Getting Started with R

1+1

[1] 2

7-8

[1] -1

(2+5i) +(3-1i)

[1] 5+4i

4/2

[1] 2

6/12

[1] 0.5

2^2

[1] 4

2^5

[1] 32

sqrt(25)

[1] 5

sqrt(16)

[1] 4

sqrt(144)    

[1] 12

log(2)#Natural log

[1] 0.6931472

log(10)

[1] 2.302585

log(2.72)

[1] 1.000632

log10(10)

[1] 1

Question 1:Compute the log base 5 of 10 and the log of 10.**

Answers:Question 1

log(10,5)#log of 10,base 5

[1] 1.430677

log(10,10)#log of 10, base 10

[1] 1

log(100,4)#Blog 100, base 4

[1] 3.321928

#Battting Average=(No. of Hits)//(No. of At Bats)
#What is the battling average of a player that bats 129 hits in 412 at bats?
BA=129/412
BA

[1] 0.3131068

#Alternative Solution
N_Hits=129
At_Bats=412
BA<-N_Hits/At_Bats
BA

[1] 0.3131068

Batting_Average=round(BA,digits=3)
Batting_Average

[1] 0.313

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

#Answers
N_hits=42
At_Bats1=212
Bat_Average<-N_Hits/At_Bats1
BattingAverage=round(Bat_Average,digits = 3)
BattingAverage

[1] 0.608

#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

OBP_Adj=round(OBP,digits=3)
OBP_Adj

[1] 0.428

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+65+3+165+7)
OBP_ad=round(OBP,digits =3)
OBP_ad

[1] 0.278

Often you will want to test whether something is less than, greater than or euqal to somehting.

3==9

[1] FALSE

2==3

[1] FALSE

1==1

[1] TRUE

3>=1

[1] TRUE

3>=9

[1] FALSE

7<=10

[1] TRUE

7<=6

[1] FALSE

3!=4

[1] TRUE

TRUE& FALSE

[1] FALSE

#Combination of statements
2<3|1==5 # 2<3 is True, 1==5 is False, True OR False is True

[1] TRUE

total_bases<-7+4
total_bases*4

[1] 44

Vectors

pitches_by_innings<-c(12,15,10,20,10)
pitches_by_innings

strikes_by_innins<-c(9,12,6,14,9)
strikes_by_innins

[1]  9 12  6 14  9

rep(2,5)

[1] 2 2 2 2 2

rep (3,3)

[1] 3 3 3

1:6

[1] 1 2 3 4 5 6

2:7

[1] 2 3 4 5 6 7

seq(1,10,by=3)

[1]  1  4  7 10

#adding vectors
pitches_by_innings+strikes_by_innins#+operator

[1] 21 27 16 34 19

#compare two vectors
pitches_by_innings

[1] 12 15 10 20 10

strikes_by_innins

[1]  9 12  6 14  9

pitches_by_innings==strikes_by_innins

[1] FALSE FALSE FALSE FALSE FALSE

length(pitches_by_innings)

[1] 5

min(pitches_by_innings)

[1] 10

 mean(pitches_by_innings)

[1] 13.4

pitches_by_innings[1]

[1] 12

pitches_by_innings[length(pitches_by_innings)]

[1] 10

pitches_by_innings

[1] 12 15 10 20 10

pitches_by_innings[c(1:3)]

[1] 12 15 10

player_positions<-c("catcher","pitcher","infielders","outfielders")
player_positions

[1] "catcher"     "pitcher"     "infielders"  "outfielders"

data.frame(bonus=c(2,3,1),active_roster=c("yes","No","Yes"),salary=c(1.5,2.5,1))

Using Tables

x<-c("yes","no","no","yes","yes")
table(x)

x
 no yes 
  2   3 

Numerical measures and center of spread

ceo_salaries<-c(12,.4,2,50,8,3,1,4.25)
mean(ceo_salaries)

[1] 10.08125

var(ceo_salaries)

[1] 275.31

sd(ceo_salaries)

[1] 16.59247

median(ceo_salaries)

[1] 3.625

fivenum(ceo_salaries)

[1]  0.400  1.500  3.625 10.000 50.000

getMode<-function(X) {
ux<-unique(x)
ux[which.max(tabulate(match(x,ux)))]
}

pitches_by_innings

[1] 12 15 10 20 10

getMode(pitches_by_innings)

[1] "yes"

strikes_by_innins

[1]  9 12  6 14  9

getMode(strikes_by_innins)

[1] "yes"

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