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ISRO CS Syllabus for Scientist/Engineer Exam. ISRO CS Original Papers and Official Keys. GATE CS Original Papers and Official Keys. In addition, this code is not only easier to understand it also runs about two to three times as fast. The NA from the second row is instead visible in all columns ( which makes way more sense, since you don't know, what category this observation IS, so you cannot know what category it IS NOT). Also, there isn't an NA-column present anymore. This code will give you the following table: id color_radio color_tv color_cinemaĪs you can see, the column cinema is now present even though our sample data-frame did not contain any observations of it. One Hot Encoding with mltools (recommended): library(data.table) Also, the spread()-function created an own column for values with NA's, treating them as a separate category. Note that there is no cinema column present, even though we defined a respective level in our sample data-frame. However, this code will give you the following table: # A tibble: 4 x 4 For an explanation, see the following code snippets: # set up sample data Even as this may be possible to accomplish in dplyr (together with tidyr), I would recommend using the function one_hot() from the library mltools. 
What you are trying to do is called one hot encoding or dummy encoding.
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