1. An Introduction to Stock Market Data Analysis with R (Part 1)

    This example is important because it shows basic tools for handling and analyzing stock market data with R. The stock data is obtained from Yahoo! Finance. Later four prices for each date (open, high, low, and close) is visualized by Japanese candlestick plot. Two transformations are caried out on the raw data:
    • Stock’s Return
    • Change of each stock per day

    Then, the author employs moving averages to smooth the series and to identify its trends. It is a nice example to play with time series data but I still think that financial data is boring.

  2. Gender Pay Gap Analysis

    This work investigates whether there is a significant the gender pay gap difference between men and women and the other factors (race, education, marriage, etc.) affecting the gap. It is found that there is actually a significant pay gap between two genders and this gaps varies depending on individual’s weight and maritial status. As seen in the figure below, especially over-weighted females are penalyzed beause they do not comply with society’s beauty standards.

    Weight vs. Income for men and women World is not just! :( p.s: There is also a nice discussion about how to handle missing values in the data set.

  3. Guardian data blog — UK general election analysis in R

    In this example the author analyzes UK’s elections of the last thirty years by using the data obtained from Guardian data blog. The beauty of this data set is itminimal requirement of data cleaning. The full R code is shared at the end of the block.

  4. Querying the Bitcoin blockchain with R

    For the ones who are investing in cryptocurrency called Bitcoin, this example mighht be helpfull. With this example you can query the exchange value of Bitcoin vs. EUR, visualize the time-series of the lastest exchange values and draw the resulting network of transactions between addresses (since all the transactions of Bitcoin is back-traceable).

  5. LOGIT REGRESSION | R DATA ANALYSIS EXAMPLES

    Actually there are two logistic regression examples in this site but I want to focus on the second one. It investigates the how GRE, GPA scores and prestige of home university of a student affect its admission chance to graduate school. The probility of admission is visualized using ggplot. This example might be helpfull once we learn more about logistic regression throughout the course.

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