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How to change the number of replicas of a Kafka topic? The problem with changing the topic replication factor using the kafka-reassign-partitions sh tool is that the user is in charge of doing the calculations to determine the best broker to host a new replica, or which replica needs to be dropped
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
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
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:
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
r - list all factor levels of a data. frame - Stack Overflow with dplyr::glimpse(data) I get more values, but no infos about number values of factor-levels Is there an automatic way to get all level informations of all factor vars in a data frame?
Generating predicted values for levels of factor variable I am regressing a number of factor variables on a continuous outcome variable using lm() For example, fit<-lm(dv~factor(hour)+factor(weekday)+factor(month)+factor(year)+count, data=df) I would like to generate predicted values (yhat) for different levels of a factor variable while holding the other variables at their median or modal value For example, how would I generate the yhat for