Additions from ROpenSci Unconference (Spring 2016) #159
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… for the weather station)
…t we clean with `clean_daily`
Merge branch 'master' of github.com:hrbrmstr/rnoaa # Conflicts: # man/clean_daily.Rd
…itors and bind results together as one dataframe
This pulls data and I think requires a loaded API key, so I don't think it will run without error on CRAN
As a simple solution, we're just going to do this function as a regular function
…with `id` as a factor, not character
| radius = 15, | ||
| year_min = 2016, | ||
| var = c("TAVG", "PRCP")) | ||
| stationsDelhi <- unique(rbind_all(stationsDelhi) %>% select(- distance)) |
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@geanders switch to use bind_rows instead, as rbind_all is deprecated, and change in import statement in rnoaa-package
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@geanders thanks for the PR - made a few comments for changes |
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Thanks so much for taking the time to review. I've made the suggested changes. |
…e aren't chopped off from final data.
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thanks @geanders merged |
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Thank you so much! I really appreciate you taking the time on this pull request. Would you let me know the next time you submit a version of rnoaa to CRAN? I have a student working on a package that builds on some of these functions, so it will be good to know for getting the dependencies straight in her package code. Best, Brooke From: Scott Chamberlain notifications@github.com thanks @geandershttps://github.com/geanders merged You are receiving this because you were mentioned. |
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thanks for all your work on this, and the work of others, i'll add you as a contributor |
This pull requests incorporates work started at the spring's ROpenSci Unconference. Specific elements of this pull request are:
rnoaato pull weather data that can then be used in conjunction with air pollution data pulled using OpenAQ ("rnoaa_openaq.RMD").meteo_nearby_stationsfunction, to identify stations near a latitude-longitude pair for Global Historical Climatology Network daily weather data (defined in "meteo_distance.R" file).meteo_pull_monitors, to pull Global Historical Climatology Network daily weather data from multiple monitors with a single function call (defined in "helpers_ghcnd.R" file).meteo_show_cacheandmeteo_clear_cachefunctions, for cache management (both files in "meteo_cache.r") (other functions we're adding do not rely on these functions).meteo_coveragefunction to determine the coverage (percent of non-missing weather observations over dates pulled) for each weather monitor (in "meteo_utils.R" file), with its own method for plotting coverage.vis_missfunction to visualize missingness in a dataframe, including weather dataframes generated withrnoaa