Webincidence implements functions and classes to compute, handle, visualise and model incidences from dates data. This vignette provides an overview of current features. It largely reproduces the content of REAME.md. Installing the package To install the current stable, CRAN version of the package, type: install.packages ( "incidence")
Overview of the incidence package
WebNov 3, 2024 · incidence implements functions and classes to compute, handle, visualise and model incidences from dates data. This vignette provides an overview of current features. It largely reproduces the content of REAME.md. Installing the package To install the current stable, CRAN version of the package, type: install.packages("incidence") WebMay 14, 2024 · 3 Answers. Sorted by: 5. We can do a group by summarise into a list and then unnest the list components into separate columns. library (tidyverse) df %>% group_by (time,age, ethnic, gender) %>% summarise (age_adjust = list (ageadjust.direct (count = count, pop = pop, rate = rate, stdpop = weight))) %>% mutate (age_adjust = map … china south korea relations computer chips
incidence package - RDocumentation
WebIncidence data, excluding zeros, can be modelled using log-linear regression of the form: log(y) = r x t + b. where y is the incidence, r is the growth rate, t is the number of days since a specific point in time (typically the start of the outbreak), and b is the intercept. Such model can be fitted to any incidence object using fit. Of course ... WebOct 15, 2024 · To aggregate this data, we can use the floor_date () function from the lubridate package which uses the following syntax: floor_date(x, unit) where: x: A vector of date objects. unit: A time unit to round to. Options include second, minute, hour, day, week, month, bimonth, quarter, halfyear, and year. The following code snippets show how to use ... WebNov 15, 2024 · Packages for time series analysis: For analyzing time series data – i.e., where the data has been collected over a period of time, e.g., the hourly temperature and precipitation at a weather station – there are three useful packages: tseries, urca, and vars. The tseries package is the backbone for time series analysis in R. china south korea japan