please refer to the following link for more info
Use the following help function to pull the data on r
I just need the code to run it.
8. Tsibble and mutate practice: Import a year of stock (of your choosing) closing price data (feel free to use gafa_stock within FPP3 or quantmod package. Convert this data to a tsibble. Plot differences and correlogram of the differences and comment on whether the differences resemble white noise (reference FPP3 2.10, #12 for code help dFB <- gafa_stock %>%
filter(Symbol == “FB”, year(Date) >= 2018) %>%
mutate(trading_day = row_number()) %>%
update_tsibble(index = trading_day, regular = TRUE) %>%
mutate(diff = difference(Close))
9. Reindexing and plotting practice: Vic_elec dataset
a. Plot daily demand year over year for vic_elec dataset (within FPP3)
b. Is temperature correlated to demand?
c. Is previous day demand correlated with current demand?
For below problems please reference https://r4ds.had.co.nz/dates-and-times.html
10. Datetime components: nycflights13 (
#ensure you have the tables loaded and preview
#check what tables are in the ‘nycflights13’ package
a. Load the flights table from the nycflights13 package
b. What day of the week has the highest average delay?
11. Time zones: Reindex vic_elec to the US Eastern timezone using the with_tz function
12. Durations and periods
a. Create a duration for your age at the start of our first lecture and print this duration.
b. Calculate your age at the end of the semester (4/27/22 8:50p) using periods