Data Science Desktop Survival Guide by Graham Williams
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Wrangling Setup

20180908 Packages used in this chapter include dplyr, FSelector, ggplot2, glue, janitor, lobstr, lubridate, randomForest, readr, stringi, stringr, tidyr, 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(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 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()