Go to TogaWare.com Home Page. Data Science Desktop Survival Guide
by Graham Williams
Duck Duck Go

KnitR Setup

20200602

Packages used in this chapter include diagram, dplyr, ggplot2, magrittr, xtable, Hmisc, and rattle.

Packages are loaded into the current R session from the local library directories on disk. Missing packages can be installed using utils::install.packages() within R. On Ubuntu R packages can be installed using wajig install r-cran-<pkgname>.

# Load required packages from local library into the R session.

library(rattle)       # Dataset: weatherAUS.
library(magrittr)     # Data pipelines: %>% %T>% %<>%.
library(ggplot2)      # Visualise data.
library(xtable)       # Format R data frames as LaTeX tables.
library(Hmisc)        # Escape special LaTeX charaters.
library(diagram)      # Produce a flowchart.
library(dplyr)        # Data wrangling.
library(scales)

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.

ds <- weatherAUS

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()

A random sample of the dataset:

ds %>% sample_frac()
## # A tibble: 176,747 x 24
##    date       location min_temp max_temp rainfall evaporation sunshine
##    <date>     <chr>       <dbl>    <dbl>    <dbl>       <dbl>    <dbl>
##  1 2009-12-31 Perth        15.2     26.6      0          11.2     12.1
##  2 2014-01-16 MountGa~     22.8     44.1      0           9       12.1
##  3 2009-02-08 Sydney       21.4     28.6      0          14.2     12.2
##  4 2014-08-23 NorahHe~     10.9     17.8      8.4        NA       NA  
##  5 2014-12-07 Richmond     17.5     29.9     33.6        NA       NA  
##  6 2014-01-18 Tuggera~     14.2     39.4      0          NA       NA  
##  7 2014-07-31 Launces~     11.5     14.7     29.2        NA       NA  
....


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Books available on Amazon include Data Mining with Rattle and Essentials of Data Science.
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