Exam 2

Question 5 Consider the following vectors representing the number of field goals made and attempted by a basketball player in five games:

Field Goals Made: c(18, 7, 6, 9, 10,13) Field Goals Attempted: c(36, 23, 12, 18, 24,22)

Calculate the field goal percentage for each game and select the correct average field goal percentage for the five games.

# Create field goals made vector
FGM <- c(18, 7, 6, 9, 10,13)

# Create field goals attempts vector
FGA <- c(36, 23, 12, 18, 24,22)

avg_per_game <- round(FGM/FGA, 3)
avg_per_game

[1] 0.500 0.304 0.500 0.500 0.417 0.591

overall_avg<-round(mean(avg_per_game),2)
overall_avg

[1] 0.49

Question 6

Consider the following vectors in R representing the number of three-pointers made (3PM) and attempted (3PA) by a basketball player over a seven-game span:

Three-Pointers Made: c(3, 5, 10, 6, 3, 7, 1)

Three-Pointers Attempted: c(9, 10, 18, 12, 11, 12, 11)

Calculate both the average of the individual per-game three-point percentages and the overall (cumulative) three-point shooting percentage across all seven games. Submit your code and knitted R HTML file or RPubs link as instructed.

# Create three point made vector
TPM <- c(4, 5, 3, 6, 7)

# Create three point attempts vector
TPA <- c(9, 10, 8, 11, 12)

avg_per_game <- round(TPM/TPA, 3)
avg_per_game

[1] 0.444 0.500 0.375 0.545 0.583

overall_total<-round(sum(TPM)/sum(TPA),3)
overall_total

[1] 0.5

Question 7

Consider the following dataset representing the performance of baseball players in a season. It includes the following variables: PlayerID, Hits, At-Bats, Home Runs (HR), Walks (BB), and Strikeouts (SO).

# Create table
Q7.df <- data.frame(
  player_id = c(1, 2, 3, 4, 5),   # Player ID vector
  hits = c(112, 124, 121, 106, 140),  # Hits vector
  at_bats = c(400, 450, 380, 500, 402), # At-Bats vector
  HR = c(25, 22, 8, 20, 11),      # Home Runs vector
  BB = c(50, 60, 19, 150, 55),    # Walks vector
  SO = c(60, 65, 67, 92, 70)      # Strikeouts vector
)

# Add OBP column: OBP = (Hits + Walks) / (At-Bats + Walks)
Q7.df$OBP <- (Q7.df$hits + Q7.df$BB) / (Q7.df$at_bats + Q7.df$BB)

Q7.df # Probably overkill but in a real life setting I would create a df

NA

# Find the player with the highest OBP
Q7.df$player_id[which.max(Q7.df$OBP)]

[1] 5

Player ID 5 has the highest OBP.