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

You can install {epikit} from CRAN:

You can install {epikit} from the R4EPI repository:

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 field epidemiologists to use in tidying their reports. The functions in {epikit} come in a few categories:

If you need a quick function to determine the number of breaks you need for a 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
```

These 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.

```
library("knitr")
library("magrittr")
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 |

There 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 10.89109
```

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
)
#> # A tibble: 3 x 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)
```

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.

```
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
```