10.55 Variable Roles
20180723 Now that we have a basic idea of the size and shape and contents of the dataset and have performed some basic data type identification and conversion we are in a position to identify the roles played by the variables within the dataset. First we will record the list of available variables so that we might reference them below.
## [1] "date" "location" "min_temp" "max_temp"
## [5] "rainfall" "evaporation" "sunshine" "wind_gust_dir"
## [9] "wind_gust_speed" "wind_dir_9am" "wind_dir_3pm" "wind_speed_9am"
## [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"By this stage of the project we will usually have identified a
business problem that is the focus of attention. In our case we will
assume it is to build a predictive analytics model to predict the
chance of it raining tomorrow given the observation of today’s
weather. In this case the variable rain_tomorrow is the
target variable. Given today’s
observations of the weather this is what we want to predict. The
dataset we have is then a training dataset of historic observations. The task is to identify any
patterns among the other observed variables that suggest that it rains
the following day.
# Note the target variable.
target <- "rain_tomorrow"
# Place the target variable at the beginning of the vars.
vars <- c(target, vars) %>% unique() %T>% print()## [1] "rain_tomorrow" "date" "location" "min_temp"
## [5] "max_temp" "rainfall" "evaporation" "sunshine"
## [9] "wind_gust_dir" "wind_gust_speed" "wind_dir_9am" "wind_dir_3pm"
## [13] "wind_speed_9am" "wind_speed_3pm" "humidity_9am" "humidity_3pm"
## [17] "pressure_9am" "pressure_3pm" "cloud_9am" "cloud_3pm"
## [21] "temp_9am" "temp_3pm" "rain_today" "risk_mm"We have taken the opportunity here to move the target variable to be
the first in the vector of variables recorded in
vars. This is common practice where the first variable in
a dataset is the target (dependent variable) and the remainder are the
variables (the independent variables) that will be used to build a
model to predict that target.
Your donation will support ongoing availability and give you access to the PDF version of this book. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Popular open source software includes rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2022 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0
