author: GreenStat - Peter van Horssen date: '12 maart, 2018' width: 2000 height: 1200
Peter van Horssen [www.greenstat.nl] ( https:www.greenstat.nl)
background in physical geography & ecology
experience in data analysis/statistics/mapping in ecological studies
- animal tagging studies
- impact assessment studies
- data visualisation
analysis : statistics, GIS, graphs, maps
- Intro
- geo-data in R
- Basics:
- convert non-spatial tot spatial data in R
do stuff yourself
.... coffee break ....
- Analysis:
- reading and writing
- some examples
do more stuff yourself
Why GIS in R ?
or maybe start with : why R?
-
R is a usefull big box with all tools needed for data-analysis
-
R is not better than other tools
-
R does not make your data-analysis problem easier/simple !
-
creation of reproducible workflow
-
scripting and documentation of workflow
-
Recently a boost in the available 'spatial' tools (based on 'simple features')
All spatial analysis functionality (and more!) is available in R, plotting and layout for maps is at a basic level.
what is geo-data?
- not all data is geodata
- formats (xlsx,csv, shp,gml,kml,.....)
- meta-data (map projection, units)
'simple features' is a data model with basic features for all spatial data:
- ISO standard for a common storage and access model of mostly two-dimensional geometries (point, line, polygon, multi-point, multi-line, etc.) used by geographic information systems. [link] (https://cran.r-project.org/web/packages/sf/vignettes/sf1.html)
This standard is 'under the hood' used in nearly all GIS packages (QGis, Esri, PostGIS, ..)
it also exports all OGC operations:
st_area, st_buffer, st_length, st_transform,....
read/write through GDAL
- why do we need this ?
- who needs this ?
r&d for small to midsize (in terms of data) analysis
very usefull for data-exploration (spatial, temporal, attributes)
keep the workflow on one platform
analysis in R, fancy (web)mapping somewhere else
Assume user with basic knowledge of R
data.frame, x[] , str()
User with workable knowledge of spatial analysis and map projections
This presentation provides 'pointers' only
Software: R-core
packages :
-
sf : package to handle spatial vector data efficiently (https://r-spatial.github.io/sf/)
(mac users : check website) -
tidyverse : 'all you can eat' data exploration tools (https://www.tidyverse.org/packages/)
- ggplot : graphs in every form
- dplyr : tools for data manipulation
- much more but we skip that for now
-
mapview : https://r-spatial.github.io/mapview/articles/index.html
Please download this before the meetup, R lives @ cran : https://cran.r-project.org/bin/windows/base/
Package are installed when R is running, choose 'Package|Install Packages' in the topbar, choose a cloudsource and select package name
Run scripts in plain R of RStudio (newest version)
test set simple example: points with coordinates
# script voor test df
n=10^3
df <- data.frame(
ID=c(1:n),
var2=runif(n),
var1=sample(LETTERS[1:4], n, replace=TRUE),
dates=sample(seq(as.Date('2016/01/01'), as.Date('2017/01/01'), by="day"), n, replace=TRUE),
X = runif(n, min= 3.36,max= 7.23), # why this min/max?
Y = runif(n, min=50.72,max=53.55)
)
head(df) ID var2 var1 dates X Y
1 1 0.1280419 D 2016-02-01 5.429107 51.72390
2 2 0.9780423 C 2016-01-09 6.033566 51.03359
3 3 0.1913305 C 2016-06-02 3.777492 51.02033
4 4 0.1235640 B 2016-10-28 4.201303 51.94631
5 5 0.8046380 B 2016-12-19 7.092073 51.80091
6 6 0.4275470 C 2016-05-05 5.222120 53.34998
library(sf)
df.sf <- st_as_sf(df,coords=c('X','Y'))
df.sfSimple feature collection with 1000 features and 4 fields
geometry type: POINT
dimension: XY
bbox: xmin: 3.364658 ymin: 50.72375 xmax: 7.227531 ymax: 53.5451
epsg (SRID): NA
proj4string: NA
First 10 features:
ID var2 var1 dates geometry
1 1 0.12804193 D 2016-02-01 POINT (5.429107 51.7239)
2 2 0.97804231 C 2016-01-09 POINT (6.033566 51.03359)
3 3 0.19133054 C 2016-06-02 POINT (3.777492 51.02033)
4 4 0.12356401 B 2016-10-28 POINT (4.201303 51.94631)
5 5 0.80463805 B 2016-12-19 POINT (7.092073 51.80091)
6 6 0.42754700 C 2016-05-05 POINT (5.22212 53.34998)
7 7 0.36428749 C 2016-08-14 POINT (5.875715 51.43441)
8 8 0.78050745 D 2016-10-04 POINT (3.669354 53.06731)
9 9 0.04795271 D 2016-06-19 POINT (5.154448 50.9267)
10 10 0.58685661 A 2016-02-28 POINT (6.345324 50.74155)
#st_as_sf(df,coords=c('X','Y','var2'), dim="XYZ")
#head(df.sf,n=2)
#head(df,n=2)mapprojection - metadata
-
basic principle: transformation from sphere to a flat surface
-
formula to project coordinates on a sphere to coordinates on a flat surface, choose centre of map projection ('datum'), important voor units: area, distance
-
map projections in PROJ.4 and import/export through GDAL
for Netherlands:
- [RD-coordinate system] (https://nl.wikipedia.org/wiki/Rijksdriehoeksco%C3%B6rdinaten) (meters) : crs 28992
- [WGS84 coordinate system for GPS data] (https://nl.wikipedia.org/wiki/WGS_84) (geografische graden) : crs 4326
- [UTM31 Universele Transversale Mercator projectie] (https://nl.wikipedia.org/wiki/Universele_transversale_mercatorprojectie) (meters) : crs 32631
in sf map projections through CRS (Coordinate Reference System) at http://spatialreference.org/
df.sf <- st_as_sf(df,coords=c('X','Y'), crs=4326)
df.sfSimple feature collection with 1000 features and 4 fields
geometry type: POINT
dimension: XY
bbox: xmin: 3.364658 ymin: 50.72375 xmax: 7.227531 ymax: 53.5451
epsg (SRID): 4326
proj4string: +proj=longlat +datum=WGS84 +no_defs
First 10 features:
ID var2 var1 dates geometry
1 1 0.12804193 D 2016-02-01 POINT (5.429107 51.7239)
2 2 0.97804231 C 2016-01-09 POINT (6.033566 51.03359)
3 3 0.19133054 C 2016-06-02 POINT (3.777492 51.02033)
4 4 0.12356401 B 2016-10-28 POINT (4.201303 51.94631)
5 5 0.80463805 B 2016-12-19 POINT (7.092073 51.80091)
6 6 0.42754700 C 2016-05-05 POINT (5.22212 53.34998)
7 7 0.36428749 C 2016-08-14 POINT (5.875715 51.43441)
8 8 0.78050745 D 2016-10-04 POINT (3.669354 53.06731)
9 9 0.04795271 D 2016-06-19 POINT (5.154448 50.9267)
10 10 0.58685661 A 2016-02-28 POINT (6.345324 50.74155)
# st_transform(df.sf, 28992)
# st_crs(df.sf)
# st_set_csr(df.sf) # error !
# st_set_crs(df.sf, 28992 ) # is this oke ?str(df.sf) # str : shows structure of objectClasses 'sf' and 'data.frame': 1000 obs. of 5 variables:
$ ID : int 1 2 3 4 5 6 7 8 9 10 ...
$ var2 : num 0.128 0.978 0.191 0.124 0.805 ...
$ var1 : Factor w/ 4 levels "A","B","C","D": 4 3 3 2 2 3 3 4 4 1 ...
$ dates : Date, format: "2016-02-01" "2016-01-09" ...
$ geometry:sfc_POINT of length 1000; first list element: Classes 'XY', 'POINT', 'sfg' num [1:2] 5.43 51.72
- attr(*, "sf_column")= chr "geometry"
- attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA
..- attr(*, "names")= chr "ID" "var2" "var1" "dates"
str(df)'data.frame': 1000 obs. of 6 variables:
$ ID : int 1 2 3 4 5 6 7 8 9 10 ...
$ var2 : num 0.128 0.978 0.191 0.124 0.805 ...
$ var1 : Factor w/ 4 levels "A","B","C","D": 4 3 3 2 2 3 3 4 4 1 ...
$ dates: Date, format: "2016-02-01" "2016-01-09" ...
$ X : num 5.43 6.03 3.78 4.2 7.09 ...
$ Y : num 51.7 51 51 51.9 51.8 ...
objects keep 'dataframe' class
df.sf[1:3,] # first three rows of dataframeSimple feature collection with 3 features and 4 fields
geometry type: POINT
dimension: XY
bbox: xmin: 3.777492 ymin: 51.02033 xmax: 6.033566 ymax: 51.7239
epsg (SRID): 4326
proj4string: +proj=longlat +datum=WGS84 +no_defs
ID var2 var1 dates geometry
1 1 0.1280419 D 2016-02-01 POINT (5.429107 51.7239)
2 2 0.9780423 C 2016-01-09 POINT (6.033566 51.03359)
3 3 0.1913305 C 2016-06-02 POINT (3.777492 51.02033)
#df.sf[,3]
df.sf[1:3,2:3] # first three rows and column 2 and 3 onlySimple feature collection with 3 features and 2 fields
geometry type: POINT
dimension: XY
bbox: xmin: 3.777492 ymin: 51.02033 xmax: 6.033566 ymax: 51.7239
epsg (SRID): 4326
proj4string: +proj=longlat +datum=WGS84 +no_defs
var2 var1 geometry
1 0.1280419 D POINT (5.429107 51.7239)
2 0.9780423 C POINT (6.033566 51.03359)
3 0.1913305 C POINT (3.777492 51.02033)
library(mapview) # R wrapper for leaflet .....
#mapview(df.sf)
mapview(df.sf,zcol="var1", legend=TRUE)
#library(tmap)
#tmap_mode("view")
#tm_shape(df.sf) + tm_dots(col="black", size=.1)
use of 'pipes' : '%>%'
select : select column
filter : filter rows
mutate : add column (and mutate value)
summarize : aggregate
library(tidyverse)
df <- df %>% mutate(maand=format.Date(dates, "%m"))
df %>% head() ID var2 var1 dates X Y maand
1 1 0.1280419 D 2016-02-01 5.429107 51.72390 02
2 2 0.9780423 C 2016-01-09 6.033566 51.03359 01
3 3 0.1913305 C 2016-06-02 3.777492 51.02033 06
4 4 0.1235640 B 2016-10-28 4.201303 51.94631 10
5 5 0.8046380 B 2016-12-19 7.092073 51.80091 12
6 6 0.4275470 C 2016-05-05 5.222120 53.34998 05
df %>% filter(var1=="B" ) %>% head() ID var2 var1 dates X Y maand
1 4 0.1235640 B 2016-10-28 4.201303 51.94631 10
2 5 0.8046380 B 2016-12-19 7.092073 51.80091 12
3 14 0.3911026 B 2016-08-27 6.344933 51.30927 08
4 17 0.7717081 B 2016-01-29 4.740575 51.08011 01
5 20 0.4170263 B 2016-12-01 3.473820 52.09161 12
6 24 0.5422442 B 2016-01-24 6.318316 51.44538 01
df %>%
ggplot(aes(x=dates,y=ID)) +
geom_point() +
#geom_line() +
theme(text = element_text(size = 25))
df %>%
ggplot(aes(x=maand,y=var2, group=maand)) +
geom_boxplot()+
theme(text = element_text(size = 25))
use data.frame ...
# conditional plot
df %>% ggplot(aes(var1,var2)) +
geom_boxplot() +
facet_wrap(~maand) +
theme(text = element_text(size = 25))
remember the classes of a sf object : 'sf' and 'data.frame'?
df.sf %>%
mutate(maand=format.Date(dates, "%m")) %>%
ggplot(aes(var1,var2)) +
geom_boxplot() +
facet_wrap(~maand) +
theme(text = element_text(size = 25))
library(mapview) # R wrapper for leaflet .....
df.sf %>%
mutate(maand=format.Date(dates, "%m")) %>%
filter((var1=="B") %>%
mapview(zcol="maand", legend=TRUE)
# alternative ....
#library(tmap)
#tmap_mode("view")
#df.sf %>% tm_shape() + tm_dots(col="black", size=.1)
do stuff yourself ....
...coffee break...
maps downloaded from web https://www.cbs.nl/nl-nl/dossier/nederland-regionaal/geografische%20data/wijk-en-buurtkaart-2017 CBS : buurt_2017.zip
- kaart met wijken, buurten en gemeenten in NL
- uitpakken: shape files
list.files("../dataUtrecht/buurt_2017") [1] "buurt_2017.cpg" "buurt_2017.dbf" "buurt_2017.prj"
[4] "buurt_2017.shp" "buurt_2017.shp.xml" "buurt_2017.shx"
[7] "gem_2017.cpg" "gem_2017.dbf" "gem_2017.prj"
[10] "gem_2017.shp" "gem_2017.shp.xml" "gem_2017.shx"
[13] "wijk_2017.cpg" "wijk_2017.dbf" "wijk_2017.prj"
[16] "wijk_2017.shp" "wijk_2017.shp.xml" "wijk_2017.shx"
Utrecht : bomenkaart.zip https://utrecht.dataplatform.nl/dataset/afa19ac8-e63e-4e27-a42e-3bb4f9082c59
- kaart met bomen in utrecht
- uitpakken : shapefile met bomen in Utrecht
list.files("../dataUtrecht/bomenkaart")[1] "Bomen_GISIB_ArcGISonline.dbf" "Bomen_GISIB_ArcGISonline.prj"
[3] "Bomen_GISIB_ArcGISonline.sbn" "Bomen_GISIB_ArcGISonline.sbx"
[5] "Bomen_GISIB_ArcGISonline.shp" "Bomen_GISIB_ArcGISonline.shx"
library(sf)
library(tidyverse)
buurt.sf <- st_read("../dataUtrecht/buurt_2017/buurt_2017.shp")Reading layer `buurt_2017' from data source `L:\GreenStat\projecten\2017-X03 maptime 030 R GIS\dataUtrecht\buurt_2017\buurt_2017.shp' using driver `ESRI Shapefile'
Simple feature collection with 13308 features and 39 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: 10425.16 ymin: 306846.2 xmax: 278026.1 ymax: 621876.3
epsg (SRID): NA
proj4string: +proj=sterea +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +units=m +no_defs
#buurt.sf %>% head(n=3)library(sf)
library(tidyverse)
buurt.sf %>% str() # show structure of objectClasses 'sf' and 'data.frame': 13308 obs. of 40 variables:
$ BU_CODE : Factor w/ 13308 levels "BU00030000","BU00030001",..: 1 2 3 4 5 6 7 8 9 10 ...
$ BU_NAAM : Factor w/ 12251 levels "'n Oaln Diek",..: 372 374 373 9715 10782 10735 786 9585 12185 11064 ...
$ WK_CODE : Factor w/ 3159 levels "WK000300","WK000500",..: 1 1 1 1 1 1 2 2 2 2 ...
$ GM_CODE : Factor w/ 389 levels "GM0003","GM0005",..: 1 1 1 1 1 1 2 2 2 2 ...
$ GM_NAAM : Factor w/ 388 levels "'s-Gravenhage",..: 20 20 20 20 20 20 28 28 28 28 ...
$ IND_WBI : num 1 1 1 1 1 1 1 1 1 1 ...
$ WATER : Factor w/ 3 levels "B","JA","NEE": 3 3 3 3 3 3 3 3 3 3 ...
$ POSTCODE : Factor w/ 3907 levels "-99999999","1011",..: 3830 3832 3831 3832 3831 3830 3782 3782 3784 3784 ...
$ DEK_PERC : num 1 5 1 4 1 1 1 1 1 1 ...
$ OAD : num 1190 894 1112 351 74 ...
$ STED : num 3 4 3 5 5 5 4 5 5 5 ...
$ AANT_INW : num 2335 3080 5955 320 100 ...
$ AANT_MAN : num 1090 1535 2865 165 50 ...
$ AANT_VROUW: num 1245 1545 3090 150 50 ...
$ P_00_14_JR: num 10 17 16 21 17 24 16 21 14 17 ...
$ P_15_24_JR: num 9 11 11 11 8 11 12 12 11 10 ...
$ P_25_44_JR: num 21 20 22 23 14 15 21 27 17 16 ...
$ P_45_64_JR: num 30 33 27 35 45 33 29 26 38 39 ...
$ P_65_EO_JR: num 30 19 25 10 17 17 22 13 21 17 ...
$ P_ONGEHUWD: num 40 43 43 50 40 46 43 48 43 41 ...
$ P_GEHUWD : num 36 47 41 44 55 47 45 47 45 51 ...
$ P_GESCHEID: num 11 7 9 3 3 6 6 4 8 7 ...
$ P_VERWEDUW: num 12 4 8 2 2 1 6 1 4 2 ...
$ BEV_DICHTH: num 2774 1950 2094 59 18 ...
$ AANTAL_HH : num 1310 1335 2735 115 40 ...
$ P_EENP_HH : num 54 27 35 18 15 21 30 18 34 19 ...
$ P_HH_Z_K : num 28 37 31 32 48 37 34 38 37 46 ...
$ P_HH_M_K : num 18 36 34 50 38 42 36 45 29 35 ...
$ GEM_HH_GR : num 1.7 2.3 2.1 2.7 2.5 2.7 2.3 2.7 2.2 2.5 ...
$ P_WEST_AL : num 6 6 9 6 2 8 4 6 5 5 ...
$ P_N_W_AL : num 4 3 9 0 1 1 3 2 4 4 ...
$ P_MAROKKO : num 0e+00 0e+00 1e+00 -1e+08 -1e+08 ...
$ P_ANT_ARU : num 1e+00 1e+00 1e+00 -1e+08 -1e+08 ...
$ P_SURINAM : num 0e+00 0e+00 1e+00 -1e+08 -1e+08 ...
$ P_TURKIJE : num 1e+00 1e+00 4e+00 -1e+08 -1e+08 ...
$ P_OVER_NW : num 2e+00 1e+00 3e+00 -1e+08 -1e+08 ...
$ OPP_TOT : num 90 163 295 559 582 769 313 2190 74 699 ...
$ OPP_LAND : num 84 158 284 540 554 ...
$ OPP_WATER : num 5 5 11 18 28 13 5 14 3 6 ...
$ geometry :sfc_MULTIPOLYGON of length 13308; first list element: List of 1
..$ :List of 1
.. ..$ : num [1:72, 1:2] 253642 253617 253599 253593 253602 ...
..- attr(*, "class")= chr "XY" "MULTIPOLYGON" "sfg"
- attr(*, "sf_column")= chr "geometry"
- attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
..- attr(*, "names")= chr "BU_CODE" "BU_NAAM" "WK_CODE" "GM_CODE" ...
library(sf)
library(tidyverse)
u.buurt.sf <-
buurt.sf %>% filter(GM_NAAM=='Utrecht') %>% select(GM_NAAM,BU_NAAM,AANT_INW)
# filter gemeente Utrecht
# selecteer alleen de kolommen GM_NAAM, BU_NAAM,AANT_INW
u.buurt.sf %>% head(n=3)Simple feature collection with 3 features and 3 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: 133872 ymin: 454563.4 xmax: 135198.4 ymax: 456063.6
epsg (SRID): NA
proj4string: +proj=sterea +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +units=m +no_defs
GM_NAAM BU_NAAM AANT_INW geometry
1 Utrecht Welgelegen, Den Hommel 1395 MULTIPOLYGON (((135198.4 45...
2 Utrecht Oog in Al 4280 MULTIPOLYGON (((134877.6 45...
3 Utrecht Halve Maan-Zuid 1435 MULTIPOLYGON (((134161.3 45...
library(sf)
library(tidyverse)
library(mapview)
#u.buurt.sf %>% mapview()
#u.buurt.sf %>% mapview(zcol="BU_NAAM")
u.buurt.sf %>% mapview(fill=NA)
# okay save this for later...
st_write(u.buurt.sf, "u_buurt.kml")
#st_write(u.buurt.sf, "u_buurt.gml")
#st_write(u.buurt.sf, "u_buurt.shp")
#st_write(u.buurt.sf, "u.buurt.GeoJSON")
# st_drivers() for possible formats
# write/read straight in postgres db!
# library(RpostgreSQL)
# conn <- dbCOnnect(PostgreSQL(),dbname='your_db_name', user='your_user_name')
# st_write_db(conn, u.buurt.sf, 'your_table_name')
library(sf)
library(tidyverse)
bomen.sf <- st_read("../dataUtrecht/bomenkaart/Bomen_GISIB_ArcGISonline.shp")Reading layer `Bomen_GISIB_ArcGISonline' from data source `L:\GreenStat\projecten\2017-X03 maptime 030 R GIS\dataUtrecht\bomenkaart\Bomen_GISIB_ArcGISonline.shp' using driver `ESRI Shapefile'
Simple feature collection with 168736 features and 10 fields
geometry type: POINT
dimension: XY
bbox: xmin: 126735.1 ymin: 448930 xmax: 141827.3 ymax: 461162.1
epsg (SRID): NA
proj4string: +proj=sterea +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +units=m +no_defs
#bomen.sf %>% head(n=3)library(sf)
library(tidyverse)
bomen.sf %>% str()Classes 'sf' and 'data.frame': 168736 obs. of 11 variables:
$ Naam_NL : Factor w/ 368 levels "acacia","Acacia",..: 179 162 266 162 163 165 165 341 179 6 ...
$ Naam_Wet : Factor w/ 558 levels "Abies concolor",..: 345 379 449 384 382 557 557 517 366 236 ...
$ Plantjaar : num 2017 2017 NA 2012 1995 ...
$ Eigenaar : Factor w/ 2 levels "Gemeentelijk",..: 1 1 2 1 1 1 1 1 1 1 ...
$ Buurt : Factor w/ 194 levels "'t Weer","1e Daalsebuurt",..: 49 47 151 166 166 55 55 55 55 55 ...
$ Wijk : Factor w/ 10 levels "Binnenstad","Leidsche Rijn",..: 4 4 4 3 3 4 4 4 4 4 ...
$ Boomnr : num 2938159 2938160 2938161 2930732 2930733 ...
$ Boomnr_Oud: Factor w/ 168154 levels "1000","10002",..: NA NA NA NA NA NA NA NA NA NA ...
$ X_coordina: num 134185 134292 134346 136882 137486 ...
$ Y_coordina: num 457957 458042 457536 457854 458126 ...
$ geometry :sfc_POINT of length 168736; first list element: Classes 'XY', 'POINT', 'sfg' num [1:2] 134185 457957
- attr(*, "sf_column")= chr "geometry"
- attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA
..- attr(*, "names")= chr "Naam_NL" "Naam_Wet" "Plantjaar" "Eigenaar" ...
nice, bomen data also has 'Wijk' en 'Buurt' ...
library(sf)
library(tidyverse)
bomen.sf %>%
ggplot(aes(Plantjaar)) +
geom_histogram(binwidth=10) +
theme(text = element_text(size = 25))
library(sf)
library(tidyverse)
bomen.sf %>%
ggplot(aes(2018-Plantjaar)) +
geom_histogram(binwidth=10) +
theme(text = element_text(size = 25))
library(sf)
library(tidyverse)
bomen.sf %>%
ggplot(aes(2018-Plantjaar, fill=Eigenaar)) +
geom_histogram(binwidth=25) +
theme(text = element_text(size = 25))
select trees > 100 yr
library(sf)
library(tidyverse)
library(mapview)
bomen100.sf <- bomen.sf %>%
mutate (leeftijd = 2018 - Plantjaar) %>%
filter(leeftijd>100) %>%
select(Naam_NL,Eigenaar,leeftijd)
bomen100.sf %>% head()Simple feature collection with 6 features and 3 fields
geometry type: POINT
dimension: XY
bbox: xmin: 134643 ymin: 453446.5 xmax: 139270.3 ymax: 456316.7
epsg (SRID): NA
proj4string: +proj=sterea +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +units=m +no_defs
Naam_NL Eigenaar leeftijd geometry
1 Gewone beuk Gemeentelijk 143 POINT (139099 453446.5)
2 Krimlinde Gemeentelijk 103 POINT (138084.9 455459.2)
3 Zomereik Gemeentelijk 144 POINT (139270.3 453553.1)
4 Zomereik Gemeentelijk 128 POINT (134643 454852.9)
5 Rode beuk Gemeentelijk 118 POINT (138136.4 456316.7)
6 Hollandse linde Gemeentelijk 108 POINT (137845 454053)
#bomen100.sf %>% mapview(cex="leeftijd") +mapview(u.buurt.sf, fill=NA)
#
# 'cex' controles size of dots, bigger dots for older trees
#spatial join with st_join
library(sf)
library(tidyverse)
library(mapview)
bomen100_inw_ubuurt.sf <-
bomen100.sf %>%
st_join(u.buurt.sf)
bomen100_inw_ubuurt.sf %>% head(n=2)Simple feature collection with 2 features and 6 fields
geometry type: POINT
dimension: XY
bbox: xmin: 138084.9 ymin: 453446.5 xmax: 139099 ymax: 455459.2
epsg (SRID): NA
proj4string: +proj=sterea +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +units=m +no_defs
Naam_NL Eigenaar leeftijd GM_NAAM BU_NAAM
1 Gewone beuk Gemeentelijk 143 Utrecht Maarschalkerweerd en Mereveld
2 Krimlinde Gemeentelijk 103 Utrecht Wilhelminapark en omgeving
AANT_INW geometry
1 175 POINT (139099 453446.5)
2 2620 POINT (138084.9 455459.2)
database join with left_join
library(sf)
library(tidyverse)
library(mapview)
# left_join is for 'sp' join 'df' only,
#'sp' left_join 'sp' gives err
bomen_per_buurt.df <-
bomen100_inw_ubuurt.sf %>%
st_set_geometry(NULL) %>%
# drop the geometry column
group_by(BU_NAAM) %>%
# group by 'BU_NAAM
summarize(n=n())
# summarize with function
# n = number of cases
# sum, mean, min, max, median,
# first, last ...
u.buurt.sf %>%
left_join(bomen_per_buurt.df, by=c('BU_NAAM'='BU_NAAM')) %>%
mapview(zcol="n", at=c(1,10,100,250,500), legend=TRUE)
# head()
join table to spatial object
library(sf)
library(tidyverse)
library(mapview)
u.buurt.sf %>%
left_join(bomen_per_buurt.df) %>%
mutate(boom_per_100 = n/(AANT_INW/100)) %>%
# aantal oude bomen per 100 inwoners
mapview(zcol="boom_per_100",
at=c(0,1,10,100,250),
legend=TRUE) +
mapview(bomen100.sf, cex="leeftijd")
#head() 
do stuff yourself
===================================================
==================================================
summarize and ...
# summarize
df %>%
group_by(maand,var1) %>%
summarize(median_var2=median(var2))# A tibble: 48 x 3
# Groups: maand [?]
maand var1 median_var2
<chr> <fct> <dbl>
1 01 A 0.515
2 01 B 0.575
3 01 C 0.422
4 01 D 0.502
5 02 A 0.479
6 02 B 0.660
7 02 C 0.493
8 02 D 0.460
9 03 A 0.462
10 03 B 0.483
# ... with 38 more rows
.. plot in one go
# conditional plot
df %>%
group_by(maand,var1) %>%
summarize(median_var2=median(var2)) %>%
ggplot() +
geom_point(aes(var1,median_var2),size=2) +
facet_wrap(~maand) +
theme(text = element_text(size = 25))
use 2 geom's in one graph
df %>%
group_by(maand) %>%
mutate(mean_var2=mean(var2)) %>%
ggplot() +
geom_boxplot(aes(var1,var2)) +
geom_hline(aes(yintercept=mean_var2), col='red') +
facet_wrap(~maand) +
theme(text = element_text(size = 25))
calculate area's
library(sf)
library(tidyverse)
u.buurt.sf %>% mutate(area = st_area(geometry)) %>% head(n=3)Simple feature collection with 3 features and 4 fields
geometry type: MULTIPOLYGON
dimension: XY
bbox: xmin: 133872 ymin: 454563.4 xmax: 135198.4 ymax: 456063.6
epsg (SRID): NA
proj4string: +proj=sterea +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +units=m +no_defs
GM_NAAM BU_NAAM AANT_INW area
1 Utrecht Welgelegen, Den Hommel 1395 394091.1 m^2
2 Utrecht Oog in Al 4280 471201.6 m^2
3 Utrecht Halve Maan-Zuid 1435 232087.7 m^2
geometry
1 MULTIPOLYGON (((135198.4 45...
2 MULTIPOLYGON (((134877.6 45...
3 MULTIPOLYGON (((134161.3 45...
## note the geometry fieldselect one polygon calculate area alter geometry from polygon to line calculate length (= perimeter)
library(sf)
library(tidyverse)
u.buurt.sf %>%
filter (BU_NAAM=='Oog in Al') %>% st_area()471201.6 m^2
# go from multipolygon to polygon to line ....
u.buurt.sf %>%
filter (BU_NAAM=='Oog in Al') %>%
st_cast('POLYGON') %>% # st_area()
st_cast('LINESTRING') %>% st_length()2854.89 m
# note the warnings ...buffer ...
#u.buurt.sf %>%
# filter (BU_NAAM=='Oog in Al') %>%
# st_buffer(100) %>% mapview(fill=NA) + mapview(u.buurt.sf %>% filter (BU_NAAM=='Oog in Al'))
# st_buffer value can also be negative ...
filter rows in a spatial object
library(tidyverse)
library(sf)
#bomen100.sf[1:10,]
bomen100.sf %>% filter(leeftijd==101)Simple feature collection with 63 features and 3 fields
geometry type: POINT
dimension: XY
bbox: xmin: 126762.4 ymin: 450519.3 xmax: 140240.7 ymax: 458069.7
epsg (SRID): NA
proj4string: +proj=sterea +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +units=m +no_defs
First 10 features:
Naam_NL Eigenaar leeftijd geometry
1 Zomereik Gemeentelijk 101 POINT (139511.3 453349.8)
2 Zomereik Gemeentelijk 101 POINT (139354.3 453603.7)
3 Gewone beuk Gemeentelijk 101 POINT (139819.7 453394.8)
4 Gewone beuk Gemeentelijk 101 POINT (138966.1 453562.4)
5 Hollandse linde Niet gemeentelijk 101 POINT (132462.1 450519.3)
6 Gewone acacia Gemeentelijk 101 POINT (139263.6 453738.8)
7 Gewone beuk Gemeentelijk 101 POINT (138858.1 453538.3)
8 Zomereik Gemeentelijk 101 POINT (140236.8 453517.1)
9 Zomereik Gemeentelijk 101 POINT (139739.5 453982.4)
10 Gewone beuk Gemeentelijk 101 POINT (139950.4 453383.6)
filter rows in a spatial object with another spatial object
library(tidyverse)
library(sf)
library(mapview)
#bomen100.sf[1:10,]
#bomen100.sf %>% filter(leeftijd==101)
een_wijk.sf <-u.buurt.sf %>% filter (BU_NAAM=='Oog in Al')
bomen100.sf[een_wijk.sf,]Simple feature collection with 51 features and 3 fields
geometry type: POINT
dimension: XY
bbox: xmin: 134419.3 ymin: 455017.3 xmax: 134847.7 ymax: 455595.9
epsg (SRID): NA
proj4string: +proj=sterea +lat_0=52.15616055555555 +lon_0=5.38763888888889 +k=0.9999079 +x_0=155000 +y_0=463000 +ellps=bessel +units=m +no_defs
First 10 features:
Naam_NL Eigenaar leeftijd
17 Venijnboom Gemeentelijk 153
48 Japanse noteboom Gemeentelijk 138
82 Venijnboom Gemeentelijk 128
208 Watercipres Gemeentelijk 108
242 Gewone acacia Gemeentelijk 128
314 Scherpe hulst Gemeentelijk 118
320 Dubbelbloemige paardenkastanje Gemeentelijk 118
342 Gewone es Gemeentelijk 108
430 Zwarte den Gemeentelijk 138
582 Rode beuk Gemeentelijk 128
geometry
17 POINT (134441.8 455039.1)
48 POINT (134668.9 455561.2)
82 POINT (134841.2 455474.4)
208 POINT (134724 455394.6)
242 POINT (134846 455457.8)
314 POINT (134752.4 455409)
320 POINT (134756.5 455417.7)
342 POINT (134746.2 455517.2)
430 POINT (134715.3 455584)
582 POINT (134794.3 455450.6)
# mapview(een_wijk.sf , fill=NA) + mapview(bomen100.sf[een_wijk.sf,], zcol="Naam_NL", legend=TRUE)# mapview(een_wijk.sf , fill=NA) +
# mapview(bomen100.sf[een_wijk.sf,], zcol="Naam_NL", legend=TRUE)