1
Introduction
1.1
Course Learning objectives
1.2
License
2
Introduction to R Statistical Software
2.1
Learning objectives
2.2
New functions & syntax
2.3
First some terms:
2.4
Getting to know Rstudio Cloud
2.5
Introduction to R & RStudio
2.6
R basics
2.6.1
A fancy calculator
2.6.2
Variables
2.6.3
Vectors
2.6.4
Data frames
3
Is this normal? Evaluating historical weather data to understand extremes, changes, and climate.
3.1
Learning objectives
3.2
New functions & syntax
3.3
The problem. How weird was yesterday’s heatwave? Using long-term weather data to understand normals and climate.
3.3.1
Using summary summary statistics to summarize weather
3.4
The Data.
3.5
Using packages in R
3.5.1
Reading in data
3.5.2
Subsetting
3.6
Summary statistics and visualizing data distributions.
3.6.1
Histograms
3.7
Probability distributions
3.7.1
Normal distributions
3.8
Using the normal probability distribution
3.9
Other distributions
3.10
Citations
4
Visualizing data
4.1
Learning objectives
4.2
New functions & syntax
4.3
Fundamentals of plotting data
4.3.1
1. Representation of data
4.3.2
2. Layout
4.3.3
3. Encoding
4.3.4
4. Simplicity
4.4
Plotting data in base R
4.5
Using
ggplot2
4.6
Plotting in ggplot2
4.7
Exploring different visualizations in ggplot2
5
Introduction to hypothesis testing
5.1
Learning objectives
5.2
New functions & syntax
5.3
Introduction to two sample t-tests
5.3.1
The data
5.3.2
Study goals and statistical hypotheses
5.3.3
Checking assumptions of test
5.3.4
Two sample t-test in R
5.4
One sample t-test in R
5.5
Introduction to chi-squared test
5.5.1
The data
5.5.2
Chi squared test
5.6
Citations
6
Introduction to linear regression
6.1
Learning objectives
6.2
New functions & syntax
6.3
Simple linear regression: Are beavers flooding the Arctic?
6.3.1
The data
6.3.2
Set up the regression
6.3.3
Checking assumptions
6.3.4
Interpreting results
6.4
Multiple linear regression: What makes an early spring?
6.4.1
The data
6.5
Panels of bivariate plots
6.6
Check for multi-collinearity
6.7
Run the regression
6.8
Interpret the regression
6.9
Citations
7
Introduction to Analysis of Variance (ANOVA)
7.1
Learning objectives
7.2
New functions & syntax
7.3
Measuring biodiversity: the data
7.4
ANOVA overview
7.5
ANOVA in R
7.5.1
Checking assumptions
7.5.2
Running the ANOVA
7.5.3
Post hoc comparisons
7.5.4
Interpreting the results
7.6
Citations
8
Advanced data wrangling. Linking soil moisture with landcover and rainfall.
8.1
Learning objectives
8.2
New functions & syntax
8.3
Soil moisture
8.3.1
Soil moisture in different types of land cover
8.3.2
The data: soil moisture
8.4
Working with dates
8.4.1
The lubridate package
8.4.2
Parsing date/time
8.4.3
Extracting information from dates
8.5
Data wrangling with
dplyr
8.5.1
Filtering data
8.5.2
Joining data tables
8.6
Conclusions
8.7
Citations
References
Introduction to Environmental Data and R
References