data:image/png;base64,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data:image/png;base64,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 to learn about youth voter turnout!3Click on the colorful buttons on the screen to learn about the percentage of youth voters from 1972 to 2016.

Should report the mean value for your data. With our sample data, this would return 94.47

LABEL will stamp text on the stage at the given font size. The direction of the text is the direction the sprite is facing, and color will match the pen color.

de:drucke _ in Größe _ ca:etiqueta _ de mida _ es:etiqueta _ de tamaño _ fr:étiquette _ d'une taille de _ Hello!12

de:behalte Elemente, die _ aus _ ca:manté els elements on _ de _ es:mantener los elementos donde _ de _ fr:garder les items tels que _ de _ 1

de:kombiniere mit _ die Elemente von _ ca:combina amb _ els elements de _ es:combinar con _ los elementos de _ fr:combine avec _ les items de _ 1

de:wenn _ dann _ sonst _ ca:si _ llavors _ si no _ es:si _ entonces _ sino _ fr:si _ alors _ sinon _

de:für _ = _ bis _ _ ca:per _ = _ fins _ _ es:para _ = _ hasta _ _ fr:pour _ allant de _ à _ _ 110i

de:fange _ _ ca:agafa _ _ es:atrapar _ _ fr:attrape _ _ cont

de:wirf _ ca:llança _ es:lanzar _ fr:lance _ catchtag

de:fange _ _ ca:agafa _ _ es:atrapar _ _ fr:attrape _ _ cont

de:wirf _ _ ca:llança _ _ es:lanzar _ _ fr:lance _ _ catchtag

de:für jedes _ von _ _ ca:per cada _ de _ _ es:para cada _ de _ _ fr:pour chaque _ de _ _

de:falls _ dann _ und pause $pause-1-255-220-0 ca:si _ fes _ i atura-ho tot $pause-1-255-220-0 es:si _ haz _ y páralo todo $pause-1-255-220-0 fr:si _ faire _ et mettre tout en pause $pause-1-255-220-0

de:ignoriere _ ca:ignora _ es:ignorar _ fr:ignore _

de:Zahlen von _ bis _ ca:nombres des de _ a _ es:números de _ a _ fr:nombres de _ à _ 110

de:wende _ an auf _ ca:mapeja _ sobre _ es:mapear _ sobre _ fr:appliquer _ à _ 1

Reports the Median - ours is 94.5

Take 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]].

1111

Reports a new list containing the items of the input list, but in the opposite order.

1inputresult

Reports 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.

Reports 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.

1even itemsmerge11#1#2

The 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.

#11datacountmapmany#

1data listscount

#1datacount1

Calculates the standard deviation. You do NOT need to alter this block.

This statistic is calculated by the (number of successes) / (total number of outcomes)

Takes any number of numbers as inputs (use the left and right arrowheads to adjust the number of input slots) and reports the result of adding them all, so (SUM (4) (100) (8)) reports 112.

pt:a soma de _ 1

Takes any number of numbers as inputs (use the left and right arrowheads to adjust the number of input slots) and reports the result of multiplying them all, so (PRODUCT (4) (100) (8)) reports 3200.

pt:o produto de _ 1

Takes any number of Boolean (true/false) inputs (use the left and right arrowheads to adjust the number of input slots) and reports TRUE only if all of the inputs are TRUE, otherwise FALSE. Like AND but for multiple inputs.

pt:todas as afirmações _ são verdadeiras 1

Takes any number of Boolean (true/false) inputs (use the left and right arrowheads to adjust the number of input slots) and reports TRUE if at least one input is TRUE, otherwise FALSE.

pt:alguma das afirmações _ é verdadeira 1

Takes a text string as input, and reports a new text string containing all but the first character of the input.

pt:_ sem o primeiro caractere strreturn str.slice(1);

All but first of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the first word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE➞LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a primeira palavra

All but first of empty sentence.

Takes a text string as input, and reports a new text string containing all but the last letter of the input.

pt:_ sem o último caractere strreturn str.slice(0,-1);

All but last of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the last word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE->LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a última palavra

arrreturn new List(arr.asArray().slice(0,-1));

All but last of empty sentence.

1 11 1

pt:a primeira palavra de _

Takes a text string as input, and reports the last character in the string.

pt:o último caractere de _ 0

pt:a última palavra de _

Last of empty sentence.

1 1

Takes a text string as input, and reports TRUE if the string has no characters in it of any kind, otherwise false.

pt:a palavra _ está vazia 0

Takes a text string as input, and reports TRUE if the input contains no characters other than spaces (therefore, no words when the string is considered as a sentence), otherwise FALSE.

pt:a frase _ está vazia

Like JOIN, takes any number of words (text strings) and reports a sentence with its inputs concatenated, but inserts a blank space between the inputs. Consider using SENTENCE (Lists palette) instead.

de:füge Wörter zusammen _ ca:uneix les paraules _ es:unir las palabras _ fr:fusionne les mots _ pt:uma frase com as palavras _ firstbffirst1

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Takes a sentence in list form and reports the sentence as a text string.

de:Liste $arrowRight Satz _ ca:llista $arrowRight frase _ es:lista $arrowRight frase _ fr:liste $arrowRight phrase _ pt:uma frase com as palavras em _

Takes a sentence in text string form and reports the sentence as a list of its words.

de:Satz $arrowRight Liste _ ca:frase $arrowRight llista _ es:frase $arrowRight lista _ fr:phrase $arrowRight liste _ pt:uma lista com as palavras da frase _

report a list in which each item is one letter from the input word

de:Wort $arrowRight Liste _ ca:paraula $arrowRight llista _ es:palabra $arrowRight lista _ fr:mot $arrowRight liste _ pt:uma lista com os caracteres da palavra _

join all the items of the input list into a single word, and report it

de:Liste $arrowRight Wort _ ca:llista $arrowRight paraula _ es:lista $arrowRight palabra _ fr:liste $arrowRight mot _ pt:uma palavra com os caracteres em _

SENTENCE is the main constructor for sentences, represented as lists of words. It takes zero or more inputs, each of which can be either a list or a text string. If a list, the input is assumed to be a list of words. If a text string, it is converted to a list of words using SENTENCE→LIST. Then all the lists of words are appended to form a new list of words. If the inputs are lists of lists rather than lists of words, SENTENCE, like APPEND, does only one level of flattening, reporting a list of all the items of all the input lists.

pt:lança o erro _

Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation).

(

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Helper function for word/sentence library. Reports its first input, unless that input is empty, in which case it gives its second input as an error message.

Takes a text string as input, and reports the first character in the string.

pt:a primeira palavra de _

1972,55.41976,48.81980,48.21984,49.11988,43.81992,521996,39.62000,40.32004,492008,51.12012,452016,46.147.3666666666666748.56.8823445617512260.7509