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knitr::opts_chunk$set(Warning = FALSE, message = FALSE, echo = FALSE) library(Covid19Wastewater) library(dplyr) library(RcppRoll)
How to find the offset between wastewater and case data. By offsetting the wastewater and case data 1 day at a time and then finding the highest correlation we can find the offset between the wastewater and case data.
data(Case_data, package = "Covid19Wastewater") totalcases <- Case_data %>% group_by(date) %>% summarise(sum_case = sum(conf_case)) %>% mutate(conf_case = roll_sum(sum_case,10,align = "right", fill = NA)) data(WasteWater_data, package = "Covid19Wastewater") data(Covariants_data, package = "Covid19Wastewater") data(Pop_data, package = "Covid19Wastewater") WasteWater_data <- WasteWater_data%>% left_join(Pop_data) data(Example_data, package = "Covid19Wastewater")
Output is not normal as only example data is used
One way of finding the offset.
offsetDF <- OffsetDFMaker(10, as.Date("2020-08-01"), as.Date("2023-01-01"), Case_data, WasteWater_data) OffsetDF_Plot(offsetDF, "All Wisconsin data over all time")
Another way to find the offset.
heatmapcorfunc(Example_data) heatmapcorfunc(Example_data, 10)
Using found offset can try to see how it changes over time and other factors.
covarstarts <- c(as.Date("2020-08-17"), as.Date("2021-03-29"), as.Date("2021-07-05"), as.Date("2021-12-20"), as.Date("2022-03-28"), as.Date("2022-05-23"), as.Date("2022-07-04")) covarends <- c(as.Date("2021-01-18"), as.Date("2021-05-24"), as.Date("2021-12-06"), as.Date("2022-03-14"), as.Date("2022-05-09"), as.Date("2022-06-06"), as.Date("2022-11-07")) covarnames <- c("Robin1", "Alpha.V1", "Delta", "Omicron.21K", "Omicron.21L", "Omicron.22C", "Omicron.22B") OffsetHeatmap("kendall_offset", 0, WasteWater_data, Case_data, Pop_data, covarstarts, covarends, covarnames, "region", TRUE, TRUE)
These functions are used in this analysis:
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