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Change dryad to figshare in SI2
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ms/data_exploration.Rmd

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@@ -90,25 +90,25 @@ grubbs_tidy <- function(x, type = 10, ...) {
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The dataset includes biodiversity metrics and environmental data measured on native, non-hybrid ferns of Japan.
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All biodiversity metrics were calculated during the `targets` workflow (`_targets.R`), and are contained in the `biodiv_ferns_cent_dryad` object in the `targets` cache.
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All biodiversity metrics were calculated during the `targets` workflow (`_targets.R`), and are contained in the `biodiv_ferns_cent_figshare` object in the `targets` cache.
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The next step uses `tar_load()` to load the dataset from the targets cache. Alternatively, this can be loaded from the data file as shown in the commented-out code below.
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```{r load-data, message = FALSE}
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# Load the data from the targets workflow
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tar_load(biodiv_ferns_cent_dryad)
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tar_load(biodiv_ferns_cent_figshare)
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# Alternatively, unzip `results.zip`
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# (FIXME: add DOI when available)
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# and read in the data from there
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# biodiv_ferns_cent_dryad <- read_csv(here::here("japan_ferns_biodiv.csv"))
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# biodiv_ferns_cent_figshare <- read_csv(here::here("japan_ferns_biodiv.csv"))
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```
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This dataframe actually has more variables than are needed for modeling, so we will subset it to only the relevant variables.
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```{r subset-vars}
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biodiv_ferns_cent_raw <-
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biodiv_ferns_cent_dryad %>%
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biodiv_ferns_cent_raw <-
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biodiv_ferns_cent_figshare %>%
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select(
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grids, lat, long, # grid-cell ID, latitude, longitude
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richness, pd_obs_z, fd_obs_z, rpd_obs_z, rfd_obs_z, # response vars

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