INSTRUCTIONS (Delete when you compile the submission version of your project.)

Modify your report YAML with the following.

Remove the comments in the dataset_chunk to load the assigned datasets.

If you are including a numerical result in your text, you should use inline code.

All plots should have informative titles and axis labels. You may also change colors.

THIS DOCUMENT WAS EDITED FOR SPELLING / GRAMMAR / CLARITY TUESDAY 02/26

MAUNA LOA CO2 MODELING AND VISUALIZATION

The Question

In 1966, the World Meteorological Organization (WMO) put forth the term “climatic change” to refer to climatic variability on time-scales longer than ten years, regardless of the cause for such change. During the next decade, scientists began to suspect that human activities had the potential to drastically alter the global climate in ways that would have negative impacts on our environment. The term evolved into “climate change” and is now used to describe both the process of change and the perceived problem. Sometimes the term “global warming” is used, though in many ways this fails to adequately describe the variability in impact, since climate change can cause both hot and cold extremes in weather. Anthropogenic climate change is change that is caused by human activity, as opposed to the Earth’s natural processes. However, in the context of environmental policy, the term “climate change” is often used to mean anthropogenic climate change.

Mauna Loa Observatory is a world-renowned atmospheric research facility. It has been continuously monitoring and collecting data since the 1950’s and its remote location makes it very well-suited for monitoring atmospheric components that can contribute to climate change, including the heat-trapping greenhouse gas carbon dioxide (CO2). Carbon overload from burning fossil fuels and deforestation is cited as the primary cause of anthropogenic climate change by proponents of such theories, while opponents assert that natural process (such as photosynthesis) contribute more to atmospheric CO2 than humans and observed changes are simply Earth’s cycle.

Monthly Mean CO2: The Last Five Years

Create your own version of the plot found here. Do not replicate it, but rather design your own. Use one of the themes found in the ggplot2 or ggthemes packages. You are encouraged to make style adjustments to help you informatively display the data.

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Monthly Mean CO2: A Major Milestone

An atmospheric CO2 level of 400 ppm is considered by many to be a symbolic threshold with regard to climate change. “In the centuries to come, history books will likely look back on September 2016 as a major milestone for the world’s climate. At a time when atmospheric carbon dioxide is usually at its minimum, the monthly value failed to drop below 400 parts per million.” (source)

Adapt your plot above to include a red dashed line at 400 ppm and a large red dot on September 2016, with appropriate annotations to indicate what these additions represent.

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Annual Mean CO2 Since 1959

Replicate as closely as possible the annual mean plot found here. Hint: It uses a ggplot theme for some of the formatting.

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Discussion

In what way could these visualizations be used to support the theory of anthropogenic climate change?

ANSWER:

Why are data such as these considered evidence rather than proof of anthropogenic climate change?

ANSWER:

ANTHROPOMETRIC MODELING AND VISUALIZATION

The Question

Are people generally happy with their heights? If not, how tall do they want to be? Dr. Thomley’s anthropometric dataset contains measurements of students’ heights and their self-selected ideal heights. You will fit a parallel slopes model to predict ideal height using measured height and gender, then interpret the results of your model.

Exploratory Data Analysis

Filter the dataset to include only students who self-identified as male or female (there are not enough data points in the other categories to create a model for them). Perform EDA to determine whether you need to perform any transformations or remove any data points before you fit your model. Create your modeling dataset and call it anthro_mod.

Fitting the Model

Create a scatterplot of ideal height versus measured height showing separate fitted linear models for males and females. Then fit a parallel slopes model with measured height and gender as predictors and save it as ideal_model. Display its summary.

Examine Residuals

Create a residual scatterplot and histogram for your model.

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Predicting Ideal Height

Create a dataset containing heights at one-inch intervals from 60 to 80 for each gender. Use your parallel slopes model to predict the ideal heights for these values. Use mutate to create a new variable in your results tibble that shows whether the ideal height is less than, equal to, or greater than height. The case_when function may be useful here. Display the results.

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Additional Visualization

Create a plot that shows the same fitted lines for males and females as your scatterplot (but without points), as well as an annotated line indicating the relationship ideal height = measured height. Format this line in some way other than the default (e.g., color, style).

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Discussion

Explain your rationale for any transformations or deletions you chose to make in the dataset.

ANSWER:

Does the model seem appropriate for the data? Be sure to include discussion of the residuals.

ANSWER:

Do the people in this sample generally seem to be happy with their heights or do their ideal heights differ? Do males and females seem to have the same attitudes regarding what is an ideal height? What group patterns do you notice? Discuss.

ANSWER:


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