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Cumulative counting seroconversion episodes per individual

cg_annotate_exposures_count.Rd

cg_annotate_exposures_count returns a cumulative count of exposures. These exposures can be vaccines, infection episodes, or seroconversion episodes (a seroconversion episode is biologically an infection - the difference between the two is concomittant symptoms, or diagnostic testing).

Usage

cg_annotate_exposures_count(
  cg,
  episode_number = episode_number,
  N_seroconversion_episode_number = N_seroconversion_episode_number,
  dose_number = dose_number,
  exposure_number = exposure_number
)

Arguments

cg

a chronogram

episode_number

the column containing infection episode numbers. cg_annotate_episodes_find() Default is episode_number.

N_seroconversion_episode_number

the column name for resulting episode flag. Generated by cg_annotate_episodes_find_seroconversion(). Default is "N_seroconversion_episode_number".

dose_number

the column name for cumulative doses. Generated by cg_annotate_vaccines_count(). Default is "dose_number".

exposure_number

the column name to return the cumulative counter. Default is "exposure_number".

Value

a chronogram, with episode numbers annotated

See also

cg_annotate_episodes_find()

Examples

library(dplyr)
library(ggplot2)

data("built_smallstudy")
cg <- built_smallstudy$chronogram

## add infections to chronogram ##
cg <- cg_add_experiment(
  cg,
  built_smallstudy$infections_to_add
)

## annotate infections ## 
cg <- cg_annotate_episodes_find(
   cg,
   infection_cols = c("LFT", "PCR", "symptoms"),
   infection_present = c("pos", "Post", "^severe")
)
#> Parsed: infection_cols and infection_present
#>           
#> Searching in the [[column]], for the "text": 
#> 
#> stringr::str_detect(.data[["LFT"]], "pos") ~ "yes"
#> 
#> stringr::str_detect(.data[["PCR"]], "Post") ~ "yes"
#> 
#> stringr::str_detect(.data[["symptoms"]], "^severe") ~ "yes"
#> 
#> 
#> ...detecting will be exact.
#>           Capitals, spelling etc must be precise
#> 
#> Joining with `by = join_by(calendar_date, elig_study_id)`

## annotate vaccines ## 
cg <- cg %>% cg_annotate_vaccines_count(
 ## the prefix to the dose columns: ##
 dose = dose,
 ## the output column name: ##
 dose_counter = dose_number,
 ## the prefix to the date columns: ##
 vaccine_date_stem = date_dose,
 ## use 14d to 'star' after a dose ##
 intermediate_days = 14)
#> Using stem: date_dose
#> Found vaccine dates
#> date_dose_1
#> 
#> date_dose_2

## annotate exposures ##
cg <- cg %>% cg_annotate_exposures_count(
 episode_number = episode_number,
 dose_number = dose_number,
 ## we have not considered episodes of seroconversion
 N_seroconversion_episode_number = NULL
 )
 
 ## Visualise
 cg %>% ggplot(
 aes(x=calendar_date,
 y= exposure_number, 
 col = elig_study_id)) + geom_line()