The descriptiveness for the documentation will vary, depending on the package author. Remember that R will always have documentation (in the help page ?diamonds) for built-in datasets. There is 1 variable that has an integer structure: price You could have a geombar () for data1 and a geompoint () for data2 if. Note that you can plot with multiple datasets for any other geom element too. This is because the first argument for many of the geom functions is the aesthetic mapping by default. There are 6 variables that are of numeric structure: carat, depth, table, x, y, z Within each geom element, you specify the name of the dataset with the argument label data. For example, there are 5 categories of diamond cuts with “Fair” being the lowest grade of cut to ideal being the highest grade. An ordered factor arranges the categorical values in a low-to-high rank order. There are 3 variables with an ordered factor structure: cut, color, & clarity. We can take a quick view of the variable names using: Notice that these variable names are in lowercase. There are 10 variables measuring various pieces of information about the diamonds. Why create your own dataset Aside from being ready to analyze, synthetic datasets offer additional advantages over real world data. ![]() How do we know? Each row of data represents a different diamond and there are 53,940 rows of data (see help page, ?diamonds) This dataset contains information about 53,940 round-cut diamonds. Here’s what we know about the diamonds dataset: An added bonus of working with a built-in dataset is that documentation giving further descriptions and explanations is available via the help page ( ?diamonds). Here, we see that there are 10 total variables (three ordered factors, one integer, and 6 numeric). Next, let’s look at the structure of each variable in diamonds (see 3.3.10 for a refresher on structures): str(diamonds) # Classes 'tbl_df', 'tbl' and 'ame': 53940 obs. Instead, every action must be explicitly specified in your code. Unlike Excel, you cannot edit your data directly cell-by-cell in RStudio. However, with more practice, viewing the dataset in this manner becomes less useful (especially when working with really big datasets). As a beginner in learning R, viewing the dataset in a familiar Excel-like format can be comforting. You can view any object in a new tab by wrapping the View() function around the object name.
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