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Epitabulate

Lifecycle: experimental CRAN status Codecov test coverage R build status

The {epitabulate} package produces tables for descriptive epidemiological analysis. It contains four functions:

  • tab_linelist() — Tabulate and describe counts of variables in a linelist
  • tab_survey() — Tabulate and describe counts of variables in a survey (with appropriate CIs)
  • tab_univariate() — Calculate Odds / Risk / Incidence Rate Ratios directly from a linelist
  • data_frame_from_2x2() — Creates a data frame from a 2x2 table for unambiguous interpretation

Installation

{epitabulate} is currently under development, but you can keep up-to-date by installing it from the R4EPIs drat repository:

# install.packages("drat")
drat::addRepo("R4EPI")
install.packages("epitabulate")

You can also install the in-development version from GitHub using the {remotes} package (but there’s no guarantee that it will be stable):

# install.packages("remotes")
remotes::install_github("R4EPI/epitabulate")

Examples

Here is an example of the tables produced by {epitabulate} from a randomly generated data set based on an MSF data dictionary for Measles data.

library(epidict)
library(matchmaker)
library(epikit)
library(dplyr)
#> 
#> Attache Paket: 'dplyr'
#> Die folgenden Objekte sind maskiert von 'package:stats':
#> 
#>     filter, lag
#> Die folgenden Objekte sind maskiert von 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(epitabulate)

linelist <- epidict::gen_data("Measles", numcases = 1000, org = "MSF")
measles_dict <- epidict::msf_dict("Measles", compact = FALSE)

# Cleaning linelist data
linelist_clean <- matchmaker::match_df(
  x = linelist,
  dictionary = measles_dict,
  from = "option_code",
  to = "option_name",
  by = "data_element_shortname",
  order = "option_order_in_set"
)
linelist_clean
#> # A tibble: 1,000 × 52
#>    case_number date_of_c…¹ patie…² patie…³ age_y…⁴ age_m…⁵ age_d…⁶ sex   pregn…⁷
#>    <chr>       <date>      <fct>   <chr>     <int>   <int>   <int> <fct> <fct>  
#>  1 A1          2018-02-17  Outpat… Villag…      27      NA      NA Unkn… Not ap…
#>  2 A2          2018-02-24  Outpat… Villag…      11      NA      NA Unkn… Not ap…
#>  3 A3          2018-03-31  Inpati… Villag…      13      NA      NA Unkn… Not ap…
#>  4 A4          2018-02-15  Outpat… Villag…      NA       4      NA Fema… Not ap…
#>  5 A5          2018-04-13  Inpati… Villag…      23      NA      NA Unkn… Not ap…
#>  6 A6          2018-02-04  Inpati… Villag…      78      NA      NA Fema… Not ap…
#>  7 A7          2018-01-27  Inpati… Villag…      47      NA      NA Unkn… Not ap…
#>  8 A8          2018-01-26  Inpati… Villag…      32      NA      NA Unkn… Not ap…
#>  9 A9          2018-01-05  Inpati… Villag…      54      NA      NA Fema… Not cu…
#> 10 A10         2018-02-21  Inpati… Villag…       4      NA      NA Fema… Not cu…
#> # … with 990 more rows, 43 more variables: trimester <fct>,
#> #   foetus_alive_at_admission <fct>, exit_status <fct>, date_of_exit <date>,
#> #   time_to_death <fct>, pregnancy_outcome_at_exit <fct>,
#> #   baby_born_with_complications <fct>, previously_vaccinated <fct>,
#> #   previous_vaccine_doses_received <fct>, detected_by <fct>,
#> #   msf_involvement <fct>, residential_status <fct>,
#> #   residential_status_brief <fct>, date_of_last_vaccination <date>, …
#> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names

Quick tabulations

the_symptoms <- tab_linelist(linelist_clean,
  cough, nasal_discharge, severe_oral_lesions,
  transpose = "value"
) 
the_symptoms
#> # A tibble: 3 × 5
#>   variable            `Yes n` `Yes proportion` `No n` `No proportion`
#>   <fct>                 <dbl>            <dbl>  <dbl>           <dbl>
#> 1 cough                   501             50.1    499            49.9
#> 2 nasal_discharge         501             50.1    499            49.9
#> 3 severe_oral_lesions     487             48.7    513            51.3
the_symptoms %>%
  epikit::rename_redundant("%" = "proportion") %>%
  epikit::augment_redundant("(n)" = "n") %>%
  knitr::kable()
variable Yes (n) % No (n) %
cough 501 50.1 499 49.9
nasal_discharge 501 50.1 499 49.9
severe_oral_lesions 487 48.7 513 51.3

2x2 tables

In R, creating 2x2 tables is as simple as using the function table(), but unfortunately, it can be difficult to interpret the values of these tables because the dimensions are often flipped around for different analyses. The function data_frame_from_2x2() will present these values labeled unambiguously.

symptoms_tf <- linelist_clean %>%
  dplyr::transmute(
    pneumonia = grepl("Yes", pneumonia),
    cough = grepl("Yes", cough),
    nasal_discharge = grepl("Yes", nasal_discharge),
    oral_lesions = grepl("Yes", severe_oral_lesions),
    contact = grepl("known case", contact_history)
  ) 

symptoms_tf
#> # A tibble: 1,000 × 5
#>    pneumonia cough nasal_discharge oral_lesions contact
#>    <lgl>     <lgl> <lgl>           <lgl>        <lgl>  
#>  1 FALSE     TRUE  FALSE           FALSE        TRUE   
#>  2 TRUE      FALSE FALSE           TRUE         FALSE  
#>  3 TRUE      TRUE  FALSE           FALSE        TRUE   
#>  4 TRUE      FALSE TRUE            FALSE        TRUE   
#>  5 FALSE     FALSE FALSE           FALSE        FALSE  
#>  6 TRUE      TRUE  FALSE           TRUE         FALSE  
#>  7 TRUE      FALSE TRUE            FALSE        FALSE  
#>  8 TRUE      TRUE  FALSE           TRUE         TRUE   
#>  9 TRUE      FALSE TRUE            FALSE        TRUE   
#> 10 TRUE      TRUE  TRUE            FALSE        FALSE  
#> # … with 990 more rows
#> # ℹ Use `print(n = ...)` to see more rows

print(pxc <- with(symptoms_tf, table(pneumonia, cough)))
#>          cough
#> pneumonia FALSE TRUE
#>     FALSE   152  171
#>     TRUE    347  330
print(pxcxc <- with(symptoms_tf, table(pneumonia, cough, contact)))
#> , , contact = FALSE
#> 
#>          cough
#> pneumonia FALSE TRUE
#>     FALSE    76   86
#>     TRUE    168  170
#> 
#> , , contact = TRUE
#> 
#>          cough
#> pneumonia FALSE TRUE
#>     FALSE    76   85
#>     TRUE    179  160

data_frame_from_2x2(pxc)
#>   A_exp_cases B_exp_controls C_unexp_cases D_unexp_controls total_cases
#> 1         152            171           347              330         499
#>   total_controls total_exposed total_unexposed total
#> 1            501           323             677  1000
data_frame_from_2x2(pxcxc)
#>       A_exp_cases B_exp_controls C_unexp_cases D_unexp_controls total_cases
#> crude         152            171           347              330         499
#> FALSE          76             86           168              170         244
#> TRUE           76             85           179              160         255
#>       total_controls total_exposed total_unexposed total
#> crude            501           323             677  1000
#> FALSE            256           162             338   500
#> TRUE             245           161             339   500

Odds / Risk / Incidence Rate Ratios

tu <- tab_univariate(symptoms_tf, 
  outcome = pneumonia, 
  cough, nasal_discharge, oral_lesions, contact,
  mergeCI = TRUE,
  extend_output = FALSE,
  measure = "OR"
)
tu %>% 
  select(-est_type) %>% 
  epikit::augment_redundant("exposed " = "exp_") %>%
  rename(odds = est_ci) %>%
  knitr::kable(digits = 3)
variable exposed cases unexposed cases exposed controls unexposed controls odds p.value
crude…1 cough 330 347 171 152 0.845 (0.648–1.102) 0.215
crude…2 nasal_discharge 348 329 153 170 1.175 (0.901–1.532) 0.233
crude…3 oral_lesions 346 331 141 182 1.349 (1.034–1.761) 0.027
crude…4 contact 339 338 161 162 1.009 (0.774–1.316) 0.946