#### Overview of this book

Managing spatial data has always been challenging and it's getting more complex as the size of data increases. Spatial data is actually big data and you need different tools and techniques to work your way around to model and create different workflows. R and QGIS have powerful features that can make this job easier. This book is your companion for applying machine learning algorithms on GIS and remote sensing data. You’ll start by gaining an understanding of the nature of spatial data and installing R and QGIS. Then, you’ll learn how to use different R packages to import, export, and visualize data, before doing the same in QGIS. Screenshots are included to ease your understanding. Moving on, you’ll learn about different aspects of managing and analyzing spatial data, before diving into advanced topics. You’ll create powerful data visualizations using ggplot2, ggmap, raster, and other packages of R. You’ll learn how to use QGIS 3.2.2 to visualize and manage (create, edit, and format) spatial data. Different types of spatial analysis are also covered using R. Finally, you’ll work with landslide data from Bangladesh to create a landslide susceptibility map using different machine learning algorithms. By reading this book, you’ll transition from being a beginner to an intermediate user of GIS and remote sensing data in no time.
Preface
Free Chapter
Setting Up R and QGIS Environments for Geospatial Tasks
Fundamentals of GIS Using R and QGIS
Creating Geospatial Data
Working with Geospatial Data
Remote Sensing Using R and QGIS
Point Pattern Analysis
Spatial Analysis
GRASS, Graphical Modelers, and Web Mapping
Classification of Remote Sensing Images
Landslide Susceptibility Mapping
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# Plotting in R

We can make a simple plot using the plot() function of R. Now we will simulate 50 values from a normal distribution using rnorm() and assign these to x and similarly generate and assign 50 normally distributed values to y. We can plot these values in the following way:

`x = rnorm(50)y = rnorm(50)# pch = 19 stands for filled dotplot(x, y, pch = 19, col = 'blue')`

This gives us the following scatterplot with blue-colored filled dots as symbols for each data point:

We can also generate a line plot type of graph by using type = "l" inside plot().

Now we will briefly look at a very strong graphical library called ggplot2 developed by Hadley Wickham. Remember, the all_prices data frame? If you don't, let's have another look at that:

`str(all_prices)`

We see that it has 12 rows and four columns, it has three numeric variables and one factor variable:

We first need to install and then load the ggplot2 package:

`install.packages("ggplot2")library(ggplot2)`
In any R session, if we want to use an R package, we need to load it using library(). But once loaded, we don't need to load it any further to use any of the functions inside the package.

Now we need to define the data frame we want to use inside the ggplot() command, and inside this command, after the data frame name, we need to write aes(), which stands for aesthetics. Inside this aes(), we define the x axis variable and the y axis variable. So, if we want to plot the prices of different items in January against these items, we can do the following:

` ggplot(all_prices, aes(x = items, y = jan_price)) + geom_point()`

Now we see the plot as follows:

We can also compute and mark the mean price in January of these different items over all the years under consideration using stat = "summary" and fun.y = "mean". We will just need to add another layer, geom_point(), and mention these arguments inside this:

`ggplot(all_prices, aes(x = items, y = jan_price)) + geom_point() + geom_point(stat = "summary", fun.y = "mean", colour = "red", size = 3)`

The following screenshot shows that along with data points, the mean values for each item are marked as red:

We can also plot the price of January against the price of June and make separate plots for each of the items using facet_grid(. ~ items):

`ggplot(all_prices, aes(x = jan_price, y = june_price)) + geom_point() + facet_grid(. ~ items)`

As a result, we see a scatterplot for three different items as follows:

We can also add a linear model fit using a stat_smooth() layer:

`ggplot(all_prices, aes(x = jan_price, y = june_price)) + geom_point() + facet_grid(. ~ items) + # se = TRUE inside stat_smooth() shows confidence interval stat_smooth(method = "lm", se = TRUE, col = "red")`

The preceding code gives a linear model fit and a 95% confidence interval along with the scatterplot:

We get this weird-looking confidence interval for the oil price and the rice price, as there are very few points available.

We can do so many more things, and we have so many other things to cover in this book that we will not be covering any more plotting functionalities here. But we will explain many other aspects of plotting as and when appropriate when dealing with spatial data in upcoming chapters. I have also listed books to refer to for a deeper understanding of R in the Further reading section.