Assignment #1

Assignment #1 Caroline Hawkins Data Analytics - Summer 2018

What are the measures of central tendency and variation of data?

The three main measures of central tendency are the mode, the median and the mean. The median is the preferred method of central tendency because it is less affected by outliers and skewed data. A real world example is compensation studies, using median would not factor in how some companies pay unreasonably high salaries and instead use more of the benchmark.

What are the different ways to create a vector
in R?

A vector is a collection of objects that can be stored in a single variable and accessed through subscripts. The different ways to create a vector in R are the following: • B = C(1,2,3,4,5) • x5<1:16;x • x2 <- c(1, 7.4, TRUE, “hello”) • Seq(1,5,by=0.2) • Seq(1,5,length.out=4)

Create the following vector and check the class (‘x’,’x’, ‘x’, 1,3,5,7,9,2,4,6,8,10)

Vec1

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

Create a vector of positive odd integers less
than 100

x<-seq(1,by=2,len=100) x [1] 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 [17] 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 [33] 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 [49] 97 99 101 103 105 107 109 111 113 115 117 119 121 123 125 127 [65] 129 131 133 135 137 139 141 143 145 147 149 151 153 155 157 159 [81] 161 163 165 167 169 171 173 175 177 179 181 183 185 187 189 191 [97] 193 195 197 199

Remove the values greater than 60 and less
than 80

x[-(60:80)][1] 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 [17] 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 [33] 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 [49] 97 99 101 103 105 107 109 111 113 115 117 161 163 165 167 169 [65] 171 173 175 177 179 181 183 185 187 189 191 193 195 197 199

Write a function to return standard deviation,
mean, and median of the vector from Question

sd(x)

[1] 4.84768

mean(x)

[1] 6

median(x)

[1] 5

Create two matrices of the form from the given
set of numbers} in two ways X1 = {2,3,7,1,6,2,3,5,1} and x2 =
{3,2,9,0,7,8,5,8,2}

mat1=matrix(x1, ncol=3)
mat1

     [,1] [,2] [,3]
[1,]    2    1    3
[2,]    3    6    5
[3,]    7    2    1

mat2=matrix(x1,nrow=3)
mat2

     [,1] [,2] [,3]
[1,]    2    1    3
[2,]    3    6    5
[3,]    7    2    1

mat3=matrix(x2, ncol=3)
mat3

     [,1] [,2] [,3]
[1,]    3    0    5
[2,]    2    7    8
[3,]    9    8    2

mat4=matrix(x2, nrow=3)
mat4

     [,1] [,2] [,3]
[1,]    3    0    5
[2,]    2    7    8
[3,]    9    8    2

Find the matrix product

(x1%*%x2)

     [,1]
[1,]  190

Find the class of ‘iris’ dataframe, find the class
of all the columns of ‘iris’ get the summary. Get rownames, get column names. Get the number
of rows and number of columns.

class(iris)

[1] "data.frame"

summary(iris)

  Sepal.Length    Sepal.Width     Petal.Length  
 Min.   :4.300   Min.   :2.000   Min.   :1.000  
 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600  
 Median :5.800   Median :3.000   Median :4.350  
 Mean   :5.843   Mean   :3.057   Mean   :3.758  
 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100  
 Max.   :7.900   Max.   :4.400   Max.   :6.900  
  Petal.Width          Species  
 Min.   :0.100   setosa    :50  
 1st Qu.:0.300   versicolor:50  
 Median :1.300   virginica :50  
 Mean   :1.199                  
 3rd Qu.:1.800                  
 Max.   :2.500

rownames(iris)

  [1] "1"   "2"   "3"   "4"   "5"   "6"   "7"   "8"   "9"  
 [10] "10"  "11"  "12"  "13"  "14"  "15"  "16"  "17"  "18" 
 [19] "19"  "20"  "21"  "22"  "23"  "24"  "25"  "26"  "27" 
 [28] "28"  "29"  "30"  "31"  "32"  "33"  "34"  "35"  "36" 
 [37] "37"  "38"  "39"  "40"  "41"  "42"  "43"  "44"  "45" 
 [46] "46"  "47"  "48"  "49"  "50"  "51"  "52"  "53"  "54" 
 [55] "55"  "56"  "57"  "58"  "59"  "60"  "61"  "62"  "63" 
 [64] "64"  "65"  "66"  "67"  "68"  "69"  "70"  "71"  "72" 
 [73] "73"  "74"  "75"  "76"  "77"  "78"  "79"  "80"  "81" 
 [82] "82"  "83"  "84"  "85"  "86"  "87"  "88"  "89"  "90" 
 [91] "91"  "92"  "93"  "94"  "95"  "96"  "97"  "98"  "99" 
[100] "100" "101" "102" "103" "104" "105" "106" "107" "108"
[109] "109" "110" "111" "112" "113" "114" "115" "116" "117"
[118] "118" "119" "120" "121" "122" "123" "124" "125" "126"
[127] "127" "128" "129" "130" "131" "132" "133" "134" "135"
[136] "136" "137" "138" "139" "140" "141" "142" "143" "144"
[145] "145" "146" "147" "148" "149" "150"

colnames(iris)

[1] "Sepal.Length" "Sepal.Width"  "Petal.Length"
[4] "Petal.Width"  "Species"

row(iris)

       [,1] [,2] [,3] [,4] [,5]
  [1,]    1    1    1    1    1
  [2,]    2    2    2    2    2
  [3,]    3    3    3    3    3
  [4,]    4    4    4    4    4
  [5,]    5    5    5    5    5
  [6,]    6    6    6    6    6
  [7,]    7    7    7    7    7
  [8,]    8    8    8    8    8
  [9,]    9    9    9    9    9
 [10,]   10   10   10   10   10
 [11,]   11   11   11   11   11
 [12,]   12   12   12   12   12
 [13,]   13   13   13   13   13
 [14,]   14   14   14   14   14
 [15,]   15   15   15   15   15
 [16,]   16   16   16   16   16
 [17,]   17   17   17   17   17
 [18,]   18   18   18   18   18
 [19,]   19   19   19   19   19
 [20,]   20   20   20   20   20
 [21,]   21   21   21   21   21
 [22,]   22   22   22   22   22
 [23,]   23   23   23   23   23
 [24,]   24   24   24   24   24
 [25,]   25   25   25   25   25
 [26,]   26   26   26   26   26
 [27,]   27   27   27   27   27
 [28,]   28   28   28   28   28
 [29,]   29   29   29   29   29
 [30,]   30   30   30   30   30
 [31,]   31   31   31   31   31
 [32,]   32   32   32   32   32
 [33,]   33   33   33   33   33
 [34,]   34   34   34   34   34
 [35,]   35   35   35   35   35
 [36,]   36   36   36   36   36
 [37,]   37   37   37   37   37
 [38,]   38   38   38   38   38
 [39,]   39   39   39   39   39
 [40,]   40   40   40   40   40
 [41,]   41   41   41   41   41
 [42,]   42   42   42   42   42
 [43,]   43   43   43   43   43
 [44,]   44   44   44   44   44
 [45,]   45   45   45   45   45
 [46,]   46   46   46   46   46
 [47,]   47   47   47   47   47
 [48,]   48   48   48   48   48
 [49,]   49   49   49   49   49
 [50,]   50   50   50   50   50
 [51,]   51   51   51   51   51
 [52,]   52   52   52   52   52
 [53,]   53   53   53   53   53
 [54,]   54   54   54   54   54
 [55,]   55   55   55   55   55
 [56,]   56   56   56   56   56
 [57,]   57   57   57   57   57
 [58,]   58   58   58   58   58
 [59,]   59   59   59   59   59
 [60,]   60   60   60   60   60
 [61,]   61   61   61   61   61
 [62,]   62   62   62   62   62
 [63,]   63   63   63   63   63
 [64,]   64   64   64   64   64
 [65,]   65   65   65   65   65
 [66,]   66   66   66   66   66
 [67,]   67   67   67   67   67
 [68,]   68   68   68   68   68
 [69,]   69   69   69   69   69
 [70,]   70   70   70   70   70
 [71,]   71   71   71   71   71
 [72,]   72   72   72   72   72
 [73,]   73   73   73   73   73
 [74,]   74   74   74   74   74
 [75,]   75   75   75   75   75
 [76,]   76   76   76   76   76
 [77,]   77   77   77   77   77
 [78,]   78   78   78   78   78
 [79,]   79   79   79   79   79
 [80,]   80   80   80   80   80
 [81,]   81   81   81   81   81
 [82,]   82   82   82   82   82
 [83,]   83   83   83   83   83
 [84,]   84   84   84   84   84
 [85,]   85   85   85   85   85
 [86,]   86   86   86   86   86
 [87,]   87   87   87   87   87
 [88,]   88   88   88   88   88
 [89,]   89   89   89   89   89
 [90,]   90   90   90   90   90
 [91,]   91   91   91   91   91
 [92,]   92   92   92   92   92
 [93,]   93   93   93   93   93
 [94,]   94   94   94   94   94
 [95,]   95   95   95   95   95
 [96,]   96   96   96   96   96
 [97,]   97   97   97   97   97
 [98,]   98   98   98   98   98
 [99,]   99   99   99   99   99
[100,]  100  100  100  100  100
[101,]  101  101  101  101  101
[102,]  102  102  102  102  102
[103,]  103  103  103  103  103
[104,]  104  104  104  104  104
[105,]  105  105  105  105  105
[106,]  106  106  106  106  106
[107,]  107  107  107  107  107
[108,]  108  108  108  108  108
[109,]  109  109  109  109  109
[110,]  110  110  110  110  110
[111,]  111  111  111  111  111
[112,]  112  112  112  112  112
[113,]  113  113  113  113  113
[114,]  114  114  114  114  114
[115,]  115  115  115  115  115
[116,]  116  116  116  116  116
[117,]  117  117  117  117  117
[118,]  118  118  118  118  118
[119,]  119  119  119  119  119
[120,]  120  120  120  120  120
[121,]  121  121  121  121  121
[122,]  122  122  122  122  122
[123,]  123  123  123  123  123
[124,]  124  124  124  124  124
[125,]  125  125  125  125  125
[126,]  126  126  126  126  126
[127,]  127  127  127  127  127
[128,]  128  128  128  128  128
[129,]  129  129  129  129  129
[130,]  130  130  130  130  130
[131,]  131  131  131  131  131
[132,]  132  132  132  132  132
[133,]  133  133  133  133  133
[134,]  134  134  134  134  134
[135,]  135  135  135  135  135
[136,]  136  136  136  136  136
[137,]  137  137  137  137  137
[138,]  138  138  138  138  138
[139,]  139  139  139  139  139
[140,]  140  140  140  140  140
[141,]  141  141  141  141  141
[142,]  142  142  142  142  142
[143,]  143  143  143  143  143
[144,]  144  144  144  144  144
[145,]  145  145  145  145  145
[146,]  146  146  146  146  146
[147,]  147  147  147  147  147
[148,]  148  148  148  148  148
[149,]  149  149  149  149  149
[150,]  150  150  150  150  150

col(iris)

       [,1] [,2] [,3] [,4] [,5]
  [1,]    1    2    3    4    5
  [2,]    1    2    3    4    5
  [3,]    1    2    3    4    5
  [4,]    1    2    3    4    5
  [5,]    1    2    3    4    5
  [6,]    1    2    3    4    5
  [7,]    1    2    3    4    5
  [8,]    1    2    3    4    5
  [9,]    1    2    3    4    5
 [10,]    1    2    3    4    5
 [11,]    1    2    3    4    5
 [12,]    1    2    3    4    5
 [13,]    1    2    3    4    5
 [14,]    1    2    3    4    5
 [15,]    1    2    3    4    5
 [16,]    1    2    3    4    5
 [17,]    1    2    3    4    5
 [18,]    1    2    3    4    5
 [19,]    1    2    3    4    5
 [20,]    1    2    3    4    5
 [21,]    1    2    3    4    5
 [22,]    1    2    3    4    5
 [23,]    1    2    3    4    5
 [24,]    1    2    3    4    5
 [25,]    1    2    3    4    5
 [26,]    1    2    3    4    5
 [27,]    1    2    3    4    5
 [28,]    1    2    3    4    5
 [29,]    1    2    3    4    5
 [30,]    1    2    3    4    5
 [31,]    1    2    3    4    5
 [32,]    1    2    3    4    5
 [33,]    1    2    3    4    5
 [34,]    1    2    3    4    5
 [35,]    1    2    3    4    5
 [36,]    1    2    3    4    5
 [37,]    1    2    3    4    5
 [38,]    1    2    3    4    5
 [39,]    1    2    3    4    5
 [40,]    1    2    3    4    5
 [41,]    1    2    3    4    5
 [42,]    1    2    3    4    5
 [43,]    1    2    3    4    5
 [44,]    1    2    3    4    5
 [45,]    1    2    3    4    5
 [46,]    1    2    3    4    5
 [47,]    1    2    3    4    5
 [48,]    1    2    3    4    5
 [49,]    1    2    3    4    5
 [50,]    1    2    3    4    5
 [51,]    1    2    3    4    5
 [52,]    1    2    3    4    5
 [53,]    1    2    3    4    5
 [54,]    1    2    3    4    5
 [55,]    1    2    3    4    5
 [56,]    1    2    3    4    5
 [57,]    1    2    3    4    5
 [58,]    1    2    3    4    5
 [59,]    1    2    3    4    5
 [60,]    1    2    3    4    5
 [61,]    1    2    3    4    5
 [62,]    1    2    3    4    5
 [63,]    1    2    3    4    5
 [64,]    1    2    3    4    5
 [65,]    1    2    3    4    5
 [66,]    1    2    3    4    5
 [67,]    1    2    3    4    5
 [68,]    1    2    3    4    5
 [69,]    1    2    3    4    5
 [70,]    1    2    3    4    5
 [71,]    1    2    3    4    5
 [72,]    1    2    3    4    5
 [73,]    1    2    3    4    5
 [74,]    1    2    3    4    5
 [75,]    1    2    3    4    5
 [76,]    1    2    3    4    5
 [77,]    1    2    3    4    5
 [78,]    1    2    3    4    5
 [79,]    1    2    3    4    5
 [80,]    1    2    3    4    5
 [81,]    1    2    3    4    5
 [82,]    1    2    3    4    5
 [83,]    1    2    3    4    5
 [84,]    1    2    3    4    5
 [85,]    1    2    3    4    5
 [86,]    1    2    3    4    5
 [87,]    1    2    3    4    5
 [88,]    1    2    3    4    5
 [89,]    1    2    3    4    5
 [90,]    1    2    3    4    5
 [91,]    1    2    3    4    5
 [92,]    1    2    3    4    5
 [93,]    1    2    3    4    5
 [94,]    1    2    3    4    5
 [95,]    1    2    3    4    5
 [96,]    1    2    3    4    5
 [97,]    1    2    3    4    5
 [98,]    1    2    3    4    5
 [99,]    1    2    3    4    5
[100,]    1    2    3    4    5
[101,]    1    2    3    4    5
[102,]    1    2    3    4    5
[103,]    1    2    3    4    5
[104,]    1    2    3    4    5
[105,]    1    2    3    4    5
[106,]    1    2    3    4    5
[107,]    1    2    3    4    5
[108,]    1    2    3    4    5
[109,]    1    2    3    4    5
[110,]    1    2    3    4    5
[111,]    1    2    3    4    5
[112,]    1    2    3    4    5
[113,]    1    2    3    4    5
[114,]    1    2    3    4    5
[115,]    1    2    3    4    5
[116,]    1    2    3    4    5
[117,]    1    2    3    4    5
[118,]    1    2    3    4    5
[119,]    1    2    3    4    5
[120,]    1    2    3    4    5
[121,]    1    2    3    4    5
[122,]    1    2    3    4    5
[123,]    1    2    3    4    5
[124,]    1    2    3    4    5
[125,]    1    2    3    4    5
[126,]    1    2    3    4    5
[127,]    1    2    3    4    5
[128,]    1    2    3    4    5
[129,]    1    2    3    4    5
[130,]    1    2    3    4    5
[131,]    1    2    3    4    5
[132,]    1    2    3    4    5
[133,]    1    2    3    4    5
[134,]    1    2    3    4    5
[135,]    1    2    3    4    5
[136,]    1    2    3    4    5
[137,]    1    2    3    4    5
[138,]    1    2    3    4    5
[139,]    1    2    3    4    5
[140,]    1    2    3    4    5
[141,]    1    2    3    4    5
[142,]    1    2    3    4    5
[143,]    1    2    3    4    5
[144,]    1    2    3    4    5
[145,]    1    2    3    4    5
[146,]    1    2    3    4    5
[147,]    1    2    3    4    5
[148,]    1    2    3    4    5
[149,]    1    2    3    4    5
[150,]    1    2    3    4    5

Get the last two rows in the last 2 columns
from iris dataset.

iris[(149:150)]

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