University of Missouri Lab 6 Columbia R Project for Sports Analytics

Lab 6 will be using the same data as with lab 5. Below, I have included some lines to load in the data and do some cleaning. Your only job for loading in the data is to make sure you have the correct file path (i.e.,reference where you have lab 5 data stored), which is denoted by the fill in below.Question 1Let’s set the stage: Coach Sausaman wants to break down the August 6th practice by drill. He wants todetermine therelative efforta player devotes to each practice drill with respect to their maximum velocity,total distance, and total player load. This means that we need to standardize the metrics (e.g., percent oftotal distance that player A spends in 11v11, or warm up, etc. . . ), and then divide the standardized metricsby the percent of time a player spent on that drill. However, he want’s you to notice that in the data set, thepractice drill “11v11” was recorded twice, so you will need to add these two sessions together. As a generaloutline, you will need to:1. Filter the rows to select only the one date,2. Remove the rows that have “Session” in them as they are the total practice summary,3. Select the appropriate column names,4. Group by player and practice session,5. Combine the 11v11 drills,6. Standardize the metrics,7. Divide the standardized metrics by time spent,8. Remove unneeded columns,9. Change to long format for plotting.After you have created the data set, you will need to plot it. I have not included any outline for the plot, soit is up to you on how to visualize it. Feel free to make it as basic as you want, but be sure that the plotmakes sense and is clean.ddf =%>%#### FILL INfilter()%>%#### FILL IN, 1.filter()%>%#### FILL IN, 2.select()%>%#### FILL IN, 3.group_by()%>%#### FILL IN, 4.summarize_at(vars(Field_Time:Maximum_Velocity),~ sum(.))%>%# this adds the 11v11 obs together, 5.mutate_at(vars(Field_Time:Maximum_Velocity), )%>%#### FILL IN, 6.ungroup()%>%mutate_at(vars(Total_Distance:Maximum_Velocity), )%>%#### FILL IN, 7.select()%>%#### FILL IN, 8.pivot_longer()#### FILL IN, 9.Answer1Question 2Now, Coach Sausaman wants which practice drills have the highest player load per minute when grouping byposition. He also wants to know the 95% confidence intervals to these estimates. He also recognizes thatsome drills are not as common as others, so he only your plot to show drills that have occurred more than 20times over the course of the given data set. Breaking this down into bite-sized steps:1. Select the appropriate columns,2. Group by practice drill,3. Keep only the drills that occur 20 or more times,4. Group by position and drill,5. Calculate the summary statistics.Again, you will need to determine how best to visualize the data. However, Coach Sausaman recognizes thatthe goal keepers have a different practice routine. Therefore, he does not want you to have the goal keepersin your visualization. Feel free to make the plot as basic as you want, but be sure that the plot makes senseand is clean.%>%#### Fill inselect()%>%#### Fill in, 1.group_by(Period_Name)%>%#### Fill in, 2.filter(n()>=20)%>%# 3.ungroup()%>%group_by(Position_Name, Period_Name)%>%#### Fill in, 4.summarise_at()#### Fill in, 5.AnswerQuestion 3There is a good chance that the figures you created for Questions 1 and 2 are different than mine. I want youto describe your figures, being sure to break them down so that a coach can understand what they are seeing.However, you have do this in 5 or less sentences per figure.Answer – Figure 1Answer – Figure 2