Data Science Desktop Survival Guide by Graham Williams
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Glue Pipelines

20180729 We can use glue::glue_data() within pipes and operate over the rows of the data that is piped into the operator.

weatherAUS %>%
  sample_n(6) %>%
  glue_data("Observation",
            " {rownames(.) %>% as.integer() %>% comma() %>% sprintf('%7s', .)}",
            " location {Location %>% sprintf('%-14s', .)}",
            " max temp {MaxTemp %>% sprintf('%5.1f', .)}")

## Observation     1.0 location Hobart         max temp  27.3
## Observation     2.0 location Witchcliffe    max temp  29.3
## Observation     3.0 location Brisbane       max temp  26.7
## Observation     4.0 location PerthAirport   max temp  20.8
## Observation     5.0 location Brisbane       max temp  21.1
## Observation     6.0 location PearceRAAF     max temp  26.6
....

It can also be useful with the tidy verse work flow.

weatherAUS %>%
  sample_n(6) %>%
  mutate(TempRange = glue("{MinTemp}-{MaxTemp}")) %>%
  glue_data("Observed temperature range at {Location} of {TempRange}")

## Observed temperature range at Williamtown of 7.7-19.7
## Observed temperature range at Albury of 15.3-34.9
## Observed temperature range at Sydney of 18.1-27.5
## Observed temperature range at Canberra of 11.8-25.7
## Observed temperature range at Nuriootpa of 14.1-30.7
## Observed temperature range at Dartmoor of 5.1-16.9
....