refer to the following link for more info https://rpubs.com/putriangelinaw/econometrics10 Use the following function to pull the data on r ??gafa_stock ??PBS ??vic_elec ??pelt I just need the code to run

refer to the following link for more info https://rpubs.com/putriangelinaw/econometrics10 Use the following 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 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 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. 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.

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