`read.simulation.results.bigpurple`

Read results of an EMOD simulation from its original location off the BigPurple filesystem

Description

Read results of an EMOD simulation from its original location off the BigPurple filesystem

Usage

read.simulation.results.bigpurple(
  experiment_path,
  scenario_name,
  summarize_columns = c("Newly.Infected", "Newly.Tested.Positive",
    "Newly.Tested.Negative", "Population", "Infected", "On_ART", "Died", "Died_from_HIV",
    "Tested.Past.Year.or.On_ART", "Tested.Ever", "Diagnosed"),
  stratify_columns = c("Year", "Gender"),
  min_age_inclusive = 15,
  max_age_inclusive = 49,
  verbose = FALSE
)

Arguments

Argument	Description
`experiment_path`	string pointing to the folder which contains the Simulation_XXXXXXXX folders. For example, /gpfs/scratch/kaftad01/experiments/Baseline-campaign_Nyanza_baseline_03112021_NoPrEP-Baseline___2022_02_17_21_34_51_660565
`scenario_name`	string for the name of the scenario being read. For example, you might use “baseline” for the baseline scenario.
`summarize_columns`	a vector of strings containing names of columns to be aggregated via summation. Note that spaces in column names are replaced by a period (“.”). For example, “Newly Infected” becomes “Newly.Infected”.
`stratify_columns`	a vector of strings containing names of columns by which we will stratify the data. For example, we might want to have a separate row in the dataset for each year, so we would set stratify_columns = c(“Year”)
`min_age_inclusive`	an integer representing the minimum age to keep while reading the data (all ages below will be filtered out)
`max_age_inclusive`	an integer representing the maximum age to keep while reading the data (all ages above will be filtered out)

Details

When a simulation is run on BigPurple, dtk-tools creates a simulation folder somewhere on BigPurple (the folder which contains the simulation folder is specified in simtools.ini using the parameter “sim_root”). In this folder is a set of folders - each one representing a different run of the simulation. These folders will look something like “Simulation_6CGUFHY7”. The results of each simulation are stored within these folders. The results of an EMOD simulation are stored in a series of csv files titled “ReportHIVByAgeAndGender.csv”. One of these files exists for each simulation run (typically 250 files). This function reads and aggregates those files into a single tibble.

Value

A tibble with columns incidence and Year

read.simulation.results.bigpurple