├── .gitignore ├── 2018_01_tidyweek ├── Instructions.md ├── R │ └── tidyweek_analysis_skeleton.Rmd ├── data │ ├── data.csv │ └── file.csv ├── data_world.md └── solution │ ├── solution.Rmd │ └── solution.nb.html ├── LICENSE ├── README.html ├── README.md ├── advanced_level └── advanced.csv ├── beginner_level └── beginner.csv ├── data └── data.csv ├── resources └── git.md ├── rest ├── 31736571 (1).png ├── README - Copy.md ├── data.world_project.png ├── data.world_r.png └── data.world_r_tool.png └── tidyweek.Rproj /.gitignore: -------------------------------------------------------------------------------- 1 | # History files 2 | .Rhistory 3 | .Rapp.history 4 | 5 | # Session Data files 6 | .RData 7 | 8 | # Example code in package build process 9 | *-Ex.R 10 | 11 | # Output files from R CMD build 12 | /*.tar.gz 13 | 14 | # Output files from R CMD check 15 | /*.Rcheck/ 16 | 17 | # RStudio files 18 | .Rproj.user/ 19 | 20 | # produced vignettes 21 | vignettes/*.html 22 | vignettes/*.pdf 23 | 24 | # OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3 25 | .httr-oauth 26 | 27 | # knitr and R markdown default cache directories 28 | /*_cache/ 29 | /cache/ 30 | 31 | # Temporary files created by R markdown 32 | *.utf8.md 33 | *.knit.md 34 | .Rproj.user 35 | -------------------------------------------------------------------------------- /2018_01_tidyweek/Instructions.md: -------------------------------------------------------------------------------- 1 | # Welcome to TidyWeek! 2 | 3 | TidyWeek is community mentorship program from #R4DS that focuses on using Rstudio, the tidyverse and tidy data principles for analysis. Our goal is to help students of R4DS by providing an experience with untidy, real world dataset and how to deal with unforseen challenges and obstacles. The role of the mentor will be mainly to act as a guide for the phases of a data science process as described by Hadley and Garrett in R4DS. 4 | 5 | # Why Should I Join In? 6 | 7 | At the end of many of these, you can put forth a portfolio that illustrates how you can apply techniques and skills to solve data science problems. 8 | 9 | # So What Do I Do? 10 | 11 | Here's a skeleton list to get you started: 12 | 13 | * Create a new project in Rstudio. 14 | * Download & Import data into that project 15 | * Explore & Tidy the Data. 16 | * Select an indicator from the dataset for analysis 17 | * Conduct your analysis (comparison, forecast, whatever!) 18 | * Create a visualization to accompany/explain your analysis 19 | * Publish your results at https://data.world 20 | * Tweet it to the world with the #TidyWeek hashtag 21 | 22 | # What's Data World? 23 | 24 | We're going to be using [Data World](https://data.world), and good ol' fashioned twitter as the platforms to share analysis and output. 25 | 26 | # This Weeks Data 27 | 28 | Dataset description: https://datacatalog.worldbank.org/dataset/world-development-indicators 29 | Data: [csv/zip](http://databank.worldbank.org/data/download/WDI_csv.zip) 30 | 31 | 32 | -------------------------------------------------------------------------------- /2018_01_tidyweek/R/tidyweek_analysis_skeleton.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "TidyWeek Week1" 3 | output: html_document 4 | --- 5 | 6 | # 1. Participants (Name of the Student & Mentor) 7 | 8 | ```{r setup, include=FALSE} 9 | #load your packages here and set your code options here. 10 | knitr::opts_chunk$set(echo = TRUE) 11 | ``` 12 | 13 | # 2. Background information & Objectives of your analysis 14 | 15 | * What sort of information does someone else reading this need to know? 16 | * What is the objective of your analysis? (What is your driving question?) 17 | * What sort of steps or structure will your analysis follow? 18 | 19 | 20 | # 3. Data 21 | 22 | * What is included in this data? 23 | * What isn't included? 24 | * Where did it come from? 25 | * Who collected it and how? 26 | * What tools were used? 27 | ```{r data} 28 | #load in your data here! 29 | ``` 30 | 31 | 32 | # 4. Tidying Data 33 | 34 | * What did you notice that was obvious about your data that needed to be fixed? 35 | * What steps will you take? 36 | * What to do about missing/incomplete data? 37 | 38 | # 5. Analysis 39 | 40 | * What is your approach to answer your question? 41 | * What methods are you using? 42 | * What are your assumptions and how are they justified? 43 | * How can someone learn more about the techniques used? 44 | * Why did you choose the visualizations that you did? 45 | 46 | # 6. Conclusions 47 | 48 | * What was the answer to your question? Why? 49 | * Is your conclusion valid? Under what circumstances? 50 | * Is there anything else that might better explain your conclusion? 51 | * What did you learn from this exercise? 52 | * How would you approach a problem like this next time? 53 | 54 | # External resources 55 | * [github repo](https://github.com/you/your-repo) 56 | * [link to yout website](https://mywebsite.com) -------------------------------------------------------------------------------- /2018_01_tidyweek/data/data.csv: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /2018_01_tidyweek/data/file.csv: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /2018_01_tidyweek/data_world.md: -------------------------------------------------------------------------------- 1 | # Data World 2 | 3 | We'll be using [data world](https://data.world/) alongside twitter (#TidyWeek) to share our accomplishments! 4 | 5 | ## Step 1: Sign Up for Data World 6 | 7 | ## Step 2: Create project 8 | ![Create project](https://github.com/rfordatascience/tidyweek/blob/master/rest/data.world_project.png) 9 | 10 | ## Step 3: Select your tools (R indeed in our case) 11 | ![Select your tools](https://github.com/rfordatascience/tidyweek/blob/master/rest/data.world_r_tool.png) 12 | 13 | ## Step 4: Create Rmd document/Open in App (and we will work with it later in RStudio/now we will only generate Rmd) 14 | ![Create Rmd document/Open in App](https://github.com/rfordatascience/tidyweek/blob/master/rest/data.world_r.png) 15 | -------------------------------------------------------------------------------- /2018_01_tidyweek/solution/solution.Rmd: -------------------------------------------------------------------------------- 1 | title: "DC rbokeh" 2 | author: "Radovan Kavicky" 3 | date: "18 March 2018" 4 | output: html_notebook 5 | 6 | data2: https://data.worldbank.org/data-catalog/world-development-indicators 7 | data: https://data.worldbank.org/data-catalog 8 | --- 9 | ###Data from [World bank](https://data.worldbank.org/data-catalog/world-development-indicators), after download, also unzip into "your Working directory/data" and choose/import "WDIData.csv". 10 | #### - First, we will load the .csv (nearly 200 MB/it will take a while) 11 | ```{r, echo=TRUE, message=FALSE, warning = FALSE} 12 | library(readr) 13 | library(dplyr) 14 | dat <- read_csv("data/WDIData.csv") 15 | ``` 16 | ## Data 17 | #### - Check the imported data 18 | ```{r, echo=TRUE, message=FALSE, warning = FALSE} 19 | dat 20 | ``` 21 | ## Data (different view) 22 | #### - Check the data structure (via dplyr's Glimpse function). 23 | ```{r, echo=TRUE, message=FALSE, warning = FALSE} 24 | glimpse(dat) 25 | ``` 26 | ## Let's filter & tidy this mess 27 | #### - Select one (GDP growth from 60's till 2016) indicator for UK, USA and EU + Make checking data via Glimpse great again! 28 | ```{r} 29 | library(tidyr) 30 | dc <- dat %>% 31 | filter(`Indicator Name`=="GDP growth (annual %)") %>% 32 | filter(`Country Name`==c("United Kingdom", "United States", "European Union")) 33 | glimpse(dc) 34 | ``` 35 | ## Still not tidy enough! 36 | #### - Let's tidy some more (select only what we need & transform from wide to long dataset & NA's out). 37 | #### - & Take a Glimpse again. 38 | ```{r} 39 | names(dc) 40 | dc1 <- select(dc, "Country Name", "Indicator Name", 5:61) 41 | dc2 <- gather(dc1, key="Year", value="GDP growth (annual %)", -"Country Name", na.rm=TRUE) 42 | glimpse(dc2) 43 | ``` 44 | ## Still not a tidy dataset! 45 | #### - Let's remove first 3 rows (no data there). 46 | #### - & Glimpse again. 47 | ```{r} 48 | dc2 49 | dc2 <- dc2[-c(1:3), ] 50 | dc2 51 | dc2$Year <- as.numeric(dc2$Year) 52 | dc2$`GDP growth (annual %)` <- as.numeric(dc2$`GDP growth (annual %)`) 53 | class(dc2$Year) 54 | class(dc2$`GDP growth (annual %)`) 55 | glimpse(dc2) 56 | ``` 57 | ## Tidy enough & We can start plotting now! 58 | #### - Let's import rbokeh and make it a line plot by a country. 59 | ```{r, echo=TRUE, message=FALSE, warning = FALSE} 60 | library(rbokeh) 61 | p <- figure(width = 600, height = 350, legend_location = "top_right", title = "GDP Growth (%) USA, EU, UK", logo = NULL) %>% 62 | ly_lines(x="Year", y="GDP growth (annual %)", data = dc2, 63 | alpha = 0.5, width = 5, col = "Country Name") 64 | p 65 | ``` 66 | ## Congratulations, your first plot! But still not publication quality... let's customize some more... 67 | #### - We will add points too. 68 | #### - Put it black and transparent (using alpha transparency) 69 | ```{r, echo=TRUE, message=FALSE, warning = FALSE} 70 | library(rbokeh) 71 | q <- figure(width = 600, height = 350, legend_location = "top_right", title = "GDP Growth (%) USA, EU, UK", logo = NULL) %>% 72 | ly_lines(x="Year", y="GDP growth (annual %)", data = dc2, 73 | alpha = 0.5, width = 5, col = "Country Name") %>% 74 | ly_points(x="Year", y="GDP growth (annual %)", data = dc2, 75 | alpha = 0.5, size = 4, col = "black") 76 | q 77 | ``` 78 | ## Not bad! But still not publication quality... what about the legend? (it is covering our points) 79 | ```{r, echo=TRUE, message=FALSE, warning = FALSE} 80 | library(rbokeh) 81 | r <- figure(width = 600, height = 350, legend_location = "top_right", title = "GDP Growth (%) USA, EU, UK", logo = NULL) %>% 82 | ly_lines(x="Year", y="GDP growth (annual %)", data = dc2, 83 | alpha = 0.5, width = 5, col = "Country Name") %>% 84 | ly_points(x="Year", y="GDP growth (annual %)", data = dc2, 85 | alpha = 0.5, size = 4, col = "black") %>% 86 | theme_legend(border_line_width = 1, background_fill_alpha = 0.1, label_text_font_size = "8pt", label_text_align = "left", label_text_font = "Garamond", label_text_font_style = "bold") 87 | r 88 | ``` 89 | ## Getting better! But still not publication quality... what about the title? 90 | ```{r, echo=TRUE, message=FALSE, warning = FALSE} 91 | library(rbokeh) 92 | s <- figure(width = 600, height = 350, legend_location = "top_right", title = "GDP Growth (%) USA, EU, UK", logo = NULL) %>% 93 | ly_lines(x="Year", y="GDP growth (annual %)", data = dc2, 94 | alpha = 0.5, width = 5, col = "Country Name") %>% 95 | ly_points(x="Year", y="GDP growth (annual %)", data = dc2, 96 | alpha = 0.5, size = 4, col = "black") %>% 97 | theme_legend(border_line_width = 1, background_fill_alpha = 0.1, label_text_font_size = "8pt", label_text_align = "left", label_text_font = "Garamond", label_text_font_style = "bold") %>% 98 | theme_title(text_align = "center", text_font = "Garamond", text_font_size = "14pt", text_baseline = "bottom") 99 | s 100 | ``` 101 | ## Definitely better! But still not publication quality... what about the axis? 102 | #### - Let's make it bold & Garamond too (Pipe it via theme_axis). 103 | ```{r, echo=TRUE, message=FALSE, warning = FALSE} 104 | library(rbokeh) 105 | t <- figure(width = 600, height = 350, legend_location = "top_right", title = "GDP Growth (%) USA, EU, UK", logo = NULL) %>% 106 | ly_lines(x="Year", y="GDP growth (annual %)", data = dc2, 107 | alpha = 0.5, width = 5, col = "Country Name") %>% 108 | ly_points(x="Year", y="GDP growth (annual %)", data = dc2, 109 | alpha = 0.5, size = 4, col = "black") %>% 110 | theme_legend(border_line_width = 1, background_fill_alpha = 0.1, label_text_font_size = "8pt", label_text_align = "left", label_text_font = "Garamond", label_text_font_style = "bold") %>% 111 | theme_title(text_align = "center", text_font = "Garamond", text_font_size = "14pt", text_baseline = "bottom") %>% 112 | theme_axis(axis_label_text_font = "Garamond", 113 | axis_label_text_font_size = "12pt", axis_label_text_font_style = "bold", major_label_text_font = "Garamond", major_label_text_font_size = "10pt", 114 | major_label_text_font_style = "bold") 115 | t 116 | ``` 117 | ## Nearly there! But still not publication quality... it is growth, right? What about to make it visually distinctive (divide positive/negative growth)? 118 | #### - rbokeh allows us to do this via ly_abline, so let's Pipe it there ([a,b] to [0,0]). 119 | ```{r, echo=TRUE, message=FALSE, warning = FALSE} 120 | library(rbokeh) 121 | library(htmlwidgets) 122 | u <- figure(width = 600, height = 350, legend_location = "top_right", title = "GDP Growth (%) USA, EU, UK", logo = NULL, tools = c("pan", "wheel_zoom", "box_zoom", "box_select", "reset", "resize")) %>% 123 | ly_lines(x="Year", y="GDP growth (annual %)", data = dc2, 124 | alpha = 0.5, width = 5, col = "Country Name") %>% 125 | ly_points(x="Year", y="GDP growth (annual %)", data = dc2, 126 | alpha = 0.5, size = 4, col = "black") %>% 127 | theme_legend(border_line_width = 1, background_fill_alpha = 0.1, label_text_font_size = "8pt", label_text_align = "left", label_text_font = "Garamond", label_text_font_style = "bold") %>% 128 | theme_title(text_align = "center", text_font = "Garamond", text_font_size = "14pt", text_baseline = "bottom") %>% 129 | theme_axis(axis_label_text_font = "Garamond", 130 | axis_label_text_font_size = "12pt", axis_label_text_font_style = "bold", major_label_text_font = "Garamond", major_label_text_font_size = "10pt", 131 | major_label_text_font_style = "bold") %>% 132 | ly_abline(a = 0, b = 0, v = NULL, h = NULL, coef = NULL, 133 | color = "black", width = 1, type = 1, legend = NULL, 134 | visible = TRUE) %>% 135 | tool_lasso_select() 136 | u 137 | saveWidget(u, file="rbokeh001.html") 138 | ``` 139 | ##Congratulations! You have now the publication quality figure ready for you & it's all interactive! -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 R for Data Science online learning community 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # TidyWeek 2 | Repo dedicated to Mentorship pilot / TidyWeek
3 | 4 | hashtag: #TidyWeek 5 | 6 | A repo dedicated to a #makeovermonday style weekly projects as result of collaboration between learners and mentors @ R4DS community. 7 | 8 | ![R for Data Science online learning community](https://github.com/rfordatascience/tidyweek/blob/master/rest/31736571%20(1).png) 9 | 10 | # Why TidyWeek? 11 | 12 | We thought it best to have a program name that: 13 | 14 | 1. Follows major themes in the `tidyverse` and R4DS, tidy data and tidy tools! 15 | 2. Reflects the target duration of most projects 16 | 3. Relatively easy to remember twitter hashtag (#TidyWeek) 17 | 18 | # What is Tidy data? Tidy tools? Tidyverse? 19 | 20 | It is a framework and theory by Hadley Wickham that has grown from its humble origins and expanded to [data](http://vita.had.co.nz/papers/tidy-data.pdf), [tools](https://www.tidyverse.org/) and workflows for [working in data science](http://r4ds.had.co.nz/). 21 | 22 | # TidyWeek Project Outline 23 | - Regular/weekly projects as output of the mentor-learner partnership during a week 24 | - Each week a data set will be published (via https://data.world and separate repo with instructions here), this weeks/test here (later will create R4DS profile there +add admins): https://data.world/radovankavicky/2018-01-tidyweek 25 | - Learners will create profile there and a project (we will use `data.world` R package, more @ CRAN here: https://cran.r-project.org/web/packages/data.world/index.html), which automatically generates and RMd notebook that will later be published on GitHub 26 | - Train & walk-through full Data Science cycle/process capturing transformation from raw, messy real-world dataset into tidy form and the output/model, visualization or final document/published via Data.World or as Jupyter notebook with IRkernel via GitHub 27 | - More on #MakeoverMonday here (for those that are not yet familiar with it): http://www.makeovermonday.co.uk 28 | - Share your work via Twitter and other social networks with #TidyWeek (as for the name & hashtag of the project + TidyWeek also because the learners will have to “tidy” the messy/real-world dataset first and will have a week to create the output from the given dataset via RPubs document or a Jupyter notebook published on GitHub with the help/oversight of the mentor) 29 | - Others can/will be able to vote & rank (https://data.world or your own GitHub repo/Rmd Notebook or Jupyter notebook) 30 | - Learners will build their Data Science portfolio/projects that they were working on together with a mentor from R4DS community 31 | 32 | # 1st week/test 33 | [01/2018 - Instructions](https://github.com/rfordatascience/tidyweek/tree/master/2018_01_tidyweek) 34 | 35 | # Schedule 36 | 37 | Test Version | Start/1st #TidyWeek 38 | ------------ | ------------- 39 | End of March | 2nd April 40 | R4DS Mentors | R4DS learners & Mentors 41 | 42 | # Helpful Resources for TidyWeek 43 | 44 | 1. [Working with Git and Github](resources/git.md) 45 | -------------------------------------------------------------------------------- /advanced_level/advanced.csv: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /beginner_level/beginner.csv: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /data/data.csv: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /resources/git.md: -------------------------------------------------------------------------------- 1 | # Working with Git and Github 2 | 3 | ## What is Git? 4 | Git is a version control system for tracking changes in computer files and coordinating work on those files among multiple people. 5 | 6 | ## What is GitHub? 7 | GitHub is a web-based hosting service for version control using git. It offers all the functionality of Git, plus a lot of wonderful extra features. GitHub makes it very easy to share projects across distributed or remote teams and greatly enhances collaboration in collaborative coding and software development. If you're new to Github, learn more through the links below! 8 | 9 | ## The Git and GitHub Workflow 10 | It's best to start with the notion that using Git and GitHub should be essential parts of your workflow. The point of a workflow is to work in a manner that is both efficient and effective. Version control will save you countless headaches and adopting it early will allow it to become second nature quickly. 11 | 12 | A great read on this is ["Excuse me, do you have a moment to talk about version control?"](https://peerj.com/preprints/3159/) 13 | 14 | Here is a great Github fork and pull request video tutorial that goes through a basic workflow in GitHub. 15 | 16 | ## Recommended Git resources for beginners: 17 | 18 | These following resources are fantastic comprehensive guides to working with Git and GitHub, and tie in with using R and RStudio. 19 | 20 | * [Happy Git and GitHub for the useR](http://happygitwithr.com/) 21 | * [Version Control with Git from the Software Carpentries](https://swcarpentry.github.io/git-novice/) 22 | * [Free DataCamp Course: Introduction to Git for Data Science](https://www.datacamp.com/courses/introduction-to-git-for-data-science) 23 | 24 | ## Markdown & GitHub 25 | 26 | * [Mastering Markdown](https://guides.github.com/features/mastering-markdown/), [(pdf)](https://guides.github.com/pdfs/markdown-cheatsheet-online.pdf) 27 | * [Markdown Cheatsheet](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet) 28 | 29 | ## GitHub Tutorial 30 | 31 | https://services.github.com/on-demand/intro-to-github/ 32 | 33 | And then here: Basic GitHub Website Instructions 34 | 35 | 36 | Video from GitHub: GitHub for Beginners 37 | 38 | This tutorial: Try Git gives a good explanation of the steps to contributing to a GitHub repo and hands on experience with using Git in the command prompt. Even if you want to use the GitHub website or GitHub Desktop, it will help you understand what the various terms mean. 39 | 40 | If you're already acquainted with the basics, Github provides tons of training videos on advanced topics on YouTube. 41 | -------------------------------------------------------------------------------- /rest/31736571 (1).png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rfordatascience/tidyweek/f53a5c2bf28fb778892b62504f3862b66d516d7c/rest/31736571 (1).png -------------------------------------------------------------------------------- /rest/README - Copy.md: -------------------------------------------------------------------------------- 1 | # tidyweek 2 | Repo dedicated to Mentorship pilot
3 | #TidyWeek & #TidyTuesday 4 | 5 | # Project Outline 6 | 7 | 8 | # Working in Github 9 | Github is a web based graphical interface based off of Git: a command line tool for version control. If you're new to Github, learn more through the links below! 10 | 11 | For beginners, start here: Good explanation of what GitHub is for 12 | 13 | And then here: Basic GitHub Website Instructions 14 | 15 | Here is a great Github fork and pull request video tutorial that goes through a basic workflow in GitHub. 16 | 17 | Another source for beginners: Happy Git and GitHub for the useR 18 | 19 | Video from GitHub: GitHub for Beginners 20 | 21 | This tutorial: Try Git gives a good explanation of the steps to contributing to a GitHub repo and hands on experience with using Git in the command prompt. Even if you want to use the GitHub website or GitHub Desktop, it will help you understand what the various terms mean. 22 | 23 | If you're already acquainted with the basics, Github provides tons of training videos on advanced topis on YouTube. 24 | -------------------------------------------------------------------------------- /rest/data.world_project.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rfordatascience/tidyweek/f53a5c2bf28fb778892b62504f3862b66d516d7c/rest/data.world_project.png -------------------------------------------------------------------------------- /rest/data.world_r.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rfordatascience/tidyweek/f53a5c2bf28fb778892b62504f3862b66d516d7c/rest/data.world_r.png -------------------------------------------------------------------------------- /rest/data.world_r_tool.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rfordatascience/tidyweek/f53a5c2bf28fb778892b62504f3862b66d516d7c/rest/data.world_r_tool.png -------------------------------------------------------------------------------- /tidyweek.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: Sweave 13 | LaTeX: pdfLaTeX 14 | --------------------------------------------------------------------------------