generalized function to convert raw agent based data to aggregate data for grouped_data (preforms per group) — agents_to_aggregate.grouped

This function converts data on an agent-based level (1 row = 1 agent) relative when an agent is in each state and aggregates it, so that the user can know how many agents are in each state at a given time point (integer based). This function takes grouped_df data.frames (from dplyr) and aggregates within grouping parameters and also provides the columns associated with the grouping.

# S3 method for grouped_df
agents_to_aggregate(
  agents,
  states,
  death = NULL,
  birth = NULL,
  min_max_time = c(0, NA),
  integer_time_expansion = TRUE
)

Arguments

agents	grouped data.frame with individual agent information
states	Name-variable pairs of the form `states = c(col1, col2, col3)`, that describe which columns contain the time one entered the state. Do not include column for original state. These need to be ordered, for example: for an SIR model, with columns "`tI`" and "`tR`" expressing the time the individual became infected and recovered (respectively), we want "`states = c(tI, tR)`".
death	string for column with death time information (default `NULL`)
birth	string for column with birth time information (default `NULL`)
min_max_time	vector (length 2) of minimum and maximum integer time, the second value can be `NA` - and if so, we estimate maximum time from the data.
integer_time_expansion	boolean if every integer time point in the range of `min_max_time` should be presented in the aggregation output. If `FALSE` (default is `TRUE`), then lines will only include those time points where

Value

grouped dataset with aggregated information per group, We label classes "X{i}" for i in 0:(length(states)).

Details

note that all parameters related to name columns can also be in a string format. More details can be found in agents_to_aggregate's documentation.

Examples

library(dplyr)

max_time <- 100
agents_g <- hagelloch_raw %>%
  filter(SEX %in% c("female", "male")) %>% group_by(SEX)
sir_group <- agents_to_aggregate(agents_g, states = c(tI, tR),
                                 min_max_time = c(0, max_time))
agents <- agents_g %>%
  filter(SEX == "female") %>% ungroup()
sir_group1 <- agents_to_aggregate(agents, states = c(tI, tR),
                                 min_max_time = c(0, max_time))
sir_group_1 <- sir_group %>% filter(SEX == "female")
assertthat::are_equal(sir_group1,
                      sir_group_1 %>% ungroup %>% select(t, X0, X1, X2))
#> [1] TRUE