CHAPTER 7 AND 8

the clean harvest The first Rdata file contains information about all the Tauntauns that have been harvested, while the second contains information about the hunters. A similar column in both datasets, hunter.id, identifies who harvested which animal.A column named hunter.id appears in both datasets, and it is the unique number that identifies each hunter. We'll use the merge feature to combine these two data frames into a single data frame.A "left join" (lower left example) will hold all records in the left table and only those in the right table where the left table matches. NA is used to fill in any missing values. A "right join" (lower right example) will hold all records in the right table and only those in the left table where the right table matches.If we use the order function, we can filter by multiple columns. Each additional sort vector is entered as an argument of the order function, separated by commas. The order function returns a vector of indices in the prescribed order. For sorting vectors, save type.There are two ways to use the aggregate function, which allows you to aggregate the data in a variety of ways. You decide what you want to aggregate, which variables you want to aggregate over, and which aggregation function to use in the first step. We want to add up the columns and count them by town. i have mastered quite a few data-handling features, both in terms of manipulating data and plotting it in various ways. So far, we've used a lot of base R functions in our data wrangling. We'll introduce you to dplyr, a R package designed for clean data manipulation.The package dplyr contains yet another collection of data wrangling functions. The tidyverse includes this kit. This chapter will include a very brief overview of dplyr and its coding structure.The arrange function provides a convenient way for arranging your data – moving columns around or sorting. For example, in the code below, you start with the starwars dataframe, and then arrange the dataset by descending mass.