Using aggregate functions on multiple columns at once in r - r

Question

Lets say I have a data set that has multiple rows and columns and I want to record the min, max and mean for each column and store this data in its own table. How do I loop through the data frame in such a way that I can find this data for each column?
Edit: My initial data is stored in a tbl that looks like this Initial Data and I want the output to look like this Output Data

Answer 1

Take a look at package dplyr, which will make this task more straightforward!
Here's an approach that just uses dplyr. The format isn't exactly what's in Output Data...
> df <- data.frame(A=c(7,2,4), B=c(5,4,6), C=c(7,9,1)) # Your Initial Data
> library(dplyr)
> df %>% summarise_all(.funs=funs(mean, min, max)) ## Approach 1: just dplyr
A_mean B_mean C_mean A_min B_min C_min A_max B_max C_max
1 4.333333 5 5.666667 2 4 1 7 6 9
Alternatively, if you also use package tidyr, you can get exactly the format you wanted for your output data:
> library(tidyr)
> df %>%
+ gather(Column, Value) %>% ## Converts dataframe from wide to long format
+ group_by(Column) %>% ## Groups by the new column containing old column names
+ summarise(Max=max(Value), Min=min(Value), Mean=mean(Value)) ## The summary functions
# A tibble: 3 x 4
Column Max Min Mean
<chr> <dbl> <dbl> <dbl>
1 A 7.00 2.00 4.33
2 B 6.00 4.00 5.00
3 C 9.00 1.00 5.67
One advantage of using these packages is that it may be more efficient, especially if df is large, than using an explicit loop.

Answer 2

I suggest you work with long tables instead of wide ones. While the last will make it simpler to the human eye, the former are easier to manipulate for data analysis. That said, I think you could use the data.table package to achieve this:
# create a data frame
df <- data.frame(A=c(7,2,4), B=c(5,4,6), C=c(7,9,1))
# load data.table package
require(data.table)
# convert df to a data.table
setDT(df)
#Explanation of the following code:
# melt: turns your wide table into a long one
# .(val_mean ...) calculate and give names to calculated variables
# by = ... : group by variable. See data.table vignette
melt(df)[, .(val_mean = mean(value),
val_min = min(value),
val_max = max(value)),
by = variable]
which produces:
variable val_mean val_min val_max
1: A 4.333333 2 7
2: B 5.000000 4 6
3: C 5.666667 1 9