In Class Activity 4

3 ==5

[1] FALSE

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

log(10) # in of 10

[1] 2.302585

log10(10)

[1] 1

#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

[1] 0.2589286

#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

[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

On_Base_Percentage=round(OBP,digits = 3)
On_Base_Percentage

[1] 0.428

#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+56+3)/(565+65+7)
On_Base_Percentage2=round(OBP,digits = 3)
On_Base_Percentage2

[1] 0.338

Total_Bases <- 6 + 5
Total_Bases*3

[1] 33

ls

function (name, pos = -1L, envir = as.environment(pos), all.names = FALSE, 
    pattern, sorted = TRUE) 
{
    if (!missing(name)) {
        pos <- tryCatch(name, error = function(e) e)
        if (inherits(pos, "error")) {
            name <- substitute(name)
            if (!is.character(name)) 
                name <- deparse(name)
            warning(gettextf("%s converted to character string", 
                sQuote(name)), domain = NA)
            pos <- name
        }
    }
    all.names <- .Internal(ls(envir, all.names, sorted))
    if (!missing(pattern)) {
        if ((ll <- length(grep("[", pattern, fixed = TRUE))) && 
            ll != length(grep("]", pattern, fixed = TRUE))) {
            if (pattern == "[") {
                pattern <- "\\["
                warning("replaced regular expression pattern '[' by  '\\\\['")
            }
            else if (length(grep("[^\\\\]\\[<-", pattern))) {
                pattern <- sub("\\[<-", "\\\\\\[<-", pattern)
                warning("replaced '[<-' by '\\\\[<-' in regular expression pattern")
            }
        }
        grep(pattern, all.names, value = TRUE)
    }
    else all.names
}
<bytecode: 0x58a201b7c7b0>
<environment: namespace:base>

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

[1] 12 15 10 20 10

wins_season<-c(85,65,78,75,55,55)
wins_season

[1] 85 65 78 75 55 55

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

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

uns_per_9innings<-c(25,12,6,1,7)
uns_per_8innings<-c(1,5,9,7,3)
uns_per_8innings

[1] 1 5 9 7 3

uns_per_9innings

[1] 25 12  6  1  7

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

[1] 12 15 10 20 10

total<-uns_per_8innings+uns_per_9innings
total

[1] 26 17 15  8 10

ls()

 [1] "BA"                  "BA_1"                "Batting_Average1"   
 [4] "OBP"                 "On_Base_Percentage"  "On_Base_Percentage2"
 [7] "pitches_by_innings"  "strikes_by_innings"  "total"              
[10] "Total_Bases"         "uns_per_8innings"    "uns_per_9innings"   
[13] "wins_season"        

length(total)

[1] 5

min(total)

[1] 8

max(uns_per_8innings)

[1] 9

#Question_5: Get the first element of hits_per_9innings.

pitches_by_innings[1]

[1] 12

pitches_by_innings[c(2, 3, 4)]

[1] 15 10 20

#Question_6: Get the last element of hits_per_9innings.

hits_per_9innings<-c(41,15,12,7,15)
hits_per_9innings

[1] 41 15 12  7 15

tail(hits_per_9innings, 1)

[1] 15

hits_per_9innings[length(hits_per_9innings)]

[1] 15

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

[1] "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 

data.frame(uns_per_8innings,uns_per_9innings)

sample(1:10, size=5)

[1] 2 4 7 8 3

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

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

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

table(x)

x
 No Yes 
  4   6 

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

#Question_7: Find the most frequent value of hits_per_9innings.

getMode(wins_season)

[1] 55

getMode(total)

[1] 26

getMode(hits_per_9innings)

[1] 15

#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")
game_day

 [1] "Saturday" "Saturday" "Sunday"   "Monday"   "Saturday" "Tuesday" 
 [7] "Sunday"   "Friday"   "Friday"   "Monday"  

getMode(game_day)

[1] "Saturday"