Using pandas you can import data and do any relevant wrangling (see our recent blog entry on pandas). Those of you who are familiar with chunks in different styles should easily be able to skim through the data wrangling. While there is a lot of repeated code, we included all the details for those of you who might be working with Python in R for the first time. The more students can think broadly and confidently about their skill set, the more impact they will have in performing data analyses.īelow we’ve provided a series of examples in markdown chunks (both Python chunks and R chunks). Whatever computational environment is used to execute instructions to the computer, it can be illuminating for students to see different implementations of the same syntax producing the same results, or alternatively, implementation of different syntax producing the same result. Below, we discuss running Python in the R Markdown environment. A previous blog entry on Jupyter discussed running Python code in its native environment. We don’t take sides in that conversation, but we do recognize that teaching students about both Python and R can give them insight into both languages and more skills for doing data science in the wild. ![]() A quick google search can quickly bring up many arguments on both sides of the heated Python vs R debate.
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