| crosstable {vegtable} | R Documentation |
Generating cross tables from database lists
Description
cross table is the most common format required by statistical packages used to analyse vegetation data (e.g. vegan).
You may use for convenience a formula as
'abundance ~ plot + species + ...'.
Additional variables used for rows (...) can be for instance the
layers.
For objects of class vegtable, the formula can also include
variables from the species list (for example AcceptedName, AuthorName)
or even taxon traits.
If required, tables already formatted as cross tables can be converted into
column-oriented tables by using the function cross2db().
Usage
crosstable(formula, data, ...)
## S4 method for signature 'formula,data.frame'
crosstable(
formula,
data,
FUN,
na_to_zero = FALSE,
use_nas = TRUE,
as_matrix = FALSE,
...
)
## S4 method for signature 'formula,vegtable'
crosstable(formula, data, FUN, na_to_zero = FALSE, use_nas = TRUE, ...)
cross2db(object, layers = FALSE, na_strings)
Arguments
formula |
A formula indicating the variables used in the cross table.
This formula can be represented as |
data |
Either a data frame or an object of class vegtable. |
... |
Further arguments passed to the function |
FUN |
Function used to aggregate values in the case of a multiple occurrence of a species in a plot, for instance. |
na_to_zero |
A logical value indicating whether zeros should be inserted into empty cells or not. |
use_nas |
Logical value indicating whether NAs should be considered as levels for categorical variables or not. |
as_matrix |
A logical value, whether output should be done as matrix or data frame. |
object |
A data frame including a cross table. |
layers |
Logical value, whether the cross table includes a layer column or not. |
na_strings |
Character vector indicating no records in the cross table. |
Value
An object of class data.frame.
Author(s)
Miguel Alvarez kamapu78@gmail.com
Examples
veg <- subset(Kenya_veg, REFERENCE == 2331, slot = "header")
## transform cover to percentage
veg <- cover_trans(veg, to = "cover_perc", rule = "middle")
## cross table of the first 5 plots
Cross <- crosstable(cover_perc ~ ReleveID + AcceptedName + AuthorName,
veg[1:5, ], mean,
na_to_zero = TRUE
)
head(Cross)