Data Science Desktop Survival Guide by Graham Williams
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Clustering Setup

20200902 Packages used in this chapter include biclust.

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

library(biclust)      # Bicluster analysis.
library(dplyr)        # Wrangling: glimpse() group_by() print() select() mutate().
library(rattle)       # Weather dataset.

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()

It is always useful to remind ourselves of the dataset with a random sample:

ds  %>% sample_frac() %>% select(date, location, sample(3:length(vars), 5))

## # A tibble: 176,747 x 7
##    date       location risk_mm pressure_3pm pressure_9am rainfall wind_g...
##    <date>     <chr>      <dbl>        <dbl>        <dbl>    <dbl> <ord> ...
##  1 2013-06-02 Penrith     13.4          NA           NA       8.4 SSW   ...
##  2 2016-01-10 Badgery~     0          1016.        1020.      0   ESE   ...
##  3 2012-04-09 Nurioot~     0          1027.        1027.      1   SW    ...
##  4 2019-11-16 Cobar        0          1014         1017       0   SW    ...
##  5 2008-10-02 Melbour~     0          1011.        1014.      0   N     ...
##  6 2013-05-20 Wollong~     0          1014.        1018.      0   WSW   ...
....