The database connections essentially remove that limitation in that you can have a database of many 100s GB, conduct queries on it directly and pull back just what you need for analysis in R. This addresses a common problem with R in that all operations are conducted in memory and thus the amount of data you can work with is limited by available memory. The benefits of doing this are that the data can be managed natively in a relational database, queries can be conducted on that database, and only the results of the query returned. An additional feature is the ability to work with data stored directly in an external database. dplyr addresses this by porting much of the computation to C++. Multiple columns are combined into one value column with a key column keeping track. The thinking behind it was largely inspired by the package plyr which has been in use for some time but suffered from being slow in some cases. dplyr::across can be used to programmatically summarize multiple columns. arrange count filter select and rename summarise and summarize. It is built to work directly with data frames. Note that I used summarize (across ()) which replaces the deprecated summarizeall (), even though with a single column couldve. This is a big change to summarise () but it should have minimal impact on existing code because it broadens the interface: all existing code. Then you can just pivot wider to get the final result you want. Were going to learn some of the most common dplyr functions: select(), filter(), mutate(), groupby(), and summarize(). To put this another way, before dplyr 1.0.0, each summary had to be a single value (one row, one column), but now we’ve lifted that restriction so each summary can generate a rectangle of arbitrary size. The package dplyr is a fairly new (2014) package that tries to provide easy tools for the most common data manipulation tasks. Theres lots of ways to go about it, but I would simplify it by pivoting to a longer data frame initially, and then grouping by var and group. Summarizing multiple columns with dplyr 246,916 Solution 1 In dplyr (>1.
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