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ISM tutorial

Material for the workshop at the Institute for Statistical Mathematics in Tokyo (JP), February 29, 2020.

Lecturers: Mark van der Loo and Edwin de Jonge

Time table

Time slot Topic
13:30 - 13:45 Opening
13:45 - 14:45 Hands-on lecture (1)
14:45 - 15:00 Break
15:00 - 16:00 Hands-on lecture (2)
16:00 - 16:15 Break
16:15 - 17:15 Hands-on lecture (3)
17:15 - 17:30 Discussion and closing

The course form is highly hands-on. Each topic starts with an approximately 10-15 minute session where you run and adapt some R code. Next, we will provide background and details on what you just did. After that there is a more in-depth assignment. Depending on time and topic we will discuss the topic more in-depth after that.

Prerequisites

Bring a laptop

Participants are expected to have a basic knowledge of R/RStudio, explicitly:

  • Work with the R command line and R scripts
  • Read/write CSV data
  • Some basic data manipulations and plots
  • We highly recommend working with RStudio projects.

Software needed for the course

  1. R See https://r-project.org
  2. (Recommended) Rstudio

Execute the following R code to install the necessary packages.

install.packages(c(
        "validate"
      , "errorlocate"
      , "simputation"
      , "rspa"
      , "daff"
      , "jsonlite"
      , "XML"
      , "readr"
      , "stringr"
      , "lumberjack")
  , dependencies=TRUE)

Course material

For users who have git installed

In RStudio go to File $\to$ New Project... $\to$ Version Control $\to$ Git, and enter the follwong URL into the Repository URL field:

https://github.com/data-cleaning/ISM2020_tutorial

Or, use your OS's command-line

git clone https://github.com/data-cleaning/ISM2020_tutorial

And then open the ISM2020_tutorial.Rproj file with RStudio.

For users who do not have git installed

At the top of this webpage click Clone or Download and then Download Zip. Unzip the file and open ISM2020_tutorial.Rproj with RStudio.


License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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Data Cleaning for Official Statistics

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