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As the #rstats course material is not public (yet?) or available as online training (yet?), I thought I am sharing some slides from the deck.

The course covers all steps of the #DataScience workflow as featured in @hadleywickham's fantastic #R4DS 📕 r4ds.had.co.nz/index.html The title slide of the workshop "Reproducible Data AnalThree avatars (customised versions of the lovely Open Peeps A chapter slide entitled "What is This Course About?&quA conceptional representation of the data science workflow:t
Let's start with session 1:
"Introduction to #rstats and #rstudio" ®️ The title slide for the first session of the "ReproduciA slide explaining what the R programming language is:  &quoA comparison of R and Rstudio, taken from ModernDive: R is tA screenshot of Rstudio with the default panes: Script (uppe
The fundamentals of R includes:

* values
* assignments and objects
* functions
* data types
* unknown values
* vectors
* factors
* packages
* tabular data
* data generation
* data import A colorful overview of how functions work: the function nameA function might return a value, which is printed on a new lA colorful representation of objects and assignments in R: tA colorful explanation of vectors that start with the vector
Read 16 tweets
Starting out in #R can be tough.

Here are the 7 packages that have helped me tremendously.

#rstats
1. #dplyr for data wrangling

github.com/tidyverse/dplyr
2. #tidyr for tidying, wrangling and pivoting

github.com/tidyverse/tidyr
Read 10 tweets

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