Getting Started With R Part 1

#complex numbers use "i" 

7-8i

[1] 7-8i

log10(10)

[1] 1

log(10) #natural log not base 10

[1] 2.302585

sqrt(25)

[1] 5

log(5,base = (10))

[1] 0.69897

Compute the log base 5 of 10 and the log of 10

#Answers:
log(10,5) #log of ten, base 5

[1] 1.430677

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

[1] 1

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

[1] 3.321928

log(10) #log of 10, natural log

[1] 2.302585

log10(10) #log of 10, base 10

[1] 1

Question 1:

#Batting Average is (# hits)/(# 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 bats?

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

[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 OBP for a player with the following general stats:

#AB=565, H=157, BB=65, HBP=3, SF=7
#OBP=(H+BB+HBP)/(At Bats+BB+HBP+SF)
AB = 565
H = 157
BB = 65
SF = 7
HBP = 3
OBP=(H+BB+HBP)/(AB+BB+HBP+SF)
OBP

[1] 0.3515625

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

4<8

[1] TRUE

3!=4

[1] TRUE

Comparison Operators

#LOGICAL OPERATOR (OR)

FALSE | FALSE #FALSE OR FALSE

[1] FALSE

FALSE | TRUE #FALSE OR TRUE

[1] TRUE

TRUE | TRUE #TRUE OR TRUE

[1] TRUE

#LOGICAL OPERATOR (AND)
FALSE & FALSE

[1] FALSE

FALSE & TRUE

[1] FALSE

TRUE & TRUE

[1] TRUE

#LOGICAL OPERATOR (NOT)
!FALSE

[1] TRUE

!TRUE

[1] FALSE

#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

Assigning Values to Variables

total_bases<-7+4
total_bases*4

[1] 44

Vectors

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

[1] 12 15 10 20 10

Question 4: Define two vectors, runs_per_innings, and hits_per_innings, each with five elements

strikes_by_innings<-c(19,12,6,14,9)
strikes_by_innings

[1] 19 12  6 14  9

runs_per_innings<-c(3,3,4,1,2)
hits_per_innings<-c(6,8,7,4,5)

rep(5,2)

[1] 5 5

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_innings # the + operator

[1] 31 27 16 34 19

#compare two vectors
pitches_by_innings==strikes_by_innings

[1] FALSE FALSE FALSE FALSE FALSE

length(pitches_by_innings)

[1] 5

#tells you the length

min(pitches_by_innings)

[1] 10

#tells you the minimum

max(pitches_by_innings)

[1] 20

#tells you the maximum

mean(pitches_by_innings)

[1] 13.4

#tells you the mean 

pitches_by_innings[1]

[1] 12

#returns 1st element (not th element at index 1)

pitches_by_innings[length(pitches_by_innings)]

[1] 10

#returns the last element

pitches_by_innings[c(2,3,4)]

[1] 15 10 20

#returning 2nd, 3rd, 4th elements
#same as saying.....
pitches_by_innings[c(2:4)]

[1] 15 10 20

Vectors as strings or logical values

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 spread

variance = std deviation squared std dev = avg distance from mean

ceo_salaries<-c(12,.4,2,50,8,3,1,4,.25)
mean(ceo_salaries)

[1] 8.961111

median(ceo_salaries)

[1] 3

var(ceo_salaries)

[1] 251.9386

sd(ceo_salaries)

[1] 15.87257

**Fivenum is Tukey’s 5 Number Summary

Gives us: min, Q1, med, Q3, max

fivenum(ceo_salaries)

[1]  0.25  1.00  3.00  8.00 50.00

# 12,.4,2,15,8,3,1,4,.25
#.25,.4,1,2,3,4,8,12,15

Design a function which gives you the MODE

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

}
#getMode is the name we gave our function
#our function gets the mode
#there is no built-in function to get the mode in R

getMode(pitches_by_innings)

[1] 10

getMode(strikes_by_innings)

[1] 19

strikes_by_innings

[1] 19 12  6 14  9

game_day<-c("Saturday", "Saturday", "Sunday", "Monday", "Saturday","Tuesday", "Sunday", "Friday", "Friday", "Monday")
getMode(game_day)

[1] "Saturday"

unlike Knit, Preview does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.