zoocat package"
In zoocat: 'zoo' Objects with Column Attributes

title: "zoocat package" author: "Ran-Ran He" date: "r Sys.Date()" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Vignette Title} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}

knitr::opts_chunk$set(
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zoocat package is an extension package of the zoo package. The aim of this package is:

Manipulate multivariate time serie conveniently.
Convert between time series data and data frame.
Represent multi-dimensional data by two-dimensional format, so we can focus on one dimension.

For using zoocat package, first load it:

library(zoocat)

Construct a zoocat object

Although column names can be used to identify columns, for "zoocat" class, a data frame is used to store information of columns, we call it column attribute table (cattr table). In the cattr table, each row represents a column of the core data in the object, and each column represents a column attribute. Using following codes to construct a zoocat object based on a matrix.

mat <- matrix(round(rnorm(24), 2), ncol = 4)
ctable <- data.frame(treatment = factor(rep(c('a', 'b'), 2), levels = c('a', 'b')), 
                     site = factor(rep(c('s1', 's2'), each = 2), levels = c('s1', 's2')))
zc <- zoocat(mat, order.by = 2011 : 2014, colattr = ctable, index.name = 'year')
print(zc)

It can be seen that there are two column attributes: "treatment" and "site", and the index means "year". So, each data value corresponds to three underlying variables, i.e., treatment, site and year.

Set and get the cattr table

Using following codes to get and set cattr table.

cattr(zc)
zc2 <- zc
cattr(zc2) <- data.frame(cattr(zc), observer = 'Jack')
print(zc2)

Merge by columns

Merging by columns is similar with "zoo" object except that the cattr tables need to be merged by rows. Note that NA is added if it is needed.

zc2 <- zc + 10
cattr(zc2) <- data.frame(site = 's3', added = rep(TRUE, 4))
zc.merge <- cbind(zc, zc2)
print(zc.merge)
cattr(zc.merge)

Melt and cast

Similar with package reshape2, you can melt and cast between data frame and zoocat.

df.melt <- melt(zc)
head(df.melt)

cast2zoocat(df.melt, index.var = 'year', value.var = 'value')

Note that casting from a data frame is a convenient method to build a "zoocat" object.

Reset the index variable

The index variable can be reset to be a variable in the cattr table.

reset_index_var(zc, index.var = 'treatment')

apply series functions

Functions can be applied for each column of the "zoocat" object, and the results will be binded with the cattr table.

apply_col(zc, FUN = mean)
apply_col(zc, FUN = function (x) {c(mean = mean(x), sd = sd(x))})

Another method is to apply a function for the whole core data, and the results can be binded with the cattr table or index.

apply_core(zc, FUN = colMeans, bind = 'cattr')
apply_core(zc, FUN = rowMeans, bind = 'index')
apply_core(zc, FUN = function (x) {t(x*2)}, bind = c('cattr', 'index'))

Filter columns

Function filter_col can be used to extract columns by specified conditions on the cattr table:

filter_col(zc, cond = treatment == 'a' & site == 's2')

Others

For "zoocat" class, all methods for the "zoo" class can be used. Addtionally, class "zoomly", based on "zoocat", is designed to manipulate monthly data.