Преобразование данных сообщества в широкоформатный для веганского пакета

• В частности, моя функция matrify2() создает строки для каждой комбинации сюжет-год (т. Е. Переделанные строки для одного и того же участка) путем дублирования названий строк участка (или среды обитания) и добавления столбца Year.

Ниже приведен код:

#Create data.frame with PLOT, YEAR, and ABUNDANCE for each SPEC:

 #Creates function that can sort the data.frame output by:
   #Columns = individual SPECS, #Rows = plot by Year
   #Note: Code modified from matrify() function from labdsv package (v. 1.6-1)

 matrify2 <-  function(data) { 
   #Data must have columns: plot, SPEC, abundance measure,Year 
   if (ncol(data) != 4) 
       stop("data frame must have four column format")
   plt <- factor(data[, 1]) 
   spc <- factor(data[, 2])
   abu <- data[, 3]
   yrs <- factor(data[, 4])
   plt.codes <- sort(levels(factor(plt)))                                                     ##object with sorted plot numbers
   spc.codes <- levels(factor(spc))                                                           ##object with sorted SPEC names
   yrs.codes <- sort(levels(factor(yrs)))                                                     ##object with sorted sampling Years
   taxa <- matrix(0, nrow = length(plt.codes)*length(yrs.codes), ncol = length(spc.codes))    ##Create empty matrix with proper dimensions (unique(plotxYear) by # of SPEC)
   plt.list <- rep(plt.codes,length(yrs.codes))                                               ##Create a list of all the plot numbers (in order of input data) to add as an ID column at end of function
   yrs.list <- rep(yrs.codes,each=length(plt.codes))                                          ##Create a list of all the Year numbers (in order of input data) to add as an ID column at end of function
   col <- match(spc, spc.codes)                                                               ##object that determines the alphabetical order ranking of each SPEC in the spc.code list
   row.plt <- match(plt, plt.codes)                                                           ##object that determines the rank order ranking of each plot of the input data in the plt.code list
   row.yrs <- match(yrs,yrs.codes)                                                            ##object that determines the rank order ranking of each Year of the input data in the yrs.code list
   for (i in 1:length(abu)) {
       row <- (row.plt[i])+length(plt.codes)*(row.yrs[i]-1)                                   ##Determine row number by assuming each row represents a specific plot & year in an object of rep(plot,each=Year)
       if(!is.na(abu[i])) {                                                                   ##ONly use value if !is.na .. [ignore all is.NA values]
         taxa[row, col[i]] <- sum(taxa[row, col[i]], abu[i])                                  ##Add abundance measure of row i to the proper SPEC column and plot/Year row. Sum across all identical individuals.
       }
   }
   taxa <- data.frame(taxa)                                                                   ##Convert to data.frame for easier manipulation
   taxa <- cbind(plt.list,yrs.list,taxa)                                                      ##Add ID columns for plot and Year to each row already representing the abundance of Each SPEC of that given plot/Year.
   names(taxa) <- c('Plot','Year',spc.codes)
   taxa
 }

Пример:

dat.y <- data.frame(Habitat = c('Hab1','Hab1','Hab2','Hab2','Hab2','Hab3','Hab3','Hab1','Hab1','Hab2','Hab2','Hab2','Hab3','Hab3'),
                    Species = c('Sp1','Sp2','Sp1','Sp3','Sp4','Sp2','Sp3','Sp1','Sp2','Sp1','Sp3','Sp4','Sp2','Sp3'),
                    Abundance = c(1,2,1,3,2,2,1,1,2,1,3,2,2,1),
                    Year = c(1,1,1,1,1,1,1,2,2,2,2,2,2,2))
print(dat.y)

   Habitat Species Abundance Year
1     Hab1     Sp1         1    1
2     Hab1     Sp2         2    1
3     Hab2     Sp1         1    1
4     Hab2     Sp3         3    1
5     Hab2     Sp4         2    1
6     Hab3     Sp2         2    1
7     Hab3     Sp3         1    1
8     Hab1     Sp1         1    2
9     Hab1     Sp2         2    2
10    Hab2     Sp1         1    2
11    Hab2     Sp3         3    2
12    Hab2     Sp4         2    2
13    Hab3     Sp2         2    2
14    Hab3     Sp3         1    2

matrify2(dat.y)

  Plot Year Sp1 Sp2 Sp3 Sp4
1 Hab1    1   1   2   0   0
2 Hab2    1   1   0   3   2
3 Hab3    1   0   2   1   0
4 Hab1    2   1   2   0   0
5 Hab2    2   1   0   3   2
6 Hab3    2   0   2   1   0

Вместе с пакетом labdsv пакет vegan предоставляет большинство стандартных инструментов описательного анализа сообщества.