Description Usage Arguments Details Aesthetics Examples
View source: R/stat-aggregate.R
aggregate SIR path visuals from agent data
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mapping |
Set of aesthetic mappings created by
|
data |
The data to be displayed in this layer. There are three options: If A A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)). |
stat |
Override the default connection between |
position |
Position adjustment, either as a string, or the result of a call to a position adjustment function |
... |
Other arguments passed on to |
na.rm |
when using the default Stat or Geom this parameter doesn't matter
as If not using either of these (which means this geom/stat doesn't need to be
used), then if |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
geom |
Override the default connection between |
This visual leverage the function agents_to_aggregate
underneath. This function converts individual agents' information on when the
agent transitions between 3 different states (ordered), to create a temporal
tally/aggregation of how many agents are in what state at each integer time
point. In this visualization tool, the user provides y
and z
to
present the agent enters the second and third state (respectively).
As mentioned in parameter details, agents_to_aggregate
encodes
NA
to capture information, and as such, the default
y
raw time when initially infected (or more
generally when the agent enters the second state)
z
raw time when started recovery (or more generally
when the agent enters the third state)
group
...
Learn more about setting these aesthetics in
vignette("ggplot2-specs")
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | library(ggplot2)
library(dplyr)
library(ggtern); EpiCompare:::update_approved_layers()
# ^ this generally need not be done
# geom_aggregate
EpiCompare::hagelloch_raw %>% filter(SEX %in% c("male", "female")) %>%
ggplot(., aes(y = tI, z = tR, color = SEX)) +
geom_aggregate() + coord_tern() +
labs(x = "S", y = "I", z = "R",
color = "Gender")
# stat_aggregate
EpiCompare::hagelloch_raw %>% filter(SEX %in% c("male", "female")) %>%
ggplot(., aes(y = tI, z = tR, color = SEX)) +
geom_path(stat = StatSirAggregate) + coord_tern() +
labs(x = "S", y = "I", z = "R",
color = "Gender")
EpiCompare::hagelloch_raw %>% dplyr::filter(SEX %in% c("male", "female")) %>%
ggplot(., aes(y = tI, z = tR, color = SEX)) +
stat_aggregate(geom = "path") +
# note geom = "path" is the default
coord_tern() +
labs(x = "S", y = "I", z = "R",
color = "Gender")
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