Discriptive stats

a <- 5
b <- 6


sprintf("a is assigned value: %s b is addigned value: %s", a,b)
## [1] "a is assigned value: 5 b is addigned value: 6"
print("a is assigned value: %s b \n is addigned value: %s", a,b)
## [1] "a is assigned value: %s b \n is addigned value: %s"
cat("a is assigned value:", a, "\nb is assigned value:", a)
## a is assigned value: 5 
## b is assigned value: 5
a + b
## [1] 11
a/b
## [1] 0.8333333
b - a
## [1] 1
a * b
## [1] 30
a %*% b #Matrix Mult
##      [,1]
## [1,]   30
log(a)
## [1] 1.609438
exp(a)
## [1] 148.4132
a^2
## [1] 25
a^10
## [1] 9765625
a^b
## [1] 15625
floor(2.3) # ROund to lower integer
## [1] 2
floor(2.7)
## [1] 2
ceiling(3.6) # rounds to upper int
## [1] 4
ceiling(3.1)
## [1] 4
round(3.3) # rounds to nearest int
## [1] 3
round(3.8)
## [1] 4
x <- 1: 10
cat("printing one to ten:",x)
## printing one to ten: 1 2 3 4 5 6 7 8 9 10
y <- 10:1
cat("printing ten to one:",y)
## printing ten to one: 10 9 8 7 6 5 4 3 2 1
z <- 1:4
cat("matrix mult if x & y gives:",x %*% y)
## matrix mult if x & y gives: 220
cat("normal mult of x & y gives:",x *y)
## normal mult of x & y gives: 10 18 24 28 30 30 28 24 18 10
x+z
## Warning in x + z: longer object length is not a multiple of shorter object
## length
##  [1]  2  4  6  8  6  8 10 12 10 12
cat("unequal length ventors addition:", x+z)
## Warning in x + z: longer object length is not a multiple of shorter object
## length
## unequal length ventors addition: 2 4 6 8 6 8 10 12 10 12
sum(x)
## [1] 55
mean(x)
## [1] 5.5
median(x)
## [1] 5.5
mode(x)
## [1] "numeric"
sum(1:50)
## [1] 1275
sd(1:50)
## [1] 14.57738
var(1:50)
## [1] 212.5
sqrt(1:50)
##  [1] 1.000000 1.414214 1.732051 2.000000 2.236068 2.449490 2.645751
##  [8] 2.828427 3.000000 3.162278 3.316625 3.464102 3.605551 3.741657
## [15] 3.872983 4.000000 4.123106 4.242641 4.358899 4.472136 4.582576
## [22] 4.690416 4.795832 4.898979 5.000000 5.099020 5.196152 5.291503
## [29] 5.385165 5.477226 5.567764 5.656854 5.744563 5.830952 5.916080
## [36] 6.000000 6.082763 6.164414 6.244998 6.324555 6.403124 6.480741
## [43] 6.557439 6.633250 6.708204 6.782330 6.855655 6.928203 7.000000
## [50] 7.071068
x^-1/3
##  [1] 0.33333333 0.16666667 0.11111111 0.08333333 0.06666667 0.05555556
##  [7] 0.04761905 0.04166667 0.03703704 0.03333333
quantile(1:20)
##    0%   25%   50%   75%  100% 
##  1.00  5.75 10.50 15.25 20.00
min(100:20)
## [1] 20
max(1:3)
## [1] 3
range(x)
## [1]  1 10
seq(10,20,0.5)
##  [1] 10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 14.5 15.0 15.5 16.0 16.5
## [15] 17.0 17.5 18.0 18.5 19.0 19.5 20.0
rep(1,10)
##  [1] 1 1 1 1 1 1 1 1 1 1
rep(1:5,2)
##  [1] 1 2 3 4 5 1 2 3 4 5
rep(1:5,each=3)
##  [1] 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5
rep(1:4,1:4)
##  [1] 1 2 2 3 3 3 4 4 4 4
a <- 5
sprintf("a has value:%s data type of a is:%s",a,class(a))
## [1] "a has value:5 data type of a is:numeric"
b <- "XYZ"
sprintf("b has value:%s data type of b is:%s",b,class(b))
## [1] "b has value:XYZ data type of b is:character"
c <- TRUE
sprintf("c has value:%s data type of c is:%s",c,class(c))
## [1] "c has value:TRUE data type of c is:logical"
a > b
## [1] FALSE
a < b
## [1] TRUE
a1 <- c(1,.2,4)
length(x)
## [1] 10
length(a1)
## [1] 3
a2 <- c("a",length=20)
class(a1)
## [1] "numeric"
x > 3
##  [1] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
x[x>3]
## [1]  4  5  6  7  8  9 10
a3 <- rbinom(100,5,.5)
table(a3)
## a3
##  0  1  2  3  4  5 
##  2 18 28 26 20  6
a4 <- rnbinom(100,5,0.5)
table(a4)
## a4
##  0  1  2  3  4  5  6  7  8  9 10 11 13 14 
##  4  7 14 13 20 13  8  5  5  4  3  1  1  2
rpois(10,.5)
##  [1] 1 1 0 0 0 1 1 1 0 1
data()
data("iris")
View(iris)
class(iris)
## [1] "data.frame"
str(iris)
## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
names(iris)
## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width" 
## [5] "Species"
colnames(iris)
## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width" 
## [5] "Species"
rownames(iris)
##   [1] "1"   "2"   "3"   "4"   "5"   "6"   "7"   "8"   "9"   "10"  "11" 
##  [12] "12"  "13"  "14"  "15"  "16"  "17"  "18"  "19"  "20"  "21"  "22" 
##  [23] "23"  "24"  "25"  "26"  "27"  "28"  "29"  "30"  "31"  "32"  "33" 
##  [34] "34"  "35"  "36"  "37"  "38"  "39"  "40"  "41"  "42"  "43"  "44" 
##  [45] "45"  "46"  "47"  "48"  "49"  "50"  "51"  "52"  "53"  "54"  "55" 
##  [56] "56"  "57"  "58"  "59"  "60"  "61"  "62"  "63"  "64"  "65"  "66" 
##  [67] "67"  "68"  "69"  "70"  "71"  "72"  "73"  "74"  "75"  "76"  "77" 
##  [78] "78"  "79"  "80"  "81"  "82"  "83"  "84"  "85"  "86"  "87"  "88" 
##  [89] "89"  "90"  "91"  "92"  "93"  "94"  "95"  "96"  "97"  "98"  "99" 
## [100] "100" "101" "102" "103" "104" "105" "106" "107" "108" "109" "110"
## [111] "111" "112" "113" "114" "115" "116" "117" "118" "119" "120" "121"
## [122] "122" "123" "124" "125" "126" "127" "128" "129" "130" "131" "132"
## [133] "133" "134" "135" "136" "137" "138" "139" "140" "141" "142" "143"
## [144] "144" "145" "146" "147" "148" "149" "150"
mean(iris$Sepal.Length)
## [1] 5.843333
sd(iris$Sepal.Width)
## [1] 0.4358663
table(iris$Species)
## 
##     setosa versicolor  virginica 
##         50         50         50
dim(iris)
## [1] 150   5
nrow(iris)
## [1] 150
NROW(iris)
## [1] 150
ncol(iris)
## [1] 5
NCOL(iris)
## [1] 5
summary(iris)
##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
## 
summary(iris$Sepal.Length)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   4.300   5.100   5.800   5.843   6.400   7.900
tapply(iris$Sepal.Length,iris$Species,mean)
##     setosa versicolor  virginica 
##      5.006      5.936      6.588
tapply(iris$Sepal.Length,iris$Species,max)
##     setosa versicolor  virginica 
##        5.8        7.0        7.9
tapply(iris$Sepal.Length,iris$Species,min)
##     setosa versicolor  virginica 
##        4.3        4.9        4.9
tapply(iris$Sepal.Length,iris$Species,sd)
##     setosa versicolor  virginica 
##  0.3524897  0.5161711  0.6358796
tapply(iris$Sepal.Length,iris$Species,var)
##     setosa versicolor  virginica 
##  0.1242490  0.2664327  0.4043429
tapply(iris$Sepal.Length,iris$Species,sum)
##     setosa versicolor  virginica 
##      250.3      296.8      329.4
data("mtcars")
#View(mtcars)
tapply(mtcars$mpg,list(mtcars$cyl,mtcars$am),mean)
##        0        1
## 4 22.900 28.07500
## 6 19.125 20.56667
## 8 15.050 15.40000
tapply(mtcars$mpg,list(mtcars$cyl,mtcars$am),sum)
##       0     1
## 4  68.7 224.6
## 6  76.5  61.7
## 8 180.6  30.8
head(mtcars)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
head(mtcars,2)
##               mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4      21   6  160 110  3.9 2.620 16.46  0  1    4    4
## Mazda RX4 Wag  21   6  160 110  3.9 2.875 17.02  0  1    4    4
mtcars[1:5]
##                      mpg cyl  disp  hp drat
## Mazda RX4           21.0   6 160.0 110 3.90
## Mazda RX4 Wag       21.0   6 160.0 110 3.90
## Datsun 710          22.8   4 108.0  93 3.85
## Hornet 4 Drive      21.4   6 258.0 110 3.08
## Hornet Sportabout   18.7   8 360.0 175 3.15
## Valiant             18.1   6 225.0 105 2.76
## Duster 360          14.3   8 360.0 245 3.21
## Merc 240D           24.4   4 146.7  62 3.69
## Merc 230            22.8   4 140.8  95 3.92
## Merc 280            19.2   6 167.6 123 3.92
## Merc 280C           17.8   6 167.6 123 3.92
## Merc 450SE          16.4   8 275.8 180 3.07
## Merc 450SL          17.3   8 275.8 180 3.07
## Merc 450SLC         15.2   8 275.8 180 3.07
## Cadillac Fleetwood  10.4   8 472.0 205 2.93
## Lincoln Continental 10.4   8 460.0 215 3.00
## Chrysler Imperial   14.7   8 440.0 230 3.23
## Fiat 128            32.4   4  78.7  66 4.08
## Honda Civic         30.4   4  75.7  52 4.93
## Toyota Corolla      33.9   4  71.1  65 4.22
## Toyota Corona       21.5   4 120.1  97 3.70
## Dodge Challenger    15.5   8 318.0 150 2.76
## AMC Javelin         15.2   8 304.0 150 3.15
## Camaro Z28          13.3   8 350.0 245 3.73
## Pontiac Firebird    19.2   8 400.0 175 3.08
## Fiat X1-9           27.3   4  79.0  66 4.08
## Porsche 914-2       26.0   4 120.3  91 4.43
## Lotus Europa        30.4   4  95.1 113 3.77
## Ford Pantera L      15.8   8 351.0 264 4.22
## Ferrari Dino        19.7   6 145.0 175 3.62
## Maserati Bora       15.0   8 301.0 335 3.54
## Volvo 142E          21.4   4 121.0 109 4.11
mtcars[1:5,]
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
mtcars[,1:5]
##                      mpg cyl  disp  hp drat
## Mazda RX4           21.0   6 160.0 110 3.90
## Mazda RX4 Wag       21.0   6 160.0 110 3.90
## Datsun 710          22.8   4 108.0  93 3.85
## Hornet 4 Drive      21.4   6 258.0 110 3.08
## Hornet Sportabout   18.7   8 360.0 175 3.15
## Valiant             18.1   6 225.0 105 2.76
## Duster 360          14.3   8 360.0 245 3.21
## Merc 240D           24.4   4 146.7  62 3.69
## Merc 230            22.8   4 140.8  95 3.92
## Merc 280            19.2   6 167.6 123 3.92
## Merc 280C           17.8   6 167.6 123 3.92
## Merc 450SE          16.4   8 275.8 180 3.07
## Merc 450SL          17.3   8 275.8 180 3.07
## Merc 450SLC         15.2   8 275.8 180 3.07
## Cadillac Fleetwood  10.4   8 472.0 205 2.93
## Lincoln Continental 10.4   8 460.0 215 3.00
## Chrysler Imperial   14.7   8 440.0 230 3.23
## Fiat 128            32.4   4  78.7  66 4.08
## Honda Civic         30.4   4  75.7  52 4.93
## Toyota Corolla      33.9   4  71.1  65 4.22
## Toyota Corona       21.5   4 120.1  97 3.70
## Dodge Challenger    15.5   8 318.0 150 2.76
## AMC Javelin         15.2   8 304.0 150 3.15
## Camaro Z28          13.3   8 350.0 245 3.73
## Pontiac Firebird    19.2   8 400.0 175 3.08
## Fiat X1-9           27.3   4  79.0  66 4.08
## Porsche 914-2       26.0   4 120.3  91 4.43
## Lotus Europa        30.4   4  95.1 113 3.77
## Ford Pantera L      15.8   8 351.0 264 4.22
## Ferrari Dino        19.7   6 145.0 175 3.62
## Maserati Bora       15.0   8 301.0 335 3.54
## Volvo 142E          21.4   4 121.0 109 4.11
mtcars[1:2,1:5]
##               mpg cyl disp  hp drat
## Mazda RX4      21   6  160 110  3.9
## Mazda RX4 Wag  21   6  160 110  3.9
head(lapply(mtcars$mpg,sum),3)
## [[1]]
## [1] 21
## 
## [[2]]
## [1] 21
## 
## [[3]]
## [1] 22.8
sapply(mtcars$mpg,mean)
##  [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2
## [15] 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4
## [29] 15.8 19.7 15.0 21.4
sapply(mtcars,class)
##       mpg       cyl      disp        hp      drat        wt      qsec 
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
##        vs        am      gear      carb 
## "numeric" "numeric" "numeric" "numeric"
head(sapply(mtcars,is.na))
##        mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
## [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
a5 <- matrix(x,2,5)
a6 <- matrix(y,5,3)
## Warning in matrix(y, 5, 3): data length [10] is not a sub-multiple or
## multiple of the number of columns [3]
a5 %*% a6
##      [,1] [,2] [,3]
## [1,]  180   55  180
## [2,]  220   70  220
dim(a5)
## [1] 2 5
union(x,y)
##  [1]  1  2  3  4  5  6  7  8  9 10
intersect(x,y)
##  [1]  1  2  3  4  5  6  7  8  9 10
union(iris$Species,mtcars$cyl)
## [1] "setosa"     "versicolor" "virginica"  "6"          "4"         
## [6] "8"
setdiff(iris$Species,mtcars$cyl)
## [1] "setosa"     "versicolor" "virginica"
matrix(x,byrow = T, nrow=2)
##      [,1] [,2] [,3] [,4] [,5]
## [1,]    1    2    3    4    5
## [2,]    6    7    8    9   10
head(as.matrix(iris))
##      Sepal.Length Sepal.Width Petal.Length Petal.Width Species 
## [1,] "5.1"        "3.5"       "1.4"        "0.2"       "setosa"
## [2,] "4.9"        "3.0"       "1.4"        "0.2"       "setosa"
## [3,] "4.7"        "3.2"       "1.3"        "0.2"       "setosa"
## [4,] "4.6"        "3.1"       "1.5"        "0.2"       "setosa"
## [5,] "5.0"        "3.6"       "1.4"        "0.2"       "setosa"
## [6,] "5.4"        "3.9"       "1.7"        "0.4"       "setosa"
cbind(x,y)
##        x  y
##  [1,]  1 10
##  [2,]  2  9
##  [3,]  3  8
##  [4,]  4  7
##  [5,]  5  6
##  [6,]  6  5
##  [7,]  7  4
##  [8,]  8  3
##  [9,]  9  2
## [10,] 10  1
rbind(x,y)
##   [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## x    1    2    3    4    5    6    7    8    9    10
## y   10    9    8    7    6    5    4    3    2     1
class(iris$Species)
## [1] "factor"
mode(iris$Species)
## [1] "numeric"
table(iris$Species)
## 
##     setosa versicolor  virginica 
##         50         50         50
unclass(iris$Species)
##   [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [36] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
##  [71] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3
## [106] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [141] 3 3 3 3 3 3 3 3 3 3
## attr(,"levels")
## [1] "setosa"     "versicolor" "virginica"
unique(iris$Species)
## [1] setosa     versicolor virginica 
## Levels: setosa versicolor virginica
if(iris$Sepal.Length[10] > 5){print("greater than3")}else{print("less than3")}
## [1] "less than3"
for(i in 1:5){print(iris$Sepal.Length[i])}
## [1] 5.1
## [1] 4.9
## [1] 4.7
## [1] 4.6
## [1] 5
a<- 5
while(a>0){print(a);a<- a-2}
## [1] 5
## [1] 3
## [1] 1
letters
##  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q"
## [18] "r" "s" "t" "u" "v" "w" "x" "y" "z"
LETTERS
##  [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q"
## [18] "R" "S" "T" "U" "V" "W" "X" "Y" "Z"
p<- 1:4
sapply(p,runif)
## [[1]]
## [1] 0.6410101
## 
## [[2]]
## [1] 0.05186213 0.26420511
## 
## [[3]]
## [1] 0.7442884 0.1544726 0.1945827
## 
## [[4]]
## [1] 0.1436498 0.5699853 0.3856235 0.9600023
runif(p)
## [1] 0.03287415 0.04154630 0.44755435 0.14264310
p[2] <- NA
is.na(p)
## [1] FALSE  TRUE FALSE FALSE
sum(p)
## [1] NA
sum(p,na.rm = T)
## [1] 8
mean(p,na.rm = T)
## [1] 2.666667
is.nan(p)
## [1] FALSE FALSE FALSE FALSE
p[5] <- 3
hist(iris$Sepal.Length)

hist(iris$Sepal.Length,20)

boxplot(iris$Sepal.Length)

boxplot(iris$Sepal.Length~iris$Species,col=c("red","blue"))
title("A Box plot")

a7 <- matrix(x,nrow=2,ncol = 3,byrow=T) 
## Warning in matrix(x, nrow = 2, ncol = 3, byrow = T): data length [10] is
## not a sub-multiple or multiple of the number of columns [3]
class(a7)
## [1] "matrix"
str(a7)
##  int [1:2, 1:3] 1 4 2 5 3 6
dim(a7)
## [1] 2 3
barplot(a7,names.arg = rev(c("a","b","c")),legend.text = c("legend text"),beside = T,main = "Title for bar graph",xlab="xaxis",ylab="yaxis",ylim = c(0,5),col = c("red","blue"))

pie(c(123,21,124,525),main="pie chart",col = rainbow(5))