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.   :-8.7   Min.   :-4.10   Min.   :  0.000   Min.   : 0.000  
##  1st Qu.: 7.5   1st Qu.:18.00   1st Qu.:  0.000   1st Qu.: 4.900  
##  Median :11.9   Median :22.70   Median :  0.000   Median : 8.500  
##  Mean   :12.1   Mean   :23.26   Mean   :  2.339   Mean   : 7.629  
##  3rd Qu.:16.8   3rd Qu.:28.20   3rd Qu.:  0.600   3rd Qu.:10.600  
##  Max.   :33.9   Max.   :49.60   Max.   :474.000   Max.   :14.500  
....

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 dplyr::filter() the observations.

# Select specific variables and observations from the dataset.

ds %>% 
  select(min_temp, max_temp, rainfall, sunshine) %>%
  filter(rainfall >= 1)

## # A tibble: 62,177 × 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 62,167 more rows

This 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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