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r4epis-website's Introduction

Sitrep

Lifecycle: maturing CRAN status Codecov test coverage R build status

The goal of {sitrep} is provide report templates for common epidemiological surveys and outbreak reports. The package further contains helper function that standardize certain analyses.

While templates are primarily for MSF analyses - they have been setup to be as generic as possible for use by the general applied epidemiology community.

Detailed information about the project and the templates can be found at https://r4epis.netlify.com.
A reference website for the functions in {sitrep} can be found at https://r4epi.github.io/sitrep.

{sitrep} includes a number of other R packages which facilitate specific analysis:
{epitabulate}: Tables for epidemiological analysis
{epidict}: Epidemiology data dictionaries and random data generators
{epikit}: Miscellaneous helper tools for epidemiologists
{apyramid}: Age pyramid construction and plotting

Installation

The {sitrep} package, is currently stored in a GitHub repository. Therefore, the procedure to install these packages have one extra step required.

To install sitrep from GitHub you must first install the remotes package.

# install.packages("remotes")
remotes::install_github("r4epi/sitrep")

If you are getting errors, check the frequently asked questions.

Available templates

Sitrep has four outbreak templates and four survey templates available. These templates will generate the following:

  1. A word document with the situation report
  2. A plain text markdown document (for conversion to other formats such as HTML or PDF)
  3. A directory with all of the figures produced

You can access the list of templates in R Studio by clicking (see example below): file > New file > R Markdown… > From Template

Example of how to open and save the cholera template

You can generate an example template by using the check_sitrep_templates() function:

library("sitrep")
output_dir <- file.path(tempdir(), "sitrep_example")
dir.create(output_dir)

# view the available templates, categorized by type
available_sitrep_templates(categorise = TRUE)
#> $outbreak
#> [1] "ajs_outbreak"        "cholera_outbreak"    "measles_outbreak"   
#> [4] "meningitis_outbreak"
#> 
#> $survey
#> [1] "mortality"         "nutrition"         "vaccination_long" 
#> [4] "vaccination_short"

# generate the measles outbreak template in the output directory
check_sitrep_templates("measles_outbreak", path = output_dir)
#> [1] "C:\\Users\\alexf\\AppData\\Local\\Temp\\Rtmpcv1H8d/sitrep_example"

# view the contents
list.files(output_dir, recursive = TRUE)
#> [1] "measles_outbreak.Rmd"

Please note that the ‘sitrep’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

r4epis-website's People

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aspina7 avatar epiamsterdam avatar nsbatra avatar pbkeating avatar zkamvar avatar

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r4epis-website's Issues

Show example code comparing HEV rdt positives v negatives

@nsbatra - dont know how much time you have. But if you do, and the other stuff is cleared.
Could you show some example code comparing HEV rdt postives versus negatives. Either in the walkthrough or in a seperate tab called statistical testing?
Run tab_univariate to produce odds ratios. And maybe example codes comparing groups with chi-squared tests, and potentially also t-tests (though preferably kruskal-wallis tests). Have example code if you need!

Originally posted by @aspina7 in https://github.com/R4EPI/R4EPIs-website/pull/17/files

Additions to R4epis website and survey templates

  1. I would suggest that we add scripts for sample size calculation considering all the parameters that we normally use: expected prevalence, precision, and Z-cut off value (or T cut off value for cluster surveys) and non response rate. Plus (in household cluster surveys): DEFF, household size, and percentage of target population in the total population. I am attaching an excel that I recently used for a household cluster sample size calculation in case it is helpful.
    sample size calcs_v2.xlsx
  2. I would suggest that we add scripts for the sampling:
  • Sampling for probability proportional to size (PPS) in clusters surveys using a a list of primary sampling units (e.g. villages or neighbourhoods). Depending on how straight forward it is, we can add that? Or just add the excel file? Or both?
  • Spatial sampling (with different options):
    Systematic random sampling using GPS random points in one area (e.g. refugee camp).
    Starting point for cluster (in cluster surveys) (e.g. we have to interview three clusters in one city, we draw the three clusters for the teams).
    We would need to explain that this would depend on the availability of GIS data (e.g. shape files for villages)
  1. Regarding the text, I had a more in depth look and I would suggest some changes. Please, see them in the attached.
    R4epis comments for website_survey templates.docx
    --> happy to have a chat if needed.. thanks!

pak::pkg_install("R4EPI/sitrep")

I see this coding on the get started part of the R4epis website: pak::pkg_install("R4EPI/sitrep")

Is this new coding format for installing packages? If so, it should also be consistent throughout our templates and tools.
So could we maybe just use the usual install.packages () for this?

Check for consistent use of the word "code"

I've noticed that we have a couple of different ways to refer to R code (code, coding, codes) and I want to make sure we have consistent usage when we talk about these things. My interpretation is:

  • code refers to any sort of programming script: "The R code for calculating the mean of x without missing data is mean(x, na.rm = TRUE). This is a mass noun and should never be plural.
  • coding/codes refers to how variables are represented: "It's common to have simplified coding to represent common variables such as 1 for Male and 2 for Female"

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