Chapter 37 Drawing maps with Leaflet
Packages for this chapter:
37.1 The brain of a cat, revisited
Earlier, we looked at an ethics study that had to do with a fictional brain of a fictional cat. I said there was actually a town called Catbrain. It’s in England, near Bristol, and seems to be home to a street of car dealerships.
Find the latitude and longitude of “Catbrain UK” (though I don’t think there are any others).
Draw a map of Catbrain using Leaflet.
Make a dataframe containing some other British cities as well as Catbrain, and find their latitudes and longitudes.
Draw a map containing the places you picked.
37.2 Making a map of Wisconsin
The file link contains the road distances (in miles) between 12 cities in Wisconsin and neighbouring states. We are going to try to draw a map of the area using Leaflet.
Read in the data, displaying the data that you read in.
Make a dataframe containing the names of the locations (get rid of the columns containing distances), and add a column of the abbreviations of the states they are in. All of them are in Wisconsin (WI), except for the last three: Dubuque is in Iowa (IA), St. Paul is in Minnesota (MN) and Chicago is in Illinois (IL).
Create a new column in which the abbreviation for the state is glued on to the end of each
location, separated by a space.Look up the latitudes and longitudes of these twelve places.
Obtain a Leaflet map of the area containing these twelve cities.
37.3 The Cross-City Line
When I went to university (in Birmingham, England, a long time ago), I was very excited because I would be travelling to campus by train. My journey was on the Cross-City Line, a metro-type service with lots of stops short distances apart, but run in those days by diesel trains (the electrification came later).
A list of the stations on the line is in http://ritsokiguess.site/datafiles/cross-city.csv. There is one column in the data file, called station. We are going to draw a map of these.
Read in and display (some of) the station names.
In preparation for geocoding, create a second column in the dataframe that consists of the station names with “station UK” on the end. (This is to improve the chances of the geocoder finding the actual railway station.)
Look up the longitudes and latitudes of all the stations, organizing your dataframe so that they are visible.
Make a Leaflet map of the stations. Use circle markers or the “pin” markers as you prefer.
Zoom in to see whether the geocoding did indeed find each of the stations. Comment briefly on what you find.
My solutions follow:
37.4 The brain of a cat, revisited
Earlier, we looked at an ethics study that had to do with a fictional brain of a fictional cat. I said there was actually a town called Catbrain. It’s in England, near Bristol, and seems to be home to a street of car dealerships.
- Find the latitude and longitude of “Catbrain UK” (though I don’t think there are any others).
Solution
Make sure you have these two packages loaded:
To find the latitude and longitude of Catbrain:
catbrain <- tibble(place = "Catbrain UK")
catbrain %>% mutate(ll = list(geocode_OSM(place))) %>%
unnest_wider(ll) %>%
unnest_wider(coords) -> catbrain## # A tibble: 1 x 5
## place query x y bbox
## <chr> <chr> <dbl> <dbl> <list>
## 1 Catbrain UK Catbrain UK -2.61 51.5 <bbox [4]>Remember that the output from geocode_OSM is a list, and it has in it a thing called coords that is the longitude and latitude together, and another thing called bbox that we don’t use. So we have to unnest twice to get the longitude (as x) and the latitude (as y) out for drawing in a moment.
\(\blacksquare\)
- Draw a map of Catbrain using Leaflet.
Solution
That goes this way:
leaflet(data = catbrain) %>%
addTiles() %>%
addCircleMarkers(lng = ~x, lat = ~y) -> catbrain_map
catbrain_mapThere are car dealerships are along Lysander Road. Zoom in to see them. Or zoom out to see where this is. You can keep zooming out until you know where you are, using the plus and minus buttons, or your mouse wheel.
The name Catbrain, according to link, means “rough stony soil”, from Middle English, and has nothing to do with cats or their brains at all.
Extra: I was actually surprised that this worked at all, because with only one point, how does it know what scale to draw the map? Also, unless your UK geography is really good, you won’t have any clue about exactly where this is. That’s the reason for the next part.
\(\blacksquare\)
- Make a dataframe containing some other British cities as well as Catbrain, and find their latitudes and longitudes.
Solution
I chose the cities below, mostly somewhere near Catbrain. You could fire up a Google map, zoom it out until it contains something you know, and pick some places you’ve heard of. (I happen to know British geography pretty well, so I just picked some mostly nearby places out of my head. I didn’t really want to pick London, but I figured this was the one you might know.)
catbrain2 <- tribble(
~where,
"Catbrain UK",
"Bristol UK",
"Taunton UK",
"Newport UK",
"Gloucester UK",
"Cardiff UK",
"Birmingham UK",
"London UK",
"Caldicot UK"
)
catbrain2 %>%
rowwise() %>%
mutate(ll = list(geocode_OSM(where))) %>%
unnest_wider(ll) %>%
unnest_wider(coords) -> catbrain2## # A tibble: 9 x 5
## where query x y bbox
## <chr> <chr> <dbl> <dbl> <list>
## 1 Catbrain UK Catbrain UK -2.61 51.5 <bbox [4]>
## 2 Bristol UK Bristol UK -2.60 51.5 <bbox [4]>
## 3 Taunton UK Taunton UK -3.10 51.0 <bbox [4]>
## 4 Newport UK Newport UK -3.00 51.6 <bbox [4]>
## 5 Gloucester UK Gloucester UK -2.25 51.9 <bbox [4]>
## 6 Cardiff UK Cardiff UK -3.18 51.5 <bbox [4]>
## 7 Birmingham UK Birmingham UK -1.90 52.5 <bbox [4]>
## 8 London UK London UK -0.128 51.5 <bbox [4]>
## 9 Caldicot UK Caldicot UK -2.75 51.6 <bbox [4]>The first time I did this, I forgot the rowwise, which we didn’t need the first time (there was only one place), but here, it causes odd problems if you omit it.
\(\blacksquare\)
- Draw a map containing the places you picked.
Solution
The map-drawing is almost the same, just changing the dataframe:
Now, if you have any sense of the geography of the UK, you know where you are. The big river (the Severn) is the border between England and Wales, so the places above and to the left of it are in Wales, including Caldicot (see question about Roman pottery).
You can zoom this map in (once you have figured out which of the circles is Catbrain) and find Lysander Road again, and also the M5 (see below).
More irrelevant extra: the M5 is one of the English “motorways” (like 400-series highways or US Interstates). The M5 goes from Birmingham to Exeter. You can tell that this is England because of the huge number of traffic circles, known there as “roundabouts”. One of the first things they teach you in British driving schools is how to handle roundabouts: which lane to approach them in, which (stick-shift) gear to be in, and when you’re supposed to signal where you’re going. I hope I still remember all that for when I next drive in England!
\(\blacksquare\)
37.5 Making a map of Wisconsin
The file link contains the road distances (in miles) between 12 cities in Wisconsin and neighbouring states. We are going to try to draw a map of the area using Leaflet.
- Read in the data, displaying the data that you read in.
Solution
##
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
## location = col_character(),
## Appleton = col_double(),
## Beloit = col_double(),
## Fort.Atkinson = col_double(),
## Madison = col_double(),
## Marshfield = col_double(),
## Milwaukee = col_double(),
## Monroe = col_double(),
## Superior = col_double(),
## Wausau = col_double(),
## Dubuque = col_double(),
## St.Paul = col_double(),
## Chicago = col_double()
## )## # A tibble: 12 x 13
## location Appleton Beloit Fort.Atkinson Madison Marshfield Milwaukee Monroe Superior Wausau Dubuque St.Paul
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Appleton 0 130 98 102 103 100 149 315 91 196 257
## 2 Beloit 130 0 33 50 185 73 33 377 186 94 304
## 3 Fort.Atk… 98 33 0 36 164 54 58 359 166 119 287
## 4 Madison 102 50 36 0 138 77 47 330 139 95 258
## 5 Marshfie… 103 185 164 138 0 184 170 219 45 186 161
## 6 Milwaukee 100 73 54 77 184 0 107 394 181 168 322
## 7 Monroe 149 33 58 47 170 107 0 362 186 61 289
## 8 Superior 315 377 359 330 219 394 362 0 223 351 162
## 9 Wausau 91 186 166 139 45 181 186 223 0 215 175
## 10 Dubuque 196 94 119 95 186 168 61 351 215 0 274
## 11 St.Paul 257 304 287 258 161 322 289 162 175 274 0
## 12 Chicago 186 97 113 146 276 93 130 467 275 184 395
## # … with 1 more variable: Chicago <dbl>The first time I did this, I had a blank line on the end of the data
file, so I had a blank location and missing values for all
the distances for it. I tidied that up before sharing the file with
you, though.
\(\blacksquare\)
- Make a dataframe containing the names of the locations (get rid of the columns containing distances), and add a column of the abbreviations of the states they are in. All of them are in Wisconsin (WI), except for the last three: Dubuque is in Iowa (IA), St. Paul is in Minnesota (MN) and Chicago is in Illinois (IL).
Solution
There seems to be a bit of base R attached to this, however you do it. I am going to create a dataframe pretending they are all in Wisconsin, and then fix it up afterwards:
## # A tibble: 12 x 2
## location state
## <chr> <chr>
## 1 Appleton WI
## 2 Beloit WI
## 3 Fort.Atkinson WI
## 4 Madison WI
## 5 Marshfield WI
## 6 Milwaukee WI
## 7 Monroe WI
## 8 Superior WI
## 9 Wausau WI
## 10 Dubuque WI
## 11 St.Paul WI
## 12 Chicago WI(I checked that in this question I didn’t need the road distances for anything, so I saved it back into the original dataframe. Also, the select is unnecessarily fancy: I could have just selected the location column, but this one says “don’t select any of the columns that are numeric”.)
Now we have to fix up the states of the last three places, which is where the base R seems to come in (but see the Extra):
## # A tibble: 12 x 2
## location state
## <chr> <chr>
## 1 Appleton WI
## 2 Beloit WI
## 3 Fort.Atkinson WI
## 4 Madison WI
## 5 Marshfield WI
## 6 Milwaukee WI
## 7 Monroe WI
## 8 Superior WI
## 9 Wausau WI
## 10 Dubuque IA
## 11 St.Paul MN
## 12 Chicago ILThe states of the last three locations are now correct.
Extra: I didn’t know about the following until literally just now, but there is a tidyverse way to do this as well (that may look familiar to those of you that know about SQL). Let’s start by pretending again that everything is in Wisconsin:
## # A tibble: 12 x 2
## location state
## <chr> <chr>
## 1 Appleton WI
## 2 Beloit WI
## 3 Fort.Atkinson WI
## 4 Madison WI
## 5 Marshfield WI
## 6 Milwaukee WI
## 7 Monroe WI
## 8 Superior WI
## 9 Wausau WI
## 10 Dubuque WI
## 11 St.Paul WI
## 12 Chicago WIand then change the ones that need changing. What you do is to make a little dataframe of the ones that need changing:
## # A tibble: 3 x 2
## location state
## <chr> <chr>
## 1 Dubuque IA
## 2 St.Paul MN
## 3 Chicago ILNote that the columns in here have exactly the same names as the ones in the original dataframe where everything was in Wisconsin.
I did this by copy-pasting the city names whose states needed changing out of the display of my wisc2. You might think that a left join is what we need now, and it almost is. Note that I want to match the locations but not the states:
## # A tibble: 12 x 3
## location state.x state.y
## <chr> <chr> <chr>
## 1 Appleton WI <NA>
## 2 Beloit WI <NA>
## 3 Fort.Atkinson WI <NA>
## 4 Madison WI <NA>
## 5 Marshfield WI <NA>
## 6 Milwaukee WI <NA>
## 7 Monroe WI <NA>
## 8 Superior WI <NA>
## 9 Wausau WI <NA>
## 10 Dubuque WI IA
## 11 St.Paul WI MN
## 12 Chicago WI ILand the story here is that if state.y has a value, use that, otherwise use the value in state.x. This can even be done: there is a function coalesce36 that will do exactly that:
## # A tibble: 12 x 4
## location state.x state.y state
## <chr> <chr> <chr> <chr>
## 1 Appleton WI <NA> WI
## 2 Beloit WI <NA> WI
## 3 Fort.Atkinson WI <NA> WI
## 4 Madison WI <NA> WI
## 5 Marshfield WI <NA> WI
## 6 Milwaukee WI <NA> WI
## 7 Monroe WI <NA> WI
## 8 Superior WI <NA> WI
## 9 Wausau WI <NA> WI
## 10 Dubuque WI IA IA
## 11 St.Paul WI MN MN
## 12 Chicago WI IL ILThe idea behind coalesce is that you give it a list of columns, and the first one of those that is not missing gives its value to the new column. So I feed it state.y first, and then state.x, and the new state contains the right things. (Can you explain what happens if you do it the other way around?)
But, there is a better way. Let’s go back to our all-Wisconsin dataframe:
## # A tibble: 12 x 2
## location state
## <chr> <chr>
## 1 Appleton WI
## 2 Beloit WI
## 3 Fort.Atkinson WI
## 4 Madison WI
## 5 Marshfield WI
## 6 Milwaukee WI
## 7 Monroe WI
## 8 Superior WI
## 9 Wausau WI
## 10 Dubuque WI
## 11 St.Paul WI
## 12 Chicago WIand our dataframe of corrections to make:
## # A tibble: 3 x 2
## location state
## <chr> <chr>
## 1 Dubuque IA
## 2 St.Paul MN
## 3 Chicago ILWe can make those changes in one step, thus:
## Matching, by = "location"## # A tibble: 12 x 2
## location state
## <chr> <chr>
## 1 Appleton WI
## 2 Beloit WI
## 3 Fort.Atkinson WI
## 4 Madison WI
## 5 Marshfield WI
## 6 Milwaukee WI
## 7 Monroe WI
## 8 Superior WI
## 9 Wausau WI
## 10 Dubuque IA
## 11 St.Paul MN
## 12 Chicago ILThis works because the first column of changes, namely location, is the one that is looked up in wisc2. (rows_update has a by in the same way that left_join does if you want to change this.) So all three locations in changes are looked up in wisc2, and any that match have their state changed to the one in changes.
In database terms, the location is known as a “key” column. That means that each city appears once only in the column, and so the replacements in wisc are only happening once. To a statistician, location is an “identifier variable”, identifying the individuals in the dataset. Unless you are doing something like repeated measures, each individual will only give you one measurement, but even then, if you have wide format, the identifier variables will still only appear once.
Magic. Now that I have learned about this, I will be using it a lot.
\(\blacksquare\)
- Create a new column in which the abbreviation for the state is glued on to the end of each
location, separated by a space.
Solution
A couple of ways: a literal gluing, using paste:
## # A tibble: 12 x 3
## location state lookup
## <chr> <chr> <chr>
## 1 Appleton WI Appleton WI
## 2 Beloit WI Beloit WI
## 3 Fort.Atkinson WI Fort.Atkinson WI
## 4 Madison WI Madison WI
## 5 Marshfield WI Marshfield WI
## 6 Milwaukee WI Milwaukee WI
## 7 Monroe WI Monroe WI
## 8 Superior WI Superior WI
## 9 Wausau WI Wausau WI
## 10 Dubuque IA Dubuque IA
## 11 St.Paul MN St.Paul MN
## 12 Chicago IL Chicago ILor the same idea using str_c from stringr (part of the tidyverse), only this time you have to supply the space yourself:
## # A tibble: 12 x 3
## location state lookup
## <chr> <chr> <chr>
## 1 Appleton WI Appleton WI
## 2 Beloit WI Beloit WI
## 3 Fort.Atkinson WI Fort.Atkinson WI
## 4 Madison WI Madison WI
## 5 Marshfield WI Marshfield WI
## 6 Milwaukee WI Milwaukee WI
## 7 Monroe WI Monroe WI
## 8 Superior WI Superior WI
## 9 Wausau WI Wausau WI
## 10 Dubuque IA Dubuque IA
## 11 St.Paul MN St.Paul MN
## 12 Chicago IL Chicago ILor a way you might have forgotten, using unite (which is the inverse of separate):
## # A tibble: 12 x 1
## lookup
## <chr>
## 1 Appleton WI
## 2 Beloit WI
## 3 Fort.Atkinson WI
## 4 Madison WI
## 5 Marshfield WI
## 6 Milwaukee WI
## 7 Monroe WI
## 8 Superior WI
## 9 Wausau WI
## 10 Dubuque IA
## 11 St.Paul MN
## 12 Chicago ILThis last one is my favourite, because it gets rid of the two constituent columns location and state that we don’t need any more. The syntax is the name of the new column, a vector of columns to unite together, and (optionally) what to separate the joined-together values with. The default for that is an underscore, but here we want a space because that’s what the geocoder (coming up) expects.
\(\blacksquare\)
- Look up the latitudes and longitudes of these twelve places.
Solution
This is geocoding, with the disentangling afterwards that is (I hope) becoming familiar:
wisc %>%
rowwise() %>%
mutate(ll = list(geocode_OSM(lookup))) %>%
unnest_wider(ll) %>%
unnest_wider(coords) -> wisc
wisc## # A tibble: 12 x 5
## lookup query x y bbox
## <chr> <chr> <dbl> <dbl> <list>
## 1 Appleton WI Appleton WI -88.4 44.3 <bbox [4]>
## 2 Beloit WI Beloit WI -89.0 42.5 <bbox [4]>
## 3 Fort.Atkinson WI Fort.Atkinson WI -88.8 42.9 <bbox [4]>
## 4 Madison WI Madison WI -89.4 43.1 <bbox [4]>
## 5 Marshfield WI Marshfield WI -90.2 44.7 <bbox [4]>
## 6 Milwaukee WI Milwaukee WI -87.9 43.0 <bbox [4]>
## 7 Monroe WI Monroe WI -89.6 42.6 <bbox [4]>
## 8 Superior WI Superior WI -92.1 46.6 <bbox [4]>
## 9 Wausau WI Wausau WI -89.6 45.0 <bbox [4]>
## 10 Dubuque IA Dubuque IA -90.7 42.5 <bbox [4]>
## 11 St.Paul MN St.Paul MN -93.1 45.0 <bbox [4]>
## 12 Chicago IL Chicago IL -87.6 41.9 <bbox [4]>Yes, I forgot the rowwise as well the first time.
\(\blacksquare\)
- Obtain a Leaflet map of the area containing these twelve cities.
Solution
The usual:
The nice thing about this map is that you can play with it: zoom it (using the plus/minus on the map or your mouse wheel), or move it around by clicking and dragging. To identify the cities: well, the big ones are obvious, and you can zoom in to identify the others. (You have to zoom in quite a long way to find Monroe, and the geocoder seems to have found its airport, which is not actually in the city.)
I like identifying the cities with circles, but there are other possibilities, such as “icon markers” that look like Google map pins:
You can even attach text to the markers that appears when you click on them, but that’s farther than I would go here.
\(\blacksquare\)
37.6 The Cross-City Line
When I went to university (in Birmingham, England, a long time ago), I was very excited because I would be travelling to campus by train. My journey was on the Cross-City Line, a metro-type service with lots of stops short distances apart, but run in those days by diesel trains (the electrification came later).
A list of the stations on the line is in http://ritsokiguess.site/datafiles/cross-city.csv. There is one column in the data file, called station. We are going to draw a map of these.
- Read in and display (some of) the station names.
Solution
Nothing terribly unexpected here:
##
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
## station = col_character()
## )## # A tibble: 24 x 1
## station
## <chr>
## 1 Redditch
## 2 Alvechurch
## 3 Barnt Green
## 4 Longbridge
## 5 Northfield
## 6 King's Norton
## 7 Bournville
## 8 Selly Oak
## 9 Birmingham University
## 10 Five Ways
## # … with 14 more rows\(\blacksquare\)
- In preparation for geocoding, create a second column in the dataframe that consists of the station names with “station UK” on the end. (This is to improve the chances of the geocoder finding the actual railway station.)
Solution
I wrote this back into the original dataframe. Create a new one if you prefer:
## # A tibble: 24 x 2
## station longname
## <chr> <chr>
## 1 Redditch Redditch station UK
## 2 Alvechurch Alvechurch station UK
## 3 Barnt Green Barnt Green station UK
## 4 Longbridge Longbridge station UK
## 5 Northfield Northfield station UK
## 6 King's Norton King's Norton station UK
## 7 Bournville Bournville station UK
## 8 Selly Oak Selly Oak station UK
## 9 Birmingham University Birmingham University station UK
## 10 Five Ways Five Ways station UK
## # … with 14 more rows\(\blacksquare\)
- Look up the longitudes and latitudes of all the stations, organizing your dataframe so that they are visible.
Solution
Two steps: the first is to do the geocoding, and the second is to disentangle what comes back.
First, then:
## # A tibble: 24 x 3
## # Rowwise:
## station longname ll
## <chr> <chr> <list>
## 1 Redditch Redditch station UK <named list [3]>
## 2 Alvechurch Alvechurch station UK <named list [3]>
## 3 Barnt Green Barnt Green station UK <named list [3]>
## 4 Longbridge Longbridge station UK <named list [3]>
## 5 Northfield Northfield station UK <named list [3]>
## 6 King's Norton King's Norton station UK <named list [3]>
## 7 Bournville Bournville station UK <named list [3]>
## 8 Selly Oak Selly Oak station UK <named list [3]>
## 9 Birmingham University Birmingham University station UK <named list [3]>
## 10 Five Ways Five Ways station UK <named list [3]>
## # … with 14 more rowsThe longitudes and latitudes are hidden in the list-column that I called ll, so the second step is to get them out:
## # A tibble: 24 x 6
## station longname query x y bbox
## <chr> <chr> <chr> <dbl> <dbl> <list>
## 1 Redditch Redditch station UK Redditch station UK -1.94 52.3 <bbox [4…
## 2 Alvechurch Alvechurch station UK Alvechurch station UK -1.97 52.3 <bbox [4…
## 3 Barnt Green Barnt Green station UK Barnt Green station UK -1.99 52.4 <bbox [4…
## 4 Longbridge Longbridge station UK Longbridge station UK -1.98 52.4 <bbox [4…
## 5 Northfield Northfield station UK Northfield station UK -1.97 52.4 <bbox [4…
## 6 King's Norton King's Norton station UK King's Norton station UK -1.93 52.4 <bbox [4…
## 7 Bournville Bournville station UK Bournville station UK -1.94 52.4 <bbox [4…
## 8 Selly Oak Selly Oak station UK Selly Oak station UK -1.94 52.4 <bbox [4…
## 9 Birmingham University Birmingham University station … Birmingham University station … -1.93 52.5 <bbox [4…
## 10 Five Ways Five Ways station UK Five Ways station UK -1.91 52.5 <bbox [4…
## # … with 14 more rowsThe two unnest_widers are because the longitudes and latitudes are hidden inside a thing called coords which is itself nested within ll. Do the first unnest_wider, and see what you have. This will tell you that you need to do another one.
The values seem reasonable; this is the UK, most of which is slightly west of the Greenwich meridian, and the latitudes look sensible given that the UK is north of southern Ontario.
\(\blacksquare\)
- Make a Leaflet map of the stations. Use circle markers or the “pin” markers as you prefer.
Solution
I used the pin markers (with addMarkers), though addCircleMarkers is as good. The code for drawing the map is always the same; the work here is in setting up the geocoding:
This seems to extend across the city of Birmingham, as it should. There are quite a lot of stations, so the pins overlap each other. Zoom in to see them in a bit more detail, or zoom out to orient yourself if your UK geography needs some work.
\(\blacksquare\)
- Zoom in to see whether the geocoding did indeed find each of the stations. Comment briefly on what you find.
Solution
The stations go south to north, so the most southerly one you find should be Redditch and the most northerly is Lichfield Trent Valley.
If you zoom in enough, you’ll see where the railway line goes (a grey line). The points seem to be mainly close to it. But if you zoom in a bit more, some of the pins are right on the railway (such as Alvechurch), and some of them, like Redditch and Barnt Green, are a bit off, because the geocoder found the centre of the place rather than its railway station. This continues as you go north; Northfield and King’s Norton are right where they should be, but Bournville is not (Bournville station is about halfway between where you see Bournville and Stirchley on the map.) Likewise, Gravelly Hill station is right where it should be, but Aston is not.37
Extra: geocode_OSM uses a free geocoder called Nominatim. This has some options. The defaults are to return only the first “hit”, and to return just the coordinates and the “bounding box”. These can be changed. Let’s see what we can find for Aston:
tibble(where = "Aston UK") %>%
mutate(info = list(geocode_OSM(where, return.first.only = FALSE,
details = TRUE))) -> d
d ## # A tibble: 1 x 2
## where info
## <chr> <list>
## 1 Aston UK <list [10]>There are now 10 things returned. Let’s unnest this and see what we have:
## # A tibble: 10 x 13
## where query coords bbox place_id osm_type osm_id place_rank display_name class type importance icon
## <chr> <chr> <list> <lis> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Aston… Aston… <dbl … <bbo… 9814552 node 96663… 18 Aston, Birmin… place town 0.5141099… https…
## 2 Aston… Aston… <dbl … <bbo… 65583750 node 59202… 30 Aston, Lovers… rail… stat… 0.4415759… https…
## 3 Aston… Aston… <dbl … <bbo… 3972412 node 48708… 19 Aston, Flints… place vill… 0.4412913… https…
## 4 Aston… Aston… <dbl … <bbo… 126341 node 26127… 19 Aston, West O… place vill… 0.4268930… https…
## 5 Aston… Aston… <dbl … <bbo… 118240 node 23777… 19 Aston, East H… place vill… 0.385 https…
## 6 Aston… Aston… <dbl … <bbo… 17080467 node 17098… 19 Aston, Cheshi… place vill… 0.385 https…
## 7 Aston… Aston… <dbl … <bbo… 2586055… relation 35941… 20 Aston, East H… boun… admi… 0.36 https…
## 8 Aston… Aston… <dbl … <bbo… 2580670… relation 14404… 20 Aston, Cheshi… boun… admi… 0.36 https…
## 9 Aston… Aston… <dbl … <bbo… 2427860 node 35624… 20 Aston, Claver… place haml… 0.36 https…
## 10 Aston… Aston… <dbl … <bbo… 5573721 node 59965… 20 Aston, Maer, … place haml… 0.36 https…There are 10 locations it found matching “Aston UK”, and for each of those there is the information you see, a total of 12 columns’ worth in addition to the name of the place we are looking up. Perhaps the most interesting are the columns class and type near the end:
## # A tibble: 10 x 3
## where class type
## <chr> <chr> <chr>
## 1 Aston UK place town
## 2 Aston UK railway station
## 3 Aston UK place village
## 4 Aston UK place village
## 5 Aston UK place village
## 6 Aston UK place village
## 7 Aston UK boundary administrative
## 8 Aston UK boundary administrative
## 9 Aston UK place hamlet
## 10 Aston UK place hamletOh look, the second one is the station.
This makes me think that with sufficient patience we could do this for all our places, and pick out the one that is the station:
##
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
## station = col_character()
## )stations %>%
mutate(longname = str_c(station, " UK")) %>%
rowwise() %>%
mutate(ll = list(geocode_OSM(longname,
return.first.only = FALSE,
details = TRUE))) -> stationsstations %>% unnest(ll) %>%
unnest_wider(ll) %>%
select(station, coords, class, type) %>%
filter(class == "railway", type == "station") %>%
unnest_wider(coords) -> d## New names:
## * `` -> ...1
## New names:
## * `` -> ...1
## New names:
## * `` -> ...1
## New names:
## * `` -> ...1
## New names:
## * `` -> ...1
## New names:
## * `` -> ...1
## New names:
## * `` -> ...1
## New names:
## * `` -> ...1
## New names:
## * `` -> ...1
## New names:
## * `` -> ...1## # A tibble: 22 x 5
## station x y class type
## <chr> <dbl> <dbl> <chr> <chr>
## 1 Redditch -1.95 52.3 railway station
## 2 Alvechurch -1.97 52.3 railway station
## 3 Barnt Green -1.99 52.4 railway station
## 4 Longbridge -1.98 52.4 railway station
## 5 Northfield -1.97 52.4 railway station
## 6 King's Norton -1.93 52.4 railway station
## 7 Bournville -1.93 52.4 railway station
## 8 Selly Oak -1.94 52.4 railway station
## 9 Five Ways -1.91 52.5 railway station
## 10 Birmingham New Street -1.90 52.5 railway station
## # … with 12 more rowsAlmost. We’re missing University and Lichfield City stations, but it looks as if we got the rest:
If you zoom in, you’ll see that the ones we got are all the actual stations, and not the area from which the station takes its name.
\(\blacksquare\)
I knew this existed, but I couldn’t remember what it was called, which made it hard to search for. My first port of call was
na_if, which converts values toNA, the opposite of what I wanted. But from its See Also I gotna_replace, and from the See Also of that, I found out whatcoalescedoes.↩If you’re a soccer fan, this Aston is what Aston Villa is named after. See if you can find the team’s stadium Villa Park on your map.↩