generalized function to convert raw agent based data to aggregate data — agents_to

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).

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

Arguments

agents	data frame with individual agent information
states	Name-variable pairs of the form `states = c(col1, col2)`, 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

dataset with aggregated information, 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)
agents <- EpiCompare::hagelloch_raw
# making babies
set.seed(5)
babies <- sample(nrow(agents),size = 5)
agents$tBIRTH <- NA
agents$tBIRTH[babies] <- agents$tI[babies] - 5

aggregate_b <- agents_to_aggregate(agents, states = c(tI, tR),
                                   death = NULL, birth = tBIRTH)

# looking at when babies where born:
agents %>% dplyr::filter(!is.na(.data$tBIRTH)) %>%
  dplyr::pull(.data$tBIRTH) %>% ceiling() %>% sort
#> [1] 23 26 29 29 32
# vs:
data.frame(counts = 1:nrow(aggregate_b),
           num_people = aggregate_b %>% select(-t) %>% apply(1, sum))
#>    counts num_people
#> 1       1        183
#> 2       2        183
#> 3       3        183
#> 4       4        183
#> 5       5        183
#> 6       6        183
#> 7       7        183
#> 8       8        183
#> 9       9        183
#> 10     10        183
#> 11     11        183
#> 12     12        183
#> 13     13        183
#> 14     14        183
#> 15     15        183
#> 16     16        183
#> 17     17        183
#> 18     18        183
#> 19     19        183
#> 20     20        183
#> 21     21        183
#> 22     22        183
#> 23     23        183
#> 24     24        184
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#> 93     93        188
#> 94     94        188


# including death
aggregate_d <- agents_to_aggregate(agents, states = c(tI, tR),
                                   death = tDEAD, birth = NULL)

# looking at when people died:
agents %>% dplyr::filter(!is.na(.data$tDEAD)) %>%
  dplyr::pull(.data$tDEAD) %>% ceiling() %>% sort
#>  [1] 20 41 44 44 44 45 46 47 47 47 49 60
# vs:
data.frame(counts = 1:nrow(aggregate_d),
           num_people = aggregate_d %>% select(-t) %>% apply(1, sum))
#>    counts num_people
#> 1       1        188
#> 2       2        188
#> 3       3        188
#> 4       4        188
#> 5       5        188
#> 6       6        188
#> 7       7        188
#> 8       8        188
#> 9       9        188
#> 10     10        188
#> 11     11        188
#> 12     12        188
#> 13     13        188
#> 14     14        188
#> 15     15        188
#> 16     16        188
#> 17     17        188
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#> 22     22        187
#> 23     23        187
#> 24     24        187
#> 25     25        187
#> 26     26        187
#> 27     27        187
#> 28     28        187
#> 29     29        187
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#> 45     45        183
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