HW1
Shariq Mian
2024-02-05
Explore the following four time series: Bricks from aus_production, Lynx from pelt, Close from gafa_stock, Demand from vic_elec.
Use ? (or help()) to find out about the data in each series. What is the time interval of each series? Use autoplot() to produce a time plot of each series. For the last plot, modify the axis labels and title.
library(fpp3)## Warning: package 'fpp3' was built under R version 4.3.2## ── Attaching packages ────────────────────────────────────────────── fpp3 0.5 ──## ✔ tibble 3.2.1 ✔ tsibble 1.1.4
## ✔ dplyr 1.1.2 ✔ tsibbledata 0.4.1
## ✔ tidyr 1.3.0 ✔ feasts 0.3.1
## ✔ lubridate 1.9.2 ✔ fable 0.3.3
## ✔ ggplot2 3.4.4 ✔ fabletools 0.3.4## Warning: package 'ggplot2' was built under R version 4.3.2## Warning: package 'tsibble' was built under R version 4.3.2## Warning: package 'tsibbledata' was built under R version 4.3.2## Warning: package 'feasts' was built under R version 4.3.2## Warning: package 'fabletools' was built under R version 4.3.2## Warning: package 'fable' was built under R version 4.3.2## ── Conflicts ───────────────────────────────────────────────── fpp3_conflicts ──
## ✖ lubridate::date() masks base::date()
## ✖ dplyr::filter() masks stats::filter()
## ✖ tsibble::intersect() masks base::intersect()
## ✖ tsibble::interval() masks lubridate::interval()
## ✖ dplyr::lag() masks stats::lag()
## ✖ tsibble::setdiff() masks base::setdiff()
## ✖ tsibble::union() masks base::union()data("aus_production")
data("pelt")
data("gafa_stock")
data("vic_elec")Bricks from aus_production: Intreval every 3 Months
autoplot(aus_production,Bricks)## Warning: Removed 20 rows containing missing values (`geom_line()`).
Lynx from pelt: Intreval Year
autoplot(pelt,Lynx)
Close from gafa_stock: Intreval Daily
autoplot(gafa_stock,Close)
Demand from vic_elec Intreval every 30 Min
autoplot(vic_elec,Demand)
# 2. Use filter() to find what days corresponded to the peak closing
price for each of the four stocks in gafa_stock.
## # A tsibble: 4 x 8 [!]
## # Key: Symbol [4]
## # Groups: Symbol [4]
## Symbol Date Open High Low Close Adj_Close Volume
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AAPL 2018-10-03 230. 233. 230. 232. 230. 28654800
## 2 AMZN 2018-09-04 2026. 2050. 2013 2040. 2040. 5721100
## 3 FB 2018-07-25 216. 219. 214. 218. 218. 58954200
## 4 GOOG 2018-07-26 1251 1270. 1249. 1268. 1268. 24056003.
Download the file tute1.csv here, 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.
tute1 <- readr::read_csv("tute1.csv")## Rows: 100 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (3): Sales, AdBudget, GDP
## date (1): Quarter
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.mytimeseries <- tute1 |>
mutate(Quarter = yearquarter(Quarter)) |>
as_tsibble(index = Quarter)
mytimeseries |>
pivot_longer(-Quarter) |>
ggplot(aes(x = Quarter, y = value, colour = name)) +
geom_line() +
facet_grid(name ~ ., scales = "free_y")
tute1 <- readr::read_csv("tute1.csv")## Rows: 100 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (3): Sales, AdBudget, GDP
## date (1): Quarter
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.mytimeseries <- tute1 |>
mutate(Quarter = yearquarter(Quarter)) |>
as_tsibble(index = Quarter)
mytimeseries |>
pivot_longer(-Quarter) |>
ggplot(aes(x = Quarter, y = value, colour = name)) +
geom_line() +
facet_grid(name ~ ., scales = "free_y")
4.The USgas package contains data on the demand for natural gas in the US.
Install the USgas package. Create a tsibble from us_total with year as the index and state as the key. 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).
library(USgas)## Warning: package 'USgas' was built under R version 4.3.2tsibble_us <- us_total %>%
as_tsibble(index = year, key = state)
ne<- tsibble_us %>%
group_by(state) %>%
filter(state %in% c('Maine', 'Vermont', 'New
Hampshire', 'Massachusetts',
'Connecticut' ,'Rhode Island')) %>%
ungroup()
ne %>% autoplot(y)
5. Download tourism.xlsx from the book website and read it into R using readxl::read_excel().
Create a tsibble which is identical to the tourism tsibble from the tsibble package. Find what combination of Region and Purpose had the maximum number of overnight trips on average. Create a new tsibble which combines the Purposes and Regions, and just has total trips by State.
tourism <- readxl::read_excel("tourism.xlsx")
tsibble_tourism <- tourism %>% mutate(Quarter = yearquarter(Quarter) ) %>%
as_tsibble(index = Quarter, key = c(Region, State, Purpose))
tsibble_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 rowstsibble_tourism %>% group_by(Region, Purpose) %>%
summarise(Trips = mean(Trips)) %>%
ungroup() %>%
filter(Trips == max(Trips))## # A tsibble: 1 x 4 [1Q]
## # Key: Region, Purpose [1]
## Region Purpose Quarter Trips
## <chr> <chr> <qtr> <dbl>
## 1 Melbourne Visiting 2017 Q4 985.new_tsibble <- tsibble_tourism %>%
group_by(State) %>% summarise(Trips = sum(Trips))%>%
ungroup()
new_tsibble## # A tsibble: 640 x 3 [1Q]
## # Key: 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 rowsPriv <- us_employment %>%
filter(Title == "Total Private")
Priv %>% autoplot(Employed)
Priv %>% gg_season(Employed)
Priv %>% gg_lag(Employed)
Priv %>% ACF(Employed)## # A tsibble: 29 x 3 [1M]
## # Key: Series_ID [1]
## Series_ID lag acf
## <chr> <cf_lag> <dbl>
## 1 CEU0500000001 1M 0.997
## 2 CEU0500000001 2M 0.993
## 3 CEU0500000001 3M 0.990
## 4 CEU0500000001 4M 0.986
## 5 CEU0500000001 5M 0.983
## 6 CEU0500000001 6M 0.980
## 7 CEU0500000001 7M 0.977
## 8 CEU0500000001 8M 0.974
## 9 CEU0500000001 9M 0.971
## 10 CEU0500000001 10M 0.968
## # ℹ 19 more rowsaus_production %>% autoplot(Bricks)## Warning: Removed 20 rows containing missing values (`geom_line()`).
aus_production %>% gg_season(Bricks)## Warning: Removed 20 rows containing missing values (`geom_line()`).
aus_production %>% gg_subseries(Bricks)## Warning: Removed 5 rows containing missing values (`geom_line()`).
aus_production %>% gg_lag(Bricks)## Warning: Removed 20 rows containing missing values (gg_lag).
aus_production %>% ACF(Bricks)## # A tsibble: 22 x 2 [1Q]
## lag acf
## <cf_lag> <dbl>
## 1 1Q 0.900
## 2 2Q 0.815
## 3 3Q 0.813
## 4 4Q 0.828
## 5 5Q 0.720
## 6 6Q 0.642
## 7 7Q 0.655
## 8 8Q 0.692
## 9 9Q 0.609
## 10 10Q 0.556
## # ℹ 12 more rows