plot(cars)
`
``
```{r}
Error: unexpected symbol in:
"``
`"
3==5
[1] FALSE
3==8
[1] FALSE
3==3
[1] TRUE
3==(2+1)
[1] TRUE
4!=4
[1] FALSE
2!=4
[1] TRUE
TRUE | FALSE
[1] TRUE
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)
[1] 2.302585
log10(10)
[1] 1
log10(100)
[1] 2
log10(1000)
[1] 3
log10(500)
[1] 2.69897
log(10,base =5)
[1] 1.430677
log(10)#ln of
[1] 2.302585
log10(10)
[1] 1
BA=(29)/(112)
BA
[1] 0.2589286
Batting_Average=round(BA,digits = 3)
Batting_Average
[1] 0.259
BA_1 =(42)/212
Batting_Average1=round(BA_1,digits = 3)
Batting_Average1
[1] 0.198
OBP=(172+84+5)/(515+84+5+6)
OBP
[1] 0.4278689
#Question_2:What is the batting average of a player that bats 42 hits in 212 at bats?
#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
[1] 0.428
OBP=(156+65+3)/(565+56+3+7)
On_Base_Percentage2=round(OBP,digits =3)
On_Base_Percentage2
[1] 0.355
#Question_3:Compute the OBP for a player with the following general stats: #AB=565,H=156,BB=65,HBP=3,SF=7
Total_bases<-136+214
Total_bases
[1] 350
ls()
[1] "BA" "BA_1" "Batting_Average" "Batting_Average1" "OBP"
[6] "On_Base_Percentage" "On_Base_Percentage2" "Total_bases"
rm(Total_bases)
ls()
[1] "BA" "BA_1" "Batting_Average" "Batting_Average1" "OBP"
[6] "On_Base_Percentage" "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_Innings <- c(5,6,9,7,14)
Strikes_Innings
[1] 5 6 9 7 14
rep(2,5)
[1] 2 2 2 2 2
1:6
[1] 1 2 3 4 5 6
seq(3,10,3)
[1] 3 6 9
Strikes_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_Innings+pitches_by_innings
[1] 17 21 19 27 24
Strikes_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
players_positions<- c("catchers","pitchers","infielders","outfielders")
players_positions
[1] "catchers" "pitchers" "infielders" "outfielders"
soccer_positions<- c("goalkeepers","defenders","midfielders","forwards")
soccer_positions
[1] "goalkeepers" "defenders" "midfielders" "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] 5 8
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
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"
getmode(game_day)