title |
output |
weeklyForecast |
html_document |
toc |
number_sections |
keep_md |
true |
true |
true |
|
|
weeklyCases <- read.csv('https://raw.githubusercontent.com/kentranz/socialMobilityCOVID/master/data/weeklyCases.csv')
cutOff <- as.Date("2020-11-29")
train <- weeklyCases %>%
filter(weekNum > 3 & as.Date(date) < cutOff)
test7 <- weeklyCases %>%
filter(weekNum > 3 & as.Date(date) >= cutOff & weekNum < 41)
test14 <- weeklyCases %>%
filter(weekNum > 3 & as.Date(date) >= cutOff) %>%
# retained lag case rates for first week only, zero out the rest
mutate(
casesTminus2.rate = case_when(weekNum == 41 ~ 0, TRUE ~ casesTminus2.rate)
)
nrow(train)