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())

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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?

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?

This question is about tidy temporal data. Below are the data on military expenditure for different countries

head(expenditure)

## # A tibble: 6 × 4
##   Entity      Code   Year military_expenditure
##   <chr>       <chr> <dbl>                <dbl>
## 1 Afghanistan AFG    1970              5373185
## 2 Afghanistan AFG    1973              6230685
## 3 Afghanistan AFG    1974              6056124
## 4 Afghanistan AFG    1975              6357396
## 5 Afghanistan AFG    1976              8108200
## 6 Afghanistan AFG    1977              8553767

To compare expenditure counts of Australia and Germany between 2015 and 2020, we are going to select the observations for those two countries and that time range. Then we want change the data format so that the expenditure for Australia and the expenditure for Germany across different years appears in two different columns.

Fill in the blanks for the following code to get our desired output:

exp2 <- expenditure %>% 
  # (a) which pivot function
  # (b) which id_cols
  # (c) which column forms names_from
  # (d) which colum forms values_from
  dplyr::---(Entity %in% c("Australia", "Germany",
                Year >= 2015) %>%
  pivot_---(id_cols = ---, 
              names_from = ---,
              values_from = ---)

1. select

   filter

   military_expenditure

   Germany
2. longer()

   wider()

   year
3. Military_expenditure

   Time

   Year
4. Country

   Entity

   Australia

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 Monash LGA?

Incorrect answers are penalised.

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

What is the proportion of crimes that entered through the front door?

Hint: If a data set contains "NA" values, it means the entries are missing. You can ignore these missing values here.

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?