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The make_data.R R script that generates the data can be generated by the following command:

# install.packages("knitr")
knitr::purl("https://raw.githubusercontent.com/viparc/clires_data/master/make_data.Rmd", documentation = 1L)

The data set, as a flat rectangular CSV file, can be downloaded from here:

# install.packages("readr")
viparc <- readr::read_csv("https://raw.githubusercontent.com/viparc/clires_data/master/data/viparc.csv",
                          col_types = paste(c("ciillidddllllll", rep("d", 45)), collapse = ""))

The data frame contains 5566 weeks of observation (rows) and 60 variables (columns). The variables are

  • farm: farm ID;
  • flock: flock ID (in a given farm);
  • week: week number (in a given flock of a given farm);
  • sampling: boolean informing whether there is feces sampling during the week (TRUE) or not (FALSE);
  • completed: boolean informing whether the flock is done (TRUE) or still ongoing (FALSE);
  • nb_chicken: total number of chicken in the farm;
  • nb_chicken_sold: total number of chicken sold from the farm;
  • chicken_disease_death number of chicken in the farm that died from disease between the previous and the current weeks;
  • chicken_sudden_death number of chicken in the farm that suddenly died (from other causes than the disease) between the previous and the current weeks;
  • respiratory, …, sudden_death: presence (TRUE) / absence (FALSE) of 6 clinical signs in the flock;
  • chicken_disease_death: number of chicken dying from disease between the previous and the current weeks;
  • chicken_sudden_death: number of chicken suddenly dying (from other cause than disease) between the previous and the current weeks;
  • amoxicillin_g, …, unknown_g: mass, in g, of the 44 antimicriobial used in the flock.

In addition, there is also information on the antimicrobial class of each antimicrobial used in the data set. These data can be download from here:

ab_classes <- readr::read_csv("https://raw.githubusercontent.com/viparc/clires_data/master/data/antimicrobial_classes.csv", col_types = "cc")

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