Compute data.frame column averages by date

Question

I have a data.frame in R where one column is a list of dates (many of which are duplicates), whereas the other column is a temperature recorded on that date. The columns in question look like this (but is several thousand rows and a few other unnecessary cols): ``` Date | Temp ----------------- 1/2/13 34.4 1/2/13 36.4 1/2/13 34.3 1/4/13 45.6 1/4/13 33.5 1/5/13 45.2 ``` I need to find a way of getting a daily average for temperature. So ideally, I could tell R to loop through the data.frame and for every date that matched, give me an average for the temperature that day. I've been googling and I know loops in R are possible, but I can't wrap my head around this conceptually given what little I know about R code. I know I can pull out a single column and average it (i.e. `mean(data.frame[[2]])`) but I'm utterly lost on how to tell R to match that mean to a single value located in the first column. Additionally, how could I generate an average for every seven calendar days (regardless of…

Accepted Answer

```
library(plyr)

ddply(df, .(Date), summarize, daily_mean_Temp = mean(Temp))
```

This is a simple example of the Split-Apply-Combine paradigm.

Alternative #1 as Ananda Mahto mentions, `dplyr` package is a higher-performance rewrite of `plyr`. He shows the syntax.

Alternative #2: `aggregate()` is also functionally equivalent, just has fewer bells-and-whistles than `plyr/dplyr`.

Additionally 'generate average for every 7 calendar days': do you mean 'average-by-week-of-year', or 'moving 7-day average (trailing/leading/centered)'?

Compute data.frame column averages by date

Solution

Problem