Build a pixel map

This function builds a choropleth map that visualises estimates and errors simultaneously through pixelation and sampling.

build_pmap(
  data = NULL,
  distribution = NULL,
  pixelGeo,
  id,
  border = NULL,
  palette = "Blues",
  q = NULL,
  limits = NULL
)

Arguments

data: A data frame.
distribution: Name of the distribution that the pixel assignments will be drawn from. It must be one of discrete, normal or uniform. If distribution = "discrete", a data frame of the quantiles that define the relative frequency distribution for the estimate must be entered for q. If distribution = "normal", the values assigned to pixels will be drawn from a normal distribution parameterised using the estimates and errors (means and standard deviations). If distribution = "uniform", values will be sampled with equal probability from a sequence of 5 numbers that spans the estimate minus its error to the estimate plus its error.
pixelGeo: An object from pixelate.
id: Name of the common column shared by the objects passed to data, pixelGeo and q (if distribution = "discrete").
border: Name of geographical borders to be added to the map. It must be one of county, france, italy, nz, state, usa or world (see documentation for map_data for more information). The borders will be refined to match latitute and longtidue coordinates provided in the data frame or spatial polygons data frame. Alternatively, you can supply a SpatialPolygonsDataFrame.
palette: Name of colour palette. It must be one of Blues, Greens, Greys, Oranges, Purples or Reds (see documentation for scale_fill_distiller for more information).
q: A data frame of quantiles which define the distribution for each estimate. Each row is an estimate, and each column is a quantile. See examples for an example of q input.
limits: Limits for the legend. Default is NULL, which takes the limits to be the range of the data.

Examples

if (FALSE) {
# This code will produce a pixelated map when run in R
# It is not run here.
data(us_geo)
ca_geo <- subset(us_geo, us_geo@data$STATE == "06")
pix <- pixelate(geoData = ca_geo, pixelSize = 70, id = "GEO_ID")

data(us_data)
us_data$GEO.id2 <- as.numeric(us_data$GEO.id2)
ca_data <- subset(us_data, us_data$GEO.id2 > 6000 & us_data$GEO.id2 < 7000)
ca_data <- read.uv(data = ca_data, estimate = "pov_rate", error = "pov_moe")
row.names(ca_data) <- seq(1, nrow(ca_data), 1)

# uniform distribution
m <- build_pmap(data = ca_data, distribution = "uniform", pixelGeo = pix, id = "GEO_ID")
view(m)

# normal distribution
ca_data$se <- ca_data$pov_moe / 1.645
ca_data <- read.uv(data = ca_data, estimate = "pov_rate", error = "se")

m <- build_pmap(data = ca_data, distribution = "normal", pixelGeo = pix, id = "GEO_ID")
view(m)

# experiment with discrete distribution
# exponential - example for q argument
ca_data.q <- with(ca_data, data.frame(p0.05 = qexp(0.05, 1/pov_rate),
 p0.25 = qexp(0.25, 1/pov_rate), p0.5 = qexp(0.5, 1/pov_rate),
 p0.75 = qexp(0.75, 1/pov_rate), p0.95 = qexp(0.95, 1/pov_rate)))

m <- build_pmap(data = ca_data, distribution = "discrete", pixelGeo = pix,
 id = "GEO_ID", q = ca_data.q)
view(m)
}

Arguments

See also

Examples