Below are the solutions to these exercises on selecting and querying data
############### # # # Exercise 1 # # # ############### a=c(4,5,6,8,3) b=c("apple","chair","jetplane","salmon","island") c=c(TRUE,TRUE,FALSE,TRUE,FALSE) ############### # # # Exercise 2 # # # ############### df=data.frame(a,b,c) ############### # # # Exercise 3 # # # ############### str(df)
## 'data.frame': 5 obs. of 3 variables: ## $ a: num 4 5 6 8 3 ## $ b: Factor w/ 5 levels "apple","chair",..: 1 2 4 5 3 ## $ c: logi TRUE TRUE FALSE TRUE FALSE
############### # # # Exercise 4 # # # ############### colnames(df)
## [1] "a" "b" "c"
############### # # # Exercise 5 # # # ############### colnames(df)=c("id","wish","real") str(df)
## 'data.frame': 5 obs. of 3 variables: ## $ id : num 4 5 6 8 3 ## $ wish: Factor w/ 5 levels "apple","chair",..: 1 2 4 5 3 ## $ real: logi TRUE TRUE FALSE TRUE FALSE
############### # # # Exercise 6 # # # ############### df['id']
## id ## 1 4 ## 2 5 ## 3 6 ## 4 8 ## 5 3
df[df['id']==3]
## [1] "3" "island" "FALSE"
df[1,]
## id wish real ## 1 4 apple TRUE
df[,2]
## [1] apple chair jetplane salmon island ## Levels: apple chair island jetplane salmon
df[3,2]
## [1] jetplane ## Levels: apple chair island jetplane salmon
############### # # # Exercise 7 # # # ############### attach(iris) ############### # # # Exercise 8 # # # ############### colnames(iris)=c("sl","sw","sl","pw","s") ############### # # # Exercise 9 # # # ############### iris[iris['sw']==3]
## [1] "4.9" "4.8" "4.3" "5.0" "4.4" ## [6] "4.8" "5.9" "5.6" "6.6" "6.7" ## [11] "5.4" "5.6" "6.1" "5.7" "7.1" ## [16] "6.5" "7.6" "6.8" "6.5" "6.1" ## [21] "7.2" "7.7" "6.0" "6.7" "6.5" ## [26] "5.9" "3.0" "3.0" "3.0" "3.0" ## [31] "3.0" "3.0" "3.0" "3.0" "3.0" ## [36] "3.0" "3.0" "3.0" "3.0" "3.0" ## [41] "3.0" "3.0" "3.0" "3.0" "3.0" ## [46] "3.0" "3.0" "3.0" "3.0" "3.0" ## [51] "3.0" "3.0" "1.4" "1.4" "1.1" ## [56] "1.6" "1.3" "1.4" "4.2" "4.5" ## [61] "4.4" "5.0" "4.5" "4.1" "4.6" ## [66] "4.2" "5.9" "5.8" "6.6" "5.5" ## [71] "5.5" "4.9" "5.8" "6.1" "4.8" ## [76] "5.2" "5.2" "5.1" "0.2" "0.1" ## [81] "0.1" "0.2" "0.2" "0.3" "1.5" ## [86] "1.5" "1.4" "1.7" "1.5" "1.3" ## [91] "1.4" "1.2" "2.1" "2.2" "2.1" ## [96] "2.1" "1.8" "1.8" "1.6" "2.3" ## [101] "1.8" "2.3" "2.0" "1.8" "setosa" ## [106] "setosa" "setosa" "setosa" "setosa" "setosa" ## [111] "versicolor" "versicolor" "versicolor" "versicolor" "versicolor" ## [116] "versicolor" "versicolor" "versicolor" "virginica" "virginica" ## [121] "virginica" "virginica" "virginica" "virginica" "virginica" ## [126] "virginica" "virginica" "virginica" "virginica" "virginica"
iris[1,2]
## [1] 3.5
head(iris)
## sl sw sl pw s ## 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
tail(iris)
## sl sw sl pw s ## 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
iris[-5]
## sl sw sl.1 pw ## 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 1.8
############### # # # Exercise 10 # # # ############### iris_2=iris[iris['sl']>5,-5]
I think the iris atachment appears to early. It is asked starting with exercise 7. But in the solutions it appears already starting with exercise 2.
Thanks for letting us know. The answer set has been updated 🙂
please provide password for other exercises