# R Codeessay

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) %>%
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?

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

https://rpubs.com/putriangelinaw/econometrics10

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

??gafa stock

??PBS

??vic elec

??pelt

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

view(dFB)).

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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