- 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:
- What are the differences between a factor and a vector in R?
I would like to know what the differences are between vector and factor, because sometimes I find it confusing when I work in which database
- r - Changing factor levels with dplyr mutate - Stack Overflow
From my understanding, the currently accepted answer only changes the order of the factor levels, not the actual labels (i e , how the levels of the factor are called)
- 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 - summarizing counts of a factor with dplyr - Stack Overflow
I want to group a data frame by a column (owner) and output a new data frame that has counts of each type of a factor at each observation The real data frame is fairly large, and there are 10 diff
- r - Re-ordering factor levels in data frame - Stack Overflow
Re-ordering factor levels in data frame [duplicate] Asked 11 years, 10 months ago Modified 3 years, 10 months ago Viewed 247k times
- r - Coerce multiple columns to factors at once - Stack Overflow
You don't even need to use funs if you only perform one transformation; mutate_at(cols, factor) is sufficient
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