Closed vamsikrishna97 closed 7 years ago
also update your version from here (upstream) as i pushed a few changes to make sure builds pass
Hi,
Thanks for your feedback.
In order to add examples, I'd have to create another sample_data
because none of the present ones has coordinateUncertainityInMeters
variable.
Are there any set guidelines to follow to create sample_data
or can I use any data which would work for my function?
I have corrected all the rookie mistakes and will update it soon.
Thanks
Vamsi
@vamsikrishna97 for a sample data set, add a documentation entry in https://github.com/ropensci/scrubr/blob/master/R/scrubr_datasets.R once you've settled on the format - try to keep the data as small as possible - save the data.frame in data/
using save()
(make sure the object saved is the same name as the filename to make it easier on everyone)
Merging #24 into master will increase coverage by
3.65%
. The diff coverage is93.33%
.
@@ Coverage Diff @@
## master #24 +/- ##
=========================================
+ Coverage 55.25% 58.9% +3.65%
=========================================
Files 15 15
Lines 333 348 +15
=========================================
+ Hits 184 205 +21
+ Misses 149 143 -6
Impacted Files | Coverage Δ | |
---|---|---|
R/coord-funs.R | 51.61% <93.33%> (+12.16%) |
:arrow_up: |
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@vamsikrishna97 let me know when you're done
Hi,
I am trying to figure out why my PR is failing the codecov
tests and I haven't been successful yet.
Is there something simple I am missing here?
Hi,
Added tests for coord_incomplete
, coord_unlikely
and coord_uncertain
in order to increase the code coverage.
Hi, I was also thinking to add more tests for the rest of the functions if you are okay with it?
I was also thinking to add more tests for the rest of the functions if you are okay with it?
no, like to keep pull requests focused on a single topic
thanks @vamsikrishna97
looks good
Hi, My motive was to take
coordinateUncertainityInMeters
into consideration while flagging imprecise points.I have created a separate function to deal with this
coord_uncertain(x, coorduncertainityLimit=30000, drop=T, ignore.na=F)
Parameters-
x
(data.frame) A data.framecoorduncertainityLimit
(numeric) numeric limit of thecoordinateUncertainityInMeters
variabledrop
(logical) Drop bad data points or notignore.na
(logical) To consider NA as a bad point or not