Getting Started with R

1+1

[1] 2

7-8

[1] -1

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

[1] 5+6i

4/2

[1] 2

6/12

[1] 0.5

2^3

[1] 8

sqrt(25)

[1] 5

sqrt(16)

[1] 4

sqrt(144)

[1] 12

log(2)

[1] 0.6931472

log(10)

[1] 2.302585

log(2.72)

[1] 1.000632

log10(10)

[1] 1

Question 1: Compue the Log base 5 of 10 and the log of 10.

log(10,5)

[1] 1.430677

log(10,10)

[1] 1

log(100,4)

[1] 3.321928

Computing some offensive metrics in Baseball

#Battling Average=(No. of Hits)/(No. of At Bats)
#What is the Battling average of a player that bats 29 hits in 412 at bats?
BA=129/412
BA

[1] 0.3131068

#Alternative Solutiuon
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
Batting_Average=round(Bat_Average,digits = 3)
Batting_Average

[1] 0.198

#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+156+7)
OBP_ad=round(OBP,digits = 3)
OBP_ad

[1] 0.281

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

3==8

[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

The logical operators are & for logical AND, | for logical OR, and ! for NOT. These are some examples:

#Logical 
FALSE|TRUE

[1] TRUE

# Logical Disjunction (or)
FALSE | FALSE # False OR False

[1] FALSE

# Logical Conjunction (and)
TRUE & FALSE #True AND False

[1] FALSE

# Negation
! FALSE # Not False

[1] TRUE

# 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

ls()

 [1] "At_Bats"         "At_Bats1"        "BA"              "Bat_Average"     "Batting_Average" "N_Hits"          "OBP"             "OBP_ad"         
 [9] "OBP_Adj"         "total_bases"    

rm(total_bases)

Warning in rm(total_bases) : object 'total_bases' not found

ls()

[1] "At_Bats"         "At_Bats1"        "BA"              "Bat_Average"     "Batting_Average" "N_Hits"          "OBP"             "OBP_ad"         
[9] "OBP_Adj"        

Vectors

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(15,12,10,8,7)
hits_per_9innings <-c(6,12,8,10,13)

runs_per_9innings

[1] 15 12 10  8  7

hits_per_9innings

[1]  6 12  8 10 13

#replicate function

rep(2,5)

[1] 2 2 2 2 2

rep(3,3)

[1] 3 3 3

# consecutuive numbers
1:6

[1] 1 2 3 4 5 6

2:7

[1] 2 3 4 5 6 7

# Sequence from 1 to 10 with a step of 2 


seq(1,10,by=3)

[1]  1  4  7 10

seq(2,13,by=3)

[1]  2  5  8 11

# adding vectors
pitches_by_innings + strikes_by_innings # + operator

[1] 21 27 16 34 19

#compare two vectors
pitches_by_innings

[1] 12 15 10 20 10

strikes_by_innings

[1]  9 12  6 14  9

pitches_by_innings == strikes_by_innings

[1] FALSE FALSE FALSE FALSE FALSE

length(pitches_by_innings)

[1] 5

min(pitches_by_innings)

[1] 10

# finding the average in the vecotr
mean(pitches_by_innings)

[1] 13.4

# findin the first vector
pitches_by_innings[1]

[1] 12

Question_5: Get the first element of hits_per_9innings.

hits_per_9innings

[1]  6 12  8 10 13

hits_per_9innings[1]

[1] 6

pitches_by_innings

[1] 12 15 10 20 10

pitches_by_innings[length(pitches_by_innings)]

[1] 10

hits_per_9innings

[1]  6 12  8 10 13

hits_per_9innings[length(hits_per_9innings)]

[1] 13

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

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,.4,2,15,8,3,1,4,.25)
mean(ceo_salaries)

[1] 5.072222

var(ceo_salaries)

[1] 252.5907

sd(ceo_salaries)

[1] 15.87257

median(ceo_salaries)

[1] 3

fivenum(ceo_salaries)

[1]  0.25  1.00  3.00  8.00 15.00

#(12,.4,2,50,8,3,1,4,.25)
#.25,.4,1,2,3,4,8,12,15

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] 10

# Most frequent value in pitches_by_innings
getmode(pitches_by_innings)

[1] 10

Question_7: Find the most frequent value of hits_per_9innings.

hits_per_9innings

[1]  6 12  8 10 13

getmode(hits_per_9innings)

[1] 6

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 

getmode(game_day)