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plot(cars)

mean(cars$speed)
## [1] 15.4
mean(cars$dist)
## [1] 42.98
max(cars$dist)
## [1] 120
max(cars$speed)
## [1] 25
4+1 #math functions
## [1] 5
5-2
## [1] 3
2^2
## [1] 4
sqrt(25)
## [1] 5
2^5
## [1] 32
log(2.72) #natural log
## [1] 1.000632
log10(5)
## [1] 0.69897
log10(10)
## [1] 1
log10(100)
## [1] 2
#Here we are computing log base ten
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 Offensive Metrics in Baseball

BA=(29)/(112)
BA
## [1] 0.2589286
Batting_Average=round(BA,digits = 3)
Batting_Average
## [1] 0.259
BA=(42)/(212)
BA
## [1] 0.1981132
Batting_Average=round(BA,digits = 3) #round BA to 3 digits
Batting_Average
## [1] 0.198
OBP=(172+84+5)/(515+172+84+5+6) #including hits in demoninator calculation
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

OBP=(156+65+3)/(565+156+65+3+7)
OBP
## [1] 0.281407
On_Base_Percentage=round(OBP, digits = 3)
On_Base_Percentage
## [1] 0.281
3==8 #true or false
## [1] FALSE
3!=8
## [1] TRUE
3<=8
## [1] TRUE
3>4
## [1] FALSE
FALSE|FALSE
## [1] FALSE
TRUE & FALSE
## [1] FALSE
!FALSE
## [1] TRUE
2<3|1==5
## [1] TRUE

#Assigning Values to variables

TotalBases<-6+5
TotalBases*3
## [1] 33
ls() #list of variables 
## [1] "BA"                 "Batting_Average"    "OBP"               
## [4] "On_Base_Percentage" "TotalBases"
rm(TotalBases)

#Vectors

pitches_by_inning<-c(12,15,10,20,10) #creating vector
pitches_by_inning
## [1] 12 15 10 20 10
strikes_by_inning<-c(9,12,6,14,9)
strikes_by_inning
## [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(3,7,1,2,4)
hits_per_9innings<-c(8,2,5,2,7)
runs_per_9innings
## [1] 3 7 1 2 4
hits_per_9innings
## [1] 8 2 5 2 7
rep(2,5) #repeating numbers
## [1] 2 2 2 2 2
rep(1,4)
## [1] 1 1 1 1
1:5 #consecutive numbers
## [1] 1 2 3 4 5
2:10
## [1]  2  3  4  5  6  7  8  9 10
seq(1,10,by=2) #sequence from number to number with intervals
## [1] 1 3 5 7 9
seq(2,13,by=3)
## [1]  2  5  8 11
pitches_by_inning+strikes_by_inning #adding vectors
## [1] 21 27 16 34 19
pitches_by_inning==strikes_by_inning #comparing vectors
## [1] FALSE FALSE FALSE FALSE FALSE
length(pitches_by_inning) #length of vector
## [1] 5
min(pitches_by_inning) #min value in vector
## [1] 10
mean(pitches_by_inning) #average value in vector
## [1] 13.4
pitches_by_inning
## [1] 12 15 10 20 10
pitches_by_inning[1] #getting first element
## [1] 12

#Question_5: Get the first element of hits_per_9innings.

hits_per_9innings[1]
## [1] 8
pitches_by_inning[length(pitches_by_inning)] #length helps show last element of vector
## [1] 10

#Question_6: Get the last element of hits_per_9innings.

hits_per_9innings[length(hits_per_9innings)]
## [1] 7
pitches_by_inning[c(2,3,4)] #getting 2nd through 4th value of a vector
## [1] 15 10 20
player_positions<-c( "catchers", "pitcher", "infielders", "outfielders")

#Data Frames, similar to a spreadsheet

data.frame(bonus = c(2,3,1), #in millions
           active_roster = c("yes", "no", "yes"),
           salary = c(1.5, 2.5, 1)) #in millions

#How to Make a Random Sample

sample(1:10, size=5)   #first shows the numbers of vector that can be selected, then size shows numbers to populate
## [1]  3  5  2  7 10
bar<-data.frame(var1 = LETTERS[1:10], var2 = 1:10)
bar #making new data frame for random sample
n<-5 #selecting sample size
samplerows<-sample(1:nrow(bar), size=n) #creates random sample from data frame, use nrow instead of entering number
samplerows
## [1]  4  3 10  5  2
barsample<-bar[samplerows, ] #extracting rows of random sample of bar, also creating new data frame
print(barsample) #prints sample
##    var1 var2
## 4     D    4
## 3     C    3
## 10    J   10
## 5     E    5
## 2     B    2
bar[sample(1:nrow(bar),n), ] #single line of code from above functions

#Using Tables

x<-c("Yes", "No", "No", "Yes", "Yes") #fan survey report
table(x) #creates table for x
## x
##  No Yes 
##   2   3

#Numerical Measures of Center and Sprea

sals<-c(12, .4, 5, 2, 50, 8,3, 1, 4, 0.25) #yearly salaries
mean(sals)
## [1] 8.565
var(sals) #variance
## [1] 225.5145
sd(sals) #standard deviation
## [1] 15.01714
median(sals)
## [1] 3.5
fivenum(sals) #shows min, lower hinge, median, upper hinge, and max
## [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

#How About the Mode

getMode<-function(x) {
  ux<- unique(x)
  ux[which.max(tabulate(match(x, ux)))]
} #Function to help us find the mode
getMode(pitches_by_inning) #finding mode
## [1] 10

#Question_7: Find the most frequent value of hits_per_9innings.

getMode(hits_per_9innings)
## [1] 2

#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"