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README.md

README.md

This is the R script repository of the "R Skills" course in the 2018/2019 Fall term, part of the MSc in Business Analytics at CEU.

Table of Contents

Syllabus

Please find in the syllabus folder of this repository.

Technical Prerequisites

Please bring your own laptop and make sure to install the below items before attending the first class:

  1. Install R from https://cran.r-project.org
  2. Install RStudio Desktop (Open Source License) from https://www.rstudio.com/products/rstudio/download
  3. Register an account at https://github.com
  4. Enter the following commands in the R console (bottom left panel of RStudio) and make sure you see a plot in the bottom right panel and no errors in the R console:
install.packages('ggplot2')
library(ggplot2)
ggplot(diamonds, aes(cut)) + geom_bar()

Optional steps I highly suggest to do as well before attending the class if you plan to use git:

  1. Bookmark, watch or star this repository so that you can easily find it later

  2. Install git from https://git-scm.com/

  3. Verify that in RStudio, you can see the path of the git executable binary in the Tools/Global Options menu's "Git/Svn" tab -- if not, then you might have to restart RStudio (if you installed git after starting RStudio) or installed git by not adding that to the PATH on Windows. Either way, browse the "git executable" manually (in some bin folder look for thee git executable file).

  4. Create an RSA key (optionally with a passphrase for increased security -- that you have to enter every time you push and pull to and from GitHub). Copy the public key and add that to you SSH keys on your GitHub profile.

  5. Create a new project choosing "version control", then "git" and paste the SSH version of the repo URL copied from GitHub in the pop-up -- now RStudio should be able to download the repo. If it asks you to accept GitHub's fingerprint, say "Yes".

  6. If RStudio/git is complaining that you have to set your identity, click on the "Git" tab in the top-right panel, then click on the Gear icon and then "Shell" -- here you can set your username and e-mail address in the command line, so that RStudio/git integration can work. Use the following commands:

    $ git config --global user.name "Your Name"
    $ git config --global user.email "Your e-mail address"
    

    Close this window, commit, push changes, all set.

Find more resources in Jenny Bryan's "Happy Git and GitHub for the useR" tutorial if in doubt or contact me.

Class Schedule

Week 1 (100 min): Writing Loops

  • Recap on generating sequence and drawing base plots: 1.R

  • Intro to writing custom functions: 1.R

  • Exercises on 1D random walk: 1.R

    • Writing loops: 1.R
    • Vectorized approach: 1.R
    • Custom function: 1.R
  • lapply, sapply, do.call: 1.R

  • Creating animation by iterating plots: 1.R

  • apply on matrices: 1.R

Week 2 (100 min): More Loops

  • Recap on 1D random walk (sim): 2.R
  • Rolling 3 dices (sim): 2.R
  • Roulette (sim): 2.R
  • Reading Excel spreadsheet: 2.R
  • Dimension reduction with MDS for plotting: 2.R
Homework
1. Set the seed to 42 and run a simulation 1000 times: playing roulette for 100 rounds, starting with $100 budget, always betting $1 on numbers 18-36 in each round. After saving the results in a helper variable, answer the below questions by using your saved results:

  - Draw a histogram on the (1000) resulting budgets at the end of the 100-100 rounds.
  - What's the average amount of lost dollars in 100 rounds?
  - How many times (out of 1000) did we win at least $1?
  
2. Draw a ggplot2 2D "map" of European cities by applying MDS on the "eurodist" dataset:

  - Start with a scatterplot.
  - Add the city names as labels to the scatterplot.
  - Fix the north-south and east-west orientation if needed.

Week 3 (100 min): Intro to Time-series Analysis

  • Recap on MDS: 3.R
  • Loading data from the Binance API: 3.R
  • Defining and visualizing time-series: 3.R
  • Decompose time-series into seasonal, trend and random components: 3.R
  • The Naïve forecasting method: 3.R
  • Moving averages: 3.R
  • Exponential smoothing: 3.R
  • ARIMA: 3.R
  • The Naïve forecasting method: 3.R
  • Analysing the amount of gasoline products supplied between 1991 and 2017: 3.R
Homework
1. Make sure to revisit the exercises we covered on Monday: https://github.com/daroczig/CEU-R-skills#week-3-100-min-intro-to-time-series-analysis
2. Make sure that all required packages are installed (both from CRAN via install.packages and from GitHub via install_github), debug error messages, search on StackOverflow -- open a GitHub issue with the details if you get stuck and I'll try to help
3. Load the fpp2 package and check the manual of the "usmelec" dataset
4. Visualize the time-series
5. Apply moving-average smoothing using a window of 12 and visualize the smoothed time-series over the raw data with a blue line
6. What's the naive forecast for the next 3 months?
7. Apply the auto.arima function and plot the forecasts for the next 5 years
8. Compute the accuracy metrics of TBATS forecasts for the next 6 months
9. Install the following R packages from CRAN before Monday: lubridate, XML, rvest, shiny, rmarkdown, pander

Week 4 (100 min): Intro to Spatial Data

  • General feedback on homework: 4.R
  • Recap on merging datasets: 4.R
  • Recap on aggregates: 4.R
  • Recap on plotting: 4.R
  • Geocoding: 4.R
  • Plotting a map: 4.R
  • Parsing data from the Internet: 4.R
Homework
1. Visit Week 31 of the TidyTuesday project at https://github.com/rfordatascience/tidytuesday and get familiar with the project data.
2. Load into R `r-downloads.csv` as a `data.table` object
3. Create a new dataset (called `countries`) describing the countries with the following variables:
   - number of overall downloads
   - number of unique users (IP addresses)
   - number of Windows downloads
   - number of OSX downloads
   - number of downloads from other operating systems
4. Compute a new column in `countries` showing the ratio of Windows vs other OS users
5. Merge the name of the country based on the 2-letter ISO country code, maybe using the `countrycode` package from CRAN
6. Get geocodes (lon, lat) for each row of the `countries` dataset
7. Create a scatterplot on the top of a worldmap, where each point stands for a country and the size is based on the number of package downloads
8. Merge GDP per capita to the dataset
9. Check if there's any association between GDP and ratio of OSX users

Week 5 (100 min): Recap on Time Series and Spatial Data

  • Extended time-series object: 5.R
  • Drawing heatmaps: 5.R
  • Plotting from multiple datasets: 5.R
  • Time periods, intervals, duration: 5.R
  • Case study of Portland's Biketown PDX data: 5.R
Homework
1. Load the Maryland Bridges dataset from https://github.com/rfordatascience/tidytuesday/tree/master/data/2018-11-27
2. Visualize the location of the bridges as a scatter plot via `ggplot2`
3. Add a map layer behind the scatter plot
4. Compute the number of months between the built date (let's suppose all bridges were finished in Dec) and most recent inspection date
5. Which bridge is the closest to Budapest, Hungary?
6. Compute the average daily traffic on all bridges per county
7. Create a scatter plot showing the daily traffic per country by placing points with varying sizes on the geometric center of the counties computed from the geolocation of the bridges

Week 6 (100 min): Presenting Insights

Final take-home assignment
Note, that this take-home assignment will make up 50% of your final grade, so please take this seriously. Clean up the R script / documents from comments and dead code, also double check that all the commands are working etc before submission. 

Dataset to be used: US Honey Production from https://github.com/rfordatascience/tidytuesday + some metadata on kaggle.com

Minimum requirements: an R Markdown or Shiny application including at least

  - a paragraph describing the dataset including min 3 inline code chunks
  - a frequency table or summary stats rendered as a table
  - a ggplot2 chart using at least 3 variables from the dataset
  - visualize the honey production per state in 2012
  - predict the total honey production in 2013 based on the available dataset

Note, that fulfilling the above minimum requirements will result in 3 stars out of the overall 5 stars given for this project, which will be taken into account with a weight of 4 plus your homeworks (each with a weight of 1) for the final grade, so if you are looking for an above average grade, I'd suggest spending more time on this project and come up with creative questions and insights on this data, then present in a nice way.

Tech requirements:

  - R Markdown: publish your document on https://rpubs.com, add the URL to your Rmd file and upload to Moodle
  - Shiny: publish your app at http://www.shinyapps.io, add the URL to your Shiny app source file(s) and upload to Moodle
  - strict deadline: Dec 21 (Friday), 2018 -- no late submissions will be accepted

Contact

File a GitHub ticket.

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