If you do not have the dplyr package in R then download and install the tidyverse which combines the main data wrangling packages we will be using in one place.
If you've ever wanted to tear your hair out over an Excel file with merged cells, data in column headers, headers mixed in data, and key information in color coding, this is the package for you.
There are many other summary statistics you could consider such sd(), min(), max(), median(), sum(), n() (returns the length of vector), first() (returns first value in vector), last() (returns last value in vector) and n_distinct() (number of distinct values in vector).
Matching to the taxonomic backbone cannot be done cause there was no match at all or several matches with too little information to keep them apart (homonyms).
Something slightly more complicated: same as above, except arrange the rows in the sleep_total column in a descending order.
This data set consists of flight arrival and departure details for all commercial flights within the USA, from October 1987 to April 2008.
GBIF is a powerful tool for obtaining taxonomic information about biodiversity in a country or a region.
But now grouping is now a top level operation performed by group_by(), and summarise() works directly on the grouped data, rather than being called from inside another function.
To make the most of dplyr, I also recommend that you familiarise yourself with the principles of tidy data: this will help you get your data into a form that works well with dplyr, ggplot2 and R's many modelling functions.
Another financial package that's useful for importing, analyzing and visualizing data, integrating aspects of other popular finance packages as well as tidyverse tools.
As you can see in the NYC Tree Map, there are a few different species of cherry tree that are planted street trees, and if you filter by species to show a Japanese Flowering Cherry variety, you'll see them.
We will be using this package a lot in the patent analysis tutorials so, if you don't have it already, now is a good time.
This is what allows it to keep track of which data sources should remain untouched, and which can be modified or overwritten as part of a pipeline.
This saves you having to manually delete files that are no longer in use, but it does mean that you must copy the output of your pipeline to a permanent location if you want to keep it around.
