[R] Zoo object problem: Find the column name of a univariate zoo object

Fri Aug 10 04:14:53 CEST 2012

On Aug 9, 2012, at 8:32 PM, jpm miao <miaojpm at gmail.com> wrote:

> Hi everyone and Achim,
> 
>   Achim, I appreciate your help about the function "NCOL". When I use
> "NCOL" instead of "ncol", I can find out the number of columns (number of
> time series) in the presence of only one time series (one variable, one
> column).
> 
>   Now I want to know how I can find out the column names of the zoo
> objects? In case of more than one time series, the function "colnames"
> works, but not for the univariate time series.
> 

That's not true. zoo allows (but does not require) the data to have a dim attribute, as with any other vector. If (and I believe only if) an object has a dim attribute and hence can have colnames. For example, see xts which is a subclass of zoo requiring dim and for which colnames and names are equivalent. 

Michael

>   Thanks,
> 
> Miao
> 
> 2012/8/9 Achim Zeileis <Achim.Zeileis at uibk.ac.at>
> 
>> On Thu, 9 Aug 2012, jpm miao wrote:
>> 
>> Hi,
>>> 
>>>  Part of my program is to calculate the number of time series in a zoo
>>> object. It works well if it has more than one time series, but it fails if
>>> it has only one. How can I access the number of column (i.e. the number of
>>> time series) when I have only one column? Why is the number of an object
>>> of
>>> only one object "NULL"? It should be one, shouldn't it? (The following
>>> example does not involve the creation of a zoo object; in reality, similar
>>> problems are encountered when I create a zoo object)
>>> 
>> 
>> Univariate zoo series are by default vectors, i.e., have no "dim"
>> attribute. You can either choose to store a matrix instead of a vector in
>> the zoo series - or you can use NCOL() instead of ncol() to extract the
>> number of columns. See the corresponding manual pages for more details. An
>> illustration is included below:
>> 
>> ## univariate series
>> 
>> R> x <- sin(1:4)
>> R> z <- zoo(x)
>> R> z
>>         1          2          3          4
>> 0.8414710  0.9092974  0.1411200 -0.7568025
>> R> dim(z)
>> NULL
>> R> ncol(z)
>> NULL
>> R> NCOL(z)
>> [1] 1
>> 
>> ## alternatives to create a 1-column matrix instead of a vector
>> 
>> R> z1 <- zoo(matrix(x, ncol = 1))
>> R> z1
>> 
>> 1  0.8414710
>> 2  0.9092974
>> 3  0.1411200
>> 4 -0.7568025
>> R> dim(z1)
>> [1] 4 1
>> 
>> R> dim(z) <- c(NROW(z), NCOL(z))
>> R> z
>> 
>> 1  0.8414710
>> 2  0.9092974
>> 3  0.1411200
>> 4 -0.7568025
>> R> dim(z)
>> [1] 4 1
>> 
>> 
>> temp5<-read.csv("A_**Consumption.csv", header=TRUE)> temp5[1:3,]    TIME
>>>>    C  C_D  C_ND
>>>> 
>>> 1 196101 70345 1051 69294
>>> 2 196102 61738  905 60833
>>> 3 196103 63838  860 62978> temp6<-temp5[,2:ncol(temp5)]> temp6[1:3,]
>>>  C  C_D  C_ND
>>> 1 70345 1051 69294
>>> 2 61738  905 60833
>>> 3 63838  860 62978> colnames(temp6)[1] "C"    "C_D"  "C_ND">
>>> temp7<-read.csv("A_FX_EUR_Q.**csv", header=TRUE)> temp7[1:3,]    TIME
>>> EUR
>>> 1 198001 1.41112
>>> 2 198002 1.39108
>>> 3 198003 1.42323> temp8<-temp7[,2:ncol(temp7)]> temp8[1:3,]Error in
>>> temp8[1:3, ] : incorrect number of dimensions
>>> 
>>> ncol(temp6)[1] 3> ncol(temp8)   # Why isn't it 1?NULL
>>>> 
>>> 
>>> temp8  [1] 1.411120 1.391080 1.423230 1.342050 1.232870
>>>> 
>>> [6] 1.115090 1.032930 1.089250 1.036320 1.001850
>>> [11] 0.950641 0.933593 0.947940 0.911204 0.860682
>>> [16] 0.843864 0.831666 0.824776 0.768626 0.732064
>>> [21] 0.684473 0.726018 0.784729 0.852911 0.922885
>>> [26] 0.958778 1.012740 1.038160 1.124550 1.149780
>>> [31] 1.128450 1.214120 1.233530 1.216270 1.113620
>>> [36] 1.170250 1.126230 1.074330 1.078480 1.127870
>>> [41] 1.205540 1.222740 1.296500 1.366550 1.341280
>>> [46] 1.187600 1.176790 1.254490 1.262610 1.271820
>>> [51] 1.385930 1.268130 1.190480 1.206840 1.150270
>>> [56] 1.140010 1.125200 1.163450 1.226830 1.240210
>>> [61] 1.273300 1.331010 1.312420 1.317350 1.287330
>>> [66] 1.254500 1.274210 1.261930 1.178970 1.143500
>>> [71] 1.093320 1.123400 1.086770 1.100380 1.117670
>>> [76] 1.176960 1.121600 1.056900 1.048600 1.038000
>>> [81] 0.986500 0.933200 0.905200 0.868300 0.923200
>>> [86] 0.872500 0.890300 0.895900 0.876600 0.918800
>>> [91] 0.983800 0.999400 1.073100 1.137200 1.124800
>>> [96] 1.189000 1.249700 1.204600 1.222000 1.297700
>>> [101] 1.311300 1.259400 1.219900 1.188400 1.202300
>>> [106] 1.258200 1.274300 1.288700 1.310600 1.348100
>>> [111] 1.373800 1.448600 1.497600 1.562200 1.505000
>>> [116] 1.318000 1.302900 1.363200 1.430300 1.477900
>>> [121] 1.382900 1.270800 1.291000 1.358300 1.368000
>>> [126] 1.439100 1.412700 1.348200 1.310800 1.281400
>>> 
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