I would like too summarize one column by another, which I know how to do, using either ddply or aggregate...But, I want to also add groups within my grouping variable to summarize the data in a custom fashion, see below:
##Create Data
year<-c(2008,2008,2008,2008,2009,2009,2009,2010,2010,2010,2010,2011,2011,2011,2011,2011)
catch<-c(1,2,0,1,1,0,1,0,1,2,1,0,1,1,1,0)
df<-data.frame(year,catch)
##I know how to aggregate by year:
yearlyAVcap<-ddply(df,~year,summarise,mean=mean(catch),sd=sd(catch))
##But I'd like to have 2008,2009,2010 summarized together and 2011 on it's own (i,e two groups- 1=2008,2009,2010 ; 2=2011)
You could try
df1 <- transform(df, yrGroup=c('2008-2010', '2011')[(year==2011)+1])
library(plyr)
ddply(df1, .(yrGroup), summarise, mean=mean(catch), sd=sd(catch))
# yrGroup mean sd
#1 2008-2010 0.9090909 0.7006490
#2 2011 0.6000000 0.5477226
Or using dplyr
df1 %>%
group_by(yrGroup) %>%
summarise_each(funs(mean, sd), catch)
#summarise_each can be used for multiple columns
# yrGroup mean sd
#1 2008-2010 0.9090909 0.7006490
#2 2011 0.6000000 0.5477226
To get two groups from different years, in the example (2008-2010 and 2011), we can use == to match for the second group year i.e. 2011. The result will be a logical vector. Then, we can transform this to numeric index by adding 1 to it so that the two groups will 1 and 2. If we add 0, the TRUE will be converted to 1 and FALSE as 0. This numeric index can be used for renaming it to 2008-2010 and 2011.
df$year==2011
#[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
#[13] TRUE TRUE TRUE TRUE
(df$year==2011)+1
#[1] 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2
c('2008-2010', '2011')[(df$year==2011)+1]
#[1] "2008-2010" "2008-2010" "2008-2010" "2008-2010" "2008-2010" "2008-2010"
#[7] "2008-2010" "2008-2010" "2008-2010" "2008-2010" "2008-2010" "2011"
#[13] "2011" "2011" "2011" "2011"
Suppose, we have multiple years, for example 2008-2010 as one group and 2011-2013 as another group, we can use %in%
(df$year %in% 2011:2013)+1