transformdata.back: Data transformation

In lozalojo/mem: The Moving Epidemic Method

Data transformation

Description

Function transformdata.back transforms data from week,rate1,...,rateN to year,week,rate format.

Usage

transformdata.back(
  i.data,
  i.name = "rates",
  i.cutoff.original = NA,
  i.range.x.final = NA,
  i.fun = mean
)

Arguments

i.data	Data frame of input data.
i.name	Name of the column that contains the values.
i.cutoff.original	Cutoff point between seasons when they have two years
i.range.x.final	Range of the surveillance period in the output dataset
i.fun	sumarize function

Details

Transforms data from the season in each column format (the one that uses mem) to the format year, week, rate in a 3 columns data.frame.

Allows to set the cutoff point to separate between two seasons when one season has two different years.

Value

transformdata.back returns a data.frame with three columns, year, week and rate.

Author(s)

Jose E. Lozano [e-mail]

References

Vega T, Lozano JE, Ortiz de Lejarazu R, Gutierrez Perez M. Modelling influenza epidemic - can we detect the beginning and predict the intensity and duration? Int Congr Ser. 2004 Jun;1263:281-3.

Vega T, Lozano JE, Meerhoff T, Snacken R, Mott J, Ortiz de Lejarazu R, et al. Influenza surveillance in Europe: establishing epidemic thresholds by the moving epidemic method. Influenza Other Respir Viruses. 2013 Jul;7(4):546-58. DOI:10.1111/j.1750-2659.2012.00422.x.

Vega T, Lozano JE, Meerhoff T, Snacken R, Beaute J, Jorgensen P, et al. Influenza surveillance in Europe: comparing intensity levels calculated using the moving epidemic method. Influenza Other Respir Viruses. 2015 Sep;9(5):234-46. DOI:10.1111/irv.12330.

Lozano JE. lozalojo/mem: Second release of the MEM R library. Zenodo [Internet]. [cited 2017 Feb 1]; Available from: https://zenodo.org/record/165983. DOI:10.5281/zenodo.165983

Examples

# Castilla y Leon Influenza Rates data
data(flucyl)
# Transform data
newdata <- transformdata.back(flucyl)$data

lozalojo/mem documentation built on July 7, 2023, 10 a.m.