Aug 4, 2022 · It is not possible to apply a simple transformation to discrete data to obtain continuous data. So can you apply continuous methods of analysis to those data, even though they're technically discrete?

This section illustrates how to convert a discrete factor variable to a continuous data object in R. For this task, we have to apply the as.numeric and as.character functions as shown below: x_cont <- as . numeric ( as . character ( x ) ) # Convert factor to …

Jun 17, 2011 · You need to convert your factor into numeric: mdf$numVariable <- as.numeric(as.character(mdf$variable)) g <- ggplot(mdf,aes(numVariable,value,group=variable,colour=t)) g + geom_point() + #scale_x_continuous() + opts() Or just do the conversion in the call to ggplot:

Oct 28, 2024 · Sometimes you’ll need to transform continuous numeric data into categories. The right method depends on your primary goal, as shown in our flowchart’s three main paths: finding natural groups, handling outliers, or analyzing distributions.

Aug 24, 2020 · One way is that you can construct a histogram based on a subset of your data (if you say it's gigantic) to get a sense of how the data is distributed, then you can fit a probability distribution based on your observations.

Jun 19, 2020 · What I would like to do is turn this categorical age range variable into a continuous one by assigning 1, 2, 3, 4, 5, 6, 7 to the age ranges. Any idea how I can do this in R? I think the as.numeric function could be useful but I've never used it before.

Apr 1, 2023 · The solution is to convert your variables into a suitable class which tells the computer that it represents date or time information and hopefully ggplot will then understand this information. Here is the conversion process: For the Year/Month Variable:

May 18, 2022 · How to change a discrete variable to a continuous variable in the R programming language. More details: https://statisticsglobe.com/convert-d...

Jan 13, 2012 · Treating that count variable as continuous would give you predicted values that are non-integers, but perhaps that’s not a big issue in your particular data set. Q: How high does the count scale have to be before you can consider it continuous?

If you are building decision trees based on large datasets, it may be costly in terms of processing power and memory to convert categorical variables into dummy variables. Furthermore, some models (e.g. randomForest in R) cannot handle categorical variables with many levels.