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
—
For more information, please see the following link.
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
