Name:
Andrew ID:
Collaborated with:
This lab is to be done in class (completed outside of class if need be). You can collaborate with your classmates, but you must identify their names above, and you must submit your own lab as an knitted HTML file on Canvas, by Sunday 11:59pm, this week. Make sure to complete your weekly check-in (which can be done by coming to lecture, recitation, lab, or any office hour), as this will count a small number of points towards your lab score.
This week’s agenda: reading in data, cleaning data, reordering data, merging data, restructuring data.
1a. Use read.table() to read into R the data sets found at http://www.stat.cmu.edu/~ryantibs/statcomp-F19/data/sprint.m.dat and http://www.stat.cmu.edu/~ryantibs/statcomp-F19/data/sprint.w.dat, and call the resulting data frames sprint.m.df and sprint.w.df, respectively. Make sure to use appropriate arguments in read.table(), you can check the lecture for what is needed. Also, make usre to set the argument stringsAsFactors to be TRUE. Verify that you end up with data frames of dimensions 2988 x 8 (for the men’s data) and 2018 x 8 (for the women’s data). Display the first five rows of both data frames.
1b. Since we set stringsAsFactors=TRUE in the previous part, the function read.table() treated the values in the Wind column as factors. (The values would’ve been strings otherwise). We want to convert these factors to numerics. Converting factors to numerics can be an annoyingly frustrating task in general, so it’s good to practice it. These next two questions will guide you on how to do this.
We provide a test string input.value below, which is “4,8”. Use functions you have seen in previous weeks on text processing to convert input.value to contain the numeric 4.8 instead. Display the converted value and check its class to ensure it is a numeric. Hint: there are multiple ways to do the conversion; perhaps the most familiar way will be to use strsplit() to separate input.value by the comma, and then use paste() function to concatenate the “4” with the “8”, separated by “.”, and then finally use as.numeric().
input.value = "4,8"wind.measurements of factors (note: not strings) for you to play around with, as a testing ground. Write a function factor.to.numeric() that takes in a vector of factors and outputs a vector of corresponding numerics. Verify that factor.to.numeric(wind.measurements) returns c(-2.0, 0.0, 0.6, 1.7) (or equivalent numbers, e.g., 2 instead of 2.0 is fine).wind.measurements = as.factor(c("-2,0", "0,0", "0,6", "+1,7"))1d. Using factor.to.numeric(), convert the Wind column of sprint.m.df and sprint.w.df into numeric variables. However, you might get exactly one NA from this process in sprint.w.df (or get no NAs depending on you how wrote your function). If you do, what was the wind entry that failed to be converted into a numeric (hence becoming NA)? In words, can you describe why this NA occurred? (This will require you to reload the sprint.w.df from the beginning to see what certain values in the Wind column were before we used the factor.to.numeric().) If needed, you should manually fix this NA. Then, display the first five rows of sprint.m.df and sprint.w.df.
1e. For each of the men’s and women’s data frames, plot the the 100m sprint time versus the wind measurements, setting the pch appropriately so that the points are solid small black dots. Label the axes and title the plot appropriately. Do you notice a trend—does more wind assistance mean faster sprint times? Where do the fastest men’s time, and for the fastest women’s time, lie among this trend? (Remark: there’s an interesting story behind the wind measurement that was recorded for the fastest women’s time, you might enjoy reading about it online …)
2a. Notice that the Birthdate and Date columns in both data frames sprint.m.df and sprint.w.df are currently factors that follow the format DAY.MONTH.YEAR. Write a function called date.to.numeric() that takes in a factor from either the Birthdate or Date columns, and outputs a numeric of the form DAY + (MONTH)*10^2 + (YEAR)*10^4. For example, date.to.numeric(as.factor("16.08.2009")) should return the numeric 20090816. Then, use one of the apply functions to iteratively use date.to.numeric() on both the Birthdate and Date columns in both the sprint.m.df and sprint.w.df data frames, converting these columns to numerics as appropriate. Print out the first five lines of sprint.m.df and sprint.w.df afterwards. Note: the dates in Birthdate have only the last two numbers of the year, while Date has all four numbers of the year (e.g., 86 vs. 1986). Your code should handle this appropriately.
2b. Reorder both data frames sprint.m.df and sprint.w.df so that their rows are in increasing order of Date. Print out the first five lines of sprint.m.df and sprint.w.df afterwards.
2c. Create a column in both sprint.m.df and sprint.w.df called City.Date, given by concatenating the entries in the City and Date columns, separated by “.”. For example, if the City is Tokyo and Date is 19641015, then City.Date should be Tokyo.19641015. Print out the first five lines of sprint.m.df and sprint.w.df afterwards.
2d. We now want to remove all duplicated sprints in each of sprint.m.df and sprint.w.df. Specifically, if multiple sprints (rows) in sprint.m.df occur on the same City.Date, we will only keep the fastest sprint and discard the rest. Do the same with sprint.w.df. Make sure at the end, all the rows in sprint.m.df and sprint.w.df are still sorted in order of Date, and if multiple sprints occur on the same date, then sort those sprints alphabetically by City. Your final sprint.m.df should have dimension 1253 x 9, while sprint.w.df should be 921 x 9. Display the first five lines of sprint.m.df and sprint.w.df afterwards. Hint: write a function to do the cleaning; then apply this function to each of the two data frames.
2e. Verify that in both sprint.m.df and sprint.w.df, each of the values in the City.Date column appear exactly once (i.e., there are no duplicated values).
3a. In preparation of merging sprint.m.df and sprint.w.df, we first wwant to find all the sprints that occur in the same race in both data frames. Specifically, remove all the rows in sprint.m.df that have a City.Date that does not occur in sprint.w.df. Likewise, remove all the rows in sprint.w.df that have a City.Date that does not occur in sprint.m.df. Then, remove the City and Date columns in both data frames. In the end, both sprint.m.df and sprint.w.df should have 377 rows and 7 columns. Print out the first five lines of sprint.m.df and sprint.w.df afterwards. Hint: you might find the %in% operator useful; try looking it its help file.
3b. We will now complete the manual merge of sprint.m.df and sprint.w.df. First, check the order of values in City.Date in sprint.m.df match exactly with those in sprint.w.df. Then, use cbind() to create a new data frame sprint.df that has 13 columns. The first column should be City.Date, the next 6 columns should contain all the remaining columns from sprint.m.df, and the last 6 columns should contain all the remaining columns form sprint.w.df. Of course, each row should correspond to sprints from the same City.Date. Print out the first five lines of sprint.df afterwards, and verify that its dimensions are 377 x 13.
3c. Ue the merge() function to recreate the merge in the previous part. This should require only one line of code; call the result sprint.df.2. In the call to merge(), make sure to set the argument suffixes=c(".m",".w"), which will help appropriately distinguish the column names after merging (a convenience of using the merge() function). The merged data frame sprint.df2 should be of dimension 377 x 13; display its first five lines. Do these match those of sprint.df from the last part? They shouldn’t match, and this is because the merge() function sorts according to the by column, by default. Take a look at the help file for merge() to see what arugment you should set in order to turn off this behavior; then check again the first five lines of the output sprint.df2, and compare to those from sprint.df.
3d. Plot the Time.w versus Time.m columns in sprint.df2, with appropriately labeled axes and an appropriate title. Looking at the the women’s versus men’s times from the common track meets—is there a positive correlation here, i.e., is there a “track meet effect”? This might suggest that there is something about the track meet itself (e.g., the weather, the atmosphere, the crowd, the specific way the track has been constructed/set up, etc.) that helps sprinters run faster. Then, use the cor.test() function to determine if there is a significant correlation between Time.w and Time.m—specifically, report the p.value from its output. In the call to cor.test(), use all default arguments.