Removing outliers in R with tools from dplyr and ggplot2 (CC232)

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  • Опубліковано 11 гру 2024

КОМЕНТАРІ • 18

  • @haraldurkarlsson1147
    @haraldurkarlsson1147 2 роки тому +2

    Pat,
    This two-part exercise was probably my most favorite of those I have observed from you.
    I am currently in Northern Indiana so not suprisingly my data looks similar to yours. The data set I used went back to the early 1890s but there was a break in data collection from about 1910-1920 (World War I?). I did not see the extreme values in precip and snow that you observe. The most extreme precip and snow values were around 200 mm and 500 mm , respectively. When looking at the ten highest snow values I found events that line up with the big snow fall in Chicago in late January 1967. The station I am using may be experiencing higher precip (snow and rain) due to the lake effect. I am not sure how far or how large the lake effect is in Michigan in your neck of the woods. Finally, I converted the snow to rain using 1:12 ratio suggested by NOAA for this region. I thought it might be interesting to compare precip and "converted from snow precip". There are some 'hint' of a trend but caution is needed since the conversion of snow to precip is highly dependent on temperature.

    • @Riffomonas
      @Riffomonas  2 роки тому

      Very cool! Great job digging into your data 👍

  • @pratish143
    @pratish143 2 роки тому +1

    Thanks!

    • @Riffomonas
      @Riffomonas  2 роки тому

      My pleasure! Thanks for tuning in 🤓

    • @Riffomonas
      @Riffomonas  2 роки тому

      I just realized that you sent me $10 - thank you so much. That is very generous and much appreciated!

  • @lalitpl4
    @lalitpl4 2 роки тому +3

    I stumbled upon one of your videos few months back. My life is much easier since then. Your videos helped immensely in my work. Thank you!

    • @Riffomonas
      @Riffomonas  2 роки тому

      Wonderful! I’m so glad you found the channel 🤓

  • @cyrillejar1914
    @cyrillejar1914 2 роки тому +1

    Very intresting and useful, as always. What do you think about tests like dixon's test or Grubb's test to identify outliers ?

    • @Riffomonas
      @Riffomonas  2 роки тому +1

      I think it’s best to understand why an observer is an outlier before removing it

  • @brianwood9610
    @brianwood9610 2 роки тому +1

    Wouldn't it be worth imputing a median value to those outliers?

    • @Riffomonas
      @Riffomonas  2 роки тому

      Hmmm. Interesting suggestion. Considering how patchy the precipitation/snow fall data are I’m not sure. Maybe for the temperature data which would be more continuous

  • @petongtitus
    @petongtitus Рік тому

    how about replacing outliers with NA instead of removing outliers? could you show me how?

  • @sarvagyavatsal9569
    @sarvagyavatsal9569 2 роки тому +1

    very useful

    • @Riffomonas
      @Riffomonas  2 роки тому

      Wonderful! I’m glad you enjoyed it 🤓

  • @sven9r
    @sven9r 2 роки тому

    First 😝

  • @yaqinguo8971
    @yaqinguo8971 2 роки тому +1

    Thanks!