### Verbs * [`.derive(, )`](https://idl.uw.edu/arquero/api/verbs#derive) The mutator. Create new columns by passing them as properties of the `newCols` object with anonymous functions. * [`.filter()`](https://idl.uw.edu/arquero/api/verbs#filter) * [`.groupby()`](https://idl.uw.edu/arquero/api/verbs#groupby) Groups. * [`.orderby()`](https://idl.uw.edu/arquero/api/verbs#orderby) Order by a column. Can also use `aq.desc()`, for example, to reverse sort. * [`.rollup()`](https://idl.uw.edu/arquero/api/verbs#rollup) Like `summarise()` in dplyr. Pass in aggregation columns as methods of `` using `op` operator functions. * [`.count()`](https://idl.uw.edu/arquero/api/verbs#count) Count things. * [`.reify()`](https://idl.uw.edu/arquero/api/verbs#reify) RIP Jameson. Materializes all the various wranglings into a settled, new table. * [`.fold()`](https://idl.uw.edu/arquero/api/verbs#fold) Like `pivot_longer()` in dplyr. * [`.pivot(, )`](https://idl.uw.edu/arquero/api/verbs#pivot) Like `pivot_wider()` in dplyr. There are, of course, many other verbs, but this should give us a great starting point to investigate our taxi data. Still, as we see in the `.rollup()` entry, it would be nice to know what kinds of operations we can do on the data. ### Operations The [`op` functions](https://idl.uw.edu/arquero/api/op) include internal type functions for working with tables. Many of the `op` functions are just reworkings of common methods of arrays, numbers, strings, and so on. It's probably more helpful to go over a few of the math functions and the aggregation functions we would use with `.rollup()`. * [`op.bin(, , , )`](https://idl.uw.edu/arquero/api/op#bin) Bin `` to bins established by the other parameters. * [`op.is_nan()`](https://idl.uw.edu/arquero/api/op#is_nan) Test for NaNness. * [`op.equal(, )`](https://idl.uw.edu/arquero/api/op#equal) Are the two values equal? * [`op.bins(, = 15, =true, , )`](https://idl.uw.edu/arquero/api/op#bins) Bin an entire column/field. * [`op.count()`](https://idl.uw.edu/arquero/api/op#count) Like `n()` in dplyr? * [`op.distinct()`](https://idl.uw.edu/arquero/api/op#distinct) Count number of distinct values. * [`op.max()`](https://idl.uw.edu/arquero/api/op#max) Get the maximum value of the column. * [`op.mean()`](https://idl.uw.edu/arquero/api/op#mean) Get the mean of the column. * [`op.stdev()`](https://idl.uw.edu/arquero/api/op#stdev) Get the sample standard deviation. These and the two example uses below should give us enough to start asking simple questions of our data.