Plot differences and correlogram of the differences and comment on whether the differences resemble white noise

please refer to the following link for more info

https://rpubs.com/putriangelinaw/econometrics10

Use the following help function to pull the data on r

??gafa_stock
??PBS
??vic_elec
??pelt

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))
view(dFB)).

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 (

library(nycflights13)

#ensure you have the tables loaded and preview
flights
weather

#check what tables are in the ‘nycflights13′ package
data(package=’nycflights13’))

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