Aggregate / summarize multiple variables per group

Question

From a data frame, is there a easy way to aggregate (sum, mean, max et c) multiple variables simultaneously?

Below are some sample data:

library(lubridate)
days = 365*2
date = seq(as.Date("2000-01-01"), length = days, by = "day")
year = year(date)
month = month(date)
x1 = cumsum(rnorm(days, 0.05)) 
x2 = cumsum(rnorm(days, 0.05))
df1 = data.frame(date, year, month, x1, x2)

I would like to simultaneously aggregate the x1 and x2 variables from the df2 data frame by year and month. The following code aggregates the x1 variable, but is it also possible to simultaneously aggregate the x2 variable?

### aggregate variables by year month
df2=aggregate(x1 ~ year+month, data=df1, sum, na.rm=TRUE)
head(df2)

Any suggestions would be greatly appreciated.

Answer 1

Where is this year() function from?

You could also use the reshape2 package for this task:

require(reshape2)
df_melt <- melt(df1, id = c("date", "year", "month"))
dcast(df_melt, year + month ~ variable, sum)
#  year month         x1           x2
1  2000     1  -80.83405 -224.9540159
2  2000     2 -223.76331 -288.2418017
3  2000     3 -188.83930 -481.5601913
4  2000     4 -197.47797 -473.7137420
5  2000     5 -259.07928 -372.4563522

Answer 2

Using the data.table package, which is fast (useful for larger datasets)

https://github.com/Rdatatable/data.table/wiki

library(data.table)
df2 <- setDT(df1)[, lapply(.SD, sum), by=.(year, month), .SDcols=c("x1","x2")]
setDF(df2) # convert back to dataframe

Using the plyr package

require(plyr)
df2 <- ddply(df1, c("year", "month"), function(x) colSums(x[c("x1", "x2")]))

Using summarize() from the Hmisc package (column headings are messy in my example though)

# need to detach plyr because plyr and Hmisc both have a summarize()
detach(package:plyr)
require(Hmisc)
df2 <- with(df1, summarize( cbind(x1, x2), by=llist(year, month), FUN=colSums))

Answer 3

Yes, in your formula, you can cbind the numeric variables to be aggregated:

aggregate(cbind(x1, x2) ~ year + month, data = df1, sum, na.rm = TRUE)
   year month         x1          x2
1  2000     1   7.862002   -7.469298
2  2001     1 276.758209  474.384252
3  2000     2  13.122369 -128.122613
...
23 2000    12  63.436507  449.794454
24 2001    12 999.472226  922.726589

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See ?aggregate, the formula argument and the examples.

Answer 4

Late to the party, but recently found another way to get the summary statistics.

library(psych) describe(data)

Will output: mean, min, max, standard deviation, n, standard error, kurtosis, skewness, median, and range for each variable.

Answer 5

Interestingly, base R aggregate's data.frame method is not showcased here, above the formula interface is used, so for completeness:

aggregate(
  x = df1[c("x1", "x2")],
  by = df1[c("year", "month")],
  FUN = sum, na.rm = TRUE
)

More generic use of aggregate's data.frame method:

Since we are providing a

• data.frame as x and
• a list (data.frame is also a list) as by, this is very useful if we need to use it in a dynamic manner, e.g. using other columns to be aggregated and to aggregate by is very simple
• also with custom-made aggregation functions

For example like so:

colsToAggregate <- c("x1")
aggregateBy <- c("year", "month")
dummyaggfun <- function(v, na.rm = TRUE) {
  c(sum = sum(v, na.rm = na.rm), mean = mean(v, na.rm = na.rm))
}

aggregate(df1[colsToAggregate], by = df1[aggregateBy], FUN = dummyaggfun)

Answer 6

With the dplyr package, you can use summarise_all, summarise_at or summarise_if functions to aggregate multiple variables simultaneously. For the example dataset you can do this as follows:

library(dplyr)
# summarising all non-grouping variables
df2 <- df1 %>% group_by(year, month) %>% summarise_all(sum)

# summarising a specific set of non-grouping variables
df2 <- df1 %>% group_by(year, month) %>% summarise_at(vars(x1, x2), sum)
df2 <- df1 %>% group_by(year, month) %>% summarise_at(vars(-date), sum)

# summarising a specific set of non-grouping variables based on condition (class)
df2 <- df1 %>% group_by(year, month) %>% summarise_if(is.numeric, sum)

The result of the latter two options:

    year month        x1         x2
   <dbl> <dbl>     <dbl>      <dbl>
1   2000     1 -73.58134  -92.78595
2   2000     2 -57.81334 -152.36983
3   2000     3 122.68758  153.55243
4   2000     4 450.24980  285.56374
5   2000     5 678.37867  384.42888
6   2000     6 792.68696  530.28694
7   2000     7 908.58795  452.31222
8   2000     8 710.69928  719.35225
9   2000     9 725.06079  914.93687
10  2000    10 770.60304  863.39337
# ... with 14 more rows

Note: summarise_each is deprecated in favor of summarise_all, summarise_at and summarise_if.

---

As mentioned in my comment above, you can also use the recast function from the reshape2-package:

library(reshape2)
recast(df1, year + month ~ variable, sum, id.var = c("date", "year", "month"))

which will give you the same result.