# makeX

##### convert a data frame to a data matrix with one-hot encoding

Converts a data frame to a data matrix suitable for input to `glmnet`

.
Factors are converted to dummy matrices via "one-hot" encoding. Options deal
with missing values and sparsity.

- Keywords
- models

##### Usage

`makeX(train, test = NULL, na.impute = FALSE, sparse = FALSE, ...)`

##### Arguments

- train
Required argument. A dataframe consisting of vectors, matrices and factors

- test
Optional argument. A dataframe matching 'train' for use as testing data

- na.impute
Logical, default

`FALSE`

. If`TRUE`

, missing values for any column in the resultant 'x' matrix are replaced by the means of the nonmissing values derived from 'train'- sparse
Logical, default

`FALSE`

. If`TRUE`

then the returned matrice(s) are converted to matrices of class "CsparseMatrix". Useful if some factors have a large number of levels, resulting in very big matrices, mostly zero- ...
additional arguments, currently unused

##### Details

The main function is to convert factors to dummy matrices via "one-hot"
encoding. Having the 'train' and 'test' data present is useful if some
factor levels are missing in either. Since a factor with k levels leads to a
submatrix with 1/k entries zero, with large k the `sparse=TRUE`

option
can be helpful; a large matrix will be returned, but stored in sparse matrix
format. Finally, the function can deal with missing data. The current
version has the option to replace missing observations with the mean from
the training data. For dummy submatrices, these are the mean proportions at
each level.

##### Value

If only 'train' was provided, the function returns a matrix 'x'. If missing values were imputed, this matrix has an attribute containing its column means (before imputation). If 'test' was provided as well, a list with two components is returned: 'x' and 'xtest'.

##### See Also

`glmnet`

##### Examples

```
# NOT RUN {
set.seed(101)
### Single data frame
X = matrix(rnorm(20), 10, 2)
X3 = sample(letters[1:3], 10, replace = TRUE)
X4 = sample(LETTERS[1:3], 10, replace = TRUE)
df = data.frame(X, X3, X4)
makeX(df)
makeX(df, sparse = TRUE)
### Single data freame with missing values
Xn = X
Xn[3, 1] = NA
Xn[5, 2] = NA
X3n = X3
X3n[6] = NA
X4n = X4
X4n[9] = NA
dfn = data.frame(Xn, X3n, X4n)
makeX(dfn)
makeX(dfn, sparse = TRUE)
makeX(dfn, na.impute = TRUE)
makeX(dfn, na.impute = TRUE, sparse = TRUE)
### Test data as well
X = matrix(rnorm(10), 5, 2)
X3 = sample(letters[1:3], 5, replace = TRUE)
X4 = sample(LETTERS[1:3], 5, replace = TRUE)
dft = data.frame(X, X3, X4)
makeX(df, dft)
makeX(df, dft, sparse = TRUE)
### Missing data in test as well
Xn = X
Xn[3, 1] = NA
Xn[5, 2] = NA
X3n = X3
X3n[1] = NA
X4n = X4
X4n[2] = NA
dftn = data.frame(Xn, X3n, X4n)
makeX(dfn, dftn)
makeX(dfn, dftn, sparse = TRUE)
makeX(dfn, dftn, na.impute = TRUE)
makeX(dfn, dftn, sparse = TRUE, na.impute = TRUE)
# }
```

*Documentation reproduced from package glmnet, version 3.0-2, License: GPL-2*