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zTd5_h7kfz#9=H{AX0)n`=wkVMkyU?XZ`}dz>$ba(iqp_YgkY-8F5*r$uMVBc`NTUK zSwB@&m;Ps>>l-~vblei3_rpB%YQVEnhs0@sqJzh^-k?eM^1A7HB}jv>FQ1 ui{d(vM*h~YzN(1yZ}}8|^Fu`fJTg@Ik;&R~hS0B7*;(7~$@=ERmH!2x36g~X literal 0 HcmV?d00001 From 48d5da9055067583f60b27613fcd2775970fc914 Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Wed, 11 Dec 2024 20:27:20 +0000 Subject: [PATCH 02/27] Create gestion-manipulation-donnees-r.md Create Markdown for new FR translation. --- fr/lecons/gestion-manipulation-donnees-r.md | 499 ++++++++++++++++++++ 1 file changed, 499 insertions(+) create mode 100644 fr/lecons/gestion-manipulation-donnees-r.md diff --git a/fr/lecons/gestion-manipulation-donnees-r.md b/fr/lecons/gestion-manipulation-donnees-r.md new file mode 100644 index 000000000..0282e2570 --- /dev/null +++ b/fr/lecons/gestion-manipulation-donnees-r.md @@ -0,0 +1,499 @@ +--- +title: "Gestion et manipulation des données avec R" +slug: gestion-manipulation-donnees-r +original: data-wrangling-and-management-in-R +layout: lesson +collection: lessons +date: 2017-07-31 +translation_date: YYYY-MM-DD +authors: +- Nabeel Siddiqui +reviewers: +- Lauren Tilton +- Ryan Deschamps +editors: +- Ian Milligan +translator: +- Damien Belvèze +translation-editor: +- Alexandre Wauthier +translation-reviewer: +- Jeanne Gissinger +- Bénédicte Garnier +review-ticket: https://github.com/programminghistorian/ph-submissions/issues/625 +difficulty: 2 +activity: transforming +topics: [data-manipulation, data-management, distant-reading, r, data-visualization] +abstract: Ce tutoriel guide les chercheurs et chercheuses dans l'organisation de leurs données pour les rendre « propres », et les aide à prendre en main les pacakages R qui leur permettront de conduire des analyses de base sur ces données. +avatar_alt: Pain de savon +doi: 10.46430/phfr0035 +--- + +{% include toc.html %} + +## Prérequis + +Cette leçon nécessite quelques prérequis sur votre compréhension de R. Si vous n'avez pas été au bout de la leçon sur les [bases de R avec des données tabulées](/en/lessons/r-basics-with-tabular-data), je vous suggère de le faire d'abord. Avoir une expérience dans un autre langage de programmation peut aussi vous aider. Si vous souhaitez savoir par où commencer, je vous recommande de parcourir les excellentes leçons de *Programming Historian* sur Python. + +## Objectifs de la leçon + +À la fin de cette leçon, vous serez en mesure de : + + 1. Comprendre comment organiser vos données pour qu'elles soient « propres », et pourquoi c'est important. + 2. Comprendre le paquet (« package » en anglais) dplyr et l'utiliser pour manipuler et administrer les données. + 3. Se familiariser avec l'opérateur pipe dans R et observer comment il peut vous aider à créer du code plus lisible. + 4. Apprendre à travailler sur des exemples de base de manipulation de données pour acquérir les fondements de l'analyse exploratoire des données. + +## Introduction + +Les données que vous rencontrerez « dans la nature » sont rarement présentées dans un format qui rend possible une analyse. Vous aurez donc à les traiter avant d'explorer les questions qui vous intéressent. Ce traitement peut prendre plus de temps que l'analyse elle-même ! Dans ce tutoriel, nous apprendrons quelques techniques de base pour manipuler, gérer et administrer nos données dans R. Nous nous fonderons notamment sur la philosophie des [« données propres »](https://perma.cc/C58C-4Y9A) (tidy data) telle que l'a présentée Hadley Wickham. + +Selon Wickham, la donnée est « propre » quand elle répond à ces trois critères : + + 1. Chaque observation dans une ligne. + 2. Chaque variable dans une colonne. + 3. Chaque valeur a sa propre cellule. + +Remplir ces critères nous permet de juger si la donnée est organisée ou pas. Ces critères nous fournissent également un schéma standard et un ensemble d'outils pour gérer les formes les plus communes de désordre dans les données : + + 1. Les entêtes de colonnes sont des valeurs et pas des noms de variables. + 2. Plusieurs variables sont stockées dans une même colonne. + 3. Des variables sont présentes à la fois dans les colonnes et dans les lignes. + 4. Plusieurs unités d'observation sont présentes dans une même table. + 5. Une même unité d'observation est présente dans plusieurs tables. + +Un avantage peut-être encore plus important est de garder nos données dans ce format propre, qui nous permet d'utiliser une galerie de paquets dans le [« tidyverse »](http://tidyverse.org/), spécifiquement conçus pour fonctionner avec des données bien structurées. En nous assurant que nos données en entrée et en sortie sont bien structurées, nous n'aurons qu'un nombre limité d'outils à utiliser pour répondre à un grand nombre de questions. De plus, nous pourrons combiner, manipuler et séparer des jeux de données comme bon nous semble. + +Dans ce tutoriel, nous nous intéresserons particulièrement au paquet [dplyr](https://cran.r-project.org/web/packages/dplyr/index.html) du tidyverse. Mais cela vaut la peine de mentionner brièvement quelques autres paquets que nous utiliserons : + +- [magittr](https://perma.cc/J622-RDNF) : donne accès à l'opérateur pipe et rend le code plus facile à lire. + +- [ggplot2](https://perma.cc/W7JT-UAEX) : utilise la [« grammaire des graphiques »](https://doi.org/10.1007/0-387-28695-0) permettant de réaliser des visualisations simples de vos données. + +- [readr](https://perma.cc/JH6R-7Z5N) : met à disposition un import plus rapide et intuitif de données tabulées, comme des fichiers CSV. +- [tibble](https://perma.cc/BV8B-8MBB) : donne accès à une nouvelle conceptualisation des tableaux de données qui seront plus simples à traiter et à imprimer. + +Si vous ne l’avez pas déjà fait, vous devez installer et charger le tidyverse avant de commencer. Assurez-vous en outre que vous disposez de la [dernière version de R](https://cran.rstudio.com/) et de la [dernière version de Rstudio](https://www.rstudio.com/products/rstudio/download/) installées sur votre système d'exploitation (Windows, Mac OS, Linux). + +Copiez le code ci-dessous dans la zone d’édition de fichiers de Rstudio. Pour l'exécuter, il faut mettre ces lignes en surbrillance et appuyer sur `Ctrl+enter` (ou `Cmd+enter` sur Mac OS) : + +``` +# Installer les bibliothèques tidyverse et charger ces bibliothèques +# Ne vous inquiétez pas si cela prend un peu de temps + +install.packages("tidyverse", repos = "http://cran.us.r-project.org") +library(tidyverse) +``` + +## Un exemple du fonctionnement de dplyr + +Utilisons un exemple pour voir comment dplyr peut aider les historien·nes, ainsi que les autres chercheur·euses en sciences humaines et sociales : importez les données de recensement décennal des États-Unis entre 1790 et 2010. Téléchargez les données depuis [le dépot de _Programming Historian_](/assets/introductory_state_example.csv) et placez le fichier téléchargé dans le dossier que vous utiliserez pour traiter les exemples présentés dans ce tutoriel. + +Comme les données sont stockées dans un fichier CSV, utilisez la commande `read_CSV()` incluse dans le paquet [readr](https://perma.cc/58PX-7LPB) du tidyverse. + +La fonction `read_csv()` doit comporter le chemin d'un fichier qu'on veut importer en tant que variable, par conséquent il faut s'assurer que ce chemin soit correctement renseigné : + +``` +# Importer le fichier CSV et le sauvegarder dans la variable us_state_populations_import +# Assurez-vous que le chemin vers le fichier est bien défini +library(tidyverse) +us_state_populations_import<-read_csv("introductory_state_example.csv") +``` + +Après avoir importé les données, vous remarquerez qu'elles sont disposées en trois colonnes : une pour la population, une pour l'année et une pour l'État. Ces données sont d'emblée dans un format propre, ce qui nous permet de nous livrer à une multitude d'explorations. + +Pour cet exemple, nous voulons visualiser la croissance de la population en Californie et dans l'État de New York afin d'avoir une meilleure compréhension des migrations vers l'Ouest. Nous allons utiliser dplyr pour filtrer nos données, de telle sorte qu’elles ne contiennent que les informations relatives aux États qui nous intéressent et nous aurons recours à ggplot2 pour visualiser cette information. Cet exercice a pour but de nous donner une idée de ce que dplyr peut faire – ne vous en faites pas si vous ne comprenez pas le code utilisé à ce stade : + +``` +# Filtrer le jeu pour ne traiter que les données relatives à la Californie et à l'État de New York +california_and_new_york_state_populations<-us_state_populations_import %>% + filter(state %in% c("California", "New York")) + +# Éditer le graphique des courbes de population de la Californie et de l'État de New York +ggplot(data=california_and_new_york_state_populations, aes(x=year, y=population, color=state)) + + geom_line() + + geom_point() +``` + +{% include figure.html filename="en-or-data-wrangling-and-management-in-R-01.png" alt="Courbe graphique des évolutions des populations de la Californie et de l'État de New-York. Les deux courbes sont ascendantes à partir de la première valeur (avant 1800 pour l'État de New-York, à partir de 1850 pour la Californie). La population de l'État de New-York reste supérieure à celle de la Californie, jusqu'à ce que les deux courbes se croisent et que le rapport s'inverse autour de 1960 ; après quoi la population californienne croît de façon très rapide, tandis que la population de New-York a une croissance beaucoup plus faible" caption="Figure 1. Vue graphique de la population de la Californie et de l’État de New York." %} + +Comme nous pouvons le voir, la population de la Californie a augmenté de façon considérable si nous la comparons à celle de l'État de New York. Cet exemple en particulier peut sembler évident au regard de l’histoire des migrations aux États-Unis, mais le code en lui-même nous fournit une base pour poser de nombreuses questions similaires. Ainsi, avec une modification rapide du code, on peut créer un graphique équivalent pour deux autres États comme le Mississippi et la Virginie. + +``` +# Filtrer le jeu pour ne traiter que les données relatives au Mississippi et à la Virginie +mississippi_and_virginia_state_populations<-us_state_populations_import %>% + filter(state %in% c("Mississippi", "Virginia")) + +# Éditer le graphique des courbes de population du Mississippi et de la Virginie +ggplot(data=mississippi_and_virginia_state_populations, aes(x=year, y=population, color=state)) + + geom_line() + + geom_point() +``` + +{% include figure.html filename="en-or-data-wrangling-and-management-in-R-02.png" alt="Courbe graphique des évolutions des populations du Mississippi et de la Virginie. Les courbes de croissance des populations de ces deux États suivent un rythme comparable jusqu'aux années 30 (avec une population globalement un peu plus nombreuse en Virginie que dans le Mississippi). A partir des années 30, la croissance de la population du Mississippi fléchit un peu, tandis que celle de la Virginie augmente à un rythme beaucoup plus rapide." caption="Figure 2. Vue graphique de la population du Mississippi et de la Virginie." %} + +La possibilité de faire des changements rapides dans le code et de visualiser nos données est un élément fondamental de l'analyse exploratoire de données (AED). Plutôt que d'essayer de « prouver » une hypothèse, l'analyse exploratoire de données nous aide à mieux comprendre nos données et à les interroger. L'AED fournit aux historien·nes et aux spécialistes des sciences humaines et sociales un moyen simple de déterminer quand approfondir un sujet ou quand prendre du recul, et c'est un domaine dans lequel R excelle. + +## L'opérateur Pipe + +Avant d’aborder dplyr, nous devons examiner ce qu'est l'opérateur pipe (`%>%`) dans R, car nous l'utiliserons souvent dans nos exemples. Comme mentionné plus haut, l'opérateur pipe fait partie du paquet [magrittr](https://perma.cc/5N2A-6L8D) mis au point par [Stefan Milton Bache](https://perma.cc/LV97-FMS8) et [Hadley Wickham](https://perma.cc/UK93-NGQB) et est inclus dans le tidyverse. Son nom rend hommage au peintre surréaliste René Magritte, connu pour son œuvre « La trahison des images » où l'on peut voir une pipe accompagnée de la légende « ceci n’est pas une pipe ». + +L'opérateur pipe nous permet déclarer ce qui est à gauche du pipe comme le premier élément de la fonction qui est indiquée à droite. Cela peut sembler étrange au premier abord, mais une fois appris, vous trouverez qu'il rend votre code plus lisible en évitant l'imbrication des opérations. Ne vous inquiétez pas si tout cela vous semble encore confus pour le moment ; cela deviendra plus clair progressivement avec des exemples. + +Supposons que nous souhaitions calculer la racine carrée de la population de chaque État, puis en calculer la somme et enfin la moyenne. À l’évidence, cette mesure ne nous sert à rien, mais elle permet de montrer à quel point un code réalisé avec R peut rapidement devenir difficile à lire. Normalement, nous pourrions imbriquer ces opérations : + +``` +mean(sum(sqrt(us_state_populations_import$population))) + +## [1] 1256925 +``` + +Comme on peut le voir, l'utilisation de commandes imbriquées rend difficile de se rappeler combien de parenthèses doivent être fournies et cela donne un code assez pataud. Pour minimiser cela, certains vont créer des vecteurs temporaires entre chaque appel de commandes. + +``` +# Obtenir la racine carrée de la population de chaque État + +sqrt_state_populations_vector<-sqrt(us_state_populations_import$population) + +# Obtenir la somme de toutes les racines carrées des variables temporaires + +sum_sqrt_state_populations_vector<-sum(sqrt_state_populations_vector) + +# Obtenir la moyenne de la variable temporaire + +mean_sum_sqrt_state_populations_vector<-mean(sum_sqrt_state_populations_vector) + +# Afficher la moyenne + +mean_sum_sqrt_state_populations_vector + +## [1] 1256925 +``` + +Vous obtenez certes la même réponse, mais de façon beaucoup plus lisible. En revanche, cela peut rapidement encombrer votre espace de travail si vous oubliez de supprimer les vecteurs temporaires. L'opérateur pipe fait tout cela pour vous. Voici le même code, mais avec l'opérateur pipe : + +``` +us_state_populations_import$population%>%sqrt%>%sum%>%mean + +## [1] 1256925 +``` + +C'est bien plus simple à lire, et vous pourriez même rendre cette commande encore plus claire en l'écrivant sur plusieurs lignes : + +``` +# Assurez-vous que l'opérateur soit à la fin de la ligne +us_state_populations_import$population%>% + sqrt%>% + sum%>% + mean + +## [1] 1256925 +``` + +Notez que les vecteurs ou les tableaux que crée l'opérateur pipe sont supprimés lorsque le processus est achevé. Si vous souhaitez les conserver, vous devrez en faire de nouvelles variables : + +``` +permanent_sqrt_and_sum_state_populations_vector <- us_state_populations_import$population%>%sqrt%>%sum%>%mean +permanent_sqrt_and_sum_state_populations_vector + +## [1] 1256925 +``` + +## Il nous faut un nouveau jeu de données + +Maintenant que nous comprenons comment fonctionne le pipe, nous sommes en mesure d’examiner et manipuler des données. Heureusement, aujourd'hui, de plus en plus de jeux de données sont mis à la disposition des historien·nes et des spécialistes des sciences humaines et sociales. Nous allons partir du paquet de [données historiques](https://perma.cc/Z7UN-4FT5) créé par [Lincoln Mullen](https://perma.cc/G46P-SGYW) + +Allons-y, installons et chargeons ce paquet : + +``` +# Installer le paquet historydata +install.packages("historydata", repos = "http://cran.us.r-project.org") + +# Charger le paquet historydata +library(historydata) +``` + +Ce paquet contient un échantillon de données historiques sur les États-Unis. Les données de recensement que nous avons utilisées précédemment font partie de cet échantillon. Jusqu'à la fin de ce tutoriel, nous allons surtout travailler avec un jeu de données en particulier : `early_colleges`, qui contient des données sur les universités (« colleges » dans le monde anglophone) fondées avant 1848. Commençons par charger les données et les observer : + +``` +# Vérifiez avant d'exécuter ce code que le paquet historydata a bien été installé et chargé + +data(early_colleges) +early_colleges + +## # A tibble: 65 × 6 +## college original_name city state established sponsorship +## +## 1 Harvard Cambridge MA 1636 Congregational; after 1805 Unitarian +## 2 William and Mary Williamsburg VA 1693 Anglican +## 3 Yale New Haven CT 1701 Congregational +## 4 Pennsylvania, Univ. of Philadelphia PA 1740 Nondenominational +## 5 Princeton College of New Jersey Princeton NJ 1746 Presbyterian +## 6 Columbia King's College New York NY 1754 Anglican +## 7 Brown Providence RI 1765 Baptist +## 8 Rutgers Queen's College New Brunswick NJ 1766 Dutch Reformed +## 9 Dartmouth Hanover NH 1769 Congregational +## 10 Charleston, Coll. Of Charleston SC 1770 Anglican +## # ... with 55 more rows +``` + +Comme vous pouvez le remarquer, ce jeu de données contient le nom actuel de chaque université, son nom à l'origine, la ville et l'État dans lequel il a été fondé, la date de sa fondation et l'organisation qui le parraine. Comme on l'a vu plus haut, avant de commencer à travailler sur ce jeu de données, il est important de penser à la façon dont on va organiser ces données. Voyons si certaines de nos données ne se trouveraient pas dans un format « impropre ». Voyez-vous des cellules qui ne répondraient pas aux trois critères que remplissent les données « propres » ? + +Si vous avez répondu le parrainage de Harvard, vous avez la bonne réponse. En plus de mentionner le premier parrainage de cette université, la cellule comporte l'information de son changement de parrainage en 1805. Habituellement, on veut conserver dans nos données autant d'information que possible, mais pour rester dans la perspective de ce tutoriel, nous allons modifier la colonne pour ne conserver que les parrainages lors de la fondation. + +``` +early_colleges[1,6] <- "Congregational" +early_colleges + +## # A tibble: 65 × 6 +## college original_name city state established sponsorship +## +## 1 Harvard Cambridge MA 1636 Congregational +## 2 William and Mary Williamsburg VA 1693 Anglican +## 3 Yale New Haven CT 1701 Congregational +## 4 Pennsylvania, Univ. of Philadelphia PA 1740 Nondenominational +## 5 Princeton College of New Jersey Princeton NJ 1746 Presbyterian +## 6 Columbia King's College New York NY 1754 Anglican +## 7 Brown Providence RI 1765 Baptist +## 8 Rutgers Queen's College New Brunswick NJ 1766 Dutch Reformed +## 9 Dartmouth Hanover NH 1769 Congregational +## 10 Charleston, Coll. Of Charleston SC 1770 Anglican +## # ... with 55 more rows +``` + +À présent que nos données se présentent dans un format propre, nous pouvons leur donner forme avec le paquet dplyr. + +## Qu'est-ce que dplyr ? + +[dplyr](https://perma.cc/33LH-QFU3) est un autre élément du tidyverse qui met à disposition des fonctions utiles pour manipuler et transformer des données. Parce que nous gardons nos données propres, nous n'avons besoin que d'un nombre réduit d'outils pour les explorer. + +Comparé au langage R de base, utiliser dplyr est souvent plus rapide et vous garantit que si les données en entrée sont propres, les données transformées le seront aussi. Ce qui est peut-être encore plus important, dplyr facilite la lecture de votre code et utilise des « verbes » qui sont intuitifs la plupart du temps. Chaque fonction dans dplyr correspond à l'un de ces verbes, dont les plus importants sont : filtrer(`filter`), sélectionner(`select`), arranger(`arrange`), recoder(`mutate`) et résumer(`summarise`, avec le *s* de l'orthographe britannique). Parcourons-les un à un pour voir comment ils fonctionnent dans la pratique. + +### select + +Si on regarde les données comprises dans `early_colleges`, on peut observer qu'il y a un grand nombre de « NA » dans la colonne des noms originaux. NA (Not Available) signifie qu’on n’a aucune donnée correspondante. On pourrait donc vouloir visualiser nos données en soustrayant cette colonne. La fonction `select()` de dplyr nous donne la possibilité de le faire. Nous prenons le tableau de données que nous voulons manipuler comme premier argument, suivi d'une liste qui indique quelles colonnes on souhaite conserver : + +``` +# Supprime la colonne des noms originaux en utiliser select() +# Notez que vous n'avez pas à préfixer le nom de la colonne par un $ +# dplyr traite automatiquement la virgule comme un AND + +select(early_colleges, college, city, state, established, sponsorship) + +## # A tibble: 65 × 5 +## college city state established sponsorship +## +## 1 Harvard Cambridge MA 1636 Congregational +## 2 William and Mary Williamsburg VA 1693 Anglican +## 3 Yale New Haven CT 1701 Congregational +## 4 Pennsylvania, Univ. of Philadelphia PA 1740 Nondenominational +## 5 Princeton Princeton NJ 1746 Presbyterian +## 6 Columbia New York NY 1754 Anglican +## 7 Brown Providence RI 1765 Baptist +## 8 Rutgers New Brunswick NJ 1766 Dutch Reformed +## 9 Dartmouth Hanover NH 1769 Congregational +## 10 Charleston, Coll. Of Charleston SC 1770 Anglican +## # ℹ 55 more rows +``` + +Allons encore plus loin et voyons comment on peut écrire ceci en utilisant l'opérateur pipe (%>%) : + +``` +early_colleges%>% + select(college, city, state, established, sponsorship) + +## # A tibble: 65 × 5 +## college city state established sponsorship +## +## 1 Harvard Cambridge MA 1636 Congregational +## 2 William and Mary Williamsburg VA 1693 Anglican +## 3 Yale New Haven CT 1701 Congregational +## 4 Pennsylvania, Univ. of Philadelphia PA 1740 Nondenominational +## 5 Princeton Princeton NJ 1746 Presbyterian +## 6 Columbia New York NY 1754 Anglican +## 7 Brown Providence RI 1765 Baptist +## 8 Rutgers New Brunswick NJ 1766 Dutch Reformed +## 9 Dartmouth Hanover NH 1769 Congregational +## 10 Charleston, Coll. Of Charleston SC 1770 Anglican +## # ℹ 55 more rows +``` + +Devoir référencer toutes les colonnes qu'on veut conserver juste pour se débarrasser d'une seule est un peu fastidieux. Nous pouvons à la place utiliser le symbole moins (`-`) pour signifier que nous voulons enlever une colonne. + +``` +early_colleges%>% + select(-original_name) + +## # A tibble: 65 × 5 +## college city state established sponsorship +## +## 1 Harvard Cambridge MA 1636 Congregational +## 2 William and Mary Williamsburg VA 1693 Anglican +## 3 Yale New Haven CT 1701 Congregational +## 4 Pennsylvania, Univ. of Philadelphia PA 1740 Nondenominational +## 5 Princeton Princeton NJ 1746 Presbyterian +## 6 Columbia New York NY 1754 Anglican +## 7 Brown Providence RI 1765 Baptist +## 8 Rutgers New Brunswick NJ 1766 Dutch Reformed +## 9 Dartmouth Hanover NH 1769 Congregational +## 10 Charleston, Coll. Of Charleston SC 1770 Anglican +## # ℹ 55 more rows +``` + +### filter + +La fonction `filter()` réalise une opération similaire à la fonction `select()` à ceci près qu'au lieu de choisir une colonne, on peut utiliser `filter()` pour sélectionner les lignes qui remplissent une certaine condition. Par exemple, on peut visualiser toutes les universités qui ont existé avant la fin du 18e siècle. + +``` +early_colleges%>% + filter(established < 1800) + +## # A tibble: 20 × 6 +## college original_name city state established sponsorship +## +## 1 Harvard Cambridge MA 1636 Congregational +## 2 William and Mary Williamsburg VA 1693 Anglican +## 3 Yale New Haven CT 1701 Congregational +## 4 Pennsylvania, Univ. of Philadelphia PA 1740 Nondenominational +## 5 Princeton College of New Jersey Princeton NJ 1746 Presbyterian +## 6 Columbia King's College New York NY 1754 Anglican +## 7 Brown Providence RI 1765 Baptist +## 8 Rutgers Queen's College New Brunswick NJ 1766 Dutch Reformed +## 9 Dartmouth Hanover NH 1769 Congregational +## 10 Charleston, Coll. Of Charleston SC 1770 Anglican +## 11 Hampden-Sydney Hampden-Sydney VA 1775 Presbyterian +## 12 Transylvania Lexington KY 1780 Disciples of Christ +## 13 Georgia, Univ. of Athens GA 1785 Secular +## 14 Georgetown Washington DC 1789 Roman Catholic +## 15 North Carolina, Univ. of Chapel Hill NC 1789 Secular +## 16 Vermont, Univ. of Burlington VT 1791 Nondenominational +## 17 Williams Williamstown MA 1793 Congregational +## 18 Tennessee, Univ. of Blount College Knoxville TN 1794 Secular +## 19 Union College Schenectady NY 1795 Presbyterian with Congregational +## 20 Marietta Marietta OH 1797 Congregational +``` + +### mutate + +La commande mutate vous permet d’ajouter une colonne à votre tableau de données. Ici nous avons la ville et l'État qui se trouvent dans deux colonnes séparées. Nous pouvons utiliser la commande `paste` (coller) pour combiner ces deux chaînes de caractères en une seule tout en gardant un séparateur. Plaçons ces deux informations dans une seule et même colonne intitulée **location** (localisation). + +``` +early_colleges%>%mutate(location=paste(city,state,sep=",")) + +## # A tibble: 65 × 7 +## college original_name city state established sponsorship location +## +## 1 Harvard Cambridge MA 1636 Congregational Cambridge,MA +## 2 William and Mary Williamsburg VA 1693 Anglican Williamsburg,VA +## 3 Yale New Haven CT 1701 Congregational New Haven,CT +## 4 Pennsylvania, Univ. of Philadelphia PA 1740 Nondenominational Philadelphia,PA +## 5 Princeton College of New Jersey Princeton NJ 1746 Presbyterian Princeton,NJ +## 6 Columbia King's College New York NY 1754 Anglican New York,NY +## 7 Brown Providence RI 1765 Baptist Providence,RI +## 8 Rutgers Queen's College New Brunswick NJ 1766 Dutch Reformed New Brunswick,NJ +## 9 Dartmouth Hanover NH 1769 Congregational Hanover,NH +## 10 Charleston, Coll. Of Charleston SC 1770 Anglican Charleston,SC +## # ... with 55 more rows + +``` + +Rappelez-vous que dplyr ne conserve pas les données ni ne transforme le document original. À la place, il crée un tableau de données temporaire à chaque étape. Si vous souhaitez le conserver, vous devez lui associer une variable permanente. + +``` +early_colleges_with_location <- early_colleges%>%mutate(location=paste(city,state, sep=",")) + +# Voir le nouveau tableau avec la colonne "location" ajoutée +early_colleges_with_location + +## # A tibble: 65 × 7 +## college original_name city state established sponsorship location +## +## 1 Harvard Cambridge MA 1636 Congregational Cambridge,MA +## 2 William and Mary Williamsburg VA 1693 Anglican Williamsburg,VA +## 3 Yale New Haven CT 1701 Congregational New Haven,CT +## 4 Pennsylvania, Univ. of Philadelphia PA 1740 Nondenominational Philadelphia,PA +## 5 Princeton College of New Jersey Princeton NJ 1746 Presbyterian Princeton,NJ +## 6 Columbia King's College New York NY 1754 Anglican New York,NY +## 7 Brown Providence RI 1765 Baptist Providence,RI +## 8 Rutgers Queen's College New Brunswick NJ 1766 Dutch Reformed New Brunswick,NJ +## 9 Dartmouth Hanover NH 1769 Congregational Hanover,NH +## 10 Charleston, Coll. Of Charleston SC 1770 Anglican Charleston,SC +## # ... with 55 more rows +``` + +### arrange + +La fonction `arrange()` permet de changer l'ordre des colonnes. Pour l'instant, les universités sont organisées par année de fondation en ordre croissant. Plaçons-les par ordre décroissant (descending en anglais). Dans notre cas, à partir de la fin de la guerre américano-mexicaine : + +``` +early_colleges%>% + arrange(desc(established)) + +## # A tibble: 65 × 6 +## college original_name city state established sponsorship +## +## 1 Wisconsin, Univ. of Madison WI 1848 Secular +## 2 Earlham Richmond IN 1847 Quaker +## 3 Beloit Beloit WI 1846 Congregational +## 4 Bucknell Lewisburg PA 1846 Baptist +## 5 Grinnell Grinnell IA 1846 Congregational +## 6 Mount Union Alliance OH 1846 Methodist +## 7 Louisiana, Univ. of New Orleans LA 1845 Secular +## 8 U.S. Naval Academy Annapolis MD 1845 Secular +## 9 Mississipps, Univ. of Oxford MI 1844 Secular +## 10 Holy Cross Worchester MA 1843 Roman Catholic +## # ... with 55 more rows +``` + +### summarise + +La dernière fonction clé de dplyr est `summarise()` (n'oubliez pas, avec un *s* comme dans la forme britannique). `summarise()` est une fonction habituellement utilisée pour créer un tableau de données statistiques récapitulatives destinées à fournir des graphiques. Nous allons l'utiliser pour calculer la date de fondation moyenne des universités avant 1848. + +``` +early_colleges%>%summarise(mean(established)) + +## # A tibble: 1 × 1 +## `mean(established)` +## +## 1 1810. +``` + +### Et si nous compilions ces fonctions ? + +Maintenant que nous avons exploré les cinq verbes principaux de dplyr, nous pouvons les utiliser pour créer rapidement une visualisation de nos données. Créons un graphique en barres pour représenter le nombre d’universités laïques ou religieuses fondées avant la guerre de 1812 : + +``` +secular_colleges_before_1812<-early_colleges%>% + filter(established < 1812)%>% + mutate(`est laïque`=ifelse(sponsorship!="Secular", "non", "oui")) + +ggplot(secular_colleges_before_1812) + + geom_bar(aes(x=`est laïque`, fill=`est laïque`))+ + labs(x="est-ce que l'université est laïque ?") + + ylab("nombre d'établissements") +``` + +{% include figure.html filename="fr-tr-gestion-manipulation-donnees-r-03.png" alt="Graphique comportant deux barres indiquant la proportion d'universités religieuses par rapport aux universités laïques fondées avant la guerre de 1812. Le graphique montre une prééminence des universités laïques sur les universités religieuses (environ 3 pour 1)" caption="Figure 3. Nombre d'universités religieuses et laïques avant la guerre de 1812." %} + +
+Note du traducteur : la variable est laïque est mise entre apostrophes arrière (backticks). Cela permet à la fois de gérer le fait qu’elle occupe deux termes séparés par un espace, au lieu d’un seul, et le i trema présent dans le mot laïque. Ce caractère ne fait pas partie des 128 présents dans la table ASCII et peut donc poser problème dans l’exécution de certains programmes. +
+ +Encore une fois, en n'apportant qu'une modification rapide à notre code, nous pouvons aussi visualiser le nombre d'universités laïques par rapport au nombre d'universités religieuses fondées depuis le début de la guerre de 1812 : + +``` +secular_colleges_after_1812<-early_colleges%>% + filter(established > 1812)%>% + mutate(`est laïque`=ifelse(sponsorship!="Secular", "non", "oui")) + +ggplot(secular_colleges_after_1812) + + geom_bar(aes(x=`est laïque`, fill=`est laïque`))+ + labs(x="Est-ce que l'université est laïque ?")+ + ylab("nombre d'établissements") +``` + +{% include figure.html filename="fr-tr-gestion-manipulation-donnees-r-04.png" alt="Graphique comportant deux barres indiquant la proportion d'universités religieuses par rapport aux universités laïques fondées après la guerre de 1812. Comparé au graphique précédent, celui-ci manifeste que la prééminence des universités laïques par rapport aux universités religieuses s'est encore accrue (rapport de 7 à 1 environ)" caption="Figure 4. Nombre d'universités religieuses et laïques après la guerre de 1812." %} + +## Conclusion + +Ce tutoriel devrait vous mettre sur la bonne voie pour bien concevoir l'organisation et la manipulation de vos données avec R. Plus tard, vous souhaiterez sans doute progresser en visualisation de vos données. Je vous recommande de regarder le paquet [ggplot2](https://perma.cc/W7JT-UAEX) pour trouver des fonctions qui seront efficaces avec dplyr. De plus, vous pouvez être tenté d'examiner quelques autres fonctions accessibles dans dplyr pour améliorer vos compétences. Dans les deux cas, ce guide vous fournit une base pour approfondir vos connaissances et vous permet de couvrir les problèmes courants que vous pourriez rencontrer dans la gestion de données. From 6997729a83510abf2f79b10f902a866f8eeaebd8 Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Wed, 11 Dec 2024 20:32:59 +0000 Subject: [PATCH 03/27] Update gestion-manipulation-donnees-r.md Add translation_date: --- fr/lecons/gestion-manipulation-donnees-r.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fr/lecons/gestion-manipulation-donnees-r.md b/fr/lecons/gestion-manipulation-donnees-r.md index 0282e2570..d993ba674 100644 --- a/fr/lecons/gestion-manipulation-donnees-r.md +++ b/fr/lecons/gestion-manipulation-donnees-r.md @@ -5,7 +5,7 @@ original: data-wrangling-and-management-in-R layout: lesson collection: lessons date: 2017-07-31 -translation_date: YYYY-MM-DD +translation_date: 2024-12-13 authors: - Nabeel Siddiqui reviewers: From e1d0617bf2dac9fa285de6a45e1de3e6b7f283b8 Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 11:45:48 +0000 Subject: [PATCH 04/27] Create assets directory /data-wrangling-and-management-in-R Create /assets/data-wrangling-and-management-in-R --- .../introductory_state_example.csv | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename assets/{ => data-wrangling-and-management-in-R}/introductory_state_example.csv (100%) diff --git a/assets/introductory_state_example.csv b/assets/data-wrangling-and-management-in-R/introductory_state_example.csv similarity index 100% rename from assets/introductory_state_example.csv rename to assets/data-wrangling-and-management-in-R/introductory_state_example.csv From a98a89edfd66cb75a22a44b37672c0af169ec296 Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 11:46:55 +0000 Subject: [PATCH 05/27] Update data-wrangling-and-management-in-R.md Update /assets link, line 129. --- en/lessons/data-wrangling-and-management-in-R.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/en/lessons/data-wrangling-and-management-in-R.md b/en/lessons/data-wrangling-and-management-in-R.md index d411988e4..e1e9f3ff3 100755 --- a/en/lessons/data-wrangling-and-management-in-R.md +++ b/en/lessons/data-wrangling-and-management-in-R.md @@ -126,7 +126,7 @@ An Example of dplyr in Action Let's go through an example to see how dplyr can aid us as historians by inputting U.S. decennial census data from 1790 to 2010. Download the data by [clicking -here](/assets/introductory_state_example.csv) +here](/assets/data-wrangling-and-management-in-R/introductory_state_example.csv) and place it in the folder that you will use to work through the examples in this tutorial. From 09f803d767d9beaff29f2022b9c97ad8ecf0e502 Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 11:47:57 +0000 Subject: [PATCH 06/27] Update administracion-de-datos-en-r.md Update /assets link, line 81. --- es/lecciones/administracion-de-datos-en-r.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/es/lecciones/administracion-de-datos-en-r.md b/es/lecciones/administracion-de-datos-en-r.md index e47e57872..da6d42725 100644 --- a/es/lecciones/administracion-de-datos-en-r.md +++ b/es/lecciones/administracion-de-datos-en-r.md @@ -78,7 +78,7 @@ Copia el siguiente código en R Studio. Para ejecutarlo tienes que marcar las l ``` ## Un ejemplo de dplyr en acción -Veamos un ejemplo de cómo dyplr nos puede ayudar a los historiadores. Vamos a cargar los datos del censo decenal de 1790 a 2010 de Estados Unidos. Descarga los datos haciendo [click aquí](/assets/ejemplo_introductorio_estados.csv)[^2] y ponlos en la carpeta que vas a utilizar para trabajar en los ejemplos de este tutorial. +Veamos un ejemplo de cómo dyplr nos puede ayudar a los historiadores. Vamos a cargar los datos del censo decenal de 1790 a 2010 de Estados Unidos. Descarga los datos haciendo [click aquí](/assets/data-wrangling-and-management-in-R/ejemplo_introductorio_estados.csv)[^2] y ponlos en la carpeta que vas a utilizar para trabajar en los ejemplos de este tutorial. Como los datos están en un archivo CSV, vamos a usar el comando de lectura ```read_csv()``` en el paquete [readr](https://cran.r-project.org/web/packages/readr/vignettes/readr.html) de "tidyverse". From cdd44308ceb1717bc470d0a0b580a7892e3e9aea Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 11:53:00 +0000 Subject: [PATCH 07/27] Update gestion-manipulation-donnees-r.md Update header level, line 461. --- fr/lecons/gestion-manipulation-donnees-r.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fr/lecons/gestion-manipulation-donnees-r.md b/fr/lecons/gestion-manipulation-donnees-r.md index d993ba674..a8cb225d7 100644 --- a/fr/lecons/gestion-manipulation-donnees-r.md +++ b/fr/lecons/gestion-manipulation-donnees-r.md @@ -458,7 +458,7 @@ early_colleges%>%summarise(mean(established)) ## 1 1810. ``` -### Et si nous compilions ces fonctions ? +## Et si nous compilions ces fonctions ? Maintenant que nous avons exploré les cinq verbes principaux de dplyr, nous pouvons les utiliser pour créer rapidement une visualisation de nos données. Créons un graphique en barres pour représenter le nombre d’universités laïques ou religieuses fondées avant la guerre de 1812 : From 7c9e182df68b26b9208e586cedb4d0f92fe5d24a Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 11:59:52 +0000 Subject: [PATCH 08/27] Move ejemplo_introductorio_estados.csv Move ES translated asset into directory. --- .../ejemplo_introductorio_estados.csv | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename assets/{ => data-wrangling-and-management-in-r}/ejemplo_introductorio_estados.csv (100%) diff --git a/assets/ejemplo_introductorio_estados.csv b/assets/data-wrangling-and-management-in-r/ejemplo_introductorio_estados.csv similarity index 100% rename from assets/ejemplo_introductorio_estados.csv rename to assets/data-wrangling-and-management-in-r/ejemplo_introductorio_estados.csv From 68639bde8e844589771c1a9124de7763e4dc28ac Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 12:21:30 +0000 Subject: [PATCH 09/27] Update gestion-manipulation-donnees-r.md Update /assets link, line 90 --- fr/lecons/gestion-manipulation-donnees-r.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/fr/lecons/gestion-manipulation-donnees-r.md b/fr/lecons/gestion-manipulation-donnees-r.md index a8cb225d7..a9b754242 100644 --- a/fr/lecons/gestion-manipulation-donnees-r.md +++ b/fr/lecons/gestion-manipulation-donnees-r.md @@ -87,7 +87,7 @@ library(tidyverse) ## Un exemple du fonctionnement de dplyr -Utilisons un exemple pour voir comment dplyr peut aider les historien·nes, ainsi que les autres chercheur·euses en sciences humaines et sociales : importez les données de recensement décennal des États-Unis entre 1790 et 2010. Téléchargez les données depuis [le dépot de _Programming Historian_](/assets/introductory_state_example.csv) et placez le fichier téléchargé dans le dossier que vous utiliserez pour traiter les exemples présentés dans ce tutoriel. +Utilisons un exemple pour voir comment dplyr peut aider les historien·nes, ainsi que les autres chercheur·euses en sciences humaines et sociales : importez les données de recensement décennal des États-Unis entre 1790 et 2010. Téléchargez les données depuis [le dépot de _Programming Historian_](/assets/data-wrangling-and-management-in-R/introductory_state_example.csv) et placez le fichier téléchargé dans le dossier que vous utiliserez pour traiter les exemples présentés dans ce tutoriel. Comme les données sont stockées dans un fichier CSV, utilisez la commande `read_CSV()` incluse dans le paquet [readr](https://perma.cc/58PX-7LPB) du tidyverse. From f7495d3b7b89b4e4621ceaa7343c1c109d89fa7b Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 12:24:40 +0000 Subject: [PATCH 10/27] Rename directory data-wrangling-and-management-in-R > data-wrangling-and-management-in-r --- .../introductory_state_example.csv | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename assets/{data-wrangling-and-management-in-R => data-wrangling-and-management-in-r}/introductory_state_example.csv (100%) diff --git a/assets/data-wrangling-and-management-in-R/introductory_state_example.csv b/assets/data-wrangling-and-management-in-r/introductory_state_example.csv similarity index 100% rename from assets/data-wrangling-and-management-in-R/introductory_state_example.csv rename to assets/data-wrangling-and-management-in-r/introductory_state_example.csv From 0a3fd0f67f51ed2b8f788768b93a7dc0582bd642 Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 12:27:38 +0000 Subject: [PATCH 11/27] Update data-wrangling-and-management-in-r.md data-wrangling-and-management-in-R > data-wrangling-and-management-in-r --- ...nagement-in-R.md => data-wrangling-and-management-in-r.md} | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) rename en/lessons/{data-wrangling-and-management-in-R.md => data-wrangling-and-management-in-r.md} (97%) diff --git a/en/lessons/data-wrangling-and-management-in-R.md b/en/lessons/data-wrangling-and-management-in-r.md similarity index 97% rename from en/lessons/data-wrangling-and-management-in-R.md rename to en/lessons/data-wrangling-and-management-in-r.md index e1e9f3ff3..cdcba493c 100755 --- a/en/lessons/data-wrangling-and-management-in-R.md +++ b/en/lessons/data-wrangling-and-management-in-r.md @@ -1,6 +1,6 @@ --- title: Data Wrangling and Management in R -slug: data-wrangling-and-management-in-R +slug: data-wrangling-and-management-in-r layout: lesson collection: lessons authors: @@ -126,7 +126,7 @@ An Example of dplyr in Action Let's go through an example to see how dplyr can aid us as historians by inputting U.S. decennial census data from 1790 to 2010. Download the data by [clicking -here](/assets/data-wrangling-and-management-in-R/introductory_state_example.csv) +here](/assets/data-wrangling-and-management-in-r/introductory_state_example.csv) and place it in the folder that you will use to work through the examples in this tutorial. From 981b7092d190cb70bca91c40ae586ed0d2d6650f Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 12:29:28 +0000 Subject: [PATCH 12/27] Update manipulacao-transformacao-dados-r.md manipulacao-transformacao-dados-R > manipulacao-transformacao-dados-r --- ...rmacao-dados-R.md => manipulacao-transformacao-dados-r.md} | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) rename pt/licoes/{manipulacao-transformacao-dados-R.md => manipulacao-transformacao-dados-r.md} (98%) diff --git a/pt/licoes/manipulacao-transformacao-dados-R.md b/pt/licoes/manipulacao-transformacao-dados-r.md similarity index 98% rename from pt/licoes/manipulacao-transformacao-dados-R.md rename to pt/licoes/manipulacao-transformacao-dados-r.md index f124ec22d..b506cbf4c 100644 --- a/pt/licoes/manipulacao-transformacao-dados-R.md +++ b/pt/licoes/manipulacao-transformacao-dados-r.md @@ -1,6 +1,6 @@ --- title: Manipulação e transformação de dados com R -slug: manipulacao-transformacao-dados-R +slug: manipulacao-transformacao-dados-r layout: lesson collection: lessons date: 2017-08-01 @@ -24,7 +24,7 @@ review-ticket: https://github.com/programminghistorian/ph-submissions/issues/397 activity: transforming topics: [data-manipulation, data-management, distant-reading, r, data-visualization] abstract: "Esta lição explora como os investigadores podem tornar seus dados organizados, entender os pacotes do R para manipulação de dados e conduzir análises de dados básicas usando esta linguagem." -original: data-wrangling-and-management-in-R +original: data-wrangling-and-management-in-r avatar_alt: Barra de sabão doi: 10.46430/phpt0035 --- From 461dc30945e342d00cb46e4371e72c3ac32cb333 Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 12:59:40 +0000 Subject: [PATCH 13/27] Delete images/data-wrangling-and-management-in-R directory To be replaced with renamed directory: 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Téléchargez les données depuis [le dépot de _Programming Historian_](/assets/data-wrangling-and-management-in-R/introductory_state_example.csv) et placez le fichier téléchargé dans le dossier que vous utiliserez pour traiter les exemples présentés dans ce tutoriel. +Utilisons un exemple pour voir comment dplyr peut aider les historien·nes, ainsi que les autres chercheur·euses en sciences humaines et sociales : importez les données de recensement décennal des États-Unis entre 1790 et 2010. Téléchargez les données depuis [le dépot de _Programming Historian_](/assets/data-wrangling-and-management-in-r/introductory_state_example.csv) et placez le fichier téléchargé dans le dossier que vous utiliserez pour traiter les exemples présentés dans ce tutoriel. Comme les données sont stockées dans un fichier CSV, utilisez la commande `read_CSV()` incluse dans le paquet [readr](https://perma.cc/58PX-7LPB) du tidyverse. @@ -115,7 +115,7 @@ ggplot(data=california_and_new_york_state_populations, aes(x=year, y=population, geom_point() ``` -{% include figure.html filename="en-or-data-wrangling-and-management-in-R-01.png" alt="Courbe graphique des évolutions des populations de la Californie et de l'État de New-York. Les deux courbes sont ascendantes à partir de la première valeur (avant 1800 pour l'État de New-York, à partir de 1850 pour la Californie). La population de l'État de New-York reste supérieure à celle de la Californie, jusqu'à ce que les deux courbes se croisent et que le rapport s'inverse autour de 1960 ; après quoi la population californienne croît de façon très rapide, tandis que la population de New-York a une croissance beaucoup plus faible" caption="Figure 1. Vue graphique de la population de la Californie et de l’État de New York." %} +{% include figure.html filename="en-or-data-wrangling-and-management-in-r-01.png" alt="Courbe graphique des évolutions des populations de la Californie et de l'État de New-York. Les deux courbes sont ascendantes à partir de la première valeur (avant 1800 pour l'État de New-York, à partir de 1850 pour la Californie). La population de l'État de New-York reste supérieure à celle de la Californie, jusqu'à ce que les deux courbes se croisent et que le rapport s'inverse autour de 1960 ; après quoi la population californienne croît de façon très rapide, tandis que la population de New-York a une croissance beaucoup plus faible" caption="Figure 1. Vue graphique de la population de la Californie et de l’État de New York." %} Comme nous pouvons le voir, la population de la Californie a augmenté de façon considérable si nous la comparons à celle de l'État de New York. Cet exemple en particulier peut sembler évident au regard de l’histoire des migrations aux États-Unis, mais le code en lui-même nous fournit une base pour poser de nombreuses questions similaires. Ainsi, avec une modification rapide du code, on peut créer un graphique équivalent pour deux autres États comme le Mississippi et la Virginie. @@ -130,7 +130,7 @@ ggplot(data=mississippi_and_virginia_state_populations, aes(x=year, y=population geom_point() ``` -{% include figure.html filename="en-or-data-wrangling-and-management-in-R-02.png" alt="Courbe graphique des évolutions des populations du Mississippi et de la Virginie. Les courbes de croissance des populations de ces deux États suivent un rythme comparable jusqu'aux années 30 (avec une population globalement un peu plus nombreuse en Virginie que dans le Mississippi). A partir des années 30, la croissance de la population du Mississippi fléchit un peu, tandis que celle de la Virginie augmente à un rythme beaucoup plus rapide." caption="Figure 2. Vue graphique de la population du Mississippi et de la Virginie." %} +{% include figure.html filename="en-or-data-wrangling-and-management-in-r-02.png" alt="Courbe graphique des évolutions des populations du Mississippi et de la Virginie. Les courbes de croissance des populations de ces deux États suivent un rythme comparable jusqu'aux années 30 (avec une population globalement un peu plus nombreuse en Virginie que dans le Mississippi). A partir des années 30, la croissance de la population du Mississippi fléchit un peu, tandis que celle de la Virginie augmente à un rythme beaucoup plus rapide." caption="Figure 2. Vue graphique de la population du Mississippi et de la Virginie." %} La possibilité de faire des changements rapides dans le code et de visualiser nos données est un élément fondamental de l'analyse exploratoire de données (AED). Plutôt que d'essayer de « prouver » une hypothèse, l'analyse exploratoire de données nous aide à mieux comprendre nos données et à les interroger. L'AED fournit aux historien·nes et aux spécialistes des sciences humaines et sociales un moyen simple de déterminer quand approfondir un sujet ou quand prendre du recul, et c'est un domaine dans lequel R excelle. From 483b88b256230b7a10bb353bc1e0d3edfc3f766d Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 13:20:18 +0000 Subject: [PATCH 16/27] Update administracion-de-datos-en-r.md data-wrangling-and-management-in-R > data-wrangling-and-management-in-r --- es/lecciones/administracion-de-datos-en-r.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/es/lecciones/administracion-de-datos-en-r.md b/es/lecciones/administracion-de-datos-en-r.md index da6d42725..418c2215c 100644 --- a/es/lecciones/administracion-de-datos-en-r.md +++ b/es/lecciones/administracion-de-datos-en-r.md @@ -18,7 +18,7 @@ translation-reviewer: - Victor Gayol review-ticket: https://github.com/programminghistorian/ph-submissions/issues/199 layout: lesson -original: data-wrangling-and-management-in-R +original: data-wrangling-and-management-in-r difficulty: 2 activity: transforming topics: [data-manipulation, data-management, distant-reading, r, data-visualization] @@ -78,7 +78,7 @@ Copia el siguiente código en R Studio. Para ejecutarlo tienes que marcar las l ``` ## Un ejemplo de dplyr en acción -Veamos un ejemplo de cómo dyplr nos puede ayudar a los historiadores. Vamos a cargar los datos del censo decenal de 1790 a 2010 de Estados Unidos. Descarga los datos haciendo [click aquí](/assets/data-wrangling-and-management-in-R/ejemplo_introductorio_estados.csv)[^2] y ponlos en la carpeta que vas a utilizar para trabajar en los ejemplos de este tutorial. +Veamos un ejemplo de cómo dyplr nos puede ayudar a los historiadores. Vamos a cargar los datos del censo decenal de 1790 a 2010 de Estados Unidos. Descarga los datos haciendo [click aquí](/assets/data-wrangling-and-management-in-r/ejemplo_introductorio_estados.csv)[^2] y ponlos en la carpeta que vas a utilizar para trabajar en los ejemplos de este tutorial. Como los datos están en un archivo CSV, vamos a usar el comando de lectura ```read_csv()``` en el paquete [readr](https://cran.r-project.org/web/packages/readr/vignettes/readr.html) de "tidyverse". From 9a9666856637b48d7359b6a6f58ac4f207faa98f Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 13:21:53 +0000 Subject: [PATCH 17/27] Update manipulacao-transformacao-dados-r.md Capital R to lowercase r in image filenames. --- pt/licoes/manipulacao-transformacao-dados-r.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pt/licoes/manipulacao-transformacao-dados-r.md b/pt/licoes/manipulacao-transformacao-dados-r.md index b506cbf4c..dbd149d8e 100644 --- a/pt/licoes/manipulacao-transformacao-dados-r.md +++ b/pt/licoes/manipulacao-transformacao-dados-r.md @@ -115,7 +115,7 @@ Neste exemplo, vamos visualizar o crescimento populacional de Brasil e Argentina geom_line() + geom_point() -{% include figure.html filename="pt-tr-manipulacao-transformacao-dados-R-01.png" alt="Imagem com a representação de um gráfico de linhas com dados da população por anos para o Brasil e a Argentina" caption="Gráfico da população de Brasil e Argentina, ao longo dos anos" %} +{% include figure.html filename="pt-tr-manipulacao-transformacao-dados-r-01.png" alt="Imagem com a representação de um gráfico de linhas com dados da população por anos para o Brasil e a Argentina" caption="Gráfico da população de Brasil e Argentina, ao longo dos anos" %} Como podemos observar, a população absoluta do Brasil é consideravelmente maior em comparação com a população da Argentina. Embora isso pareça óbvio devido ao tamanho do território brasileiro, o código nos fornece uma base sobre a qual podemos formular uma infinidade de questões similares. Por exemplo, com uma pequena mudança no código podemos criar um gráfico similar com dois países diferentes, como Portugal e Bélgica. @@ -130,7 +130,7 @@ Como podemos observar, a população absoluta do Brasil é consideravelmente mai geom_line() + geom_point() -{% include figure.html filename="pt-tr-manipulacao-transformacao-dados-R-02.png" alt="Imagem com a representação de um gráfico de linhas com dados da população por anos para a Bélgica e Portugal" caption="Gráfico da população de Portugal e Bégica, ao longo dos anos" %} +{% include figure.html filename="pt-tr-manipulacao-transformacao-dados-r-02.png" alt="Imagem com a representação de um gráfico de linhas com dados da população por anos para a Bélgica e Portugal" caption="Gráfico da população de Portugal e Bégica, ao longo dos anos" %} Promover mudanças rápidas no código e revisar nossos dados é parte fundamental do processo de análise exploratória de dados (AED). Ao invés de tentar "provar" uma hipótese, a análise exploratória nos ajuda a entender melhor os dados e a levantar questões sobre eles. Para os historiadores, a AED fornece uma forma fácil de saber quando aprofundar mais em um tema e quando voltar atrás, e esta é uma área onde o R se sobressai. @@ -398,7 +398,7 @@ Agora, após termos visto os cinco principais verbos do dplyr, podemos criar rap geom_bar(aes(x = expectativa_2007, fill = expectativa_2007)) + labs(x = "A expectativa de vida é maior que 50 anos?") -{% include figure.html filename="pt-tr-manipulacao-transformacao-dados-R-03.png" alt="Imagem com a representação de um gráfico de barras com dados sobre o número de países onde a expectativa de vida é maior ou menor que 50 anos, em 2007" caption="Expectativa de vida nos países em 2007" %} +{% include figure.html filename="pt-tr-manipulacao-transformacao-dados-r-03.png" alt="Imagem com a representação de um gráfico de barras com dados sobre o número de países onde a expectativa de vida é maior ou menor que 50 anos, em 2007" caption="Expectativa de vida nos países em 2007" %} Novamente, fazendo uma pequena mudança no nosso código, podemos ver também o número de países com expectativa de vida maior que 50 anos, em 1952. @@ -410,7 +410,7 @@ Novamente, fazendo uma pequena mudança no nosso código, podemos ver também o geom_bar(aes(x = expectativa_1952, fill = expectativa_1952)) + labs(x = "A expectativa de vida é maior que 50 anos?") -({% include figure.html filename="pt-tr-manipulacao-transformacao-dados-R-04.png" alt="Imagem com a representação de um gráfico de barras com dados sobre o número de países onde a expectativa de vida é maior ou menor que 50 anos, em 1952" caption="Expectativa de vida nos países em 1952" %} +({% include figure.html filename="pt-tr-manipulacao-transformacao-dados-r-04.png" alt="Imagem com a representação de um gráfico de barras com dados sobre o número de países onde a expectativa de vida é maior ou menor que 50 anos, em 1952" caption="Expectativa de vida nos países em 1952" %} ## Conclusão From 7fa1c4b5c91818ada33ad59be565d72f96dfb594 Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 17:11:32 +0000 Subject: [PATCH 18/27] Update data-wrangling-and-management-in-r.md R > r --- en/lessons/data-wrangling-and-management-in-r.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/en/lessons/data-wrangling-and-management-in-r.md b/en/lessons/data-wrangling-and-management-in-r.md index cdcba493c..5c72ce12a 100755 --- a/en/lessons/data-wrangling-and-management-in-r.md +++ b/en/lessons/data-wrangling-and-management-in-r.md @@ -164,7 +164,7 @@ time. geom_line() + geom_point() -{% include figure.html filename="en-or-data-wrangling-and-management-in-R-01.png" caption="Graph of California and New York population" %} +{% include figure.html filename="en-or-data-wrangling-and-management-in-r-01.png" caption="Graph of California and New York population" %} As we can see, the population of California has grown considerably compared to New York. While this particular example may seem obvious @@ -182,7 +182,7 @@ with two different states such as Mississippi and Virginia. geom_line() + geom_point() -{% include figure.html filename="en-or-data-wrangling-and-management-in-R-02.png" caption="Graph of Mississippi and Virginia population" %} +{% include figure.html filename="en-or-data-wrangling-and-management-in-r-02.png" caption="Graph of Mississippi and Virginia population" %} Quickly making changes to our code and reanalyzing our data is a fundamental part of exploratory data analysis (EDA). Rather than trying @@ -579,7 +579,7 @@ colleges founded before the U.S. War of 1812: geom_bar(aes(x=is_secular, fill=is_secular))+ labs(x="Is the college secular?") -{% include figure.html filename="en-or-data-wrangling-and-management-in-R-03.png" caption="Number of secular and non-secular colleges before War of 1812" %} +{% include figure.html filename="en-or-data-wrangling-and-management-in-r-03.png" caption="Number of secular and non-secular colleges before War of 1812" %} Again, by making a quick change to our code, we can also look at the number of secular versus non-secular colleges founded after the start of @@ -593,7 +593,7 @@ the War of 1812: geom_bar(aes(x=is_secular, fill=is_secular))+ labs(x="Is the college secular?") -({% include figure.html filename="en-or-data-wrangling-and-management-in-R-04.png" caption="Number of secular and non-secular colleges after War of 1812" %} +({% include figure.html filename="en-or-data-wrangling-and-management-in-r-04.png" caption="Number of secular and non-secular colleges after War of 1812" %} Conclusion ========== From ab35a6f02b971a0c489cf3e829b31b8eb0f114e1 Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 17:12:52 +0000 Subject: [PATCH 19/27] Rename data-wrangling-and-management-in-r.png R > r --- ...R.png => data-wrangling-and-management-in-r.png} | Bin 1 file changed, 0 insertions(+), 0 deletions(-) rename gallery/{data-wrangling-and-management-in-R.png => data-wrangling-and-management-in-r.png} (100%) diff --git a/gallery/data-wrangling-and-management-in-R.png b/gallery/data-wrangling-and-management-in-r.png similarity index 100% rename from gallery/data-wrangling-and-management-in-R.png rename to gallery/data-wrangling-and-management-in-r.png From 7cf4d8b5019be567d9e7d69ac742dcb885542206 Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 17:18:11 +0000 Subject: [PATCH 20/27] Update beginners-guide-to-twitter-data.md R > r --- en/lessons/beginners-guide-to-twitter-data.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/en/lessons/beginners-guide-to-twitter-data.md b/en/lessons/beginners-guide-to-twitter-data.md index b5919b9fa..17d4621cb 100644 --- a/en/lessons/beginners-guide-to-twitter-data.md +++ b/en/lessons/beginners-guide-to-twitter-data.md @@ -130,7 +130,7 @@ At this point, your data has gone from the long list of single tweet IDs to a ro Each tweet now has lots of useful metadata, including the time created, the included hashtags, number of retweets and favorites, and some geo info. One can imagine how this information can be used for a wide variety of explorations, including to map discourse around an issue on social media, explore the relationship between sentiment and virality, or even text analysis of language of the tweets. -All of these processes will probably include some light data work to format this dataset so that you can produce useful insights: [statistical analyses](/en/lessons/data-wrangling-and-management-in-R), [maps](/en/lessons/mapping-with-python-leaflet), [social network analyses](/en/lessons/exploring-and-analyzing-network-data-with-python), [discourse analyses](/en/lessons/corpus-analysis-with-antconc). But regardless of where you go from here, you have a pretty robust dataset that can be used for a variety of academic pursuits. +All of these processes will probably include some light data work to format this dataset so that you can produce useful insights: [statistical analyses](/en/lessons/data-wrangling-and-management-in-r), [maps](/en/lessons/mapping-with-python-leaflet), [social network analyses](/en/lessons/exploring-and-analyzing-network-data-with-python), [discourse analyses](/en/lessons/corpus-analysis-with-antconc). But regardless of where you go from here, you have a pretty robust dataset that can be used for a variety of academic pursuits. You might have noticed we didn't get any latitude/longitude location information, but we did get a "place" column with less exact, textualized location information. Non-coordinate location data needs to be [geocoded](https://en.wikipedia.org/wiki/Geocode), which in this case means using a geocoder to [geoparse](https://en.wikipedia.org/wiki/Toponym_Resolution#Geoparsing) the reported locations and assign lat/long values to them. Different programs do this to greater or lesser success. [Tableau](https://www.tableau.com), for instance, has a hard time interpolating a set of locations if it's not at a consistent geographical level (city, state, etc.). For that reason, I generated latitude and longitude information with the Google geocoder following this *Programming Historian* [lesson](/en/lessons/mapping-with-python-leaflet), and then inputted that information into Tableau for mapping. There's plenty of good mapping [tools](https://digitalfellows.commons.gc.cuny.edu/2019/06/03/finding-the-right-tools-for-mapping/) out there that you can feel free to use: the key here is getting specific, accurate location information from the list of place names in the dataset. From 2ac2d308c2fdb974ba79d52470ae984c7152a5de Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 17:18:53 +0000 Subject: [PATCH 21/27] Update geospatial-data-analysis.md R > r --- en/lessons/geospatial-data-analysis.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/en/lessons/geospatial-data-analysis.md b/en/lessons/geospatial-data-analysis.md index 8724f3ccc..e4513d779 100644 --- a/en/lessons/geospatial-data-analysis.md +++ b/en/lessons/geospatial-data-analysis.md @@ -174,7 +174,7 @@ Now we have a large dataframe called `County_Aggregate_Data` which has our count ```r religion <- read.csv("./data/Religion/Churches.csv", as.is=TRUE) ``` -Depending on the state of the data you may need to do some data transformations in order to merge it back with the DataFrame. For complex transformations, see tutorials in R on working with data such as [Data Wrangling and Management in R tutorial](/en/lessons/data-wrangling-and-management-in-R) [data transforms](http://r4ds.had.co.nz/transform.html). In essence, you need to have a common field in both datasets to merge upon. Often this is a geographic id for the county and state represented by `GEOID`. It could also be the unique FIPS Code given by the US Census. Below I am using state and county `GEOID`. In this example, we are converting one data frame's common fields to numeric so that they match the variable type of the other dataframe: +Depending on the state of the data you may need to do some data transformations in order to merge it back with the DataFrame. For complex transformations, see tutorials in R on working with data such as [Data Wrangling and Management in R tutorial](/en/lessons/data-wrangling-and-management-in-r) [data transforms](http://r4ds.had.co.nz/transform.html). In essence, you need to have a common field in both datasets to merge upon. Often this is a geographic id for the county and state represented by `GEOID`. It could also be the unique FIPS Code given by the US Census. Below I am using state and county `GEOID`. In this example, we are converting one data frame's common fields to numeric so that they match the variable type of the other dataframe: ```r religion$STATEFP <- religion$STATE From a0d62f8b0c30dd750b4dbbdd9dd515ff9273a155 Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 17:19:34 +0000 Subject: [PATCH 22/27] Update sentiment-analysis-syuzhet.md R > r --- en/lessons/sentiment-analysis-syuzhet.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/en/lessons/sentiment-analysis-syuzhet.md b/en/lessons/sentiment-analysis-syuzhet.md index e648bb8a2..6768f18b5 100644 --- a/en/lessons/sentiment-analysis-syuzhet.md +++ b/en/lessons/sentiment-analysis-syuzhet.md @@ -41,7 +41,7 @@ Although the lesson is not intended for advanced R users, it is expected that yo * Taylor Arnold and Lauren Tilton, '[Basic Text Processing in R](/en/lessons/basic-text-processing-in-r)', *Programming Historian* 6 (2017), https://doi.org/10.46430/phen0061 * Taryn Dewar, '[R Basics with Tabular Data](/en/lessons/r-basics-with-tabular-data)', *Programming Historian* 5 (2016), https://doi.org/10.46430/phen0056 -* Nabeel Siddiqui, '[Data Wrangling and Management in R](/en/lessons/data-wrangling-and-management-in-R)', *Programming Historian* 6 (2017), https://doi.org/10.46430/phen0063 +* Nabeel Siddiqui, '[Data Wrangling and Management in R](/en/lessons/data-wrangling-and-management-in-r)', *Programming Historian* 6 (2017), https://doi.org/10.46430/phen0063 You may also be interested in other sentiment analysis lessons: From 6f086546b47247293ac9777b823d7c82fe412d58 Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 17:20:32 +0000 Subject: [PATCH 23/27] Update shiny-leaflet-newspaper-map-tutorial.md R > r --- en/lessons/shiny-leaflet-newspaper-map-tutorial.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/en/lessons/shiny-leaflet-newspaper-map-tutorial.md b/en/lessons/shiny-leaflet-newspaper-map-tutorial.md index dd38bd29b..375edd197 100644 --- a/en/lessons/shiny-leaflet-newspaper-map-tutorial.md +++ b/en/lessons/shiny-leaflet-newspaper-map-tutorial.md @@ -36,7 +36,7 @@ In this lesson, you will learn: - The concept and practice of 'reactive programming', as implemented by Shiny applications. Specifically, you'll learn how you can use Shiny to 'listen' for certain inputs, and how they are connected to outputs displayed in your app.

-Note that this lesson doesn't teach any coding in R, other than what's necessary to create the web application, nor does it cover publishing the finished application to the web. A basic knowledge of R, particularly using the tidyverse, would be very useful. +Note that this lesson doesn't teach any coding in R, other than what's necessary to create the web application, nor does it cover publishing the finished application to the web. A basic knowledge of R, particularly using the tidyverse, would be very useful.
### Graphical User Interfaces and the Digital Humanities @@ -108,7 +108,7 @@ First, however, you need to set up the correct programming environment and creat To get started with this tutorial, you should install the latest versions of [R](https://cran.rstudio.com/) and [Rstudio](https://www.rstudio.com/products/rstudio/download/) on your local machine. The R programming language has a very popular IDE (Integrated Development Environment) called RStudio, which is often used alongside R, as it provides a large set of features to make coding in the language more convenient. We'll use RStudio throughout the lesson. -Previous *Programming Historian* lessons have covered [working with R](/en/lessons/r-basics-with-tabular-data) and [working with the tidyverse](/en/lessons/data-wrangling-and-management-in-R). It would be useful to go through these lessons beforehand, to learn the basics of installing R and using the tidyverse for data wrangling. +Previous *Programming Historian* lessons have covered [working with R](/en/lessons/r-basics-with-tabular-data) and [working with the tidyverse](/en/lessons/data-wrangling-and-management-in-r). It would be useful to go through these lessons beforehand, to learn the basics of installing R and using the tidyverse for data wrangling. ### Create a new RStudio Project From e2b02b02988cd1ea338e46b580771396c277402d Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 17:23:06 +0000 Subject: [PATCH 24/27] Update analise-sentimento-R-syuzhet.md R > r --- pt/licoes/analise-sentimento-R-syuzhet.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pt/licoes/analise-sentimento-R-syuzhet.md b/pt/licoes/analise-sentimento-R-syuzhet.md index 93676c69e..f32d40df8 100644 --- a/pt/licoes/analise-sentimento-R-syuzhet.md +++ b/pt/licoes/analise-sentimento-R-syuzhet.md @@ -31,7 +31,7 @@ doi: 10.46430/phpt0022 # Objetivos -Esta lição usa a metodologia de análise de sentimentos e emoções através da linguagem de programação R para investigar documentos textuais de modo individual. Embora a lição não seja destinada a usuários avançados de R, é necessário que se tenha algum conhecimento dessa linguagem; assumimos que se tenha o R instalado e saiba como importar pacotes. Também recomendamos o download do RStudio. Se não estiver familiarizado com R, é melhor trabalhar primeiro através das lições [Processamento básico de texto em R](/pt/licoes/processamento-basico-texto-r), [Noções básicas de R com dados tabulares](/pt/licoes/nocoes-basicas-R-dados-tabulares) ou [Data Wrangling and Management in R](/en/lessons/data-wrangling-and-management-in-R) (em inglês). Ao final desta lição, o(a) pesquisador(a) será capaz de: +Esta lição usa a metodologia de análise de sentimentos e emoções através da linguagem de programação R para investigar documentos textuais de modo individual. Embora a lição não seja destinada a usuários avançados de R, é necessário que se tenha algum conhecimento dessa linguagem; assumimos que se tenha o R instalado e saiba como importar pacotes. Também recomendamos o download do RStudio. Se não estiver familiarizado com R, é melhor trabalhar primeiro através das lições [Processamento básico de texto em R](/pt/licoes/processamento-basico-texto-r), [Noções básicas de R com dados tabulares](/pt/licoes/nocoes-basicas-R-dados-tabulares) ou [Data Wrangling and Management in R](/en/lessons/data-wrangling-and-management-in-r) (em inglês). Ao final desta lição, o(a) pesquisador(a) será capaz de: - Colocar perguntas de pesquisa com base na análise quantitativa de sentimentos em textos de tipo ensaístico e/ou narrativo. - Usar a linguagem de programação R, o ambiente RStudio e o pacote `syuzhet` com o dicionário NRC para gerar o indicador de sentimento de um texto em diferentes linguagens. From 056c8db3ec784f5275606bb72371319f2968c558 Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 17:25:32 +0000 Subject: [PATCH 25/27] Update aplicacao-web-interativa-r-shiny-leaflet.md R > r --- pt/licoes/aplicacao-web-interativa-r-shiny-leaflet.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pt/licoes/aplicacao-web-interativa-r-shiny-leaflet.md b/pt/licoes/aplicacao-web-interativa-r-shiny-leaflet.md index 7bab853b9..575b012e0 100644 --- a/pt/licoes/aplicacao-web-interativa-r-shiny-leaflet.md +++ b/pt/licoes/aplicacao-web-interativa-r-shiny-leaflet.md @@ -47,7 +47,7 @@ Nesta lição, irá aprender: - O conceito e a prática de 'programação reativa', conforme implementado por aplicações Shiny. Especificamente, aprenderá como usar Shiny para 'entender' informações de *input* e como elas estão conectadas aos resultados a serem exibidos na sua aplicação.
-Saiba que esta lição não ensina nenhuma codificação em R, além da suficiente para criar a aplicação, nem aborda a publicação da aplicação finalizada na Web. É recomendado um conhecimento básico em linguagem de programação R, particularmente com o tidyverse. +Saiba que esta lição não ensina nenhuma codificação em R, além da suficiente para criar a aplicação, nem aborda a publicação da aplicação finalizada na Web. É recomendado um conhecimento básico em linguagem de programação R, particularmente com o tidyverse.
### Interfaces Gráficas do Utilizador e Humanidades Digitais From dfdfc898e1cfad6510ee7f3066390380946374bb Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 17:27:36 +0000 Subject: [PATCH 26/27] Update visualizacao-animacao-tabelas-historicas-R.md R > r --- pt/licoes/visualizacao-animacao-tabelas-historicas-R.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pt/licoes/visualizacao-animacao-tabelas-historicas-R.md b/pt/licoes/visualizacao-animacao-tabelas-historicas-R.md index d2cb3871e..52b093e22 100644 --- a/pt/licoes/visualizacao-animacao-tabelas-historicas-R.md +++ b/pt/licoes/visualizacao-animacao-tabelas-historicas-R.md @@ -55,7 +55,7 @@ Nesta lição, mostraremos uma das muitas maneiras pelas quais ficheiros que re # Pré-requisitos -Esta lição exige um entendimento básico de R, que pode ser obtido nas lições [Noções básicas de R com dados tabulares](/pt/licoes/nocoes-basicas-R-dados-tabulares) de Trayn Dewar e [Manipulação e transformação de dados em R](/en/lessons/data-wrangling-and-management-in-R) (em inglês) de Nabeel Siddiqui. +Esta lição exige um entendimento básico de R, que pode ser obtido nas lições [Noções básicas de R com dados tabulares](/pt/licoes/nocoes-basicas-R-dados-tabulares) de Trayn Dewar e [Manipulação e transformação de dados em R](/en/lessons/data-wrangling-and-management-in-r) (em inglês) de Nabeel Siddiqui. Além do R, será necessário possuir o ambiente de desenvolvimento [RStudio](https://www.rstudio.com/products/rstudio/download/#download) instalado. Se ainda não o tiver, [este vídeo](https://www.youtube.com/watch?v=A8A-OU_nlsw) fornece um guia sobre como baixá-lo e instalá-lo. From 7d72f8ef023cea4c373e3289b50e8fb13b2f4479 Mon Sep 17 00:00:00 2001 From: Anisa Hawes <87070441+anisa-hawes@users.noreply.github.com> Date: Thu, 12 Dec 2024 17:38:36 +0000 Subject: [PATCH 27/27] Update aplicacao-web-interativa-r-shiny-leaflet.md R > r --- pt/licoes/aplicacao-web-interativa-r-shiny-leaflet.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pt/licoes/aplicacao-web-interativa-r-shiny-leaflet.md b/pt/licoes/aplicacao-web-interativa-r-shiny-leaflet.md index 575b012e0..10d40ea2f 100644 --- a/pt/licoes/aplicacao-web-interativa-r-shiny-leaflet.md +++ b/pt/licoes/aplicacao-web-interativa-r-shiny-leaflet.md @@ -120,7 +120,7 @@ No entanto, antes de chegar a estes pontos é preciso configurar o ambiente corr Instale as [últimas versões do R](https://cran.rstudio.com/) (em inglês) e do [RStudio](https://www.rstudio.com/products/rstudio/download/) (em inglês) na sua máquina local para concluir esta lição. O R tem uma IDE (Ambiente de Desenvolvimento Integrado) muito popular (embora separado) chamado RStudio, que é frequentemente usado junto com o R, pois fornece um grande conjunto de recursos para tornar a codificação na linguagem mais conveniente. Usaremos ao longo da lição. -Lições anteriores do *Programming Historian* abordaram como [trabalhar com R](/pt/licoes/nocoes-basicas-R-dados-tabulares) e [trabalhar com o tydeverse](/pt/licoes/manipulacao-transformacao-dados-R). Seria útil passar por essas lições primeiro, para aprender os fundamentos da instalação do R e usar o tydeverse para organizar os dados. +Lições anteriores do *Programming Historian* abordaram como [trabalhar com R](/pt/licoes/nocoes-basicas-R-dados-tabulares) e [trabalhar com o tydeverse](/pt/licoes/manipulacao-transformacao-dados-r). Seria útil passar por essas lições primeiro, para aprender os fundamentos da instalação do R e usar o tydeverse para organizar os dados. ### Criar um novo projeto no RStudio