Summary and Setup

  • Define common time series terms and familiarize yourself with the field site
  • Clean data collected from the field
  • Manipulate a time series data frame
  • Calculate basic analyses on the time series data
  • Visualize time series data in R

Timeseries in R

The best way to learn how to program is by doing! In this lesson we will learn some of the basics for working with timeseries in R. We assume that you have some experience with programming in R already for this course.

To successfully participate in this course, we ask that Learners meet the following prerequisites:

  • Have an understanding of how file explorer works - creating folders, how to access and download files and moving them into the approriate folder (uses daily)
  • Be able to use and download files from internet (uses daily)
  • Be able to open, use basic functions, and edit in excel (uses weekly to monthly)
  • Some very basic statistical knowledge (what is a mean, median, boxplot, distribution etc.) (any previous use)
  • An overall understanding of why timeseries data is important (any previous use)
  • Have some familiarity with R: it is downloaded and up to date, Learners have made projects, used basic functions (View, summary, mean, sd, etc.) and can load a dataset and install packages (any previous use)
  • R coding: Base R: understanding of aggregrate() and some experience with tidyverse (have used at least twice before)

We will study changes in surfacewater and groundwater temperatures and stage (water level) over time. The datasets are uploaded as separate .csv files (comma-separated values), one for surfacewater data and one for groundwater data. Each row holds information for a single time point (in 15 minute intervals) and the columns represent the different variables we will study.

Data Sets

Make a new folder on your Desktop for this lesson: Timeseries_R Within your Timeseries_R folder, create an .Rproject for this workshop with the following folders: - data - data_processed - scripts Download the KNZ_GW_temp_stage.csv and KNZ_SW_temp_stage.csv and move into the ‘data’ folder

We will: - load data into the R enviornment - clean and manipulate the data - calculate basic metrics - visulize timeseries data

Software Setup

This course requires the following packages:

  • tidyverse (includes dyplr, ggplot2, readr, zoo, and lubridate)

R 


install.packages("tidyverse")