R Notebook Activity 4

4+3

[1] 7

4-3

[1] 1

4==3

[1] FALSE

3^2

[1] 9

sqrt(4)

[1] 2

Does 3 equals 8?

3==8

[1] FALSE

3==5

[1] FALSE

3 != 8

[1] TRUE

4!=4

[1] FALSE

2!=4

[1] TRUE

3 <= 8

[1] TRUE

3>4

[1] FALSE

TRUE | FALSE

[1] TRUE

FALSE | FALSE

[1] FALSE

TRUE & FALSE

[1] FALSE

!FALSE

[1] TRUE

!TRUE

[1] FALSE

!FALSE & FALSE | TRUE

[1] TRUE

!FALSE & !TRUE | TRUE

[1] TRUE

2>5|1==3

[1] FALSE

11>7|4==3

[1] TRUE

log (10) #ln, natural log, base e=2.72

log10 (10)
log10 (100)
log10 (1000)

#Question_1: Compute the log base 5 of 10 and the log of 10.

log (10, base = 5)
log10(10)

#Batting Average=(No. of Hits)/(No. of At Bats)
#What is the batting average of a player that bats 29 hits in 112 at bats?
BA=(29)/(112)
BA

Batting_Average=round(BA,digits = 3)
Batting_Average

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

BA_1=(42)/212
Batting_Average1=round(BA_1,digits = 3)
Batting_Average1

#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

On_Base_Percentage=round(OBP,digits = 3)
On_Base_Percentage

#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+7)
On_Base_Percentage=round(OBP,digits = 3)
On_Base_Percentage

[1] 0.35

Total_Bases <- 136+214
Total_Bases

[1] 350

ls()

[1] "BA"                  "BA_1"               
[3] "Batting_Average"     "Batting_Average1"   
[5] "OBP"                 "On_Base_Percentage" 
[7] "On_Base_Percentage2" "Total_Bases"        

rm(Total_Bases)

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

ls()

[1] "BA"                  "BA_1"               
[3] "Batting_Average"     "Batting_Average1"   
[5] "OBP"                 "On_Base_Percentage" 
[7] "On_Base_Percentage2"

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

[1] 12 15 10 20 10

Wins_Season<-c(94,88,96,87,79)
Wins_Season

[1] 94 88 96 87 79

strikes_by_innings <- c(5, 6, 9, 7, 14)
strikes_by_innings

[1]  5  6  9  7 14

rep(2, 5)

1:6

[1] 1 2 3 4 5 6

seq(3,10,3)

[1] 3 6 9

strikes_by_innings

[1]  5  6  9  7 14

Wins_Season

[1] 94 88 96 87 79

pitches_by_innings

[1] 12 15 10 20 10

strikes_by_innings+pitches_by_innings

[1] 17 21 19 27 24

strikes_by_innings==pitches_by_innings

[1] FALSE FALSE FALSE FALSE FALSE

length(pitches_by_innings)

[1] 5

min(pitches_by_innings)

[1] 10

max(pitches_by_innings)

[1] 20

mean(pitches_by_innings)

[1] 13.4

pitches_by_innings[3]

[1] 10

pitches_by_innings[1]

[1] 12

pitches_by_innings[5]

[1] 10

pitches_by_innings[length(pitches_by_innings)]

[1] 10

pitches_by_innings[c(2,3,4)]

[1] 15 10 20

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

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

soccer_positions <- c("goalkeeper", "defender", "Midfield", "forwards")
soccer_positions

[1] "goalkeeper" "defender"   "Midfield"   "forwards"  

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

sample(1:9,size = 2)

[1] 2 9

x <- c("Yes","No","No","No","Yes","Yes","Yes","Yes","Yes","Yes") 
x

 [1] "Yes" "No"  "No"  "No"  "Yes" "Yes" "Yes" "Yes" "Yes" "Yes"

table(x)

x
 No Yes 
  3   7 

sals <- c(12, .4, 5, 2, 50, 8, 3, 1, 4, 0.25)
mean(sals) 

[1] 8.565

var(sals)

[1] 225.5145

sd(sals)

[1] 15.01714

median(sals)

[1] 3.5

# Tukey's five number summary, usefull for boxplots
# five numbers: min, lower hinge, median, upper hinge, max
fivenum(sals)

[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 

# Function to find the mode, i.e. most frequent value
getMode <- function(x) {
     ux <- unique(x)
     ux[which.max(tabulate(match(x, ux)))]
 }

getMode(pitches_by_innings)

[1] 10

getMode(Wins_Season)

[1] 94

Wins_Season

[1] 94 88 96 87 79

#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"