Assignment 10
Md Rizwanul Hasan
11/17/2020
Introduction
This assignment explores different visualization techniques using tmap function.
Cleaning the Global environment and the console
rm(list = ls()) # Clears the global environment for a fresh start
cat('\f') # Cleans the consoleLoading libraries
my_libtrary <- c("sf", "dplyr", "raster", "tmap", "ggplot2", "leaflet")
lapply(my_libtrary, library, character.only = TRUE)## Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## Loading required package: sp##
## Attaching package: 'raster'## The following object is masked from 'package:dplyr':
##
## selectLoading the data
For this assignment we used the Shelby Census tract shapefile along with our own data for population density, median income and other variables. We looked for NA values.
Shelby <- st_read("Data/Assignment_5/Shelby.shp")## Reading layer `Shelby' from data source `C:\Rizwan\Education\UofM PhD Work\Seminar in Earth Sciences\R-Spatial\Data\Assignment_5\Shelby.shp' using driver `ESRI Shapefile'
## Simple feature collection with 221 features and 9 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: -90.3103 ymin: 34.99419 xmax: -89.63278 ymax: 35.40948
## geographic CRS: NAD83Data <- as_tibble(read.csv("Data/Assignment_5/Data.csv"))
cat("Number of NA value in the data table is: ", sum(is.na(Data)), sep = '')## Number of NA value in the data table is: 0We explore the data using str function and inspected basic statistics of the data using summary function.
str(Shelby)## Classes 'sf' and 'data.frame': 221 obs. of 10 variables:
## $ STATEFP : chr "47" "47" "47" "47" ...
## $ COUNTYFP: chr "157" "157" "157" "157" ...
## $ TRACTCE : chr "021312" "021530" "021726" "022024" ...
## $ AFFGEOID: chr "1400000US47157021312" "1400000US47157021530" "1400000US47157021726" "1400000US47157022024" ...
## $ GEOID : chr "47157021312" "47157021530" "47157021726" "47157022024" ...
## $ NAME : chr "213.12" "215.30" "217.26" "220.24" ...
## $ LSAD : chr "CT" "CT" "CT" "CT" ...
## $ ALAND : num 1362074 10068590 1433803 2581685 1056567 ...
## $ AWATER : num 0 0 2132 0 45527 ...
## $ geometry:sfc_POLYGON of length 221; first list element: List of 1
## ..$ : num [1:26, 1:2] -89.8 -89.8 -89.8 -89.8 -89.8 ...
## ..- attr(*, "class")= chr [1:3] "XY" "POLYGON" "sfg"
## - attr(*, "sf_column")= chr "geometry"
## - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA
## ..- attr(*, "names")= chr [1:9] "STATEFP" "COUNTYFP" "TRACTCE" "AFFGEOID" ...str(Data)## tibble [221 x 17] (S3: tbl_df/tbl/data.frame)
## $ FID : int [1:221] 0 1 2 3 4 5 6 7 8 9 ...
## $ STATEFP : int [1:221] 47 47 47 47 47 47 47 47 47 47 ...
## $ COUNTYFP : int [1:221] 157 157 157 157 157 157 157 157 157 157 ...
## $ GEO_ID : chr [1:221] "1400000US47157021312" "1400000US47157021530" "1400000US47157021726" "1400000US47157022024" ...
## $ OBJECTID : int [1:221] 1161 1176 1186 1201 1001 1006 1021 1029 1044 1059 ...
## $ ALAND : int [1:221] 1362074 10068590 1433803 2581685 1056567 2037629 1356159 1413184 1418277 1726745 ...
## $ AWATER : int [1:221] 0 0 2132 0 45527 154343 0 0 0 0 ...
## $ Area_sqmil: num [1:221] 0.528 3.886 0.554 0.994 0.421 ...
## $ Tot_Pop : int [1:221] 2108 5396 3486 3429 790 2286 2030 3138 2253 1402 ...
## $ Med_Inc : int [1:221] 54769 129375 26731 49667 14345 14299 45729 56111 20724 27344 ...
## $ Tot_Wht : int [1:221] 1902 4530 1012 167 0 42 882 2417 18 21 ...
## $ Tot_AA : int [1:221] 99 308 2423 3250 762 2244 857 595 2229 1379 ...
## $ Tot_Asian : int [1:221] 97 479 0 0 0 0 110 80 0 1 ...
## $ Pop_Den : num [1:221] 3990 1388 6294 3449 1877 ...
## $ PopDen_Wht: num [1:221] 3600 1166 1827 168 0 ...
## $ PopDen_AA : num [1:221] 187.4 79.3 4375.1 3269 1810.7 ...
## $ PopDen_Asi: num [1:221] 184 123 0 0 0 ...summary(Data)## FID STATEFP COUNTYFP GEO_ID OBJECTID
## Min. : 0 Min. :47 Min. :157 Length:221 Min. :1000
## 1st Qu.: 55 1st Qu.:47 1st Qu.:157 Class :character 1st Qu.:1055
## Median :110 Median :47 Median :157 Mode :character Median :1110
## Mean :110 Mean :47 Mean :157 Mean :1110
## 3rd Qu.:165 3rd Qu.:47 3rd Qu.:157 3rd Qu.:1165
## Max. :220 Max. :47 Max. :157 Max. :1220
## ALAND AWATER Area_sqmil Tot_Pop
## Min. : 555457 Min. : 0 Min. : 0.2163 Min. : 0
## 1st Qu.: 1940071 1st Qu.: 0 1st Qu.: 0.7730 1st Qu.: 2625
## Median : 3843270 Median : 0 Median : 1.4855 Median : 3958
## Mean : 8948641 Mean : 251458 Mean : 3.5522 Mean : 4240
## 3rd Qu.: 6099276 3rd Qu.: 8845 3rd Qu.: 2.4992 3rd Qu.: 5426
## Max. :138912108 Max. :32514243 Max. :66.1540 Max. :16377
## Med_Inc Tot_Wht Tot_AA Tot_Asian
## Min. : 0 Min. : 0 Min. : 0 Min. : 0.0
## 1st Qu.: 25296 1st Qu.: 167 1st Qu.: 787 1st Qu.: 0.0
## Median : 39375 Median : 845 Median :1800 Median : 23.0
## Mean : 48137 Mean : 1667 Mean :2270 Mean : 108.3
## 3rd Qu.: 65382 3rd Qu.: 2535 3rd Qu.:3425 3rd Qu.: 97.0
## Max. :165321 Max. :10300 Max. :8736 Max. :2219.0
## Pop_Den PopDen_Wht PopDen_AA PopDen_Asi
## Min. : 0 Min. : 0.0 Min. : 0.0 Min. : 0.00
## 1st Qu.: 1722 1st Qu.: 122.9 1st Qu.: 456.9 1st Qu.: 0.00
## Median : 2993 Median : 432.5 Median :1473.2 Median : 16.44
## Mean : 3039 Mean :1008.1 Mean :1822.5 Mean : 52.86
## 3rd Qu.: 4197 3rd Qu.:1754.7 3rd Qu.:2922.1 3rd Qu.: 72.23
## Max. :11705 Max. :5276.3 Max. :9842.2 Max. :622.92Merging the Shape File and the Dataset
We merged the shapefile and the data file to combine all the data as attribute of the Shelby County shapefile.
Shelby_merged <- merge(Shelby,Data, by.x = "AFFGEOID", by.y = "GEO_ID")tmap basics
The below shows the basic plotting with tmap. The tm_shape function plots the polygons stored in the Shelby_merged object. The tm_fill function fills the polygons with uniform color, and the tm_borders function draws the borders of the polygons. To arrange different maps side by side, we used tmap_arrange function.
t1 <- tm_shape(Shelby_merged) +
tm_fill()
t2 <- tm_shape(Shelby_merged) +
tm_borders()
t3 <- tm_shape(Shelby_merged) +
tm_fill() +
tm_borders()
tmap_arrange(t1, t2, t3, ncol = 3, nrow = 1)
Aesthetics
The aesthetics of the maps can be changed by changing the properties of the tm_fill and tm_borders functions.
ma1 = tm_shape(Shelby_merged) + tm_fill(col = "red")
ma2 = tm_shape(Shelby_merged) + tm_fill(col = "red", alpha = 0.3)
ma3 = tm_shape(Shelby_merged) + tm_borders(col = "blue")
ma4 = tm_shape(Shelby_merged) + tm_borders(lwd = 3)
ma5 = tm_shape(Shelby_merged) + tm_borders(lty = 2)
ma6 = tm_shape(Shelby_merged) + tm_fill(col = "red", alpha = 0.3) +
tm_borders(col = "blue", lwd = 3, lty = 2)
tmap_arrange(ma1, ma2, ma3, ma4, ma5, ma6)
The legend title can be changed by changing the title properties of the tm_fill function.
legend_title = expression("Area (Miles"^2*")")
tm_shape(Shelby_merged)+
tm_fill(col = "Area_sqmil", title = legend_title)+
tm_borders()
Color settings
The following shows different color scheme to represent a variable in the map where breaks can be manually set as per the user requirements. The color palette can also be selected by the user.
legend_title = "Median Income"
c1 <- tm_shape(Shelby_merged) + tm_polygons(col = "Med_Inc", title=legend_title)
breaks <- c(0, 10, 15, 20) * 10000
c2 <- tm_shape(Shelby_merged) + tm_polygons(col = "Med_Inc", breaks = breaks, title=legend_title)
c3 <- tm_shape(Shelby_merged) + tm_polygons(col = "Med_Inc", n = 10, title=legend_title)
c4 <- tm_shape(Shelby_merged) + tm_polygons(col = "Med_Inc", palette = "BuGn", title=legend_title)
tmap_arrange(c1, c2, c3, c4)
Color settings with different styles and color
The style properties of the tm_polygons function can be used to select different style of data representations like jenks, quantile, pretty, etc.
legend_title = "Population Density"
c5 <- tm_shape(Shelby_merged) + tm_polygons(col = "Pop_Den", title=legend_title, style="pretty", palette = "OrRd")
c6 <- tm_shape(Shelby_merged) + tm_polygons(col = "Pop_Den", title=legend_title, style="equal", palette = "BuGn")
c7 <- tm_shape(Shelby_merged) + tm_polygons(col = "Pop_Den", title=legend_title, style="quantile", palette = "YlOrBr")
c8 <- tm_shape(Shelby_merged) + tm_polygons(col = "Pop_Den", title=legend_title, style="jenks", palette = "Blues")
tmap_arrange(c5, c6, c7, c8)
Layouts
The tm_compass function inserts a north arrow to the map. The position of the map can be selected by the postion argument of the function. A scale bar for the map can be inserted using tm_scale_bar function.
l1 <- tm_shape(Shelby_merged) +
tm_polygons(col = "Med_Inc", style="quantile", palette = "YlOrBr")
l1 +
tm_compass(type = "8star", position = c("left", "bottom")) +
tm_scale_bar(breaks = c(0, 5, 10), text.size = 1)
The tm_layout function helps to change the layout of the map. The code below is an example to use the function with user defined parameters.
l1 +
tm_layout(title = "Median Income",
bg.color = "lightblue", frame.lwd = 2,
legend.outside = TRUE)+
tm_compass(type = "8star", position = c("left", "top"))+
tm_scale_bar(breaks = c(0, 5, 10), text.size = 1, position = c("left", "bottom"))
The tm_style function have some predefined layout styles which can be useful too.
s1 <- l1 + tm_style("bw")
s2 <- l1 + tm_style("classic")
s3 <- l1 + tm_style("cobalt")
s4 <- l1 + tm_style("col_blind")
tmap_arrange(s1, s2, s3, s4)
Interactive maps
The maps generated maps can be showed interactively by changing the tmap_mode to view. It will allow users to interactively see the map. The map can be zoomed in or zoomed out, and is layed over a real time basemap.
tmap_mode("view")## tmap mode set to interactive viewingl1