Reputation: 3
Apologies if this question has already been dealt with already on SO, but I cannot seem to find a quick solution as of yet.
I am trying to aggregate a dataset by a specific year. My data frame consists of hourly climate data over a period of 10 years.
head(df)
# day month year hour rain temp pressure wind
#1 1 1 2005 0 0 7.6 1016 15
#2 1 1 2005 1 0 8.0 1015 14
#3 1 1 2005 2 0 7.7 1014 15
#4 1 1 2005 3 0 7.8 1013 17
#5 1 1 2005 4 0 7.3 1012 17
#6 1 1 2005 5 0 7.6 1010 17
To calculate daily means from the above dataset, I use this aggregate function
g <- aggregate(cbind(temp,pressure,wind) ~ day + month + year, d, mean)
options(digits=2)
head(g)
# day month year temp pressure wind
#1 1 1 2005 6.6 1005 25
#2 2 1 2005 6.5 1018 25
#3 3 1 2005 9.7 1019 22
#4 4 1 2005 7.5 1010 25
#5 5 1 2005 7.3 1008 25
#6 6 1 2005 9.6 1009 26
Unfortunately, I get a huge dataset spanning the whole 10 years (2005 to 2014). I am wondering if anybody would be able to help me tweak the above aggregate code so as I would be able to summaries daily means over a specific year as opposed to all of them in one swipe?
Upvotes: 0
Views: 1028
Reputation: 8611
Dplyr
also does it nicely.
library(dplyr)
df %>%
filter(year==2005) %>%
group_by(day, month, year) %>%
summarise_each(funs(mean), temp, pressure, wind)
Upvotes: 0
Reputation: 887088
You can use the subset
argument in aggregate
aggregate(cbind(temp,pressure,wind) ~ day + month + year, df,
subset=year %in% 2005:2014, mean)
Upvotes: 1