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Category: Ggplot map

Ggplot map

If specified and inherit. You must supply mapping if there is no plot mapping.

ggplot map

If NULLthe default, the data is inherited from the plot data as specified in the call to ggplot. A data. All objects will be fortified to produce a data frame. See fortify for which variables will be created. A function will be called with a single argument, the plot data. The return value must be a data.

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A function can be created from a formula e. Other arguments passed on to layer. Data frame that contains the map coordinates. This will typically be created using fortify on a spatial object.

It must contain columns x or longy or latand region or id. If TRUEmissing values are silently removed. Should this layer be included in the legends? NAthe default, includes if any aesthetics are mapped. It can also be a named logical vector to finely select the aesthetics to display. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.

This is pure annotation, so does not affect position scales. The data to be displayed in this layer. There are three options: If NULLthe default, the data is inherited from the plot data as specified in the call to ggplot.Enroll now!

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Learn more. You will need a computer with internet access to complete this lesson and the data for week 4 of the course. In the previous lesson, you used base plot to create a map of vector data - your roads data - in R.

ggplot map

In this lesson you will create the same maps, however instead you will use ggplot. 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. 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.

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!You can report issue about the content on this page here Want to share your content on R-bloggers?

Inset maps enable multiple places to be shown in the same geographic data visualisation, as described in the Inset maps section 8. The topic of inset maps has gained attention and recently Enrico Spinielli asked inset maps could be created for data in unusual coordinate systems:.

However, the main idea stays the same: we need to create at least two maps: a larger one, called the main map, that shows the central story and a smaller one, called the inset map, that puts the main map in context. This blog post shows how to create inset maps with ggplot2 for visualization. The approach also uses the sf package for spatial data reading and handling, cowplot to arrange inset maps, and rcartocolor for additional color palettes.

To reproduce the results on your own computer, after installing them, these packages can be attached as follows:. The first step is to read and prepare the data we want to visualize. Both objects should have the same coordinate reference system crs. We also need to have the borders of the area we want to highlight use in the main map.

The second step is to create both inset and main maps independently. The inset map should show the context larger area and highlight the area of interest. Here we show the number of births between and in the North Carolina counties the BIR74 variable using the Mint color palette from the rcartocolor palette.

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We also customize the legend position and size — this way, the legend is a part of the map, instead of being somewhere outside the map frame. The final step is to join two maps. This can be done using functions from the cowplot package. For our purpose, we are interested in the continental 48 states and the District of Columbia only; therefore, we remove the rest of the divisions using subset.

We also extract the bounding box of the main object here.

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However, instead of using it directly, we add a buffer of 10, meters around it. This output will be handy in both inset and main maps. On the other hand, the main map looks better when we provide some additional context to our data. One of the ways to achieve it is to add the borders of the neighboring states. Importantly, we also need to limit the extent of our main map to the range of the frame in the inset map. The above examples can be adjusted to any spatial data and location.

The main map can also be enhanced with the north arrow and scale bar using the ggsn package. As always with R, there are many possible options to create inset maps.

2. Mapping the US

You can find two examples of inset maps created using the tmap package in the Geocomputation with R book. The second example is a classic map of the United States, which consists of the contiguous United States, Hawaii, and Alaska. However, Hawaii and Alaska are displayed at different geographic scales than the main map there. This problem can also be solved in R, which you can see in the Making maps of the USA with R: alternative layout blogpost and the Alternative layout for maps of the United States repository.In the previous part, we presented general concepts with a map with little information country borders only.

The modular approach of ggplot2 allows to successively add additional layers, for instance study sites or administrative delineations, as will be illustrated in this part. The full list of packages necessary for this series of tutorials can be installed with:. We start by loading the basic packages necessary for all maps, i.

The package rnaturalearth provides a map of countries of the entire world. The function can return sp classes default or directly sf classes, as defined in the argument returnclass :. We start by defining two study sites, according to their longitude and latitude, stored in a regular data. As such, we can adjust all characteristics of points e. A better, more flexible alternative is to use the power of sf : Converting the data frame to a sf object allows to rely on sf to handle on the fly the coordinate system both projection and extentwhich can be very useful if the two objects here world map, and sites are not in the same projection.

To achieve the same result, the projection here WGS84, which is the CRS code has to be a priori defined in the sf object:. It would be informative to add finer administrative information on top of the previous map, starting with state borders and names.

The package maps which is automatically installed and loaded with ggplot2 provides maps of the USA, with state and county borders, that can be retrieved and converted as sf objects:. State names are part of this data, as the ID variable.

ggplot map

A simple but not necessarily optimal way to add state name is to compute the centroid of each state polygon as the coordinates where to draw their names. WGS84 are not exact, which is perfectly fine for our drawing purposes. State names, which are not capitalized in the data from mapscan be changed to title case using the function toTitleCase from the package tools :.

County data are also available from the package mapsand can be retrieved with the same approach as for state data. We can also fill in the county using their area to visually identify the largest counties. To make a more complete map of Florida, main cities will be added to the map.

We first prepare a data frame with the five largest cities in the state of Florida, and their geographic coordinates:. Once you have your API key, you can run the following code to automatically retrieve geographic coordinates of the five cities:. We can now convert the data frame with coordinates to sf format:. This is not really satisfactory, as the names overlap on the points, and they are not easy to read on the grey background.

For the final map, we put everything together, having a general background map based on the world map, with state and county delineations, state labels, main city names and locations, as well as a theme adjusted with titles, subtitles, axis labels, and a scale bar:.

This example fully demonstrates that adding layers on ggplot2 is relatively straightforward, as long as the data is properly stored in an sf object.

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The full list of packages necessary for this series of tutorials can be installed with: install.Plotting geospatial data is a common visualisation task, and one that requires specialised tools. Typically the problem can be decomposed into two problems: using one data source to draw a map, and adding metadata from another information source to the map.

This chapter will help you tackle both problems. Next, Sections 6. Finally, Section 6. In this data set we have four variables: lat and long specify the latitude and longitude of a vertex i. In this plot, each row in the data frame is plotted as a single point, producing a scatterplot that shows the corners of every county.

This is illustrated in the right panel below. To illustrate what an sf data set looks like, we import a data set depicting the borders of Australian states and territories:. This output shows some of the metadata associated with the data discussed momentarilyand tells us that the data is essentially a tibble with 11 rows and 4 columns. One advantage to sf data is immediately apparent, we can easily see the overall structure of the data: Australia is comprised of six states, four territories and Macquarie Island, which is politically part of Tasmania.

There are 11 distinct geographical units, so there are 11 rows in this tibble cf. The most important column is geometrywhich specifies the spatial geometry for each of the states and territories.

Each element in the geometry column is a multipolygon object which, as the name suggests, contains data specifying the vertices of one or more polygons that demark the border of a region. This aesthetic can be specified in one of three ways:. This is useful if you have multiple geometry columns. In some instances you may want to overlay one map on top of another. To do this, there are two preprocessing steps to perform.

It is worth noting that the first layer to this plot maps the fill aesthetic in onto a variable in the data. In this instance the NAME variable is a categorical variable and does not convey any additional information, but the same approach can be used to visualise other kinds of area metadata. For example, while an Australian audience might be reasonably expected to know the names of the Australian states and are left unlabelled in the plot above few Australians would know the names of different electorates in the Sydney metropolitan region.

In order to draw an electoral map of Sydney, then, we would first need to extract the map data for the relevant elextorates, and then add the label. The warning message is worth noting. This assumption is not strictly warranted, and in some cases e. For this reason, the sf package produces warning messages when it relies on this approximation. The code below illustrates how this is done:.

At the start of the chapter I drew maps by plotting longitude and latitude on a Cartesian plane, as if geospatial data were no different to other kinds of data one might want to plot.

There are two fundamental problems with the approach. The first issue is the shape of the planet. The Earth is neither a flat plane, nor indeed is it a perfect sphere.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I was able to create a US map with this tutorial.

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When I tried to add additional points to it they all ended up in South Dakota, no matter what I input for data. One thing the author also did not see fit to do is provide support for anything but choropleths. Your problem is that the map is in one coordinate system and your points are in another.

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If you can use non-CRAN packages, albersusa which was around for a while before the usamap author did the copypasta package provides the necessary glue:. However, if you look up the help on state. As of usmap 0.

There is also a new vignette called Advanced Mapping which shows this in more detail. Learn more. Add points to usmap with ggplot in r Ask Question.

Asked 1 year, 11 months ago. Active 1 year ago. Viewed 2k times. MikeF MikeF 3 3 silver badges 15 15 bronze badges. Active Oldest Votes. I'm the maintainer of usmap. I wasn't aware of how to do that when I was creating the package but I would be more than happy to include it in the appropriate place.

Paolo Id also like to point out that usmap does have a value added feature. It does not rely on non R libraries such as geos and gdalwhich can be problematic if you don't have admin access to your environment. So thank you for the package. Is there a way to project state and county boundaries using contrasting colors with your package?The process from start to finish was extremely easy. If a problem arose, the only answer I heard from the professionals at Web Design Express was "we can fix it" and then they did.

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