Week 3

[gvegayon]

This week, although we will continue looking into R fundamentals, we will make some room (lots of it) for the R package data.table (here is why). Here are the readings for the week:

Readings

• Chapters 14 through 16 of Peng's R Programming for Data Science.
• Introduction to data.table

Optional reading

• Chapters 6 and 9 of Hadley Wickam's Advanced R.
• Chapter 7 of the same book.

[rberch]

Hello, George, I am having an error when I try to render the gfm file. @gvegayon @chipmanj

Error in eval(xfun::parse_only(name)) : object 'gfm' not found Calls: ... create_output_format -> create_output_format_function -> eval -> eval Execution halted

[hyejung0]

Hi @gvegayon , I have questions on the Part 3. I don't think I understood how to solve the problem. Starting with Question 1, you are asking what is the median station in terms of temperature, wind speed, and atmospheric pressure. Are we supposed to find one station that has the median value of temperature, and another station with the medical wind speed, and another station that has the median atmospheric pressure? @LindaAmoafo said that there are multiple stations with the median value so we can't choose a unique station.

[gvegayon]

Hello, George, I am having an error when I try to render the gfm file. @gvegayon @chipmanj

Error in eval(xfun::parse_only(name)) : object 'gfm' not found Calls: ... create_output_format -> create_output_format_function -> eval -> eval Execution halted

Were you able to compile it using quarto, @rberch?

[gvegayon]

Hi @gvegayon , I have questions on the Part 3. I don't think I understood how to solve the problem. Starting with Question 1, you are asking what is the median station in terms of temperature, wind speed, and atmospheric pressure. Are we supposed to find one station that has the median value of temperature, and another station with the medical wind speed, and another station that has the median atmospheric pressure? @LindaAmoafo said that there are multiple stations with the median value so we can't choose a unique station.

@hyejung0 and @LindaAmoafo, one way to approach the problem is to look at the average values at a given time window for each station. In other words, collapse the dataset by station and compute the average temperature, wind speed, and pressure. Once you have that, you can compute the quantiles for each of the average values and find the station closest to each of them.