in-class activity 1

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plot(cars)

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mean(cars$speed)

[1] 15.4

mean(cars$dist)

[1] 42.98

max(cars$dist)

[1] 120

max(cars$speed)

[1] 25

2^5

[1] 32

log(2.72)

[1] 1.000632

log10(5)

[1] 0.69897

log10(10)

[1] 1

log10(100)

[1] 2

log(10,base=5)

[1] 1.430677

log(10,base=2)

[1] 3.321928

log(1000,base=10)

[1] 3

log(10,base=5)

[1] 1.430677

log(10,base=10)

[1] 1

BA=42/212
BA

[1] 0.1981132

Batting_Average = round(BA,digits = 3)
Batting_Average

[1] 0.198

AB=565+156+65+3+7
BW=156+65+3
BV=(BW/AB)
OBP=round(BV,digits = 3)
OBP

[1] 0.281

3==8

[1] FALSE

3!=8

[1] TRUE

3>5

[1] FALSE

3<5

[1] TRUE

FALSE|TRUE

[1] TRUE

TRUE|FALSE

[1] TRUE

FALSE & FALSE

[1] FALSE

!FALSE & TRUE

[1] TRUE

!TRUE & TRUE

[1] FALSE

2<5 & 1 > 0

[1] TRUE

Total_Bases <- 1*3
Total_Bases*12

[1] 36

ls()

 [1] "a"               "AB"             
 [3] "at1"             "at2"            
 [5] "b"               "BA"             
 [7] "Batting_Average" "blood"          
 [9] "bt1"             "bt2"            
[11] "BV"              "BW"             
[13] "color_pct"       "color_table"    
[15] "flu_status"      "gender"         
[17] "ins_model"       "ins_model2"     
[19] "insurance"       "launch"         
[21] "m"               "m.cubist"       
[23] "m.rpart"         "MAE"            
[25] "model"           "model_table"    
[27] "new.pkg"         "OBP"            
[29] "p.cubist"        "p.rpart"        
[31] "pkg"             "pt_data"        
[33] "r"               "reg"            
[35] "regression1"     "sdr_a"          
[37] "sdr_b"           "subject_name"   
[39] "subject1"        "symptoms"       
[41] "tee"             "temperature"    
[43] "Total_Bases"     "usedcars"       
[45] "wine"            "wine_test"      
[47] "wine_train"     

rm(Total_Bases)
ls()

 [1] "a"               "AB"             
 [3] "at1"             "at2"            
 [5] "b"               "BA"             
 [7] "Batting_Average" "blood"          
 [9] "bt1"             "bt2"            
[11] "BV"              "BW"             
[13] "color_pct"       "color_table"    
[15] "flu_status"      "gender"         
[17] "ins_model"       "ins_model2"     
[19] "insurance"       "launch"         
[21] "m"               "m.cubist"       
[23] "m.rpart"         "MAE"            
[25] "model"           "model_table"    
[27] "new.pkg"         "OBP"            
[29] "p.cubist"        "p.rpart"        
[31] "pkg"             "pt_data"        
[33] "r"               "reg"            
[35] "regression1"     "sdr_a"          
[37] "sdr_b"           "subject_name"   
[39] "subject1"        "symptoms"       
[41] "tee"             "temperature"    
[43] "usedcars"        "wine"           
[45] "wine_test"       "wine_train"     

pitches_by_innings <- c(21,15,10,12)
pitches_by_innings

[1] 21 15 10 12

runs_per9innings= c(1,1,2,4,15)
hits_per9innings= c(3,3,5,10,25)
runs_per9innings

[1]  1  1  2  4 15

hits_per9innings

[1]  3  3  5 10 25

rep(2,5)

[1] 2 2 2 2 2

rep(10,4)

[1] 10 10 10 10

1:10

 [1]  1  2  3  4  5  6  7  8  9 10

10:5

[1] 10  9  8  7  6  5

seq(1,20, by=2)

 [1]  1  3  5  7  9 11 13 15 17 19

pitches_by_innings+runs_per9innings

[1] 22 16 12 16 36

pitches_by_innings!=runs_per9innings

[1] TRUE TRUE TRUE TRUE TRUE

length(runs_per9innings)

[1] 5

min(runs_per9innings)

[1] 1

max(pitches_by_innings)

[1] 21

mean(runs_per9innings)

[1] 4.6

runs_per9innings[3]

[1] 2

runs_per9innings[length(runs_per9innings)]

[1] 15

hits_per9innings[length(hits_per9innings)]

[1] 25

runs_per9innings[c(2,4)]

[1] 1 4

players = c("Ohtani","Skubal","Judge")
players

[1] "Ohtani" "Skubal" "Judge" 

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(12:57,size=6)

[1] 28 22 44 54 53 39

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

n=5
samplerows= sample(1:nrow(bar),size=n)
samplerows

[1] 1 5 3 7 8

barsample=bar[samplerows, ]
barsample

x=c("BMW","Honda","Nissan","BMW")
table(x)

x
   BMW  Honda Nissan 
     2      1      1 

sals=c(5,10,53,12,4.2,3.7)
mean(sals)

[1] 14.65

var(sals)

[1] 364.319

sd(sals)

[1] 19.08714

median(sals)

[1] 7.5

fivenum(sals)

[1]  3.7  4.2  7.5 12.0 53.0

fivenum(sals)

[1]  3.7  4.2  7.5 12.0 53.0

summary(sals)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   3.70    4.40    7.50   14.65   11.50   53.00 

getMode= function(x)
{
ux=unique(x)
ux[which.max(tabulate(match(x, ux)))]
}

getMode(x)

[1] "BMW"

getMode(hits_per9innings)

[1] 3

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"

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