This function visualizes the map with different variables. The input data frame can be either the long or wide format.
Usage
hatchPlot(
data,
variables,
values = NULL,
labels = NULL,
geo,
by.data,
by.geo,
is.long = FALSE,
lower,
upper,
lim = NULL,
lim.CI = NULL,
breaks.CI = NULL,
ncol = 4,
hatch = NULL,
border = NULL,
size = 1,
legend.label = NULL,
per1000 = FALSE,
direction = 1,
...
)Arguments
- data
a data frame with variables to be plotted
- variables
vector of variables to be plotted. If long format of data is used, only one variable can be selected
- values
the column corresponding to the values to be plotted, only used when long format of data is used
- labels
vector of labels to use for each variable, only used when wide format of data is used
- geo
SpatialPolygonsDataFrame object for the map
- by.data
column name specifying region names in the data
- by.geo
variable name specifying region names in the data
- is.long
logical indicator of whether the data is in the long format, default to FALSE
- lower
column name of the lower bound of the CI
- upper
column name of the upper bound of the CI
- lim
fixed range of values for the variables to plot
- lim.CI
fixed range of the CI widths to plot
- breaks.CI
a vector of numerical values that decides the breaks in the CI widths to be shown
- ncol
number of columns for the output tabs
- hatch
color of the hatching lines.
- border
color of the polygon borders.
- size
line width of the polygon borders.
- legend.label
Label for the color legend.
- per1000
logical indicator to plot mortality rates as rates per 1,000 live births. Note that the added comparison data should always be in the probability scale.
- direction
Direction of the color scheme. It can be either 1 (smaller values are darker) or -1 (higher values are darker). Default is set to 1.
- ...
unused.
Examples
if (FALSE) { # \dontrun{
years <- levels(DemoData[[1]]$time)
# obtain direct estimates
data <- getDirectList(births = DemoData,
years = years,
regionVar = "region", timeVar = "time",
clusterVar = "~clustid+id",
ageVar = "age", weightsVar = "weights",
geo.recode = NULL)
# obtain direct estimates
data_multi <- getDirectList(births = DemoData, years = years,
regionVar = "region", timeVar = "time", clusterVar = "~clustid+id",
ageVar = "age", weightsVar = "weights", geo.recode = NULL)
data <- aggregateSurvey(data_multi)
fit2 <- smoothDirect(data = data, geo = geo, Amat = mat,
year_label = years.all, year_range = c(1985, 2019),
rw = 2, is.yearly=TRUE, m = 5, type.st = 4)
out2 <- getSmoothed(fit2)
plot(out2, is.yearly=TRUE, is.subnational=TRUE)
hatchPlot(data = subset(out2, is.yearly==FALSE), geo = geo,
variables=c("years"), values = c("median"),
by.data = "region", by.geo = "REGNAME",
lower = "lower", upper = "upper", is.long=TRUE)
} # }