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)

[1] 0.6931472

log(10)

[1] 2.302585

log(2.72)

[1] 1.000632

log10(10)

[1] 1

Question 1: Compute the log base of 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

#Batting Average=(No. of Hits)/(No. of At Bats)
#What is the Batting 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] 0.198

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

#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

3 != 8

[1] TRUE

3 <= 8

[1] TRUE

3>4

[1] FALSE

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

# 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

2<1| 2==3

[1] FALSE

total_bases<-7+4
total_bases*4

[1] 44

ls()

 [1] "At_bats"         "At_Bats"         "At_Bats1"       
 [4] "BA"              "Bat_Average"     "Batting_Average"
 [7] "BattingAverage"  "N_hits"          "N_Hits"         
[10] "OBP"             "OBP_ad"          "OBP_Adj"        
[13] "total_bases"    

rm(total_bases)
ls()

 [1] "At_bats"         "At_Bats"         "At_Bats1"       
 [4] "BA"              "Bat_Average"     "Batting_Average"
 [7] "BattingAverage"  "N_hits"          "N_Hits"         
[10] "OBP"             "OBP_ad"          "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(1, 3, 2, 1, 4)
runs_per_9innings

[1] 1 3 2 1 4

hits_per_9innings <- c(2, 4, 6, 1, 2)
hits_per_9innings

[1] 2 4 6 1 2

# replicate function
rep(2, 5)

rep(3,3)

[1] 3 3 3

# consecutive numbers
1:5

[1] 1 2 3 4 5

2:10

[1]  2  3  4  5  6  7  8  9 10

# sequence from 1 to 10 with a step of 2
seq(1, 10, by=2)

[1] 1 3 5 7 9

seq(1,10,by=3)

[1]  1  4  7 10

seq(2,13,by=3)

[1]  2  5  8 11

pitches_by_innings+strikes_by_innings#+

[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

mean(pitches_by_innings)

[1] 13.4

pitches_by_innings[1]

[1] 12

hits_per_9innings[1]

[1] 2

pitches_by_innings[length(pitches_by_innings)]

[1] 10

hits_per_9innings[5]

[1] 2

pitches_by_innings[c(2,3,4)]

[1] 15 10 20

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 Frames

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 a spread

ceo_salaries<-c(12,.4,2,15,8,3,1,4,.25)
mean(ceo_salaries)

[1] 5.072222

var(ceo_salaries)

[1] 28.95944

sd(ceo_salaries)

[1] 5.381398

median(ceo_salaries)

[1] 3

summary(ceo_salaries)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.250   1.000   3.000   5.072   8.000  15.000 

fivenum(ceo_salaries)
# 12,.4,2,15,8,3,1,4,.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

#Question_7: Find the most frequent value of hits_per_9innings.

strikes_by_innings

[1]  9 12  6 14  9

getMode(strikes_by_innings)

[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")

table(game_day)

game_day
  Friday   Monday Saturday   Sunday  Tuesday 
       2        2        3        2        1 

getMode(game_day)

[1] "Saturday"