Just as a very minor point "read.csv" returns a data.frame. Therefore the
data.frame in "data <- data.frame(read.csv("hw1_data.csv"))" is redundant and
just adds clutter to the code.
John Kane
Kingston ON Canada
> -----Original Message-----
> From: g...@st-andrews.ac.uk
> Sent: Fri, 1 Jul 2016 02:11:48 -0700
> To: r-help@r-project.org
> Subject: [R] Difficulty subsetting data frames using logical operators
>
> So, I uploaded a data set via my directory using the command data <-
> data.frame(read.csv("hw1_data.csv")) and then tried to subset that data
> using logical operators. Specifically, I was trying to make it so that I
> got all the rows in which the values for "Ozone" (a column in the data
> set)
> were greater than 31 (I was trying to get the mean of all said values).
> Then, I tried using the command data[ , "Ozone">31]. Additionally, I had
> trouble getting it so that I had all the rows where all the values in
> "Ozone">31 & "Temp">90 simultaneously. There were some NA values in both
> of
> those columns, so that might be it. If someone could help me to figure
> out
> how to remove those values, that'd be great as well. I'm using a Mac (OS
> X)
> with the latest version of R (3.1.2. I think??).
>
> Here is some of the code I used:
>
> >data <- data.frame(read.csv("hw1_data.csv"))
>> data
> 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 6
> 7 23 299 8.6 65 5 7
> 8 19 99 13.8 59 5 8
> 9 8 19 20.1 61 5 9
> 10 NA 194 8.6 69 5 10
> 11 7 NA 6.9 74 5 11
> 12 16 256 9.7 69 5 12
> 13 11 290 9.2 66 5 13
> 14 14 274 10.9 68 5 14
> 15 18 65 13.2 58 5 15
> 16 14 334 11.5 64 5 16
> 17 34 307 12.0 66 5 17
> 18 6 78 18.4 57 5 18
> 19 30 322 11.5 68 5 19
> 20 11 44 9.7 62 5 20
> 21 1 8 9.7 59 5 21
> 22 11 320 16.6 73 5 22
> 23 4 25 9.7 61 5 23
> 24 32 92 12.0 61 5 24
> 25 NA 66 16.6 57 5 25
> 26 NA 266 14.9 58 5 26
> 27 NA NA 8.0 57 5 27
> 28 23 13 12.0 67 5 28
> 29 45 252 14.9 81 5 29
> 30 115 223 5.7 79 5 30
> 31 37 279 7.4 76 5 31
> 32 NA 286 8.6 78 6 1
> 33 NA 287 9.7 74 6 2
> 34 NA 242 16.1 67 6 3
> 35 NA 186 9.2 84 6 4
> 36 NA 220 8.6 85 6 5
> 37 NA 264 14.3 79 6 6
> 38 29 127 9.7 82 6 7
> 39 NA 273 6.9 87 6 8
> 40 71 291 13.8 90 6 9
> 41 39 323 11.5 87 6 10
> 42 NA 259 10.9 93 6 11
> 43 NA 250 9.2 92 6 12
> 44 23 148 8.0 82 6 13
> 45 NA 332 13.8 80 6 14
> 46 NA 322 11.5 79 6 15
> 47 21 191 14.9 77 6 16
> 48 37 284 20.7 72 6 17
> 49 20 37 9.2 65 6 18
> 50 12 120 11.5 73 6 19
> 51 13 137 10.3 76 6 20
> 52 NA 150 6.3 77 6 21
> 53 NA 59 1.7 76 6 22
> 54 NA 91 4.6 76 6 23
> 55 NA 250 6.3 76 6 24
> 56 NA 135 8.0 75 6 25
> 57 NA 127 8.0 78 6 26
> 58 NA 47 10.3 73 6 27
> 59 NA 98 11.5 80 6 28
> 60 NA 31 14.9 77 6 29
> 61 NA 138 8.0 83 6 30
> 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 39 83 6.9 81 8 1
> 94 9 24 13.8 81 8 2
> 95 16 77 7.4 82 8 3
> 96 78 NA 6.9 86 8 4
> 97 35 NA 7.4 85 8 5
> 98 66 NA 4.6 87 8 6
> 99 122 255 4.0 89 8 7
> 100 89 229 10.3 90 8 8
> 101 110 207 8.0 90 8 9
> 102 NA 222 8.6 92 8 10
> 103 NA 137 11.5 86 8 11
> 104 44 192 11.5 86 8 12
> 105 28 273 11.5 82 8 13
> 106 65 157 9.7 80 8 14
> 107 NA 64 11.5 79 8 15
> 108 22 71 10.3 77 8 16
> 109 59 51 6.3 79 8 17
> 110 23 115 7.4 76 8 18
> 111 31 244 10.9 78 8 19
> 112 44 190 10.3 78 8 20
> 113 21 259 15.5 77 8 21
> 114 9 36 14.3 72 8 22
> 115 NA 255 12.6 75 8 23
> 116 45 212 9.7 79 8 24
> 117 168 238 3.4 81 8 25
> 118 73 215 8.0 86 8 26
> 119 NA 153 5.7 88 8 27
> 120 76 203 9.7 97 8 28
> 121 118 225 2.3 94 8 29
> 122 84 237 6.3 96 8 30
> 123 85 188 6.3 94 8 31
> 124 96 167 6.9 91 9 1
> 125 78 197 5.1 92 9 2
> 126 73 183 2.8 93 9 3
> 127 91 189 4.6 93 9 4
> 128 47 95 7.4 87 9 5
> 129 32 92 15.5 84 9 6
> 130 20 252 10.9 80 9 7
> 131 23 220 10.3 78 9 8
> 132 21 230 10.9 75 9 9
> 133 24 259 9.7 73 9 10
> 134 44 236 14.9 81 9 11
> 135 21 259 15.5 76 9 12
> 136 28 238 6.3 77 9 13
> 137 9 24 10.9 71 9 14
> 138 13 112 11.5 71 9 15
> 139 46 237 6.9 78 9 16
> 140 18 224 13.8 67 9 17
> 141 13 27 10.3 76 9 18
> 142 24 238 10.3 68 9 19
> 143 16 201 8.0 82 9 20
> 144 13 238 12.6 64 9 21
> 145 23 14 9.2 71 9 22
> 146 36 139 10.3 81 9 23
> 147 7 49 10.3 69 9 24
> 148 14 20 16.6 63 9 25
> 149 30 193 6.9 70 9 26
> 150 NA 145 13.2 77 9 27
> 151 14 191 14.3 75 9 28
> 152 18 131 8.0 76 9 29
> 153 20 223 11.5 68 9 30
>
> >colnames(data) <- c("Ozone", "Solar.R", "Wind", "Temp", "Month", "Day")
>
>> mean(data[, Ozone])
> Error in `[.data.frame`(data, , Ozone) : object 'Ozone' not found
>
> mean(data[, "Ozone">31])
> [1] NA
> Warning message:
> In mean.default(data[, "Ozone" > 31]) :
> argument is not numeric or logical: returning NA
>> mean(data[, "Ozone">31 & "Ozone"[!is.na("Ozone")]])
> Error in "Ozone" > 31 & "Ozone"[!is.na("Ozone")] :
> operations are possible only for numeric, logical or complex types
>
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>
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