Data Science Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface Data Science Introducing R R Constructs R Tasks R Strings R Read, Write, and Create Data Template Data Exploration Data Wrangling Data Visualisation Statistics ML Template ML Scenarios ML Activities ML Applications ML Algorithms Cluster Analysis Decision Trees Computer Vision Graph Data Privacy Literate Data Science Coding with Style Resources Bibliography Index

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Create Data Setup

20201022 Packages used in this chapter include dplyr, magrittr, randomForest, readxl, wakefield, xlsx, 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(dplyr)        # Wrangling: select() sample_frac().
library(magrittr)     # Data pipelines: %>% %<>% %T>% equals().
library(randomForest) # Model: randomForest() na.roughfix() for missing data.
library(readxl)       # Read Excel spreadsheets: read_excel().
library(wakefield)    # Generate random datasets.
library(xlsx)         # Write Excel spreadsheets: write.xlsx() saveWorkbook().
library(rattle)       # Dataset: weather.

After loading the required packages into the library we access the rattle::weatherAUS dataset and save it into the template dataset named ds, as per the template based approach introduced in Williams (2017). The dataset is modestly large and is used extensively in this book to illustrate the capabilities of R for the Data Scientist.

# Initialise the dataset as per the template.

ds <- weatherAUS

ds %>% sample_frac()

## # A tibble: 176,747 x 24
##    Date       Location MinTemp MaxTemp Rainfall Evaporation Sunshine
##    <date>     <chr>      <dbl>   <dbl>    <dbl>       <dbl>    <dbl>
##  1 2010-02-03 Wollong~    21.9    26.2      0          NA       NA  
##  2 2012-08-05 Witchcl~     5.4    17.7      0.2        NA       NA  
##  3 2012-10-06 Sydney      15.6    18.3      0           7.6      0  
##  4 2011-07-31 Launces~     2.9    14        0.2        NA       NA  
##  5 2013-11-06 Wollong~    12      21.7      0          NA       NA  
##  6 2014-07-26 Mildura      6.8    18.3      0           2        8.2
##  7 2009-12-02 Richmond    13.2    24.2      0.6         4.6     NA  
....