Error using matlab.internal.math.checkDataVariables Invalid grouping variables.
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kindly help me solve this issue
load("stormData");
summaryRegionCosts = groupsummary(stormData, "stormData.Region",['min','max','median','mean'] , "stormData.Total_Cost")
stormDataPos = stormData.Total_cost> 0
summaryRegionPosCosts = groupsummary(stormData, "stormData.Region", ['min','max','median','mean'],"stormData.stormDataPos")
1 个评论
Juan Carlos Hernandez
2023-2-18
Hi,
Here is your problem solved:
load("stormData");
summaryRegionCosts=groupsummary(stormData,"Region",["min","median","mean","max"],"Total_Cost")
stormDataPos=stormData(stormData.Total_Cost>=1,:)
summaryRegionPosCosts=groupsummary(stormDataPos,"Region",{"min","median","mean","max"},"Total_Cost")
回答(1 个)
Steven Lord
2022-8-20
The second input for the groupsummary function as you're calling it should be the name of a variable in your table. "stormData.Region" isn't the name of a variable, it's an expression that would extract a variable in the table. Try using just "Region" in your call instead.
15 个评论
Walter Roberson
2022-8-23
You are attempting to pass in a logical mask stormDatapos as the 4th parameter to groupsummary, with the first parameter being a table
When you use 4 parameters to groupsummary and the first one is a table, the permitted syntaxes are
G = groupsummary(T,GROUPVARS,GROUPBINS,METHOD)
G = groupsummary(T,GROUPVARS,METHOD,DATAVARS)
Your {'min', 'max', 'median', 'mean'} or ["min", "max", "median", "mean"] is not grouping bins, so the first of the two possibilities does not apply.
Your logical vector is not a data variable name within the table, so the second of the two possibilities does not appy.
If you want to summarize only for a subset of a table, you need to construct the subset first and summarize on it. For example,
sdss = stormData(stormDataPos, :);
summaryRegionPostCosts = groupsummary(sdss, "Region", ["min", "max", "median", "mean"])
In practice you will probably need to add a list of variables to apply the summary to, as it is not clear that taking mean() of your categorical SuperRegion is meaningful.
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