dftype#

Purpose#

Set the types of columns in a matrix or dataframe.

Format#

x_meta = dfType(X, types[, columns])#

Parameters:

X (NxK matrix) – data.
types (Mx1 vector) – Specifies types to be assigned to columns specified in columns. Valid options include: "string", "date", "number", and "category".
columns (Mx1 vector) – Optional argument, indices of columns in X to assign types to. Default = all columns.

Returns:

x_meta (NxK dataframe) – Data with the types specified in types assigned to the columns specified in columns.

Remarks#

Date Variables#

When a numeric column is set to type date with dftype():

The data from the column is interpreted as POSIX time (seconds since Jan 1, 1970).
The default date format will be used. This can be changed with asdate().

Categorical and String Variables#

When a numeric column is set to type category, or string with dftype():

The labels will be the string version of the number. The keys will be integers from 0 to n-1, where n is the number of unique values in the original data. The keys will be assigned to the labels such that the original order is maintained.

When a categorical or string variable is converted to a numeric column;

The updated column will contain the numeric keys associated with the string or category labels.

Examples#

Example 1: POSIX time numeric column to date column#

secs_per_day = 24 * 60 * 60;

// Create a 2x1 vector
x = 0 | secs_per_day;

// Set the numeric vector to be a date
x = dftype(x, "date", 1);
print x;

Since the date vector is interpreted as seconds since Jan 1, 1970, the code above will print:

        X1
1970-01-01
1970-01-02

Example 2: Category to number#

// Load 'cycles' and load 'amplitude' as a categorical variable
fname = getGAUSSHome("examples/yarn.xlsx");
yarn = loadd(fname, "cat(amplitude) + cycles");

// Set the first column to be a numeric column
yarn_n = dftype(yarn, "number", 1);

After the above code, the first few rows look like this:

yarn = amplitude   cycles   yarn_n = amplitude   cycles
             low      674                    1      674
             low      370                    1      370
             low      292                    1      292
             med      338                    2      338

Example 3: Integer column to category#

x = { 4,
      1,
      4,
      6 };

// Make 'x' a dataframe and set its
// only column to be a category
x = dftype(x, "category");

After the above code, x will be a datframe as shown below:

We can get the categorical labels and key values like this:

{ labels, keys } = getcollabels(x, 1);

They will equal:

labels = "1"   keys = 0
         "4"          1
         "6"          2

We can set new labels with recodecatlabels() like this:

// Set the labels for 0, 1, and 2 to be
// alpha, beta and gamma
x = recodecatlabels(x, labels, "alpha"$|"beta"$|"gamma", 1);

Now x will be the following dataframe:

   X1
 beta
alpha
 beta
gamma