notes in a shoebox

Quantum Forest

Category: teaching Page 2 of 10

Reducing friction in R to avoid Excel

When you have students working in a project there is always an element of quality control. Some times the results just make sense, while others we are suspicious about something going wrong. This means going back to check the whole analysis process: can we retrace all the steps in a calculation (going back to data collection) and see if there is anything funny going on? So we sat with the student and started running code (in RStudio, of course) and I noticed something interesting: there was a lot of redundancy, pieces of code that didn’t do anything or were weirdly placed. These are typical signs of code copied from several sources, which together with the presence of setwd() showed unfamiliarity with R and RStudio (we have a mix of students with a broad range of R skills).

But the part that really caught my eye was that the script read many Near Infrared spectra files, column bound them together with the sample ID (which was 4 numbers separated by hyphens) and saved the 45 MB file to a CSV file. Then the student opened the file and split the sample ID into 4 columns, deleted the top row, saved the file and read it again into R to continue the process.

New Zealand Electoral Commission results website. It held really well in election night.

Collecting results of the New Zealand General Elections

I was reading an article about the results of our latest elections where I was having a look at the spatial pattern for votes in my city.

I was wondering how would I go over obtaining the data for something like that and went to the Electoral Commission, which has this neat page with links to CSV files with results at the voting place level. The CSV files have results for each of the candidates in the first few rows (which I didn’t care about) and at the party level later in the file.

Where are New Zealand’s bellwether electorates?

I was reading a piece by Graeme Edgeler who, near the end, asked “Where are New Zealand’s bellwether electorates?”. I didn’t know where the data came from or how was the “index of disproportionality for each electorate” calculated, but I saw it mostly as an opportunity to whip up some quick code to practice the use of R and look at other packages that play well with the tidyverse.

The task can be described as: fetch Wikipedia page with results of the 2014 parliamentary election, extract the table with results by electorate, calculate some form of deviation from the national results, get the top X electorates with lowest deviation from national results.

Functions with multiple results in tidyverse

I have continued playing with the tidyverse for different parts of a couple of projects.

Often I need to apply a function by groups of observations; sometimes, that function returns more than a single number. It could be something like for each group fit a distribution and return the distribution parameters. Or, simpler for the purposes of this exploration, calculate and return a bunch of numbers.

Door in Valparaíso.

Turtles all the way down

One of the main uses for R is for exploration and learning. Let’s say that I wanted to learn simple linear regression (the bread and butter of statistics) and see how the formulas work. I could simulate a simple example and fit the regression with R:

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