15.1 Machine Learning Setup
20200514 Packages used in this chapter include magrittr (Bache and Wickham 2026), and rattle (G. Williams 2026).
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 also 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 by Graham J. Williams (2017). See
Chapter 8 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.
## rain_tomorrow ~ .## # A tibble: 275,410 × 24
## date location min_temp max_temp rainfall evaporation sunshine
## <date> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2012-02-10 GoldCoast 19.9 27.5 5.8 NA NA
## 2 2025-04-28 Albany 12 20.2 0.5 NA NA
## 3 2015-05-02 WaggaWagga 9.8 23.1 0 3.8 6.3
## 4 2025-03-23 Darwin 26.7 31.6 8.2 1 2.5
## 5 2023-01-19 Richmond 18.4 19.3 18.4 NA NA
## 6 2012-10-08 Mildura 4.4 22.9 0 5.6 10.9
## 7 2021-10-15 Moree 12.4 23.5 0 NA NA
## 8 2013-12-08 Bendigo 11 31.2 0 6.2 NA
## 9 2018-06-02 PearceRAAF 8.6 20 0 NA 9.2
## 10 2011-03-31 Albany 15.9 23 0 4.4 10.7
## # ℹ 275,400 more rows
## # ℹ 17 more variables: wind_gust_dir <ord>, wind_gust_speed <dbl>,
## # wind_dir_9am <ord>, wind_dir_3pm <ord>, wind_speed_9am <dbl>,
## # wind_speed_3pm <dbl>, humidity_9am <int>, humidity_3pm <int>,
## # pressure_9am <dbl>, pressure_3pm <dbl>, cloud_9am <int>, cloud_3pm <int>,
## # temp_9am <dbl>, temp_3pm <dbl>, rain_today <fct>, risk_mm <dbl>,
## # rain_tomorrow <fct>References
Bache, Stefan Milton, and Hadley Wickham. 2026. Magrittr: A Forward-Pipe Operator for r. https://magrittr.tidyverse.org.
Williams, Graham. 2026. Rattle: R Data Science Supporting Rattle. https://togaware.com/projects/rattle/.
Williams, Graham J. 2017. The Essentials of Data Science: Knowledge Discovery Using r. The r Series. CRC Press.
Your donation will support ongoing availability and give you access to the PDF version of this book. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Popular open source software includes rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2022 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0
