Package 'cholera'

Title: Amend, Augment and Aid Analysis of John Snow's Cholera Map
Description: Amends errors, augments data and aids analysis of John Snow's map of the 1854 London cholera outbreak.
Authors: lindbrook [aut, cre]
Maintainer: lindbrook <[email protected]>
License: GPL (>= 2)
Version: 0.8.0.9486
Built: 2024-11-26 22:22:42 UTC
Source: https://github.com/lindbrook/cholera

Help Index

cholera

Description

Amend errors, augment data and aid analysis of John Snow's map of the 1854 London cholera outbreak.

Details

  • Fixes two sets of errors in Dodson and Tobler's 1992 digitization of Snow's map: 1) three misplaced cases/fatalities and 2) one missing road segment (part of Clifford Street).

  • "Unstacks" the data in two ways to make analysis and visualization easier and more meaningful.

  • Computes and visualizes "pump neighborhoods" based on Voronoi tessellation, Euclidean distance, and walking distance.

  • Overlay graphical elements and features like kernel density estimates, Voronoi diagrams, Snow's Broad Street neighborhood, and notable landmarks (John Snow's residence, the Lion Brewery, etc.) via 'add*()' functions.

  • Includes a variety of functions to highlight specific cases, roads, pumps and paths.

  • Appends actual street names to roads data.

  • Includes the revised pump data used in the second version of Snow's map from the Vestry report, which includes the "correct" location of the Broad Street pump.

  • Adds two different aggregate time series fatalities data sets, taken from the Vestry report.

  • Support for parallel computation on Linux, macOS and Windows.

  • With 'cholera' version >= 0.8.0, preliminary and provisional support for georeferenced (longitude and latitude) versions of data and functions.

To learn more, see the vignettes:

vignette("duplicate.missing.cases")

vignette("kernel.density")

vignette("parallelization")

vignette("pump.neighborhoods")

vignette("roads")

vignette("tiles.polygons")

vignette("time.series")

vignette("unstacking.bars")

Author(s)

Maintainer: lindbrook [email protected]

See Also

Useful links:

Add observed case(s) to plot.

Description

Add case(s), as "anchor", "fatality" or "orthogonal" as points or IDs, to a plot.

Usage

addCase(case = 1, latlong = FALSE, type = "observed", token = "both",
  text.size = 0.5, pch = 1, cex = 1, point.lwd = 2, col = "black",
  pos = 1)

Arguments

case

Numeric or Character. Vector of case ID(s). "anchor" plots anchor cases; "fatality" plots all cases; "orthogonal" plot projected addresses.
latlong

Logical.
type

Character. Type of case: "observed" or "expected".
token

Character. Type of token to plot: "point", "id" or "both".
text.size

Numeric. Size of case ID text.
pch

Numeric. pch.
cex

Numeric. cex.
point.lwd

Numeric. Point lwd.
col

Character. Color.
pos

Numeric. Text position.

Note

type, token, text.size, pch, cex, point.lwd and pos relevant only when case is numeric.

Examples

snowMap(add.cases = FALSE)
addCase(1)

snowMap(add.cases = FALSE)
addCase(100)

Add Delaunay triangles.

Description

Add Delaunay triangles.

Usage

addDelaunay(pump.select = NULL, vestry = FALSE, color = "black",
  line.type = "solid", line.width = 1, latlong = FALSE)

Arguments

pump.select

Numeric. Default is NULL; all pumps are used. Otherwise, selection by a vector of numeric IDs: 1 to 13 for pumps; 1 to 14 for pumps.vestry. Exclusion (negative selection) is possible (e.g., -6).
vestry

Logical. FALSE for original 13 pumps. TRUE for 14 pumps in Vestry Report.
color

Character. Color of triangle edges.
line.type

Character. Type of line for triangle edges.
line.width

Numeric. Width of cell edges: lwd.
latlong

Logical. Use estimated longitude and latitude.

Note

This function uses deldir::deldir().

Examples

snowMap()
addDelaunay()

Add Euclidean path from case/landmark to nearest or selected pump. (prototype)

Description

Add Euclidean path from case/landmark to nearest or selected pump. (prototype)

Usage

addEuclideanPath(origin = 1, destination = NULL, type = "case-pump",
  vestry = FALSE, latlong = FALSE, case.set = "observed",
  location = "nominal", weighted = TRUE, distance.unit = "meter",
  time.unit = "second", walking.speed = 5, include.landmarks = TRUE,
  mileposts = TRUE, milepost.unit = "distance", milepost.interval = NULL,
  alpha.level = 1)

Arguments

origin

Numeric. Vector of origin(s) (numeric ID or character name landmark/pump ).
destination

Numeric. Vector of destination(s) (numeric or landmark/pump name).
type

Character. Path case to pump. FALSE is all other combinations of cases, landmarks and pumps.
vestry

Logical. TRUE uses the 14 pumps from the map in the Vestry Report. FALSE uses the 13 pumps from the original map.
latlong

Logical.
case.set

Character. "observed" or "expected".
location

Character. For cases and pumps. "nominal", "anchor" or "orthogonal".
weighted

Logical. TRUE computes shortest path in terms of road length. FALSE computes shortest path in terms of the number of nodes.
distance.unit

Character. Unit of distance: "meter" or "yard".
time.unit

Character. "hour", "minute", or "second".
walking.speed

Numeric. Walking speed in km/hr.
include.landmarks

Logical. Include landmarks as cases.
mileposts

Logical. Plot mile/time posts.
milepost.unit

Character. "distance" or "time".
milepost.interval

Numeric. Mile post interval unit of distance (yard or meter) or unit of time (seconds).
alpha.level

Numeric. Alpha level transparency for path: a value in [0, 1].

Add map border to plot.

Description

Add map border to plot.

Usage

addFrame(latlong = FALSE, col = "black", ...)

Arguments

latlong

Logical. Use estimated longitude and latitude.
col

Character. Color
...

Additional plotting parameters.

Highlight index case at 40 Broad Street.

Description

Highlight index case at 40 Broad Street.

Usage

addIndexCase(latlong = FALSE, cex = 2, col = "red", pch = 1,
  add.label = FALSE, text.size = 0.5)

Arguments

latlong

Logical.
cex

Numeric. Size of point.
col

Character. Color of point.
pch

Numeric. Type of of point.
add.label

Logical. Add text annotation: "40 Broad Street"
text.size

Numeric. Size of text label.

Value

Add base R point and (optionally) text to a graphics plot.

Examples

segmentLocator("216-1")
addIndexCase()

Add 2D kernel density contours.

Description

Add 2D kernel density contours based on selected sets of observations.

Usage

addKernelDensity(pump.subset = "pooled", pump.select = NULL,
  neighborhood.type = "walking", data = "unstacked", bandwidth = 0.5,
  color = "black", line.type = "solid", multi.core = FALSE,
  latlong = FALSE)

Arguments

pump.subset

Character or Numeric: "pooled", "individual", or numeric vector. "pooled" treats all observations as a single set. "individual" is a shortcut for all individual pump neighborhoods. Use of vector of numeric pump IDs to subset from the neighborhoods defined by pump.select. Negative selection possible. NULL selects all pumps in pump.select.
pump.select

Numeric. Vector of numeric pump IDs to define pump neighborhoods (i.e., the "population"). Negative selection possible. NULL selects all pumps.
neighborhood.type

Character. "voronoi" or "walking"
data

Character. Unit of observation: "unstacked" uses fatalities.unstacked; "address" uses fatalities.address; "fatality" uses fatalities.
bandwidth

Numeric. Bandwidth for kernel density estimation.
color

Character. Color of contour lines.
line.type

Character. Line type for contour lines.
multi.core

Logical or Numeric. TRUE uses parallel::detectCores(). FALSE uses one, single core. You can also specify the number logical cores. See vignette("Parallelization") for details.
latlong

Logical.

Value

Add contours to a graphics plot.

Note

This function uses KernSmooth::bkde2D().

Examples

## Not run: 
snowMap()
addKernelDensity()

snowMap()
addKernelDensity("individual")

snowMap()
addKernelDensity(c(6, 8))

snowMap()
addKernelDensity(pump.select = c(6, 8))

## End(Not run)

Add landmarks to plot.

Description

Add landmarks to plot.

Usage

addLandmarks(text.size = 0.5, text.col = "black",
  highlight.perimeter = TRUE, latlong = FALSE)

Arguments

text.size

Numeric. cex for text labels.
text.col

Character. col for text labels.
highlight.perimeter

Logical. Highlight Lion Brewery and Model Housing.
latlong

Logical. Use estimated longitude and latitude.

Value

Base R points and text.

Note

The location of 18 Sackville Street and 28 Dean Street are approximate. Falconberg Court & Mews form an isolate: they are not part of the network of roads and are technically unreachable. Adam and Eve Court and its pump also form an isolate.

Examples

snowMap(add.landmarks = FALSE)
addLandmarks()

Add Golden and Soho Squares to plot.

Description

Add Golden and Soho Squares to plot.

Usage

addLandmarkSquares(latlong = FALSE, text.size = 0.5, text.col = "black")

Arguments

latlong

Logical. Use estimated longitude and latitude.
text.size

Numeric. cex for text labels.
text.col

Character. col for text labels.

Value

Base R points and text.

Examples

snowMap()
addLandmarkSquares()

Add distance or time based "mileposts" to an observed walking neighborhood plot.

Description

Add distance or time based "mileposts" to an observed walking neighborhood plot.

Usage

addMilePosts(pump.subset = NULL, pump.select = NULL, vestry = FALSE,
  unit = "distance", interval = NULL, walking.speed = 5,
  type = "arrows", multi.core = TRUE, dev.mode = FALSE)

Arguments

pump.subset

Numeric. Vector of numeric pump IDs to subset from the neighborhoods defined by pump.select. Negative selection possible. NULL uses all pumps in pump.select.
pump.select

Numeric. Numeric vector of pumps to define possible pump neighborhoods (i.e. the "population"). Negative selection is possible. NULL selects all "observed" pumps (i.e., pumps with at least one case).
vestry

Logical. TRUE uses the 14 pumps from the Vestry Report. FALSE uses the 13 from the original map.
unit

Character. Milepost unit of measurement: "distance" or "time".
interval

Numeric. Interval between mileposts: 50 meters for "distance"; 60 seconds for "time".
walking.speed

Numeric. Walking speed in km/hr.
type

Character. "arrows" or "points".
multi.core

Logical or Numeric. TRUE uses parallel::detectCores(). FALSE uses one, single core. You can also specify the number logical cores. See vignette("Parallelization") for details.
dev.mode

Logical. Development mode uses parallel::parLapply().

Value

R base graphics arrows or points.

Add observed neighborhood cases.

Description

Add cases to a plot as "nominal" or "fatalities" and as points or IDs.

Usage

addNeighborhoodCases(pump.subset = NULL, pump.select = NULL,
  metric = "walking", case.set = "observed", location = "nominal",
  token = "point", text.size = 0.5, pch = 16, point.size = 0.5,
  vestry = FALSE, weighted = TRUE, color = NULL, alpha.level = 0.5,
  latlong = FALSE, multi.core = TRUE)

Arguments

pump.subset

Numeric. Vector of numeric pump IDs to subset from the neighborhoods defined by pump.select. Negative selection possible. NULL uses all pumps in pump.select.
pump.select

Numeric. Numeric vector of pump IDs that define which pump neighborhoods to consider (i.e., specify the "population"). Negative selection possible. NULL selects all pumps.
metric

Character. Type of neighborhood: "euclidean" or "walking".
case.set

Character. "observed" or "expected".
location

Character. "nominal", "anchor" or "orthogonal".
token

Character. Type of token to plot: "point" or "id".
text.size

Numeric. Size of case ID text.
pch

Numeric.
point.size

Numeric.
vestry

Logical. TRUE uses the 14 pumps from the Vestry Report. FALSE uses the 13 in the original map.
weighted

Logical. TRUE computes shortest walking path weighted by road length. FALSE computes shortest walking path in terms of the number of nodes.
color

Character. Use a single color for all paths. NULL uses neighborhood colors defined by snowColors().
alpha.level

Numeric. Alpha level transparency for area plot: a value in [0, 1].
latlong

Logical. Longitude and latitude coordinates.
multi.core

Logical or Numeric. TRUE uses parallel::detectCores(). FALSE uses one, single core. You can also specify the number logical cores. See vignette("Parallelization") for details.

Examples

## Not run: 
snowMap(add.cases = FALSE)
addNeighborhoodCases()

snowMap(add.cases = FALSE)
addNeighborhoodCases(pump.subset = c(6, 10))

snowMap(add.cases = FALSE)
addNeighborhoodCases(pump.select = c(6, 10))

snowMap(add.cases = FALSE, latlong = TRUE)
addNeighborhoodCases(latlong = TRUE)

snowMap(add.cases = FALSE, latlong = TRUE)
addNeighborhoodCases(pump.subset = c(6, 10), latlong = TRUE)

snowMap(add.cases = FALSE, latlong = TRUE)
addNeighborhoodCases(pump.select = c(6, 10), latlong = TRUE)

## End(Not run)

Add expected Euclidean pump neighborhoods.

Description

Add expected Euclidean pump neighborhoods.

Usage

addNeighborhoodEuclidean(pump.subset = NULL, pump.select = NULL,
  vestry = FALSE, location = "nominal", type = "star",
  alpha.level = 0.5, multi.core = TRUE, dev.mode = FALSE)

Arguments

pump.subset

Numeric. Vector of numeric pump IDs to subset from the neighborhoods defined by pump.select. Negative selection possible. NULL selects all pumps in pump.select.
pump.select

Numeric. Vector of numeric pump IDs to define pump neighborhoods (i.e., the "population"). Negative selection possible. NULL selects all pumps.
vestry

Logical. TRUE uses the 14 pumps from the Vestry Report. FALSE uses the 13 in the original map.
location

Character. "nominal" or "orthogonal". "orthogonal" is the x-y coordinates of sim.ortho.proj. "nominal" is the x-y coordinates of regular.cases.
type

Character. Type of plot: "star", "area.points" or "area.polygons".
alpha.level

Numeric. Alpha level transparency for area plot: a value in [0, 1].
multi.core

Logical or Numeric. TRUE uses parallel::detectCores(). FALSE uses one, single core. You can also specify the number logical cores. See vignette("Parallelization") for details.
dev.mode

Logical. Development mode uses parallel::parLapply().

Value

R graphic elements.

Examples

## Not run: 
streetNameLocator("marshall street", zoom = 0.5, highlight = FALSE,
  add.subtitle = FALSE)
addNeighborhoodEuclidean()

streetNameLocator("marshall street", zoom = 0.5, highlight = FALSE,
  add.subtitle = FALSE)
addNeighborhoodEuclidean(type = "area.points")

## End(Not run)

Add expected walking neighborhoods.

Description

Add expected walking neighborhoods.

Usage

addNeighborhoodWalking(pump.select = NULL, pump.subset = NULL,
  vestry = FALSE, weighted = TRUE, path = NULL, path.color = NULL,
  path.width = 3, alpha.level = 0.25, polygon.type = "solid",
  polygon.col = NULL, polygon.lwd = 2, multi.core = TRUE,
  dev.mode = FALSE, latlong = FALSE)

Arguments

pump.select

Numeric. Numeric vector of pump IDs that define which pump neighborhoods to consider (i.e., specify the "population"). Negative selection possible. NULL selects all pumps.
pump.subset

Numeric. Vector of numeric pump IDs to subset from the neighborhoods defined by pump.select. Negative selection possible. NULL uses all pumps in pump.select.
vestry

Logical. TRUE uses the 14 pumps from the Vestry Report. FALSE uses the 13 in the original map.
weighted

Logical. TRUE computes shortest path weighted by road length. FALSE computes shortest path in terms of the number of nodes.
path

Character. "expected" or "observed".
path.color

Character. Use a single color for all paths. NULL uses neighborhood colors defined by snowColors().
path.width

Numeric. Set width of paths.
alpha.level

Numeric. Alpha level transparency for area plot: a value in [0, 1].
polygon.type

Character. "perimeter" or "solid".
polygon.col

Character.
polygon.lwd

Numeric.
multi.core

Logical or Numeric. TRUE uses parallel::detectCores(). FALSE uses one, single core. You can also specify the number logical cores. See vignette("Parallelization") for details.
dev.mode

Logical. Development mode uses parallel::parLapply().
latlong

Logical. Use estimated longitude and latitude.

Examples

## Not run: 
streetNameLocator("marshall street", zoom = 0.5)
addNeighborhoodWalking()

## End(Not run)

Add plague pit (Marshall Street).

Description

Draws a polygon that approximates the plague pit located around Marshall Street. From Vestry Report map.

Usage

addPlaguePit(color = "black", line.type = "solid")

Arguments

color

Character. Color of polygon.
line.type

Character. Polygon line type.

Value

Adds a polygon to a graphics plot.

Note

In progress.

Examples

snowMap(add.landmarks = FALSE)
addPlaguePit()

Add selected pump(s) to plot.

Description

Add selected pump(s) to plot.

Usage

addPump(pump.select = NULL, vestry = FALSE, col = NULL, pch = 24,
  label = TRUE, pos = 1, cex = 1, latlong = FALSE)

Arguments

pump.select

Numeric or Integer. Vector of water pump numerical ID(s). With vestry = TRUE, whole number(s) between 1 and 14. With vestry = FALSE, whole number(s) between 1 and 13. See pumps.vestry and pumps for IDs and details about specific pumps. NULL plots all pumps. Negative selection allowed.
vestry

Logical. TRUE for the 14 pumps from Vestry Report. FALSE for the original 13 pumps.
col

Character. Color of pump points.
pch

Numeric. Shape of point character.
label

Logical. TRUE adds text label.
pos

Numeric. Position of label.
cex

Numeric. point cex.
latlong

Logical. Use c("lon". "lat") or c("x", "y").

Add all streets and roads to plot.

Description

Add all streets and roads to plot.

Usage

addRoads(latlong = FALSE, col = "gray")

Arguments

latlong

Logical. Use estimated longitude and latitude.
col

Character. Color

Adds Snow's graphical annotation of the Broad Street pump walking neighborhood.

Description

Adds Snow's graphical annotation of the Broad Street pump walking neighborhood.

Usage

addSnow(latlong = FALSE, type = "area", color = "dodgerblue",
  alpha.level = 0.25, line.width = 2)

Arguments

latlong

Logical.
type

Character. Type of annotation plot: "area" or "perimeter".
color

Character. Neighborhood color.
alpha.level

Numeric. Alpha level transparency: a value in [0, 1] when type = "area".
line.width

Numeric. Line width for type = "street" and type = "perimeter".

Examples

## Not run: 
plot(neighborhoodVoronoi())
addSnow()

## End(Not run)

Add Voronoi cells.

Description

Add Voronoi cells.

Usage

addVoronoi(pump.select = NULL, vestry = FALSE, case.location = "nominal",
  color = "black", line.type = "solid", line.width = 1,
  latlong = FALSE)

Arguments

pump.select

Numeric. Default is NULL; all pumps are used. Otherwise, selection by a vector of numeric IDs: 1 to 13 for pumps; 1 to 14 for pumps.vestry. Exclusion (negative selection) is possible (e.g., -6).
vestry

Logical. FALSE for original 13 pumps. TRUE for 14 pumps in Vestry Report.
case.location

Character. For observed = FALSE: "address" or "nominal". "nominal" is the x-y coordinates of regular.cases.
color

Character. Color of cell edges.
line.type

Character. Type of line for cell edges: lty.
line.width

Numeric. Width of cell edges: lwd.
latlong

Logical. Use estimated longitude and latitude.

Note

This function uses deldir::deldir().

Examples

snowMap()
# addVoronoi()

Add walking path from case/landmark to nearest or selected pump. (prototype)

Description

Add walking path from case/landmark to nearest or selected pump. (prototype)

Usage

addWalkingPath(origin = 1, destination = NULL, type = "case-pump",
  vestry = FALSE, latlong = FALSE, case.set = "observed",
  location = "nominal", weighted = TRUE, distance.unit = "meter",
  time.unit = "second", walking.speed = 5, include.landmarks = TRUE,
  mileposts = TRUE, milepost.unit = "distance", milepost.interval = NULL,
  alpha.level = 1, ...)

Arguments

origin

Numeric. Vector of origin(s) (numeric or case/landmark name).
destination

Numeric. Vector of destination(s) (numeric or landmark/pump name).
type

Character. Path case to pump. FALSE is all other combinations of cases, landmarks and pumps.
vestry

Logical. TRUE uses the 14 pumps from the map in the Vestry Report. FALSE uses the 13 pumps from the original map.
latlong

Logical.
case.set

Character. "observed" or "expected".
location

Character. For cases and pumps. "anchor, "fatality" or "orthogonal.
weighted

Logical. TRUE computes shortest path in terms of road length. FALSE computes shortest path in terms of the number of nodes.
distance.unit

Character. Unit of distance: "meter" or "yard".
time.unit

Character. "hour", "minute", or "second".
walking.speed

Numeric. Walking speed in km/hr.
include.landmarks

Logical. Include landmarks as cases.
mileposts

Logical. Plot mile/time posts.
milepost.unit

Character. "distance" or "time".
milepost.interval

Numeric. Mile post interval unit of distance (yard or meter) or unit of time (seconds).
alpha.level

Numeric. Alpha level transparency for path: a value in [0, 1].
...

Additional plotting parameters.

Add Rev. Henry Whitehead's Broad Street pump neighborhood.

Description

A circle (polygon), centered around a desired pump with a radius of 210 yards. The Broad Street pump is the default.

Usage

addWhitehead(pump = "Broad Street", radius = 210, distance.unit = "yard",
  color = "black", line.type = "solid", vestry = FALSE,
  add.subtitle = FALSE, walking.speed = 5)

Arguments

pump

Character or Numeric. Name (road name) or numerical ID of selected pump. See pumps or pumps.vestry.
radius

Numeric. Distance from a pump.
distance.unit

Character. Unit of distance: "meter", "yard" or "native". "native" returns the map's native scale. See vignette("roads") for information on conversion.
color

Character. Color of circle.
line.type

Character. Circle line type.
vestry

Logical. TRUE uses the 14 pumps and locations from Vestry report. FALSE uses original 13 pumps.
add.subtitle

Logical. Add subtitle with estimated "walking" time in seconds.
walking.speed

Numeric. Walking speed in km/hr.

Value

Adds a circle (polygon) to a graphics plot.

Examples

snowMap(add.landmarks = FALSE)
addWhitehead()

Anchor or base case of each stack of fatalities.

Description

Data frame that links a fatality to its stack, a stack's base case. For use with caseLocator.

Usage

anchor.case

Format

case

numerical case ID

anchor

numerical case ID of anchor.case

Note

unstackFatalities documents the code for these data.

Numeric IDs of line segments that create the map's border frame.

Description

Vector of ordered numbers that identify the line segments that make up the frame of the map. For use with sp::Polygon().

Usage

border

Format

border

numerical ID

Compute distance between case fatalities (meters).

Description

Compute distance between case fatalities (meters).

Usage

caseDistance(a = 19, b = 263, latlong = FALSE)

Arguments

a

Numeric. Case ID.
b

Numeric. Case ID.
latlong

Logical.

Locate case by numerical ID.

Description

Highlight selected observed or simulated case and its home road segment.

Usage

caseLocator(case = 1, zoom = FALSE, observed = TRUE, latlong = FALSE,
  add.title = TRUE, highlight.segment = TRUE, data = FALSE,
  add = FALSE, col = "red", vestry = FALSE)

Arguments

case

Numeric or Integer. Whole number between 1 and 578.
zoom

Logical or Numeric.A numeric value >= 0 controls the degree of zoom. The default is 1.
observed

Logical. TRUE for observed. FALSE for simulated.
latlong

Logical. Longitude and latitude coordinates
add.title

Logical. Include title.
highlight.segment

Logical. Highlight case's segment.
data

Logical. Output data.
add

Logical. Add to existing plot or separate plot.
col

Character. Point color.
vestry

Logical. TRUE uses the 14 pumps from the Vestry report. FALSE uses the 13 in the original map.

Value

A base R graphics plot.

Examples

caseLocator(290)
caseLocator(290, zoom = TRUE)
caseLocator(290, observed = FALSE)
caseLocator(290, latlong = TRUE, zoom = TRUE)

Compute Euclidean path coordinates from observed case/landmark to nearest/selected pump.

Description

Compute Euclidean path coordinates from observed case/landmark to nearest/selected pump.

Usage

euclideanPath(origin = 1, destination = NULL, type = "case-pump",
  vestry = FALSE, latlong = FALSE, case.set = "observed",
  location = "nominal", weighted = TRUE, distance.unit = "meter",
  time.unit = "second", walking.speed = 5, include.landmarks = TRUE)

Arguments

origin

Numeric. Vector of origin(s) (numeric ID or character name landmark/pump ).
destination

Numeric. Vector of destination(s) (numeric or landmark/pump name).
type

Character. Path case to pump. FALSE is all other combinations of cases, landmarks and pumps.
vestry

Logical. TRUE uses the 14 pumps from the map in the Vestry Report. FALSE uses the 13 pumps from the original map.
latlong

Logical.
case.set

Character. "observed" or "expected".
location

Character. For cases and pumps. "nominal", "anchor" or "orthogonal".
weighted

Logical. TRUE computes shortest path in terms of road length. FALSE computes shortest path in terms of the number of nodes.
distance.unit

Character. Unit of distance: "meter" or "yard".
time.unit

Character. "hour", "minute", or "second".
walking.speed

Numeric. Walking speed in km/hr.
include.landmarks

Logical. Include landmarks as cases.

Amended Dodson and Tobler's cholera data.

Description

An amended version of Dodson and Tobler's digitization of John Snow's map of the 1854 London cholera outbreak. It removes 3 duplicate observations and imputes the location for 3 "missing" observation. This information is also available in HistData::Snow.deaths2 (>= ver. 0.7-8).

Usage

fatalities

Format

A data frame with 3 variable that records the position and the nearest pump for the 578 bars on Snow's map.

case

numeric case ID

x

x-coordinate

y

y-coordinate

lon

longitude

lat

latitude

Note

fixFatalities documents the code for these data. For details, see vignette("duplicate.missing.cases").

See Also

caseLocator

streetNameLocator

streetNumberLocator

caseLocator

streetNameLocator

streetNumberLocator

"Unstacked" amended cholera data with address as unit of observation.

Description

An "unstacked" version of the fatalities dataset. It changes the unit of observation from the case (bar) to the "address", the x-y coordinates of the case at the base of a stack, and makes the number of fatalities an attribute of the "address".

Usage

fatalities.address

Format

A data frame with 4 variables for 321 addresses

anchor

numerical case ID of address

x

x-coordinate

y

y-coordinate

case.count

number of fatalities at address

lon

longitude

lat

latitude

Note

unstackFatalities documents the code for these data. For details, see vignette("unstacking.fatalities").

See Also

caseLocator

streetNameLocator

streetNumberLocator

"Unstacked" amended cholera fatalities data with fatality as unit of observation.

Description

An "unstacked" version of the fatalities dataset. It changes the unit of observation from the case (bar) to the "address", the x-y coordinates of the case at the base of a stack, and assigns the base case's coordinates to all cases in the stack.

Usage

fatalities.unstacked

Format

A data frame with 3 variable that records the position of the 578 bars on Snow's map.

case

numerical case ID

x

x-coordinate

y

y-coordinate

lon

longitude

lat

latitude

Note

unstackFatalities documents the code for these data. For details, see vignette("unstacking.fatalities").

See Also

caseLocator

streetNameLocator

streetNumberLocator

Fix errors in Dodson and Tobler's digitization of Snow's map.

Description

Fixes two apparent coding errors using three misplaced cases.

Usage

fixFatalities()

Value

An R data frame.

See Also

vignette("duplicate.missing.cases")

Map frame data c("x", "y") and c("lon", "lat").

Description

Map frame data c("x", "y") and c("lon", "lat").

Usage

frame.data

Format

A data frame with 106 observations (points) and 8 variables.

street

street number

n

street street component number

x

native x-coordinate

y

native y-coordinate

id

segment numeric ID

name

street name

lon

longitude

lat

latitude

Partitioned map frame points (segment endpoints).

Description

Partitioned map frame points (segment endpoints).

Usage

frame.sample

Format

A list of 3 vectors length 19, 19 and 18 from cholera::roads$id.

frame.sample

cholera::roads$id

Centers of city squares.

Description

Centers of city squares.

Usage

landmark.squares

Format

A data frame with 6 variables that records the position of the orthogonal projection of landmarks onto the network of roads.

name

square name

x

x-coordinate

y

y-coordinate

case

numeric case ID

Centers of city squares.

Description

Centers of city squares.

Usage

landmark.squaresB

Format

A data frame with 2 observations and 6 variables that records the position of landmark square labels.

case

numeric case ID

x

x-coordinate

y

y-coordinate

lon

longitude

lat

latitude

name

square name

Landmark data.

Description

Nominal and orthogonal coordinates

Usage

landmarkData(multi.core = TRUE, dev.mode = FALSE)

Arguments

multi.core

Logical or Numeric. TRUE uses parallel::detectCores(). FALSE uses one, single core. You can also specify the number logical cores. See vignette("Parallelization") for details.
dev.mode

Logical. Development mode uses parallel::parLapply().

Orthogonal projection of landmarks onto road network.

Description

Orthogonal projection of landmarks onto road network.

Usage

landmarks

Format

A data frame with 12 variables that records the position of the orthogonal projection of landmarks onto the network of roads.

road.segment

"address" road segment

x.proj

orthogonal x-coordinate

y.proj

orthogonal y-coordinate

ortho.dist

orthogonal distance to home road segment

x

nominal x-coordinate

y

nominal y-coordinate

name

landmark name

case

numeric case ID

lon

longitude

lat

latitude

lon.proj

orthogonal longitude

lat.proj

orthogonal latitude

Note

landmarkData document the code for these data.

Landmark coordinates.

Description

Landmark coordinates.

Usage

landmarksB

Format

A data frame of landmark coordinates with 20 observationas and 11 variables.

case

numeric case ID

road.segment

"address" road segment

x

nominal x-coordinate

y

nominal y-coordinate

x.lab

label x-coordinate

y.lab

label y-coordinate

lon

longitude

lat

latitude

lon.lab

label longitude

lat.lab

label latitude

name

landmark name

Note

landmarkData document the code for these data.

Orthogonal projection of observed address (latlong) cases onto road network.

Description

Orthogonal projection of observed address (latlong) cases onto road network.

Usage

latlong.ortho.addr

Format

A data frame with 7 variables that records the position of the orthogonal projection of the 321 cases onto the network of roads.

road.segment

"address" road segment

x.proj

x-coordinate

y.proj

y-coordinate

ortho.dist

orthogonal distance to home road segment

case

numeric case ID

lon

longitude

lat

latitude

Note

unstackFatalities documents the code for these data.

Orthogonal projection of 13 original pumps (latlong).

Description

Orthogonal projection of 13 original pumps (latlong).

Usage

latlong.ortho.pump

Format

A data frame with 7 variables that records the position of the orthogonal projection of the 13 original pumps onto the network of roads.

road.segment

"address" road segment

x.proj

x-coordinate

y.proj

y-coordinate

ortho.dist

orthogonal distance to home road segment

id

numeric ID

lon

longitude

lat

latitude

Note

pumpData documents the code for these data.

Orthogonal projection of the 14 pumps from the Vestry Report (latlong).

Description

Orthogonal projection of the 14 pumps from the Vestry Report (latlong).

Usage

latlong.ortho.pump.vestry

Format

A data frame with 7 variables that records the position of the orthogonal projection of the 14 pumps onto the network of roads.

road.segment

"address" road segment

x.proj

x-coordinate

y.proj

y-coordinate

ortho.dist

orthogonal distance to home road segment

id

numeric ID

lon

longitude

lat

latitude

Note

pumpData documents the code for these data.

"Expected" cases (latlong).

Description

The result of using sp::spsample() and sp::Polygon() to generate 19,993 regularly spaced simulated Cartesian/geodesic cases within the map's borders.

Usage

latlong.regular.cases

Format

A data frame with 4 variables that records the position of 19,993 "expected" cases fitted by sp::spsample().

x

x-coordinate

y

y-coordinate

lon

longitude

lat

latitude

Road "address" of simulated (i.e., "expected") cases (latlong).

Description

Road "address" of simulated (i.e., "expected") cases (latlong).

Usage

latlong.sim.ortho.proj

Format

A data frame with 8 variables that records the "address" of 19,993 regularly spaced simulated Cartesian/geodesic cases regularly spaced across map.

case

numeric case ID

road.segment

"address" road segment

x.proj

x-coordinate

y.proj

y-coordinate

dist

Euclidean or orthogonal distance to home road segment

type

type of projection: Euclidean ("eucl") or orthogonal ("ortho")

lon

longitude

lat

latitude

Compute xlim and ylim of Snow's map.

Description

Compute xlim and ylim of Snow's map.

Usage

mapRange(latlong = FALSE)

Arguments

latlong

Logical. Use estimated longitude and latitude.

Compute shortest distances to selected pumps.

Description

Compute shortest distances to selected pumps.

Usage

nearestPump(pump.select = NULL, metric = "walking", vestry = FALSE,
  weighted = TRUE, case.set = "observed", latlong = FALSE,
  multi.core = TRUE, dev.mode = FALSE)

Arguments

pump.select

Numeric. Pump candidates to consider. Default is NULL: all pumps are used. Otherwise, selection by a vector of numeric IDs: 1 to 13 for pumps; 1 to 14 for pumps.vestry. Negative selection allowed.
metric

Character. "euclidean" or "walking".
vestry

Logical. TRUE uses the 14 pumps from the Vestry Report. FALSE uses the 13 in the original map.
weighted

Logical. TRUE computes shortest path in terms of road length. FALSE computes shortest path in terms of the number of nodes.
case.set

Character. "observed" or "expected" # or "snow".
latlong

Logical. TRUE Use longitude and latitude coordinates.
multi.core

Logical or Numeric. TRUE uses parallel::detectCores(). FALSE uses one, single core. You can also specify the number logical cores. See vignette("Parallelization") for details.
dev.mode

Logical. Development mode uses parallel::parLapply().

Compute network graph of roads, cases and pumps.

Description

Assembles cases, pumps and road into a network graph.

Usage

neighborhoodData(vestry = FALSE, case.set = "observed", embed = TRUE,
  embed.landmarks = TRUE)

Arguments

vestry

Logical. Use Vestry Report pump data.
case.set

Character. "observed", "expected", or "snow". "snow" captures John Snow's annotation of the Broad Street pump neighborhood printed in the Vestry report version of the map.
embed

Logical. Embed cases and pumps into road network.
embed.landmarks

Logical. Embed landmarks into road network.

Value

An R list of nodes, edges and an 'igraph' network graph.

Compute network graph of roads, cases and pumps (prototype).

Description

Assembles cases, pumps and road into a network graph.

Usage

neighborhoodDataB(vestry = FALSE, case.set = "observed",
  embed.addr = TRUE, embed.landmarks = TRUE, embed.pumps = TRUE,
  latlong = FALSE)

Arguments

vestry

Logical. Use Vestry Report pump data.
case.set

Character. "observed", "expected", or "snow". "snow" captures John Snow's annotation of the Broad Street pump neighborhood printed in the Vestry report version of the map.
embed.addr

Logical. Embed cases into road network.
embed.landmarks

Logical. Embed landmarks into road network.
embed.pumps

Logical. Embed pumps into road network.
latlong

Logical. Use estimated longitude and latitude.

Value

An R list of nodes, edges and an 'igraph' network graph.

Compute Euclidean path pump neighborhoods.

Description

Plots star graph from pump to its cases.

Usage

neighborhoodEuclidean(pump.select = NULL, vestry = FALSE,
  case.set = "observed", case.select = "address", latlong = FALSE,
  location = "nominal", brute.force = FALSE, multi.core = TRUE,
  dev.mode = FALSE)

Arguments

pump.select

Numeric. Vector of numeric pump IDs to define pump neighborhoods (i.e., the "population"). Negative selection possible. NULL selects all pumps.
vestry

Logical. TRUE uses the 14 pumps from the Vestry Report. FALSE uses the 13 in the original map.
case.set

Character. "observed" or "expected".
case.select

Character. Fatalities: "all" or "address".
latlong

Logical. Longitude and latitude coordinates
location

Character. "nominal", "anchor" or "orthogonal".
brute.force

Logical. For latlong = FALSE. TRUE computes nearest pump for each case. FALSE uses Voronoi cells as shortcut.
multi.core

Logical or Numeric. TRUE uses parallel::detectCores(). FALSE uses one, single core. You can also specify the number logical cores. See vignette("Parallelization") for details.
dev.mode

Logical. Development mode uses parallel::parLapply().

Value

An R list.

Compute Voronoi pump neighborhoods.

Description

Group cases into neighborhoods using Voronoi tessellation.

Usage

neighborhoodVoronoi(pump.select = NULL, vestry = FALSE, latlong = FALSE,
  location = "nominal", polygon.vertices = FALSE)

Arguments

pump.select

Numeric. Vector of numeric pump IDs to define pump neighborhoods (i.e., the "population"). Negative selection possible. NULL selects all pumps.
vestry

Logical. TRUE uses the 14 pumps from the Vestry report. FALSE uses the 13 in the original map.
latlong

Logical. Longitude and latitude coordinates
location

Character. "nominal" or "orthogonal". "nominal" uses the x-y coordinates of fatalities.address. "orthogonal"uses the x-y coordinates of ortho.proj.
polygon.vertices

Logical. TRUE returns a list of x-y coordinates of the vertices of Voronoi cells. Useful for sp::point.in.polygon() as used in print.voronoi() method.

Value

An R list with 12 objects.

  • pump.id: vector of selected pumps

  • voronoi: output from deldir::deldir().

  • snow.colors: neighborhood color based on snowColors().

  • x.rng: range of x for plot.

  • y.rng: range of y for plot.

  • select.string: description of "pump.select" for plot title.

  • expected.data: expected neighborhood fatality counts, based on Voronoi cell area.

  • coordinates: polygon vertices of Voronoi cells.

  • statistic.data: observed neighborhood fatality counts.

  • pump.select: "pump.select" from neighborhoodVoronoi().

  • statistic: "statistic" from neighborhoodVoronoi().

  • vestry: "vestry" from neighborhoodVoronoi().

Examples

## Not run: 
neighborhoodVoronoi()
neighborhoodVoronoi(vestry = TRUE)
neighborhoodVoronoi(pump.select = 6:7)
neighborhoodVoronoi(pump.select = -6)
neighborhoodVoronoi(pump.select = -6, polygon.vertices = TRUE)

# coordinates for vertices also available in the returned object.
dat <- neighborhoodVoronoi(pump.select = -6)
dat$coordinates

## End(Not run)

Compute walking path pump neighborhoods.

Description

Group cases into neighborhoods based on walking distance.

Usage

neighborhoodWalking(pump.select = NULL, vestry = FALSE, weighted = TRUE,
  case.set = "observed", multi.core = TRUE, dev.mode = FALSE,
  latlong = FALSE)

Arguments

pump.select

Numeric. Vector of numeric pump IDs to define pump neighborhoods (i.e., the "population"). Negative selection possible. NULL selects all pumps. Note that you can't just select the pump on Adam and Eve Court (#2) because it's technically an isolate.
vestry

Logical. TRUE uses the 14 pumps from the Vestry report. FALSE uses the 13 in the original map.
weighted

Logical. TRUE computes shortest path weighted by road length. FALSE computes shortest path in terms of the number of nodes.
case.set

Character. "observed", "expected" or "snow". "snow" captures John Snow's annotation of the Broad Street pump neighborhood printed in the Vestry report version of the map.
multi.core

Logical or Numeric. TRUE uses parallel::detectCores(). FALSE uses one, single core. You can also specify the number logical cores. See vignette("Parallelization") for details.
dev.mode

Logical. Development mode uses parallel::parLapply().
latlong

Logical.

Examples

## Not run: 
neighborhoodWalking()
neighborhoodWalking(pump.select = -6)

## End(Not run)

Orthogonal projection of observed cases onto road network.

Description

Orthogonal projection of observed cases onto road network.

Usage

ortho.proj

Format

A data frame with 5 variables that records the position of the orthogonal projection of the 578 cases onto the network of roads.

road.segment

"address" road segment

x.proj

x-coordinate

y.proj

y-coordinate

ortho.dist

orthogonal distance to home road segment

case

numeric case ID

Note

unstackFatalities documents the code for these data.

Orthogonal projection of 13 original pumps.

Description

Orthogonal projection of 13 original pumps.

Usage

ortho.proj.pump

Format

A data frame with 6 variables that records the position of the orthogonal projection of the 13 original pumps onto the network of roads.

pump.id

numeric ID

road.segment

"address" road segment

x.proj

x-coordinate

y.proj

y-coordinate

ortho.dist

orthogonal distance to home road segment

node

node ID

Note

pumpData documents the code for these data.

Orthogonal projection of the 14 pumps from the Vestry Report.

Description

Orthogonal projection of the 14 pumps from the Vestry Report.

Usage

ortho.proj.pump.vestry

Format

A data frame with 6 variables that records the position of the orthogonal projection of the 14 pumps onto the network of roads.

pump.id

numeric ID

road.segment

"address" road segment

x.proj

x-coordinate

y.proj

y-coordinate

ortho.dist

orthogonal distance to home road segment

node

node ID

Note

pumpData documents the code for these data.

Oxford monthly weather data, January 1853 - February 2024.

Description

Extract from UK Met Office (https://www.metoffice.gov.uk/pub/data/weather/uk/climate/stationdata/oxforddata.txt): Lat 51.761 Lon -1.262, 63 metres amsl. Approximate 90 km (55 miles) northwest of Soho.

Usage

oxford.weather

Format

A data frame with 9 variables and 2054 observations.

year

Year

month

Month

tmax

Mean daily maximum temperature Celsius

tmin

Mean daily minimum temperature Celsius

af

Days of air frost

rain

Total rainfall (mm)

sun

Total sunshine duration hours

prov

Provisional data

date

Date

Weather data recorded in Oxford (Met Office UK).

Description

Add and use last day of month as unit of observation to oxford.weather.

Usage

oxfordWeather()

Value

An R data frame.

Note

December 1860 observation is dropped due to missing "tmin" value.

Compute Pearson Residuals (prototype)

Description

Compute Pearson Residuals (prototype)

Usage

pearsonResiduals(x)

Arguments

x

An object created by neighborhoodEuclidean(), neighborhoodVoronoi() or neighborhoodWalking().

Value

An R vector.

Examples

## Not run: 
pearsonResiduals(neighborhoodEuclidean())
pearsonResiduals(neighborhoodVoronoi())
pearsonResiduals(neighborhoodWalking())

## End(Not run)

Plague pit coordinates.

Description

Coordinates for polygon() or sp::Polygon(). In progress.

Usage

plague.pit

Format

A data frame with 13 observations and 2 variables.

x

x-coordinate

y

y-coordinate

Plot method for neighborhoodEuclidean().

Description

Plot method for neighborhoodEuclidean().

Usage

## S3 method for class 'euclidean'
plot(x, type = "star", add.observed.points = TRUE,
  add.title = TRUE, ...)

Arguments

x

An object of class "euclidean" created by neighborhoodEuclidean().
type

Character. "star", "area.points" or "area.polygons". "area" flavors only valid when case.set = "expected".
add.observed.points

Logical. Add observed fatality "addresses".
add.title

Logical. Add title.
...

Additional plotting parameters.

Value

A base R plot.

Note

This uses an approximate computation of polygons, using the 'TSP' package, that may produce non-simple and/or overlapping polygons.

Examples

## Not run: 
plot(neighborhoodEuclidean())
plot(neighborhoodEuclidean(-6))
plot(neighborhoodEuclidean(pump.select = 6:7))
plot(neighborhoodEuclidean(case.set = "expected"), type = "area.points")
plot(neighborhoodEuclidean(case.set = "expected"), type = "area.polygons")

## End(Not run)

Plot the path of the Euclidean distance between cases and/or pumps.

Description

Plot the path of the Euclidean distance between cases and/or pumps.

Usage

## S3 method for class 'euclidean_path'
plot(x, zoom = TRUE, long.title = TRUE,
  mileposts = TRUE, milepost.unit = "distance", milepost.interval = NULL,
  alpha.level = 1, ...)

Arguments

x

An object of class "euclidean_path" created by euclideanPath().
zoom

Logical or Numeric. A numeric value >= 0 controls the degree of zoom. The default is 0.5.
long.title

Logical. Tile with names.
mileposts

Logical. Plot mile/time posts.
milepost.unit

Character. "distance" or "time".
milepost.interval

Numeric. Mile post interval unit of distance (yard or meter) or unit of time (seconds).
alpha.level

Numeric. Alpha level transparency for path: a value in [0, 1].
...

Additional plotting parameters.

Value

A base R plot.

Plot method for euclideanLatlong()

Description

Plot method for euclideanLatlong()

Usage

## S3 method for class 'euclideanLatlong'
plot(x, type = "star", ...)

Arguments

x

Object.
type

Character. "star", "area.points" or "area.polygons". "area" flavors only valid when case.set = "expected".
...

Additional plotting parameters.

Plot method for neighborhoodData().

Description

Visualize underlying road network (with or without cases and pumps).

Usage

## S3 method for class 'neighborhood_data'
plot(x, ...)

Arguments

x

An 'igraph' object of class "neighborhood_data" created by neighborhoodData().
...

Additional plotting parameters.

Value

A base R plot.

Examples

plot(neighborhoodData())
plot(neighborhoodData(embed = FALSE))

Plot method for oxfordWeather().

Description

Plot method for oxfordWeather().

Usage

## S3 method for class 'oxfordWeather'
plot(x, statistic = "temperature",
  month = "september", end.year = NULL, unit.observation = "month", ...)

Arguments

x

object.
statistic

Character.
month

Character. "august" or "september".
end.year

Numeric.
unit.observation

Character. "day" or "month".
...

Additional plotting parameters.

Value

A base R plot.

Plot method for povertyLondon().

Description

Plot method for povertyLondon().

Usage

## S3 method for class 'povertyLondon'
plot(x, district = c("City", "Westminster",
  "Marylebone", "St. Giles"), ...)

Arguments

x

object.
district

Character. Selected district(s).
...

Additional plotting parameters.

Plot method for profilePerspective().

Description

Plot method for profilePerspective().

Usage

## S3 method for class 'profile_perspective'
plot(x, ...)

Arguments

x

An object of class "profile" created by profilePerspective().
...

Additional plotting parameters.

Plot aggregate time series data from Vestry report.

Description

Plot aggregate fatality data and indicates the date of the removal of the handle of the Broad Street pump.

Usage

## S3 method for class 'time_series'
plot(x, statistic = "fatal.attacks",
  pump.handle = TRUE, main = "Removal of the Broad Street Pump Handle",
  type = "o", xlab = "Date", ylab = "Fatalities", ...)

Arguments

x

An object of class "time_series" from timeSeries().
statistic

Character. Fatality measure: either "fatal.attacks" or "deaths".
pump.handle

Logical. Indicate date of removal of Broad Street pump handle.
main

Character. Title of graph.
type

Character. R plot type.
xlab

Character. x-axis label.
ylab

Character. y-axis label.
...

Additional plotting parameters.

See Also

timeSeries

Examples

plot(timeSeries())
plot(timeSeries(), statistic = "deaths")
plot(timeSeries(), bty = "n", type = "h", lwd = 4)

Plot Voronoi neighborhoods.

Description

Plot Voronoi neighborhoods.

Usage

## S3 method for class 'voronoi'
plot(x, delaunay.voronoi = "voronoi",
  euclidean.paths = FALSE, ...)

Arguments

x

An object of class "voronoi" created by voronoiNominal().
delaunay.voronoi

Character "delaunay", "voronoi", or "both".
euclidean.paths

Logical. Plot all Euclidean paths (star graph).
...

Additional plotting parameters.

Value

A base R graph.

See Also

voronoiNominal()

addVoronoi()

Plot method for voronoiLatlong()

Description

Plot method for voronoiLatlong()

Usage

## S3 method for class 'voronoiLatlong'
plot(x, add.pumps = TRUE,
  delaunay.voronoi = "voronoi", euclidean.paths = FALSE, ...)

Arguments

x

Object.
add.pumps

Logical.
delaunay.voronoi

Character "delaunay", "voronoi", or "both".
euclidean.paths

Logical.
...

Additional plotting parameters.

Plot method for walkingNominal().

Description

Plot method for walkingNominal().

Usage

## S3 method for class 'walking'
plot(x, type = "roads", tsp.method = "repetitive_nn",
  ...)

Arguments

x

An object of class "walking" created by walkingNominal().
type

Character. "roads", "area.points" or "area.polygons". "area" flavors only valid when case.set = "expected".
tsp.method

Character. Traveling salesperson problem algorithm.
...

Additional plotting parameters.

Value

A base R plot.

Note

When plotting area graphs with simulated data (i.e., case.set = "expected"), there may be discrepancies between observed cases and expected neighborhoods, particularly between neighborhoods. type = "roads" inspired by Shiode et. al. (2015).

Plot the walking path between selected cases and/or pumps.

Description

Plot the walking path between selected cases and/or pumps.

Usage

## S3 method for class 'walking_path'
plot(x, zoom = TRUE, long.title = TRUE,
  mileposts = TRUE, milepost.unit = "distance", milepost.interval = NULL,
  alpha.level = 1, ...)

Arguments

x

An object of class "walking_path" created by walkingPath().
zoom

Logical or Numeric. A numeric value >= 0 controls the degree of zoom. A value of 1 equals zoom = TRUE.
long.title

Logical. Tile with names.
mileposts

Logical. Plot mile/time posts.
milepost.unit

Character. "distance" or "time".
milepost.interval

Numeric. Mile post interval unit of distance (yard or meter) or unit of time (seconds).
alpha.level

Numeric. Alpha level transparency for path: a value in [0, 1].
...

Additional plotting parameters.

Value

A base R plot.

Plot method for walkingB().

Description

Plot method for walkingB().

Usage

## S3 method for class 'walkingB'
plot(x, type = "area.points",
  tsp.method = "repetitive_nn", ...)

Arguments

x

An object of class "walking" created by walkingNominal().
type

Character. "roads", "area.points" or "area.polygons". "area" flavors only valid when case.set = "expected".
tsp.method

Character. Traveling salesperson problem algorithm.
...

Additional plotting parameters.

Value

A base R plot.

Note

When plotting area graphs with simulated data (i.e., case.set = "expected"), there may be discrepancies between observed cases and expected neighborhoods, particularly between neighborhoods. type = "roads" inspired by Shiode et. al. (2015).

Plot method for walkingLatlong().

Description

Plot method for walkingLatlong().

Usage

## S3 method for class 'walkingLatlong'
plot(x, type = "roads", ...)

Arguments

x

An object of class "latlong_walking" created by walkingLatlongwalkingLatlong().
type

Character. "area.points", "area.polygons" or "roads". For walkingLatlong(case.set = "expected").
...

Additional plotting parameters.

Value

A base R plot.

Plot method for winterTemperatures().

Description

Plot method for winterTemperatures().

Usage

## S3 method for class 'winterTemperatures'
plot(x, end.date = "1859-6-1", ...)

Arguments

x

object.
end.date

Date. "yyyy-mm-dd" or NULL.
...

Additional plotting parameters.

Value

A base R plot.

Examples

plot(winterTemperatures())

Poverty and Born in London.

Description

Gareth Stedman Jones, p. 132. Census and Charles Booth Data, 1881.

Usage

povertyLondon()

Print method for neighborhoodEuclidean().

Description

Parameter values for neighborhoodEuclidean().

Usage

## S3 method for class 'euclidean'
print(x, ...)

Arguments

x

An object of class "euclidean" created by neighborhoodEuclidean().
...

Additional parameters.

Value

A list of argument values.

Examples

## Not run: 
neighborhoodEuclidean()
print(neighborhoodEuclidean())

## End(Not run)

Print method for euclideanPath().

Description

Summary output.

Usage

## S3 method for class 'euclidean_path'
print(x, ...)

Arguments

x

An object of class "euclidean_path" created by euclideanPath().
...

Additional parameters.

Value

An R data frame.

Print summary data for timeSeries().

Description

Return summary results.

Usage

## S3 method for class 'time_series'
print(x, ...)

Arguments

x

An object of class "time_series" created by timeSeries().
...

Additional parameters.

Value

An R data frame.

Examples

timeSeries()
print(timeSeries())

Print method for voronoiNominal().

Description

Parameter values for voronoiNominal().

Usage

## S3 method for class 'voronoi'
print(x, ...)

Arguments

x

An object of class "voronoi" created by voronoiNominal().
...

Additional arguments.

Value

A list of argument values.

Print method for voronoiLatlong().

Description

Parameter values for voronoiLatlong().

Usage

## S3 method for class 'voronoiLatlong'
print(x, ...)

Arguments

x

An object of class "voronoiLatlong" created by voronoiLatlong().
...

Additional arguments.

Value

A list of argument values.

Print method for walkingNominal().

Description

Parameter values for neighborhoodWalking().

Usage

## S3 method for class 'walking'
print(x, ...)

Arguments

x

An object of class "walking" created by neighborhoodWalking().
...

Additional parameters.

Value

A list of argument values.

Print method for walkingPath().

Description

Summary output.

Usage

## S3 method for class 'walking_path'
print(x, ...)

Arguments

x

An object of class "latlong_walking_path" created by latlongWalkingPath().
...

Additional parameters.

Value

An R data frame.

2D Profile .

Description

2D Profile .

Usage

profile2D(angle = 0, pump = 7, vestry = FALSE, graphics = "base",
  multi.core = FALSE)

Arguments

angle

Numeric. Angle of perspective axis in degrees.
pump

Numeric. Select pump as focal point.
vestry

Logical. TRUE uses the 14 pumps from the Vestry Report. FALSE uses the 13 in the original map.
graphics

Character. Type of graphic: "base" or "ggplot2".
multi.core

Logical or Numeric. TRUE uses parallel::detectCores(). FALSE uses one, single core. You can also specify the number logical cores. See vignette("Parallelization") for details.

Examples

## Not run: 
profile2D(angle = 30)
profile2D(angle = 30, graphics = "ggplot2")

## End(Not run)

3D Profile.

Description

3D Profile.

Usage

profile3D(pump.select = NULL, pump.subset = NULL, vestry = FALSE,
  drop.neg.subset = FALSE, multi.core = TRUE)

Arguments

pump.select

Numeric. Vector of numeric pump IDs to define pump neighborhoods (i.e., the "population"). Negative selection possible. NULL selects all pumps.
pump.subset

Numeric. Vector of numeric pump IDs to subset from the neighborhoods defined by pump.select. Negative selection possible. NULL selects all pumps in pump.select.
vestry

Logical. TRUE uses the 14 pumps from the Vestry Report. FALSE uses the 13 in the original map.
drop.neg.subset

Logical. Drop negative subset selection
multi.core

Logical or Numeric. TRUE uses parallel::detectCores(). FALSE uses one, single core. You can also specify the number logical cores. See vignette("Parallelization") for details.

Examples

## Not run: 
profile3D(pump.select = 6:7)
profile3D(pump.subset = -7)
profile3D(pump.subset = -7, drop.neg.subset = TRUE)

## End(Not run)

Extract numeric case IDs by pump neighborhood.

Description

Extract numeric case IDs by pump neighborhood.

Usage

pumpCase(x, case)

Arguments

x

An object created by neighborhoodEuclidean(), neighborhoodVoronoi() or neighborhoodWalking().
case

Character. "address" or "fatality"

Value

An R list of numeric ID of cases by pump neighborhoods.

Examples

## Not run: 
pumpCase(neighborhoodEuclidean())
pumpCase(neighborhoodVoronoi())
pumpCase(neighborhoodWalking())

## End(Not run)

Compute pump coordinates.

Description

Returns either the set of x-y coordinates for the pumps themselves or for their orthogonally projected "addresses" on the network of roads.

Usage

pumpData(vestry = FALSE, orthogonal = FALSE, multi.core = TRUE)

Arguments

vestry

Logical. TRUE uses the 14 pumps from the Vestry report. FALSE uses the 13 in the original map.
orthogonal

Logical. TRUE returns pump "addresses": the coordinates of the orthogonal projection from a pump's location onto the network of roads. FALSE returns pump location coordinates.
multi.core

Logical or Numeric. TRUE uses parallel::detectCores(). FALSE uses one, single core. With Numeric, you specify the number logical cores (rounds with as.integer()). See vignette("Parallelization") for details.

Value

An R data frame.

Note

Note: The location of the fourteenth pump, at Hanover Square, and the "correct" location of the Broad Street pump are approximate. This function documents the code that generates pumps, pumps.vestry, ortho.proj.pump and ortho.proj.pump.vestry.

See Also

pumpLocator

Compute fatalities by pump.

Description

Compute fatalities by pump.

Usage

pumpFatalities(pump.select = NULL, metric = "walking", vestry = FALSE,
  latlong = FALSE, multi.core = TRUE)

Arguments

pump.select

Numeric. Pump candidates to consider. Default is NULL: all pumps are used. Otherwise, selection by a vector of numeric IDs: 1 to 13 for pumps; 1 to 14 for pumps.vestry. Negative selection allowed.
metric

Character. "euclidean" or "walking".
vestry

Logical. TRUE uses the 14 pumps from the Vestry Report. FALSE uses the 13 in the original map.
latlong

Logical. Use estimated longitude and latitude.
multi.core

Logical or Numeric. TRUE uses parallel::detectCores(). FALSE uses one, single core. You can also specify the number logical cores. See vignette("Parallelization") for details.

Examples

## Not run: 
pumpFatalities(pump.select = -7)
pumpFatalities(latlong = TRUE)
pumpFatalities(metric = "euclidean", vestry = TRUE)

## End(Not run)

Locate water pump by numerical ID.

Description

Highlight selected water pump.

Usage

pumpLocator(id = 7, zoom = 1, vestry = FALSE, add.title = TRUE,
  highlight.segment = TRUE, data = FALSE)

Arguments

id

Numeric or Integer. With vestry = TRUE, a whole number between 1 and 14. With vestry = FALSE, a whole number between 1 and 13. See cholera::pumps.vestry and cholera::pumps for IDs and details about specific pumps.
zoom

Logical or Numeric. A numeric value >= 0 controls the degree of zoom. The default is 1.
vestry

Logical. TRUE for the 14 pumps from Vestry Report. FALSE for the original 13 pumps.
add.title

Logical. Include title.
highlight.segment

Logical. Highlight case's segment.
data

Logical. Output data.

Value

A base R graphics plot.

See Also

pumpData

Examples

pumpLocator()
pumpLocator(zoom = TRUE)
pumpLocator(14, vestry = TRUE, zoom = TRUE)

Dodson and Tobler's pump data with street name.

Description

Adds and amends road locations for water pumps from John Snow's map to Dodson and Tobler's street data. The latter are available at Michael Friendly's HistData::Snow.streets.

Usage

pumps

Format

A data frame with 13 observations and 4 variables that describe the pumps on Snow's map.

id

pump number between 1 and 13

street

nearest street

x

x-coordinate

y

y-coordinate

lon

longitude

lat

latitude

Note

pumpData documents the code for these data.

See Also

pumpLocator

Vestry report pump data.

Description

These data include the fourteenth pump, at Hanover Square, and the "corrected" location of the Broad Street pump that Snow includes in the second version of his map in the Vestry report.

Usage

pumps.vestry

Format

A data frame with 14 observations and 4 variables.

id

pump number between 1 and 14

street

nearest street

x

x-coordinate

y

y-coordinate

lon

longitude

lat

latitude

Note

pumpData documents the code for these data.

See Also

pumpLocator

Sample of road intersections (segment endpoints).

Description

Sample of road intersections (segment endpoints).

Usage

rd.sample

Format

A list with 2 variables that list randomly re-arranges unique road intersections (segment endpoints).

one

endpoints with 1 intersection

three

endpoints with 3 intersections

Rectangular filter data.

Description

Coordinates to filter out frame shadow using sp::point.in.polygon().

Usage

rectangle.filter

Format

A data frame with 2 variables and 4 observations.

x

longitude

y

latitude

"Expected" cases.

Description

The result of using sp::spsample() and sp::Polygon() to generate 19,993 regularly spaced simulated cases within the map's borders.

Usage

regular.cases

Format

A data frame with 2 variable that records the position of 19,993 "expected" cases fitted by sp::spsample().

x

x-coordinate

y

y-coordinate

Note

simulateFatalities documents the code for these data.

Dodson and Tobler's street data transformed into road segments.

Description

This data set transforms Dodson and Tobler's street data to give each straight line segment of a "road" a unique ID.

Usage

road.segments

Format

A data frame with 658 observations and 7 variables. The data describe the straight line segments used to recreate the roads on Snow's map.

street

numeric street ID, which range between 1 and 528

id

character segment ID

name

road name

x1

x-coordinate of first endpoint

y1

y-coordinate of first endpoint

x2

x-coordinate of second endpoint

y2

y-coordinate of second endpoint

Note

roadSegments documents the code for these data.

See Also

roads

vignette("road.names")

streetNameLocator

streetNumberLocator

segmentLocator

Dodson and Tobler's street data with appended road names.

Description

This data set adds road names from John Snow's map to Dodson and Tobler's street data. The latter are also available from HistData::Snow.streets.

Usage

roads

Format

A data frame with 1243 observations and 6 variables. The data describe the roads on Snow's map.

street

street segment number, which range between 1 and 528

n

number of points in this street line segment

x

x-coordinate

y

y-coordinate

id

unique numeric ID

name

road name

lon

longitude

lat

latitude

See Also

road.segments

vignette("road.names")

streetNameLocator

streetNumberLocator

segmentLocator

Reshape 'roads' data frame into 'road.segments' data frame.

Description

Used to integrate pumps and cases into road network when computing walking neighborhoods.

Usage

roadSegments(latlong = FALSE)

Arguments

latlong

Logical. Use estimated longitude and latitude.

Value

An R data frame.

Note

This function documents the code that generates road.segments.

Highlight segment by ID.

Description

Highlight segment by ID.

Usage

segmentHighlight(id, highlight = TRUE, col = "red", rotate.label = FALSE,
  latlong = FALSE)

Arguments

id

Character. Segment ID: a concatenation of a street's numeric ID, a whole number between 1 and 528, and a second number to identify the segment.
highlight

Logical. Color segment.
col

Character. Highlight color.
rotate.label

Logical. Rotate segment ID label.
latlong

Logical. Use estimated longitude and latitude.

Value

A base R graphics segment(s).

Examples

streetNameLocator("Soho Square", zoom = TRUE, highlight = FALSE)
ids <- road.segments[road.segments$name == "Soho Square", "id"]
invisible(lapply(ids, function(x) segmentHighlight(x, highlight = FALSE)))

Compute length of road segment.

Description

Compute length of road segment.

Usage

segmentLength(id = "216-1", distance.unit = "meter", latlong = FALSE)

Arguments

id

Character. A concatenation of a street's numeric ID, a whole number between 1 and 528, and a second number used to identify the sub-segments.
distance.unit

Character. Unit of distance: "meter", "yard" or "native". "native" returns the map's native scale. See vignette("roads") for information on conversion. latlong = TRUE only returns meters.
latlong

Logical.

Value

An R vector of length one.

Examples

segmentLength("242-1")
segmentLength("242-1", distance.unit = "yard")

Plot/Locate road segment by ID.

Description

Highlight selected road segment(s) and cases.

Usage

segmentLocator(segment.id = "216-1", zoom = TRUE, latlong = FALSE,
  cases = "address", token = "id", vestry = FALSE, add.pump = TRUE,
  add.title = TRUE, add.subtitle = TRUE, highlight = TRUE,
  distance.unit = "meter", time.unit = "second", walking.speed = 5,
  cex.text = 0.67)

Arguments

segment.id

Character. A vector of segment IDs. See Note.
zoom

Logical or Numeric. Positive value zoom in. Negative values zoom out.
latlong

Logical. Longitude and latitude coordinates
cases

Character. Cases to plot: NULL, "address" or "fatality".
token

Character. Cases as "id" or "point".
vestry

Logical. TRUE uses the 14 pumps from the Vestry report. FALSE uses the 13 in the original map.
add.pump

Logical. Include pumps.
add.title

Logical. Include title.
add.subtitle

Logical. Include subtitle.
highlight

Logical. Highlight selected segment(s) and cases.
distance.unit

Character. Unit of distance: "meter", "yard" or "native". "native" returns the map's native scale. See vignette("roads") for information on conversion.
time.unit

Character. "hour", "minute", or "second".
walking.speed

Numeric. Walking speed in km/hr.
cex.text

Numeric.

Value

A base R graphics plot.

Note

With Dodson and Tobler's data, a street (e.g., Broad Street) is often comprised of multiple straight line segments. To identify each segment individually, an additional number is appended to form a text string ID (e.g., "116-2"). See cholera::road.segments.

Examples

segmentLocator("216-1")
segmentLocator("216-1", zoom = -10)
segmentLocator("216-1", latlong = TRUE, zoom = -10)
segmentLocator("216-1", distance.unit = "yard")
segmentLocator("216-1", zoom = FALSE)

Road "address" of simulated (i.e., "expected") cases.

Description

Road "address" of simulated (i.e., "expected") cases.

Usage

sim.ortho.proj

Format

A data frame with 6 variables that records the "address" of 19,993 simulate cases along the network of roads.

case

numeric case ID

road.segment

"address" road segment

x.proj

x-coordinate

y.proj

y-coordinate

dist

Euclidean or orthogonal distance to home road segment

type

type of projection: Euclidean ("eucl") or orthogonal ("ortho")

Note

simulateFatalities documents the code for these data.

List of "simulated" fatalities grouped by walking-distance pump neighborhood.

Description

List of "simulated" fatalities grouped by walking-distance pump neighborhood.

Usage

sim.pump.case

Format

A list 4972 IDs spread over 13 vectors.

sim.pump.case

numerical ID

Note

neighborhoodWalking documents the code for these data. For details, see vignette("pump.neighborhoods").

Examples

## Not run: 
pumpCase(neighborhoodWalking(case.set = "expected"))

## End(Not run)

Walking distance to Broad Street Pump (#7).

Description

Walking distance to Broad Street Pump (#7).

Usage

sim.walking.distance

Format

A data frames with 5 variables.

case

case ID

pump

pump ID

pump.name

pump name

distance

walking distance in meters

time

walking time in seconds based on 5 km/hr walking speed

Project simulated fatalities onto road network

Description

Places regularly spaced "simulated" or "expected" cases across the face of the map and then finds the "addresses" of those cases via orthogonal projection or simple proximity to road graph network. These data are used to generate "expected" pump neighborhoods.

Usage

simulateFatalities(recompute.regular.cases = FALSE, simulated.obs = 20000L,
  multi.core = TRUE)

Arguments

recompute.regular.cases

Logical. TRUE re-computes regular data. FALSE uses pre-computed data. For replication of data used in the package.
simulated.obs

Numeric. Number of regular cases. For use with recompute.regular.cases = TRUE.
multi.core

Logical or Numeric. TRUE uses parallel::detectCores(). FALSE uses one, single core. With Numeric, you specify the number logical cores (rounds with as.integer()). See vignette("Parallelization") for details.

Value

An R data frame: sim.ortho.proj.

Note

This function is computationally intensive. With "simulated.obs" set to 20,000 (generating 19,993 cases). This function documents the code that generates sim.ortho.proj and regular.cases. In real world terms, the distance between simulated cases is approximately 6 meters.

Snow neighborhood fatalities.

Description

Numeric IDs of fatalities from Dodson and Tobler that fall within Snow's Broad Street pump neighborhood.

Usage

snow.neighborhood

Format

A vector with 384 observations.

snow.neighborhood

numeric case ID

Create a set of colors for pump neighborhoods.

Description

Uses RColorBrewer::brewer.pal().

Usage

snowColors(vestry = FALSE)

Arguments

vestry

Logical. TRUE uses the 14 pumps in the Vestry Report. FALSE uses the original 13.

Value

A character vector of colors.

Note

Built with 'RColorBrewer' package.

Plot John Snow's cholera map.

Description

Plot John Snow's cholera map.

Usage

snowMap(vestry = FALSE, stacked = TRUE, add.axes_box = TRUE,
  add.cases = TRUE, add.landmarks = FALSE, add.pumps = TRUE,
  add.roads = TRUE, add.frame = TRUE, main = NA, case.col = "gray",
  case.pch = 15, latlong = FALSE, ...)

Arguments

vestry

Logical. TRUE uses the 14 pumps from the map in the Vestry Report. FALSE uses the 13 pumps from the original map.
stacked

Logical. Use stacked fatalities.
add.axes_box

Logical. Add plot axes and plot box.
add.cases

Logical. Add observed cases.
add.landmarks

Logical. Add landmarks.
add.pumps

Logical. Add pumps.
add.roads

Logical. Add roads.
add.frame

Logical. Add map frame.
main

Character. Title of graph.
case.col

Character. Color of fatalities.
case.pch

Character. Color of fatalities.
latlong

Logical. Use estimated longitude and latitude.
...

Additional plotting parameters.

Value

A base R graphics plot.

Note

Uses amended version of Dodson and Tobler's data included in this package.

Examples

snowMap()
snowMap(vestry = TRUE, stacked = FALSE)

Plotting data for Snow's graphical annotation of the Broad Street pump neighborhood.

Description

Computes "missing" and split road segments data, and area plot data.

Usage

snowNeighborhood(latlong = FALSE)

Arguments

latlong

Logical. Use estimated longitude and latitude.

Value

An R list of edge IDs and simulated case IDs.

Highlight road by name.

Description

Highlight road by name.

Usage

streetHighlight(road.name, col = "red", lwd = 3, latlong = FALSE)

Arguments

road.name

Character vector. The function tries to correct for case and remove extra spaces (includes "Map Frame").
col

Character. Highlight color.
lwd

Numeric. Line width.
latlong

Logical. Use estimated longitude and latitude.

Value

A base R graphics segment(s).

Examples

snowMap()
streetHighlight("Broad Street")

Compute length of selected street.

Description

Compute length of selected street.

Usage

streetLength(road = "Oxford Street", distance.unit = "meter",
  latlong = FALSE)

Arguments

road

Character or Numeric. Road name or number. For names, the function tries to correct for case and to remove extra spaces.
distance.unit

Character. Unit of distance: "meter", "yard" or "native". "native" returns the map's native scale. See vignette("roads") for information on conversion.
latlong

Logical. Use estimated longitude and latitude.

Value

An R vector of length one.

Examples

streetLength("Oxford Street")
streetLength("oxford street")
streetLength("oxford street", distance.unit = "yard")

Locate street(s) by name(s).

Description

Highlight selected road(s) and cases.

Usage

streetNameLocator(street.name = "Broad Street", zoom = TRUE,
  latlong = FALSE, cases = "address", token = "id", vestry = FALSE,
  add.pump = TRUE, add.title = TRUE, add.subtitle = TRUE,
  highlight = TRUE, distance.unit = "meter", time.unit = "second",
  walking.speed = 5, cex.text = 0.67)

Arguments

street.name

Character. A street name or vector of street names (e.g., "Broad Street", "Poland Street").
zoom

Logical or Numeric. Positive values zoom in. Negative values zoom out.
latlong

Logical. Longitude and latitude coordinates
cases

Character. Cases to plot: NULL, "address" or "fatality".
token

Character. Cases as "id" or "point".
vestry

Logical. TRUE uses the 14 pumps from the Vestry report. FALSE uses the 13 in the original map.
add.pump

Logical. Include pumps.
add.title

Logical. Include title.
add.subtitle

Logical. Include subtitle.
highlight

Logical. Highlight selected segment(s) and cases.
distance.unit

Character. Unit of distance: "meter", "yard" or "native". "native" returns the map's native scale. See vignette("roads") for information on conversion.
time.unit

Character. "hour", "minute", or "second".
walking.speed

Numeric. Walking speed in km/hr.
cex.text

Numeric.

Value

A base R graphics plot.

Note

See streetNames().

Examples

streetNameLocator("broad street")
streetNameLocator("Broad Street", zoom = -10)
streetNameLocator("Broad Street", latlong = TRUE, zoom = -10)
streetNameLocator("Broad Street", distance.unit = "yard")
streetNameLocator("Broad Street", zoom = FALSE)

Street names (alphabetized).

Description

Unique road names from Snow's cholera map.

Usage

streetNames()

Value

An R character vector.

Note

See vignette("roads"), and roads and road.segment data frames.

Locate street by its numerical ID.

Description

Highlight selected road segment(s) and cases.

Usage

streetNumberLocator(street.number = 216, zoom = TRUE, latlong = FALSE,
  cases = "address", token = "id", vestry = FALSE, add.pump = TRUE,
  add.title = TRUE, add.subtitle = TRUE, highlight = TRUE,
  distance.unit = "meter", time.unit = "second", walking.speed = 5,
  cex.text = 0.67)

Arguments

street.number

Character. A vector of segment IDs. See Note.
zoom

Logical or Numeric. Positive value zoom in. Negative values zoom out.
latlong

Logical. Longitude and latitude coordinates
cases

Character. Cases to plot: NULL, "address" or "fatality".
token

Character. Cases as "id" or "point".
vestry

Logical. TRUE uses the 14 pumps from the Vestry report. FALSE uses the 13 in the original map.
add.pump

Logical. Include pumps.
add.title

Logical. Include title.
add.subtitle

Logical. Include subtitle.
highlight

Logical. Highlight selected segment(s) and cases.
distance.unit

Character. Unit of distance: "meter", "yard" or "native". "native" returns the map's native scale. See vignette("roads") for information on conversion.
time.unit

Character. "hour", "minute", or "second".
walking.speed

Numeric. Walking speed in km/hr.
cex.text

Numeric.

Value

A base R graphics plot.

Note

See cholera::roads.

Examples

streetNumberLocator(216)
streetNumberLocator(216, zoom = -10)
streetNumberLocator(216, latlong = TRUE, zoom = -10)
streetNumberLocator(216, distance.unit = "yard")
streetNumberLocator(216, zoom = FALSE)

Summary method for neighborhoodEuclidean().

Description

Return computed counts for Euclidean neighborhoods.

Usage

## S3 method for class 'euclidean'
summary(object, ...)

Arguments

object

Object. An object of class "euclidean" created by neighborhoodEuclidean().
...

Additional parameters.

Value

A vector of counts by neighborhood.

Examples

## Not run: 
summary(neighborhoodEuclidean())

## End(Not run)

Summary method for voronoiNominal().

Description

Return computed counts for Voronoi neighborhoods.

Usage

## S3 method for class 'voronoi'
summary(object, ...)

Arguments

object

Object. An object of class "voronoi" created by voronoiNominal().
...

Additional arguments.

Value

A vector of counts by neighborhood.

See Also

addVoronoi() plot.voronoi()

Summary method for walkingNominal().

Description

Return computed counts for walking neighborhoods.

Usage

## S3 method for class 'walking'
summary(object, ...)

Arguments

object

Object. An object of class "walking" created by walkingNominal().
...

Additional parameters.

Value

An R vector.

Tanaka contour plot.

Description

Soho elevation data.

Usage

tanakaContourPlot(add = FALSE)

Arguments

add

Logical. Add to exisiting plot.

Aggregate time series fatality data from the Vestry report.

Description

Aggregate time series fatality data from the Vestry report.

Usage

timeSeries(vestry = FALSE)

Arguments

vestry

Logical. TRUE returns the data from the Vestry committee (Appendix B, p. 175). FALSE returns John Snow's contribution to the report (p.117).

Value

A R list with two objects: "data" and "source" ("snow" or "vestry").

  • date: Calendar date.

  • day: Day of the week.

  • deaths: Measure of fatality.

  • fatal.attacks: Measure of fatality.

Note

The "snow" data appears on p. 117 of the report; the "vestry" data appear in Appendix B on p.175.

See Also

plot.time_series, print.time_series, vignette("time.series")

Examples

timeSeries(vestry = TRUE)
plot(timeSeries())

Unstack "stacks" in Snow's cholera map.

Description

Unstacks fatalities data by 1) assigning the coordinates of the base case to all cases in a stack and 2) setting the base case as an "address" and making the number of fatalities an attribute.

Usage

unstackFatalities(multi.core = TRUE, compute = FALSE, dev.mode = FALSE)

Arguments

multi.core

Logical or Numeric. TRUE uses parallel::detectCores(). FALSE uses one, single core. With Numeric, you specify the number logical cores. See vignette("Parallelization") for details.
compute

Logical. TRUE computes data. FALSE uses pre-computed data.
dev.mode

Logical. Development mode uses parallel::parLapply().

Value

An R list that includes anchor.case, fatalities.address, fatalities.unstacked and ortho.proj.

Note

This function is computationally intensive. This function documents the code that generates anchor.case, fatalities.address, fatalities.unstacked and ortho.proj.

See Also

vignette("unstacking.fatalities")

Coordinates of Voronoi polygon vertices for original map.

Description

Coordinates of Voronoi polygon vertices for original map.

Usage

voronoi.polygons

Format

A list of 13 data frames frames with 5 variables.

vertex

vertex ID

x

x-coordinate

y

y-coordinate

lon

longitude

lat

latitude

Coordinates of Voronoi polygon vertices for Vestry Report map.

Description

Coordinates of Voronoi polygon vertices for Vestry Report map.

Usage

voronoi.polygons.vestry

Format

A list of 14 data frames frames with 5 variables.

vertex

vertex ID

x

x-coordinate

y

y-coordinate

lon

longitude

lat

latitude

Extract vertices of Delaunay triangles and Dirichelet (Voronoi) tiles.

Description

For construction and plotting of Delaunay and Voronoi polygons.

Usage

voronoiPolygons(sites, rw.data = NULL, rw = NULL, type = "tiles",
  output = "vertices", latlong = FALSE)

Arguments

sites

Object. Data frame of sites to compute Delaunay triangulation and Dirichelet (Voronoi) tessellation with variables "x" and "y".
rw.data

Object. Data frame of secondary source of data to set the rectangular window or bounding box: observations, cases, etc. with variables "x" and "y".
rw

Numeric. Alternative to rw.data: vector of corners to define the rectangular window or bounding box: xmin, xmax, ymin, ymax.
type

Character. "tiles" (tessellation) or "triangles" (triangulation) vertices.
output

Character. "vertices" or "polygons". "vertices" re "polygons" will draw base R polygons() to an existing plot.
latlong

Logical. Use estimated longitude and latitude.

Value

An R list of data frames or base R graphics polygon()'s'.

Note

This function relies on the 'deldir' package.

Examples

snowMap()
voronoiPolygons(pumps, output = "polygons")

snowMap()
voronoiPolygons(pumps, roads, output = "polygons")

snowMap()
voronoiPolygons(pumps, roads, type = "triangles", output = "polygons")

vertices <- voronoiPolygons(pumps, roads)
snow.colors <- grDevices::adjustcolor(snowColors(), alpha.f = 1/3)
snowMap(add.cases = FALSE)
invisible(lapply(seq_along(vertices), function(i) {
  polygon(vertices[[i]], col = snow.colors[[i]])
}))

Compute walking path pump neighborhoods.

Description

Group cases into neighborhoods based on walking distance.

Usage

walkingB(pump.select = NULL, vestry = FALSE, weighted = TRUE,
  case.set = "observed", latlong = FALSE, multi.core = TRUE)

Arguments

pump.select

Numeric. Vector of numeric pump IDs to define pump neighborhoods (i.e., the "population"). Negative selection possible. NULL selects all pumps. Note that you can't just select the pump on Adam and Eve Court (#2) because it's technically an isolate.
vestry

Logical. TRUE uses the 14 pumps from the Vestry report. FALSE uses the 13 in the original map.
weighted

Logical. TRUE computes shortest path weighted by road length. FALSE computes shortest path in terms of the number of nodes.
case.set

Character. "observed", "expected" or "snow". "snow" captures John Snow's annotation of the Broad Street pump neighborhood printed in the Vestry report version of the map.
latlong

Logical.
multi.core

Logical or Numeric. TRUE uses parallel::detectCores(). FALSE uses one, single core. You can also specify the number logical cores. See vignette("Parallelization") for details.

Compute walking path from case/landmark to nearest or selected pump.

Description

Compute walking path from case/landmark to nearest or selected pump.

Usage

walkingPath(origin = 1, destination = NULL, type = "case-pump",
  vestry = FALSE, latlong = FALSE, case.set = "observed",
  location = "nominal", weighted = TRUE, distance.unit = "meter",
  time.unit = "second", walking.speed = 5, include.landmarks = TRUE)

Arguments

origin

Numeric. Vector of origin(s) (numeric or case/landmark name).
destination

Numeric. Vector of destination(s) (numeric or landmark/pump name).
type

Character. Path case to pump. FALSE is all other combinations of cases, landmarks and pumps.
vestry

Logical. TRUE uses the 14 pumps from the map in the Vestry Report. FALSE uses the 13 pumps from the original map.
latlong

Logical.
case.set

Character. "observed" or "expected".
location

Character. For cases and pumps. "anchor, "fatality" or "orthogonal.
weighted

Logical. TRUE computes shortest path in terms of road length. FALSE computes shortest path in terms of the number of nodes.
distance.unit

Character. Unit of distance: "meter" or "yard".
time.unit

Character. "hour", "minute", or "second".
walking.speed

Numeric. Walking speed in km/hr.
include.landmarks

Logical. Include landmarks as cases.

Average Winter Temperatures.

Description

Gareth Stedman Jones Appendix 2, Table 12, p.384.

Usage

winterTemperatures()

Examples

plot(winterTemperatures(), "1859-6-1")