data:image/png;base64,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data:image/png;base64,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23456Guess a letter to save a life!2345623456Guess a letter to save a life!used lettershintTaftCanadaMarioHudson RiverCheeseTentTaftMarioHudsonCanadaASUtentTaftMarioHudsonCanadaASUtentTaftMarioHudsonCanadaASUtentTaftMarioHudsonCanadaASUtentTaftMarioHudsonCanadaASUTentTaftMarioHudsonCanadaASUTentTake any number of input lists, and create a new list containing the items of the input lists. So APPEND [A B] [C D] where the [...] are lists reports the list [A B C D] not [[A B] [C D]].

ca:concatena _ 11111Reports a new list containing the items of the input list, but in the opposite order.

ca:capgira _ 1inputresultReports a new list whose items are the same as in the input list, except that if two or more equal items appear in the input list, only the last one is kept in the result.

ca:elimina els duplicats de _ 1Reports a sorted version of the list in its first input slot, using the comparison function in the second input slot. For a list of numbers, using < as the comparison function will sort from low to high; using > will sort from high to low.

ca:ordena _ segons criteri _ copy of datasplitmerge11#1#2The second input is an "association list," a list of two-item lists. Each of those smaller lists has a "key" as its first item and a "value" as its second. ASSOC reports the first key-value pair in the association list whose key matches the first input.

ca:associació _ _ 11This block applies the given function to each item of the given list, like the primitive MAP block, but with two differences: 1. The # variable provided will contain the item number of the item currently being used in the given function: 1 for item 1, and so on. You read the variable in your function; don't set it. 2. You can provide more than one input list. If so, the lengths of all the lists should be the same, and the function you supply must expect as many inputs as you have lists (i.e., that many empty input slots in the function).

ca:_ mapeja _ sobre _ #11datacountmapmany##1#1data listscountThis block makes a new list containing selected items from the given list, like the primitive KEEP block. What's different is that it provides the # variable for use in your predicate function. It will contain the item number (in the original list) of the item being examined by the predicate.

ca:_ manté els elements on _ de _ #1datacount1This block carries out the given script for each item of the given list, like the primitive FOR EACH. What's different is that it provides the # variable, which will contain the item number in the list of each item in turn, 1 while processing item 1, and so on.

ca:_ per cada _ de _ _ 5Husky,Taft,Canada,Mario,Hudson,Cheese,TentCanadaC,a,n,a,d,aA,N,D