10.1 Wrangling Setup

20180908 Packages used in this chapter include dplyr (Wickham et al. 2021), FSelector (Romanski and Kotthoff 2018), ggplot2 (Wickham et al. 2020), glue (Hester 2020), janitor (Firke 2021), lobstr (Wickham 2019a), lubridate (Spinu, Grolemund, and Wickham 2021), randomForest (Breiman et al. 2018), readr (Wickham and Hester 2020), stringi (Gagolewski et al. 2020), stringr (Wickham 2019b), tidyr (Wickham 2021), magrittr (Bache and Wickham 2020), and rattle (G. Williams 2021).

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(rattle)       # weather dataset.
library(readr)        # Efficient reading of CSV data.
library(dplyr)        # Wrangling: glimpse().
library(lobstr)       # Inspect R data structures.
library(tidyr)        # Prepare a tidy dataset, gather().
library(magrittr)     # Pipes %>% and %T>% and equals().
library(glue)         # Format strings.
library(janitor)      # Cleanup: clean_names().
library(lubridate)    # Dates and time.
library(FSelector)    # Feature selection, information.gain().
library(stringi)      # String concat operator %s+%.
library(stringr)      # String operations.
library(randomForest) # Impute missing values with na.roughfix().
library(ggplot2)      # Visualise data.
library(purrr)        # simplify(), set_names()

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()


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