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setup

Open Notebook

Open a new R Notebook to work in.

File > New File > R Notebook

Name (eg. Vectors) and save it

Load libraries

Load the libraries we’ll be using for this section of the workshop

library(sf)
library(ggplot2)
library(dplyr)
library(spData)

Vector data

Geographic vector data model is based on points, usually located within a coordinate reference system (CRS).

Most point geometries contain only two dimensions \(x\) & \(y\) but 3 dimensional CRSs contain an additional \(z\) value -> height above sea level.

Coordinates consist of two numbers representing distance from an origin in the \(x\) & \(y\) dimensions.

simple features

The Simple Features data model is a widely supported model that underlies vector data structures in many GIS applications.

It is a hierarchical model that represents a wide range of geometry types.

  • single points -> self-standing features (e.g. sampling location)
  • Points can be linked together to form more complex geometries:
    • lines
    • polygons
  • ‘multi’ versions of each represent groups of features of the same type into a single feature.
  • geometry collections, which can contain multiple geometry types in a single object.

*Figure 2.2: The subset of the Simple Features class hierarchy supported by sf. Image source: https://geocompr.robinlovelace.net/figures/sf-classes.png*

Of 68 geometry types supported by the specification, only 7 are used in the vast majority of geographic research

package sf

sf is an R package providing a class system for geographic vector data using the simple features data model.

Supercedes and combines the functionality of three previously used packages: - sp for the class system, - rgdal for reading and writing data, - rgeos for spatial operations undertaken by GEOS in a single, cohesive whole.

Benefits of sf vs sp classes

  • Fast reading and writing of data
  • Enhanced plotting performance
  • sf objects can be treated as data frames in most operations
  • sf functions can be combined using the pipe (%>%) operator and works well with the tidyverse collection of R packages
  • sf function names are relatively consistent and intuitive (all begin with st_)
  • geometry a list in geometry column regardless of geometry type but can easily be transformed to a Spatial class used in sp using function as_Spatial().

simple feature anatomy

Simple feature objects are hierarchically organised as follows:

  • sf: simple feature, data.frame with spatial list-column (geom or geometry) as well as additional data associated with the spatial geometries.

  • sfc: simple feature column. A list-column containing multiple geometries + information about the coordinate reference system.

  • sfg: simple feature geometry. a single simple feature geometry

Creating sf vector data

sf provides a number of function for creating simple feature geometries, bringing multiple geometries together in a simple feature column.

a single point feature

To create single points, we can use function sf::st_point() and supply a vector of x & y coordinates as argument x

st_point(x = c(0,0))

multiple point feature

For multiple points in a single geometry, we can use function sf::st_multipoint() and supply a two column numeric matrix with x & y coordinates of points in rows.

st_multipoint(x = matrix(c(0, 0, 2, 0), ncol = 2, byrow = T))

Let’s assign this to an object:

points <- st_multipoint(x = matrix(c(0, 0, 2, 0), ncol = 2, byrow = T))

We can check the class of the points we just created:

class(points)
[1] "XY"         "MULTIPOINT" "sfg"

And we can also quickly plot geometries to inspect them:

plot(points)

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line feature

Similarly, we can create line features using function sf::st_linestring() and supplying a two column numeric matrix with x & y coordinates of points in rows.

line <- st_linestring(x = matrix(c(-1, -2, -0.5, -3, 2.5, -3, 3, -2), 
                                 ncol = 2, byrow = T))

line
plot(line)

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Combining sfgs into an sfc

We can then combine our geometries into an simple feature list-column (sfc).

sfc <- st_sfc(points, line)
class(sfc)
[1] "sfc_GEOMETRY" "sfc"         
plot(sfc)

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Creating an sf and adding attribute data

We can now add some attribute data, eg names for the shapes we created, and create a simple feature (sf).

sf <- st_sf(shape = c("eyes", "mouth"), geom = sfc)
sf
Simple feature collection with 2 features and 1 field
geometry type:  GEOMETRY
dimension:      XY
bbox:           xmin: -1 ymin: -3 xmax: 3 ymax: 0
epsg (SRID):    NA
proj4string:    NA
  shape                           geom
1  eyes          MULTIPOINT (0 0, 2 0)
2 mouth LINESTRING (-1 -2, -0.5 -3,...
class(sf)
[1] "sf"         "data.frame"
plot(sf)

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Exercise

1) add a nose!

Create a nose geometry, combine all the shapes into a single sf and then plot the face.

Working with sf vector data

world dataset in pkg spData

Package spData provides spatial datasets in a variety of formats, including a number of sf data.

One of these is the spData::world data set, containing the current boundaries of countries and including additional demographic, geographic attribute data

We can get more information on the data containing through r help

?world

Let’s have a look at the data

world
Simple feature collection with 177 features and 10 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs
First 10 features:
   iso_a2        name_long     continent region_un          subregion
1      FJ             Fiji       Oceania   Oceania          Melanesia
2      TZ         Tanzania        Africa    Africa     Eastern Africa
3      EH   Western Sahara        Africa    Africa    Northern Africa
4      CA           Canada North America  Americas   Northern America
5      US    United States North America  Americas   Northern America
6      KZ       Kazakhstan          Asia      Asia       Central Asia
7      UZ       Uzbekistan          Asia      Asia       Central Asia
8      PG Papua New Guinea       Oceania   Oceania          Melanesia
9      ID        Indonesia          Asia      Asia South-Eastern Asia
10     AR        Argentina South America  Americas      South America
                type    area_km2       pop  lifeExp gdpPercap
1  Sovereign country    19289.97    885806 69.96000  8222.254
2  Sovereign country   932745.79  52234869 64.16300  2402.099
3      Indeterminate    96270.60        NA       NA        NA
4  Sovereign country 10036042.98  35535348 81.95305 43079.143
5            Country  9510743.74 318622525 78.84146 51921.985
6  Sovereign country  2729810.51  17288285 71.62000 23587.338
7  Sovereign country   461410.26  30757700 71.03900  5370.866
8  Sovereign country   464520.07   7755785 65.23000  3709.082
9  Sovereign country  1819251.33 255131116 68.85600 10003.089
10 Sovereign country  2784468.59  42981515 76.25200 18797.548
                             geom
1  MULTIPOLYGON (((180 -16.067...
2  MULTIPOLYGON (((33.90371 -0...
3  MULTIPOLYGON (((-8.66559 27...
4  MULTIPOLYGON (((-122.84 49,...
5  MULTIPOLYGON (((-122.84 49,...
6  MULTIPOLYGON (((87.35997 49...
7  MULTIPOLYGON (((55.96819 41...
8  MULTIPOLYGON (((141.0002 -2...
9  MULTIPOLYGON (((141.0002 -2...
10 MULTIPOLYGON (((-68.63401 -...

Looks like a normal data.frame right? Well it is, apart from it’s got a CRS attached to it and an extra (geom) column containing the geometries.

Let’s have a look at it with function sf::st_crs():

st_crs(world)
Coordinate Reference System:
  EPSG: 4326 
  proj4string: "+proj=longlat +datum=WGS84 +no_defs"

Manipulating sf objects

As discussed the data is effectively a data.frame, with an additional geom column containing the geographic data. As such it can be manipulated as any other data.frame.

getting attribute information

names(world)
 [1] "iso_a2"    "name_long" "continent" "region_un" "subregion"
 [6] "type"      "area_km2"  "pop"       "lifeExp"   "gdpPercap"
[11] "geom"

indexing

We can index sf objects like any other data.frame.

E.g. we can index columns using the $ notation:

world$iso_a2
  [1] FJ   TZ   EH   CA   US   KZ   UZ   PG   ID   AR   CL   CD   SO   KE  
 [15] SD   TD   HT   DO   RU   BS   FK   <NA> GL   TF   TL   ZA   LS   MX  
 [29] UY   BR   BO   PE   CO   PA   CR   NI   HN   SV   GT   BZ   VE   GY  
 [43] SR   <NA> EC   PR   JM   CU   ZW   BW   NA   SN   ML   MR   BJ   NE  
 [57] NG   CM   TG   GH   CI   GN   GW   LR   SL   BF   CF   CG   GA   GQ  
 [71] ZM   MW   MZ   SZ   AO   BI   IL   LB   MG   PS   GM   TN   DZ   JO  
 [85] AE   QA   KW   IQ   OM   VU   KH   TH   LA   MM   VN   KP   KR   MN  
 [99] IN   BD   BT   NP   PK   AF   TJ   KG   TM   IR   SY   AM   SE   BY  
[113] UA   PL   AT   HU   MD   RO   LT   LV   EE   DE   BG   GR   TR   AL  
[127] HR   CH   LU   BE   NL   PT   ES   IE   NC   SB   NZ   AU   LK   CN  
[141] TW   IT   DK   GB   IS   AZ   GE   PH   MY   BN   SI   FI   SK   CZ  
[155] ER   JP   PY   YE   SA   AQ   <NA> CY   MA   EG   LY   ET   DJ   <NA>
[169] UG   RW   BA   MK   RS   ME   XK   TT   SS  
173 Levels: AE AF AL AM AO AQ AR AT AU AZ BA BD BE BF BG BI BJ BN BO ... ZW

Or by using [,] indexing, in this case, by supplying a vector of column names to the second argument of the square brackets.

world[, c("iso_a2", "name_long")]
Simple feature collection with 177 features and 2 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs
First 10 features:
   iso_a2        name_long                           geom
1      FJ             Fiji MULTIPOLYGON (((180 -16.067...
2      TZ         Tanzania MULTIPOLYGON (((33.90371 -0...
3      EH   Western Sahara MULTIPOLYGON (((-8.66559 27...
4      CA           Canada MULTIPOLYGON (((-122.84 49,...
5      US    United States MULTIPOLYGON (((-122.84 49,...
6      KZ       Kazakhstan MULTIPOLYGON (((87.35997 49...
7      UZ       Uzbekistan MULTIPOLYGON (((55.96819 41...
8      PG Papua New Guinea MULTIPOLYGON (((141.0002 -2...
9      ID        Indonesia MULTIPOLYGON (((141.0002 -2...
10     AR        Argentina MULTIPOLYGON (((-68.63401 -...

Note that although we selected only two columns, the geom column is still retained.

And we can index rows by supplying eg the row number(s) required to the first argument of the square brackets.

world[1,]
Simple feature collection with 1 feature and 10 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -180 ymin: -18.28799 xmax: 180 ymax: -16.02088
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs
  iso_a2 name_long continent region_un subregion              type
1     FJ      Fiji   Oceania   Oceania Melanesia Sovereign country
  area_km2    pop lifeExp gdpPercap                           geom
1 19289.97 885806   69.96  8222.254 MULTIPOLYGON (((180 -16.067...

dplyr functions and piping

But even nicer is that we can use dplyr functions with sfs. Especially exciting is the ability to set up pipelines using the dplyr pipe (%>%).

selecting

We can pipe the spData::world sf into function dplyr::select() to select specific columns. Note that in dplyr functions, you can use column names bare (ie without "...").

world %>% select(iso_a2, name_long)
Simple feature collection with 177 features and 2 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs
First 10 features:
   iso_a2        name_long                           geom
1      FJ             Fiji MULTIPOLYGON (((180 -16.067...
2      TZ         Tanzania MULTIPOLYGON (((33.90371 -0...
3      EH   Western Sahara MULTIPOLYGON (((-8.66559 27...
4      CA           Canada MULTIPOLYGON (((-122.84 49,...
5      US    United States MULTIPOLYGON (((-122.84 49,...
6      KZ       Kazakhstan MULTIPOLYGON (((87.35997 49...
7      UZ       Uzbekistan MULTIPOLYGON (((55.96819 41...
8      PG Papua New Guinea MULTIPOLYGON (((141.0002 -2...
9      ID        Indonesia MULTIPOLYGON (((141.0002 -2...
10     AR        Argentina MULTIPOLYGON (((-68.63401 -...

filtering

We can also filter rows using function dplyr::filter(). Let’s try and get the row for Greece, which is represented by iso code "GR":

world %>% filter(iso_a2 == "GR")
Simple feature collection with 1 feature and 10 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: 20.15002 ymin: 34.91999 xmax: 26.6042 ymax: 41.8269
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs
  iso_a2 name_long continent region_un       subregion              type
1     GR    Greece    Europe    Europe Southern Europe Sovereign country
  area_km2      pop  lifeExp gdpPercap                           geom
1 131964.6 10892413 81.38537  24081.63 MULTIPOLYGON (((26.29 35.29...

summarising

We can even summarise our attribute data using, for example, function base::summary().

summary(world)
     iso_a2          name_long           continent 
 AE     :  1   Afghanistan:  1   Africa       :51  
 AF     :  1   Albania    :  1   Asia         :47  
 AL     :  1   Algeria    :  1   Europe       :39  
 AM     :  1   Angola     :  1   North America:18  
 AO     :  1   Antarctica :  1   South America:13  
 (Other):168   Argentina  :  1   Oceania      : 7  
 NA's   :  4   (Other)    :171   (Other)      : 2  
                   region_un            subregion                 type    
 Africa                 :51   Western Asia   :18   Country          : 11  
 Americas               :31   Eastern Africa :16   Dependency       :  4  
 Antarctica             : 1   Western Africa :15   Disputed         :  1  
 Asia                   :47   South America  :13   Indeterminate    :  3  
 Europe                 :39   Southern Europe:12   Sovereign country:158  
 Oceania                : 7   Eastern Europe :10                          
 Seven seas (open ocean): 1   (Other)        :93                          
    area_km2             pop               lifeExp        gdpPercap       
 Min.   :    2417   Min.   :5.630e+04   Min.   :50.62   Min.   :   597.1  
 1st Qu.:   46185   1st Qu.:3.755e+06   1st Qu.:64.96   1st Qu.:  3752.4  
 Median :  185004   Median :1.040e+07   Median :72.87   Median : 10734.1  
 Mean   :  832558   Mean   :4.282e+07   Mean   :70.85   Mean   : 17106.0  
 3rd Qu.:  621860   3rd Qu.:3.075e+07   3rd Qu.:76.78   3rd Qu.: 24232.7  
 Max.   :17018507   Max.   :1.364e+09   Max.   :83.59   Max.   :120860.1  
                    NA's   :10          NA's   :10      NA's   :17        
            geom    
 MULTIPOLYGON :177  
 epsg:4326    :  0  
 +proj=long...:  0

extracting geometries

We can extract the geometry list-column from an sf with function sf::st_geometry.

st_geometry(world) 
Geometry set for 177 features 
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -180 ymin: -90 xmax: 180 ymax: 83.64513
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs
First 5 geometries:

extracting coordinates

We can also extract a matrix of coordinates of an sf possibly followed by integer indicators L1,…,L3 that point out to which structure the coordinate belongs:

  • for POINT this is absent (each coordinate is a feature)
  • for LINESTRING L1 refers to the feature
  • for MULTIPOLYGON
    • L1 refers to the main ring or holes
    • L2 to the ring id in the MULTIPOLYGON,
    • and L3 to the simple feature.
world %>% filter(iso_a2 == "GR") %>% st_coordinates()
             X        Y L1 L2 L3
 [1,] 26.29000 35.29999  1  1  1
 [2,] 26.16500 35.00500  1  1  1
 [3,] 24.72498 34.91999  1  1  1
 [4,] 24.73501 35.08499  1  1  1
 [5,] 23.51498 35.27999  1  1  1
 [6,] 23.69998 35.70500  1  1  1
 [7,] 24.24667 35.36802  1  1  1
 [8,] 25.02502 35.42500  1  1  1
 [9,] 25.76921 35.35402  1  1  1
[10,] 25.74502 35.18000  1  1  1
[11,] 26.29000 35.29999  1  1  1
[12,] 22.95238 41.33799  1  2  1
[13,] 23.69207 41.30908  1  2  1
[14,] 24.49264 41.58390  1  2  1
[15,] 25.19720 41.23449  1  2  1
[16,] 26.10614 41.32890  1  2  1
[17,] 26.11704 41.82690  1  2  1
[18,] 26.60420 41.56211  1  2  1
[19,] 26.29460 40.93626  1  2  1
[20,] 26.05694 40.82412  1  2  1
[21,] 25.44768 40.85255  1  2  1
[22,] 24.92585 40.94706  1  2  1
[23,] 23.71481 40.68713  1  2  1
[24,] 24.40800 40.12499  1  2  1
[25,] 23.89997 39.96201  1  2  1
[26,] 23.34300 39.96100  1  2  1
[27,] 22.81399 40.47601  1  2  1
[28,] 22.62630 40.25656  1  2  1
[29,] 22.84975 39.65931  1  2  1
[30,] 23.35003 39.19001  1  2  1
[31,] 22.97310 38.97090  1  2  1
[32,] 23.53002 38.51000  1  2  1
[33,] 24.02502 38.21999  1  2  1
[34,] 24.04001 37.65501  1  2  1
[35,] 23.11500 37.92001  1  2  1
[36,] 23.40997 37.40999  1  2  1
[37,] 22.77497 37.30501  1  2  1
[38,] 23.15423 36.42251  1  2  1
[39,] 22.49003 36.41000  1  2  1
[40,] 21.67003 36.84499  1  2  1
[41,] 21.29501 37.64499  1  2  1
[42,] 21.12003 38.31032  1  2  1
[43,] 20.73003 38.76999  1  2  1
[44,] 20.21771 39.34023  1  2  1
[45,] 20.15002 39.62500  1  2  1
[46,] 20.61500 40.11001  1  2  1
[47,] 20.67500 40.43500  1  2  1
[48,] 20.99999 40.58000  1  2  1
[49,] 21.02004 40.84273  1  2  1
[50,] 21.67416 40.93127  1  2  1
[51,] 22.05538 41.14987  1  2  1
[52,] 22.59731 41.13049  1  2  1
[53,] 22.76177 41.30480  1  2  1
[54,] 22.95238 41.33799  1  2  1

extracting crs

We can also retrieve the coordinate reference system from sf or sfc object with function sf::st_crs()

world %>% st_crs()
Coordinate Reference System:
  EPSG: 4326 
  proj4string: "+proj=longlat +datum=WGS84 +no_defs"

transforming CRSs

We can transform the CRS of an sf by using function sf::st_transform(). Let’s transform the world CRS from WGS 84 to the Mercator projection (epsg:3785).

world_merc <- world %>% st_transform(crs = 3785)
world_merc
Simple feature collection with 177 features and 10 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -20037510 ymin: -20801250 xmax: 20037510 ymax: 18440000
epsg (SRID):    3785
proj4string:    +proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +wktext +no_defs
First 10 features:
   iso_a2        name_long     continent region_un          subregion
1      FJ             Fiji       Oceania   Oceania          Melanesia
2      TZ         Tanzania        Africa    Africa     Eastern Africa
3      EH   Western Sahara        Africa    Africa    Northern Africa
4      CA           Canada North America  Americas   Northern America
5      US    United States North America  Americas   Northern America
6      KZ       Kazakhstan          Asia      Asia       Central Asia
7      UZ       Uzbekistan          Asia      Asia       Central Asia
8      PG Papua New Guinea       Oceania   Oceania          Melanesia
9      ID        Indonesia          Asia      Asia South-Eastern Asia
10     AR        Argentina South America  Americas      South America
                type    area_km2       pop  lifeExp gdpPercap
1  Sovereign country    19289.97    885806 69.96000  8222.254
2  Sovereign country   932745.79  52234869 64.16300  2402.099
3      Indeterminate    96270.60        NA       NA        NA
4  Sovereign country 10036042.98  35535348 81.95305 43079.143
5            Country  9510743.74 318622525 78.84146 51921.985
6  Sovereign country  2729810.51  17288285 71.62000 23587.338
7  Sovereign country   461410.26  30757700 71.03900  5370.866
8  Sovereign country   464520.07   7755785 65.23000  3709.082
9  Sovereign country  1819251.33 255131116 68.85600 10003.089
10 Sovereign country  2784468.59  42981515 76.25200 18797.548
                             geom
1  MULTIPOLYGON (((20037508 -1...
2  MULTIPOLYGON (((3774144 -10...
3  MULTIPOLYGON (((-964649 320...
4  MULTIPOLYGON (((-13674486 6...
5  MULTIPOLYGON (((-13674486 6...
6  MULTIPOLYGON (((9724867 631...
7  MULTIPOLYGON (((6230351 505...
8  MULTIPOLYGON (((15696072 -2...
9  MULTIPOLYGON (((15696072 -2...
10 MULTIPOLYGON (((-7640303 -6...

Plotting sf

sf has reasonable native plotting behaviour which can be useful for quick checks of your data.

world %>% plot()
Warning: plotting the first 9 out of 10 attributes; use max.plot = 10 to
plot all

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world_merc %>% plot()
Warning: plotting the first 9 out of 10 attributes; use max.plot = 10 to
plot all

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We can easily select and plot information for a single variable

world %>% select(lifeExp) %>% plot()

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We can also extract and just plot out the geometries.

world %>% st_geometry() %>% plot()

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Exercises

2) What are the coordinates for the 10th point in the Mexico polygon?

3) How about in CRS Mexico ITRF92 / UTM zone 15N

4) Are these coordinates projected or not? Can you tell by just looking at the spatial information in the transformed sf object?

A working example

Molecular data on salamanders

The data we will work with are from the paper: Tracking climate change in a dispersal‐limited species: reduced spatial and genetic connectivity in a montane salamander (2013) https://doi.org/10.1111/mec.12310

The researchers where interested in examining how climate and landscape features in montane regions affect population genetic structure of montane salamander Pseudoeurycea leprosa.

To address this they used ecological niche modelling (ENM) and measured spatial connectivity and gene flow across extant populations of P. leprosa in the Trans‐Mexican Volcanic Belt (TVB).

To do this they had to combine their molecular data with environmental data. This is what we will try and reproduce during this workshop.

I’ve created a .csv of the published data containing the following fields, and saved it in file data/csv/salamander_mol.csv.

field description
id sample ID
locality sample locality
n sample size
mountain_chain mountain chain
region region
latitude latitude
longitude longitude
na average number of alleles
he expected heterozygosity
ar allelic richness
par private allelic richness

Let’s read it in using function readr::read_csv(). I’m also using function here::here() to specify the paths in a way that is both portable and will work across different systems.

mol_df <- readr::read_csv(here::here("data", "csv", "salamander_mol.csv"))

Note also the use of ::. This allows to call a function without loading the library (so long as the package has been installed).

Now, let’s have a look at the data we just loaded.

mol_df
# A tibble: 15 x 11
      id locality       n mountain_chain   region latitude longitude    na
   <int> <chr>      <int> <chr>            <chr>     <dbl>     <dbl> <dbl>
 1     1 Nevado de…    12 Nevado de Toluca Centr…     19.2     -99.8  5.44
 2     2 Texcalyac…    29 Sierra de las C… Centr…     19.1     -99.5  8.22
 3     3 Desierto …     7 Sierra de las C… Centr…     19.3     -99.3  4.44
 4     4 Ajusco         8 Sierra de las C… Centr…     19.2     -99.3  4.22
 5     8 Calpan        34 Sierra Nevada    Centr…     19.1     -98.6 11.9 
 6     9 Atzompa       43 Sierra Nevada    Centr…     19.2     -98.6 10.3 
 7    10 Llano Gra…    15 Sierra Nevada (… Centr…     19.3     -98.7  7.78
 8    11 Rio Frio      27 Sierra Nevada (… Centr…     19.4     -98.7  7.56
 9    12 Nanacamil…    14 Sierra Nevada (… Centr…     19.5     -98.6  6.22
10    13 MalincheS      8 Malinche         Centr…     19.2     -98.0  5.00
11    14 MalincheW     17 Malinche         Centr…     19.3     -98.1  6.67
12    16 MalincheE     13 Malinche         Centr…     19.2     -98.0  6.11
13    17 Texmalaqu…     8 Pico de Orizaba  South…     18.9     -97.3  6.00
14    18 Xometla       16 Pico de Orizaba  South…     19.0     -97.2  9.11
15    19 Vigas         48 Cofre de Perote  North…     19.6     -97.1 11.8 
# ... with 3 more variables: he <dbl>, ar <dbl>, par <dbl>

Converting lat/lon data to simple features

Our data contains geographical coordinates in lat/lot decimal degrees. We can convert these sampling locations to sf geographic points using function sf::st_as_sf(). We can also assign a CRS, in this case we’ll assign WGS 84 which corresponds to epsg:4326.

mol_sf <- sf::st_as_sf(mol_df, coords = c("longitude", "latitude"), 
                       crs = 4326)
mol_sf
Simple feature collection with 15 features and 9 fields
geometry type:  POINT
dimension:      XY
bbox:           xmin: -99.84806 ymin: 18.94194 xmax: -97.09056 ymax: 19.63083
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs
# A tibble: 15 x 10
      id locality       n mountain_chain   region    na    he    ar    par
   <int> <chr>      <int> <chr>            <chr>  <dbl> <dbl> <dbl>  <dbl>
 1     1 Nevado de…    12 Nevado de Toluca Centr…  5.44 0.620  4.56 0.350 
 2     2 Texcalyac…    29 Sierra de las C… Centr…  8.22 0.660  5.14 0.500 
 3     3 Desierto …     7 Sierra de las C… Centr…  4.44 0.590  4.44 0.180 
 4     4 Ajusco         8 Sierra de las C… Centr…  4.22 0.490  4.05 0.0200
 5     8 Calpan        34 Sierra Nevada    Centr… 11.9  0.730  6.48 0.290 
 6     9 Atzompa       43 Sierra Nevada    Centr… 10.3  0.690  5.79 0.0800
 7    10 Llano Gra…    15 Sierra Nevada (… Centr…  7.78 0.650  5.80 0.250 
 8    11 Rio Frio      27 Sierra Nevada (… Centr…  7.56 0.570  4.77 0.130 
 9    12 Nanacamil…    14 Sierra Nevada (… Centr…  6.22 0.590  4.91 0.100 
10    13 MalincheS      8 Malinche         Centr…  5.00 0.580  4.69 0.0900
11    14 MalincheW     17 Malinche         Centr…  6.67 0.600  4.76 0.0600
12    16 MalincheE     13 Malinche         Centr…  6.11 0.560  4.73 0.210 
13    17 Texmalaqu…     8 Pico de Orizaba  South…  6.00 0.710  5.64 0.910 
14    18 Xometla       16 Pico de Orizaba  South…  9.11 0.830  6.86 0.490 
15    19 Vigas         48 Cofre de Perote  North… 11.8  0.660  5.75 1.31  
# ... with 1 more variable: geometry <POINT [°]>

Plotting sf with ggplot2

Another great new feature of sf is that ggplot2 provides a dedicated function, ggplot2::geom_sf(), for mapping sf.

Plotting sampling locations

Let’s plot the sampling points we just specified.

mol_sf %>% ggplot() +
  geom_sf()

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Although this doesn’t look AMAZING (yet), the coordinates are positioned correctly in space. And it also means we have the full power of ggplot2 to add more information and customise the look of our maps.

For example, maybe we want to colour the points according to the number of alleles in the population.

mol_sf %>% ggplot() +
  geom_sf(aes(colour = na))

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Or maybe we want to identify points by region

mol_sf %>% ggplot() +
  geom_sf(aes(colour = region))

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The default geom_sf() assumes we are plotting polygons, hence the odd legend displaying both a colour (outline) and a fill key. To get it to plot an appropriate legend for points we need to include show.legend = "point" in geom_sf.

mol_sf %>% ggplot() +
  geom_sf(aes(colour = region), show.legend = "point")

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Locating the study area

We might also want to include a plot of the study area, and located in the context of the whole country.

Country polygon

We can source country boundaries for Mexico from the spData::world sf.

mx <- world %>% filter(name_long == "Mexico")
mx
Simple feature collection with 1 feature and 10 fields
geometry type:  MULTIPOLYGON
dimension:      XY
bbox:           xmin: -117.1278 ymin: 14.53883 xmax: -86.81198 ymax: 32.72083
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs
  iso_a2 name_long     continent region_un       subregion
1     MX    Mexico North America  Americas Central America
               type area_km2       pop lifeExp gdpPercap
1 Sovereign country  1969480 124221600  76.753   16622.6
                            geom
1 MULTIPOLYGON (((-117.1278 3...

Study area bounding box

Now, we also need to get the bounding box of our study area. We can use sf::st_bbox()

study_bbox <- mol_sf %>% sf::st_bbox()
study_bbox
     xmin      ymin      xmax      ymax 
-99.84806  18.94194 -97.09056  19.63083

This just returns the coordinates specifying the boundaries of our sf in each dimension. We can turn this into a rectangular polygon in an sfc with function sf::st_as_sfc.

study_bbox <- study_bbox %>% st_as_sfc()
study_bbox
Geometry set for 1 feature 
geometry type:  POLYGON
dimension:      XY
bbox:           xmin: -99.84806 ymin: 18.94194 xmax: -97.09056 ymax: 19.63083
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs

Let’s plot all this together:

ggplot() + 
    geom_sf(data = mx, colour = "black", fill = "lightgrey") +
    geom_sf(data = study_bbox, colour = "black", fill = "white")

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Still kinda ugly. Let’s try making the panel background a light blue.

p <- ggplot() + 
    theme(panel.background = 
              element_rect(fill = "lightblue"))
p

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p + 
    geom_sf(data = mx, colour = "black", fill = "lightgrey") +
    geom_sf(data = study_bbox, colour = "black", fill = "white")

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in CRS Mexico ITRF92 / UTM zone 15N

mx_utm15 <- st_transform(mx, crs = 4488)

p + 
    geom_sf(data = mx_utm15, colour = "black", fill = "lightgrey") +
    geom_sf(data = study_bbox, colour = "black", fill = "white")

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Before sf_geom, we could still plot geographic data. However, it took a lot more code to do so. Here’s what we’d need to code the first Mexico plot:

mx_coords <- st_coordinates(mx) %>% as.data.frame()
bbox_coords <- st_coordinates(study_bbox) %>% as.data.frame()

p + 
    geom_polygon(data = mx_coords, aes(x = X, y = Y), 
                 colour = "black", fill = "lightgrey") +
    geom_polygon(data = bbox_coords, aes(x = X, y = Y), 
                 colour = "black", fill = "white") +
    coord_quickmap()

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So, firstly we needed our data to be in a data.frame with a column for each of the x and y coordinates. Then we would need to use the appropriate geom_*() function according to the shape we are trying to plot (in this case geom_polygon(). If we wanted to plot points we would use geom_point()). We need to specify the columns that contain the x & y coordinates and finally, we also need to include coord_quickmap() which projects our points geographically.

That’s a lot more work that’s handled automatically by geom_sf. Most importantly, when overlaying shapes, ggplot2 has no idea about projections!

If you remember, in our previous UTM 15 example, we only transformed the first layer we plotted (ie mx to mx_utm15). When study_bbox was plotted subsequently, it’s CRS was automatically transformed to that of the mx_utm15.

If we try the same with geom_polygon, the coordinates for the two layers are now in completely different CRSs and the study bounding box does not even show up on the map!

mx_utm15_coords <- st_coordinates(mx_utm15) %>% as.data.frame()
bbox_coords <- st_coordinates(study_bbox) %>% as.data.frame()

p + 
    geom_polygon(data = mx_utm15_coords, 
                 aes(x = X, y = Y), colour = "black", fill = "lightgrey") +
    geom_polygon(data = bbox_coords, 
                 aes(x = X, y = Y), colour = "black", fill = "white") +
    coord_quickmap()

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Also, the axis units… yuk!

Saving sf objects

Package sf provides a variety of drivers allowing us to write geospatial vector data to a number of file formats:

sf::st_drivers() %>% DT::datatable()

Let’s first create a new folder to save our sf.

dir.create(here::here("data", "sf"))

shapefiles (.shp)

Let’s now save our file in the most popular geospatial vector data format, the shapefile(.shp). It is developed and regulated by Esri as a (mostly) open specification for data interoperability among Esri and other GIS software products.

write_sf(mol_sf, here::here("data", "sf", "salamander.shp"))
Warning in abbreviate_shapefile_names(obj): Field names abbreviated for
ESRI Shapefile driver
Warning in CPL_write_ogr(obj, dsn, layer, driver, as.character(dataset_options), : GDAL Message 1: One or several characters couldn't be converted correctly from UTF-8 to ISO-8859-1.
This warning will not be emitted anymore.

Hmmmmm, that’s a bit of a worrying warning…but let’s have a quick look at what we just wrote out anyways.

If you look in the sf/ folder, you will see that four files have been created for each sf we wrote. Here’s what each file contains:

  • .shp: This file contains the geometry of each feature.

  • .dbf: This is a dBase file which contains the attribute data for all of the features in the dataset. The dBase file is very similar to a sheet in a spreadsheet and can even be opened in Excel.

  • .shx: The .shx is the spatial index, it allows GIS systems to find features within the .shp file more quickly.

  • .prj: The .prj is the projection file. It contains information about the “projection” and “coordinate system” of the data.

All of them are required to fully recreate our sf but when to read the data in, you only specify the path to the .shp file

Now, as noted, I really didn’t like the look of that previous warning, so let’s read in the file and have a look at it.

read_sf(here::here("data", "sf", "salamander.shp"))
Simple feature collection with 15 features and 9 fields
geometry type:  POINT
dimension:      XY
bbox:           xmin: -99.84806 ymin: 18.94194 xmax: -97.09056 ymax: 19.63083
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs
# A tibble: 15 x 10
      id localty         n mntn_ch        region     na    he    ar    par
   <int> <chr>       <int> <chr>          <chr>   <dbl> <dbl> <dbl>  <dbl>
 1     1 Nevado de …    12 Nevado de Tol… Centra…  5.44 0.620  4.56 0.350 
 2     2 Texcalyacac    29 Sierra de las… Centra…  8.22 0.660  5.14 0.500 
 3     3 Desierto d…     7 Sierra de las… Centra…  4.44 0.590  4.44 0.180 
 4     4 Ajusco          8 Sierra de las… Centra…  4.22 0.490  4.05 0.0200
 5     8 Calpan         34 Sierra Nevada  Central 11.9  0.730  6.48 0.290 
 6     9 Atzompa        43 Sierra Nevada  Central 10.3  0.690  5.79 0.0800
 7    10 Llano Gran…    15 Sierra Nevada… Central  7.78 0.650  5.80 0.250 
 8    11 Rio Frio       27 Sierra Nevada… Central  7.56 0.570  4.77 0.130 
 9    12 Nanacamilpa    14 Sierra Nevada… Central  6.22 0.590  4.91 0.100 
10    13 MalincheS       8 Malinche       Centra…  5.00 0.580  4.69 0.0900
11    14 MalincheW      17 Malinche       Centra…  6.67 0.600  4.76 0.0600
12    16 MalincheE      13 Malinche       Centra…  6.11 0.560  4.73 0.210 
13    17 Texmalaqui…     8 Pico de Oriza… Southe…  6.00 0.710  5.64 0.910 
14    18 Xometla        16 Pico de Oriza… Southe…  9.11 0.830  6.86 0.490 
15    19 Vigas          48 Cofre de Pero… Northe… 11.8  0.660  5.75 1.31  
# ... with 1 more variable: geometry <POINT [°]>

Gah!! What’s happened to the column names?! This is in fact a well known problem with the shapefile format which cannot handle field (column) names longer than 7 characters. When your column names are longer than that, write_sf() quietly runs base::abbreviate() on them before writing the files out. This does not sit well with me in terms of good data provenance tracking and reproducibility. So let’s try a different format instead.

GeoJSON (.geojson)

GeoJSON is an open standard format designed for representing simple geographical features, along with their non-spatial attributes. It differs from other GIS standards in that it was written and is maintained not by a formal standards organization, but by an Internet working group of developers. As such, it does not play well with Esri products like ArcGIS (although they can be converted to formats that will). However, if you are not planning to use your data with Esri products, this format is fine.

write_sf(mol_sf, here::here("data", "sf", "salamander.geojson"))

Let’s read it back in and check it:

read_sf(here::here("data", "sf", "salamander.geojson"))
Simple feature collection with 15 features and 9 fields
geometry type:  POINT
dimension:      XY
bbox:           xmin: -99.84806 ymin: 18.94194 xmax: -97.09056 ymax: 19.63083
epsg (SRID):    4326
proj4string:    +proj=longlat +datum=WGS84 +no_defs
# A tibble: 15 x 10
      id locality       n mountain_chain   region    na    he    ar    par
   <int> <chr>      <int> <chr>            <chr>  <dbl> <dbl> <dbl>  <dbl>
 1     1 Nevado de…    12 Nevado de Toluca Centr…  5.44 0.620  4.56 0.350 
 2     2 Texcalyac…    29 Sierra de las C… Centr…  8.22 0.660  5.14 0.500 
 3     3 Desierto …     7 Sierra de las C… Centr…  4.44 0.590  4.44 0.180 
 4     4 Ajusco         8 Sierra de las C… Centr…  4.22 0.490  4.05 0.0200
 5     8 Calpan        34 Sierra Nevada    Centr… 11.9  0.730  6.48 0.290 
 6     9 Atzompa       43 Sierra Nevada    Centr… 10.3  0.690  5.79 0.0800
 7    10 Llano Gra…    15 Sierra Nevada (… Centr…  7.78 0.650  5.80 0.250 
 8    11 Rio Frio      27 Sierra Nevada (… Centr…  7.56 0.570  4.77 0.130 
 9    12 Nanacamil…    14 Sierra Nevada (… Centr…  6.22 0.590  4.91 0.100 
10    13 MalincheS      8 Malinche         Centr…  5.00 0.580  4.69 0.0900
11    14 MalincheW     17 Malinche         Centr…  6.67 0.600  4.76 0.0600
12    16 MalincheE     13 Malinche         Centr…  6.11 0.560  4.73 0.210 
13    17 Texmalaqu…     8 Pico de Orizaba  South…  6.00 0.710  5.64 0.910 
14    18 Xometla       16 Pico de Orizaba  South…  9.11 0.830  6.86 0.490 
15    19 Vigas         48 Cofre de Perote  North… 11.8  0.660  5.75 1.31  
# ... with 1 more variable: geometry <POINT [°]>

Beautiful! The file is accurately reproduced with all column names intact 💪, so no need to go updating your data README or attribute metadata table.

Session information

sessionInfo()
R version 3.4.4 (2018-03-15)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.3

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] bindrcpp_0.2.2 spData_0.2.9.3 dplyr_0.7.6    ggplot2_3.0.0 
[5] sf_0.6-3      

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4  purrr_0.2.5       colorspace_1.3-2 
 [4] htmltools_0.3.6   emo_0.0.0.9000    yaml_2.1.19      
 [7] utf8_1.1.3        rlang_0.2.1       later_0.7.3      
[10] R.oo_1.21.0       e1071_1.6-8       pillar_1.2.1     
[13] glue_1.2.0.9000   withr_2.1.2       DBI_1.0.0        
[16] R.utils_2.6.0     bindr_0.1.1       plyr_1.8.4       
[19] stringr_1.3.1     munsell_0.5.0     gtable_0.2.0     
[22] workflowr_1.0.1   R.methodsS3_1.7.1 htmlwidgets_1.2  
[25] evaluate_0.11     labeling_0.3      knitr_1.20       
[28] httpuv_1.4.4.2    crosstalk_1.0.0   class_7.3-14     
[31] highr_0.6         Rcpp_0.12.18      xtable_1.8-2     
[34] readr_1.1.1       promises_1.0.1    scales_1.0.0     
[37] backports_1.1.2   classInt_0.1-24   DT_0.4           
[40] jsonlite_1.5      mime_0.5          hms_0.4.2        
[43] digest_0.6.15     stringi_1.2.4     shiny_1.1.0      
[46] grid_3.4.4        rprojroot_1.3-2   here_0.1         
[49] cli_1.0.0         tools_3.4.4       magrittr_1.5     
[52] lazyeval_0.2.1    tibble_1.4.2      crayon_1.3.4     
[55] whisker_0.3-2     pkgconfig_2.0.2   lubridate_1.7.4  
[58] assertthat_0.2.0  rmarkdown_1.10    R6_2.2.2         
[61] units_0.6-0       git2r_0.21.0      compiler_3.4.4

This reproducible R Markdown analysis was created with workflowr 1.0.1