Chapter 6 Exploring the dimensions of your data frame - Week 7

You should aim to complete this chapter in Week 7, Semester 1

6.1 Top to tail - some useful functions to explore

Log into posit cloud and open up your tribolium_parasites project.

Lets start by reminding ourselves of what these data look like. Try using the following functions on yourtribolium_combo data frame, copy them into your script and comment # next to each when you work out what it does.

# Some functions to play with
nrow()
ncol()
colnames()
str()
view()
tail()

Hopefully you can see that these functions help give you some good insights into the dimensions of your data set, especially when used alongside functions that you are already be familiar with, like head() and glimpse().

6.2 Whats your sample size?

With this data set its not hard to work out what the sample size is. But data sets can become huge and its not always easy or effective to scroll through them. Sometimes you really just need to know how many observations are in your data set, thankfully each of our beetles has a unique identifyer so we can count how many unique entries there are in the beetle_id variable. Try the following;

# Use the n_distinct function to count unique/distinct entries in the beetle_id column
n_distinct(tribolium_combo$beetle_id)

Side Note notice the use of the $ above. This is effectively directing R to a specific variable/column within a data frame.

You may have noticed that we have two categorical variables in this data set, sex and treatment. It would be really useful to know what your sample size is for each of these variables. Try the following;

# Use the tabyl function from the janitor package, comment on what it does. 
tabyl(tribolium_combo, sex , treatment)

Stuck? Click here for a breakdown of the code above

In this case the tabyl function has returned the number of individuals of each sex in each treatment. In this experiment there were 5 females kept at 30°C and 5 females kept at 35°C, likewise for males, 5 males were kept 30°C and 5 males were kept at 35°C in this experiment.

So hopefully you can see from the outputs of the summarise and tabyl functions that we have a total sample size of 20 beetles and that 5 of each sex were exposed to each treatment. Next week we will start to explore how we can visualise some of these data.

6.3 Before you leave!

Log out of posit Cloud and make sure you save your script!

6.4 References

Firke, Sam. 2021. Janitor: Simple Tools for Examining and Cleaning Dirty Data. https://github.com/sfirke/janitor.
Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the tidyverse.” Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.
Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2021. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.
Wickham, Hadley, Jim Hester, and Jennifer Bryan. 2021. Readr: Read Rectangular Text Data. https://CRAN.R-project.org/package=readr.