The R Language Definition is handy for answering these types of questions:
R has three basic indexing operators, with syntax displayed by the following examples
x[i]
x[i, j]
x[[i]]
x[[i, j]]
x$a
x$"a"
For vectors and matrices the [[ forms are rarely used, although they have some slight semantic differences from the [ form (e.g. it drops any names or dimnames attribute, and that partial matching is used for character indices). When indexing multi-dimensional structures with a single index, x[[i]] or x[i] will return the ith sequential element of x.
For lists, one generally uses [[ to select any single element, whereas [ returns a list of the selected elements.
The [[ form allows only a single element to be selected using integer or character indices, whereas [ allows indexing by vectors. Note though that for a list, the index can be a vector and each element of the vector is applied in turn to the list, the selected component, the selected component of that component, and so on. The result is still a single element.
By using the merge function and its optional parameters:
Inner join: merge(df1, df2) will work for these examples because R automatically joins the frames by common variable names, but you would most likely want to specify merge(df1, df2, by = "CustomerId") to make sure that you were matching on only the fields you desired. You can also use the by.x and by.y parameters if the matching variables have different names in the different data frames.
Outer join: merge(x = df1, y = df2, by = "CustomerId", all = TRUE)
Left outer: merge(x = df1, y = df2, by = "CustomerId", all.x = TRUE)
Right outer: merge(x = df1, y = df2, by = "CustomerId", all.y = TRUE)
Cross join: merge(x = df1, y = df2, by = NULL)
Just as with the inner join, you would probably want to explicitly pass "CustomerId" to R as the matching variable. I think it's almost always best to explicitly state the identifiers on which you want to merge; it's safer if the input data.frames change unexpectedly and easier to read later on.
You can merge on multiple columns by giving by a vector, e.g., by = c("CustomerId", "OrderId").
If the column names to merge on are not the same, you can specify, e.g., by.x = "CustomerId_in_df1", by.y = "CustomerId_in_df2" where CustomerId_in_df1 is the name of the column in the first data frame and CustomerId_in_df2 is the name of the column in the second data frame. (These can also be vectors if you need to merge on multiple columns.)
Best Solution
There's not much of one in everyday work.
However, according to the documentation for both functions (accessed by putting a
?before the function name and hitting enter),requireis used inside functions, as it outputs a warning and continues if the package is not found, whereaslibrarywill throw an error.