A satisfying and important aspect of geographic research is communicating the results. Map making — the art of cartography — is an ancient skill that involves communication, intuition, and an element of creativity.
Static mapping is straightforward with plotas we saw in Section 2. It is possible to create advanced maps using base R methods Murrellbut this chapter focuses on dedicated map-making packages.
When learning a new skill, it makes sense to gain depth-of-knowledge in one area before branching out. In addition to being fun and creative, cartography also has important practical applications. A carefully crafted map is vital for effectively communicating the results of your work Brewer :. Maps have been used for several thousand years for a wide variety of purposes. Map making has historically been an activity undertaken only by, or on behalf of, the elite.How to create a dynamic map chart with drop-down (works with ANY Excel version)
Maps are also often the best way to present the findings of geocomputational research in a way that is accessible. Map making is therefore a critical part of geocomputation and its emphasis not only on describing, but also changing the world. This chapter shows how to make a wide range of maps. The next section covers a range of static maps, including aesthetic considerations, facets and inset maps.
Sections 8. Finally, Section 8. Static maps are the most common type of visual output from geocomputation. Standard formats include. Initially, static maps were the only type of maps that R could produce. Things advanced with the release of sp see Pebesma and Bivand and many techniques for map making have been developed since then. However, despite the innovation of interactive mapping, static plotting was still the emphasis of geographic data visualisation in R a decade later Cheshire and Lovelace The generic plot function is often the fastest way to create static maps from vector and raster spatial objects see sections 2.
Sometimes, simplicity and speed are priorities, especially during the development phase of a project, and this is where plot excels. The base R approach is also extensible, with plot offering dozens of arguments. Another approach is the grid package which allows low level control of static maps, as illustrated in Chapter 14 of Murrell This section focuses on tmap and emphasizes the important aesthetic and layout options.
It has a concise syntax that allows for the creation of attractive maps with minimal code which will be familiar to ggplot2 users. Finally, it accepts a wider range of spatial classes including raster objects than alternatives such as ggplot2 see the vignettes tmap-getstarted and tmap-changes-v2as well as Tennekesfor further documentation.
This layering is demonstrated in the chunk below, which generates the maps presented in Figure 8. This layering is illustrated in the right panel of Figure 8.
A useful feature of tmap is its ability to store objects representing maps.Boundaries are crucial for the safety and ethical responsibility of every person who enjoys the outdoors. See exactly where private land stops and public land begins. View detailed land owner information including clear boundaries, owner name, parcel size and more. A full suite of weather tools let you plan your next outdoor adventure.
SmartMarkers are your window into understanding patterns with your hunts to bring you more success in the outdoors. View detailed season dates for each hunting unit. Know exactly when you can hunt, the weapon options available, quotas and more. BaseMap is taking the guesswork out of your next hunt, one layer at a time. When used with the nationwide parcel boundaries layer, you will see who owns the land surrounding any water body. Create an offline map in BaseMap before heading out on your adventure.
Nationwide Parcel Boundaries. Save Favorite Locations. Use on Mobile, Tablet, or Web. Sync Data Across All Devices. Multiple Topo and Imagery Options. Includes Basic Features. Nationwide Owner Names. Color Coded Govt Lands. Hundreds of Map Layers. Google Earth Integration.
In-App Location Sharing. Unlimited Offline Maps. Nationwide Season Dates. Western Big Game Harvest Data. The Best Nationwide Hunting Maps. Try Free. Land Ownership See exactly where private land stops and public land begins. Hunting Maps Know when, where and what to hunt.Leaflet supports basemaps using map tilespopularized by Google Maps and now used by nearly all interactive web maps.
The easiest way to add tiles is by calling addTiles with no arguments; by default, OpenStreetMap tiles are used. Alternatively, many popular free third-party basemaps can be added using the addProviderTiles function, which is implemented using the leaflet-providers plugin.
See here for the complete set. As a convenience, leaflet also provides a named list of all the third-party tile providers that are supported by the plugin. You can also use names providers to view all of the options. Note that some tile set providers require you to register; see the project page for more information.
If you happen to have a custom map tile URL template to use, you can provide it as an argument to addTiles. This generally only makes sense if the front tiles consist of semi transparent tiles, or have an adjusted opacity via the options argument. Leaflet for R. Using Basemaps Leaflet supports basemaps using map tilespopularized by Google Maps and now used by nearly all interactive web maps. Third-Party Tiles Alternatively, many popular free third-party basemaps can be added using the addProviderTiles function, which is implemented using the leaflet-providers plugin.Enroll now!
Learn more. You will need a computer with internet access to complete this lesson and the data for week 4 of the course.Bg david koh
In the previous lesson, you used base plot to create a map of vector data - your roads data - in R. In this lesson you will create the same maps, however instead you will use ggplot.Yandere quiz rp
Compared to base plot, you will find creating custom legends to be simpler and cleaner, and creating nicely formatted themed maps to be simpler as well. However, you will have to convert your data from spatial sp objects to data. Data Tip: If your data attribute values are not read in as factors, you can convert the categorical attribute values using as.
It looks like you have some missing values in your road types.Liigu sisu juurde
You want to plot all road types even those that are NA. However ggplot requires a data. Thus you will need to convert your data.
Rainbow Six Siege Maps
You can convert he data using the tidy function from the broom package in R. Data Tip: The tidy function used to be the fortify function! The code for the tidy function is exactly the same as the fortify code. Note the following when you plot.
You can think of this as temporarily grouping the data by the RTTYP category for plotting purposes only. Notice that above the colors are applied to each category C, M, S and Unknown in order. In this case the order is alphabetical. Finally you can remove the axis ticks and labels using a theme element.
Themes are used in ggplot to customize the look of a plot.
You can customize any element of the plot including fonts, colors and more! Data Tip: There are many different ways to ensure ggplot plots data using x and y axis distances that represent the data properly. If you want a unique line width for each factor level or attribute category in your spatial object, you can use a similar syntax to the one you used for colors. Similar to the colors set above, ggplot will apply the line width in the order of the factor levels in the data. This is by default alphabetical.
Like this:. The map above looks ok but you have multiple legends when you really just want one legend for both color and size. You can merge the legend using the guides function. Here you specify each legend element that you wish to merge together as follows:. Create your own custom map of roads. Adjust the line width and the colors of the roads to make a map that emphasizes roads of value S thicker lines and that de-emphasizes roads with an RTTYP attribute value of unknown thinner lines, lighter color.Matokeo ya kurunyemi sekondari kidato cha ine kigoma
This data represents study plot locations from your field work in southern California. Next you can tidy up the data as you did before… or can you? Note that this time you imported point data.
Instead, you simply convert it to a data. Next you have a few options - your roads layer is a much larger spatial extent compared to your plots layer.
Create maps in R in 10 (fairly) easy steps
A better approach is to crop your data to a study region. It looks like your data are not all in the same coordinate reference system CRS.Raw on GitHub. Edit this page. For a long time, R has had a relatively simple mechanism, via the maps package, for making simple outlines of maps and plotting lat-long points and paths on them. More recently, with the advent of packages like sprgdaland rgeosR has been acquiring much of the functionality of traditional GIS packages like ArcGIS, etc.
This is an exciting development, but not always easily accessible for the beginner, as it requires installation of specialized external libraries that may, on some platforms, not be straightforward and considerable familiarity with GIS concepts. More recently, a third approach to convenient mapping, using ggmap has been developed that allows the tiling of detailed base maps from Google Earth or Open Street Maps, upon which spatial data may be plotted.
Nor will cover the somewhat more simplified approach to projections using the mapproj package. It is pretty sweet, but does not support different projections.
I feel that the above twp topics should cover a large part of what people will need for making useful maps of field sites, or sampling locations, or fishing track lines, etc. Here is no fill, with a red line. Remember, fixed value of aesthetics go outside the aes function. This is just like it is above, but we can map fill to region and make sure the the lines of state borders are white. We now have the numbers that we want, but we need to attach those to every point on polygons of the counties.
If you were needing a little more elbow room in the great Golden State, this shows you where you can find it:. I personally like more color than ggplot uses in its default gradient. Can we do something similar with ggplot? Note that the scale of these maps from package maps are not great. We can zoom in to the Bay region, and it sort of works scale-wise, but if we wanted to zoom in more, it would be tough. The ggmap package is the most exciting R mapping tool in a long time!
You might be able to get better looking maps at some resolutions by using shapefiles and rasters from naturalearthdata.
That was a fail, but we got a warning about it too. Zoom levels go from 3 world scale to 20 house scale. For this, I have whittled down some stuff in the coded wire tag data base to georeferenced marine locations in British Columbia where at least one Chinook salmon was recovered in between and inclusive.
To see how I did all that you can check out this. These locations in BC are hierarchically structured.Do you have some data with geolocation information that you want to map? You may not think of R when you're looking for a GIS platform, but new packages and standards have helped make the R programming language a surprisingly robust platform for some geospatial analysis.
These examples will demonstrate how to map election results, but the concepts can easily be used for any other kind of color-coded choropleth map. I'll show how to handle a straightforward two-person race and a more complex race with three or more candidates. We'll be using two mapping packages in this tutorial: tmap and tmaptools for quick static maps and leaflet for interactive maps.
You can install and load them now with. I'll start with the New Hampshire Democratic primary resultswhich are available from the New Hampshire secretary of state's office as a downloadable Excel spreadsheet. Getting election data into the proper format for mapping is one of this project's biggest challenges — more so than actually creating the map.
For simplicity, let's stick to results by county instead of drilling down to individual towns and precincts. One common problem: Results data need to have one column with all election district names — whether counties, precincts or states — and candidate names as column headers. Many election returns, though, are reported with each election district in its own column and candidate results by row. That's the case with the official New Hampshire results.
I transposed the data to fix that and otherwise cleaned up the spreadsheet a bit before importing it into R such as removing ", d" after each candidate's name.
The first column now has county names, while every additional column is a candidate name; each row is a county result. I also got rid of the "total" row at the bottom, which can interfere with data sorting.
You can do the same — or, if you'd like to download the data file and all the other files I'm using, including R code, head to the "Mapping with R" file download page.
Free Insider registration needed. Bonus: You'll be helping me convince my boss that I ought to write more of these types of tutorials. If you download and unzip the mapping with R file, look for NHD To make your R mapping script as reusable as possible, I suggest putting data file names at the top of the script — that makes it easy to swap in different data files without having to hunt through code to find where a file name appears.
You can put this toward the top of your R script:. Note: My data file isn't in the same working directory as my R script; I have it in a data subdirectory.An alternative is to use Ra free software environment for statistical computing and graphics.
R has many features that allow it to read GIS data and produce both static and interactive maps. This document which is an R Notebook shows how to make maps with R and RStudiousing R base graphics and the maps and mapdata packages, in addition to the leaflet and tmap packages.
We will also map U. Census data with help from the tigris and acs packages. It is assumed that the reader has some familiarity with R and RStudio. See also Why I love R Notebooks. Also please see Habitat structure and phenotypic variation in the invading butterfly Coenonympha tulliaan R Notebook that illustrates how to use R and RStudio to write a complete academic manuscript. To install R, go to the R download site.
You must install R before installing RStudio. After installing R, visit the RStudio download site to download the RStudio installer for your computer platform. Some R packages require the command-line tools so that they can be compiled and installed. This R Notebook may be viewed onlinewhere you can download the R Notebook document click the first button labeled Codethen click Download Rmd. The notebook is a plain text file named maps.
Rmdwhich can be opened in RStudio. Output is visible immediately beneath the code chunk. Within a code chunk, the symbol is used to denote comments, which are ignored by the R interpreter and serve to document the code. A code chunk begins and ends with three backticks found to the left of the numeral 1 on your keyboard. Following the initial three backticks is a pair of curly braces containing the letter r to signify that it is an R code chunk.
The curly braces can contain additional R options, and all R code must be contained inside of the backticks.
Making Maps with R
You can use the Insert menu of the RStudio text editor to insert an empty R code chunk ready for coding. The following chunk establishes global options that apply to all of the other chunks in the document. Each chunk has a short title, following the r after the first curly brace, making it easier to navigate the chunks in the RStudio text editor.
Next we install a series of R packagesand other packages that those packages require, which are needed for the examples. The code first makes a list of the packages we want to install, then checks if they are already installed. The packages and their dependencies are then installed and loaded into the R environment. If you are running this for the first time after you have installed R and RStudio, be patient, as the packages may take a while to download and install.
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