Load Airport Data
%%R
myDF <- read.csv("/anvil/projects/tdm/data/flights/subset/airports.csv")
head(myDF)
Just try this R code using the seminar kernel (not the seminar-r kernel) and make sure that you can see the first six rows of the airports data frame.
Now do this again, using the seminar-r kernel with R, and notice that you do NOT need the %%R cell magic with the seminar-r kernel. You can do all of this in the same Jupyter Lab notebook, just by changing the kernel.
myDF <- read.csv("/anvil/projects/tdm/data/flights/subset/airports.csv")
head(myDF)
A Jupyter notebook is made up of cells, which you can edit and then run. There are two types of cells we’ll work in for this class:
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Markdown cells. These are where your writing, titles, sections, and paragraphs will go. Double clicking a markdown cell puts it in
editmode, and then clicking the play button near the top of the screen runs the cell, which puts it in its formatted form. More on this in a second. For now, just recognize that most markdown looks like regular text with extra characters like#,*, and-to specify bolding, indentation font, size, and more! -
Code cells. These are where you will write and run all your code! Clicking the play button will run the code in that cell, and the programming language is specified by the language or languages known by the kernel that you chose.
For each question in The Data Mine, please always be sure to put some comments after your cells, which describe all of the work that you are doing in the cells, as well as your thinking and insights about the results.
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Some common Jupyter notebooks shortcuts:
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