Data 624 Homework 1

Author

Jonnathan Zuna

2.1 Explore the following four time series: Bricks from aus_production, Lynx from pelt, Close from gafa_stock, Demand from vic_elec.

# Loading required packages
library(fpp3)
aus_production |>
  autoplot(Bricks)
Warning: Removed 20 rows containing missing values or values outside the scale range
(`geom_line()`).

The plot shows a series that raises from around 1950 up until 1980 and then it flattens with some fluctuations.

pelt |>
  autoplot(Lynx)

In this plot we can see a cyclic series it swings in a similar pattern every 10 years

gafa_stock |>
  autoplot(Close)

The stock market plot shows four tech companies and the stock market performance since 2014. It is clear that Google and amazon have outperform considerably.

vic_elec |>
  autoplot(Demand) +
  labs(
    title = "Electricity Deman",
    x = "Time",
    y = "Demand"
  )

Demand of electricity has had some high spikes at the beginning of each year.

2.2 Use filter() to find what days corresponded to the peak closing price for each of the four stocks in gafa_stock.

gafa_stock |>
  group_by(Symbol) |>
  filter(Close == max(Close)) |>
  select(Symbol, Date, Close) |>
  ungroup()
# A tsibble: 4 x 3 [!]
# Key:       Symbol [4]
  Symbol Date       Close
  <chr>  <date>     <dbl>
1 AAPL   2018-10-03  232.
2 AMZN   2018-09-04 2040.
3 FB     2018-07-25  218.
4 GOOG   2018-07-26 1268.

2.3 Download the file tute1.csv from the book website, open it in Excel (or some other spreadsheet application), and review its contents. You should find four columns of information. Columns B through D each contain a quarterly series, labelled Sales, AdBudget and GDP. Sales contains the quarterly sales for a small company over the period 1981-2005. AdBudget is the advertising budget and GDP is the gross domestic product. All series have been adjusted for inflation.

  1. Read data into R
library(readr)
tute1 <- read_csv("https://otexts.com/fpp3/extrafiles/tute1.csv")
  1. Converting the data to time series
mytimeseries <- tute1 |>
  mutate(Quarter = yearquarter(Quarter)) |>
  as_tsibble(index = Quarter)
  1. Constructing time series plots of each of the three series
mytimeseries |>
  pivot_longer(-Quarter) |>
  ggplot(aes(x = Quarter, y = value, colour = name)) +
  geom_line() +
  facet_grid(name ~ ., scales = "free_y")

mytimeseries |>
  pivot_longer(-Quarter) |>
  ggplot(aes(x = Quarter, y = value, colour = name)) +
  geom_line()

Dropping facet_grid puts all three lines on one shared axisw which changes considerably GDP by making it look flatter

2.4 The USgas package contains data on the demand for natural gas in the US.

  1. Install the USgas package.
# install.packages("USgas")
  1. Create a tsibble from us_total with year as the index and state as the key.
library(USgas)

tsibble_us <- us_total |>
  as_tsibble(key = state, index = year)
  1. Plot the annual natural gas consumption by state for the New England area (comprising the states of Maine, Vermont, New Hampshire, Massachusetts, Connecticut and Rhode Island).
new_england <- c(
  "Maine", "Vermont", "New Hampshire",
  "Massachusetts", "Connecticut", "Rhode Island"
)

tsibble_us |>
  filter(state %in% new_england) |>
  autoplot(y) +
  labs(
    title = "Annual Natural Gas Consumption for the New England area",
    x = "Year",
    y = "Consumption"
  )

2.5

  1. Download tourism.xlsx from the book website and read it into R using readxl::read_excel().
tourism_excel <- readxl::read_excel("data/tourism.xlsx")
  1. Create a tsibble which is identical to the tourism tsibble from the tsibble package.
tourism
# A tsibble: 24,320 x 5 [1Q]
# Key:       Region, State, Purpose [304]
   Quarter Region   State           Purpose  Trips
     <qtr> <chr>    <chr>           <chr>    <dbl>
 1 1998 Q1 Adelaide South Australia Business  135.
 2 1998 Q2 Adelaide South Australia Business  110.
 3 1998 Q3 Adelaide South Australia Business  166.
 4 1998 Q4 Adelaide South Australia Business  127.
 5 1999 Q1 Adelaide South Australia Business  137.
 6 1999 Q2 Adelaide South Australia Business  200.
 7 1999 Q3 Adelaide South Australia Business  169.
 8 1999 Q4 Adelaide South Australia Business  134.
 9 2000 Q1 Adelaide South Australia Business  154.
10 2000 Q2 Adelaide South Australia Business  169.
# ℹ 24,310 more rows
tourism_excel
# A tibble: 24,320 × 5
   Quarter    Region   State           Purpose  Trips
   <chr>      <chr>    <chr>           <chr>    <dbl>
 1 1998-01-01 Adelaide South Australia Business  135.
 2 1998-04-01 Adelaide South Australia Business  110.
 3 1998-07-01 Adelaide South Australia Business  166.
 4 1998-10-01 Adelaide South Australia Business  127.
 5 1999-01-01 Adelaide South Australia Business  137.
 6 1999-04-01 Adelaide South Australia Business  200.
 7 1999-07-01 Adelaide South Australia Business  169.
 8 1999-10-01 Adelaide South Australia Business  134.
 9 2000-01-01 Adelaide South Australia Business  154.
10 2000-04-01 Adelaide South Australia Business  169.
# ℹ 24,310 more rows

The difference between the two datasets is that that the excel contgains the specific date as oppossed to a data and an additiona quarter column, we can fix that by doing the following

tourism_excel |>
  mutate(Quarter = yearquarter(Quarter)) |>
  as_tsibble(key = c(Region, State, Purpose), index = Quarter)
# A tsibble: 24,320 x 5 [1Q]
# Key:       Region, State, Purpose [304]
   Quarter Region   State           Purpose  Trips
     <qtr> <chr>    <chr>           <chr>    <dbl>
 1 1998 Q1 Adelaide South Australia Business  135.
 2 1998 Q2 Adelaide South Australia Business  110.
 3 1998 Q3 Adelaide South Australia Business  166.
 4 1998 Q4 Adelaide South Australia Business  127.
 5 1999 Q1 Adelaide South Australia Business  137.
 6 1999 Q2 Adelaide South Australia Business  200.
 7 1999 Q3 Adelaide South Australia Business  169.
 8 1999 Q4 Adelaide South Australia Business  134.
 9 2000 Q1 Adelaide South Australia Business  154.
10 2000 Q2 Adelaide South Australia Business  169.
# ℹ 24,310 more rows
  1. Find what combination of Region and Purpose had the maximum number of overnight trips on average.
tourism_excel |>
  as_tibble() |>
  group_by(Region, Purpose) |>
  summarise(avg_trips = mean(Trips))|>
  arrange(desc(avg_trips))
`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by Region and Purpose.
ℹ Output is grouped by Region.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(Region, Purpose))` for per-operation grouping
  (`?dplyr::dplyr_by`) instead.
# A tibble: 304 × 3
# Groups:   Region [76]
   Region          Purpose  avg_trips
   <chr>           <chr>        <dbl>
 1 Sydney          Visiting      747.
 2 Melbourne       Visiting      619.
 3 Sydney          Business      602.
 4 North Coast NSW Holiday       588.
 5 Sydney          Holiday       550.
 6 Gold Coast      Holiday       528.
 7 Melbourne       Holiday       507.
 8 South Coast     Holiday       495.
 9 Brisbane        Visiting      493.
10 Melbourne       Business      478.
# ℹ 294 more rows

d.Create a new tsibble which combines the Purposes and Regions, and just has total trips by State.

tourism_excel <- tourism_excel |>
  mutate(Quarter = yearquarter(Quarter))

tourism_by_state <- tourism_excel |>
  as_tibble() |>
  group_by(State, Quarter) |>
  summarise(Trips = sum(Trips)) |>
  as_tsibble(key = State, index = Quarter)
`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by State and Quarter.
ℹ Output is grouped by State.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(State, Quarter))` for per-operation grouping
  (`?dplyr::dplyr_by`) instead.
tourism_by_state
# A tsibble: 640 x 3 [1Q]
# Key:       State [8]
# Groups:    State [8]
   State Quarter Trips
   <chr>   <qtr> <dbl>
 1 ACT   1998 Q1  551.
 2 ACT   1998 Q2  416.
 3 ACT   1998 Q3  436.
 4 ACT   1998 Q4  450.
 5 ACT   1999 Q1  379.
 6 ACT   1999 Q2  558.
 7 ACT   1999 Q3  449.
 8 ACT   1999 Q4  595.
 9 ACT   2000 Q1  600.
10 ACT   2000 Q2  557.
# ℹ 630 more rows

2.8 Use the following graphics functions: autoplot(), gg_season(), gg_subseries(), gg_lag(), ACF() and explore features from the following time series: “Total Private” Employed from us_employment, Bricks from aus_production, Hare from pelt, “H02” Cost from PBS, and Barrels from us_gasoline.

Can you spot any seasonality, cyclicity and trend? What do you learn about the series? What can you say about the seasonal patterns? Can you identify any unusual years?

total_private <- us_employment |>
  filter(Title == "Total Private")

total_private |>
  autoplot(Employed)

total_private |>
  gg_season(Employed)

total_private |>
  gg_subseries(Employed)

total_private |>
  gg_lag(Employed)

total_private |>
  ACF(Employed) |>
  autoplot()

aus_production |>
  autoplot(Bricks)
Warning: Removed 20 rows containing missing values or values outside the scale range
(`geom_line()`).

aus_production |>
  gg_season(Bricks)
Warning: Removed 20 rows containing missing values or values outside the scale range
(`geom_line()`).

aus_production |>
  gg_subseries(Bricks)
Warning: Removed 20 rows containing missing values or values outside the scale range
(`geom_line()`).

aus_production |>
  gg_lag(Bricks)
Warning: Removed 20 rows containing missing values (gg_lag).

aus_production |>
  ACF(Bricks) |>
  autoplot()

pelt |>
  autoplot(Hare)

pelt |>
  gg_subseries(Hare)

pelt |>
  gg_lag(Hare)

pelt |>
  ACF(Hare) |>
  autoplot()

h02 <- PBS |>
  filter(ATC2 == "H02") |>
  summarise(Cost = sum(Cost))

h02 |>
  autoplot(Cost)

h02 |>
  gg_season(Cost)

h02 |>
  gg_subseries(Cost)

h02 |>
  gg_lag(Cost)

h02 |>
  ACF(Cost) |>
  autoplot()

us_gasoline |>
  autoplot(Barrels)

us_gasoline |>
  gg_season(Barrels)

us_gasoline |>
  gg_subseries(Barrels)

us_gasoline |>
  gg_lag(Barrels)

us_gasoline |>
  ACF(Barrels) |>
  autoplot()