head(airquality)## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6library(datasets)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionFilter
filter(airquality, Temp > 70)## Ozone Solar.R Wind Temp Month Day
## 1 36 118 8.0 72 5 2
## 2 12 149 12.6 74 5 3
## 3 7 NA 6.9 74 5 11
## 4 11 320 16.6 73 5 22
## 5 45 252 14.9 81 5 29
## 6 115 223 5.7 79 5 30
## 7 37 279 7.4 76 5 31
## 8 NA 286 8.6 78 6 1
## 9 NA 287 9.7 74 6 2
## 10 NA 186 9.2 84 6 4
## 11 NA 220 8.6 85 6 5
## 12 NA 264 14.3 79 6 6
## 13 29 127 9.7 82 6 7
## 14 NA 273 6.9 87 6 8
## 15 71 291 13.8 90 6 9
## 16 39 323 11.5 87 6 10
## 17 NA 259 10.9 93 6 11
## 18 NA 250 9.2 92 6 12
## 19 23 148 8.0 82 6 13
## 20 NA 332 13.8 80 6 14
## 21 NA 322 11.5 79 6 15
## 22 21 191 14.9 77 6 16
## 23 37 284 20.7 72 6 17
## 24 12 120 11.5 73 6 19
## 25 13 137 10.3 76 6 20
## 26 NA 150 6.3 77 6 21
## 27 NA 59 1.7 76 6 22
## 28 NA 91 4.6 76 6 23
## 29 NA 250 6.3 76 6 24
## 30 NA 135 8.0 75 6 25
## 31 NA 127 8.0 78 6 26
## 32 NA 47 10.3 73 6 27
## 33 NA 98 11.5 80 6 28
## 34 NA 31 14.9 77 6 29
## 35 NA 138 8.0 83 6 30
## 36 135 269 4.1 84 7 1
## 37 49 248 9.2 85 7 2
## 38 32 236 9.2 81 7 3
## 39 NA 101 10.9 84 7 4
## 40 64 175 4.6 83 7 5
## 41 40 314 10.9 83 7 6
## 42 77 276 5.1 88 7 7
## 43 97 267 6.3 92 7 8
## 44 97 272 5.7 92 7 9
## 45 85 175 7.4 89 7 10
## 46 NA 139 8.6 82 7 11
## 47 10 264 14.3 73 7 12
## 48 27 175 14.9 81 7 13
## 49 NA 291 14.9 91 7 14
## 50 7 48 14.3 80 7 15
## 51 48 260 6.9 81 7 16
## 52 35 274 10.3 82 7 17
## 53 61 285 6.3 84 7 18
## 54 79 187 5.1 87 7 19
## 55 63 220 11.5 85 7 20
## 56 16 7 6.9 74 7 21
## 57 NA 258 9.7 81 7 22
## 58 NA 295 11.5 82 7 23
## 59 80 294 8.6 86 7 24
## 60 108 223 8.0 85 7 25
## 61 20 81 8.6 82 7 26
## 62 52 82 12.0 86 7 27
## 63 82 213 7.4 88 7 28
## 64 50 275 7.4 86 7 29
## 65 64 253 7.4 83 7 30
## 66 59 254 9.2 81 7 31
## 67 39 83 6.9 81 8 1
## 68 9 24 13.8 81 8 2
## 69 16 77 7.4 82 8 3
## 70 78 NA 6.9 86 8 4
## 71 35 NA 7.4 85 8 5
## 72 66 NA 4.6 87 8 6
## 73 122 255 4.0 89 8 7
## 74 89 229 10.3 90 8 8
## 75 110 207 8.0 90 8 9
## 76 NA 222 8.6 92 8 10
## 77 NA 137 11.5 86 8 11
## 78 44 192 11.5 86 8 12
## 79 28 273 11.5 82 8 13
## 80 65 157 9.7 80 8 14
## 81 NA 64 11.5 79 8 15
## 82 22 71 10.3 77 8 16
## 83 59 51 6.3 79 8 17
## 84 23 115 7.4 76 8 18
## 85 31 244 10.9 78 8 19
## 86 44 190 10.3 78 8 20
## 87 21 259 15.5 77 8 21
## 88 9 36 14.3 72 8 22
## 89 NA 255 12.6 75 8 23
## 90 45 212 9.7 79 8 24
## 91 168 238 3.4 81 8 25
## 92 73 215 8.0 86 8 26
## 93 NA 153 5.7 88 8 27
## 94 76 203 9.7 97 8 28
## 95 118 225 2.3 94 8 29
## 96 84 237 6.3 96 8 30
## 97 85 188 6.3 94 8 31
## 98 96 167 6.9 91 9 1
## 99 78 197 5.1 92 9 2
## 100 73 183 2.8 93 9 3
## 101 91 189 4.6 93 9 4
## 102 47 95 7.4 87 9 5
## 103 32 92 15.5 84 9 6
## 104 20 252 10.9 80 9 7
## 105 23 220 10.3 78 9 8
## 106 21 230 10.9 75 9 9
## 107 24 259 9.7 73 9 10
## 108 44 236 14.9 81 9 11
## 109 21 259 15.5 76 9 12
## 110 28 238 6.3 77 9 13
## 111 9 24 10.9 71 9 14
## 112 13 112 11.5 71 9 15
## 113 46 237 6.9 78 9 16
## 114 13 27 10.3 76 9 18
## 115 16 201 8.0 82 9 20
## 116 23 14 9.2 71 9 22
## 117 36 139 10.3 81 9 23
## 118 NA 145 13.2 77 9 27
## 119 14 191 14.3 75 9 28
## 120 18 131 8.0 76 9 29filter(airquality, Temp > 80 & Month > 5)## Ozone Solar.R Wind Temp Month Day
## 1 NA 186 9.2 84 6 4
## 2 NA 220 8.6 85 6 5
## 3 29 127 9.7 82 6 7
## 4 NA 273 6.9 87 6 8
## 5 71 291 13.8 90 6 9
## 6 39 323 11.5 87 6 10
## 7 NA 259 10.9 93 6 11
## 8 NA 250 9.2 92 6 12
## 9 23 148 8.0 82 6 13
## 10 NA 138 8.0 83 6 30
## 11 135 269 4.1 84 7 1
## 12 49 248 9.2 85 7 2
## 13 32 236 9.2 81 7 3
## 14 NA 101 10.9 84 7 4
## 15 64 175 4.6 83 7 5
## 16 40 314 10.9 83 7 6
## 17 77 276 5.1 88 7 7
## 18 97 267 6.3 92 7 8
## 19 97 272 5.7 92 7 9
## 20 85 175 7.4 89 7 10
## 21 NA 139 8.6 82 7 11
## 22 27 175 14.9 81 7 13
## 23 NA 291 14.9 91 7 14
## 24 48 260 6.9 81 7 16
## 25 35 274 10.3 82 7 17
## 26 61 285 6.3 84 7 18
## 27 79 187 5.1 87 7 19
## 28 63 220 11.5 85 7 20
## 29 NA 258 9.7 81 7 22
## 30 NA 295 11.5 82 7 23
## 31 80 294 8.6 86 7 24
## 32 108 223 8.0 85 7 25
## 33 20 81 8.6 82 7 26
## 34 52 82 12.0 86 7 27
## 35 82 213 7.4 88 7 28
## 36 50 275 7.4 86 7 29
## 37 64 253 7.4 83 7 30
## 38 59 254 9.2 81 7 31
## 39 39 83 6.9 81 8 1
## 40 9 24 13.8 81 8 2
## 41 16 77 7.4 82 8 3
## 42 78 NA 6.9 86 8 4
## 43 35 NA 7.4 85 8 5
## 44 66 NA 4.6 87 8 6
## 45 122 255 4.0 89 8 7
## 46 89 229 10.3 90 8 8
## 47 110 207 8.0 90 8 9
## 48 NA 222 8.6 92 8 10
## 49 NA 137 11.5 86 8 11
## 50 44 192 11.5 86 8 12
## 51 28 273 11.5 82 8 13
## 52 168 238 3.4 81 8 25
## 53 73 215 8.0 86 8 26
## 54 NA 153 5.7 88 8 27
## 55 76 203 9.7 97 8 28
## 56 118 225 2.3 94 8 29
## 57 84 237 6.3 96 8 30
## 58 85 188 6.3 94 8 31
## 59 96 167 6.9 91 9 1
## 60 78 197 5.1 92 9 2
## 61 73 183 2.8 93 9 3
## 62 91 189 4.6 93 9 4
## 63 47 95 7.4 87 9 5
## 64 32 92 15.5 84 9 6
## 65 44 236 14.9 81 9 11
## 66 16 201 8.0 82 9 20
## 67 36 139 10.3 81 9 23Mutate ##### Mutate is used to add new variables to the data. For example, let’s adds a new column that displays the temperature in Celsius.
mutate(airquality, TempInC = (Temp - 32) * 5 / 9)## Ozone Solar.R Wind Temp Month Day TempInC
## 1 41 190 7.4 67 5 1 19.44444
## 2 36 118 8.0 72 5 2 22.22222
## 3 12 149 12.6 74 5 3 23.33333
## 4 18 313 11.5 62 5 4 16.66667
## 5 NA NA 14.3 56 5 5 13.33333
## 6 28 NA 14.9 66 5 6 18.88889
## 7 23 299 8.6 65 5 7 18.33333
## 8 19 99 13.8 59 5 8 15.00000
## 9 8 19 20.1 61 5 9 16.11111
## 10 NA 194 8.6 69 5 10 20.55556
## 11 7 NA 6.9 74 5 11 23.33333
## 12 16 256 9.7 69 5 12 20.55556
## 13 11 290 9.2 66 5 13 18.88889
## 14 14 274 10.9 68 5 14 20.00000
## 15 18 65 13.2 58 5 15 14.44444
## 16 14 334 11.5 64 5 16 17.77778
## 17 34 307 12.0 66 5 17 18.88889
## 18 6 78 18.4 57 5 18 13.88889
## 19 30 322 11.5 68 5 19 20.00000
## 20 11 44 9.7 62 5 20 16.66667
## 21 1 8 9.7 59 5 21 15.00000
## 22 11 320 16.6 73 5 22 22.77778
## 23 4 25 9.7 61 5 23 16.11111
## 24 32 92 12.0 61 5 24 16.11111
## 25 NA 66 16.6 57 5 25 13.88889
## 26 NA 266 14.9 58 5 26 14.44444
## 27 NA NA 8.0 57 5 27 13.88889
## 28 23 13 12.0 67 5 28 19.44444
## 29 45 252 14.9 81 5 29 27.22222
## 30 115 223 5.7 79 5 30 26.11111
## 31 37 279 7.4 76 5 31 24.44444
## 32 NA 286 8.6 78 6 1 25.55556
## 33 NA 287 9.7 74 6 2 23.33333
## 34 NA 242 16.1 67 6 3 19.44444
## 35 NA 186 9.2 84 6 4 28.88889
## 36 NA 220 8.6 85 6 5 29.44444
## 37 NA 264 14.3 79 6 6 26.11111
## 38 29 127 9.7 82 6 7 27.77778
## 39 NA 273 6.9 87 6 8 30.55556
## 40 71 291 13.8 90 6 9 32.22222
## 41 39 323 11.5 87 6 10 30.55556
## 42 NA 259 10.9 93 6 11 33.88889
## 43 NA 250 9.2 92 6 12 33.33333
## 44 23 148 8.0 82 6 13 27.77778
## 45 NA 332 13.8 80 6 14 26.66667
## 46 NA 322 11.5 79 6 15 26.11111
## 47 21 191 14.9 77 6 16 25.00000
## 48 37 284 20.7 72 6 17 22.22222
## 49 20 37 9.2 65 6 18 18.33333
## 50 12 120 11.5 73 6 19 22.77778
## 51 13 137 10.3 76 6 20 24.44444
## 52 NA 150 6.3 77 6 21 25.00000
## 53 NA 59 1.7 76 6 22 24.44444
## 54 NA 91 4.6 76 6 23 24.44444
## 55 NA 250 6.3 76 6 24 24.44444
## 56 NA 135 8.0 75 6 25 23.88889
## 57 NA 127 8.0 78 6 26 25.55556
## 58 NA 47 10.3 73 6 27 22.77778
## 59 NA 98 11.5 80 6 28 26.66667
## 60 NA 31 14.9 77 6 29 25.00000
## 61 NA 138 8.0 83 6 30 28.33333
## 62 135 269 4.1 84 7 1 28.88889
## 63 49 248 9.2 85 7 2 29.44444
## 64 32 236 9.2 81 7 3 27.22222
## 65 NA 101 10.9 84 7 4 28.88889
## 66 64 175 4.6 83 7 5 28.33333
## 67 40 314 10.9 83 7 6 28.33333
## 68 77 276 5.1 88 7 7 31.11111
## 69 97 267 6.3 92 7 8 33.33333
## 70 97 272 5.7 92 7 9 33.33333
## 71 85 175 7.4 89 7 10 31.66667
## 72 NA 139 8.6 82 7 11 27.77778
## 73 10 264 14.3 73 7 12 22.77778
## 74 27 175 14.9 81 7 13 27.22222
## 75 NA 291 14.9 91 7 14 32.77778
## 76 7 48 14.3 80 7 15 26.66667
## 77 48 260 6.9 81 7 16 27.22222
## 78 35 274 10.3 82 7 17 27.77778
## 79 61 285 6.3 84 7 18 28.88889
## 80 79 187 5.1 87 7 19 30.55556
## 81 63 220 11.5 85 7 20 29.44444
## 82 16 7 6.9 74 7 21 23.33333
## 83 NA 258 9.7 81 7 22 27.22222
## 84 NA 295 11.5 82 7 23 27.77778
## 85 80 294 8.6 86 7 24 30.00000
## 86 108 223 8.0 85 7 25 29.44444
## 87 20 81 8.6 82 7 26 27.77778
## 88 52 82 12.0 86 7 27 30.00000
## 89 82 213 7.4 88 7 28 31.11111
## 90 50 275 7.4 86 7 29 30.00000
## 91 64 253 7.4 83 7 30 28.33333
## 92 59 254 9.2 81 7 31 27.22222
## 93 39 83 6.9 81 8 1 27.22222
## 94 9 24 13.8 81 8 2 27.22222
## 95 16 77 7.4 82 8 3 27.77778
## 96 78 NA 6.9 86 8 4 30.00000
## 97 35 NA 7.4 85 8 5 29.44444
## 98 66 NA 4.6 87 8 6 30.55556
## 99 122 255 4.0 89 8 7 31.66667
## 100 89 229 10.3 90 8 8 32.22222
## 101 110 207 8.0 90 8 9 32.22222
## 102 NA 222 8.6 92 8 10 33.33333
## 103 NA 137 11.5 86 8 11 30.00000
## 104 44 192 11.5 86 8 12 30.00000
## 105 28 273 11.5 82 8 13 27.77778
## 106 65 157 9.7 80 8 14 26.66667
## 107 NA 64 11.5 79 8 15 26.11111
## 108 22 71 10.3 77 8 16 25.00000
## 109 59 51 6.3 79 8 17 26.11111
## 110 23 115 7.4 76 8 18 24.44444
## 111 31 244 10.9 78 8 19 25.55556
## 112 44 190 10.3 78 8 20 25.55556
## 113 21 259 15.5 77 8 21 25.00000
## 114 9 36 14.3 72 8 22 22.22222
## 115 NA 255 12.6 75 8 23 23.88889
## 116 45 212 9.7 79 8 24 26.11111
## 117 168 238 3.4 81 8 25 27.22222
## 118 73 215 8.0 86 8 26 30.00000
## 119 NA 153 5.7 88 8 27 31.11111
## 120 76 203 9.7 97 8 28 36.11111
## 121 118 225 2.3 94 8 29 34.44444
## 122 84 237 6.3 96 8 30 35.55556
## 123 85 188 6.3 94 8 31 34.44444
## 124 96 167 6.9 91 9 1 32.77778
## 125 78 197 5.1 92 9 2 33.33333
## 126 73 183 2.8 93 9 3 33.88889
## 127 91 189 4.6 93 9 4 33.88889
## 128 47 95 7.4 87 9 5 30.55556
## 129 32 92 15.5 84 9 6 28.88889
## 130 20 252 10.9 80 9 7 26.66667
## 131 23 220 10.3 78 9 8 25.55556
## 132 21 230 10.9 75 9 9 23.88889
## 133 24 259 9.7 73 9 10 22.77778
## 134 44 236 14.9 81 9 11 27.22222
## 135 21 259 15.5 76 9 12 24.44444
## 136 28 238 6.3 77 9 13 25.00000
## 137 9 24 10.9 71 9 14 21.66667
## 138 13 112 11.5 71 9 15 21.66667
## 139 46 237 6.9 78 9 16 25.55556
## 140 18 224 13.8 67 9 17 19.44444
## 141 13 27 10.3 76 9 18 24.44444
## 142 24 238 10.3 68 9 19 20.00000
## 143 16 201 8.0 82 9 20 27.77778
## 144 13 238 12.6 64 9 21 17.77778
## 145 23 14 9.2 71 9 22 21.66667
## 146 36 139 10.3 81 9 23 27.22222
## 147 7 49 10.3 69 9 24 20.55556
## 148 14 20 16.6 63 9 25 17.22222
## 149 30 193 6.9 70 9 26 21.11111
## 150 NA 145 13.2 77 9 27 25.00000
## 151 14 191 14.3 75 9 28 23.88889
## 152 18 131 8.0 76 9 29 24.44444
## 153 20 223 11.5 68 9 30 20.00000Summarise ##### The summarise function is used to summarise multiple values into a single value. It is very powerful when used in conjunction with the other functions in the dplyr package, as demonstrated below. na.rm = TRUE will remove all NA values while calculating the mean, so that it doesn’t produce spurious results.
summarise(airquality, mean(Temp, na.rm = TRUE))## mean(Temp, na.rm = TRUE)
## 1 77.88235Group By ##### The group_by function is used to group data by one or more variables. For example, we can group the data together based on the Month, and then use the summarise function to calculate and display the mean temperature for each month.
summarise(group_by(airquality, Month), mean(Temp, na.rm = TRUE))## # A tibble: 5 x 2
## Month `mean(Temp, na.rm = TRUE)`
## <int> <dbl>
## 1 5 65.5
## 2 6 79.1
## 3 7 83.9
## 4 8 84.0
## 5 9 76.9Sample ##### The sample function is used to select random rows from a table. The first line of code randomly selects ten rows from the dataset, and the second line of code randomly selects 15 rows (10% of the original 153 rows) from the dataset.
sample_n(airquality, size = 10)## Ozone Solar.R Wind Temp Month Day
## 70 97 272 5.7 92 7 9
## 66 64 175 4.6 83 7 5
## 23 4 25 9.7 61 5 23
## 107 NA 64 11.5 79 8 15
## 12 16 256 9.7 69 5 12
## 135 21 259 15.5 76 9 12
## 1 41 190 7.4 67 5 1
## 52 NA 150 6.3 77 6 21
## 6 28 NA 14.9 66 5 6
## 126 73 183 2.8 93 9 3sample_frac(airquality, size = 0.1)## Ozone Solar.R Wind Temp Month Day
## 54 NA 91 4.6 76 6 23
## 138 13 112 11.5 71 9 15
## 135 21 259 15.5 76 9 12
## 59 NA 98 11.5 80 6 28
## 37 NA 264 14.3 79 6 6
## 34 NA 242 16.1 67 6 3
## 35 NA 186 9.2 84 6 4
## 50 12 120 11.5 73 6 19
## 38 29 127 9.7 82 6 7
## 45 NA 332 13.8 80 6 14
## 100 89 229 10.3 90 8 8
## 58 NA 47 10.3 73 6 27
## 99 122 255 4.0 89 8 7
## 56 NA 135 8.0 75 6 25
## 11 7 NA 6.9 74 5 11Count ##### The count function tallies observations based on a group. It is slightly similar to the table function in the base package. For example:
count(airquality, Month)## # A tibble: 5 x 2
## Month n
## <int> <int>
## 1 5 31
## 2 6 30
## 3 7 31
## 4 8 31
## 5 9 30Arrange ##### The arrange function is used to arrange rows by variables. Currently, the airquality dataset is arranged based on Month, and then Day. We can use the arrange function to arrange the rows in the descending order of Month, and then in the ascending order of Day.
arrange(airquality, desc(Month), Day)## Ozone Solar.R Wind Temp Month Day
## 1 96 167 6.9 91 9 1
## 2 78 197 5.1 92 9 2
## 3 73 183 2.8 93 9 3
## 4 91 189 4.6 93 9 4
## 5 47 95 7.4 87 9 5
## 6 32 92 15.5 84 9 6
## 7 20 252 10.9 80 9 7
## 8 23 220 10.3 78 9 8
## 9 21 230 10.9 75 9 9
## 10 24 259 9.7 73 9 10
## 11 44 236 14.9 81 9 11
## 12 21 259 15.5 76 9 12
## 13 28 238 6.3 77 9 13
## 14 9 24 10.9 71 9 14
## 15 13 112 11.5 71 9 15
## 16 46 237 6.9 78 9 16
## 17 18 224 13.8 67 9 17
## 18 13 27 10.3 76 9 18
## 19 24 238 10.3 68 9 19
## 20 16 201 8.0 82 9 20
## 21 13 238 12.6 64 9 21
## 22 23 14 9.2 71 9 22
## 23 36 139 10.3 81 9 23
## 24 7 49 10.3 69 9 24
## 25 14 20 16.6 63 9 25
## 26 30 193 6.9 70 9 26
## 27 NA 145 13.2 77 9 27
## 28 14 191 14.3 75 9 28
## 29 18 131 8.0 76 9 29
## 30 20 223 11.5 68 9 30
## 31 39 83 6.9 81 8 1
## 32 9 24 13.8 81 8 2
## 33 16 77 7.4 82 8 3
## 34 78 NA 6.9 86 8 4
## 35 35 NA 7.4 85 8 5
## 36 66 NA 4.6 87 8 6
## 37 122 255 4.0 89 8 7
## 38 89 229 10.3 90 8 8
## 39 110 207 8.0 90 8 9
## 40 NA 222 8.6 92 8 10
## 41 NA 137 11.5 86 8 11
## 42 44 192 11.5 86 8 12
## 43 28 273 11.5 82 8 13
## 44 65 157 9.7 80 8 14
## 45 NA 64 11.5 79 8 15
## 46 22 71 10.3 77 8 16
## 47 59 51 6.3 79 8 17
## 48 23 115 7.4 76 8 18
## 49 31 244 10.9 78 8 19
## 50 44 190 10.3 78 8 20
## 51 21 259 15.5 77 8 21
## 52 9 36 14.3 72 8 22
## 53 NA 255 12.6 75 8 23
## 54 45 212 9.7 79 8 24
## 55 168 238 3.4 81 8 25
## 56 73 215 8.0 86 8 26
## 57 NA 153 5.7 88 8 27
## 58 76 203 9.7 97 8 28
## 59 118 225 2.3 94 8 29
## 60 84 237 6.3 96 8 30
## 61 85 188 6.3 94 8 31
## 62 135 269 4.1 84 7 1
## 63 49 248 9.2 85 7 2
## 64 32 236 9.2 81 7 3
## 65 NA 101 10.9 84 7 4
## 66 64 175 4.6 83 7 5
## 67 40 314 10.9 83 7 6
## 68 77 276 5.1 88 7 7
## 69 97 267 6.3 92 7 8
## 70 97 272 5.7 92 7 9
## 71 85 175 7.4 89 7 10
## 72 NA 139 8.6 82 7 11
## 73 10 264 14.3 73 7 12
## 74 27 175 14.9 81 7 13
## 75 NA 291 14.9 91 7 14
## 76 7 48 14.3 80 7 15
## 77 48 260 6.9 81 7 16
## 78 35 274 10.3 82 7 17
## 79 61 285 6.3 84 7 18
## 80 79 187 5.1 87 7 19
## 81 63 220 11.5 85 7 20
## 82 16 7 6.9 74 7 21
## 83 NA 258 9.7 81 7 22
## 84 NA 295 11.5 82 7 23
## 85 80 294 8.6 86 7 24
## 86 108 223 8.0 85 7 25
## 87 20 81 8.6 82 7 26
## 88 52 82 12.0 86 7 27
## 89 82 213 7.4 88 7 28
## 90 50 275 7.4 86 7 29
## 91 64 253 7.4 83 7 30
## 92 59 254 9.2 81 7 31
## 93 NA 286 8.6 78 6 1
## 94 NA 287 9.7 74 6 2
## 95 NA 242 16.1 67 6 3
## 96 NA 186 9.2 84 6 4
## 97 NA 220 8.6 85 6 5
## 98 NA 264 14.3 79 6 6
## 99 29 127 9.7 82 6 7
## 100 NA 273 6.9 87 6 8
## 101 71 291 13.8 90 6 9
## 102 39 323 11.5 87 6 10
## 103 NA 259 10.9 93 6 11
## 104 NA 250 9.2 92 6 12
## 105 23 148 8.0 82 6 13
## 106 NA 332 13.8 80 6 14
## 107 NA 322 11.5 79 6 15
## 108 21 191 14.9 77 6 16
## 109 37 284 20.7 72 6 17
## 110 20 37 9.2 65 6 18
## 111 12 120 11.5 73 6 19
## 112 13 137 10.3 76 6 20
## 113 NA 150 6.3 77 6 21
## 114 NA 59 1.7 76 6 22
## 115 NA 91 4.6 76 6 23
## 116 NA 250 6.3 76 6 24
## 117 NA 135 8.0 75 6 25
## 118 NA 127 8.0 78 6 26
## 119 NA 47 10.3 73 6 27
## 120 NA 98 11.5 80 6 28
## 121 NA 31 14.9 77 6 29
## 122 NA 138 8.0 83 6 30
## 123 41 190 7.4 67 5 1
## 124 36 118 8.0 72 5 2
## 125 12 149 12.6 74 5 3
## 126 18 313 11.5 62 5 4
## 127 NA NA 14.3 56 5 5
## 128 28 NA 14.9 66 5 6
## 129 23 299 8.6 65 5 7
## 130 19 99 13.8 59 5 8
## 131 8 19 20.1 61 5 9
## 132 NA 194 8.6 69 5 10
## 133 7 NA 6.9 74 5 11
## 134 16 256 9.7 69 5 12
## 135 11 290 9.2 66 5 13
## 136 14 274 10.9 68 5 14
## 137 18 65 13.2 58 5 15
## 138 14 334 11.5 64 5 16
## 139 34 307 12.0 66 5 17
## 140 6 78 18.4 57 5 18
## 141 30 322 11.5 68 5 19
## 142 11 44 9.7 62 5 20
## 143 1 8 9.7 59 5 21
## 144 11 320 16.6 73 5 22
## 145 4 25 9.7 61 5 23
## 146 32 92 12.0 61 5 24
## 147 NA 66 16.6 57 5 25
## 148 NA 266 14.9 58 5 26
## 149 NA NA 8.0 57 5 27
## 150 23 13 12.0 67 5 28
## 151 45 252 14.9 81 5 29
## 152 115 223 5.7 79 5 30
## 153 37 279 7.4 76 5 31filteredData <- filter(airquality, Month != 5)
groupedData <- group_by(filteredData, Month)
summarise(groupedData, mean(Temp, na.rm = TRUE))## # A tibble: 4 x 2
## Month `mean(Temp, na.rm = TRUE)`
## <int> <dbl>
## 1 6 79.1
## 2 7 83.9
## 3 8 84.0
## 4 9 76.9airquality %>%
filter(Month != 5) %>%
group_by(Month) %>%
summarise(mean(Temp, na.rm = TRUE))## # A tibble: 4 x 2
## Month `mean(Temp, na.rm = TRUE)`
## <int> <dbl>
## 1 6 79.1
## 2 7 83.9
## 3 8 84.0
## 4 9 76.9