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plot(cars)

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mean(cars$speed)
## [1] 15.4
mean(cars$dist)
## [1] 42.98
max(cars$speed)
## [1] 25
max(cars$dist)
## [1] 120
4+1
## [1] 5
5-2
## [1] 3
2^2
## [1] 4
sqrt(25)
## [1] 5
2^5
## [1] 32
log(2.72) #natural log of 2
## [1] 1.000632
log10(5)
## [1] 0.69897
log10(10)
## [1] 1
log10(100)
## [1] 2
#Compute log base 10
log(10,base=5)
## [1] 1.430677
log(10,base=2)
## [1] 3.321928
log(1000,base=10)
## [1] 3
#Question_1: Compute the log base 5 of 10 and the log of 10.
log(10, base=5)
## [1] 1.430677
log(10,base=10)
## [1] 1

Computing some offensive metrics in Baseball

BA = (29/112)
BA
## [1] 0.2589286
Batting_Avg = round(BA,digits = 3)
Batting_Avg
## [1] 0.259
#Question_2:What is the batting average of a player that bats 42 hits in 212 at bats?.
BA2= round ((42/212),digits=3)
BA2
## [1] 0.198

Compute the OBP for a player with the following general stats

#AB=515,H=172,BB=84,HBP=5,SF=6 #OBP=(H+BB+HBP)/(At Bats+H+BB+HBP+SF)

OBP=(172+84+5)/(515+172+84+5+6)
OBP
## [1] 0.3337596
#Question_3:Compute the OBP for a player with the following general stats:
#AB=565,H=156,BB=65,HBP=3,SF=7

OBP3=round(((156+65+3)/(565+156+65+3+7)),digits = 3)
OBP3
## [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

Assigning Values to Variables

Total_Bases <- 6 + 5
Total_Bases*3
## [1] 33

#To see the variables that are currently defined, use ls (as in “list”)

ls()
## [1] "BA"          "BA2"         "Batting_Avg" "OBP"         "OBP3"       
## [6] "Total_Bases"

#To delete a variable, use rm (as in “remove”)

rm(Total_Bases)

Vectors

#Question_4: Define two vectors,runs_per_9innings and hits_per_9innings, each with five elements. 

runs_per_9innings <- c (10,15,20,30,1)
hits_per_9innings <- c (8,12,9,12,15)

runs_per_9innings
## [1] 10 15 20 30  1
hits_per_9innings
## [1]  8 12  9 12 15

Data Frames

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

How to Make a Random Sample

sample(1:10, size=5)
## [1] 6 8 1 3 7
bar <- data.frame(var1 = LETTERS[1:10], var2 = 1:10)
# Check data frame
bar
n <- 5

samplerows <- sample(1:nrow(bar), size=n) 
# print sample rows
samplerows
## [1]  8 10  2  3  4
# extract rows
barsample <- bar[samplerows, ]
# print sample
print(barsample)
##    var1 var2
## 8     H    8
## 10    J   10
## 2     B    2
## 3     C    3
## 4     D    4
bar[sample(1:nrow(bar), n), ]

Numerical measures of center and spread

Suppose, MLB Teams’ CEOs yearly compensations are sampled and the following are found (in millions)

12 .4 5 2 50 8 3 1 4 0.25

sals <- c(12, .4, 5, 2, 50, 8, 3, 1, 4, 0.25)
# the average
mean(sals) 
## [1] 8.565
# the variance
var(sals)
## [1] 225.5145
# the standard deviation
sd(sals)
## [1] 15.01714
# the median
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 statistics
summary(sals)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.250   1.250   3.500   8.565   7.250  50.000

How about the mode?

#Question_5: Get the first element of hits_per_9innings.

hits_per_9innings[1]
## [1] 8
#Question_6: Get the last element of hits_per_9innings.

hits_per_9innings[length((hits_per_9innings))]
## [1] 15
#Question_7: Find the most frequent value of hits_per_9innings.

#Define function to find the most frequent value
getMostFrequent <- function(X) {
  ux <- unique(X)
  ux[which.max(tabulate(match(X,ux)))]
}

getMostFrequent (hits_per_9innings)
## [1] 12
#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
#Question_9: What is the most frequent answer recorded in the survey? Use the getMode function to compute results. 

getMostFrequent(game_day)
## [1] "Saturday"

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