Get these slides at:

• https://github.com/uashogeschoolutrecht/work_flows" (source code)
• https://uashogeschoolutrecht.github.io/ (slides only)

1 - Data Science programming languages

2 - Data Science infrastructure & software

3 - Data Science learning tools

• A little introduction to data formats and shapes
• Introducing the tools; Github + RStudio + HU ResearchDRive
• Introducing RStudio and the {tidyverse}
• Getting materials from Github LIVE DEMO
• Getting access to HU ResearchDrive LIVE DEMO
• (Getting access to HU ResearchDrive from within RStudio LIVE DEMO)

Look at these two tables, what do you notice?

## # A tibble: 3 x 4
##   country      year type          count
##   <chr>       <int> <chr>         <int>
## 1 Afghanistan  1999 cases           745
## 2 Afghanistan  1999 population 19987071
## 3 Afghanistan  2000 cases          2666

## # A tibble: 3 x 3
##   country      year rate           
##   <chr>       <int> <chr>          
## 1 Afghanistan  1999 745/19987071   
## 2 Afghanistan  2000 2666/20595360  
## 3 Brazil       1999 37737/172006362

Both tables are build-in datasets from the {tidyr} package belonging to the {tidyverse} suite of Data Science R packages

• Suite of R-packages for Data Science and functional programming
• https://www.tidyverse.org/
• Connect to many other tools in R

From: Storybench

1. Each variable goes in its own column
2. Each observation goes in its own row
3. Each cell contains only one value

From: “R for Data Science”, Grolemund and Wickham

## [[1]]
## # A tibble: 3 x 4
##   country     century year  rate           
##   <chr>       <chr>   <chr> <chr>          
## 1 Afghanistan 19      99    745/19987071   
## 2 Afghanistan 20      00    2666/20595360  
## 3 Brazil      19      99    37737/172006362
## 
## [[2]]
## # A tibble: 3 x 3
##   country     `1999` `2000`
##   <chr>        <int>  <int>
## 1 Afghanistan    745   2666
## 2 Brazil       37737  80488
## 3 China       212258 213766

What steps would we need to tidy them?

table4a %>% 
  pivot_longer(
    cols = `1999`:`2000`, 
    names_to = "year",
    values_to = "cases"
  ) -> table4a_tidy
table4a_tidy

## # A tibble: 6 x 3
##   country     year   cases
##   <chr>       <chr>  <int>
## 1 Afghanistan 1999     745
## 2 Afghanistan 2000    2666
## 3 Brazil      1999   37737
## 4 Brazil      2000   80488
## 5 China       1999  212258
## 6 China       2000  213766

table5 %>%
  separate(
    col = rate,
    into = c("cases", "population"),
    remove = TRUE
  ) -> table5_tidy
table5_tidy

## # A tibble: 6 x 5
##   country     century year  cases  population
##   <chr>       <chr>   <chr> <chr>  <chr>     
## 1 Afghanistan 19      99    745    19987071  
## 2 Afghanistan 20      00    2666   20595360  
## 3 Brazil      19      99    37737  172006362 
## 4 Brazil      20      00    80488  174504898 
## 5 China       19      99    212258 1272915272
## 6 China       20      00    213766 1280428583

Spiegelhalter, 2020, “The Art of Statistics”

• https://github.com
• You can create personal and private repos
• Adding a README.md to each repo is a good idea
• The HU Github Data Science repos: https://github.com/uashogeschoolutrecht

• Integrated Development for R (and Python, Stan, C++, D3, SQL)
• Favorite IDE for using R
• Many integrated productivity tools (auto-completion, syntax highlighting, code-formatting, git-integrations, building tools)
• Send me an email if you want to use R/RStudio yourself!

• Service brought to HU by SURF
• Application: https://bibliotheek.hu.nl/onderzoekers/datamanagement/
• Access though webinterfacte and other software
• SFTP software CyberDuck (you need admin rights)
• Rclone (commandline interface)

To make this work you will need a WebDav token from HU ResearchDrive

Profile -> Security -> Create App

Live Demo

Ejoy the summer & StaRt leaRning:

https://r4ds.had.co.nz/

https://rstudio.com/resources/webinars/

Find all the slides on: https://uashogeschoolutrecht.github.io/