This dataset contains 1,157 baseball players including their handedness (right or left handed), height (in inches), weight (in pounds), batting average, and home runs.

In this project I analyze the data for performance of baseball players based on their Height, Weight and Handedness. I observe how these factors can affect the number of Home Runs(HR) and Batting Average(BA) a player can have.

I create two plots, one showing the relationship between Height, Batting Average(BA), and Home Runs(HR); another one showing the relationship between Weight, Home Runs(HR), and Handedness.

To create a useful visualization that can help my audience understand the relationship, I tried to use simple elements and colors to avoid confusion. An informative visualization can be simple and at the same time transfer the message I intend to share with my audience quickly.

Since for each player their height, weight, and handedness are provided in the dataset, I wanted to make use of all these variables in drawing my conclusions depending on whether I can find any patterns.

Before making the decision on the type of my visualization, I first did a bit of exploratory analysis using R on the data. I plotted simple histograms to see how the data is distributed among players.

In this plot, I first drew a 2 dimension plot, displaying height against batting average(BA). After the initial drawing, and with minimal modifications, it was visible that the batting average(BA) tends to drop as players' height increases. I grouped the data by height and I could immediately see a negative correlation between these two variables. As the height of players increased, their batting average (BA) intended to drop. The best way to show this correlation was to use a line plot, and adding points for each height group.

After a bit of searching on how weight and height of baseball players can affect their performance, I decided to add home runs(HR) to the same visualization to observe its relationship with height. Since I already was using lines to show height and batting average(BA), I decided to this time use bubbles to show height and home runs(HR) scores. I found that:

• The number of home runs (HR) seemed to have been more stable for those who were 70-75 inch tall
• There seems to be a sudden rise in the number of home runs (HR) for those who were 65-inch tall. Those players also have the highest number of batting average(BA) amongst the rest of the players.

To make the visualization more interactive, I used tooltips to show height, home runs and batting average as the mouse hovers over the bubbles or points on lines.

One thing I would like to have in the dataset was a category showing how experienced a player is. It could be that the number of home runs(HR) or batting average(BA) score have correlations with the level of experience a player has.

In my second plot, I decided to observe how weight can/might affect the number of home runs(HR). I also wanted to use the handedness variable to see how weight and handedness are related.

My initial plot was a simple 2D visualization hightlighting the relation of weight with home runs(HR). With this plot, I could see an overall positive correlation between the number of home runs(HR) and the weight of a player-- heavier players tend to have higher score of their home runs(HR).

To make the plot a bit more interactive, I decided to bring in handedness variable. After adding this variable, it seemed to me that left-handed players tend to score higher on home runs(HR). I made the interaction more appealing by bringing in another variable, the batting average. I used a bubble chart to demonstrate these relationships:

• Grouped the players based on their weight (i.e. aggregated average weight) and handedness
• Used three colors to divide players based on their handedness: Left, right or both-handed
• Used size of bubbles to show the batting average(BA) score: Higher batting average(BA) is shown with bigger bubbles

Based on this plot, I saw a positive correlation between weight of players and the number of home runs(HR) they scored:

• As the weight of a player increases, the number of the home runs(HR) increases as well
• There also seems to be a trend for higher number of home runs(HR) among left-handed players

• https://bost.ocks.org/mike/selection/
• https://blog.decayingcode.com/post/Easy-Charting-in-JavaScript-with-d3js-and-Dimple-from-CSV-data/
• https://gist.github.com/rgilredondo/4580cf3269e5461efb866d47a6e94c4c
• https://gist.github.com/Amoozegar/b48cfdb57b52174a3c71fba0998f0258
• https://www.sitepoint.com/create-data-visualizations-javascript-dimple-d3/
• https://bost.ocks.org/mike/bar/