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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/.
You can install {epikit} from CRAN (see details for the latest version):
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):
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:
A couple of functions are dedicated to constructing age categories and partitioning them into separate chunks.
age_categories() takes in a vector of numbers and returns formatted age categories.group_age_categories() will take a data frame with different age categories in columns (e.g. years, months, weeks) and combine them into a single column, selecting the column with the lowest priority.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 yearsThere are three functions that will provide quick statistics for different rates based on binomial estimates of proportions from binom::binom.wilson()
attack_rate()case_fatality_rate()mortality_rate()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 10.89109In 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$linelist,
outcome == "Death",
group = gender,
add_total = TRUE,
mergeCI = TRUE
)
#> Warning: There was 1 warning in `dplyr::mutate()`.
#> ℹ In argument: `gender = forcats::fct_explicit_na(gender, "(Missing)")`.
#> Caused by warning:
#> ! `fct_explicit_na()` was deprecated in forcats 1.0.0.
#> ℹ Please use `fct_na_value_to_level()` instead.
#> ℹ The deprecated feature was likely used in the epikit package.
#> Please report the issue at <https://github.com/R4EPI/epikit/issues>.
#> # A tibble: 3 × 5
#> gender deaths population cfr ci
#> <fct> <int> <int> <dbl> <chr>
#> 1 f 1291 2280 56.6 (54.58-58.64)
#> 2 m 1273 2247 56.7 (54.59-58.69)
#> 3 Total 2564 4527 56.6 (55.19-58.08)The inline functions make it easier to print estimates with confidence intervals in reports with the correct number of digits.
fmt_ci() formats confidence intervals from three numbers. (e.g. fmt_ci(50, 10, 80) produces 50.00% (CI 10.00-80.00)fmt_pci() formats confidence intervals from three fractions, multiplying by 100 beforehand.The _df suffixes (fmt_ci_df(), fmt_pci_df()) will print the confidence intervals for data stored in data frames. These are designed to work with the outputs of the rates functions. For example, fmt_ci_df(attack_rate(10, 50)) will produce 20.00% (CI 11.24-33.04). All of these suffixes will have three options e, l, and u. These refer to estimate, lower, and upper column positions or names.
fmt_count() will count a condition in a data frame and present the number and percent of TRUE values. For example, if you wanted to count the number of women patients from Rokupa hospital, you would write: fmt_count(ebola_sim_clean$linelist, gender == "f", hospital == "Rokupa Hospital") and it would produce: 210 (3.6%)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:
merge_ci_df() and merge_pci_df() will merge just the values of the confidence interval and leave the estimate alone. Note: this WILL remove the lower and upper columns.unite_ci() merges both the confidence interval and the estimate into a single character column. This generally has more options than merge_ci()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)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 100To 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 67Type 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 20These functions all modify the appearance of a table displayed in a report and work best with the knitr::kable() function.
rename_redundant() renames redundant columns with a single name. (e.g. hopitalized_percent and confirmed_percent can both be renamed to %)augment_redundant() is similar to rename_redundant(), but it modifies the redundant column names (e.g. hospitalized_n and confirmed_n can become hospitalized (n) and confirmed (n))merge_ci() combines estimate, lower bound, and upper bound columns into a single column.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 |
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.