I found an issue where a bunch (500ish) rows had mostly missing values in the objects data frame, which caused an error when converting dateUploaded to a date object. For now I just filtered them out on
Line 17 in cedd9f3dplyr::filter(!is.na(.data$dateUploaded)) %>%
, but we should really dive in and find out where these rows originate and why the fields are missing:
Determine why fields are missing from a bunch of rows and fix the issue
Additionally, some of the tests I wrote with hard coded values for things like number of datasets in a particular month are failing. This means that at some point we calculated that there were say 100 datasets submitted between January 1 and February 1 of 2021, but for some reason now we calculate that 103 datasets were submitted then. We need to figure out what is causing our counts to (slightly) change. It might be related to the above
Carried over from issue #11 -
I found an issue where a bunch (500ish) rows had mostly missing values in the objects data frame, which caused an error when converting dateUploaded to a date object. For now I just filtered them out on
arcticreport/R/plot_cumulative_volume.R
Line 17 in cedd9f3
dplyr::filter(!is.na(.data$dateUploaded)) %>%, but we should really dive in and find out where these rows originate and why the fields are missing:Additionally, some of the tests I wrote with hard coded values for things like number of datasets in a particular month are failing. This means that at some point we calculated that there were say 100 datasets submitted between January 1 and February 1 of 2021, but for some reason now we calculate that 103 datasets were submitted then. We need to figure out what is causing our counts to (slightly) change. It might be related to the above