Let's say I have several vectors
(def coll-a [{:name "foo"} ...])
(def coll-b [{:name "foo"} ...])
(def coll-c [{:name "foo"} ...])
and that I would like to see if the names of the first elements are equal.
I could
(= (:name (first coll-a)) (:name (first coll-b)) (:name (first coll-c)))
but this quickly gets tiring and overly verbose as more functions are composed. (Maybe I want to compare the last letter of the first element's name?)
To directly express the essence of the computation it seems intuitive to
(apply = (map (comp :name first) [coll-a coll-b coll-c]))
but it leaves me wondering if there's a higher level abstraction for this sort of thing.
I often find myself comparing / otherwise operating on things which are to be computed via a single composition applied to multiple elements, but the map syntax looks a little off to me.
If I were to home brew some sort of operator, I would want syntax like
(-op- (= :name first) coll-a coll-b coll-c)
because the majority of the computation is expressed in (= :name first).
I'd like an abstraction to apply to both the operator & the functions applied to each argument. That is, it should be just as easy to sum as compare.
(def coll-a [{:name "foo" :age 43}])
(def coll-b [{:name "foo" :age 35}])
(def coll-c [{:name "foo" :age 28}])
(-op- (+ :age first) coll-a coll-b coll-c)
; => 106
(-op- (= :name first) coll-a coll-b coll-c)
; => true
Something like
(defmacro -op-
[[op & to-comp] & args]
(let [args' (map (fn [a] `((comp ~#to-comp) ~a)) args)]
`(~op ~#args')))
Is there an idiomatic way to do this in clojure, some standard library function I could be using?
Is there a name for this type of expression?
For your addition example, I often use transduce:
(transduce
(map (comp :age first))
+
[coll-a coll-b coll-c])
Your equality use case is trickier, but you could create a custom reducing function to maintain a similar pattern. Here's one such function:
(defn all? [f]
(let [prev (volatile! ::no-value)]
(fn
([] true)
([result] result)
([result item]
(if (or (= ::no-value #prev)
(f #prev item))
(do
(vreset! prev item)
true)
(reduced false))))))
Then use it as
(transduce
(map (comp :name first))
(all? =)
[coll-a coll-b coll-c])
The semantics are fairly similar to your -op- macro, while being both more idiomatic Clojure and more extensible. Other Clojure developers will immediately understand your usage of transduce. They may have to investigate the custom reducing function, but such functions are common enough in Clojure that readers can see how it fits an existing pattern. Also, it should be fairly transparent how to create new reducing functions for use cases where a simple map-and-apply wouldn't work. The transducing function can also be composed with other transformations such as filter and mapcat, for cases when you have a more complex initial data structure.
You may be looking for the every? function, but I would enhance clarity by breaking it down and naming the sub-elements:
(let [colls [coll-a coll-b coll-c]
first-name (fn [coll] (:name (first coll)))
names (map first-name colls)
tgt-name (first-name coll-a)
all-names-equal (every? #(= tgt-name %) names)]
all-names-equal => true
I would avoid the DSL, as there is no need and it makes it much harder for others to read (since they don't know the DSL). Keep it simple:
(let [colls [coll-a coll-b coll-c]
vals (map #(:age (first %)) colls)
result (apply + vals)]
result => 106
I don't think you need a macro, you just need to parameterize your op function and compare functions. To me, you are pretty close with your (apply = (map (comp :name first) [coll-a coll-b coll-c])) version.
Here is one way you could make it more generic:
(defn compare-in [op to-compare & args]
(apply op (map #(get-in % to-compare) args)))
(compare-in + [0 :age] coll-a coll-b coll-c)
(compare-in = [0 :name] coll-a coll-b coll-c)
;; compares last element of "foo"
(compare-in = [0 :name 2] coll-a coll-b coll-c)
I actually did not know you can use get on strings, but in the third case you can see we compare the last element of each foo.
This approach doesn't allow the to-compare arguments to be arbitrary functions, but it seems like your use case mainly deals with digging out what elements you want to compare, and then applying an arbitrary function to those values.
I'm not sure this approach is better than the transducer version supplied above (certainly not as efficient), but I think it provides a simpler alternative when that efficiency is not needed.
I would split this process into three stages:
transform items in collections into the data in collections you want to operate
on - (map :name coll);
Operate on transformed items in collections, returning collection of results - (map = transf-coll-a transf-coll-b transf-coll-c)
Finally, selecting which result in resulting collection to return - (first calculated-coll)
When playing with collections, I try to put more than one item into collection:
(def coll-a [{:name "foo" :age 43} {:name "bar" :age 45}])
(def coll-b [{:name "foo" :age 35} {:name "bar" :age 37}])
(def coll-c [{:name "foo" :age 28} {:name "bra" :age 30}])
For example, matching items by second char in :name and returning result for items in second place:
(let
[colls [coll-a coll-b coll-c]
transf-fn (comp #(nth % 1) :name)
op =
fetch second]
(fetch (apply map op (map #(map transf-fn %) colls))))
;; => false
In transducers world you can use sequence function which also works on multiple collections:
(let
[colls [coll-a coll-b coll-c]
transf-fn (comp (map :name) (map #(nth % 1)))
op =
fetch second]
(fetch (apply sequence (map op) (map #(sequence transf-fn %) colls))))
Calculate sum of ages (for all items at the same level):
(let
[colls [coll-a coll-b coll-c]
transf-fn (comp (map :age))
op +
fetch identity]
(fetch (apply sequence (map op) (map #(sequence transf-fn %) colls))))
;; => (106 112)
Related
I have (for instance) a mix of data structures such as {:name "Peter" :children "Mark"} and {:name "Mark" :children ["Julia" "John"] i.e. :children value is either a single string or a collection of strings. Other functions in my code expect that the value of :children is always a collection of strings, so I need to adapt the data for them.
Of course I can use something like:
(defn data-adapter [m]
(let [children (:children m)]
(assoc m :children
(if (coll? children)
children
[children]))))
But is there a more idiomatic/laconic way?
I think you will have to take no for an answer.
(if (coll? x) x [x]) is about as terse and expressive as it gets. It’s what people usually use for this problem (sometimes with sequential? instead of coll?).
cond-> enthusiasts like me sometimes try to use it in place of a simple conditional, but here it is no improvement:
(cond-> x (not (coll? x)) vector)
In the context of your code, however, you can do a little better. A lookup and association is best expressed with update:
(defn data-adapter [m]
(update m :children #(if (coll? %) % [%])))
the only advice would be to abstract that logic to some function, to keep your actual business logic clean.
(defn data-adapter [m]
(let [children (:children m)]
(assoc m :children (ensure-coll children))))
or, more concise, with update:
(defn data-adapter [m]
(update m :children ensure-coll))
where ensure-coll could be something like this:
(defn iffun [check & {:keys [t f] :or {t identity f identity}}]
#((if (check %) t f) %))
(def ensure-coll (iffun coll? :f list))
(or whatever another implementation you like)
user> (data-adapter {:children 1})
;;=> {:children (1)}
user> (data-adapter {:children [1]})
;;=> {:children [1]}
Perhaps not idiomatic, but laconic:
(flatten [x])
https://clojuredocs.org/clojure.core/flatten
Not sure what is going on here, but I have this code, where the map function successfully executes in my repl without being wrapped in a defined function:
(def dogs '({:name "scout" :age 5} {:name "rux" :age 3} {:name "fenley" :age 2}))
(def ages (atom {:above-four '() :below-four '()}))
(map
#(if (> (get-in % [:age]) 4)
(swap! ages update-in [:above-four] merge %)
(swap! ages update-in [:below-four] merge %)) dogs)
#ages
=> {:above-four ({:name "scout", :age 5}), :below-four ({:name "fenley", :age 2} {:name "rux", :age 3})}
Yet, when I define the map function as such:
(def ages (atom {:above-four '() :below-four '()}))
(def dogs '({:name "scout" :age 5} {:name "rux" :age 3} {:name "fenley" :age 2}))
(defn test-dogs []
(map
#(if (> (get-in % [:age]) 4)
(swap! ages update-in [:above-four] merge %)
(swap! ages update-in [:below-four] merge %)) dogs)
#ages)
I get the following result:
=> {:above-four (), :below-four ()}
I'm very confused, because this function taken straight from the Clojure docs works just fine:
(def m1 (atom {:a "A" :b "B"}))
(defn update-m1 []
(swap! m1 assoc :a "Aaay")
#m1)
=> {:a "Aaay", :b "B"}
Because test-dogs uses map, it returns a lazy sequence. The elements of lazy sequences aren't realized until they're needed.
The problem with your set up is you're trying to use map to run a side effect (the call to swap!; an impure action), and never actually use the result of map. Because you never request results from map, the mapping function containing swap! never runs.
By using mapv (which returns a non-lazy vector), or doseq (which is meant to carry out side effects):
(doseq [dog dogs]
(let [k (if (> (:age dog) 4)
:above-four
:below-four)]
(swap! ages update k merge dog)))
You can force the side effects to run.
I cleaned up the code a bit. The -in versions you were using were unnecessary; as was the the call to get-in. I also got rid of the redundant calls to swap!.
Note though that at least in your example, use of atoms is entirely unnecessary. Even if you have a more complicated use case, make sure their use is justified. Mutable variables just aren't as common in languages like Clojure.
I'm attempting to modify a specific field in a data structure, described below (a filled example can be found here:
[{:fields "There are a few other fields here"
:incidents [{:fields "There are a few other fields here"
:updates [{:fields "There are a few other fields here"
:content "THIS is the field I want to replace"
:translations [{:based_on "Based on the VALUE of this"
:content "Replace with this value"}]}]}]}]
I already have this implemented it in a number of functions, as below:
(defn- translation-content
[arr]
(:content (nth arr (.indexOf (map :locale arr) (env/get-locale)))))
(defn- translate
[k coll fn & [k2]]
(let [k2 (if (nil? k2) k k2)
c ((keyword k2) coll)]
(assoc-in coll [(keyword k)] (fn c))))
(defn- format-update-translation
[update]
(dissoc update :translations))
(defn translate-update
[update]
(format-update-translation (translate :content update translation-content :translations)))
(defn translate-updates
[updates]
(vec (map translate-update updates)))
(defn translate-incident
[incident]
(translate :updates incident translate-updates))
(defn translate-incidents
[incidents]
(vec (map translate-incident incidents)))
(defn translate-service
[service]
(assoc-in service [:incidents] (translate-incidents (:incidents service))))
(defn translate-services
[services]
(vec (map translate-service services)))
Each array could have any number of entries (though the number is likely less than 10).
The basic premise is to replace the :content in each :update with the relevant :translation based on a provided value.
My Clojure knowledge is limited, so I'm curious if there is a more optimal way to achieve this?
EDIT
Solution so far:
(defn- translation-content
[arr]
(:content (nth arr (.indexOf (map :locale arr) (env/get-locale)))))
(defn- translate
[k coll fn & [k2]]
(let [k2 (if (nil? k2) k k2)
c ((keyword k2) coll)]
(assoc-in coll [(keyword k)] (fn c))))
(defn- format-update-translation
[update]
(dissoc update :translations))
(defn translate-update
[update]
(format-update-translation (translate :content update translation-content :translations)))
(defn translate-updates
[updates]
(mapv translate-update updates))
(defn translate-incident
[incident]
(translate :updates incident translate-updates))
(defn translate-incidents
[incidents]
(mapv translate-incident incidents))
(defn translate-service
[service]
(assoc-in service [:incidents] (translate-incidents (:incidents service))))
(defn translate-services
[services]
(mapv translate-service services))
I would start more or less as you do, bottom-up, by defining some functions that look like they will be useful: how to choose a translation from among a list of translations, and how to apply that choice to an update. But I wouldn't make the functions so tiny as yours: the logic is all spread out into a lot of places, and it's not easy to get an overall idea of what is going on. Here are the two functions I'd start with:
(letfn [(choose-translation [translations]
(let [applicable (filter #(= (:locale %) (get-locale))
translations)]
(when (= 1 (count applicable))
(:content (first applicable)))))
(translate-update [update]
(-> update
(assoc :content (or (choose-translation (:translations update))
(:content update)))
(dissoc :translations)))]
...)
Of course you can defn them instead if you'd like, and I suspect many people would, but they're only going to be used in one place, and they're intimately involved with the context in which they're used, so I like a letfn. These two functions are really all the interesting logic; the rest is just some boring tree-traversal code to apply this logic in the right places.
Now to build out the body of the letfn is straightforward, and easy to read if you make your code be the same shape as the data it manipulates. We want to walk through a series of nested lists, updating objects on the way, and so we just write a series of nested for comprehensions, calling update to descend into the right keyspace:
(for [user users]
(update user :incidents
(fn [incidents]
(for [incident incidents]
(update incident :updates
(fn [updates]
(for [update updates]
(translate-update update))))))))
I think using for here is miles better than using map, although of course they are equivalent as always. The important difference is that as you read through the code you see the new context first ("okay, now we're doing something to each user"), and then what is happening inside that context; with map you see them in the other order and it is difficult to keep tack of what is happening where.
Combining these, and putting them into a defn, we get a function that you can call with your example input and which produces your desired output (assuming a suitable definition of get-locale):
(defn translate [users]
(letfn [(choose-translation [translations]
(let [applicable (filter #(= (:locale %) (get-locale))
translations)]
(when (= 1 (count applicable))
(:content (first applicable)))))
(translate-update [update]
(-> update
(assoc :content (or (choose-translation (:translations update))
(:content update)))
(dissoc :translations)))]
(for [user users]
(update user :incidents
(fn [incidents]
(for [incident incidents]
(update incident :updates
(fn [updates]
(for [update updates]
(translate-update update))))))))))
we can try to find some patterns in this task (based on the contents of the snippet from github gist, you've posted):
simply speaking, you need to
1) update every item (A) in vector of data
2) updating every item (B) in vector of A's :incidents
3) updating every item (C) in vector of B's :updates
4) translating C
The translate function could look like this:
(defn translate [{translations :translations :as item} locale]
(assoc item :content
(or (some #(when (= (:locale %) locale) (:content %)) translations)
:no-translation-found)))
it's usage (some fields are omitted for brevity):
user> (translate {:id 1
:content "abc"
:severity "101"
:translations [{:locale "fr_FR"
:content "abc"}
{:locale "ru_RU"
:content "абв"}]}
"ru_RU")
;;=> {:id 1,
;; :content "абв",
;; :severity "101",
;; :translations [{:locale "fr_FR", :content "abc"} {:locale "ru_RU", :content "абв"}]}
then we can see that 1 and 2 are totally similar, so we can generalize that:
(defn update-vec-of-maps [data k f]
(mapv (fn [item] (update item k f)) data))
using it as a building block you can make up the whole data transformation:
(defn transform [data locale]
(update-vec-of-maps
data :incidents
(fn [incidents]
(update-vec-of-maps
incidents :updates
(fn [updates] (mapv #(translate % locale) updates))))))
(transform data "it_IT")
returns what you need.
then you can generalize it further, making the utility function for arbitrary depth transformations:
(defn deep-update-vec-of-maps [data ks terminal-fn]
(if (seq ks)
((reduce (fn [f k] #(update-vec-of-maps % k f))
terminal-fn (reverse ks))
data)
data))
and use it like this:
(deep-update-vec-of-maps data [:incidents :updates]
(fn [updates]
(mapv #(translate % "it_IT") updates)))
I recommend you look at https://github.com/nathanmarz/specter
It makes it really easy to read and update clojure data structures. Same performance as hand-written code, but much shorter.
Clojure beginner here..
If I have a set of maps, such as
(def kids #{{:name "Noah" :age 5}
{:name "George":age 3}
{:name "Reagan" :age 1.5}})
I know I can get names like this
(map :name kids)
1) How do I select a specific map? For example
I want to get back the map where name="Reagan".
{:name "Reagan" :age 1.5}
Can this be done using a filter?
2) How about returning the name where age = 3?
Yes, you can do it with filter:
(filter #(= (:name %) "Reagan") kids)
(filter #(= (:age %) 3) kids)
There's clojure.set/select:
(clojure.set/select set-of-maps #(-> % :age (= 3)))
And similarly with name and "Reagan". The return value in this case will be a set.
You could also use filter without any special preparations, since filter calls seq on its collection argument (edit: as already described by ffriend while I was typing this):
(filter #(-> % :age (= 3))) set-of-maps)
Here the return value will be a lazy seq.
If you know there will only be one item satisfying your predicate in the set, some will be more efficient (as it will not process any additional elements after finding the match):
(some #(if (-> % :age (= 3)) %) set-of-maps)
The return value here will be the matching element.
It seems to be a powerful macro, yet I'm failing to apply it to anything but silly examples. Can you show me some real use of it?
Thanks!
Compare:
user> (:baz (:bar (:foo {:foo {:bar {:baz 123}}})))
123
user> (java.io.BufferedReader. (java.io.FileReader. "foo.txt"))
#<BufferedReader java.io.BufferedReader#6e1f8f>
user> (vec (reverse (.split (.replaceAll (.toLowerCase "FOO,BAR,BAZ") "b" "x") ",")))
["xaz" "xar" "foo"]
to:
user> (-> {:foo {:bar {:baz 123}}} :foo :bar :baz)
123
user> (-> "foo.txt" java.io.FileReader. java.io.BufferedReader.)
#<BufferedReader java.io.BufferedReader#7a6c34>
user> (-> "FOO,BAR,BAZ" .toLowerCase (.replaceAll "b" "x") (.split ",") reverse vec)
["xaz" "xar" "foo"]
-> is used when you want a concise way to nest calls. It lets you list the calls in the order they'll be called rather than inside-out, which can be more readable. In the third example, notice how much distance is between some of the arguments and the function they belong to; -> lets you group arguments and function calls a bit more cleanly. Because it's a macro it also works for Java calls, which is nice.
-> isn't that powerful, it just saves you a few parens now and then. Using it or not is a question of style and readability.
Look at the bottom of clojure.zip for extreme examples of how this is helpful.
(-> dz next next next next next next next next next remove up (append-child 'e) root)
Taken from the wiki I've always found this example impressive:
user=> (import '(java.net URL) '(java.util.zip ZipInputStream))
user=> (-> "http://clojure.googlecode.com/files/clojure_20081217.zip"
URL. .openStream ZipInputStream. .getNextEntry bean :name)
As Brian said - it isn't 'useful' so much as 'different style'. I find for all java interop this form of 'start with X' then do Y and Z ... more readable than do Z to Y of X.
Basically you have 4 options:
; imperative style named steps:
(let [X something
b (Y X)
c (Z b)] c)
; nested calls
(Z (Y X))
; threaded calls
(-> X Y Z)
; functional composition
((comp Z Y) X)
I find -> really shines for java interop but avoid it elsewhere.
(defn search-tickets-for [term]
(-> term search zip-soup first :content
((partial filter #(= :body (:tag %)))) first :content
((partial filter #(= :div (:tag %))))
((partial filter #(= "content" ((comp :id :attrs) %))))
((partial map :content)) first ((partial map :content))
((partial map first)) ((partial filter #(= :ul (:tag %)))) first :content
((partial map :content))
((partial map first))
((partial mapcat :content))
((partial filter #(= :h4 (:tag %))))
((partial mapcat :content))
((partial filter #(= :a (:tag %))))
((partial mapcat :content))))
clojurebot from #clojure uses this to search assembla tickets