{"action":"created","comment":{"url":"https://api.github.com/repos/cienciadedatos/r4ds/pulls/comments/217920442","pull_request_review_id":155750955,"id":217920442,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDIxNzkyMDQ0Mg==","diff_hunk":"@@ -140,122 +123,114 @@ If you've used R before, you might wonder why we're not using `read.csv()`. Ther\n     read_csv(\"a;b\\n1;3\")\n     ```\n \n-## Parsing a vector\n+## segmentando un vector\n+\n+Antes de entrar en detalles sobre cómo **readr** lee archivos del disco, necesitamos desviarnos un poco para hablar sobre las funciones `parse_*()`. Estas funciones toman un vector de caracteres y devuelven un vector más especializado, como un vector lógico, numérico, o fecha:\n \n-Before we get into the details of how readr reads files from disk, we need to take a little detour to talk about the `parse_*()` functions. These functions take a character vector and return a more specialised vector like a logical, integer, or date:\n \n ```{r}\n str(parse_logical(c(\"TRUE\", \"FALSE\", \"NA\")))\n str(parse_integer(c(\"1\", \"2\", \"3\")))\n str(parse_date(c(\"2010-01-01\", \"1979-10-14\")))\n ```\n \n-These functions are useful in their own right, but are also an important building block for readr. Once you've learned how the individual parsers work in this section, we'll circle back and see how they fit together to parse a complete file in the next section.\n+Estas funciones son útiles por sí mismas, pero también son un bloque estructural importante para **readr**. Una vez que aprendas cómo funcionan los segmentadores individuales en esta sección, volveremos atrás y veremos cómo se combinan entre ellos para segmentar un archivo completo en la próxima sección.\n+\n+Como todas las funciones en **tidyverse**, las funciones `parse_*()` son uniformes: el primer argumento es un vector de caracteres a segmentar, y el argumento `na` especifica qué cadenas deberían ser tratadas como faltantes: \n+\n \n-Like all functions in the tidyverse, the `parse_*()` functions are uniform: the first argument is a character vector to parse, and the `na` argument specifies which strings should be treated as missing:\n \n ```{r}\n parse_integer(c(\"1\", \"231\", \".\", \"456\"), na = \".\")\n ```\n \n-If parsing fails, you'll get a warning:\n+Si la segmentación falla, obtendrás una advertencia:\n \n ```{r}\n x <- parse_integer(c(\"123\", \"345\", \"abc\", \"123.45\"))\n ```\n-\n-And the failures will be missing in the output:\n+Y las fallas se perderán en la salida: \n \n ```{r}\n x\n ```\n \n-If there are many parsing failures, you'll need to use `problems()` to get the complete set. This returns a tibble, which you can then manipulate with dplyr.\n+Si hay muchas fallas de segmentación, necesitarás utilizar `problems()` (del inglés, problemas) para obtener la totalidad de ellas. Esto devuelve una tibble que puedes luego manipular con **dplyr**.\n+\n \n ```{r}\n problems(x)\n ```\n+Utilizar los segmentadores es más que nada una cuestión de entender qué está disponible y cómo trabajan con diferentes tipos de entradas. Hay ocho segmentadores particularmente importantes. \n \n-Using parsers is mostly a matter of understanding what's available and how they deal with different types of input. There are eight particularly important parsers:\n+1.  `parse_logical()` y `parse_integer()` segmentan valores lógicos y números enteros respectivamente.No hay prácticamente nada que pueda salir mal con estos segmentadores así que no los describiré en más detalle aquí.\n \n-1.  `parse_logical()` and `parse_integer()` parse logicals and integers\n-    respectively. There's basically nothing that can go wrong with these\n-    parsers so I won't describe them here further.\n-    \n-1.  `parse_double()` is a strict numeric parser, and `parse_number()` \n-    is a flexible numeric parser. These are more complicated than you might\n-    expect because different parts of the world write numbers in different\n-    ways.\n-    \n-1.  `parse_character()` seems so simple that it shouldn't be necessary. But\n-    one complication makes it quite important: character encodings.\n+1. `parse_double()` es un segmentador numérico estricto, y `parse_number()` es un segmentador numérico flexible. Son más complicados de lo que podrías esperar porque diferentes partes del mundo escriben los números en diferentes formas.\n \n-1.  `parse_factor()` create factors, the data structure that R uses to represent\n-    categorical variables with fixed and known values.\n+1. `parse_character()` parece tan simple que no debiera ser necesario. Pero una complicación lo hace bastante importante: la codificación de caracteres.\n \n-1.  `parse_datetime()`, `parse_date()`, and `parse_time()` allow you to\n-    parse various date & time specifications. These are the most complicated\n-    because there are so many different ways of writing dates.\n+1.  `parse_factor()` crea factores, la estructura de datos que R usa para representar variables categóricas con valores fijos y conocidos.\n \n-The following sections describe these parsers in more detail.\n+1.  `parse_datetime()`, `parse_date()`, y `parse_time()` te permiten segmentar diversas especificaciones de fechas y horas. Estos son los más complicados porque hay muchas formas diferentes de escribir fechas. \n \n-### Numbers\n+Las secciones siguintes describen estos segmentadores en mayor detalle.\n \n-It seems like it should be straightforward to parse a number, but three problems make it tricky:\n+### Números\n \n-1. People write numbers differently in different parts of the world.\n-   For example, some countries use `.` in between the integer and fractional \n-   parts of a real number, while others use `,`.\n-   \n-1. Numbers are often surrounded by other characters that provide some\n-   context, like \"$1000\" or \"10%\".\n+Pareciera que segmentar un número debiera ser sencillo, pero hay tres problemas que pueden complicarlo: \n+\n+1. Las personas escriben los números de forma distinta en diferentes partes del mundo. Por ejemplo, algunos países utilizan `.` entre el entero y la fracción de un número real, mientras que otros utilizan `,`.\n+\n+\n+1. A menudo los números están rodeados por otros caracteres que proporcionan algún contexto, como \"$1000\" o \"10%\".\n+\n+1. Los números frecuentemente contienen caracteres de \"agrupación\", haciéndolos más fáciles de leer, como \"1,000,000\". y esos caracteres de agrupación varían alrededor del mundo. \n+\n+Para responder al primer problema, **readr** tiene el concepto de \"locale\", un objeto que especifica las opciones de segmentación que difieren de un lugar a otro. Cuando segmentamos números, la opción más importante es el caracter que utilizas como símbolo decimal. Puedes sobreescribir el valor por defecto de `.` creando un nuevo locale y estableciendo el argumento `decimal_mark` (del inglés, \"símbolo decimal\"):\n \n-1. Numbers often contain \"grouping\" characters to make them easier to read, \n-   like \"1,000,000\", and these grouping characters vary around the world.\n \n-To address the first problem, readr has the notion of a \"locale\", an object that specifies parsing options that differ from place to place. When parsing numbers, the most important option is the character you use for the decimal mark. You can override the default value of `.` by creating a new locale and setting the `decimal_mark` argument:\n \n ```{r}\n parse_double(\"1.23\")\n parse_double(\"1,23\", locale = locale(decimal_mark = \",\"))\n ```\n+El locale por defecto de **readr** es EEUU-céntrico, porque generalmente R es EEUU-céntrico (i.e. la documentación de R base está escrita en inglés americano). Una aproximación alternativa podría ser probar y adivinar las opciones por defecto de tu sistema operativo. Es difícil de hacerlo bien, y  lo que es más importante, hace que tu código sea frágil. Incluso si funciona en tu computadora, puede fallar cuando lo envíes a un colega en otro país.\n \n-readr's default locale is US-centric, because generally R is US-centric (i.e. the documentation of base R is written in American English). An alternative approach would be to try and guess the defaults from your operating system. This is hard to do well, and, more importantly, makes your code fragile: even if it works on your computer, it might fail when you email it to a colleague in another country.\n-\n-`parse_number()` addresses the second problem: it ignores non-numeric characters before and after the number. This is particularly useful for currencies and percentages, but also works to extract numbers embedded in text.\n+`parse_number()` responde al segundo problema: ignora los caracteres no-numéricos antes y después del número. Esto es particularmente útil para monedas y porcentajes, pero también sirve para extraer números insertos en texto.\n \n ```{r}\n parse_number(\"$100\")\n parse_number(\"20%\")\n parse_number(\"It cost $123.45\")\n ```\n \n-The final problem is addressed by the combination of `parse_number()` and the locale as `parse_number()` will ignore the \"grouping mark\":\n+El problema final se responde por la combinación de `parse_number()` y el locale, ya que `parse_number()` ignorará el \"símbolo decimal\":\n \n ```{r}\n-# Used in America\n+# Utilizado en América\n parse_number(\"$123,456,789\")\n \n-# Used in many parts of Europe\n+# Utilizado en muchas regiones de Europa\n parse_number(\"123.456.789\", locale = locale(grouping_mark = \".\"))\n \n-# Used in Switzerland\n+# Utilizado en Suiza\n parse_number(\"123'456'789\", locale = locale(grouping_mark = \"'\"))\n ```\n \n-### Strings {#readr-strings}\n+### Cadenas de texto {#readr-strings}\n+\n+En apariencia, `parse_character()` debería ser realmente simple --- podría tan solo devolver su entrada. Desafortunadamente la vida no es tan simple, dado que existen múltiples formas de representar la misma cadena de texto. Para entender qué está pasando, necesitamos bucear en los detalles de cómo las computadoras representan las cadenas de texto. En R, podemos acceder a la representación subyacente de una cadena de texto empleando `charToRaw()`:\n \n-It seems like `parse_character()` should be really simple --- it could just return its input. Unfortunately life isn't so simple, as there are multiple ways to represent the same string. To understand what's going on, we need to dive into the details of how computers represent strings. In R, we can get at the underlying representation of a string using `charToRaw()`:\n \n ```{r}\n charToRaw(\"Hadley\")\n ```\n+Cada número hexadecimal representa un byte de información: `48` es H, `61` es a, y así. El mapeo desde un número hexadecimal a caracteres se denomina la codificación, y en este caso la codificación se denomina ASCII. ASCII realiza un gran trabajo representando caracteres ingleses, ya que es la ***American** Standard Code for Information Interchange* (del inglés, Código Americano estandarizado para el intercambio de información).","path":"import.Rmd","position":346,"original_position":346,"commit_id":"e887a340da695c57e6aa032b8d184717a553247f","original_commit_id":"e887a340da695c57e6aa032b8d184717a553247f","user":{"login":"luisDVA","id":5580938,"node_id":"MDQ6VXNlcjU1ODA5Mzg=","avatar_url":"https://avatars1.githubusercontent.com/u/5580938?v=4","gravatar_id":"","url":"https://api.github.com/users/luisDVA","html_url":"https://github.com/luisDVA","followers_url":"https://api.github.com/users/luisDVA/followers","following_url":"https://api.github.com/users/luisDVA/following{/other_user}","gists_url":"https://api.github.com/users/luisDVA/gists{/gist_id}","starred_url":"https://api.github.com/users/luisDVA/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/luisDVA/subscriptions","organizations_url":"https://api.github.com/users/luisDVA/orgs","repos_url":"https://api.github.com/users/luisDVA/repos","events_url":"https://api.github.com/users/luisDVA/events{/privacy}","received_events_url":"https://api.github.com/users/luisDVA/received_events","type":"User","site_admin":false},"body":"caracteres en inglés <- caracteres ingleses","created_at":"2018-09-16T16:41:27Z","updated_at":"2018-09-16T16:41:27Z","html_url":"https://github.com/cienciadedatos/r4ds/pull/33#discussion_r217920442","pull_request_url":"https://api.github.com/repos/cienciadedatos/r4ds/pulls/33","author_association":"COLLABORATOR","_links":{"self":{"href":"https://api.github.com/repos/cienciadedatos/r4ds/pulls/comments/217920442"},"html":{"href":"https://github.com/cienciadedatos/r4ds/pull/33#discussion_r217920442"},"pull_request":{"href":"https://api.github.com/repos/cienciadedatos/r4ds/pulls/33"}}},"pull_request":{"url":"https://api.github.com/repos/cienciadedatos/r4ds/pulls/33","id":214388358,"node_id":"MDExOlB1bGxSZXF1ZXN0MjE0Mzg4MzU4","html_url":"https://github.com/cienciadedatos/r4ds/pull/33","diff_url":"https://github.com/cienciadedatos/r4ds/pull/33.diff","patch_url":"https://github.com/cienciadedatos/r4ds/pull/33.patch","issue_url":"https://api.github.com/repos/cienciadedatos/r4ds/issues/33","number":33,"state":"open","locked":false,"title":"Traduccion lina","user":{"login":"LinaMorenoAzocar","id":40702588,"node_id":"MDQ6VXNlcjQwNzAyNTg4","avatar_url":"https://avatars0.githubusercontent.com/u/40702588?v=4","gravatar_id":"","url":"https://api.github.com/users/LinaMorenoAzocar","html_url":"https://github.com/LinaMorenoAzocar","followers_url":"https://api.github.com/users/LinaMorenoAzocar/followers","following_url":"https://api.github.com/users/LinaMorenoAzocar/following{/other_user}","gists_url":"https://api.github.com/users/LinaMorenoAzocar/gists{/gist_id}","starred_url":"https://api.github.com/users/LinaMorenoAzocar/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/LinaMorenoAzocar/subscriptions","organizations_url":"https://api.github.com/users/LinaMorenoAzocar/orgs","repos_url":"https://api.github.com/users/LinaMorenoAzocar/repos","events_url":"https://api.github.com/users/LinaMorenoAzocar/events{/privacy}","received_events_url":"https://api.github.com/users/LinaMorenoAzocar/received_events","type":"User","site_admin":false},"body":"Capítulos Workflow-scripts e Import traducidos, borré los archivos que había agregado pero me siguen apareciendo en mi repositorio... Algunos detalles a corregir;\r\n-en Scripts, en en nro 13 aparece un llamado en el texto a una parte del chunk, y no supe hacerlo igual. \r\n-En Import, hay algunos términos más de lenguaje de programación que no conocía cómo traducirlos y requieren revisión de alguien que sepa! 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