<- "weatherAUS" dsname <- get(dsname) ds <- nrow(ds) nobs <- names(ds) vnames %<>% clean_names(numerals="right") ds names(vnames) <- names(ds) <- names(ds) vars <- "rain_tomorrow" target <- c(target, vars) %>% unique() %>% rev()vars
We also do a little more to set the data up for demonstrating various approaches to visualisation. As with the model template, a number of template variables are identified here. We also a little data wrangling to remove all missing values by performing a missing value imputation with randomForest::na.roughfix().
<- "risk_mm" risk <- c("date", "location") id <- c(risk, id) ignore <- setdiff(vars, ignore) vars <- setdiff(vars, target) inputs %<>% na.roughfix()ds[vars]
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