Working with Spatial Data and using tmap()
Samantha A. Alger
4/26/2017
Modified from:
INSTALL PACKAGES and SET WORKING DIRECTORY
rm(list=ls())
x <- c("rgdal","tmap")
#install.packages(x)
lapply(x, library, character.only = TRUE)
setwd("~/CompBio/MapTutorial/") # setting working directorySTRUCTURE OF SPATIAL DATA IN R
Spatial objects such as lnd are made up of different ‘slots’, key slots being '@data‘(non geographic attribute data) and @polygons (or @lines for line data) (polygons that make up the physicial boundaries). Specific slots are accessed using the’@‘symbol. Using the’$’ symbol to see attribute in the @ data slot.
#Read in the data:
lnd <- readOGR("data", "london_sport") # Read in the data
head(lnd@data) # use the @ symbol to refer to the data slot of the lnd object
mean(lnd$Partic_Per) # use the $ symbol to refer to an attribute in the 'data' slot
sapply(lnd@data, class) # To check the classes of all the variabes in a spatial dataset, you can use this command
lnd$Pop_2001 <- as.numeric(as.character(lnd$Pop_2001)) # Unexpectedly, Pop_2001 is a factor, coerce the variable into the correct, numeric format...
# type "lnd@" and then tab to see which slots are in lnd...
#check number of rows and columns...
nrow(lnd)
ncol(lnd)BASIC PLOTTING
plot(lnd)
DATA QUERYING AND PLOTTING
# select rows of lnd@data where sports participation is less than 15
lnd@data[lnd$Partic_Per < 15, ] # This is interrogating the attribute data slot only of the lnd object..## ons_label name Partic_Per Pop_2001
## 17 00AQ Harrow 14.8 206822
## 21 00BB Newham 13.1 243884
## 32 00AA City of London 9.1 7181#Using the same logic to subset and plot spatial objects (polygon slot)
# Select zones where sports participation is between 20 and 25%
sel <- lnd$Partic_Per > 20 & lnd$Partic_Per < 25
head(sel) # test output of previous selection## [1] TRUE FALSE TRUE FALSE TRUE FALSEplot(lnd[sel, ]) # plotting all TRUE Cases
# To see these areas in context with other areas of the maps... use add= TRUE..
plot(lnd, col = "lightgrey") # plot the london_sport object
sel <- lnd$Partic_Per > 25
plot(lnd[ sel, ], col = "turquoise", add = TRUE) # add selected zones to map
#Areas of high sports participation are now highlighted in blue!USING tmap()
Thematic maps are geographical maps in which spatial data distributions are visualized. This package offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps.
library(tmap) # load tmap package
tmap_mode="plot"
# Check out it's capabilities!
#vignette("tmap-nutshell")Quick Thematic Maps (qtm):
# load spatial data included in the tmap package
data("World", "metro")
# Try typing head(World$...) or head(metro@...) to see the type of data...
head(World@data)## iso_a3 name sovereignt continent
## 2 AFG Afghanistan Afghanistan Asia
## 3 AGO Angola Angola Africa
## 5 ALB Albania Albania Europe
## 8 ARE United Arab Emirates United Arab Emirates Asia
## 9 ARG Argentina Argentina South America
## 10 ARM Armenia Armenia Asia
## subregion area pop_est pop_est_dens gdp_md_est gdp_cap_est
## 2 Southern Asia 652860 28400000 43.50090 22270 784.1549
## 3 Middle Africa 1246700 12799293 10.26654 110300 8617.6635
## 5 Southern Europe 27400 3639453 132.82675 21810 5992.6588
## 8 Western Asia 83600 4798491 57.39822 184300 38407.9078
## 9 South America 2736690 40913584 14.95003 573900 14027.1261
## 10 Western Asia 28470 2967004 104.21510 18770 6326.2469
## economy income_grp life_exp well_being
## 2 7. Least developed region 5. Low income 48.7 4.758381
## 3 7. Least developed region 3. Upper middle income 51.1 4.206092
## 5 6. Developing region 4. Lower middle income 76.9 5.268937
## 8 6. Developing region 2. High income: nonOECD 76.5 7.196803
## 9 5. Emerging region: G20 3. Upper middle income 75.9 6.441067
## 10 6. Developing region 4. Lower middle income 74.2 4.367811
## HPI
## 2 36.75366
## 3 33.20143
## 5 54.05118
## 8 31.77827
## 9 54.05504
## 10 46.00319head(metro@data)## name name_long iso_a3 pop1950 pop1960 pop1970
## 2 Kabul Kabul AFG 170784 285352 471891
## 8 Algiers El Djazair (Algiers) DZA 516450 871636 1281127
## 13 Luanda Luanda AGO 138413 219427 459225
## 16 Buenos Aires Buenos Aires ARG 5097612 6597634 8104621
## 17 Cordoba Cordoba ARG 429249 605309 809794
## 25 Rosario Rosario ARG 554483 671349 816230
## pop1980 pop1990 pop2000 pop2010 pop2020 pop2030
## 2 977824 1549320 2401109 3722320 5721697 8279607
## 8 1621442 1797068 2140577 2432023 2835218 3404575
## 13 771349 1390240 2591388 4508434 6836849 10428756
## 16 9422362 10513284 12406780 14245871 15894307 16956491
## 17 1009521 1200168 1347561 1459268 1562509 1718192
## 25 953491 1083819 1152387 1298073 1453814 1606993# quick plot example:
qtm(World, fill = "income_grp", text = "iso_a3", text.size = "AREA") # use "World$" to see the two attributes: income_grp and iso_a3, text.size= area: text is sized increasingly with coutry area size.
MORE COMPLEX MAPPING
For More Complex Maps use the main plotting method for readability
# calculate annual growth rate
metro$growth <- (metro$pop2020 - metro$pop2010) / (metro$pop2010 * 10) * 100
# plot
tm_shape(World) +
tm_polygons("income_grp", palette = "-Blues",
title = "Income class", contrast = 0.7, border.col = "grey30", id = "name") +
tm_text("iso_a3", size = "AREA", col = "grey30", root=3) +
tm_shape(metro) +
tm_bubbles("pop2010", col = "growth", border.col = "black",
border.alpha = 0.5,
breaks = c(-Inf, 0, 2, 4, 6, Inf) ,
palette = "-RdYlGn",
title.size = "Metro population (2010)",
title.col = "Annual growth rate (%)",
id = "name",
popup.vars=c("pop2010", "pop2020", "growth")) +
tm_format_World() +
tm_style_gray()
INTERACTIVE VIEW MODE
Interactive viewing with tmap
tmap_mode("view") # sets the tmap mode to interactive viewing## tmap mode set to interactive viewinglast_map() # returns the last map to be modified or created## Warning: Text labels not supported in view mode.