Intro:
I would like to compute the mean, standard deviation, and standard error of a numeric vector in a given dataframe and then create three new vectors using these summary statistics. I then need to combine them with the original dataframe.
Example Code:
## Creating our dataframe:
datetime <- c("5/12/2017 16:15:00","5/16/2017 16:45:00","5/19/2017 17:00:00")
datetime <- as.POSIXct(datetime, format = "%m/%d/%Y %H:%M:%S")
values <- c(1,2,3)
df <- data.frame(datetime, values)
## Here's the current output:
head(df)
datetime values
1 2017-05-12 16:15:00 1
2 2017-05-16 16:45:00 2
3 2017-05-19 17:00:00 3
## And here's the desired output:
head(df1)
datetime values mean sd se
1 2017-05-12 16:15:00 1 2 0.816 0.471
2 2017-05-16 16:45:00 2 2 0.816 0.471
3 2017-05-19 17:00:00 3 2 0.816 0.471
Thanks in advance!
For those who are curious as to why I am trying to do this, I am following this tutorial. I need to make one of those line graph plots with errorbars for some calibrations between a low-cost sensor and an expensive reference instrument.
You can do the assignment simultaneously. Suppose you already have the helper function for you choice of sd and se:
sd0 <- function(x){sd(x) / sqrt(length(x)) * sqrt(length(x) - 1)}
se0 <- function(x){ sd0(x) / sqrt(length(x))}
Then you can try:
df[c('mean', 'sd', 'se')] <- lapply(list(mean, sd0, se0), function(f) f(df$values))
# > df
# datetime values mean sd se
# 1 2017-05-12 16:15:00 1 2 0.8164966 0.4714045
# 2 2017-05-16 16:45:00 2 2 0.8164966 0.4714045
# 3 2017-05-19 17:00:00 3 2 0.8164966 0.4714045