Objectives

  • Perform a numerical and graphical data exploration

Data

Your group will need the mander.csv data file.

Instructions:

As a group, use R to explore the redbacked salamander (Plethodon cinereus) data.

The data include various measurements of salamanders at several sites in Ithaca, NY.

These variables within the data set include:

  • Date of collection
  • Collector
  • Year of collection (2014)
  • Season of collection (limited to Fall for this dataset)
  • Site (A, B, C, D)
  • Snout-to-vent length (SVL) in mm
  • Total length in mm
  • Sex (male, female, or unknown)

For this exercise, you should go through the process of describing the central tendency and dispersion of the ‘Total_length’ column using the correct metrics.

Report

Instructions

You should then as a group create a report with the following elements:

  • Numerical Data Exploration. Include the following:
    1. Number of observations.
    2. An estimate of the central tendency of total length.
    3. An estimate of the dispersion of total length.
Click to show hint about making a simple formatted table in RMarkdown

You can turn a data frame into a formatted table in RMarkdown using the kable() function.

Here’s an example:

# First make a data.frame:

table_data = data.frame(
  n_observations = 555,
  median = 12345.6789101112,
  range = "12 to 45.9"
)


# Now give it nicer column names:
names(table_data) = 
  c("N. Observations", "Median", "Range")

# Finally use kable() to render the table:

knitr::kable(table_data)
N. Observations Median Range
555 12345.68 12 to 45.9
  • Graphical Data Exploration. Include two figures:
    1. A histogram showing the distribution of the total length.
    2. One additional plot of your choice.
  • Methods and Results.
    1. Description of the metric you used to quantify central tendency and why you chose that metric.
    2. Description of the metric you used to quantify dispersion and why you chose that metric.
    3. Description of the second plot that you chose and why you selected the plot type.
    4. Qualitative description of what the histograms tells us about the salamanders in the dataset.
    5. Qualitative description of what your second plot tells us about the salamanders in the dataset.