[datatable-help] What statistical test to use for species abundance between wet and dry season?

[BondHR]

Hi, thanks for the reply, at the moment my data is laid out like this;

Species        Wet Season     Dry Season
Jaguar                6                   4
Ocelot                 3                   2
currassow           17                 14
etc...

I want to find out that if there is a significant difference between the abundance of all species in wet and dry seasons. I hope to find out that as the wet season is likely to be more profitable for both predators and prey so most of the species should be more abundant?

Would it still be using a test for two samples? e.g. wilcoxon or t-test.

thanks



________________________________
From: carlsutton [via R] <ml-node+s789695n4727957h41 at n4.nabble.com>
Sent: 08 January 2017 15:20
To: BondHR
Subject: Re: What statistical test to use for species abundance between wet and dry season?

Really difficult to answer since I do not know what it really is you are attempting to determine.  It appears your data consists of:
Long (tidy) format layout
a) species- mode character or mode factor (categorical if you want them that way).  If you know how R treats factors and what functions to use for factors, then it's just a choice.  If not familiar with how R treats factors then definitely character and save yourself some unwelcome surprises
b) season - same treatment as species, or perhaps better described by date???  Read the lubridate package vignette to learn how it makes working with dates simple.
c) counts-numerical

Wide data format
a)  each species a column each per season  such as black bear- wet,turtle - dry etc
b)  counts filing the columns along with numerous NA
definitely an "untidy" data set but perhaps easiest to manually enter and verify?

You can always use the data.table melt function to go from wide to long format when or if you want, or use cast to go the other way.

Here is a toy example in the long format layout (I'm to lazy to type the wide one)

#  critter pics
library(data.table)
library(lubridate)
species <- c("bear","chipmunk","garter_snake","cardinal")
date <- mdy(c("01-15-2016", "02-25-2016", "06-01-2016", "08-31-2016"))
counts<- c(15,20,2,50)
pics_data <- data.table(species,date,counts)
pics_data
str(pics_data)

Data printout


        species       date counts
1:         bear 2016-01-15     15
2:    chipmunck 2016-02-25     20
3: garter_snake 2016-06-01      2
4:     cardinal 2016-08-31     50
> str(pics_data)
Classes ‘data.table’ and 'data.frame':  4 obs. of  3 variables:
 $ species: chr  "bear" "chipmunck" "garter_snake" "cardinal"
 $ date   : Date, format: "2016-01-15" "2016-02-25" ...
 $ counts : num  15 20 2 50
 - attr(*, ".internal.selfref")=<externalptr>




>



Carl Sutton


On Saturday, January 7, 2017 8:15 PM, BondHR <[hidden email]</user/SendEmail.jtp?type=node&node=4727957&i=0>> wrote:


Sorry if this question is trivial,

I have a collection of camera trap data of recorded species during the rainy
season and the dry season.

I am confused how to lay out a table to import to R, do I have species down
the left (then counts of how many of that species) and columns of wet season
and dry season?

Also, I am confused on what statistical test to use :(


I've read so many books and websites and every thing seems to be
contradicting each other, please can anyone help!



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