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())

Image failed to load

By looking at the above plots, select all statements that are TRUE. 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 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 wrangling data. Here is the table flights from the nycflights13 package that we have wrangled previously in class.

glimpse(flights)

## Rows: 336,776
## Columns: 19
## $ year           <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013,…
## $ month          <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ day            <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ dep_time       <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 558…
## $ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 600…
## $ dep_delay      <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2, …
## $ arr_time       <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 84…
## $ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 85…
## $ arr_delay      <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7, …
## $ carrier        <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6",…
## $ flight         <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301,…
## $ tailnum        <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N3…
## $ origin         <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LGA…
## $ dest           <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IAD…
## $ air_time       <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149,…
## $ distance       <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 733…
## $ hour           <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6,…
## $ minute         <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59,…
## $ time_hour      <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01…

For the following questions, write down the verbs and columns that you would need to use to do the calculations to answer it from the flights table. We provide the code structure and a list of possible verbs for you to select from:

What hour of day should you plan to fly if you want to avoid arrival delays as much as possible?

flights %>% 
  # step 1
  ___(hour) %>%
  # step 2
  ___(avg_delay = mean(___, na.rm = TRUE)) %>% 
  # step 3
  ___(avg_delay)

1. The verb for the first step is group_by()

   The verb for the first step is mutate()

   The verb for the first step is count()
2. The verb for the second step is mutate()

   The verb for the second step is summarise()
3. The appropriate column to compute the mean from is arr_delay

   The appropriate column to compute the mean from is dep_delay
4. The verb for the third step is sort()

   The verb for the third step is arrange()

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 extracted by the code below?

Select all answers that you think are correct. Incorrect answers are 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))

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 visualisation.

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