"Make the data usable" could mean different things depending on the characteristics of the dataset you have uploaded.
Here are some things to consider:
Because our dataset might be too big or contain things we don't want to include in our visualizations, we will want to filter it.
Here is the basic code structure to make a filter:
({namefilter} <- filter ({data}, {variablename} == {quality or quantity}))
Example:
(TopDogs <- filter(Dog_Names, Count >53))
This will filter the data to only the top dog names because the count has to be more than 53 to be included in this filtered dataset.
We could make all different sorts of filters with the Dog Name data. We could use any of the following in our filter:
== equals
> greater than
< less than
>= greater than or equal to
<= less than or equal to
Wrangling data like this uses the dplyr package. "Filter" is just one of many functions in the dyplr package. See the Resources tab for sources that will explain other functions that will help make your data usable.