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Summary of data for each year in R

I have data with two columns. In one column is the date, and in the other column is the stream data.

I was able to read data as date and stream data. I used the following code:

creek <- read.csv("creek.csv") library(ggplot2) creek[1:10,] colnames(creek) <- c("date","flow") creek$date <- as.Date(creek$date, "%m/%d/%Y")

Link to my details https://www.dropbox.com/s/eqpena3nk82x67e/creek.csv

Now I want to find a summary of each year. I want to know especially average, average, maximum, etc.

Thanks.

Regards, Jdbaba

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3 answers

Base R

Here are two methods from base R.

The first uses cut , split and lapply along with summary .

 creekFlowSummary <- lapply(split(creek, cut(creek$date, "1 year")), function(x) summary(x[2]))

This creates a list . You can view a summary of different years by referring to the appropriate list or list name.

 creekFlowSummary[1] # $`1999-01-01` # flow # Min. :0.3187 # 1st Qu.:0.3965 # Median :0.4769 # Mean :0.6366 # 3rd Qu.:0.5885 # Max. :7.2560 # creekFlowSummary["2000-01-01"] # $`2000-01-01` # flow # Min. :0.1370 # 1st Qu.:0.1675 # Median :0.2081 # Mean :0.2819 # 3rd Qu.:0.2837 # Max. :2.3800

The second uses aggregate :

 aggregate(flow ~ cut(date, "1 year"), creek, summary) # cut(date, "1 year") flow.Min. flow.1st Qu. flow.Median flow.Mean flow.3rd Qu. flow.Max. # 1 1999-01-01 0.3187 0.3965 0.4770 0.6366 0.5885 7.2560 # 2 2000-01-01 0.1370 0.1675 0.2081 0.2819 0.2837 2.3800 # 3 2001-01-01 0.1769 0.2062 0.2226 0.2950 0.2574 2.9220 # 4 2002-01-01 0.1279 0.1781 0.2119 0.5346 0.4966 14.3900 # 5 2003-01-01 0.3492 0.4761 0.7173 1.0350 1.0840 10.1500 # 6 2004-01-01 0.4178 0.5379 0.6524 0.9691 0.9020 11.7100 # 7 2005-01-01 0.4722 0.6094 0.7279 1.2340 1.0900 17.7200 # 8 2006-01-01 0.2651 0.3275 0.4282 0.5459 0.5758 3.3510 # 9 2007-01-01 0.2784 0.3557 0.4041 0.6331 0.6125 9.6290 # 10 2008-01-01 0.4131 0.5430 0.6477 0.8792 0.9540 4.5960 # 11 2009-01-01 0.3877 0.4572 0.5945 0.8465 0.8309 6.3830

Be careful with the aggregate solution, though: All summary information is a single matrix. Check out the str output to see what I mean.

xts

There are, of course, other ways to do this. One way is to use the xts package.

Convert your data to xts :

 library(xts) creekx <- xts(creek$flow, order.by=creek$date)

Then use apply.yearly and any functions that interest you.

Below is the average of the year:

 apply.yearly(creekx, mean) # [,1] # 1999-12-31 0.6365604 # 2000-12-31 0.2819057 # 2001-12-31 0.2950348 # 2002-12-31 0.5345666 # 2003-12-31 1.0351742 # 2004-12-31 0.9691180 # 2005-12-31 1.2338066 # 2006-12-31 0.5458652 # 2007-12-31 0.6331271 # 2008-12-31 0.8792396 # 2009-09-30 0.8465300

And the annual maximum:

 apply.yearly(creekx, max) # [,1] # 1999-12-31 7.256 # 2000-12-31 2.380 # 2001-12-31 2.922 # 2002-12-31 14.390 # 2003-12-31 10.150 # 2004-12-31 11.710 # 2005-12-31 17.720 # 2006-12-31 3.351 # 2007-12-31 9.629 # 2008-12-31 4.596 # 2009-09-30 6.383

Or, connect them as follows: apply.yearly(creekx, function(x) cbind(mean(x), sum(x), max(x)))

data.table

The data.table package data.table also be of interest to you, especially if you are dealing with a lot of data. Here is a data.table . The key is to use as.IDate in the "date" column when you read your data:

 library(data.table) DT <- data.table(date = as.IDate(creek$date), creek[-1]) DT[, list(mean = mean(flow), tot = sum(flow), max = max(flow)), by = year(date)] # year mean tot max # 1: 1999 0.6365604 104.3959 7.256 # 2: 2000 0.2819057 103.1775 2.380 # 3: 2001 0.2950348 107.6877 2.922 # 4: 2002 0.5345666 195.1168 14.390 # 5: 2003 1.0351742 377.8386 10.150 # 6: 2004 0.9691180 354.6972 11.710 # 7: 2005 1.2338066 450.3394 17.720 # 8: 2006 0.5458652 199.2408 3.351 # 9: 2007 0.6331271 231.0914 9.629 # 10: 2008 0.8792396 321.8017 4.596 # 11: 2009 0.8465300 231.1027 6.383
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You can use the @ananda code to create an additional column with a year, a row with a cut. Assuming the column name is year, you can use ddply from the plyr package:

 ddply(creek, .(year), summarise, mm = mean(flow), me = median(flow), ...etc)
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Code

@Pauls can be rewritten with the next iteration of the plyr package, dplyr package .

  • Create year variable
  • Create a separate data frame that splits the original data frame by year
  • Calculate summary statistics for each year in a data frame using summarize()
 creek <- mutate(creek, year = as.POSIXlt(date)$year + 1900) years <- group_by(creek, year) summarize(years, mm= mean(flow), tot= sum(flow), max = max(flow, na.rm = TRUE))

Here is a good dplyr tutorial from Roger Pan.

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