10.1 Wrangling Setup

20180908 Packages used in this chapter include dplyr (Wickham et al. 2021), FSelector (Romanski, Kotthoff, and Schratz 2021), 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. 2021), stringr (Wickham 2019b), tidyr (Wickham 2021), magrittr (Bache and Wickham 2020), and rattle (G. Williams 2020).

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()


Bache, Stefan Milton, and Hadley Wickham. 2020. Magrittr: A Forward-Pipe Operator for r. https://CRAN.R-project.org/package=magrittr.
Breiman, Leo, Adele Cutler, Andy Liaw, and Matthew Wiener. 2018. randomForest: Breiman and Cutler’s Random Forests for Classification and Regression. https://www.stat.berkeley.edu/~breiman/RandomForests/.
Firke, Sam. 2021. Janitor: Simple Tools for Examining and Cleaning Dirty Data. https://github.com/sfirke/janitor.
Gagolewski, Marek, Bartek Tartanus, and others; IBM, Unicode, Inc., and others. 2021. Stringi: Character String Processing Facilities. https://CRAN.R-project.org/package=stringi.
Hester, Jim. 2020. Glue: Interpreted String Literals. https://CRAN.R-project.org/package=glue.
Romanski, Piotr, Lars Kotthoff, and Patrick Schratz. 2021. FSelector: Selecting Attributes. https://github.com/larskotthoff/fselector.
Spinu, Vitalie, Garrett Grolemund, and Hadley Wickham. 2021. Lubridate: Make Dealing with Dates a Little Easier. https://CRAN.R-project.org/package=lubridate.
———. 2019a. Lobstr: Visualize r Data Structures with Trees. https://github.com/r-lib/lobstr.
———. 2019b. Stringr: Simple, Consistent Wrappers for Common String Operations. https://CRAN.R-project.org/package=stringr.
———. 2021. Tidyr: Tidy Messy Data. https://CRAN.R-project.org/package=tidyr.
Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, and Dewey Dunnington. 2020. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2.
Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2021. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.
Wickham, Hadley, and Jim Hester. 2020. Readr: Read Rectangular Text Data. https://CRAN.R-project.org/package=readr.
Williams, Graham. 2020. Rattle: Graphical User Interface for Data Science in r. https://rattle.togaware.com/.
Williams, Graham J. 2017. The Essentials of Data Science: Knowledge Discovery Using r. The r Series. CRC Press.

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