20210103 Continuing with our pipeline example, we might want a base::summary() of the dataset.
# Summarise subset of variables for observations with rainfall. %>% ds select(min_temp, max_temp, rainfall, sunshine) %>% filter(rainfall >= 1) %>% summary()
## min_temp max_temp rainfall sunshine ## Min. :-8.50 Min. :-4.10 Min. : 1.000 Min. : 0.000 ## 1st Qu.: 8.40 1st Qu.:15.50 1st Qu.: 2.200 1st Qu.: 2.300 ## Median :12.20 Median :19.30 Median : 4.800 Median : 5.400 ## Mean :12.69 Mean :20.15 Mean : 9.822 Mean : 5.336 ## 3rd Qu.:17.10 3rd Qu.:24.40 3rd Qu.: 11.200 3rd Qu.: 8.100 ## Max. :28.90 Max. :46.30 Max. :474.000 Max. :14.200 ....
It could be useful to contrast this with a base::summary() of those observations where there was little or no rain.
# Summarise observations with little or no rainfall. %>% ds select(min_temp, max_temp, rainfall, sunshine) %>% filter(rainfall < 1) %>% summary()
## min_temp max_temp rainfall sunshine ## Min. :-8.70 Min. :-2.10 Min. :0.00000 Min. : 0.00 ## 1st Qu.: 7.20 1st Qu.:19.00 1st Qu.:0.00000 1st Qu.: 6.10 ## Median :11.80 Median :23.70 Median :0.00000 Median : 9.30 ## Mean :11.91 Mean :24.18 Mean :0.05973 Mean : 8.35 ## 3rd Qu.:16.60 3rd Qu.:29.10 3rd Qu.:0.00000 3rd Qu.:11.00 ## Max. :33.90 Max. :48.90 Max. :0.90000 Max. :14.50 ....
Any number of functions can be included in a pipeline to achieve the results we desire. In the following chapters we will see many examples and some will string together ten or more functions. Each step along the way is of itself generally easily understandable. The power is in what we can achieve by stringing together many simple steps to produce something more complex.
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