Variables

The dataset provided for this project contains the following columns:

• siteID is simply an identifier for each location
• site.date refers to the site and date of the 8 day period corresponding to the row
• date refers to the date only of the 8 day period corresponding to the row
• ndvi stands for , which is a measure of how “green” the location is. Locations have high values of ndvi if they have a lot of vegetation, if the vegetation is of high quality and very green, or some combination of the two.
• gazelleThomsons.count refers to the count of Thomson’s gazelles that were spotted over the 8-day period.
• gazelleThomsons.present is a simple indicator function showing if any Thomsons gazelle were present during the 8-day period.
• topi.count refers to the count of Topi that were spotted over the 8-day period.
• topi.present is a simple indicator function showing if any Topi were present during the 8-day period.
• zebra.count refers to the count of Zebra that were spotted over the 8-day period.
• zebra.present is a simple indicator function showing if any Zebra were present during the 8-day period.
• fire refers to the presence of a detected wildfire in the 30 days preceeding the time period.
• amRivDist is the distance from the location to the nearest river. I honestly don’t know the units, it’s just some sort of length.
• TM100 counts the number of termite mounds within 100 meters of the location. Termite mounds are an indicator of soil quality.
• LriskDry is a measure of how “risky” the location is to lion predation. It is computed based on the number of previous recorded lion attacks at the location. It has no units.
• T50 is a count of the number of trees within 50 meters of the location.

Downloading the data

To load the data, use the code below.

serengeti <- read.csv("https://sta712-f22.github.io/projects/serengeti.csv")

Abstract

This is the first section in your report, but it is actually the last thing you will write. Called an abstract in academia, and an executive summary in industry, this one paragraph summary of your entire paper is the first thing people will read. For example:

Estimating the number of pandas inhabiting a national panda preserve is critical to understanding the health of the panda species as a whole. In this report, we discuss the process of estimating the number of pandas in the Wolong National Nature Reserve. Data were collected from volunteers walking trails in the park, and we applied capture-recapture estimation process to then estimate the total number of pandas in the park. We detail the steps of the process, and compare our results to two other possible methods of estimating pandas. Based on our estimation, there are between 140 and 155 pandas in the surveyed area. We discuss our findings, and limitations of the study, in the following paper.

Section 1: Introduction

Write 2-3 paragraphs in which you:

• Motivate the research questions
• Describe where the data came from
• Provide background on the dataset
• Finish by summarizing what you will do in the report

Section 2: Data

In this section, you will summarize and explore your data. First, write 1-2 paragraphs which:

• Describe details of the data. What does a row in the data represent? How many rows and columns? What information do the variables record? Are there any missing data?
• Describe any data manipulation. Did you remove any missing values? Did you focus only on a subset of the data? Did you create any new variables?

Next, perform exploratory data analysis. Your goal is to create any visualizations necessary to explore the data before fitting a model (e.g. tables, histograms, empirical log mean plots to choose transformations, etc.). Make sure that:

• You create a plot showing the distribution of your response variable(s)
• Your figures have axis labels, captions, and are numbered Figure 2.1, Figure 2.2, etc., where the 2. represents the section number, and the number after the decimal represents how many plots so far in this section
• Any plot that is shown should be discussed in writing in your report, and referenced by number (e.g., “As shown in Figure 2.1…”). What information does this graph give us, and why is that important for us to know when before we start building our model in the next section?

Section 3: Modeling

In this section, you will build one or more models to answer the two research questions. For each model, make sure to include the population form of the model you are using and explain why this model is appropriate for these data.

You will then fit a model and write down the equation of the fitted model using appropriate notation.

Section 4: Discussion

In this section, you are providing a conclusion. Write 1–2 paragraphs which summarize what you learned from your analysis, and how it addresses the original research questions. Also discuss any limitations to your analysis, and what you other relationships you might explore in future.

Writing

The report should be written like an article or research paper: in full sentences and paragraphs, with headings for each section. You should not write your report with question numbers or as a list of bullet points. Scientific articles are generally written in third person, though “we” can also be acceptable (“we can see from Figure 2.1 …”) in some disciplines.

Code

In full reports, the only output that should be visible from code chunks are figures and tables. If a code chunk does not produce a figure or table, you can hide it from the knitted document with include=F:

```{r, include=F}

```

If a code chunk produces a figure or table, only the figure or table should be visible in the knitted document. You can hide the chunk but display the output with echo=F, message=F, warning=F:

```{r, echo=F, message=F, warning=F}

```

Figures

Figures should have labeled axes, and should be clear and easy to read. Figures should also be captioned and numbered; to caption a figure, use fig.cap = "..." in the chunk options. For example (scroll to the right on the code to see it all),

```{r, echo=F, message=F, warning=F, fig.cap="Figure 2.1: Bill depth vs. bill length for penguins near Palmer Station, Antarctica."}
penguins %>%
  ggplot(aes(x = bill_length_mm, 
             y = bill_depth_mm)) +
  geom_point() +
  labs(x = "Bill length (mm)",
       y = "Bill depth (mm)") +
  theme_bw()
```

is displayed as

Figure 1: Bill depth vs. bill length for penguins near Palmer Station, Antarctica.

Captions should provide enough information to understand what is being plotted, but interpretation can be left to the main text. Refer to figures by their number in the text. Make sure that any figures you include are discussed in the text.

Tables

Tables should be nicely formatted, and have a number and caption. This can be done with the kable function in the knitr package.

For example,

```{r, echo=F, message=F}`r''`
table(penguins$island, penguins$species) %>%
  knitr::kable(caption = "Table 3.2: Penguins by island and species")
```

is displayed as

Writing math in R Markdown

If you want to write mathematical notation, we need to tell Markdown, “Hey, we’re going to make a math symbol!” To do that, you use dollar signs. For instance, to make \(\widehat{\beta}_1\), you simply put $\widehathat{\beta}_1$ into the white space (not a chunk) in your Markdown.

Here are some examples of writing math, which you can adapt: