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

If you would like to calculate the proportion of the different crime types by location which code do you need to use?

Hint: Missing values is typically denoted by "NA" in the dataset, we can ignore these values by passing the option "na.rm = TRUE" to the appropriate R command.

Incorrect answers will be 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 distribution of the pedestrian counts over weekdays in March across 24hrs, comparing 2019 to 2020.

ped %>% 
  ggplot(aes(x=Time, y=Count, group=Date, colour=as.factor(year))) +
    geom_boxplot() +
    facet_wrap(~ year, ncol= 1, scales = "free") + 
    scale_colour_brewer("", palette="Dark2") + 
    theme(legend.position="bottom", legend.title = element_blank())

Image failed to load

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

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.

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

What is the total number of arson crime incidents recorded in this data set for Newtown with entry point being ROOF?

This question is about working with temporal data. The example data is on pedestrian counts in the city of Melbourne. What time periods of Melbourne pedestrian traffic are NOT extracted by the code below?

Select all answers that apply. Incorrect answers will be penalised.

library(lubridate)
library(rwalkr)
ped_2020 <- melb_walk(from=Sys.Date() - 7L)
ped_2019 <- melb_walk(from=Sys.Date() - 30L - years(1), to=Sys.Date() - years(1))

The following question is about workflow and reproducibility. Suppose you are writing a report with Rmarkdown that will be presented to an important client. You have a time consuming calculation that is required for downstream chunks for making tables and charts but that isn’t necessary to show the client.

Which of the following chunks will compute the output but not print the resulting code in the report? Note there may be more than one correct answer. Incorrect answers are penalised.

{r chunk-A, eval = FALSE, echo = FALSE}

{r chunk-B, eval = FALSE, echo = TRUE}

{r chunk-C, eval = TRUE, echo = FALSE}

{r chunk-D, include = FALSE}

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())

Image failed to load

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

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

entry_point
lga
crime_type
count
FRONT DOOR
Paddington
arson
100
FRONT DOOR
CBD
arson
60
FRONT DOOR
Newtown
arson
90
WINDOW
Paddington
burglary
65
WINDOW
CBD
burglary
55
WINDOW
Newtown
burglary
100
ROOF
Paddington
burglary
10
ROOF
CBD
burglary
NA
ROOF
Newtown
burglary
NA

Which of the following statements about the variable count are TRUE?

Incorrect answers will be penalised.