R Studio and Producing Diagrams
Students will need to be confident importing data into R Studio, and producing different diagrams. The Leckie Student Book has a list of all the R Code you need, and you can find R Code in the Pre-Release Data Booklet too.
Tasks/Ideas
- Leckie Higher Applications Student Book – Chapter 7 – Exercise 7A
- A fantastic introduction to R Studio is the ‘Cow to Cone’ resource from the SRUC and Education Scotland. You’ll find it here (Glow login required). The R code is more advanced than you’ll need, but it’s a rich (and real!) dataset and is well explained.
- Once students have got the hang of importing data, nothing will beat just giving them a dataset and getting them to explore. Below are 3 datasets that are good for importing and generating tables or diagrams:
- UK Smoking Data – e.g. produce a two-way table showing gender and whether the person smokes, produce a pie-chart showing the breakdown of the different regions sampled.
- Exam grades – e.g. produce boxplots showing the results of the different exams and compare them.
- Classic books – e.g. produce a scatterplot showing Downloads on Publication Year.
Normality and Measures of Spread and Central location
At Higher Apps students can use a histogram to interpret whether data are normally distributed or skewed. Based on this, students will need to either find the median and inter-quartile range, or find the mean and standard deviation. Again, the Leckie Student Book has a list of all the R Code you need, and you can find R Code in the Pre-Release Data Booklet too.
Tasks/Ideas
- Leckie Higher Applications Student Book – Chapter 7 – Exercise 7B
- Applying Maths – Normal or Skewed – Interactive Powepoint
- An online quiz covering normal and skewed data: siyavula.com
- A worksheet by me on which average should be use – Which Average
- If you need to create your own normal data, use this code in Excel: =NORMINV(RAND(), [Mean], [Standard Deviation]). Replace [Mean] and [Standard Deviation] with the numbers you want for them, and then drag down. You can do this in R too using this function: x <- rnorm([Amount of Numbers], mean=[mean], sd=[sd]).
- This Skew The Script lesson explores skewed data and choosing the correct average.