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I have a data frame of dates from 2013 to 2018, with a daily precipitation value for a location I am studying. I want to calculate the mean precipitation of each date for the years mentioned above. For example, the mean precipitation on June 1 from 2013 to 2018. I will use this daily mean to then study the daily deviations from the mean daily precipitation from 2013 to 2018.

This is what my data looks like DataTable

I don't know how to write the loop for this, so I don't have any code to show.

The output for this loop should be a list of dates of the year with a long term mean for each date, something like below: 

Date (dd/mm)    Mean
01-01           1.5
02-01           4.6
03-01             5
  .
  .
31-12            6

Thank you!

Pooja Choksi
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    rather than add a screenshot, please `dput` your df and paste the code in your question so there's a reproducible data sample. – nycrefugee Apr 27 '19 at 22:38

1 Answers1

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This has been answered in a plethora of other posts. A combination of

  1. Converting to date as described in this question
  2. Calculating the mean of a column in this question and/or
  3. Calculating grouped mean as described in this question

would answer your question.

I'll assume your dataTable is actually either a data.table or data.frame, and or shorthand I'll call the data data. I'll provide a base R version using with and ave function, and i'll provide an example with data.table using their subsetting option

library(lubridate)
library(data.table)
#Data frame way
with(data, ave(Vil_481690, #variable to calculate mean 
               year(Vil_dates), #which variables to group (can be any number of variables)
               FUN = mean #already standard
               ))
#data.table way
setDT(data) #change data to a data.table
data[, mean(Vil_481690), by = year(Vil_dates)])]

Note these have not been tested as there was no copy-able data, so there might be typo one place or another.

Oliver
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