In-class activity #1

#Let us complete some basic operations using R
1+2

[1] 3

1-4

[1] -3

2*5

[1] 10

sqrt(9)

[1] 3

log(10)

[1] 2.302585

log(2.72)#Natural log (Ln)

[1] 1.000632

log10(5)

[1] 0.69897

log10(10)

[1] 1

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

[1] 0.2589286

Batting_Average=round(BA,digits = 3)
Batting_Average

[1] 0.259

#what is the batting average of a player that bats 42 hits in 212 at bats?
BA1 <- 42 / 212
BA1

[1] 0.1981132

#On Base Percentage
#OBP=(H+BB+HBP)/(At Bats+H+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+172+84+5+6)
OBP

[1] 0.3337596

On_Base_Percentage=round(OBP,digits = 3)
On_Base_Percentage

[1] 0.334

#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+156+65+3+7)
OBP

[1] 0.281407

3 == 8# Does 3 equals 8?

[1] FALSE

3 != 8# Is 3 different from 8?

[1] TRUE

3 <= 8# Is 3 less than or equal to 8?

[1] TRUE

3>4

[1] FALSE

# 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

Total_Bases <- 6 + 5
Total_Bases*3

[1] 33

ls()

 [1] "BA"                 "BA1"                "bar"                "barsample"          "Batting_Average"   
 [6] "game_day"           "getMode"            "hits_per_9innings"  "n"                  "OBP"               
[11] "On_Base_Percentage" "pitches_by_innings" "player_positions"   "runs_per_9innings"  "sals"              
[16] "samplerows"         "strikes_by_innings" "Total_Bases"        "x"                 

rm(Total_Bases)

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, 6, 8, 2, 1) 
runs_per_9innings

[1] 1 6 8 2 1

hits_per_9innings <- c(3, 5, 9, 14, 4)
hits_per_9innings

[1]  3  5  9 14  4

# replicate function
rep(2, 5)

[1] 2 2 2 2 2

rep(1,4)

[1] 1 1 1 1

# 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(2,13,by=3)

[1]  2  5  8 11

# add vectors
pitches_by_innings+strikes_by_innings

[1] 21 27 16 34 19

# compare vectors
pitches_by_innings == strikes_by_innings

[1] FALSE FALSE FALSE FALSE FALSE

# find length of vector
length(pitches_by_innings)

[1] 5

# find minimum value in vector
min(pitches_by_innings)

[1] 10

# find average value in vector
mean(pitches_by_innings)

[1] 13.4

pitches_by_innings

[1] 12 15 10 20 10

# If you want to get the first element:
pitches_by_innings[1]

[1] 12

#Question_5: Get the first element of hits_per_9innings.

hits_per_9innings[1]

[1] 3

pitches_by_innings[length(pitches_by_innings)]

[1] 10

#Question_6: Get the last element of hits_per_9innings.

hits_per_9innings[length(hits_per_9innings)]

[1] 4

pitches_by_innings[c(2, 3, 4)]

[1] 15 10 20

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

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(1:10, size=5)

[1]  4  9  6 10  1

bar <- data.frame(var1 = LETTERS[1:10], var2 = 1:10)
# Check data frame
bar

NA

n <- 5

samplerows <- sample(1:nrow(bar), size=n) 
# print sample rows
samplerows

[1]  6  5 10  1  7

# extract rows
barsample <- bar[samplerows, ]
# print sample
print(barsample)

bar[sample(1:nrow(bar), n), ]

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

x
 No Yes 
  2   3 

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 

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

# Most frequent value in pitches_by_innings
getMode(pitches_by_innings)

[1] 10

#Question_7: Find the most frequent value of hits_per_9innings.

getMode(hits_per_9innings)

[1] 3

#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. 

getMode(game_day)

[1] "Saturday"