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+s789695n4727957...@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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