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plot(cars)

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mean(cars$speed)
## [1] 15.4
mean(cars$dist)
## [1] 42.98
max(cars$dist)
## [1] 120
max(cars$speed)
## [1] 25

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2^5
## [1] 32
log(2.72)
## [1] 1.000632
log10(5)
## [1] 0.69897
log10(10)
## [1] 1
log10(1)
## [1] 0
log(10,base = 5)
## [1] 1.430677
log(10,base = 2)
## [1] 3.321928
log(1000,base = 10)
## [1] 3

#Question_1: Compute the log base 5 of 10 and the log of 10.

log(10, base = 5)
## [1] 1.430677
log(10,base = 10)
## [1] 1

Computing some offensive metrics in Baseball #Batting Average=(No. of Hits)/(No. of At Bats) #What is the batting average of a player that bats 29 hits in 112 at bats?

BA=(29)/(112)
BA
## [1] 0.2589286

#Question_2:What is the batting average of a player that bats 42 hits in 212 at bats?

hits <- 42
at_bats <- 212

batting_average <- hits / at_bats
batting_average
## [1] 0.1981132
#On Base Percentage
#OBP=(H+BB+HBP)/(At Bats+H+BB+HBP+SF)
#Let us compute the OBP for a player with the following general stats
#AB=515,H=172,BB=84,HBP=5,SF=6
OBP=(172+84+5)/(515+172+84+5+6)
OBP
## [1] 0.3337596
On_Base_Percentage=round(OBP,digits = 3)
On_Base_Percentage
## [1] 0.334

#Question_3:Compute the OBP for a player with the following general stats: #AB=565,H=156,BB=65,HBP=3,SF=7

AB <- 565
H <- 156
BB <- 65
HBP <- 3
SF <- 7

OBP <- (H + BB + HBP) / (AB + H + BB + HBP + SF)
OBP
## [1] 0.281407
On_Base_Percentage <- round(OBP, 3)
On_Base_Percentage
## [1] 0.281
3 == 8# Does 3 equals 8?
## [1] FALSE
3 != 8# Is 3 different from 8?
## [1] TRUE
3 <= 8# Is 3 less than or equal to 8?
## [1] TRUE
3>4
## [1] FALSE
Total_Bases <- 6 + 5
Total_Bases*3
## [1] 33
## [1] 33


## [1] "BA"                 "Batting_Average"    "OBP"               
## [4] "On_Base_Percentage" "Total_Bases"

#Question_4: Define two vectors,runs_per_9innings and hits_per_9innings, each with five elements.

runs_per_9innings <- c(4, 3, 5, 2, 6)
hits_per_9innings <- c(9, 11, 8, 10, 7)

runs_per_9innings
## [1] 4 3 5 2 6
hits_per_9innings
## [1]  9 11  8 10  7

#Question_5: Get the first element of hits_per_9innings.

hits_per_9innings[1]
## [1] 9

#Question_6: Get the last element of hits_per_9innings.

hits_per_9innings[length(hits_per_9innings)]
## [1] 7
# Function to find the mode, i.e. most frequent value
getMode <- function(x) {
  ux <- unique(x)
  ux[which.max(tabulate(match(x, ux)))]
}
data.frame(bonus = c(2, 3, 1),#in millions 
           active_roster = c("yes", "no", "yes"), 
           salary = c(1.5, 2.5, 1))#in millions

#Question_7: Find the most frequent value of hits_per_9innings.

getMode(hits_per_9innings)
## [1] 9

#Question_8: Summarize the following survey with the table() command: #What is your favorite day of the week to watch baseball? A total of 10 fans submitted this survey. #Saturday, Saturday, Sunday, Monday, Saturday,Tuesday, Sunday, Friday, Friday, Monday game_day<-c(“Saturday”, “Saturday”, “Sunday”, “Monday”, “Saturday”,“Tuesday”, “Sunday”, “Friday”, “Friday”, “Monday”)

game_day <- c("Saturday", "Saturday", "Sunday", "Monday", "Saturday",
              "Tuesday", "Sunday", "Friday", "Friday", "Monday")

table(game_day)
## game_day
##   Friday   Monday Saturday   Sunday  Tuesday 
##        2        2        3        2        1

#Question_9: What is the most frequent answer recorded in the survey? Use the getMode function to compute results.

getMode(game_day)
## [1] "Saturday"