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(a) Read in the data file, and check that each column was stored correctly in R using str().

16 / 01 / 2019 Statistics

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Before we get to some regression models, let’s get comfortable using R while answering some basic questions about the dataset.
1. To begin with, let’s read in and format the data. There are no marks for this section so you don’t have to show anything, but you still have to do it in order to answer the questions in the next section.
(a) Read in the data file, and check that each column was stored correctly in R using str(). You can use the line read.csv("NFL draft.csv", head=T, strip.white=T, stringsAsFactors=F) The second argument stores the header line as variable names; the third removes extra spaces at the beginning and end of records; the fourth stores all non-numeric records as character strings. You should store this object in a data frame. (b) Position should be a factor but before you do that, you should change anyone with the Long Snapper position to the Center position. They are pretty much the same position. (c) Some of these positions are naturally similar. Make a new factor with three levels. The “Linemen” are the players with positions {C, OG, OT, TE, DT, DE}. The “Small Backs” are the players with positions {CB, WR, FS}. Everyone else can be considered a “Big Back”. (d) You’ll need to change those heights to a proper number in inches. The heights as given are in the format “Ft-In” and there are 12 inches in a foot. (e) The last column has lots of interesting stuff in it. Too bad it’s all in one text string. Break that column into four separate columns and store each in your data frame. See the sample code for some regex hints. (f) We need the overall draft pick as a number, so remove all the text that’s not a number and store this column as a numeric instead. If you start to get frustrated with the last three tasks, you can always fix these things with brute force so that you can continue with the assignment. But, a programmatic solution is better if you have the patience.
2. Let’s summarize the Draft data with tables and graphs, and answer some exploratory questions.
(a) Which pro team had the most draft picks this year? How many picks did they have?
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(b) Which college team had the most players drafted, and how many were drafted? (c) Present a barplot showing the number of draft picks by player position, in decreasing (pareto) order. Include an informative title. (d) Display the 5-number summary of player heights in this year’s draft. What is the average height for an NFL draftee? (e) Show a histogram of player heights. Set an informative title and x-axis label. (f) Who is the shortest player(s) in the draft? (g) Present a plot of a player’s 40-yd dash time (Y) vs. their Weight (X). Does the relationship look linear? Are there any outliers visible? What is unique about these outliers? (h) Look at a plot of the 3-Cone drill time (Y) vs. the shuttle drill time (X). Does it look like there is a linear association present? (i) The preceding plot would look better if grouped by the three positional groups you constructed in the first section. Present the same plot, but each group should be plotted with a unique colour AND plotting symbol. Also include a legend that shows which symbol/colour combination is which. It’s OK if your assignment isn’t printed in colour, but then the colours must be easily distinguished (ie. use three shades of grey). (j) Present a plot of the Broad jump score vs. Bench press score, for linemen only. Does there seem to be a relationship between these two test scores? (k) Which player had the shortest broad jump? (If there’s a tie, choose the lighter player since he should have jumped farther). What round did this player get drafted in? (l) Which player had the longest broad jump? How many bench press reps did this player have?
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