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Giles Bischoff

2016-Jul-01 09:11 UTC

[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

	[[alternative HTML version deleted]]

jim holtman

2016-Jul-01 18:47 UTC

head link

[R] Difficulty subsetting data frames using logical operators

You may need to re-read the Intro to R.

data[data$Ozone > 31,]

or

subset(data, Ozone > 31)


Jim Holtman
Data Munger Guru

What is the problem that you are trying to solve?
Tell me what you want to do, not how you want to do it.

On Fri, Jul 1, 2016 at 5:11 AM, Giles Bischoff <gab4 at st-andrews.ac.uk>
wrote:
> 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
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
	[[alternative HTML version deleted]]

David Winsemius

2016-Jul-01 19:17 UTC

head link

[R] Difficulty subsetting data frames using logical operators

> On Jul 1, 2016, at 2:11 AM, Giles Bischoff <gab4 at st-andrews.ac.uk>
wrote:
> 
> 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:
> 
Bad idea to use `data` as an data-object name. There is an R function by that
name.

Look at ?data and see the fortune:

fortunes::fortune("dog")

The threat that it will "clash" is not actually correct, but it is
guaranteed to deliver error messages that do not make sense and result in code
that will be difficult to read.
>> 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
The mean function has an 'na.rm' argument which you should be setting to
TRUE. You should also test the whole column when using logical indexing in the
"i" positional argument to "[" rather than trying to do the
selection in the second argument to "["

 mean(data[ data$Ozone>31, "Ozone"], na.rm=TRUE)


> Warning message:
> In mean.default(data[, "Ozone" > 31]) :
>  argument is not numeric or logical: returning NA
>> mean(data[, "Ozone">31 &
"Ozone"[!is.na("Ozone")]])
If you want to do selection and rejection of NA's inside the "["
function, both operations should be done in the first argument rather than the
second:

 mean( data[ data$Ozone > 31 & !is.na(data$Ozone) , "Ozone"])

Alternates (but not recommended for programming use):

with( data, mean( data[ Ozone > 31 & !is.na(Ozone) , "Ozone"]

mean( subset( data, Ozone > 31, select=Ozone) )
> Error in "Ozone" > 31 &
"Ozone"[!is.na("Ozone")] :
>  operations are possible only for numeric, logical or complex types
> 
> 	[[alternative HTML version deleted]]
> And finally, the r-help mailing list uses plain text. HTML is discouraged due to
formatting weirdnesses, although in this instance does not seem to have caused
any problems.

> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
David Winsemius
Alameda, CA, USA

Ulrik Stervbo

2016-Jul-02 09:16 UTC

head link

[R] Difficulty subsetting data frames using logical operators

Hi Giles,

Look at ?mean

In addition it seems you need to read a few tutorials on R. Several are
already mentioned on this list otherwise Google can direct.

Hope this helps,
Ulrik

Giles Bischoff <gab4 at st-andrews.ac.uk> schrieb am Fr., 1. Juli 2016
18:55:
> 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
>
>         [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
	[[alternative HTML version deleted]]

John Kane

2016-Jul-02 11:44 UTC

head link

[R] Difficulty subsetting data frames using logical operators

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: gab4 at st-andrews.ac.uk
> Sent: Fri, 1 Jul 2016 02:11:48 -0700
> To: r-help at 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
> 
> 	[[alternative HTML version deleted]]
> 
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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R help - Jul 2016 - Difficulty subsetting data frames using logical operators

[R] Difficulty subsetting data frames using logical operators

[R] Difficulty subsetting data frames using logical operators

[R] Difficulty subsetting data frames using logical operators

[R] Difficulty subsetting data frames using logical operators

[R] Difficulty subsetting data frames using logical operators