We Helped With This R Language Programming Assignment: Have A Similar One?

Data Analysis Script 2

Goal:

This final assignment will provide you with the opportunity to demonstrate the full range of skills you have developed during the course. This Task will comprise mainly summative elements.

Product:

You will work individually to provide an R script of no more than 3000 words that details your working and outlines (as script annotations) how you would go about solving a real-world ecological problem. Submission should comprise a single MS Word file. Construct this file by copying the contents of your R script into a blank Word file, and then saving it with the appropriate name.

Submission:

Submit your Word file containing your R script via SafeAssign on the Assessment tab of the Course’s BlackBoard page before 23:59 on Friday 28 October (Week 13).

Format:

From the list of problems provided, select ONE, and write an annotated R script to:

1.       Import the data into R;

2.       Manipulate the data into a form that will allow exploratory analysis and model building;

3.       Undertake basic exploratory analysis of the data, including plots, where appropriate;

4.       Construct, fit and assess statistical models, including, where necessary, appropriate hypothesis tests;

5.       Summarise outcomes of the model-fitting process;

6.       Interpret the results clearly and succinctly;

7.       Output MS Office-compatible Tables and Figures, as appropriate, to aid in the interpretation of your results;

8.       Store outputs, as needed; and

9.       Using annotations in your script, explain the rationale of each step and summarise associated results; interpret main patterns from plots or findings from hypothesis tests; and draw conclusions.

NOTE that NO pre-processing is to be done in Excel. Instead, ALL steps in the process must be coded in R. I will assess the work by running your script on the original data, so if there are steps missing, it will cost you marks.

Assessment Criteria:

You will be assessed on:

·         Clarity and completeness of scripting;

·         Appropriateness of data manipulation and analysis;

·         Quality of resulting outputs; and

·         Depth and appropriateness and accuracy of rationale/explanation and interpretation of results.

You’re a wildlife manager, who is currently working on badgers. You are investigating the factors predicting badger sett (burrow) presence in England and Wales. Specifically, you’re interested in which of the available habitats in the landscape is preferred by the badger. Previous research suggests that badgers might prefer either woodland or grassland habitat. On the other hand, you know that, unfortunately, a good number of badgers get killed on the roads every year. And sadly, badgers are still nowadays culled by farmers, either to protect their produce or for the misconception that badgers are going to increase the risk of tuberculosis on their cattle.

Over the past eight years, you collected data each time an inhabited badger sett was detected, based on badger presence or signs of recent badger activity. You have also collected data on random sites to know the environmental properties of sites where badgers are not present. The English government has made available a map of the land use of your study area (habitat composition). You classify that into woodland, grassland or human disturbance (including crops and suburban infrastructure). The geographic coordinates of the badger setts allow you to assign them to a habitat type and region and to calculate distance to the nearest road. Finally, you source a historical database of culling licences granted to farmers by local governments, and you identify whether the site was being culled during the sampling interval.

The file Badgers.csv contains your results. The first sheet in the file contains metadata (a description of the variables), whist the second contains the data.

Construct models to address the following questions:

a.       Are the number of surveyed points (for presence/absence) on culling areas relative to those on non-culling areas independent of region and habitat type? Fit one model and produce one figure.  [20 %]

b.       Did habitat type, culling practices or distance to road affect likelihood of badger sett presence, and did this hold equally across the regions sampled? Fit one model and produce at least two figures.  [80 %]

Be sure to explain all significant results in terms of your research questions (i.e., interpret model coefficients both graphically and in words), and speculate about what this result might mean in terms of badger conservation.

Assignment Description