melb_walk_fast() to be compatible with the data API change.Fixed a typo in melb_weather().
Added a new set of functions to scrape Melbourne microclimate data. (@sa-lee)
melb_walk().melb_walk_directional().melb_walk_directional() to access minute by minute directional pedestrian counts for the last hour from pedestrian sensor devices located across the city.pull_sensor() due to the Socrata API URL change.lookup_sensor(), walk_melb(), run_melb() and shine_melb() in favour of suffixed function names.melb_walk_fast() (previously run_melb()) due to Socrata API changes.walk_melb(), run_melb() and shine_melb() in favour of suffixed function names.tweak in walk_melb(), as the sensor names from the data source match with run_melb().match column in the data frame called from lookup_sensor().tbl_ts) instead of data.frame.run_melb(na.rm = FALSE).walk_melb(tweak = TRUE).lookup_sensor().run_melb(), pull_sensor(), and lookup_sensor() using Socrata.shine_melb() to use walk_melb(tweak = TRUE).run_melb() pulls Melbourne pedestrian data using Socrata, which is faster than walk_melb().pull_sensor() pulls Melbourne pedestrian sensor locations using Socrata.lookup_sensor() provides a dictionary for sensor names used in walk_melb() and run_melb().na.rm = FALSE and tweak = FALSE to the function walk_melb(). If na.rm = TRUE, it removes NAs from the data. If tweak = TRUE, it ensures the consistency of sensor names to run_melb().shine_melb() to launch a shiny app. It provides two basic plots to take a glimpse at the data: one is an overlaying time series plot and the other showing a dot plot of missing values.NEWS.md file to track changes to the package.walk_melb() to scrape Melbourne pedestrian data.