Transforming data.table to data.frame or Matrix in R (4 Examples)
In this tutorial, I’ll explain how to change the structure of a data.table to data.frame and matrix in R programming, and how to do it the other way around.
Preparing the Examples
First, install and load the data.table package.
install.packages("data.table") # Install & load data.table package library("data.table") |
For our examples, we take the iris dataset.
data(iris) # Load iris data set head(iris) # Printing the data head # Sepal.Length Sepal.Width Petal.Length Petal.Width Species # 1 5.1 3.5 1.4 0.2 setosa # 2 4.9 3.0 1.4 0.2 setosa # 3 4.7 3.2 1.3 0.2 setosa # 4 4.6 3.1 1.5 0.2 setosa # 5 5.0 3.6 1.4 0.2 setosa # 6 5.4 3.9 1.7 0.4 setosa str(iris) # See the structure of the data # 'data.frame': 150 obs. of 5 variables: # $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... # $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... # $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... # $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... # $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ... |
Example 1: Converting a data.frame to a data.table
The iris dataset is a data.frame. We now copy the data and convert it into a data.table with the following lines of code.
iris_DT_1 <- data.table::copy(iris) # Replicate the iris data set iris_DT_1 <- setDT(iris_DT_1) # Convert to data.table str(iris_DT_1) # See the structure of the data # Classes ‘data.table’ and 'data.frame': 150 obs. of 5 variables: # $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... # $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... # $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... # $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... # $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ... # - attr(*, ".internal.selfref")=<externalptr> head(iris_DT_1) # Printing the data head # Sepal.Length Sepal.Width Petal.Length Petal.Width Species # 1: 5.1 3.5 1.4 0.2 setosa # 2: 4.9 3.0 1.4 0.2 setosa # 3: 4.7 3.2 1.3 0.2 setosa # 4: 4.6 3.1 1.5 0.2 setosa # 5: 5.0 3.6 1.4 0.2 setosa # 6: 5.4 3.9 1.7 0.4 setosa |
Example 2: Converting a data.table to a data.frame
In this example, we switch the direction of the transformation. We now transform a data.table into a data.frame.
iris_DF_1 <- data.table::copy(iris_DT_1) # Replicate the dataset iris_DT_1 iris_DF_1 <- as.data.frame(iris_DF_1) # Convert to data.table str(iris_DF_1) # See the structure of the data # 'data.frame': 150 obs. of 5 variables: # $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... # $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... # $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... # $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... # $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ... head(iris_DF_1) # Printing the data head # Sepal.Length Sepal.Width Petal.Length Petal.Width Species # 1 5.1 3.5 1.4 0.2 setosa # 2 4.9 3.0 1.4 0.2 setosa # 3 4.7 3.2 1.3 0.2 setosa # 4 4.6 3.1 1.5 0.2 setosa # 5 5.0 3.6 1.4 0.2 setosa # 6 5.4 3.9 1.7 0.4 setosa |
Example 3: Converting a data.table to a Matrix
For transforming a data.table into a matrix, we have to be aware of the following: When there is only one non-numeric column in the data.table, all cell entries in the corresponding matrix will be characters. We therefore only select the numeric columns of the iris data.table in the following. The resulting dataset is called iris_DT_2.
iris_DT_2 <- data.table::copy(iris_DT_1) # Replicate the dataset iris_DT_1 num_cols <- unlist(lapply(iris_DT_2, is.numeric)) # Identify the numeric columns iris_DT_2 <- iris_DT_2[ , .SD, .SDcols = names(num_cols)[num_cols] ] # Reduce the data such that it only contains numeric columns head(iris_DT_2) # Printing the data head # Sepal.Length Sepal.Width Petal.Length Petal.Width # 1: 5.1 3.5 1.4 0.2 # 2: 4.9 3.0 1.4 0.2 # 3: 4.7 3.2 1.3 0.2 # 4: 4.6 3.1 1.5 0.2 # 5: 5.0 3.6 1.4 0.2 # 6: 5.4 3.9 1.7 0.4 |
You can simply use as.matrix() to transform the data.table into a matrix.
iris_mat_2 <- as.matrix(iris_DT_2) # Convert a data.table to a matrix str(iris_mat_2) # See the structure of the data # num [1:150, 1:4] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... # - attr(*, "dimnames")=List of 2 # ..$ : NULL # ..$ : chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" head(iris_mat_2) # Printing the data head # Sepal.Length Sepal.Width Petal.Length Petal.Width # [1,] 5.1 3.5 1.4 0.2 # [2,] 4.9 3.0 1.4 0.2 # [3,] 4.7 3.2 1.3 0.2 # [4,] 4.6 3.1 1.5 0.2 # [5,] 5.0 3.6 1.4 0.2 # [6,] 5.4 3.9 1.7 0.4 |
Example 4: Converting a Matrix to a data.table
The conversion of a matrix to a data.table is straightforward by use of as.data.table(), as you can see in the following.
iris_DT_2b <- as.data.table(iris_mat_2) # Coerce to data.table str(iris_DT_2b) # See the structure of the data # Classes ‘data.table’ and 'data.frame': 150 obs. of 4 variables: # $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... # $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... # $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... # $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... # - attr(*, ".internal.selfref")=<externalptr> head(iris_DT_2b) # Printing the data head # Sepal.Length Sepal.Width Petal.Length Petal.Width # 1: 5.1 3.5 1.4 0.2 # 2: 4.9 3.0 1.4 0.2 # 3: 4.7 3.2 1.3 0.2 # 4: 4.6 3.1 1.5 0.2 # 5: 5.0 3.6 1.4 0.2 # 6: 5.4 3.9 1.7 0.4 |
Further Resources
Please find some further R programming tutorials on topics such as lists, data conversion, variables, and matrices in the following list:
- Convert Data Frame to Uppercase – All Character String Columns
- How to Convert Data Frame from Long to Wide Format
- Store List of Lists in Data Matrix
- Transpose Data Matrix & Maintain First Variable as Column Names
- How to Convert Data Frame to matrix Class in R
Note: This article was created in collaboration with Anna-Lena Wölwer. Anna-Lena is a researcher and programmer who creates tutorials on statistical methodology as well as on the R programming language. You may find more info about Anna-Lena and her other articles on her profile page.