R Codeessay

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

For more information, please see the following link.


To get the info about r, use the following help function.

??gafa stock


??vic elec


I just need the code to make it work.

8. Tsibble and mutate practice: Import a year’s worth of stock closing price data (you can use gafa stock within FPP3 or the quantmod package). Convert this information to a tsibble. Plot the differences and their correlograms, and remark on whether the differences approximate white noise (see to FPP3 2.10, #12 for code help dFB). – gafa stock percentage > percentage

percent > percent filter(Symbol == “FB”, year(Date) >= 2018)

percent > percent mutate(trading day = row number())

percent > percent update tsibble(index = trading day, regular = TRUE)

(diff = difference(Close)) mutate


9. Vic elec dataset reindexing and plotting practice

a. Plot daily demand year over year for vic_elec dataset (within FPP3)

b. Is temperature

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