![]() 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.
0 Comments
Leave a Reply. |