3.9 Pipeline
20210103 Suppose we want to produce a base::summary() of a selection of numeric variables. We can pipe the output of dplyr::select() into base::summary().
# Select variables from the dataset and summarise the result.
ds %>%
select(min_temp, max_temp, rainfall, sunshine) %>%
summary()## min_temp max_temp rainfall sunshine
## Min. :-8.70 Min. :-4.10 Min. : 0.000 Min. : 0.00
## 1st Qu.: 7.50 1st Qu.:17.90 1st Qu.: 0.000 1st Qu.: 4.90
## Median :11.90 Median :22.60 Median : 0.000 Median : 8.50
## Mean :12.09 Mean :23.21 Mean : 2.348 Mean : 7.63
## 3rd Qu.:16.80 3rd Qu.:28.20 3rd Qu.: 0.600 3rd Qu.:10.60
## Max. :33.90 Max. :48.90 Max. :474.000 Max. :14.50
....Perhaps we would like to review only those observations where there is more than a little rain on the day of the observation. To do so we stats::filter() the observations.
# Select specific variables and observations from the dataset.
ds %>%
select(min_temp, max_temp, rainfall, sunshine) %>%
filter(rainfall >= 1)## # A tibble: 51,598 × 4
## min_temp max_temp rainfall sunshine
## <dbl> <dbl> <dbl> <dbl>
## 1 17.5 32.3 1 NA
## 2 13.1 30.1 1.4 NA
## 3 15.9 21.7 2.2 NA
## 4 15.9 18.6 15.6 NA
## 5 12.6 21 3.6 NA
## 6 13.5 22.9 16.8 NA
## 7 11.2 22.5 10.6 NA
## 8 12.5 24.2 1.2 NA
## 9 18.8 35.2 6.4 NA
## 10 14.6 29 3 NA
## # ℹ 51,588 more rowsThis sequence of functions operating on the original rattle::weatherAUS dataset returns a subset of that dataset where all observations have at least 1mm of rain.
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