Data Science Desktop Survival Guide by Graham Williams
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Computer Vision Setup

20200514 Packages used in this chapter include magrittr, and rattle.

Packages are loaded into the currently running R session from your local library directories on disk. Missing packages can be installed using utils::install.packages() within R. On Ubuntu, for example, R packages can be installed using wajig install r-cran-<pkgname>.

# Load required packages from local library into the R session.

library(magrittr)     # Data pipelines: %>% %<>% %T>% equals().
library(rattle)       # Dataset: weather.

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 variable form is used in this chapter as the formula describing the model to be built.

form

## risk_mm ~ .

ds  %>% sample_frac()

## # A tibble: 176,747 x 24
##    date       location min_temp max_temp rainfall evaporation sunshine
##    <date>     <chr>       <dbl>    <dbl>    <dbl>       <dbl>    <dbl>
##  1 2015-09-05 Nurioot~      1       16.2      0.1         0.6      9.5
##  2 2014-07-27 Penrith       3.4     21        0.6        NA       NA  
##  3 2019-03-12 Wollong~     20.2     34.6      0          NA       NA  
....