I have an ffdf object called 'group1' that has a million rows of data that looks like this:
Location DateandTime Reading Group
1 1 01/01/2012 00:00:00 0.8 1
2 1 01/01/2012 00:30:00 0.4 1
3 1 01/01/2012 01:00:00 0.7 1
4 1 01/01/2012 01:30:00 0.2 1
I'm trying to get an average 'Reading' and standard deviation for each 'DateandTime' and create a new df to look something like this:
DateTime mean sd
1 01/01/2012 00:00:00 0.8 .2
2 01/01/2012 00:30:00 0.5 .5
3 01/01/2012 01:00:00 0.2 .3
4 01/01/2012 01:30:00 0.8 .8
Or you can use dplyr
package
library(dplyr)
group1.stat <- group1 %>%
select(DateandTime, Reading) %>%
group_by(DateandTime) %>%
summarise_each(funs(mean = mean(., na.rm = TRUE), sd = sd(., na.rm = TRUE)))
group1.stat
This dplyr
approach will also work:
library(dplyr)
newdf <- group1 %>%
group_by(DateandTime) %>%
summarise(mean = mean(Reading), sd = sd(Reading))
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