{"id":557,"date":"2018-10-08T02:03:24","date_gmt":"2018-10-08T09:03:24","guid":{"rendered":"https:\/\/blogs.ubc.ca\/yiwang28\/?p=557"},"modified":"2018-10-08T02:04:19","modified_gmt":"2018-10-08T09:04:19","slug":"my-r-learning-notes-2-a-simple-way-to-calculate-the-daily-mean-from-hourly-data-with-a-different-time-interval","status":"publish","type":"post","link":"https:\/\/blogs.ubc.ca\/yiwang28\/2018\/10\/08\/my-r-learning-notes-2-a-simple-way-to-calculate-the-daily-mean-from-hourly-data-with-a-different-time-interval\/","title":{"rendered":"My R learning notes 2: a simple way to calculate the daily mean from hourly data with a different time interval"},"content":{"rendered":"

Recently I received a surprising email from a scholar that I haven’t met before. He told me that he came across my blog about aggregating minutely data into hourly data when he was trying to calculate the daily mean from a different time interval (e.g., from 09:00 am to 08:00 am at the following day).<\/p>\n

At first, I was so surprised, as I thought that this blog could not be indexed by Google. Then, I felt a little bit embarrassed, because I\u00a0didn’t write the posts very carefully, so the contents must have lots of grammar errors, and might not make sense. However, I felt happy in the end, as it was wonderful to know that my notes had enlightened others who faced the same problem. So I want to express my gratitude to all who have read my posts and borne my poor writing. I also hope to receive opinions from every reader \ud83d\ude42<\/p>\n

In this post, I would like to share my idea with you on how to calculate daily mean with a different time interval (e.g., from 9:00 am to 8:00 at the other day). I think that the quickest way is to calculate the mean of each column by every 24 rows in the data frame. For example, here is the initial data sheet:<\/p>\n

\"\"\r\n                        Fig. 1 Initial data example<\/pre>\n
In order to make the R code simple, I delete the first two rows, so that the record started from 9:00 am. Now, let’s put the data frame into R as data1.<\/p>\n
\"\"\r\n                      Fig. 2 Data arrangement in R.<\/pre>\n
Then, let’s build a simple function\u00a0to calculate the mean of every 24 rows.The code was taken from Vlo’s answer from the following website: https:\/\/stackoverflow.com\/questions\/30359427\/calculate-the-mean-of-every-13-rows-in-data-frame.\u00a0 If you would like to calculate the sum instead of the mean, please just adjust the\u00a0 “FUN=\u00a0 \u00a0 \u00a0 ” commend.<\/p>\n
n.colmeans = function(df, n = 24){<\/span>
\naggregate(x = df,<\/span>
\nby = list(gl(ceiling(nrow(df)\/n), n)[1:nrow(df)]),<\/span>
\nFUN = mean)<\/span>
\n} ### because I have 12 column in the data frame, so n > 12. This function will calculate the average of every 24 rows. For example, the result comes from row 1 to row 24, then from row 25 to row 49, etc.)<\/span><\/span><\/p>\n
After we run the above codes, we input the following code:<\/p>\n
data2<- n.colmeans(data1, 24<\/span>) ### this is\u00a0used to apply the above function to calculate our own dataset.<\/span><\/p>\n
The\u00a0daily means of every column in the data2 were calculated with the time interval from 9:00 am to 8:00 at the other day. You can export the result by using the following code:<\/p>\n
data2 <-write.xlsx(\u00a0 )<\/span><\/p>\n
Finally we got what we want. Yay! However, it is note that If your data have missing rows, then this method is not applicable.<\/p>\n
There are definitely other ways to aggregate the data, but the codes in this blog, in my opinion, is the most simple ones. If you have different opinions, or you’d like to share your codes, please feel free to leave your comments.<\/p>\n
Thank you for reading it! \ud83d\ude42<\/p>\n
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Recently I received a surprising email from a scholar that I haven’t met before. He told me that he came across my blog about aggregating minutely data into hourly data when he was trying to calculate the daily mean from a different time interval (e.g., from 09:00 am to 08:00 am at the following day). … Continue reading My R learning notes 2: a simple way to calculate the daily mean from hourly data with a different time interval<\/span> →<\/span><\/a><\/p>\n","protected":false},"author":40393,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[67],"tags":[],"class_list":["post-557","post","type-post","status-publish","format-standard","hentry","category-blogs"],"_links":{"self":[{"href":"https:\/\/blogs.ubc.ca\/yiwang28\/wp-json\/wp\/v2\/posts\/557","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.ubc.ca\/yiwang28\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.ubc.ca\/yiwang28\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/yiwang28\/wp-json\/wp\/v2\/users\/40393"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/yiwang28\/wp-json\/wp\/v2\/comments?post=557"}],"version-history":[{"count":9,"href":"https:\/\/blogs.ubc.ca\/yiwang28\/wp-json\/wp\/v2\/posts\/557\/revisions"}],"predecessor-version":[{"id":569,"href":"https:\/\/blogs.ubc.ca\/yiwang28\/wp-json\/wp\/v2\/posts\/557\/revisions\/569"}],"wp:attachment":[{"href":"https:\/\/blogs.ubc.ca\/yiwang28\/wp-json\/wp\/v2\/media?parent=557"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.ubc.ca\/yiwang28\/wp-json\/wp\/v2\/categories?post=557"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.ubc.ca\/yiwang28\/wp-json\/wp\/v2\/tags?post=557"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}