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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Chapter: R Constructs

20210103 Here we introduce the language constructs of R including the powerful concept of pipes as a foundation for building sophisticated data processing pipelines in R. To illustrate the R constructs we will take a copy of the rattle::weatherAUS dataset (daily weather observations from Australia) and save it as a variable named ds. The column names in the dataset are also normalised as we would normally do as data scientists. The operations here will become familiar as we progress through the book. To aid our understanding for now though, we can ready the code as:

Obtain the weatherAUS dataset from the rattle package; clean up the column names using the clean_names function from the janitor package, choosing to align numerals to the right; save the resulting dataset into an R variable named ds.

rattle::weatherAUS %>%
  janitor::clean_names(numerals="right") ->
ds

For reference the resulting column names (variables) are:

names(ds)

##  [1] "date"            "location"        "min_temp"        "max_temp"   ...
##  [5] "rainfall"        "evaporation"     "sunshine"        "wind_gust_di...
##  [9] "wind_gust_speed" "wind_dir_9am"    "wind_dir_3pm"    "wind_speed_9...
## [13] "wind_speed_3pm"  "humidity_9am"    "humidity_3pm"    "pressure_9am...
## [17] "pressure_3pm"    "cloud_9am"       "cloud_3pm"       "temp_9am"   ...
## [21] "temp_3pm"        "rain_today"      "risk_mm"         "rain_tomorrow"