lastdiffed_month: The month in which the bug is last modified.creation_year: The year in which the bug is created.creation_month: The month in which the bug is created.isprivate: TRUE if the attachment should be private and FALSE if the attachment should be public.isobsolete: Whether attachment is marked obsolete.submitter_id: Unique numeric identifier for who submitted the bug.filename :Path-less file-name of attachment.ispatch: Whether attachment is a patch.mimetype: Content type of the attachment like text/plain or image/png.description: Text describing the attachment.modification_time: The date and time on which the attachment was last modified.attach_id: Unique numeric identifier for attachment.bug_id: Unique numeric identifier for bug.The brief description about the columns as follows: #converting the required fields in the correct datatype format bugs_df % mutate_at( vars( "creation_ts", "delta_ts", "lastdiffed", "deadline"), as.Date) # Taking the columns which are useful bugs_df % select( "bug_id", "bug_severity", "bug_status", "creation_ts", "delta_ts", "op_sys", "priority", "resolution", "component_id", "version", "lastdiffed", "deadline") #for quick view of the datatypes and the structure of data skim(bugs_df) Data summary Nameīugs_attach_df <- tbl(con, "attachments") # Converting `bugs_attach_df` to `dataframe` bugs_attach_df <- as.ame(bugs_attach_df) #for quick view of the datatypes and the structure of data skim(bugs_attach_df) Data summary NameĪbout the bugs_activity and attachments Data Used for Analysis I’ve taken the 15 columns under consideration to Analyse the Data. ![]() ![]() Also there are columns which are empty or they have same value, so it is not interesting for further analysis: So, It can’t be transformed to Date format datatype. Note:The Column estimated_time and remaining_time only contains the integer value. ): Decimal MySQL column 25 imported asįrom the above table we can conclude that the few of the columns are having wrong datatype like: #for quick view of the datatypes and the structure of data skim(bugs_df) # Warning in.
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