GIS_Homework_Jul_4_2021_Part1
Samuel Kalibala
7/4/2021
This is part1 of the GIS homework. It covers steps 1 to 7. Step 8 onwards are in part2. I had to split the file because it was not uploading in Rpubs.
Load tidyverse, ggplot2 and fansi
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.2 v dplyr 1.0.6
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggplot2)
library(fansi)Load the required packages
# install.packages("tmap")
# install.packages("tmaptools")
# install.packages("sf")
# install.packages("leaflet")
library("tmap")
library("tmaptools")
library("sf")## Linking to GEOS 3.9.0, GDAL 3.2.1, PROJ 7.2.1library("leaflet")# install.packages("rio")
library(rio)Step 1. Load the data
nhdatafile <- "NHD2016.xlsx"setwd("C:/Documents - Copy/PERSONAL/Data 110_MC_Class/GIS/GIS")
#nhdata <- import(nhdatafile)
nhdata <- rio::import(nhdatafile)view(nhdata)The data frame has 29 columns and 10 entries. The largest numbers are for Sanders and Clinton.
Eliminate columns for minor candidates and just use County, Clinton and Sanders columns. I had to rename the data frame as nhdatacs for it to show only data for Clinton and Sanders.
nhdata <- nhdata[,c("County", "Clinton", "Sanders")]Step 2: Decide what data to map
Add columns for percents and margins:
nhdata$SandersMarginVotes <- nhdata$Sanders - nhdata$Clinton
nhdata$SandersPct <- (nhdata$Sanders) / (nhdata$Sanders + nhdata$Clinton)
# Will use formatting later to multiply by a hundred
nhdata$ClintonPct <- (nhdata$Clinton) / (nhdata$Sanders + nhdata$Clinton)
nhdata$SandersMarginPctgPoints <- nhdata$SandersPct - nhdata$ClintonPctStep 3: Get geographic data files
Read in the shapefile for US states and counties:
#install.packages("raster")
#install.packages("rgdal")
setwd("C:/Documents - Copy/PERSONAL/Data 110_MC_Class/GIS/GIS")
library(raster)## Loading required package: sp##
## Attaching package: 'raster'## The following object is masked from 'package:dplyr':
##
## select## The following object is masked from 'package:tidyr':
##
## extractlibrary(rgdal)## rgdal: version: 1.5-23, (SVN revision 1121)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: C:/Users/user/OneDrive/Documents/R/win-library/4.1/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: C:/Users/user/OneDrive/Documents/R/win-library/4.1/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-5
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.
## Overwritten PROJ_LIB was C:/Users/user/OneDrive/Documents/R/win-library/4.1/rgdal/projusgeo <- shapefile("cb_2014_us_county_5m/cb_2014_us_county_5m.shp")## Warning in rgdal::readOGR(dirname(x), fn, stringsAsFactors = stringsAsFactors, :
## Z-dimension discardedTake a look at the data
view(usgeo)The data frame has 9 columns and 1000 entries.
Do a quick plot (qtm stands for quick thematic map) of the shapefile and check its structure:
qtm(usgeo)
str(usgeo)## Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots
## ..@ data :'data.frame': 3233 obs. of 9 variables:
## .. ..$ STATEFP : chr [1:3233] "01" "13" "19" "40" ...
## .. ..$ COUNTYFP: chr [1:3233] "059" "111" "109" "115" ...
## .. ..$ COUNTYNS: chr [1:3233] "00161555" "00351094" "00465243" "01101845" ...
## .. ..$ AFFGEOID: chr [1:3233] "0500000US01059" "0500000US13111" "0500000US19109" "0500000US40115" ...
## .. ..$ GEOID : chr [1:3233] "01059" "13111" "19109" "40115" ...
## .. ..$ NAME : chr [1:3233] "Franklin" "Fannin" "Kossuth" "Ottawa" ...
## .. ..$ LSAD : chr [1:3233] "06" "06" "06" "06" ...
## .. ..$ ALAND : chr [1:3233] "1641580723" "1002370118" "2519332669" "1219467406" ...
## .. ..$ AWATER : chr [1:3233] "32904833" "13560697" "4154722" "35708892" ...
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## ..$ comment: chr "TRUE"Extracting geodata just for New Hampshire
nhgeo <- usgeo[usgeo@data$STATEFP=="33",]Take a look at the data frame
view(nhgeo)The data frame has 9 variables and 10 entries.
tmap test plot of the New Hampshire data
qtm(nhgeo)
Step 4: Merge spatial and results data
Before merging, do the data frames have the same names for counties?
str(str(nhgeo$NAME))## chr [1:10] "Grafton" "Hillsborough" "Coos" "Belknap" "Rockingham" ...
## NULLstr(nhdata$County)## chr [1:10] "Belknap" "Carroll" "Cheshire" "Coos" "Grafton" "Hillsborough" ...Change the factors to character strings with:
nhgeo$NAME <- as.character(nhgeo$NAME)Next, it can be helpful to sort both data sets by county name and then compare.
nhgeo <- nhgeo[order(nhgeo$NAME),]
nhdata <- nhdata[order(nhdata$County),]Are the two county columns identical now? They should be; let’s check:
identical(nhgeo$NAME,nhdata$County )## [1] TRUENow we can join the two files.
I could not load append_data. So I used the function merge.
library(sf)
nhmap <- merge(nhgeo, nhdata, by.x = "NAME", by.y = "County")
# See the new data structure with
str(nhmap)## Formal class 'SpatialPolygonsDataFrame' [package "sp"] with 5 slots
## ..@ data :'data.frame': 10 obs. of 15 variables:
## .. ..$ NAME : chr [1:10] "Belknap" "Carroll" "Cheshire" "Coos" ...
## .. ..$ STATEFP : chr [1:10] "33" "33" "33" "33" ...
## .. ..$ COUNTYFP : chr [1:10] "001" "003" "005" "007" ...
## .. ..$ COUNTYNS : chr [1:10] "00873174" "00873175" "00873176" "00873177" ...
## .. ..$ AFFGEOID : chr [1:10] "0500000US33001" "0500000US33003" "0500000US33005" "0500000US33007" ...
## .. ..$ GEOID : chr [1:10] "33001" "33003" "33005" "33007" ...
## .. ..$ LSAD : chr [1:10] "06" "06" "06" "06" ...
## .. ..$ ALAND : chr [1:10] "1036582289" "2411458935" "1830366195" "4648216798" ...
## .. ..$ AWATER : chr [1:10] "177039345" "158933434" "57990901" "90773891" ...
## .. ..$ Clinton : num [1:10] 3495 3230 5132 2013 6918 ...
## .. ..$ Sanders : num [1:10] 6005 5638 12441 3639 14245 ...
## .. ..$ SandersMarginVotes : num [1:10] 2510 2408 7309 1626 7327 ...
## .. ..$ SandersPct : num [1:10] 0.632 0.636 0.708 0.644 0.673 ...
## .. ..$ ClintonPct : num [1:10] 0.368 0.364 0.292 0.356 0.327 ...
## .. ..$ SandersMarginPctgPoints: num [1:10] 0.264 0.272 0.416 0.288 0.346 ...
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## .. ..- attr(*, "dimnames")=List of 2
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## ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
## .. .. ..@ projargs: chr "+proj=longlat +datum=NAD83 +no_defs"
## .. .. ..$ comment: chr "GEOGCRS[\"NAD83\",\n DATUM[\"North American Datum 1983\",\n ELLIPSOID[\"GRS 1980\",6378137,298.257222"| __truncated__I deleted this output because the file was too big and was not loading in Rpubs.
Step 5: Create a static map
The hard part is done: finding data, getting it into the right format and merging it with geospatial data. Now, creating a simple static map of Sanders’ margins by county in number of votes is as easy as:
qtm(nhmap, "SandersMarginVotes")## Some legend labels were too wide. These labels have been resized to 0.62, 0.62, 0.62, 0.57, 0.53. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.
Some legend labels were too wide. These labels have been resized to 0.54, 0.54, 0.54, 0.50, 0.46. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.
And mapping margins by percentage:
qtm(nhmap, "SandersMarginPctgPoints")
Legend labels were too wide. The labels have been resized to 0.66, 0.66, 0.66, 0.66, 0.66, 0.66. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.
For more control over the map’s colors, borders and such, use the tm_shape() function, which uses a ggplot2-like syntax to set fill, border and other attributes:
tm_shape(nhmap) +
tm_fill("SandersMarginVotes", title="Sanders Margin, Total Votes", palette = "PRGn") +
tm_borders(alpha=.5) +
tm_text("NAME", size=0.8)## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Some legend labels were too wide. These labels have been resized to 0.62, 0.62, 0.62, 0.57, 0.53. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.
There are also a few built-in tmap themes, such as tm_style_classic:
I got an error message with tm_style_classic so I used tm_style(“classic”)
nhstaticmap <- tm_shape(nhmap) +
tm_fill("SandersMarginVotes", title="Sanders Margin, Total Votes", palette = "viridis") + #I like viridis
tm_borders(alpha=.5) +
tm_text("NAME", size=0.8) +
tm_style("classic")Save static map created by tmap by using the save_tmap() function:
I got an error message with the save_tmap() function. So I used:
tmap_save(nhstaticmap, filename="nhdemprimary.jpg") ## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output
## Warning in sp::proj4string(obj): CRS object has comment, which is lost in output## Map saved to C:\Documents - Copy\PERSONAL\Data 110_MC_Class\nhdemprimary.jpg## Resolution: 1501.336 by 2937.385 pixels## Size: 5.004452 by 9.791282 inches (300 dpi)Step 6: Create palette and pop-ups for interactive map
Create a Leaflet palette with this syntax:
`I got an error message: could not find function “colorFunction”
Next I ran this one:
clintonPalette <- colorNumeric(palette = "Blues", domain=nhmap$ClintonPct)Content for a pop-up window is just a simple combination of HTML and R variables, such as:
library(formattable)##
## Attaching package: 'formattable'## The following object is masked from 'package:raster':
##
## areaAt first I got an error message saying percent function not known. I had to install “formattable” package that contains the percent function.
nhpopup <- paste0("County: ", nhmap$County,
"Sanders ", percent(nhmap$SandersPct), " - Clinton ", percent(nhmap$ClintonPct))Step 7: Generate an interactive map
Now, the map code:
leaflet(nhmap) %>%
addProviderTiles("CartoDB.Positron") %>%
addPolygons(stroke=FALSE,
smoothFactor = 0.2,
fillOpacity = .8,
popup=nhpopup,
color= ~clintonPalette(nhmap$ClintonPct)
)