Generating cross tables from database lists

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 formula,data.frame
crosstable(
  formula,
  data,
  FUN,
  na_to_zero = FALSE,
  use_nas = TRUE,
  as_matrix = FALSE,
  ...
)

# S4 method for formula,vegtable
crosstable(
  formula,
  data,
  FUN,
  level,
  include_lower = FALSE,
  na_to_zero = FALSE,
  use_nas = TRUE,
  ...
)

cross2db(object, ...)

# S3 method for data.frame
cross2db(object, layers = FALSE, na_strings, ...)

# S3 method for matrix
cross2db(object, ...)

Arguments

formula: A formula indicating the variables used in the cross table. This formula can be represented as 'abundance ~ cols + rows', where 'abundance' is the numeric variable quantified for a row in a column, for instance the abundance of a species in a plot. Further variables can be set as additional rows indices in a cross table.
data: Either a data frame or an object of class vegtable.
...: Further arguments passed to the function stats::aggregate().
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.
level: A character vector with taxonomic ranks (levels) requested in the cross table.
include_lower: A logical value indicating wether lower value to the requested levels should be merged or not. It works only if 'level' is not missing. Note that if you like to include higher ranks or rankless taxa in the cross table, you will rahter need to run merge_taxa() on slot species.
object: A data frame or a matrix including a cross table. Note that cross2db() assumes observations as columns and species (and layers) as rows in the data.frame-method but species as columns and observations as rows in the matrix-method.
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

Miguel Alvarez kamapu78@gmail.com

Examples

# Produce a subset
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,
  data = veg[1:5, ], FUN = mean, na_to_zero = TRUE)
head(Cross)
#>               AcceptedName                AuthorName 1940 1937 1938 1939 1941
#> 1      Commiphora africana          (A. Rich.) Engl.  0.5    0    0    0  0.0
#> 2     Eragrostis macilenta         (A. Rich.) Steud.  3.0    3    3    3  0.0
#> 3       Helinus mystacinus (Aiton) E. Mey. ex Steud.  0.0    0    0    0  3.0
#> 4   Eragrostis cilianensis        (All.) F. T. Hubb.  3.0    3    3    3  3.0
#> 5 Astripomoea lachnosperma        (Choisy) A. Meeuse  3.0    3    0    3  3.0
#> 6       Coccinia trilobata        (Cogn.) C. Jeffrey  3.0    0    3    3  0.5

## cross table of recorded genera
Cross <- crosstable(cover_perc ~ ReleveID + AcceptedName + AuthorName,
    data = veg, FUN = mean, level = "genus")
head(Cross[ , 1:7])
#>   AcceptedName       AuthorName 2005 2059 2061 2080 2102
#> 1  Cyphostemma (Planch.) Alston  0.5  0.5  0.5  0.5  0.5
#> 2  Cassipourea            Aubl.   NA   NA   NA   NA   NA
#> 3       Dregea          E. Mey.   NA   NA   NA   NA   NA
#> 4    Digitaria           Haller   NA   NA   NA   NA   NA
#> 5 Chlorophytum        Ker Gawl.   NA   NA   NA   NA   NA
#> 6 Plectranthus          L'HÃ©r.   NA   NA   NA   NA   NA

## cross table of data merged to genus
Cross <- crosstable(cover_perc ~ ReleveID + AcceptedName + AuthorName,
    data = veg, FUN = sum, level = "genus", include_lower = TRUE)
head(Cross[ , 1:7])
#>      AcceptedName                                 AuthorName 2008 2102 2112
#> 1       Bullockia      (Bridson) Razafim., Lantz & B. Bremer  0.5   NA   NA
#> 2          Scutia                     (Comm. ex DC.) Brongn.   NA  0.5  0.5
#> 3   Dichrostachys                         (DC.) Wight & Arn.   NA   NA   NA
#> 4 Lepidotrichilia (Harms) J.-F. Leroy ex T.D. Penn. & Styles   NA   NA   NA
#> 5      Afrocarpus        (J. Buchholz & E.G. Gray) C.N. Page   NA   NA   NA
#> 6        Leonotis                             (Pers.) R. Br.   NA   NA   NA
#>   2121 2165
#> 1   NA   NA
#> 2  0.5    1
#> 3   NA   NA
#> 4 10.0   NA
#> 5   NA   NA
#> 6   NA   NA

## the same for families
Cross <- crosstable(cover_perc ~ ReleveID + AcceptedName + AuthorName,
    data = veg, FUN = sum, level = "family", include_lower = TRUE)
head(Cross[ , 1:7])
#>     AcceptedName       AuthorName 1966 1980 1988 2005 2010
#> 1 Passifloraceae Juss. ex Roussel  0.5  0.5  0.5  0.5  0.5
#> 2    Acanthaceae               NA   NA 22.0 10.0  0.5 19.0
#> 3    Achariaceae               NA   NA   NA   NA   NA   NA
#> 4      Aizoaceae               NA   NA   NA   NA  3.0   NA
#> 5  Amaranthaceae               NA  3.0   NA 19.0 36.0  3.0
#> 6 Amaryllidaceae               NA   NA   NA   NA   NA   NA