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Convert data. frame column format from character to factor The complete conversion of every character variable to factor usually happens when reading in data, e g , with stringsAsFactors = TRUE, but this is useful when say, you've read data in with read_excel() from the readxl package and want to train a random forest model that doesn't accept character variables
when to use factor () when plotting with ggplot in R? Is the general rule to use factor when the variable being used to determine the shape size colour is discrete, and not continuous? Or is there another use of factor in this context? It seems like the first command can be made like the second with the right legend, even without factor thanks edit: I get this when I use the colour=gear:
Pandas - make a column dtype object or Factor - Stack Overflow In pandas, how can I convert a column of a DataFrame into dtype object? Or better yet, into a factor? (For those who speak R, in Python, how do I as factor()?) Also, what's the difference between
How to reorder factor levels in a tidy way? - Stack Overflow A couple comments: reordering a factor is modifying a data column The dplyr command to modify a data column is mutate All arrange does is re-order rows, this has no effect on the levels of the factor and hence no effect on the order of a legend or axis in ggplot All factors have an order for their levels The difference between an ordered = TRUE factor and a regular factor is how the
r - Convert factor to integer - Stack Overflow Does anyone know of a way to coerce a factor into an integer? Using as character() will convert it to the correct character, but then I cannot immediately perform an operation on it, and as integer() or as numeric() will convert it to the number that system is storing that factor as, which is not helpful
When or why convert a numeric variable to factor? - Stack Overflow 0 I generally only convert a variable to a factor if one or more of the following are true: the values of the variable represent some form of grouping, i e the variable is categorical in nature there are substantial memory savings to be had - this is usually the case where character variables have been used to identify group levels
Example to use scale_factor and add_offset in netCDF4 with Python? If you want to know how to use add_offset and scale_factor parameters to pack or unpack data in nc file, you can read here When you read netCDF4 file with python (download NCEP reanalysis I data for example), you can reference following code:
as. factor() in numeric conversion R - Stack Overflow But conversion from factor to a character strips out the letters that make up the factor Then conversion from character to numeric turns number characters to actual numbers