A friend of yours complained about copying the same R code many times.

Should you tell them that they should put the code into a function?

True or False?

The "bow()" function checks if the data can be scraped appropriately.

The following question is about visualisation.

The data shows calories of a selection of chocolate bars, 100g equivalents. Which of the following statements are true?

The following question is about tidy data. The table below contains looks at crime occurrence in different locations across Victoria:

What proportion of the crimes were recorded in the Alpine LGA?

Incorrect answers are penalised.

The following question is about visualisation.

The data shows calories of a selection of chocolate bars, 100g equivalents. Calories mapped to the vertical axis. For the following statement:

Dark chocolates are higher in calories than milk chocolates.

This question is about data visualisation. Below are two plots of the Melbourne Central pedestrian traffic, for 2019.

Answer the following questions:

• 1. In plot A, which variable is mapped to the x axis? (Put the answer in lower case).

• 1. Is this the same information used in plot B?

• 1. Which plot did this code produce?

ggplot(walk_tidy, aes(x = Time, y = Count, group = Date)) + 
  geom_line(alpha=0.3)

2. TRUE

   FALSE
3. A

   B

The following question is about tidy data. The table below contains looks at crime data in different locations across Victoria:

As usually we need to first inspect the variables and observations in this data set. What is the dimension of the data set?

The following question is about visualisation.

The data shows calories of a selection of chocolate bars, 100g equivalents. Calories mapped to the vertical axis. If you are wanting the reader to compare the inter quantile range of calories of milk and dark chocolates, which part of the plot do you need to observe?

Suppose you are working with the US air traffic on time database. You are interested in examining the length of the flights between New York City and Los Angeles. 

You difference the arrival time in New York City from the departure time in Los Angeles, for all non-stop flights yesterday, and get an average flight time of 9 hours. 

What is the reasonable explanation? Incorrect answers are penalised.

This question is about visualising temporal data.

The example data is on pedestrian counts in the city of Melbourne. The below plot looks at the pedestrian counts over weekdays in March, comparing 2019 to 2020.

ped %>% 
  ggplot(aes(x=Time, y=Count, group=Date, colour=as.factor(year))) +
    geom_line() +
    facet_wrap(~wday, ncol=7) + 
    scale_colour_brewer("", palette="Dark2") + 
    theme(legend.position="bottom", legend.title = element_blank())

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By looking at the above plots, select all statements that are TRUE. Incorrect answers are penalised.