In avallecam/incidenceflow: What the Package Does (One Line, Title Case)

knitr::opts_chunk$set(
  collapse = TRUE,
  warning = FALSE,
  message = FALSE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
knitr::opts_chunk$set(fig.width=10, fig.height=4)
options(tidyverse.quiet = TRUE)

Disclaimer

This package is a work in progress. It has been released to get feedback from users that we can incorporate in future releases.

incidenceflow

The goal of incidenceflow is to provide tidy workflows using incidence and EpiEstim with tidyverse and purrr.

Installation

if(!require("remotes")) install.packages("remotes")
remotes::install_github("avallecam/incidenceflow")

Example

Here are two basic examples which shows you how to solve common problems:

library(incidenceflow)
## basic example code

Workflow 01

get_info_tidy: generates a tibble of incidence::get_info(). click here for more information.
tidy_incidence: generates a complete summary tibble from incidence fit paramteter estimates
glance_incidence: generates a complete summary tibble from incidence fit model performance

# packages ----------------------------------------------------------------

if(!require("devtools")) install.packages("devtools")
# if(!require("avallecam")) devtools::install_github("avallecam/avallecam") #improvements

library(tidyverse) #magrittr and purrr packages
library(lubridate) #ymd
library(outbreaks) #sample data
library(incidence) #core functions

# example outbreak --------------------------------------------------------

dat <- ebola_sim$linelist$date_of_onset
i.7 <- incidence(dat, interval=7)
# plot(i.7)
f1 <- fit(i.7[1:20])
f2 <- fit_optim_split(i.7)

# broom like functions ----------------------------------------------------

# tidy
f1 %>% tidy_incidence()
f2 %>% pluck("fit") %>% tidy_incidence()

# glance
f1 %>% glance_incidence()
f2 %>% pluck("fit") %>% glance_incidence()


# using purrr -------------------------------------------------------------

# using purrr::map family function allows easy stratification
# for gender and could be extrapolated to administrative levels
# in country level analysis

incidence_purrr <- ebola_sim$linelist %>% 
  as_tibble() %>% 
  #filter observations explicitly before incidence()
  filter(date_of_onset<lubridate::ymd(20141007)) %>% 
  #stratify by any group of covariates
  group_by(gender) %>% 
  nest() %>% 
  mutate(incidence_strata=map(.x = data,
                              .f = ~incidence(.x %>% pull(date_of_onset),
                                              interval=7))) %>% 
  mutate(strata_fit=map(.x = incidence_strata,
                        .f = possibly(fit,NA_real_)
                        )) %>% 
  mutate(strata_fit_tidy=map(.x = strata_fit,
                                  .f = possibly(tidy_incidence,tibble()))) %>% 
  mutate(strata_fit_glance=map(.x = strata_fit,
                                       .f = possibly(glance_incidence,tibble())))

# keep only the tibbles
incidence_purrr_tibble <- incidence_purrr %>% 
  select(-data,-incidence_strata,-strata_fit)

# tidy_incidence output
incidence_purrr_tibble %>% 
  unnest(cols = c(strata_fit_tidy))

# glance_incidence output
incidence_purrr_tibble %>% 
  unnest(cols = c(strata_fit_glance))

Run this as a `learnr` tutorial

# install package
if(!require("remotes")) install.packages("remotes")
remotes::install_github("avallecam/incidenceflow")
# install learner and run tutorial
if(!require("learnr")) install.packages("learnr")
learnr::run_tutorial(name = "taller",package = "incidenceflow")

Workflow 02

create_nest_dynamics: estimate Rt per strata
create_nest_summary: create figure and tables of incidence and Rt

linelist_raw <- ebola_sim$linelist %>% 
  as_tibble() %>% 
  #filter observations explicitly before incidence()
  # filter(date_of_onset<lubridate::ymd(20141007)) %>% 
  mutate(all="all")

dictionary <- linelist_raw %>% 
  count(all,gender) %>% 
  rownames_to_column(var = "code")

# linelist_raw %>% 
#   group_by(gender) %>% 
#   skimr::skim()

time_delay_set = 7
#### execute -------------------------------

nest_dynamics <- create_nest_dynamics(linelist = linelist_raw,
                                      dictionary = dictionary,
                                      strata_major = all,
                                      strata_minor = gender,
                                      strata_minor_code = code, # unico para diccionario
                                      date_incidence_case = date_of_onset,
                                      date_of_analysis_today=FALSE,
                                      issue_number_set = 0)

nest_dynamics #nest_dynamics %>% glimpse()

#### nested figures -------------------------------
nest_summary <- create_nest_summary(nest_dynamics = nest_dynamics,
                                    time_limit_fig02 = Inf)

nest_summary #%>% glimpse()

#### if a shapefile is available ----------------------
# nest_summary <- create_nest_summary_map(nest_dynamics = nest_dynamics,
#                                                    geometry = ubigeo_geometria_per2,
#                                                    strata_major=nm_pais,
#                                                    strata_minor=nm_depa)

#### if you want to write it -------------------------
# rt_write_rds(nest_summary = nest_summary,
#              rute = "",
#              name = "admx")

region_name <- "all"

nest_summary %>% 
  filter(strata_major==region_name) %>% 
  pull(fig01) %>% pluck(1)
nest_summary %>% 
  filter(strata_major==region_name) %>% 
  pull(fig02) %>% pluck(1)
nest_summary %>% 
  filter(strata_major==region_name) %>% 
  pull(fig03) %>% pluck(1)
# nest_summary %>% 
#   filter(strata_major==region_name) %>% 
#   pull(fig04) %>% pluck(1)

nest_summary %>% 
  filter(strata_major==region_name) %>% 
  pull(tab01) %>% pluck(1)
nest_summary %>% 
  filter(strata_major==region_name) %>% 
  pull(tab02) %>% pluck(1)
nest_summary %>% 
  filter(strata_major==region_name) %>% 
  pull(tab03) %>% pluck(1)
nest_summary %>% 
  filter(strata_major==region_name) %>% 
  pull(tab04) %>% pluck(1)