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This vignette discusses the default usage of reshaping functions melt wide to long and dcast long to wide for data. The melt and dcast functions for data. The extended functionalities are in line with data. From v1. You just need to load data. We could accomplish this using melt by specifying id. By default, variable column Looming of type factor.

Set variable.

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By default, the molten columns are automatically named variable and value. By default, when one of id. When neither id. In addition, a warning message is issued highlighting Lloking columns that are automatically considered to be id. In the previous section, we saw how to get from wide form to long form.

We can accomplish it using dcast as follows:.

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You can also pass a function to aggregate by in dcast with the argument fun. This is particularly essential Looking for sum n a fun the formula provided does aa identify single observation for each cell. However, there are situations we might run into where fub desired operation is not expressed in a straightforward manner.

For example, consider the data. Using the current functionality, we can do something like this:. What we wanted to do was to combine all the dob and gender type columns together respectively.

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Instead we Looking for sum n a fun combining everything together, and then splitting them again. What we are doing is more or less to combine all the clothes together, and then split them back on to shelves 1 and 3!

The columns to melt may be of different types, as in this case character and integer types.

By melting them all together, the columns will be coerced in result, as explained by the warning message above and shown from output of str DT. We are generating an additional column by splitting the variable column into two columns, whose purpose is quite cryptic.

We do it because we need it for casting in the next step. Looking for sum n a fun, we cast the data set. In fhn, stats:: It is an extremely useful and often underrated function. You should definitely give it a try! The idea is quite simple.

We pass a list of columns to measure.

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We can use the function patternsimplemented for convenience, to provide regular expressions for the columns to be combined together. The above operation can be rewritten as:.

The functionality is implemented entirely in C, and is therefore both fast and memory efficient in addition to being straightforward. Okay great! We can now melt into multiple columns sjm. Now given the data set DT. We can now provide multiple value.

Everything is taken care of internally, and efficiently. In addition to being fast, it is also very memory efficient.

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You can also provide multiple functions to fun. Check the examples in?

Data We will load the data sets directly within sections. Introduction The melt and dcast functions for data.

In this vignette, we will first briefly look at the default melting and casting of data. Default functionality a melt ing data.

We can also specify column indices instead of names.

We can accomplish it using dcast as follows: By order of hierarchy, the molten data value column will be of type 'character'. All measure variables not of type 'character' will be coerced too.

Classes 'data. Issues What we wanted to do was to combine all the dob and gender type columns together respectively. The above operation can be rewritten as: We can remove the variable column if necessary. Attributes are preserved in result wherever possible. Multiple functions to fun.