Data Science Desktop Survival Guide by Graham Williams
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Algorithms Data and Variables

20210103

The rattle::weatherAUS dataset is loaded into the template variable ds and further template variables are setup as introduced in Williams (2017). See Chapter 7 for details.

dsname <- "weatherAUS"
ds     <- get(dsname)

nobs   <- nrow(ds)

vnames <- names(ds)
ds    %<>% clean_names(numerals="right")
names(vnames) <- names(ds)

vars   <- names(ds)
target <- "rain_tomorrow"
vars   <- c(target, vars) %>% unique() %>% rev()

The rattle::weatherAUS dataset suggests the following the template variables (Williams, 2017) for predictive modelling. See Chapter 7 for details.

risk   <- "risk_mm"
id     <- c("date", "location")
ignore <- c(risk, id)
vars   <- setdiff(vars, ignore)
inputs <- setdiff(vars, target)

form   <- formula(target %s+% " ~ .")

ds[vars] %<>% na.roughfix()

SPLIT <- c(0.70, 0.15, 0.15)

nobs %>% sample(SPLIT[1]*nobs)                               -> tr
nobs %>% seq_len() %>% setdiff(tr) %>% sample(SPLIT[2]*nobs) -> tu
nobs %>% seq_len() %>% setdiff(tr) %>% setdiff(tu)           -> te

ds %>% slice(tr) %>% pull(target) -> actual_tr
ds %>% slice(tu) %>% pull(target) -> actual_tu
ds %>% slice(te) %>% pull(target) -> actual_te

ds %>% slice(tr) %>% pull(risk) -> risk_tr
ds %>% slice(tu) %>% pull(risk) -> risk_tu
ds %>% slice(te) %>% pull(risk) -> risk_te