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data:image/png;base64,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

Draws the Pie Chart

54360The statement will calculate and combine the gender's number of people who got a score of 5 AND 4 and then divide it by the total number of the gender, multiplying it by 360 which is the number of "sides" to a circle and then round, checking when the direction is that to change the hue, signifying of a new score group.543360The statement will calculate and combine the gender's number of people who got a score of 5 AND 4 AND 3 and then divide it by the total number of the gender, multiplying it by 360 which is the number of "sides" to a circle and then round, checking when the direction is that to change the hue, signifying of a new score group.5432360The statement will calculate and combine the gender's number of people who got a score of 5 AND 4 AND 3 AND 2 and then divide it by the total number of the gender, multiplying it by 360 which is the number of "sides" to a circle and then round, checking when the direction is that to change the hue, signifying of a new score group.54321360The statement will calculate and combine the gender's number of people who got a score of 5 AND 4 AND 3 AND 2 AND 1 and then divide it by the total number of the gender, multiplying it by 360 which is the number of "sides" to a circle and then round, checking when the direction is that to change the hue, signifying of a new score group.Draws the circle

pt:o texto multilinha _ Reports the part of the first string up to the first instance of the second string inside it. If the second string isn't found, reports the entire first string.

pt:o texto de _ antes de _ Reports the portion of the first input string starting after the first occurrence of the second string. If the second string isn't found in the first string, reports an empty string.

pt:o texto de _ depois de _ Reports the character position (starting from 1) of the beginning of the first input string inside the second input string. If it's not found, reports 0.

pt:a posição de _ em _ If input is TRUE, comparisons made by functions in the string library will be case-independent (so "FOO" = "foo"). This is the default. If input is FALSE, comparisons will be exact.

pt:altera comparações ignorando distinção minúsculas/maiúsculas para _ Reports the portion of the first input (string) starting at the position given by the second input (counting from 1, like LETTER n OF) and ending at the position given by the third input (also counting from 1). If the third input is empty, reports the portion from the first position to the end of the string. If a position number is negative, counts from the end backward, so -1 is the last character, -2 is the next to last, etc.

pt:o texto de _ entre as posições _ e _ , inclusive Reports True if the first input string contains the second input string, otherwise false. Comparison is case-independent by default; use USE CASE-INDEPENDENT COMPARISONS to change that.

pt:o texto de _ antes de _ Reports the input text with lower case letters instead of capital letters in the input. Uses the user's locale's rules for case conversion.

pt:em minúsculas _ 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.

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 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 _ _

de:$flash umgekehrt _ pt:$flash uma lista inversa de _ ca:$flash capgira _

1Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation). Will not work on circular lists.

( )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.

ZIP takes any number of lists as inputs. The lists should all be the same length. ZIP reports a list of lists in which the first item is a list of all the first items, the second item is a list of all the second items, etc. Viewing the inputs as the rows of a matrix, ZIP reports its transpose.

1The identity function reports its input.

Takes as input a function of N inputs and N lists. The function is called with item 1 of all the lists as its inputs, with item 2 of all the lists as its inputs, and so on. (The lists should all be the same length.)

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 _

0AP CSP,1637,5,MAP CSP,1002,5,FAP CSP,2414,4,MAP CSP,692,4,FAP CSP,4133,3,MAP CSP,650,3,FAP CSP,1861,2,MAP CSP,369,2,FAP CSP,906,1,MAP CSP,607,1,F0