In serotracker/serosurvr: SeroTracker Serosurvey Data Analysis Tools

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Set File Options

# whether to import new data or use cached data
import_new_data = TRUE

# path to cache folder to read from and write to for this project
path_to_cache_folder = ".cache/example_analysis"

# set random seed
set.seed(12345)

Load Necessary Libraries

SeroTracker research code also uses the tidyverse, an ecosystem of data analysis tools including libraries like ggplot2, dplyr, and tibble. Prioritize finding and implementing tidyverse approaches to your data input, transformation, and plotting needs, where possible.

library(tidyverse)

The custom serosurvr library is contained in a public GitHub repo: serotracker/serosurvr.

library("devtools")
devtools::install_github("serotracker/serosurvr")
library("serosurvr")      # load newest serosurvr

Get Data

Sample Data Request

Sample functions that demonstrate how to get data.

#create a data request
fs_males_req <- serosurvr::datareq_params(reqname = "france_spain_males",
                                          research_fields = TRUE,
                                          estimates_subgroup = 'prioritize_estimates',
                                          prioritize_estimates_mode = "analysis_dynamic",
                                          publication_end_date = "2020-12-31",
                                          filters = list())

# pipe data request to get_data function to request data
fs_males_tbl <-
  fs_males_req %>%
  serosurvr::get_data(import_new_data = import_new_data,
                      path_to_cache_folder = path_to_cache_folder)

fs_males_tbl

Get Filter Options for Data Requests

Get a list of the filter options that can be used in data requests.

serosurvr::filter_options()

Clean Data

Sample functions that demonstrate how to clean data.

# enforce_st_dtypes enforces correct dtypes on all columns
fs_males_tbl_cleaned <-
  fs_males_req %>%
  serosurvr::get_data(import_new_data = import_new_data,
                      path_to_cache_folder = path_to_cache_folder) %>%
  serosurvr::enforce_st_dtypes()

fs_males_tbl_cleaned

# enforce_st_dtypes enforces correct dtypes on all columns
ab_on_tbl_cleaned <-
  serosurvr::datareq_params(reqname = "ab_on_studies",
                            research_fields = TRUE,
                            estimates_subgroup = 'prioritize_estimates',
                            prioritize_estimates_mode = "dashboard",
                            filters = list(country = list('Canada'),
                                           state = list('Alberta', 'Ontario'))) %>%
  serosurvr::get_data(import_new_data = import_new_data,
                      path_to_cache_folder = path_to_cache_folder,
                      server = 'dev') %>%
  serosurvr::enforce_st_dtypes()

ab_on_tbl_cleaned