R For Hydrologists – Loading and Plotting Data Part 1: Exercises

LOADING AND PLOTTING THE DATA (Part 1)

Working with hydro-meteorological data can be very time consuming and exhausting. Luckily, R can provide a framework to easily import and process data in order to implement statistical analysis and models. In these tutorials, we are going to explore and analyze data from a tropical basin in order to create a simple forecast model.
Let’s have a look at these exercises and examples.

Answers to these exercises are available here.

Exercise 1
First, let’s import the daily levels of a river and the rainfall data from the basin, stored in a CSV file. Please download the data here (PAICOL.csv) and import it with the function read.csv.
Then, assign it to river_data.

Remember that river_data is a data frame, so we can access the attributes of it with $; for example, you can get the date values with river_data$DATE.

Exercise 2
To guarantee that the DATE column has the proper format, it is crucial to convert the string values into dates with the function as.Date. Please replace the value of DATE with formatted dates.

Exercise 3
Create a summary of the river_data.

Learn more about loading different types of data into r in the online course R Data Pre-Processing & Data Management – Shape your Data!. In this course you will learn how to:

• Import data into R in several ways while also beeing able to identify a suitable import tool,
• use SQL code within R,
• And much more.

Exercise 4
Normally we can use the build in R functions; but, this time, we will use the ggplot package. In my opinion, it is able to create better plots. Before we start, install it and load it to be able to use it.

install.packages("ggplot2")
library(ggplot2)


Create a line plot of the LEVEL with the ggplot function.

Exercise 5
Create a scatter plot of the RAIN against LEVEL.

Exercise 6
Create a plot of the RAIN and LEVEL.

Exercise 7
Find and plot circles on the LEVEL plot at the maximum and minimum value.

Exercise 8
Plot the LEVEL for the year “2001.”

Related exercise sets:

R FOR HYDROLOGISTS – Part 1: Correlation and Information Theory Measurements R FOR HYDROLOGISTS – Seasonality and Trend Decomposition Spatial Data Analysis: Introduction to Raster Processing: Part 4 R FOR HYDROLOGISTS – Part 2: Loading and Plotting Data: Exercises R FOR HYDROLOGISTS – Part 3: Loading and Plotting Data: Exercises