#addition
2+3
[1] 5
#substraction
2-5
[1] -3
# Exponentiation
2^3 
[1] 8
# log
log(10)
[1] 2.302585
log(2.72,base = 2.72)
[1] 1
log10(10)
log10(100)
log(10,base =5)
#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
Batting_Average=round(BA,digits = 3)
Batting_Average
[1] 0.259
#Question_2:What is the batting average of a player that bats 42 hits in 212 at bats?

BA=(42)/(212)
BA
[1] 0.1981132
Batting_Average=round(BA,digits = 3)
Batting_Average
[1] 0.198
On_Base_Percentage
[1] 0.428
#On Base Percentage
#OBP=(H+BB+HBP)/(At Bats+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+84+5+6)
OBP
[1] 0.4278689
#Question_3:Compute the OBP for a player with the following general stats:
#AB=565,H=156,BB=65,HBP=3,SF=7
#OBP=(H+BB+HBP)/(At Bats+BB+HBP+SF)
OBP=(156+65+3)/(565+65+3+7)
On_Base_Percentage=round(OBP,digits = 3)
On_Base_Percentage
[1] 0.35
3 <= 8# Is 3 less than or equal to 8?
[1] TRUE
3>=5
[1] FALSE
On_Base_Percentage=round(OBP,digits = 3)
# Combination of statements
2 < 3 | 1 == 5 # 2<3 is True, 1==5 is False, True OR False is True
[1] TRUE
2> 3|2==3
[1] FALSE
2>1 & 3>=3
[1] TRUE
2>1 & 3>=4
[1] FALSE
Total_Bases <- 6 + 5
Total_Bases*3
[1] 33
## [1] "BA"                 "Batting_Average"    "OBP"               
## [4] "On_Base_Percentage" "Total_Bases"
rm(Total_Bases)
pitches_by_innings <- c(12, 15, 10, 20, 10) 
pitches_by_innings
[1] 12 15 10 20 10
strikes_by_innings <- c(9, 12, 6, 14, 9)
strikes_by_innings
[1]  9 12  6 14  9
#Question_4: Define two vectors,runs_per_9innings and hits_per_9innings, each with five elements.
runs_per_9innings<-c(2,5,7,11,13)
hits_per_9innings<-c(11,13,16,18,19)
runs_per_9innings
[1]  2  5  7 11 13
# compare vectors
runs_per_9innings == hits_per_9innings
[1] FALSE FALSE FALSE FALSE FALSE
hits_per_9innings
[1] 11 13 16 18 19
hits_per_9innings[length(hits_per_9innings)]
[1] 19
rep(3,5)
[1] 3 3 3 3 3
2:4
[1] 2 3 4
seq(1,20, by=3)
[1]  1  4  7 10 13 16 19
player_positions <- c("catcher", "pitcher", "infielders", "outfielders")
#Data Frames

data.frame(bonus = c(2, 3, 1),#in millions 
           active_roster = c("yes", "no", "yes"), 
           salary = c(1.5, 2.5, 1))#in millions 
#random sample
sample(1:10, size=5)
[1]  8  9  4  1 10
x <- c("Yes","No","No","Yes","Yes") 
table(x)
x
 No Yes 
  2   3 
sals <- c(12, .4, 5, 2, 50, 8, 3, 1, 4, 0.25)
# the average
mean(sals) 
[1] 8.565
# the average
mean(sals) 
[1] 8.565
# the variance
var(sals)
[1] 225.5145
# the standard deviation
sd(sals)
[1] 15.01714
# the median
median(sals)
[1] 3.5
# summary statistics
summary(sals)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.250   1.250   3.500   8.565   7.250  50.000 
# Tukey's five number summary, usefull for boxplots
# five numbers: min, lower hinge, median, upper hinge, max
fivenum(sals)
[1]  0.25  1.00  3.50  8.00 50.00
summary(sals)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.250   1.250   3.500   8.565   7.250  50.000 
# Function to find the mode, i.e. most frequent value
# mode does not exist in R
getMode <- function(x) {
     ux <- unique(x)
     ux[which.max(tabulate(match(x, ux)))]
 }
getMode(strike_per_9inning)
Error: object 'strike_per_9inning' not found
#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")
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"
On_Base_Percentage
[1] 0.428
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QmFzZV9QZXJjZW50YWdlCmBgYA==