Apply List of functions on List of columns based on different combinations

Question

I have a dataframe df with three categorical variables cat1,cat2,cat3 and two continuous variables con1,con2. I would like to compute list of functions sd,mean on list of columns con1,con2 based on different combinations of list of columns cat1,cat2,cat3. I have done them explicitly subsetting all different combinations.

# Random generation of values for categorical data
set.seed(33)
df <- data.frame(cat1 = sample( LETTERS[1:2], 100, replace=TRUE ), 
                cat2 = sample( LETTERS[3:5], 100, replace=TRUE ),
                cat3 = sample( LETTERS[2:4], 100, replace=TRUE ),
                con1 = runif(100,0,100),
                con2 = runif(100,23,45))

# Introducing null values 
df$con1[c(23,53,92)] <- NA
df$con2[c(33,46)] <- NA

results <- data.frame()
funs <- list(sd=sd, mean=mean)

# calculation of mean and sd on total observations
sapply(funs, function(x) sapply(df[,c(4,5)], x, na.rm=T))

# calculation of mean and sd on different levels of cat1 
sapply(funs, function(x) sapply(df[df$cat1=='A',c(4,5)], x, na.rm=T))
sapply(funs, function(x) sapply(df[df$cat1=='B',c(4,5)], x, na.rm=T))

# calculation of mean and sd on different levels of cat1 and cat2
sapply(funs, function(x) sapply(df[df$cat1=='A' & df$cat2=='C' ,c(4,5)], x, na.rm=T))
.
.
.
sapply(funs, function(x) sapply(df[df$cat1=='B' & df$cat2=='E' ,c(4,5)], x, na.rm=T))

# Similarly for the combinations of three cat variables cat1, cat2, cat3

I would like to write a function on dynamically computing the list of functions for list of columns based on different combinations. Could you please give some suggestions. Thanks !

Edit: I have already got some smart suggestions using dplyr. It would be great if someone provides suggestions using the apply family functions as it will help in using them(dataframes) in the further requirements.

Answer 1

This is a simple one-line base solution:

> do.call(cbind, lapply(funs, function(x) aggregate(cbind(con1, con2) ~ cat1 + cat2 + cat3, data = df, FUN = x, na.rm = TRUE)))
   sd.cat1 sd.cat2 sd.cat3  sd.con1   sd.con2 mean.cat1 mean.cat2 mean.cat3 mean.con1 mean.con2
1        A       C       B       NA        NA         A         C         B  25.52641  37.40603
2        B       C       B 32.67192  6.966547         B         C         B  46.70387  34.85437
3        A       D       B 31.05224  6.530313         A         D         B  37.91553  37.13142
4        B       D       B 23.80335  6.001468         B         D         B  59.75107  30.29681
5        A       E       B 22.79285  1.526472         A         E         B  38.54742  25.23007
6        B       E       B 32.92139  2.621067         B         E         B  51.56253  29.52367
7        A       C       C 26.98661  5.710335         A         C         C  36.32045  36.42465
8        B       C       C 20.22217  8.117184         B         C         C  60.60036  34.98460
9        A       D       C 33.39273  7.367412         A         D         C  40.77786  35.03747
10       B       D       C 12.95351  8.829061         B         D         C  49.77160  33.21836
11       A       E       C 33.73433  4.689548         A         E         C  55.53135  32.38279
12       B       E       C 25.38637  9.172137         B         E         C  46.69063  31.56733
13       A       C       D 36.12545  6.323929         A         C         D  48.34187  32.36789
14       B       C       D 30.01992  7.130869         B         C         D  53.87571  33.12760
15       A       D       D 15.94151 11.756115         A         D         D  35.89909  31.76871
16       B       D       D 10.89030  6.829829         B         D         D  22.86577  32.53725
17       A       E       D 24.88410  6.108631         A         E         D  47.32549  35.22782
18       B       E       D 12.73711  8.151424         B         E         D  33.95569  36.70167