Data Manipulation recap

Basic R tools
- colnames() to both get and change column names
- head() and tail() to inspect top/bottom rows
- nrow() to get number of rows
- “bracket notation” to get data subsets
- class() to help you deal with unusual object types
- str() shows the internal structure of an R object
Useful tidyverse packages:
- dplyr: for lots of stuff, including:
  - filter() to get a subset of rows
  - select() to get a subset of columns
  - left_join() (and other ..._join() functions) for merging data frames
  - group_by() and summarize() for aggregation
- tidyr: for reshaping
  - pivot_longer() to “stack” multiple columns into a single column
    - … and add a column that indicates the old column names
  - pivot_wider() to “spread out” a column into multiple columns
    - … using a 2nd column to indicate the new column names
Examples below in the choropleth code

Choropleths: maps with colors

Basics and the `sf` package

Choropleths are like a special type of heatmap that uses geographical areas.
There are different ways to access geographical data.
The sf package in R provides:
- detailed access to standards-based geo data called “Simple Features”
  - https://en.wikipedia.org/wiki/Simple_Features
- convenient methods for merging with other data and plotting with ggplot2
Extensive documentation for the sf package:
- https://r-spatial.github.io/sf/index.html
- link to “Spatial Data Science” web book: https://r-spatial.org/book/
ggplot2 package has a geom_sf() function specifically for sf objects.
You may need to install additional utilities on your system to be able to fully use the sf package. See the detailed descriptions for your operating system here: https://r-spatial.github.io/sf/index.html#installing

library(sf)

## Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUE

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.2     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(lubridate)
library(patchwork)

Finding data

You need to find data that has the right “geometry” information to create the map.
Then you need to be able to “convert” the data format into something you can work with.
The sf package supports reading multiple types of spatial data.
One popular type of data is GeoJSON
See examples here:
- https://data.imap.maryland.gov/
“Detailed” vs. “Generalized”

md_county_map <- st_read("Maryland_Physical_Boundaries_-_County_Boundaries_(Generalized).geojson")

## Reading layer `BNDY_CountyPhyBoundaryGen_DoIT' from data source 
##   `/Users/patel/Library/CloudStorage/OneDrive-UniversityofMaryland/AY 2024-25/Spring 2025/TA INST 462 Data Vis/Week 14/Maryland_Physical_Boundaries_-_County_Boundaries_(Generalized).geojson' 
##   using driver `GeoJSON'
## Simple feature collection with 24 features and 9 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -79.4873 ymin: 37.92397 xmax: -75.04921 ymax: 39.72288
## Geodetic CRS:  WGS 84

head(md_county_map)

## Simple feature collection with 6 features and 9 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -79.06692 ymin: 38.31896 xmax: -75.70742 ymax: 39.72288
## Geodetic CRS:  WGS 84
##   OBJECTID         county district county_fip countynum          creation_d
## 1        1       Allegany        6          1         1 2007-08-07 20:00:00
## 2        2   Anne Arundel        5          3         2 2007-01-08 19:00:00
## 3        3      Baltimore        4          5         3 2009-11-16 19:00:00
## 4        4 Baltimore City        0        510        24 2006-04-17 20:00:00
## 5        5        Calvert        5          9         4 2007-01-08 19:00:00
## 6        6       Caroline        2         11         5 2007-05-20 20:00:00
##            last_updat shape_Length shape_Area                       geometry
## 1 2012-01-19 19:00:00    2.6488703 0.11494995 MULTIPOLYGON (((-78.38474 3...
## 2 2008-06-30 20:00:00    2.2298037 0.12117518 MULTIPOLYGON (((-76.53695 3...
## 3 2012-02-14 19:00:00    2.8951218 0.16815116 MULTIPOLYGON (((-76.44945 3...
## 4 2009-11-15 19:00:00    0.6428553 0.02481090 MULTIPOLYGON (((-76.52967 3...
## 5 2008-07-07 20:00:00    1.4357386 0.05935892 MULTIPOLYGON (((-76.52993 3...
## 6 2010-01-27 19:00:00    1.7949442 0.08731723 MULTIPOLYGON (((-75.74764 3...

summary(md_county_map)

##     OBJECTID        county             district       county_fip    
##  Min.   : 1.00   Length:24          Min.   :0.000   Min.   :  1.00  
##  1st Qu.: 6.75   Class :character   1st Qu.:2.000   1st Qu.: 14.50  
##  Median :12.50   Mode  :character   Median :3.500   Median : 26.00  
##  Mean   :12.50                      Mean   :3.625   Mean   : 44.96  
##  3rd Qu.:18.25                      3rd Qu.:5.250   3rd Qu.: 37.50  
##  Max.   :24.00                      Max.   :7.000   Max.   :510.00  
##    countynum       creation_d                    last_updat                 
##  Min.   : 1.00   Min.   :2006-04-17 20:00:00   Min.   :2007-01-07 19:00:00  
##  1st Qu.: 6.75   1st Qu.:2006-09-11 02:00:00   1st Qu.:2009-02-21 01:00:00  
##  Median :12.50   Median :2007-01-08 19:00:00   Median :2010-04-07 08:00:00  
##  Mean   :12.50   Mean   :2007-05-28 07:00:00   Mean   :2010-03-29 09:00:00  
##  3rd Qu.:18.25   3rd Qu.:2007-08-07 20:00:00   3rd Qu.:2012-01-09 19:00:00  
##  Max.   :24.00   Max.   :2012-01-09 19:00:00   Max.   :2012-02-14 19:00:00  
##   shape_Length      shape_Area               geometry 
##  Min.   :0.6429   Min.   :0.02481   MULTIPOLYGON :24  
##  1st Qu.:1.9787   1st Qu.:0.08833   epsg:4326    : 0  
##  Median :2.1978   Median :0.11806   +proj=long...: 0  
##  Mean   :2.2453   Mean   :0.11449                     
##  3rd Qu.:2.4790   3rd Qu.:0.13282                     
##  Max.   :3.7699   Max.   :0.17982

md_county_map_detailed <- st_read("Maryland_Physical_Boundaries_-_County_Boundaries_(Detailed).geojson")

## Reading layer `BNDY_CountyPhyBoundaryDetailed_SHA' from data source 
##   `/Users/patel/Library/CloudStorage/OneDrive-UniversityofMaryland/AY 2024-25/Spring 2025/TA INST 462 Data Vis/Week 14/Maryland_Physical_Boundaries_-_County_Boundaries_(Detailed).geojson' 
##   using driver `GeoJSON'
## Simple feature collection with 24 features and 9 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -79.4873 ymin: 37.89358 xmax: -75.0492 ymax: 39.72287
## Geodetic CRS:  WGS 84

head(md_county_map_detailed)

## Simple feature collection with 6 features and 9 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -79.06692 ymin: 38.31637 xmax: -75.70741 ymax: 39.72287
## Geodetic CRS:  WGS 84
##   OBJECTID         COUNTY DISTRICT COUNTY_FIP COUNTYNUM          CREATION_D
## 1        1       Allegany        6          1         1 2010-01-27 19:00:00
## 2        2   Anne Arundel        5          3         2 2006-04-17 20:00:00
## 3        3      Baltimore        4          5         3 2006-10-08 20:00:00
## 4        4 Baltimore City        0        510        24 2006-04-17 20:00:00
## 5        5        Calvert        5          9         4 2010-01-27 19:00:00
## 6        6       Caroline        2         11         5 2007-05-20 20:00:00
##            LAST_UPDAT Shape_Length Shape_Area                       geometry
## 1 2010-01-27 19:00:00     3.814949 0.11502849 MULTIPOLYGON (((-78.34284 3...
## 2 2006-04-17 20:00:00    10.040214 0.11148263 MULTIPOLYGON (((-76.606 39....
## 3 2006-10-08 20:00:00     9.470194 0.16250630 MULTIPOLYGON (((-76.56796 3...
## 4 2009-11-15 19:00:00     1.978908 0.02198736 MULTIPOLYGON (((-76.52967 3...
## 5 2010-01-27 19:00:00     3.576389 0.05735034 MULTIPOLYGON (((-76.64212 3...
## 6 2008-07-29 20:00:00     3.078015 0.08607846 MULTIPOLYGON (((-75.74764 3...

summary(md_county_map_detailed)

##     OBJECTID        COUNTY             DISTRICT       COUNTY_FIP    
##  Min.   : 1.00   Length:24          Min.   :0.000   Min.   :  1.00  
##  1st Qu.: 6.75   Class :character   1st Qu.:2.000   1st Qu.: 14.50  
##  Median :12.50   Mode  :character   Median :3.500   Median : 26.00  
##  Mean   :12.50                      Mean   :3.625   Mean   : 44.96  
##  3rd Qu.:18.25                      3rd Qu.:5.250   3rd Qu.: 37.50  
##  Max.   :24.00                      Max.   :7.000   Max.   :510.00  
##    COUNTYNUM       CREATION_D                    LAST_UPDAT                 
##  Min.   : 1.00   Min.   :2006-04-17 20:00:00   Min.   :2006-04-17 20:00:00  
##  1st Qu.: 6.75   1st Qu.:2006-08-26 08:00:00   1st Qu.:2007-05-06 02:00:00  
##  Median :12.50   Median :2007-03-07 19:00:00   Median :2008-08-23 08:00:00  
##  Mean   :12.50   Mean   :2007-09-21 19:00:00   Mean   :2008-08-30 06:00:00  
##  3rd Qu.:18.25   3rd Qu.:2008-09-13 02:00:00   3rd Qu.:2009-12-04 01:00:00  
##  Max.   :24.00   Max.   :2010-01-27 19:00:00   Max.   :2012-01-16 19:00:00  
##   Shape_Length      Shape_Area               geometry 
##  Min.   : 1.940   Min.   :0.02199   MULTIPOLYGON :24  
##  1st Qu.: 3.539   1st Qu.:0.08590   epsg:4326    : 0  
##  Median : 5.376   Median :0.11326   +proj=long...: 0  
##  Mean   : 7.050   Mean   :0.10952                     
##  3rd Qu.: 8.199   3rd Qu.:0.12679                     
##  Max.   :25.068   Max.   :0.17963

Digging deeper into geometry
- There’s a lot of data in each “cell”!
- This is why having specialized packages like sf is so helpful.

md_county_map$geometry[1]

## Geometry set for 1 feature 
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -79.06692 ymin: 39.43853 xmax: -78.31348 ymax: 39.72288
## Geodetic CRS:  WGS 84

## MULTIPOLYGON (((-78.38474 39.62421, -78.38468 3...

class(md_county_map$geometry[1])

## [1] "sfc_MULTIPOLYGON" "sfc"

str(md_county_map$geometry[1])

## sfc_MULTIPOLYGON of length 1; first list element: List of 1
##  $ :List of 1
##   ..$ : num [1:2813, 1:2] -78.4 -78.4 -78.4 -78.4 -78.4 ...
##  - attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg"

md_county_map$geometry[1][[1]] # list of many values!

## MULTIPOLYGON (((-78.38474 39.62421, -78.38468 39.62382, -78.38458 39.6233, -78.38443 39.6228, -78.38405 39.62209, -78.38379 39.6214, -78.38292 39.62038, -78.38169 39.61903, -78.38066 39.61817, -78.37929 39.61695, -78.37734 39.61545, -78.37523 39.61378, -78.37452 39.6132, -78.37358 39.61269, -78.37315 39.61226, -78.373 39.61191, -78.37302 39.61141, -78.37313 39.611, -78.37337 39.61049, -78.37378 39.61005, -78.37417 39.60977, -78.37476 39.6095, -78.37601 39.60911, -78.37638 39.60907, -78.37784 39.60918, -78.37823 39.60931, -78.3793 39.6094, -78.38004 39.60948, -78.38134 39.60979, -78.38244 39.6101, -78.38309 39.61025, -78.38439 39.61071, -78.3868 39.61166, -78.38853 39.6124, -78.41299 39.62253, -78.41361 39.62278, -78.41398 39.62302, -78.41707 39.62418, -78.4195 39.62486, -78.42066 39.62513, -78.423 39.62541, -78.42413 39.6255, -78.42413 39.6255, -78.42413 39.6255, -78.42528 39.62545, -78.42722 39.62514, -78.42864 39.62482, -78.4304 39.6241, -78.43123 39.62361, -78.43273 39.62249, -78.4335 39.62176, -78.43457 39.62025, -78.4347 39.61972, -78.43462 39.61866, -78.43425 39.61718, -78.43369 39.61595, -78.43308 39.61489, -78.4327 39.6142, -78.43206 39.61304, -78.43091 39.61184, -78.43029 39.6112, -78.42838 39.60922, -78.42719 39.60799, -78.42368 39.60435, -78.42168 39.60303, -78.42029 39.60215, -78.41932 39.60145, -78.41809 39.60053, -78.417 39.59945, -78.41617 39.59883, -78.41475 39.59802, -78.41419 39.59788, -78.41302 39.59733, -78.41177 39.59658, -78.41021 39.5959, -78.40814 39.59512, -78.40565 39.59407, -78.40339 39.59329, -78.40219 39.59267, -78.40063 39.59173, -78.39898 39.59073, -78.3977 39.58986, -78.39706 39.5891, -78.39642 39.58798, -78.39592 39.58688, -78.39574 39.58601, -78.39586 39.58518, -78.39613 39.58453, -78.39682 39.58367, -78.39717 39.58321, -78.3987 39.58226, -78.40026 39.5815, -78.40096 39.58123, -78.40207 39.58058, -78.40355 39.58007, -78.40483 39.5797, -78.40615 39.57956, -78.40688 39.57951, -78.40817 39.57966, -78.41034 39.57992, -78.4126 39.58033, -78.41471 39.58091, -78.4157 39.58116, -78.41712 39.58171, -78.41791 39.58207, -78.41896 39.58262, -78.41999 39.58331, -78.42091 39.58412, -78.42193 39.585, -78.42279 39.58571, -78.42353 39.58631, -78.42467 39.58692, -78.42643 39.58753, -78.42818 39.58795, -78.42925 39.58815, -78.42979 39.58832, -78.43027 39.58849, -78.43052 39.58868, -78.43117 39.58885, -78.43315 39.58923, -78.43434 39.58961, -78.43561 39.59012, -78.43669 39.59045, -78.43796 39.59083, -78.43937 39.59114, -78.44047 39.59136, -78.44126 39.59157, -78.44171 39.59174, -78.44203 39.59191, -78.44355 39.59235, -78.44521 39.59244, -78.44522 39.59244, -78.44522 39.59244, -78.44701 39.59233, -78.4485 39.59209, -78.44973 39.59184, -78.45121 39.59149, -78.45247 39.59104, -78.45444 39.59004, -78.45491 39.58969, -78.45533 39.58953, -78.457 39.5886, -78.45736 39.58826, -78.45752 39.58804, -78.45804 39.58758, -78.45847 39.58664, -78.45863 39.58579, -78.4587 39.58507, -78.4588 39.58466, -78.45903 39.58374, -78.45908 39.58307, 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39.56033, -78.43138 39.56019, -78.43079 39.55995, -78.43013 39.55957, -78.42959 39.55929, -78.42911 39.55878, -78.4289 39.5585, -78.42819 39.55809, -78.42743 39.55778, -78.42666 39.55744, -78.42627 39.55725, -78.42615 39.55718, -78.42615 39.55718, -78.42615 39.55718, -78.42609 39.5572, -78.42596 39.55732, -78.42562 39.55732, -78.42511 39.55678, -78.42416 39.55564, -78.4233 39.55476, -78.42286 39.55416, -78.4224 39.55355, -78.422 39.55334, -78.42169 39.55319, -78.42146 39.55291, -78.4212 39.55242, -78.42065 39.55166, -78.41999 39.5511, -78.41973 39.5508, -78.41939 39.55059, -78.41827 39.54947, -78.41803 39.54876, -78.41802 39.54824, -78.4181 39.548, -78.41851 39.54736, -78.41915 39.54679, -78.41987 39.5463, -78.42059 39.54599, -78.42084 39.54579, -78.42112 39.54578, -78.4223 39.54568, -78.42277 39.54568, -78.42345 39.54578, -78.42399 39.54599, -78.42461 39.54644, -78.42553 39.54721, -78.42659 39.54833, -78.42693 39.54869, -78.4278 39.54964, -78.42826 39.55033, -78.42856 39.55124, 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A simple plot of a map with `geom_sf()`

The geom_sf() function creates a map based on the features in the data.

basic_counties <- ggplot(data = md_county_map) +
  geom_sf()+
  theme(plot.caption = element_text(size = 10, hjust = 0),
    panel.grid.minor = element_line(linetype = "blank"),
    axis.title = element_text(size = 13),
    axis.text.y = element_text(size = 16),
    plot.title = element_text(size = 16),
    panel.background = element_rect(fill = NA,
        linetype = "dashed")) +labs(title = "Map of counties in Maryland",
    x = "latitude", y = "longitude", colour = "red",
    subtitle = "We are in... PGC!", caption = "I am a caption. Just letting you know the context of the dataset and its limitations.")


detailed_counties <- ggplot(data = md_county_map_detailed) + geom_sf() + theme(plot.subtitle = element_text(size = 10, hjust = 0),
    axis.title = element_text(size = 13),
    axis.text.x = element_text(size = 16),
    axis.text.y = element_text(size = 16),
    plot.title = element_text(size = 16),
    panel.background = element_rect(fill = NA)) +labs(title = "Map of counties in Maryland: detailed",
    x = "longitude", y = "latitude", subtitle = "Helpful details here: no insights in this map just yet",
    caption = "This is where you can add helpful contextual information about your dataset and limitations making inferences")

basic_counties / detailed_counties

basic_counties

detailed_counties

Finding more data and merging

Now that we have a map of counties, we’d like to add county-level data.
- https://opendata.maryland.gov/
- https://opendata.maryland.gov/browse?q=county&sortBy=relevance
- Let’s look at some foreclosure data.

foreclosures <- read_csv("Maryland_Foreclosure_Data_by_County_20240311.csv")

## Rows: 96 Columns: 27
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): Date, Type
## dbl (25): Allegany County, Anne Arundel County, Baltimore City, Baltimore Co...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(foreclosures)

## # A tibble: 6 × 27
##   Date            Type  `Allegany County` `Anne Arundel County` `Baltimore City`
##   <chr>           <chr>             <dbl>                 <dbl>            <dbl>
## 1 02/01/2024 12:… Noti…                76                   522              769
## 2 02/01/2024 12:… Noti…                11                    47              156
## 3 02/01/2024 12:… Fore…                 7                    15               45
## 4 01/01/2024 12:… Noti…                83                   535              914
## 5 01/01/2024 12:… Noti…                17                    56              123
## 6 01/01/2024 12:… Fore…                 7                    12               54
## # ℹ 22 more variables: `Baltimore County` <dbl>, `Calvert County` <dbl>,
## #   `Caroline County` <dbl>, `Carroll County` <dbl>, `Cecil County` <dbl>,
## #   `Charles County` <dbl>, `Dorchester County` <dbl>,
## #   `Frederick County` <dbl>, `Garrett County` <dbl>, `Harford County` <dbl>,
## #   `Howard County` <dbl>, `Kent County` <dbl>, `Montgomery County` <dbl>,
## #   `Prince George's County` <dbl>, `Queen Anne's County` <dbl>,
## #   `St. Mary's County` <dbl>, `Somerset County` <dbl>, …

colnames(foreclosures)

##  [1] "Date"                   "Type"                   "Allegany County"       
##  [4] "Anne Arundel County"    "Baltimore City"         "Baltimore County"      
##  [7] "Calvert County"         "Caroline County"        "Carroll County"        
## [10] "Cecil County"           "Charles County"         "Dorchester County"     
## [13] "Frederick County"       "Garrett County"         "Harford County"        
## [16] "Howard County"          "Kent County"            "Montgomery County"     
## [19] "Prince George's County" "Queen Anne's County"    "St. Mary's County"     
## [22] "Somerset County"        "Talbot County"          "Washington County"     
## [25] "Wicomico County"        "Worcester County"       "(blank)"

Clean up the data:
- remove the “(blank)” column
- filter down to just the “Notice of Foreclosure” type
- convert the Date column into a proper data format with lubridate
- reshape the data with a column for county, so that we can merge in with the map/spatial data
- remove the “County” part of the county names, to match what’s in the map data
Merge the cleaned up data with the map data

colnames(foreclosures)

##  [1] "Date"                   "Type"                   "Allegany County"       
##  [4] "Anne Arundel County"    "Baltimore City"         "Baltimore County"      
##  [7] "Calvert County"         "Caroline County"        "Carroll County"        
## [10] "Cecil County"           "Charles County"         "Dorchester County"     
## [13] "Frederick County"       "Garrett County"         "Harford County"        
## [16] "Howard County"          "Kent County"            "Montgomery County"     
## [19] "Prince George's County" "Queen Anne's County"    "St. Mary's County"     
## [22] "Somerset County"        "Talbot County"          "Washington County"     
## [25] "Wicomico County"        "Worcester County"       "(blank)"

foreclosures$`(blank)` <- NULL # removes the column
colnames(foreclosures)

##  [1] "Date"                   "Type"                   "Allegany County"       
##  [4] "Anne Arundel County"    "Baltimore City"         "Baltimore County"      
##  [7] "Calvert County"         "Caroline County"        "Carroll County"        
## [10] "Cecil County"           "Charles County"         "Dorchester County"     
## [13] "Frederick County"       "Garrett County"         "Harford County"        
## [16] "Howard County"          "Kent County"            "Montgomery County"     
## [19] "Prince George's County" "Queen Anne's County"    "St. Mary's County"     
## [22] "Somerset County"        "Talbot County"          "Washington County"     
## [25] "Wicomico County"        "Worcester County"

# filter to one type of foreclosure
unique(foreclosures$Type)

## [1] "Notice of Intent to Foreclose"     "Notice of Foreclosure"            
## [3] "Foreclosure Property Registration"

nrow(foreclosures)

## [1] 96

foreclosures <- filter(foreclosures, Type %in% "Notice of Foreclosure")
unique(foreclosures$Type)

## [1] "Notice of Foreclosure"

nrow(foreclosures)

## [1] 32

# convert the Date column to date-type data
head(foreclosures$Date)

## [1] "02/01/2024 12:00:00 AM" "01/01/2024 12:00:00 AM" "12/01/2023 12:00:00 AM"
## [4] "11/01/2023 12:00:00 AM" "10/01/2023 12:00:00 AM" "09/01/2023 12:00:00 AM"

foreclosures <- mutate(foreclosures, Date = mdy_hms(Date))
head(foreclosures$Date)

## [1] "2024-02-01 UTC" "2024-01-01 UTC" "2023-12-01 UTC" "2023-11-01 UTC"
## [5] "2023-10-01 UTC" "2023-09-01 UTC"

# reshape the data
colnames(foreclosures)

##  [1] "Date"                   "Type"                   "Allegany County"       
##  [4] "Anne Arundel County"    "Baltimore City"         "Baltimore County"      
##  [7] "Calvert County"         "Caroline County"        "Carroll County"        
## [10] "Cecil County"           "Charles County"         "Dorchester County"     
## [13] "Frederick County"       "Garrett County"         "Harford County"        
## [16] "Howard County"          "Kent County"            "Montgomery County"     
## [19] "Prince George's County" "Queen Anne's County"    "St. Mary's County"     
## [22] "Somerset County"        "Talbot County"          "Washington County"     
## [25] "Wicomico County"        "Worcester County"

fore_long <- pivot_longer(foreclosures, 
                          cols = `Allegany County`:`Worcester County`,
                          names_to = "county", 
                          values_to = "foreclosures")
head(fore_long)

## # A tibble: 6 × 4
##   Date                Type                  county              foreclosures
##   <dttm>              <chr>                 <chr>                      <dbl>
## 1 2024-02-01 00:00:00 Notice of Foreclosure Allegany County               11
## 2 2024-02-01 00:00:00 Notice of Foreclosure Anne Arundel County           47
## 3 2024-02-01 00:00:00 Notice of Foreclosure Baltimore City               156
## 4 2024-02-01 00:00:00 Notice of Foreclosure Baltimore County             108
## 5 2024-02-01 00:00:00 Notice of Foreclosure Calvert County                14
## 6 2024-02-01 00:00:00 Notice of Foreclosure Caroline County               10

# an alternative way to specify columns
fore_long2 <- pivot_longer(foreclosures,
                           cols = matches("(County)|(City)"), 
                           names_to = "county",
                           values_to = "foreclosures")
head(fore_long2)

## # A tibble: 6 × 4
##   Date                Type                  county              foreclosures
##   <dttm>              <chr>                 <chr>                      <dbl>
## 1 2024-02-01 00:00:00 Notice of Foreclosure Allegany County               11
## 2 2024-02-01 00:00:00 Notice of Foreclosure Anne Arundel County           47
## 3 2024-02-01 00:00:00 Notice of Foreclosure Baltimore City               156
## 4 2024-02-01 00:00:00 Notice of Foreclosure Baltimore County             108
## 5 2024-02-01 00:00:00 Notice of Foreclosure Calvert County                14
## 6 2024-02-01 00:00:00 Notice of Foreclosure Caroline County               10

Merging data can be tricky
- Do lots of checks before and after merging

# look at the data frames we want to merge
head(fore_long)

## # A tibble: 6 × 4
##   Date                Type                  county              foreclosures
##   <dttm>              <chr>                 <chr>                      <dbl>
## 1 2024-02-01 00:00:00 Notice of Foreclosure Allegany County               11
## 2 2024-02-01 00:00:00 Notice of Foreclosure Anne Arundel County           47
## 3 2024-02-01 00:00:00 Notice of Foreclosure Baltimore City               156
## 4 2024-02-01 00:00:00 Notice of Foreclosure Baltimore County             108
## 5 2024-02-01 00:00:00 Notice of Foreclosure Calvert County                14
## 6 2024-02-01 00:00:00 Notice of Foreclosure Caroline County               10

head(md_county_map)

## Simple feature collection with 6 features and 9 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -79.06692 ymin: 38.31896 xmax: -75.70742 ymax: 39.72288
## Geodetic CRS:  WGS 84
##   OBJECTID         county district county_fip countynum          creation_d
## 1        1       Allegany        6          1         1 2007-08-07 20:00:00
## 2        2   Anne Arundel        5          3         2 2007-01-08 19:00:00
## 3        3      Baltimore        4          5         3 2009-11-16 19:00:00
## 4        4 Baltimore City        0        510        24 2006-04-17 20:00:00
## 5        5        Calvert        5          9         4 2007-01-08 19:00:00
## 6        6       Caroline        2         11         5 2007-05-20 20:00:00
##            last_updat shape_Length shape_Area                       geometry
## 1 2012-01-19 19:00:00    2.6488703 0.11494995 MULTIPOLYGON (((-78.38474 3...
## 2 2008-06-30 20:00:00    2.2298037 0.12117518 MULTIPOLYGON (((-76.53695 3...
## 3 2012-02-14 19:00:00    2.8951218 0.16815116 MULTIPOLYGON (((-76.44945 3...
## 4 2009-11-15 19:00:00    0.6428553 0.02481090 MULTIPOLYGON (((-76.52967 3...
## 5 2008-07-07 20:00:00    1.4357386 0.05935892 MULTIPOLYGON (((-76.52993 3...
## 6 2010-01-27 19:00:00    1.7949442 0.08731723 MULTIPOLYGON (((-75.74764 3...

# simplify the map data for merging
md_county_geometry <- select(md_county_map, county, geometry)

# check/compare the values of the columns you want to merge on
unique(fore_long$county)

##  [1] "Allegany County"        "Anne Arundel County"    "Baltimore City"        
##  [4] "Baltimore County"       "Calvert County"         "Caroline County"       
##  [7] "Carroll County"         "Cecil County"           "Charles County"        
## [10] "Dorchester County"      "Frederick County"       "Garrett County"        
## [13] "Harford County"         "Howard County"          "Kent County"           
## [16] "Montgomery County"      "Prince George's County" "Queen Anne's County"   
## [19] "St. Mary's County"      "Somerset County"        "Talbot County"         
## [22] "Washington County"      "Wicomico County"        "Worcester County"

unique(md_county_geometry$county)

##  [1] "Allegany"        "Anne Arundel"    "Baltimore"       "Baltimore City" 
##  [5] "Calvert"         "Caroline"        "Carroll"         "Cecil"          
##  [9] "Charles"         "Dorchester"      "Frederick"       "Garrett"        
## [13] "Harford"         "Howard"          "Kent"            "Montgomery"     
## [17] "Prince George's" "Queen Anne's"    "Somerset"        "St. Mary's"     
## [21] "Talbot"          "Washington"      "Wicomico"        "Worcester"

sum(! fore_long$county %in% md_county_geometry$county)

## [1] 736

sum(! md_county_geometry$county %in% fore_long$county)

## [1] 23

# clean up columns so the values will match for merging
# the gsub(x, y, z) function substitutes every instance of "x" in "z"
# with the string "y"
fore_long <- mutate(fore_long, county = gsub(" County", "", county))
unique(fore_long$county)

##  [1] "Allegany"        "Anne Arundel"    "Baltimore City"  "Baltimore"      
##  [5] "Calvert"         "Caroline"        "Carroll"         "Cecil"          
##  [9] "Charles"         "Dorchester"      "Frederick"       "Garrett"        
## [13] "Harford"         "Howard"          "Kent"            "Montgomery"     
## [17] "Prince George's" "Queen Anne's"    "St. Mary's"      "Somerset"       
## [21] "Talbot"          "Washington"      "Wicomico"        "Worcester"

unique(md_county_geometry$county)

##  [1] "Allegany"        "Anne Arundel"    "Baltimore"       "Baltimore City" 
##  [5] "Calvert"         "Caroline"        "Carroll"         "Cecil"          
##  [9] "Charles"         "Dorchester"      "Frederick"       "Garrett"        
## [13] "Harford"         "Howard"          "Kent"            "Montgomery"     
## [17] "Prince George's" "Queen Anne's"    "Somerset"        "St. Mary's"     
## [21] "Talbot"          "Washington"      "Wicomico"        "Worcester"

sum(! fore_long$county %in% md_county_geometry$county)

## [1] 0

sum(! md_county_geometry$county %in% fore_long$county)

## [1] 0

# merge() is a base R function that has a method for `sf` objects
nrow(md_county_geometry)

## [1] 24

nrow(fore_long)

## [1] 768

md_fore_map_all <- merge(md_county_geometry, fore_long, by = "county", all = FALSE)
nrow(md_fore_map_all)

## [1] 768

head(md_fore_map_all)

## Simple feature collection with 6 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -79.06692 ymin: 39.43853 xmax: -78.31348 ymax: 39.72288
## Geodetic CRS:  WGS 84
##     county       Date                  Type foreclosures
## 1 Allegany 2024-02-01 Notice of Foreclosure           11
## 2 Allegany 2022-12-01 Notice of Foreclosure           13
## 3 Allegany 2023-05-01 Notice of Foreclosure           12
## 4 Allegany 2021-11-01 Notice of Foreclosure            3
## 5 Allegany 2022-04-01 Notice of Foreclosure           14
## 6 Allegany 2022-09-01 Notice of Foreclosure           11
##                         geometry
## 1 MULTIPOLYGON (((-78.38474 3...
## 2 MULTIPOLYGON (((-78.38474 3...
## 3 MULTIPOLYGON (((-78.38474 3...
## 4 MULTIPOLYGON (((-78.38474 3...
## 5 MULTIPOLYGON (((-78.38474 3...
## 6 MULTIPOLYGON (((-78.38474 3...

# the dplyr package has join() functions
md_fore_map_all2 <- left_join(md_county_geometry, fore_long, by = "county")
head(md_fore_map_all2)

## Simple feature collection with 6 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -79.06692 ymin: 39.43853 xmax: -78.31348 ymax: 39.72288
## Geodetic CRS:  WGS 84
##     county       Date                  Type foreclosures
## 1 Allegany 2024-02-01 Notice of Foreclosure           11
## 2 Allegany 2024-01-01 Notice of Foreclosure           17
## 3 Allegany 2023-12-01 Notice of Foreclosure           10
## 4 Allegany 2023-11-01 Notice of Foreclosure           13
## 5 Allegany 2023-10-01 Notice of Foreclosure            9
## 6 Allegany 2023-09-01 Notice of Foreclosure           18
##                         geometry
## 1 MULTIPOLYGON (((-78.38474 3...
## 2 MULTIPOLYGON (((-78.38474 3...
## 3 MULTIPOLYGON (((-78.38474 3...
## 4 MULTIPOLYGON (((-78.38474 3...
## 5 MULTIPOLYGON (((-78.38474 3...
## 6 MULTIPOLYGON (((-78.38474 3...

Some example plots

Mapping the fill to foreclosures
- the data has a row for each county (24) for each date (32) (24 * 32 = 768 rows)
- if we ignore Date, we don’t get a very meaningful result

ggplot(data = md_fore_map_all) +
  geom_sf(aes(fill = foreclosures))+
  scale_fill_distiller(palette = "Reds", direction = 1) +
  theme(plot.caption = element_text(size = 12, hjust = 0),
    axis.text.x = element_text(size = 12),
    axis.text.y = element_text(size = 12),
    panel.grid.minor = element_line(linetype = "blank"),
    axis.title = element_text(size = 12),
    plot.title = element_text(size = 15),
    panel.background = element_rect(fill = NA,
            linetype = "dashed")) +
            labs(title = "Number of foreclosures in Maryland counties (YMin - YMax)", x = "", y = "", colour = "orange", subtitle = "County X had the most foreclosures", caption = "Source: opendata.maryland.gov")

We need to aggregate, or split!
- Sum up foreclosures over Dates, then plot this sum

head(fore_long)

## # A tibble: 6 × 4
##   Date                Type                  county         foreclosures
##   <dttm>              <chr>                 <chr>                 <dbl>
## 1 2024-02-01 00:00:00 Notice of Foreclosure Allegany                 11
## 2 2024-02-01 00:00:00 Notice of Foreclosure Anne Arundel             47
## 3 2024-02-01 00:00:00 Notice of Foreclosure Baltimore City          156
## 4 2024-02-01 00:00:00 Notice of Foreclosure Baltimore               108
## 5 2024-02-01 00:00:00 Notice of Foreclosure Calvert                  14
## 6 2024-02-01 00:00:00 Notice of Foreclosure Caroline                 10

fore_grouped <- group_by(fore_long, county)
fore_total <- summarize(fore_grouped, 
                        total_foreclosures = sum(foreclosures, na.rm = TRUE))
head(fore_total)

## # A tibble: 6 × 2
##   county         total_foreclosures
##   <chr>                       <dbl>
## 1 Allegany                      321
## 2 Anne Arundel                 1681
## 3 Baltimore                    3077
## 4 Baltimore City               3640
## 5 Calvert                       404
## 6 Caroline                      157

# re-merge with the geometry info
md_fore_map_totals <- left_join(md_county_geometry, fore_total, by = "county")

# ggplot(data = md_fore_map_totals) + geom_sf(aes(fill = total_foreclosures))

ggplot(data = md_fore_map_totals) +
  geom_sf(aes(fill = total_foreclosures))+
  scale_fill_distiller(palette = "Reds", direction = 1) +
  theme(plot.caption = element_text(size = 12, hjust = 0),
    axis.text.x = element_text(size = 12),
    axis.text.y = element_text(size = 12),
    panel.grid.minor = element_line(linetype = "blank"),
    axis.title = element_text(size = 12),
    plot.title = element_text(size = 15),
    panel.background = element_rect(fill = NA,
            linetype = "dashed")) +
            labs(title = "Number of foreclosures in Maryland counties (YMin - YMax)", x = "", y = "", colour = "orange", subtitle = "County X had the most foreclosures", caption = "Source: opendata.maryland.gov")

summary(md_fore_map_totals$total_foreclosures)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    68.0   170.5   412.5   874.3   852.8  4324.0

#Create a lollipop plot to see the distribution of foreclosures by county

lol <- ggplot(data = md_fore_map_totals) +
  geom_segment(aes(x = reorder(county, total_foreclosures), xend = county, y = 0, yend = total_foreclosures)) +
  geom_point(aes(x = reorder(county, total_foreclosures), y = total_foreclosures)) +
  coord_flip() +
  theme(plot.caption = element_text(size = 12, hjust = 0),
    axis.text.x = element_text(size = 16),
    axis.text.y = element_text(size = 16),
    panel.grid.minor = element_line(linetype = "blank"),
    axis.title = element_text(size = 14),
    axis.title.x = element_text(angle = 0, hjust = 1, margin = margin(t = 10)),
    plot.title = element_text(size = 20),
    panel.background = element_rect(fill = NA,
            linetype = "dashed")) +
            labs(title = "Number of foreclosures in Maryland counties (X-Y)", x = "", y = "foreclosures", colour = "orange", subtitle = "Prince Georges County topped the charts with 4324 foreclosures", caption = "Source: opendata.maryland.gov")

ggsave(lol, filename = "lollipop.png", width = 10, height = 8, dpi = 700)

Adjusting the colors with scale_fill_distiller()
- a version of scale_fill_brewer that is designed for continuous data
- the classic Color Brewer scales are for categorical/discrete data

ggplot(data = md_fore_map_totals) + geom_sf(aes(fill = total_foreclosures)) +
  scale_fill_distiller(palette = "Reds", direction = 1)+
  theme_minimal()

Another option: scale_fill_fermenter()
- creates bins

ggplot(data = md_fore_map_totals) + geom_sf(aes(fill = total_foreclosures)) +
  scale_fill_fermenter(palette = "Reds", direction = 1)+
  theme_minimal()

Now go back to non-aggregated data, and use Date to make plots over time

# ggplot(data = md_fore_map_all) +
#   geom_sf(aes(fill = foreclosures)) + 
#   scale_fill_distiller(palette = "Reds", direction = 1) +
#   facet_wrap(~ Date)+
#   theme_minimal()

facetedChoropleth <- ggplot(data = md_fore_map_all) +
  geom_sf(aes(fill = foreclosures))+
  scale_fill_distiller(palette = "Reds", direction = 1) +
  theme(plot.caption = element_text(size = 12, hjust = 0),
    strip.text = element_text(size = 10),
    strip.background = element_blank(),
    #remove x-axis text
    axis.text.x = element_blank(),
    # remove y-axis text
    axis.text.y = element_blank(),
    panel.grid.minor = element_line(linetype = "blank"),
    axis.title = element_text(size = 0),
    plot.title = element_text(size = 20),
    panel.spacing = unit(.25, "cm"),
    legend.position = "right",
    #legend.justification = c(0.5, 0.5),
    #legend.direction = "horizontal",
    legend.title = element_text(size = 12),
    legend.text = element_text(size = 12),
    legend.background = element_rect(),
    panel.background = element_rect(fill = NA,
            linetype = "dashed")) +
            labs(title = "Number of foreclosures in MD counties",
                 x = "",
                 y = "",
                 colour = "orange",
                 subtitle = "BGC had the most foreclosures",
                 caption = "Source: opendata.maryland.gov")+
    facet_wrap(~Date, labeller = label_wrap_gen(10))

facetedChoropleth

# Save the faceted choropleth plot
ggsave(facetedChoropleth, filename = "facetedChoropleth.png", width = 10, height = 8, dpi = 700)

# Export the data to a CSV file
write.csv(md_fore_map_totals, "md_fore_map_totals.csv")

Upshot

The sf package makes it easy(-er) to bring spatial/geographical data into R.
If you can find some map data (like GeoJSON) and some other data to merge (like the foreclosure data), you can combine those data sets in R.
With a combined data set, you can leverage all the ability of ggplot2 to create plots that change the fill/color of the geographical shapes based on a variable in your data – that’s a choropleth!

INST 462: maps and choropleths in R

Spring 2025, Sections 101–103

Scott Jackson

Data Manipulation recap

Choropleths: maps with colors

Basics and the `sf` package

Finding data

A simple plot of a map with `geom_sf()`

Finding more data and merging

Some example plots

Upshot

INST 462: maps and choropleths in R

Spring 2025, Sections 101–103

Scott Jackson

Data Manipulation recap

Choropleths: maps with colors

Basics and the sf package

Finding data

A simple plot of a map with geom_sf()

Finding more data and merging

Some example plots

Upshot

Basics and the `sf` package

A simple plot of a map with `geom_sf()`