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Copy file name to clipboardExpand all lines: vignettes/slowstart.Rmd
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As for national level data any gaps in reported data are filled with NAs.
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For example, data for France Level 1 regions over time can be accessed using:
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For example, data for Lithuania Level 1 regions over time can be accessed using:
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```{r}
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get_regional_data(country = "france")
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get_regional_data(country = "lithuania")
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```
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This data then has the following format:
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```{r, echo=FALSE, eval=TRUE, message=FALSE}
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start_using_memoise()
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knitr::kable(
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tail(get_regional_data(country = "france"), n = 5)
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tail(get_regional_data(country = "lithuania"), n = 5)
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)
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```
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Alternatively, the same data can be accessed using the underlying class as follows (the France object now contains data at each processing step and the methods used at each step),
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Alternatively, the same data can be accessed using the underlying class as follows (the Lithuania object now contains data at each processing step and the methods used at each step),
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```{r, eval=FALSE, message=FALSE}
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france <- France$new(get = TRUE)
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france$return()
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lithuania <- Lithuania$new(get = TRUE)
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lithuania$return()
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```
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### Level 1 and Level 2 regions
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All countries included in the package (see below,"Coverage") have data for regions at the admin-1 level, the largest administrative unit of the country (e.g. state in the USA). Some countries also have data for smaller areas at the admin-2 level (e.g. county in the USA).
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Data for Level 2 units can be returned by using the `level = "2"` argument. The dataset will still show the corresponding Level 1 region.
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An example of a country with Level 2 units is France, where Level 2 units are French departments:
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An example of a country with Level 2 units is Lithuania, where Level 2 units are Lithuanian municipalities:
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