14.2 ML Data and Variables
20210104
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()It is always useful to remind ourselves of the dataset with a random sample:
ds %>% sample_frac() %>% select(date, location, sample(3:length(vars), 5))## # A tibble: 226,868 × 7
## date location wind_dir_9am rainfall pressure_9am rain_tomorrow sunshine
## <date> <chr> <ord> <dbl> <dbl> <fct> <dbl>
## 1 2009-08-03 Sydney WNW 0 1020. No 8.8
## 2 2011-11-02 Watsonia WSW 0.4 1016. Yes 0
## 3 2015-04-26 Badgery… WSW 18 1008. No NA
## 4 2011-12-21 Brisbane W 0 1015. Yes 9.3
## 5 2019-02-01 Penrith SSW 0.4 NA Yes NA
## 6 2021-02-18 Norfolk… E 0 1018. Yes NA
## 7 2013-08-07 Katheri… SE 0 1017. No NA
## 8 2018-09-11 Adelaide NNE 0 1011. No NA
....References
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
