epikit

Lifecycle: experimental CRAN status Codecov test coverage R-CMD-check

The goal of {epikit} is to provide miscellaneous functions for applied epidemiologists. This is a product of the R4EPIs project; learn more at https://r4epis.netlify.app/.

Installation

You can install {epikit} from CRAN (see details for the latest version):

install.packages("epikit")

Click here for alternative installation options

If there is a bugfix or feature that is not yet on CRAN, you can install it via the {drat} package:

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/epikit") 

library("epikit")

The {epikit} was primarily designed to house convenience functions for applied epidemiologists to use in tidying their reports. The functions in {epikit} come in a few categories:

Age categories

A couple of functions are dedicated to constructing age categories and partitioning them into separate chunks.

library("knitr")
library("magrittr")

set.seed(1)
x <- sample(0:100, 20, replace = TRUE)
y <- ifelse(x < 2, sample(48, 20, replace = TRUE), NA)
df <- data.frame(
  age_years = age_categories(x, upper = 80), 
  age_months = age_categories(y, upper = 16, by = 6)
)
df %>% 
  group_age_categories(years = age_years, months = age_months)
#>    age_years age_months age_category
#> 1      60-69       <NA>  60-69 years
#> 2      30-39       <NA>  30-39 years
#> 3        0-9        16+   16+ months
#> 4      30-39       <NA>  30-39 years
#> 5        80+       <NA>    80+ years
#> 6      40-49       <NA>  40-49 years
#> 7      10-19       <NA>  10-19 years
#> 8        80+       <NA>    80+ years
#> 9      50-59       <NA>  50-59 years
#> 10     50-59       <NA>  50-59 years
#> 11       80+       <NA>    80+ years
#> 12       80+       <NA>    80+ years
#> 13     20-29       <NA>  20-29 years
#> 14     50-59       <NA>  50-59 years
#> 15     70-79       <NA>  70-79 years
#> 16       0-9       <NA>    0-9 years
#> 17     70-79       <NA>  70-79 years
#> 18     70-79       <NA>  70-79 years
#> 19       80+       <NA>    80+ years
#> 20     30-39       <NA>  30-39 years

Quick proportions with conficence intervals

There are three functions that will provide quick statistics for different rates based on binomial estimates of proportions from binom::binom.wilson()

attack_rate(10, 50)
#>   cases population ar    lower    upper
#> 1    10         50 20 11.24375 33.03711
case_fatality_rate(2, 50)
#>   deaths population cfr    lower    upper
#> 1      2         50   4 1.103888 13.46009
mortality_rate(40, 50000)
#>   deaths population mortality per 10 000   lower    upper
#> 1     40      50000                    8 5.87591 10.89109

In addition, it’s possible to rapidly calculate Case fatality rate from a linelist, stratified by different groups (e.g. gender):

library("outbreaks")
case_fatality_rate_df(ebola_sim_clean$linelist, 
  outcome == "Death", 
  group = gender,
  add_total = TRUE,
  mergeCI = TRUE
)
#> Warning: There was 1 warning in `dplyr::mutate()`.
#> ℹ In argument: `gender = forcats::fct_explicit_na(gender, "(Missing)")`.
#> Caused by warning:
#> ! `fct_explicit_na()` was deprecated in forcats 1.0.0.
#> ℹ Please use `fct_na_value_to_level()` instead.
#> ℹ The deprecated feature was likely used in the epikit package.
#>   Please report the issue at <https://github.com/R4EPI/epikit/issues>.
#> # A tibble: 3 × 5
#>   gender deaths population   cfr ci           
#>   <fct>   <int>      <int> <dbl> <chr>        
#> 1 f        1291       2280  56.6 (54.58-58.64)
#> 2 m        1273       2247  56.7 (54.59-58.69)
#> 3 Total    2564       4527  56.6 (55.19-58.08)

Inline functions

The inline functions make it easier to print estimates with confidence intervals in reports with the correct number of digits.

The _df suffixes (fmt_ci_df(), fmt_pci_df()) will print the confidence intervals for data stored in data frames. These are designed to work with the outputs of the rates functions. For example, fmt_ci_df(attack_rate(10, 50)) will produce 20.00% (CI 11.24-33.04). All of these suffixes will have three options e, l, and u. These refer to estimate, lower, and upper column positions or names.

Confidence interval manipulation

The confidence interval manipulation functions take in a data frame and combine their confidence intervals into a single character string much like the inline functions do. There are two flavors:

This is useful for reporting models:

fit <- lm(100/mpg ~ disp + hp + wt + am, data = mtcars)
df  <- data.frame(v = names(coef(fit)), e = coef(fit), confint(fit), row.names = NULL)
names(df) <- c("variable", "estimate", "lower", "upper")
print(df)
#>      variable    estimate        lower       upper
#> 1 (Intercept) 0.740647656 -0.774822875 2.256118188
#> 2        disp 0.002702925 -0.002867999 0.008273849
#> 3          hp 0.005274547 -0.001400580 0.011949674
#> 4          wt 1.001303136  0.380088737 1.622517536
#> 5          am 0.155814790 -0.614677730 0.926307310

# unite CI has more options
unite_ci(df, "slope (CI)", estimate, lower, upper, m100 = FALSE, percent = FALSE)
#>      variable        slope (CI)
#> 1 (Intercept) 0.74 (-0.77-2.26)
#> 2        disp 0.00 (-0.00-0.01)
#> 3          hp 0.01 (-0.00-0.01)
#> 4          wt  1.00 (0.38-1.62)
#> 5          am 0.16 (-0.61-0.93)

# merge_ci just needs to know where the estimate is
merge_ci_df(df, e = 2)
#>      variable    estimate           ci
#> 1 (Intercept) 0.740647656 (-0.77-2.26)
#> 2        disp 0.002702925 (-0.00-0.01)
#> 3          hp 0.005274547 (-0.00-0.01)
#> 4          wt 1.001303136  (0.38-1.62)
#> 5          am 0.155814790 (-0.61-0.93)

Give me a break

If you need a quick function to determine the number of breaks you need for a grouping or color scale, you can use find_breaks(). This will always start from 1, so that you can include zero in your scale when you need to.

find_breaks(100) # four breaks from 1 to 100
#> [1]  1 26 51 76
find_breaks(100, snap = 20) # four breaks, snap to the nearest 20
#> [1]  1 41 81
find_breaks(100, snap = 20, ceiling = TRUE) # include the highest number
#> [1]   1  41  81 100

Pull together population counts

To quickly pull together population counts for use in surveys or demographic pyramids the gen_population() function can help. If you only know the proportions in each group the function will convert this to counts for you - whereas if you have counts, you can type those in directly. The default proportions are based on Doctors Without Borders general emergency intervention standard values.

# get population counts based on proportion, stratified
gen_population(groups = c("0-4","5-14","15-29","30-44","45+"), 
               strata = c("Male", "Female"), 
               proportions = c(0.079, 0.134, 0.139, 0.082, 0.067))
#> Warning in gen_population(groups = c("0-4", "5-14", "15-29", "30-44", "45+"), : Given proportions (or counts) is not the same as
#> groups multiplied by strata length, they will be repeated to match
#> # A tibble: 10 × 4
#>    groups strata proportions     n
#>    <fct>  <fct>        <dbl> <dbl>
#>  1 0-4    Male         0.079    79
#>  2 5-14   Male         0.134   134
#>  3 15-29  Male         0.139   139
#>  4 30-44  Male         0.082    82
#>  5 45+    Male         0.067    67
#>  6 0-4    Female       0.079    79
#>  7 5-14   Female       0.134   134
#>  8 15-29  Female       0.139   139
#>  9 30-44  Female       0.082    82
#> 10 45+    Female       0.067    67

Type in counts directly to get the groups in a data frame.

# get population counts based on counts, stratified - type out counts
# for each group and strata
gen_population(groups = c("0-4","5-14","15-29","30-44","45+"), 
               strata = c("Male", "Female"), 
               counts = c(20, 10, 30, 40, 0, 0, 40, 30, 20, 20))
#> # A tibble: 10 × 4
#>    groups strata proportions     n
#>    <fct>  <fct>        <dbl> <dbl>
#>  1 0-4    Male        0.0952    20
#>  2 5-14   Male        0.0476    10
#>  3 15-29  Male        0.143     30
#>  4 30-44  Male        0.190     40
#>  5 45+    Male        0          0
#>  6 0-4    Female      0          0
#>  7 5-14   Female      0.190     40
#>  8 15-29  Female      0.143     30
#>  9 30-44  Female      0.0952    20
#> 10 45+    Female      0.0952    20

Table modification

These functions all modify the appearance of a table displayed in a report and work best with the knitr::kable() function.

df <- data.frame(
  `a n` = 1:6,
  `a prop` = round((1:6) / 6, 2),
  `a deff` = round(pi, 2),
  `b n` = 6:1,
  `b prop` = round((6:1) / 6, 2),
  `b deff` = round(pi * 2, 2),
  check.names = FALSE
)
knitr::kable(df)
a n a prop a deff b n b prop b deff
1 0.17 3.14 6 1.00 6.28
2 0.33 3.14 5 0.83 6.28
3 0.50 3.14 4 0.67 6.28
4 0.67 3.14 3 0.50 6.28
5 0.83 3.14 2 0.33 6.28
6 1.00 3.14 1 0.17 6.28
df %>%
  rename_redundant("%" = "prop", "Design Effect" = "deff") %>%
  augment_redundant(" (n)" = " n$") %>%
  knitr::kable()
a (n) % Design Effect b (n) % Design Effect
1 0.17 3.14 6 1.00 6.28
2 0.33 3.14 5 0.83 6.28
3 0.50 3.14 4 0.67 6.28
4 0.67 3.14 3 0.50 6.28
5 0.83 3.14 2 0.33 6.28
6 1.00 3.14 1 0.17 6.28