default) based on the following criteria (note: you will need to assign the results of these statements to appropriately named variables, I’ll leave it up to you to use informative names!):
at depths greater than 1200 m
gradient steeper than 200 degrees water noise level of ‘low’
all observations from between latitudes 60.0 and 61.0 and longitudes -6.0 and -4.0 all rows that do not have a water noise level of medium
17. A really neat feature of extracting rows based on conditional statements is that you can include R functions within the statement itself. To practice this, modify your answer to the gradient question to use the median() function rather than hard coding the value 132.
18. However, when using functions in conditional statements you need to be careful. For example, write some code to extract all rows from the dataframe whale with depths greater than 1500 m and with a greater number of whales spotted than average (hint: use the mean() function in your conditional statement). Can you see a problem with the output? Check the help file for mean and in particular pay attention to the na.rm parameter.
19. Although you have concentrated on using the square bracket [ ] notation to extract rows and columns from your dataframe, there are of course many other approaches. One such approach is to use the subset() function. Look this function up in the manual or the help files.
20. Another useful way to manipulated your dataframes is to sort the rows based on the value of a variable (or combinations of variables). Rather counter-intuitively you should use the order() function to sort your dataframes, not the sort() function (see Section 3.4.3 of the Introduction to R book for an explanation).
21. Now for something a little more complicated. Sort all rows in the whale dataframe by ascending order of depth within each level of water noise. The trick here is to remember that you can order by more than one variable when using the order() function (see Section 3.4.3 again). Don’t forget to assign your sorted dataframe to a new variable with a sensible name. Repeat the previous ordering but this time order by descending order of depth within each level of water noise. Part 3
22. Often, we would like to summarise variables by, for example, calculating a mean, median or counting the number of observations. To do this for a single variable it’s fairly straight forward :
mean(whale$time.at.station) # mean time at station
median(whale$depth) # median depth
length(whale$number.whales) # number of observations
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Perhaps more interestingly, you might want summarise one variable conditional on the level of another factor variable. For example, write some R code to calculate the mean number of whales sighted at each of the three levels of water noise (see Section 3.5
for a few hints). Next, calculate the median number of whales sighted at each level of water noise and for each month.
23. Another useful function for summarising dataframes is aggregate(). Refer back to the book (search for aggregate) to look up how to use this function (or see ?aggregate). Use the aggregate() function to calculate the mean of time at station, number of whales, depth and gradient for each level of water noise (don’t forget about that sneaky NA value). Next calculate the mean of time at station, number of whales, depth and gradient for each level of water noise for each month.
24. Knowing how many observations are present for each factor level (or combinations of factor levels) is useful to determine whether you have an adequate sample size (for subsequent modelling for example). Use the table() function to determine the number of observations for each level of water noise (see Section 3.5 again for more information). Next use the same function to display the number of observations for each combination of water noise and month. (Optional): The xtabs() function is very flexible for creating tables of counts for factor combinations (aka contingency tables). Take a look at the Introduction to R book, the help file or Google to figure out how to use the xtabs() function to replicate your use of the table() function.
25. Ok, we have spent quite a bit of time (and energy) learning how to import and manipulate dataframes. The last thing we need to cover is how to export dataframes from R to an external file (see Section 3.6 of the book for more details). Let’s say you want to export the dataframe whale.num you created previously to a file called whale_num.csv. To do this you will need to use the write.table() function. You want to include the the variable names in the first row of the file, but you don’t want to include the row names. Also, make sure the file is a comma delimited file. Once you have created your file, download it to your local computer and try to open it in spreadsheet software.
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