Fitting Data into a Map in R

Introduction

Fitting data into a map is an essential step in many geographic information systems (GIS) and spatial analysis applications. In this article, we will explore how to fit data into a map using R and the maptools package.

Background

The maptools package provides functions for working with shapefiles, which are a common format for storing geographic data. Shapefiles can be used to represent features such as boundaries, polygons, and lines on a map. To fit data into a map, we need to merge our data with the shapefile of the region that contains our data.

Reading Shape Files

To get started, we need to read in our shapefile using the readShapePoly() function from the maptools package.

# Packages

library(maptools)

# Reading shape files

estados <- readShapePoly(fn = 'estados_2010.shp')

In this example, we are reading in a shapefile called “estados_2010.shp” into a data frame called estados.

Adding an Order Vector

To ensure that our data is ordered correctly when merging it with the shapefile, we need to add an order vector to the data frame.

# Adding an order vector in the data frame

estados@data$order <- 1:nrow(estados@data)

In this example, we are creating a vector of increasing numbers from 1 to the number of rows in the data frame. This will ensure that our data is ordered correctly when merging it with the shapefile.

Data Preparation

Before merging our data with the shapefile, we need to prepare our data by converting it into a format that can be used with the merge() function.

# Data to fit in the map

teste <- c(0.567072774466371,
           -0.144934811865636,
           0.595967865326944,
           0.479904421295722,
           0.251495518688696,
           0.195815073242536,
           0.548223329774688,
           0.463845521774005,
           0.272145251828454,
           0.580334048486482,
           0.649672015127772,
           0.584054575484519,
           0.671373957229477,
           0.436391947145788,
           0.522605768431274,
           0.641067907529059,
           0.570339843565797,
           0.381297082036819,
           0.474587396032187,
           0.462810571821925,
           0.558684078070584,
           -0.197881941278306,
           0.720551728476599,
           0.489189630257170,
           -0.538560893510226,
           0.732280520987057,
           0.555364159479124)

# Adding the data vector in the data frame

estados@data$dados <- teste

In this example, we are creating a new column called dados and filling it with our prepared data.

Merging the Data

Now that our data is prepared, we can merge it with the shapefile using the merge() function.

# Merging the data

colorOfdados2 <- data.frame(dados2=levels(estados@data$dados2),
                            colors=unique(myColorsBlue))

estados@data <- merge(estados@data, colorOfdados2)

In this example, we are creating a new data frame called colorOfdados2 that contains the unique values of our dados2 column and their corresponding colors. We then merge this data frame with the original shapefile using the merge() function.

Plotting the Data

Now that our data is merged with the shapefile, we can plot it using the plot() function.

# Plot

plot(estados, col=as.character(estados@data$colors),
     lty=0)

In this example, we are plotting the shapefile with a color gradient based on our prepared data.

Adding Additional Data

To add additional data to the plot, we can use the add() function.

# Add

plot(estados2, add=T) # Destaca as linhas dos estados no mapa

In this example, we are adding a new layer to the plot by plotting the shapefile again with the add=T argument.

Creating a Legend

To create a legend for our plot, we can use the legend() function.

# Legend

color.legend(xl=-70, xr=-60,yb=-25,yt=-26,
             legend=c('-0.53','','0.73'), 
             rect.col=myColorsBlue, gradient='x',
             cex=.8, pos=c(1,1,1))

In this example, we are creating a legend that highlights the values of -0.53 and 0.73 on our plot.

Conclusion

Fitting data into a map is an essential step in many GIS and spatial analysis applications. In this article, we explored how to fit data into a map using R and the maptools package. We covered topics such as reading shape files, adding order vectors, preparing data for merging, and creating legends. By following these steps, you can easily merge your data with a shapefile and create a color gradient plot that highlights your data.

Additional Tips

Make sure to explore the documentation of the maptools package to learn more about its functions and features.
Use the ggplot2 package to create plots with additional features such as faceting, layering, and customization.
Experiment with different color palettes and gradients to find one that suits your data best.
Consider using other libraries such as leaflet or shiny to create interactive maps.

References

“maptools: A package for reading and manipulating map data” by Robin Lovelace
“ggplot2: Elegant Statistical Graphics in R” by Hadley Wickham

Last modified on 2023-10-26