dplyr change many data types

Dplyr across function has superseded _if, _at, and _all. See vignette("colwise").

dat %>% 
mutate(across(all_of(l1), as.factor),
       across(all_of(l2), as.numeric))

You can use the standard evaluation version of mutate_each (which is mutate_each_) to change the column classes:

dat %>% mutate_each_(funs(factor), l1) %>% mutate_each_(funs(as.numeric), l2)

Edit (as of 2021-03)

As also pointed out in Eric's answer, mutate_[at|if|all] has been superseded by a combination of mutate() and across(). For reference, I will add the respective pendants to the examples in the original answer (see below):

# convert all factor to character
dat %>% mutate(across(where(is.factor), as.character))

# apply function (change encoding) to all character columns 
dat %>% mutate(across(where(is.character), 
               function(x){iconv(x, to = "ASCII//TRANSLIT")}))

# subsitute all NA in numeric columns
dat %>% mutate(across(where(is.numeric), function(x) tidyr::replace_na(x, 0)))

Original answer

Since Nick's answer is deprecated by now and Rafael's comment is really useful, I want to add this as an Answer. If you want to change all factor columns to character use mutate_if:

dat %>% mutate_if(is.factor, as.character)

Also other functions are allowed. I for instance used iconv to change the encoding of all character columns:

dat %>% mutate_if(is.character, function(x){iconv(x, to = "ASCII//TRANSLIT")})

or to substitute all NA by 0 in numeric columns:

dat %>% mutate_if(is.numeric, function(x){ifelse(is.na(x), 0, x)})

EDIT - The syntax of this answer has been deprecated, loki's updated answer is more appropriate.

ORIGINAL-

From the bottom of the ?mutate_each (at least in dplyr 0.5) it looks like that function, as in @docendo discimus's answer, will be deprecated and replaced with more flexible alternatives mutate_if, mutate_all, and mutate_at. The one most similar to what @hadley mentions in his comment is probably using mutate_at. Note the order of the arguments is reversed, compared to mutate_each, and vars() uses select() like semantics, which I interpret to mean the ?select_helpers functions.

dat %>% mutate_at(vars(starts_with("fac")),funs(factor)) %>%   
  mutate_at(vars(starts_with("dbl")),funs(as.numeric))

But mutate_at can take column numbers instead of a vars() argument, and after reading through this page, and looking at the alternatives, I ended up using mutate_at but with grep to capture many different kinds of column names at once (unless you always have such obvious column names!)

dat %>% mutate_at(grep("^(fac|fctr|fckr)",colnames(.)),funs(factor)) %>%
  mutate_at(grep("^(dbl|num|qty)",colnames(.)),funs(as.numeric))

I was pretty excited about figuring out mutate_at + grep, because now one line can work on lots of columns.

EDIT - now I see matches() in among the select_helpers, which handles regex, so now I like this.

dat %>% mutate_at(vars(matches("fac|fctr|fckr")),funs(factor)) %>%
  mutate_at(vars(matches("dbl|num|qty")),funs(as.numeric))

Another generally-related comment - if you have all your date columns with matchable names, and consistent formats, this is powerful. In my case, this turns all my YYYYMMDD columns, which were read as numbers, into dates.

  mutate_at(vars(matches("_DT$")),funs(as.Date(as.character(.),format="%Y%m%d")))