map in r

The above is pretty cool, but it seems like it would be a lot cooler if we could plot some information about those counties. 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 sp, rgdal, and rgeos, R has been acquiring much of the functionality of traditional GIS packages (like ArcGIS, etc).). In that respect I gravitate more toward Matlab’s default color gradient. Not good. R is a scriptable language that allows the user to write out a code in which it will execute the commands specified. ; Add layers (i.e., features) to the map by using layer functions (e.g. By Sharon Machlis. Here is a small data frame of points from the Sisquoc River. # Don't bother installing if you already have them, # the github version of ggmap, which recently pulled in a small fix I had, #> long lat group order region subregion, #> 1 -101.4078 29.74224 1 1 main , #> 2 -101.3906 29.74224 1 2 main , #> 3 -101.3620 29.65056 1 3 main , #> 4 -101.3505 29.63911 1 4 main , #> 5 -101.3219 29.63338 1 5 main , #> 6 -101.3047 29.64484 1 6 main , #> long lat group order region subregion, #> 7247 -122.6187 48.37482 10 7247 whidbey island , #> 7248 -122.6359 48.35764 10 7248 whidbey island , #> 7249 -122.6703 48.31180 10 7249 whidbey island , #> 7250 -122.7218 48.23732 10 7250 whidbey island , #> 7251 -122.7104 48.21440 10 7251 whidbey island , #> 7252 -122.6703 48.17429 10 7252 whidbey island , #> long lat group order region subregion, #> 1 226.6336 58.42416 1 1 Canada , #> 2 226.6314 58.42336 1 2 Canada , #> 3 226.6122 58.41196 1 3 Canada , #> 4 226.5911 58.40027 1 4 Canada , #> 5 226.5719 58.38864 1 5 Canada , #> 6 226.5528 58.37724 1 6 Canada , #> long lat group order region subregion, #> 2276817 125.0258 11.18471 2284 2276817 Philippines Leyte, #> 2276818 125.0172 11.17142 2284 2276818 Philippines Leyte, #> 2276819 125.0114 11.16110 2284 2276819 Philippines Leyte, #> 2276820 125.0100 11.15555 2284 2276820 Philippines Leyte, #> 2276821 125.0111 11.14861 2284 2276821 Philippines Leyte, #> 2276822 125.0155 11.13887 2284 2276822 Philippines Leyte, # we already did this, but we can do it again, #> long lat group order region subregion, #> 1 -87.46201 30.38968 1 1 alabama , #> 2 -87.48493 30.37249 1 2 alabama , #> 3 -87.52503 30.37249 1 3 alabama , #> 4 -87.53076 30.33239 1 4 alabama , #> 5 -87.57087 30.32665 1 5 alabama , #> 6 -87.58806 30.32665 1 6 alabama , #> long lat group order region subregion, #> 15594 -106.3295 41.00659 63 15594 wyoming , #> 15595 -106.8566 41.01232 63 15595 wyoming , #> 15596 -107.3093 41.01805 63 15596 wyoming , #> 15597 -107.9223 41.01805 63 15597 wyoming , #> 15598 -109.0568 40.98940 63 15598 wyoming , #> 15599 -109.0511 40.99513 63 15599 wyoming , #> long lat group order region subregion, #> 667 -120.0060 42.00927 4 667 california , #> 668 -120.0060 41.20139 4 668 california , #> 669 -120.0060 39.70024 4 669 california , #> 670 -119.9946 39.44241 4 670 california , #> 671 -120.0060 39.31636 4 671 california , #> 672 -120.0060 39.16166 4 672 california , #> long lat group order region subregion, #> 6965 -121.4785 37.48290 157 6965 california alameda, #> 6966 -121.5129 37.48290 157 6966 california alameda, #> 6967 -121.8853 37.48290 157 6967 california alameda, #> 6968 -121.8968 37.46571 157 6968 california alameda, #> 6969 -121.9254 37.45998 157 6969 california alameda, #> 6970 -121.9483 37.47717 157 6970 california alameda, # give names and make population and area numeric, #> subregion long lat group order region population area, #> 1 alameda -121.4785 37.48290 157 6965 california 1578891 738, #> 2 alameda -121.5129 37.48290 157 6966 california 1578891 738, #> 3 alameda -121.8853 37.48290 157 6967 california 1578891 738, #> 4 alameda -121.8968 37.46571 157 6968 california 1578891 738, #> 5 alameda -121.9254 37.45998 157 6969 california 1578891 738, #> 6 alameda -121.9483 37.47717 157 6970 california 1578891 738, # prepare to drop the axes and ticks but leave the guides and legends. modified. When you have eliminated the JavaScript , whatever remains must be an empty page. Content on this website is a government work in the public domain in the U.S. and under the CC0 1.0 internationally. It is pretty sweet, but does not support different projections. I recently started teaching myself R Shiny and one of my first projects was making an interactive map of earthquake data (click the link below to play around with the map). Working from the small spatial scale up to a larger spatial scale. get_map is a smart wrapper that queries the map server of your choosing—Google Maps, OpenStreetMap, or Stamen Maps—and returns a map at a spe… The _if and _at variants take a predicate function .p that Let’s take the log-base-10 of the population density. In this tutorial I will show some basic GIS functionality in R. Basic packages That was about as easy as could be. Notice that region names are unique overall (not just within N or S) so we can just color by region name. positions. Using just a few simple lines of code, we can make a beautiful map in R. I’ll assume you already have R and R Studio on your computer. Plot the state first but let’s ditch the axes gridlines, and gray background by using the super-wonderful theme_nothing(). Martin was born in Czech Republic and studied at the University of Natural Resources and Life Sciences, Vienna. How about another example. Building a map follows those 2 steps: Find data, load it in R: region boundaries can be stored in shapefiles or geoJSON files.Some R libraries also provide the data for the most common places. #> Warning: bounding box given to google - spatial extent only approximate. That was a fail, but we got a warning about it too. The make_bbox function has never really worked for me. type as the input. We start by defining two study sites, according to their longitude andlatitude, stored in a regular data.frame: The quickest way to add point coordinates is with the general-purposefunction geom_point, which works on any X/Y coordinates, of regulardata points (i.e. Self-Organising Maps Self-Organising Maps (SOMs) are an unsupervised data visualisation technique that can be used to visualise high-dimensional data sets in lower (typically 2) dimensional representations. Let’s make a function: you pass it the region and it makes the plot. Note that the scale of these maps from package maps are not great. modify() family for versions that return an object of the same vectors of the corresponding type (or die trying). 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 2000 and 2012 inclusive. If a component is not present, the value of See all sources at the course. .default will be returned. So, with that function we just need to cycle over the regions and make all those plots. In this post, we examine the use of R to create a SOM for customer segmentation. use in pipe. Enable JavaScript to see Google Maps. Within a list, wrap strings in get-attr() invoke, lmap, Only those elements where .p evaluates to i'm familiar with the maps package but i can't get the colors to cooperate. To do this, we just specified the name of the location (more on that later). Press question mark to learn the rest of the keyboard shortcuts First we used get_map() to retrieve the map from Google Maps. Using R to create maps brings these benefits to mapping. It is time to really put this thing through its paces. How's this site built? Topics to be covered include: Introduction to R; Working with data types, strings, and dates in R; Manipulating data frames in R; Data visualization in R …and lunch will be provided! You are going to need to install a few extra packages to follow along with this lecture.