nycdata <- read.csv("https://raw.githubusercontent.com/engine2031/Winter-Bridge-R-2021/main/environmental.csv")
nycdata <- data.frame(nycdata)
nycdata
## X ozone radiation temperature wind
## 1 1 41 190 67 7.4
## 2 2 36 118 72 8.0
## 3 3 12 149 74 12.6
## 4 4 18 313 62 11.5
## 5 5 23 299 65 8.6
## 6 6 19 99 59 13.8
## 7 7 8 19 61 20.1
## 8 8 16 256 69 9.7
## 9 9 11 290 66 9.2
## 10 10 14 274 68 10.9
## 11 11 18 65 58 13.2
## 12 12 14 334 64 11.5
## 13 13 34 307 66 12.0
## 14 14 6 78 57 18.4
## 15 15 30 322 68 11.5
## 16 16 11 44 62 9.7
## 17 17 1 8 59 9.7
## 18 18 11 320 73 16.6
## 19 19 4 25 61 9.7
## 20 20 32 92 61 12.0
## 21 21 23 13 67 12.0
## 22 22 45 252 81 14.9
## 23 23 115 223 79 5.7
## 24 24 37 279 76 7.4
## 25 25 29 127 82 9.7
## 26 26 71 291 90 13.8
## 27 27 39 323 87 11.5
## 28 28 23 148 82 8.0
## 29 29 21 191 77 14.9
## 30 30 37 284 72 20.7
## 31 31 20 37 65 9.2
## 32 32 12 120 73 11.5
## 33 33 13 137 76 10.3
## 34 34 135 269 84 4.0
## 35 35 49 248 85 9.2
## 36 36 32 236 81 9.2
## 37 37 64 175 83 4.6
## 38 38 40 314 83 10.9
## 39 39 77 276 88 5.1
## 40 40 97 267 92 6.3
## 41 41 97 272 92 5.7
## 42 42 85 175 89 7.4
## 43 43 10 264 73 14.3
## 44 44 27 175 81 14.9
## 45 45 7 48 80 14.3
## 46 46 48 260 81 6.9
## 47 47 35 274 82 10.3
## 48 48 61 285 84 6.3
## 49 49 79 187 87 5.1
## 50 50 63 220 85 11.5
## 51 51 16 7 74 6.9
## 52 52 80 294 86 8.6
## 53 53 108 223 85 8.0
## 54 54 20 81 82 8.6
## 55 55 52 82 86 12.0
## 56 56 82 213 88 7.4
## 57 57 50 275 86 7.4
## 58 58 64 253 83 7.4
## 59 59 59 254 81 9.2
## 60 60 39 83 81 6.9
## 61 61 9 24 81 13.8
## 62 62 16 77 82 7.4
## 63 63 122 255 89 4.0
## 64 64 89 229 90 10.3
## 65 65 110 207 90 8.0
## 66 66 44 192 86 11.5
## 67 67 28 273 82 11.5
## 68 68 65 157 80 9.7
## 69 69 22 71 77 10.3
## 70 70 59 51 79 6.3
## 71 71 23 115 76 7.4
## 72 72 31 244 78 10.9
## 73 73 44 190 78 10.3
## 74 74 21 259 77 15.5
## 75 75 9 36 72 14.3
## 76 76 45 212 79 9.7
## 77 77 168 238 81 3.4
## 78 78 73 215 86 8.0
## 79 79 76 203 97 9.7
## 80 80 118 225 94 2.3
## 81 81 84 237 96 6.3
## 82 82 85 188 94 6.3
## 83 83 96 167 91 6.9
## 84 84 78 197 92 5.1
## 85 85 73 183 93 2.8
## 86 86 91 189 93 4.6
## 87 87 47 95 87 7.4
## 88 88 32 92 84 15.5
## 89 89 20 252 80 10.9
## 90 90 23 220 78 10.3
## 91 91 21 230 75 10.9
## 92 92 24 259 73 9.7
## 93 93 44 236 81 14.9
## 94 94 21 259 76 15.5
## 95 95 28 238 77 6.3
## 96 96 9 24 71 10.9
## 97 97 13 112 71 11.5
## 98 98 46 237 78 6.9
## 99 99 18 224 67 13.8
## 100 100 13 27 76 10.3
## 101 101 24 238 68 10.3
## 102 102 16 201 82 8.0
## 103 103 13 238 64 12.6
## 104 104 23 14 71 9.2
## 105 105 36 139 81 10.3
## 106 106 7 49 69 10.3
## 107 107 14 20 63 16.6
## 108 108 30 193 70 6.9
## 109 109 14 191 75 14.3
## 110 110 18 131 76 8.0
## 111 111 20 223 68 11.5
summary(nycdata)
## X ozone radiation temperature
## Min. : 1.0 Min. : 1.0 Min. : 7.0 Min. :57.00
## 1st Qu.: 28.5 1st Qu.: 18.0 1st Qu.:113.5 1st Qu.:71.00
## Median : 56.0 Median : 31.0 Median :207.0 Median :79.00
## Mean : 56.0 Mean : 42.1 Mean :184.8 Mean :77.79
## 3rd Qu.: 83.5 3rd Qu.: 62.0 3rd Qu.:255.5 3rd Qu.:84.50
## Max. :111.0 Max. :168.0 Max. :334.0 Max. :97.00
## wind
## Min. : 2.300
## 1st Qu.: 7.400
## Median : 9.700
## Mean : 9.939
## 3rd Qu.:11.500
## Max. :20.700
mean(nycdata$ozone)
## [1] 42.0991
median(nycdata$ozone)
## [1] 31
mean(nycdata$radiation)
## [1] 184.8018
median(nycdata$radiation)
## [1] 207
nycdata2 <- data.frame(O3=nycdata$ozone, temp = nycdata$temperature)
nycdata2
## O3 temp
## 1 41 67
## 2 36 72
## 3 12 74
## 4 18 62
## 5 23 65
## 6 19 59
## 7 8 61
## 8 16 69
## 9 11 66
## 10 14 68
## 11 18 58
## 12 14 64
## 13 34 66
## 14 6 57
## 15 30 68
## 16 11 62
## 17 1 59
## 18 11 73
## 19 4 61
## 20 32 61
## 21 23 67
## 22 45 81
## 23 115 79
## 24 37 76
## 25 29 82
## 26 71 90
## 27 39 87
## 28 23 82
## 29 21 77
## 30 37 72
## 31 20 65
## 32 12 73
## 33 13 76
## 34 135 84
## 35 49 85
## 36 32 81
## 37 64 83
## 38 40 83
## 39 77 88
## 40 97 92
## 41 97 92
## 42 85 89
## 43 10 73
## 44 27 81
## 45 7 80
## 46 48 81
## 47 35 82
## 48 61 84
## 49 79 87
## 50 63 85
## 51 16 74
## 52 80 86
## 53 108 85
## 54 20 82
## 55 52 86
## 56 82 88
## 57 50 86
## 58 64 83
## 59 59 81
## 60 39 81
## 61 9 81
## 62 16 82
## 63 122 89
## 64 89 90
## 65 110 90
## 66 44 86
## 67 28 82
## 68 65 80
## 69 22 77
## 70 59 79
## 71 23 76
## 72 31 78
## 73 44 78
## 74 21 77
## 75 9 72
## 76 45 79
## 77 168 81
## 78 73 86
## 79 76 97
## 80 118 94
## 81 84 96
## 82 85 94
## 83 96 91
## 84 78 92
## 85 73 93
## 86 91 93
## 87 47 87
## 88 32 84
## 89 20 80
## 90 23 78
## 91 21 75
## 92 24 73
## 93 44 81
## 94 21 76
## 95 28 77
## 96 9 71
## 97 13 71
## 98 46 78
## 99 18 67
## 100 13 76
## 101 24 68
## 102 16 82
## 103 13 64
## 104 23 71
## 105 36 81
## 106 7 69
## 107 14 63
## 108 30 70
## 109 14 75
## 110 18 76
## 111 20 68
summary(nycdata2)
## O3 temp
## Min. : 1.0 Min. :57.00
## 1st Qu.: 18.0 1st Qu.:71.00
## Median : 31.0 Median :79.00
## Mean : 42.1 Mean :77.79
## 3rd Qu.: 62.0 3rd Qu.:84.50
## Max. :168.0 Max. :97.00
mean(nycdata2$O3)
## [1] 42.0991
median(nycdata2$O3)
## [1] 31
mean(nycdata2$temp)
## [1] 77.79279
median(nycdata2$temp)
## [1] 79
nycdata2$temp <- replace(nycdata2$temp,nycdata2$temp == 90, "Rename1")
nycdata2$temp <- replace(nycdata2$temp,nycdata2$temp == 73, "Rename2")
nycdata2$O3 <- replace(nycdata2$O3,nycdata2$O3 > 99, "Rename3")
nycdata2
## O3 temp
## 1 41 67
## 2 36 72
## 3 12 74
## 4 18 62
## 5 23 65
## 6 19 59
## 7 8 61
## 8 16 69
## 9 11 66
## 10 14 68
## 11 18 58
## 12 14 64
## 13 34 66
## 14 6 57
## 15 30 68
## 16 11 62
## 17 1 59
## 18 11 Rename2
## 19 4 61
## 20 32 61
## 21 23 67
## 22 45 81
## 23 Rename3 79
## 24 37 76
## 25 29 82
## 26 71 Rename1
## 27 39 87
## 28 23 82
## 29 21 77
## 30 37 72
## 31 20 65
## 32 12 Rename2
## 33 13 76
## 34 Rename3 84
## 35 49 85
## 36 32 81
## 37 64 83
## 38 40 83
## 39 77 88
## 40 97 92
## 41 97 92
## 42 85 89
## 43 10 Rename2
## 44 27 81
## 45 7 80
## 46 48 81
## 47 35 82
## 48 61 84
## 49 79 87
## 50 63 85
## 51 16 74
## 52 80 86
## 53 Rename3 85
## 54 20 82
## 55 52 86
## 56 82 88
## 57 50 86
## 58 64 83
## 59 59 81
## 60 39 81
## 61 9 81
## 62 16 82
## 63 Rename3 89
## 64 89 Rename1
## 65 Rename3 Rename1
## 66 44 86
## 67 28 82
## 68 65 80
## 69 22 77
## 70 59 79
## 71 23 76
## 72 31 78
## 73 44 78
## 74 21 77
## 75 9 72
## 76 45 79
## 77 Rename3 81
## 78 73 86
## 79 76 97
## 80 Rename3 94
## 81 84 96
## 82 85 94
## 83 96 91
## 84 78 92
## 85 73 93
## 86 91 93
## 87 47 87
## 88 32 84
## 89 20 80
## 90 23 78
## 91 21 75
## 92 24 Rename2
## 93 44 81
## 94 21 76
## 95 28 77
## 96 9 71
## 97 13 71
## 98 46 78
## 99 18 67
## 100 13 76
## 101 24 68
## 102 16 82
## 103 13 64
## 104 23 71
## 105 36 81
## 106 7 69
## 107 14 63
## 108 30 70
## 109 14 75
## 110 18 76
## 111 20 68