Week 1 Tutorial
Amanda - ydon6937
24/02/2020
DATA FRAMES
- There should be a default dataset in R called iris. Use the head function to inspect the first few lines of the data frame and use class to check that iris is in fact a data frame.
head.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 setosaclass(iris)## [1] "data.frame"- What are the column names of the iris data frame? How many rows are there? (dim() and nrow())
dim(iris)## [1] 150 5nrow(iris)## [1] 150- Extract the species column using the $ operator?
iris$Species## [1] setosa setosa setosa setosa setosa setosa
## [7] setosa setosa setosa setosa setosa setosa
## [13] setosa setosa setosa setosa setosa setosa
## [19] setosa setosa setosa setosa setosa setosa
## [25] setosa setosa setosa setosa setosa setosa
## [31] setosa setosa setosa setosa setosa setosa
## [37] setosa setosa setosa setosa setosa setosa
## [43] setosa setosa setosa setosa setosa setosa
## [49] setosa setosa versicolor versicolor versicolor versicolor
## [55] versicolor versicolor versicolor versicolor versicolor versicolor
## [61] versicolor versicolor versicolor versicolor versicolor versicolor
## [67] versicolor versicolor versicolor versicolor versicolor versicolor
## [73] versicolor versicolor versicolor versicolor versicolor versicolor
## [79] versicolor versicolor versicolor versicolor versicolor versicolor
## [85] versicolor versicolor versicolor versicolor versicolor versicolor
## [91] versicolor versicolor versicolor versicolor versicolor versicolor
## [97] versicolor versicolor versicolor versicolor virginica virginica
## [103] virginica virginica virginica virginica virginica virginica
## [109] virginica virginica virginica virginica virginica virginica
## [115] virginica virginica virginica virginica virginica virginica
## [121] virginica virginica virginica virginica virginica virginica
## [127] virginica virginica virginica virginica virginica virginica
## [133] virginica virginica virginica virginica virginica virginica
## [139] virginica virginica virginica virginica virginica virginica
## [145] virginica virginica virginica virginica virginica virginica
## Levels: setosa versicolor virginica- Extract rows 1 to 10 from the iris data frame.
iris[1:10,]## 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
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa- Make a new data frame called Setosa which only contains rows that belongs to the Setosa species.
Setosa<-subset(iris,Species=="setosa")
Setosa## 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
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosaFACTORS
- The Species column is a factor. Check that this is true.
class(iris$Species) == 'factor'## [1] TRUE- How many levels are there in Species? (levels())
levels(iris$Species)## [1] "setosa" "versicolor" "virginica"VECTORS
- Extract the Petal.lengths into a new vector called petallengths.
petal_lengths<- iris$Petal.Length- How many elements are there in petal_lengths? (length)
length(petal_lengths)## [1] 150- Extract the 5th to the 10th element from petal_lengths.
petal_lengths[5:10]## [1] 1.4 1.7 1.4 1.5 1.4 1.5- Add one more element to petal_lengths using c().
c(petal_lengths,100)## [1] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 1.5
## [12] 1.6 1.4 1.1 1.2 1.5 1.3 1.4 1.7 1.5 1.7 1.5
## [23] 1.0 1.7 1.9 1.6 1.6 1.5 1.4 1.6 1.6 1.5 1.5
## [34] 1.4 1.5 1.2 1.3 1.4 1.3 1.5 1.3 1.3 1.3 1.6
## [45] 1.9 1.4 1.6 1.4 1.5 1.4 4.7 4.5 4.9 4.0 4.6
## [56] 4.5 4.7 3.3 4.6 3.9 3.5 4.2 4.0 4.7 3.6 4.4
## [67] 4.5 4.1 4.5 3.9 4.8 4.0 4.9 4.7 4.3 4.4 4.8
## [78] 5.0 4.5 3.5 3.8 3.7 3.9 5.1 4.5 4.5 4.7 4.4
## [89] 4.1 4.0 4.4 4.6 4.0 3.3 4.2 4.2 4.2 4.3 3.0
## [100] 4.1 6.0 5.1 5.9 5.6 5.8 6.6 4.5 6.3 5.8 6.1
## [111] 5.1 5.3 5.5 5.0 5.1 5.3 5.5 6.7 6.9 5.0 5.7
## [122] 4.9 6.7 4.9 5.7 6.0 4.8 4.9 5.6 5.8 6.1 6.4
## [133] 5.6 5.1 5.6 6.1 5.6 5.5 4.8 5.4 5.6 5.1 5.1
## [144] 5.9 5.7 5.2 5.0 5.2 5.4 5.1 100.0Matrix
- Can you force the iris data frame to be a Matrix? (as.matrix(iris)). Check that the elements have been forced into the character type.
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"
## [7,] "4.6" "3.4" "1.4" "0.3" "setosa"
## [8,] "5.0" "3.4" "1.5" "0.2" "setosa"
## [9,] "4.4" "2.9" "1.4" "0.2" "setosa"
## [10,] "4.9" "3.1" "1.5" "0.1" "setosa"
## [11,] "5.4" "3.7" "1.5" "0.2" "setosa"
## [12,] "4.8" "3.4" "1.6" "0.2" "setosa"
## [13,] "4.8" "3.0" "1.4" "0.1" "setosa"
## [14,] "4.3" "3.0" "1.1" "0.1" "setosa"
## [15,] "5.8" "4.0" "1.2" "0.2" "setosa"
## [16,] "5.7" "4.4" "1.5" "0.4" "setosa"
## [17,] "5.4" "3.9" "1.3" "0.4" "setosa"
## [18,] "5.1" "3.5" "1.4" "0.3" "setosa"
## [19,] "5.7" "3.8" "1.7" "0.3" "setosa"
## [20,] "5.1" "3.8" "1.5" "0.3" "setosa"
## [21,] "5.4" "3.4" "1.7" "0.2" "setosa"
## [22,] "5.1" "3.7" "1.5" "0.4" "setosa"
## [23,] "4.6" "3.6" "1.0" "0.2" "setosa"
## [24,] "5.1" "3.3" "1.7" "0.5" "setosa"
## [25,] "4.8" "3.4" "1.9" "0.2" "setosa"
## [26,] "5.0" "3.0" "1.6" "0.2" "setosa"
## [27,] "5.0" "3.4" "1.6" "0.4" "setosa"
## [28,] "5.2" "3.5" "1.5" "0.2" "setosa"
## [29,] "5.2" "3.4" "1.4" "0.2" "setosa"
## [30,] "4.7" "3.2" "1.6" "0.2" "setosa"
## [31,] "4.8" "3.1" "1.6" "0.2" "setosa"
## [32,] "5.4" "3.4" "1.5" "0.4" "setosa"
## [33,] "5.2" "4.1" "1.5" "0.1" "setosa"
## [34,] "5.5" "4.2" "1.4" "0.2" "setosa"
## [35,] "4.9" "3.1" "1.5" "0.2" "setosa"
## [36,] "5.0" "3.2" "1.2" "0.2" "setosa"
## [37,] "5.5" "3.5" "1.3" "0.2" "setosa"
## [38,] "4.9" "3.6" "1.4" "0.1" "setosa"
## [39,] "4.4" "3.0" "1.3" "0.2" "setosa"
## [40,] "5.1" "3.4" "1.5" "0.2" "setosa"
## [41,] "5.0" "3.5" "1.3" "0.3" "setosa"
## [42,] "4.5" "2.3" "1.3" "0.3" "setosa"
## [43,] "4.4" "3.2" "1.3" "0.2" "setosa"
## [44,] "5.0" "3.5" "1.6" "0.6" "setosa"
## [45,] "5.1" "3.8" "1.9" "0.4" "setosa"
## [46,] "4.8" "3.0" "1.4" "0.3" "setosa"
## [47,] "5.1" "3.8" "1.6" "0.2" "setosa"
## [48,] "4.6" "3.2" "1.4" "0.2" "setosa"
## [49,] "5.3" "3.7" "1.5" "0.2" "setosa"
## [50,] "5.0" "3.3" "1.4" "0.2" "setosa"
## [51,] "7.0" "3.2" "4.7" "1.4" "versicolor"
## [52,] "6.4" "3.2" "4.5" "1.5" "versicolor"
## [53,] "6.9" "3.1" "4.9" "1.5" "versicolor"
## [54,] "5.5" "2.3" "4.0" "1.3" "versicolor"
## [55,] "6.5" "2.8" "4.6" "1.5" "versicolor"
## [56,] "5.7" "2.8" "4.5" "1.3" "versicolor"
## [57,] "6.3" "3.3" "4.7" "1.6" "versicolor"
## [58,] "4.9" "2.4" "3.3" "1.0" "versicolor"
## [59,] "6.6" "2.9" "4.6" "1.3" "versicolor"
## [60,] "5.2" "2.7" "3.9" "1.4" "versicolor"
## [61,] "5.0" "2.0" "3.5" "1.0" "versicolor"
## [62,] "5.9" "3.0" "4.2" "1.5" "versicolor"
## [63,] "6.0" "2.2" "4.0" "1.0" "versicolor"
## [64,] "6.1" "2.9" "4.7" "1.4" "versicolor"
## [65,] "5.6" "2.9" "3.6" "1.3" "versicolor"
## [66,] "6.7" "3.1" "4.4" "1.4" "versicolor"
## [67,] "5.6" "3.0" "4.5" "1.5" "versicolor"
## [68,] "5.8" "2.7" "4.1" "1.0" "versicolor"
## [69,] "6.2" "2.2" "4.5" "1.5" "versicolor"
## [70,] "5.6" "2.5" "3.9" "1.1" "versicolor"
## [71,] "5.9" "3.2" "4.8" "1.8" "versicolor"
## [72,] "6.1" "2.8" "4.0" "1.3" "versicolor"
## [73,] "6.3" "2.5" "4.9" "1.5" "versicolor"
## [74,] "6.1" "2.8" "4.7" "1.2" "versicolor"
## [75,] "6.4" "2.9" "4.3" "1.3" "versicolor"
## [76,] "6.6" "3.0" "4.4" "1.4" "versicolor"
## [77,] "6.8" "2.8" "4.8" "1.4" "versicolor"
## [78,] "6.7" "3.0" "5.0" "1.7" "versicolor"
## [79,] "6.0" "2.9" "4.5" "1.5" "versicolor"
## [80,] "5.7" "2.6" "3.5" "1.0" "versicolor"
## [81,] "5.5" "2.4" "3.8" "1.1" "versicolor"
## [82,] "5.5" "2.4" "3.7" "1.0" "versicolor"
## [83,] "5.8" "2.7" "3.9" "1.2" "versicolor"
## [84,] "6.0" "2.7" "5.1" "1.6" "versicolor"
## [85,] "5.4" "3.0" "4.5" "1.5" "versicolor"
## [86,] "6.0" "3.4" "4.5" "1.6" "versicolor"
## [87,] "6.7" "3.1" "4.7" "1.5" "versicolor"
## [88,] "6.3" "2.3" "4.4" "1.3" "versicolor"
## [89,] "5.6" "3.0" "4.1" "1.3" "versicolor"
## [90,] "5.5" "2.5" "4.0" "1.3" "versicolor"
## [91,] "5.5" "2.6" "4.4" "1.2" "versicolor"
## [92,] "6.1" "3.0" "4.6" "1.4" "versicolor"
## [93,] "5.8" "2.6" "4.0" "1.2" "versicolor"
## [94,] "5.0" "2.3" "3.3" "1.0" "versicolor"
## [95,] "5.6" "2.7" "4.2" "1.3" "versicolor"
## [96,] "5.7" "3.0" "4.2" "1.2" "versicolor"
## [97,] "5.7" "2.9" "4.2" "1.3" "versicolor"
## [98,] "6.2" "2.9" "4.3" "1.3" "versicolor"
## [99,] "5.1" "2.5" "3.0" "1.1" "versicolor"
## [100,] "5.7" "2.8" "4.1" "1.3" "versicolor"
## [101,] "6.3" "3.3" "6.0" "2.5" "virginica"
## [102,] "5.8" "2.7" "5.1" "1.9" "virginica"
## [103,] "7.1" "3.0" "5.9" "2.1" "virginica"
## [104,] "6.3" "2.9" "5.6" "1.8" "virginica"
## [105,] "6.5" "3.0" "5.8" "2.2" "virginica"
## [106,] "7.6" "3.0" "6.6" "2.1" "virginica"
## [107,] "4.9" "2.5" "4.5" "1.7" "virginica"
## [108,] "7.3" "2.9" "6.3" "1.8" "virginica"
## [109,] "6.7" "2.5" "5.8" "1.8" "virginica"
## [110,] "7.2" "3.6" "6.1" "2.5" "virginica"
## [111,] "6.5" "3.2" "5.1" "2.0" "virginica"
## [112,] "6.4" "2.7" "5.3" "1.9" "virginica"
## [113,] "6.8" "3.0" "5.5" "2.1" "virginica"
## [114,] "5.7" "2.5" "5.0" "2.0" "virginica"
## [115,] "5.8" "2.8" "5.1" "2.4" "virginica"
## [116,] "6.4" "3.2" "5.3" "2.3" "virginica"
## [117,] "6.5" "3.0" "5.5" "1.8" "virginica"
## [118,] "7.7" "3.8" "6.7" "2.2" "virginica"
## [119,] "7.7" "2.6" "6.9" "2.3" "virginica"
## [120,] "6.0" "2.2" "5.0" "1.5" "virginica"
## [121,] "6.9" "3.2" "5.7" "2.3" "virginica"
## [122,] "5.6" "2.8" "4.9" "2.0" "virginica"
## [123,] "7.7" "2.8" "6.7" "2.0" "virginica"
## [124,] "6.3" "2.7" "4.9" "1.8" "virginica"
## [125,] "6.7" "3.3" "5.7" "2.1" "virginica"
## [126,] "7.2" "3.2" "6.0" "1.8" "virginica"
## [127,] "6.2" "2.8" "4.8" "1.8" "virginica"
## [128,] "6.1" "3.0" "4.9" "1.8" "virginica"
## [129,] "6.4" "2.8" "5.6" "2.1" "virginica"
## [130,] "7.2" "3.0" "5.8" "1.6" "virginica"
## [131,] "7.4" "2.8" "6.1" "1.9" "virginica"
## [132,] "7.9" "3.8" "6.4" "2.0" "virginica"
## [133,] "6.4" "2.8" "5.6" "2.2" "virginica"
## [134,] "6.3" "2.8" "5.1" "1.5" "virginica"
## [135,] "6.1" "2.6" "5.6" "1.4" "virginica"
## [136,] "7.7" "3.0" "6.1" "2.3" "virginica"
## [137,] "6.3" "3.4" "5.6" "2.4" "virginica"
## [138,] "6.4" "3.1" "5.5" "1.8" "virginica"
## [139,] "6.0" "3.0" "4.8" "1.8" "virginica"
## [140,] "6.9" "3.1" "5.4" "2.1" "virginica"
## [141,] "6.7" "3.1" "5.6" "2.4" "virginica"
## [142,] "6.9" "3.1" "5.1" "2.3" "virginica"
## [143,] "5.8" "2.7" "5.1" "1.9" "virginica"
## [144,] "6.8" "3.2" "5.9" "2.3" "virginica"
## [145,] "6.7" "3.3" "5.7" "2.5" "virginica"
## [146,] "6.7" "3.0" "5.2" "2.3" "virginica"
## [147,] "6.3" "2.5" "5.0" "1.9" "virginica"
## [148,] "6.5" "3.0" "5.2" "2.0" "virginica"
## [149,] "6.2" "3.4" "5.4" "2.3" "virginica"
## [150,] "5.9" "3.0" "5.1" "1.8" "virginica"- Now do this again, but this time leave out the Species column. Check that the elements are now numeric.
as.matrix(subset(iris,select = -c(Species)))## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.1 3.5 1.4 0.2
## 2 4.9 3.0 1.4 0.2
## 3 4.7 3.2 1.3 0.2
## 4 4.6 3.1 1.5 0.2
## 5 5.0 3.6 1.4 0.2
## 6 5.4 3.9 1.7 0.4
## 7 4.6 3.4 1.4 0.3
## 8 5.0 3.4 1.5 0.2
## 9 4.4 2.9 1.4 0.2
## 10 4.9 3.1 1.5 0.1
## 11 5.4 3.7 1.5 0.2
## 12 4.8 3.4 1.6 0.2
## 13 4.8 3.0 1.4 0.1
## 14 4.3 3.0 1.1 0.1
## 15 5.8 4.0 1.2 0.2
## 16 5.7 4.4 1.5 0.4
## 17 5.4 3.9 1.3 0.4
## 18 5.1 3.5 1.4 0.3
## 19 5.7 3.8 1.7 0.3
## 20 5.1 3.8 1.5 0.3
## 21 5.4 3.4 1.7 0.2
## 22 5.1 3.7 1.5 0.4
## 23 4.6 3.6 1.0 0.2
## 24 5.1 3.3 1.7 0.5
## 25 4.8 3.4 1.9 0.2
## 26 5.0 3.0 1.6 0.2
## 27 5.0 3.4 1.6 0.4
## 28 5.2 3.5 1.5 0.2
## 29 5.2 3.4 1.4 0.2
## 30 4.7 3.2 1.6 0.2
## 31 4.8 3.1 1.6 0.2
## 32 5.4 3.4 1.5 0.4
## 33 5.2 4.1 1.5 0.1
## 34 5.5 4.2 1.4 0.2
## 35 4.9 3.1 1.5 0.2
## 36 5.0 3.2 1.2 0.2
## 37 5.5 3.5 1.3 0.2
## 38 4.9 3.6 1.4 0.1
## 39 4.4 3.0 1.3 0.2
## 40 5.1 3.4 1.5 0.2
## 41 5.0 3.5 1.3 0.3
## 42 4.5 2.3 1.3 0.3
## 43 4.4 3.2 1.3 0.2
## 44 5.0 3.5 1.6 0.6
## 45 5.1 3.8 1.9 0.4
## 46 4.8 3.0 1.4 0.3
## 47 5.1 3.8 1.6 0.2
## 48 4.6 3.2 1.4 0.2
## 49 5.3 3.7 1.5 0.2
## 50 5.0 3.3 1.4 0.2
## 51 7.0 3.2 4.7 1.4
## 52 6.4 3.2 4.5 1.5
## 53 6.9 3.1 4.9 1.5
## 54 5.5 2.3 4.0 1.3
## 55 6.5 2.8 4.6 1.5
## 56 5.7 2.8 4.5 1.3
## 57 6.3 3.3 4.7 1.6
## 58 4.9 2.4 3.3 1.0
## 59 6.6 2.9 4.6 1.3
## 60 5.2 2.7 3.9 1.4
## 61 5.0 2.0 3.5 1.0
## 62 5.9 3.0 4.2 1.5
## 63 6.0 2.2 4.0 1.0
## 64 6.1 2.9 4.7 1.4
## 65 5.6 2.9 3.6 1.3
## 66 6.7 3.1 4.4 1.4
## 67 5.6 3.0 4.5 1.5
## 68 5.8 2.7 4.1 1.0
## 69 6.2 2.2 4.5 1.5
## 70 5.6 2.5 3.9 1.1
## 71 5.9 3.2 4.8 1.8
## 72 6.1 2.8 4.0 1.3
## 73 6.3 2.5 4.9 1.5
## 74 6.1 2.8 4.7 1.2
## 75 6.4 2.9 4.3 1.3
## 76 6.6 3.0 4.4 1.4
## 77 6.8 2.8 4.8 1.4
## 78 6.7 3.0 5.0 1.7
## 79 6.0 2.9 4.5 1.5
## 80 5.7 2.6 3.5 1.0
## 81 5.5 2.4 3.8 1.1
## 82 5.5 2.4 3.7 1.0
## 83 5.8 2.7 3.9 1.2
## 84 6.0 2.7 5.1 1.6
## 85 5.4 3.0 4.5 1.5
## 86 6.0 3.4 4.5 1.6
## 87 6.7 3.1 4.7 1.5
## 88 6.3 2.3 4.4 1.3
## 89 5.6 3.0 4.1 1.3
## 90 5.5 2.5 4.0 1.3
## 91 5.5 2.6 4.4 1.2
## 92 6.1 3.0 4.6 1.4
## 93 5.8 2.6 4.0 1.2
## 94 5.0 2.3 3.3 1.0
## 95 5.6 2.7 4.2 1.3
## 96 5.7 3.0 4.2 1.2
## 97 5.7 2.9 4.2 1.3
## 98 6.2 2.9 4.3 1.3
## 99 5.1 2.5 3.0 1.1
## 100 5.7 2.8 4.1 1.3
## 101 6.3 3.3 6.0 2.5
## 102 5.8 2.7 5.1 1.9
## 103 7.1 3.0 5.9 2.1
## 104 6.3 2.9 5.6 1.8
## 105 6.5 3.0 5.8 2.2
## 106 7.6 3.0 6.6 2.1
## 107 4.9 2.5 4.5 1.7
## 108 7.3 2.9 6.3 1.8
## 109 6.7 2.5 5.8 1.8
## 110 7.2 3.6 6.1 2.5
## 111 6.5 3.2 5.1 2.0
## 112 6.4 2.7 5.3 1.9
## 113 6.8 3.0 5.5 2.1
## 114 5.7 2.5 5.0 2.0
## 115 5.8 2.8 5.1 2.4
## 116 6.4 3.2 5.3 2.3
## 117 6.5 3.0 5.5 1.8
## 118 7.7 3.8 6.7 2.2
## 119 7.7 2.6 6.9 2.3
## 120 6.0 2.2 5.0 1.5
## 121 6.9 3.2 5.7 2.3
## 122 5.6 2.8 4.9 2.0
## 123 7.7 2.8 6.7 2.0
## 124 6.3 2.7 4.9 1.8
## 125 6.7 3.3 5.7 2.1
## 126 7.2 3.2 6.0 1.8
## 127 6.2 2.8 4.8 1.8
## 128 6.1 3.0 4.9 1.8
## 129 6.4 2.8 5.6 2.1
## 130 7.2 3.0 5.8 1.6
## 131 7.4 2.8 6.1 1.9
## 132 7.9 3.8 6.4 2.0
## 133 6.4 2.8 5.6 2.2
## 134 6.3 2.8 5.1 1.5
## 135 6.1 2.6 5.6 1.4
## 136 7.7 3.0 6.1 2.3
## 137 6.3 3.4 5.6 2.4
## 138 6.4 3.1 5.5 1.8
## 139 6.0 3.0 4.8 1.8
## 140 6.9 3.1 5.4 2.1
## 141 6.7 3.1 5.6 2.4
## 142 6.9 3.1 5.1 2.3
## 143 5.8 2.7 5.1 1.9
## 144 6.8 3.2 5.9 2.3
## 145 6.7 3.3 5.7 2.5
## 146 6.7 3.0 5.2 2.3
## 147 6.3 2.5 5.0 1.9
## 148 6.5 3.0 5.2 2.0
## 149 6.2 3.4 5.4 2.3
## 150 5.9 3.0 5.1 1.8Basic statistics
- Use the summary function to extract the median, 1st quartile and 3rd quartile data from the Sepal.Length column.
summary(iris$Sepal.Length)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.300 5.100 5.800 5.843 6.400 7.900- Find the max, min, standard deviation and mean of the Sepal.Length (max(), min(), sd(), mean())
max(iris$Sepal.Length)## [1] 7.9min(iris$Sepal.Length)## [1] 4.3sd(iris$Sepal.Length)## [1] 0.8280661mean(iris$Sepal.Length)## [1] 5.843333- Find the mean Sepal.Length of each Species.
by(iris,iris$Species,function(x){
mean.sl<- mean(x$Sepal.Length)
})## iris$Species: setosa
## [1] 5.006
## --------------------------------------------------------
## iris$Species: versicolor
## [1] 5.936
## --------------------------------------------------------
## iris$Species: virginica
## [1] 6.588Basic plotting
- Make a boxplot of the species versus the Sepal.Length using boxplot().
plot(iris$Species,iris$Sepal.Length)
- Plot Sepal.Length against Petal.Length using plot().
plot(iris$Sepal.Length,iris$Petal.Length)
File I/O
- Download the iris.csv file from the Canvas page and use the read.csv command to read in the csv file into R. Save the data into a data frame called iris2.
iris2<-read.csv(file="~/OneDrive - DMG/Desktop/USYD/STAT5003/Week1/Tutorial/iris.csv",header=TRUE,sep=",")- Write the iris2 data frame to a file called myiris.csv. Use the write.table command.
iris2<- read.csv(file="~/OneDrive - DMG/Desktop/USYD/STAT5003/Week1/Tutorial/iris.csv",header=TRUE,sep=",")
write.table(iris2,file="~/OneDrive - DMG/Desktop/USYD/STAT5003/Week1/Tutorial/iris.csv")