├── 2018 ├── img │ └── fundacja.png ├── logo_IBID_kolko.png ├── install_packages.R ├── introduction.Rmd ├── data │ ├── data2.csv │ └── data1.csv ├── readme.md ├── dplyr.R └── ggplot2.R ├── .gitignore ├── install_packages.R ├── introduction.Rmd ├── data ├── data2.csv └── data1.csv ├── readme.md ├── dplyr.R └── ggplot2.R /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .RData 4 | .Ruserdata 5 | IBID-R-introduction.Rproj 6 | -------------------------------------------------------------------------------- /2018/img/fundacja.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DominikRafacz/IBID-R-introduction/master/2018/img/fundacja.png -------------------------------------------------------------------------------- /install_packages.R: -------------------------------------------------------------------------------- 1 | install.packages(c("tidyverse", "ggbeeswarm"), repos = "https://cloud.r-project.org/") 2 | -------------------------------------------------------------------------------- /2018/logo_IBID_kolko.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DominikRafacz/IBID-R-introduction/master/2018/logo_IBID_kolko.png -------------------------------------------------------------------------------- /2018/install_packages.R: -------------------------------------------------------------------------------- 1 | install.packages(c("tidyverse", "mlr", "ranger", "ggbeeswarm"), repos = "https://cloud.r-project.org/") 2 | -------------------------------------------------------------------------------- /introduction.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "IBID: Introduction to R" 3 | output: ioslides_presentation 4 | --- 5 | 6 | ```{r setup, include=FALSE} 7 | knitr::opts_chunk$set(echo = FALSE) 8 | ``` 9 | 10 | ## What's R? 11 | 12 | **R** is a powerful environment for statistical computations and presentation of their results. 13 | 14 | ## What's R? 15 | 16 | **R** is a powerful environment for statistical computations and presentation of their results. 17 | 18 | In fact, this presentation is made in **R**. 19 | 20 | ## Agenda 21 | 22 | - 9:00 Part 1 (data processing). 23 | - 12:00 Lunch. 24 | - 13:00 Part II (IDEs in R). 25 | - 13:30 Part III: Data visualization. 26 | 27 | ## What we will learn? 28 | 29 | How to process (alter, aggregate, filter) and visualize typical data in **R** using *tidyverse* tools. 30 | 31 | ## What we won't learn? 32 | 33 | **R**. 34 | 35 | ## What we won't learn? 36 | 37 | **R**. 38 | 39 | *tidyverse* offers solutions only to the most common problems. The majority of these solutions is very good and surpasses base **R** tools. 40 | 41 | Nevertheless, **R** is a much broader environment that could be used in a variety of applications. 42 | 43 | ## Who we are? 44 | 45 | 46 | 47 | ## Why R? 2018 conference 48 | 49 | 50 | 51 | ## Let's start 52 | 53 | https://tinyurl.com/IBID-R-intro 54 | -------------------------------------------------------------------------------- /2018/introduction.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "IBID: Introduction to R" 3 | output: ioslides_presentation 4 | --- 5 | 6 | ```{r setup, include=FALSE} 7 | knitr::opts_chunk$set(echo = FALSE) 8 | ``` 9 | 10 | ## What's R? 11 | 12 | **R** is a powerful environment for statistical computations and presentation of their results. 13 | 14 | ## What's R? 15 | 16 | **R** is a powerful environment for statistical computations and presentation of their results. 17 | 18 | In fact, this presentation is made in **R**. 19 | 20 | ## Agenda 21 | 22 | - 9:00 Part 1 (data processing). 23 | - 12:00 Lunch. 24 | - 13:00 Part II (IDEs in R). 25 | - 13:30 Part III: Data visualization. 26 | 27 | ## What we will learn? 28 | 29 | How to process (alter, aggregate, filter) and visualize typical data in **R** using *tidyverse* tools. 30 | 31 | ## What we won't learn? 32 | 33 | **R**. 34 | 35 | ## What we won't learn? 36 | 37 | **R**. 38 | 39 | *tidyverse* offers solutions only to the most common problems. The majority of these solutions is very good and surpasses base **R** tools. 40 | 41 | Nevertheless, **R** is a much broader environment that could be used in a variety of applications. 42 | 43 | ## Who we are? 44 | 45 | 46 | 47 | ## Why R? 2018 conference 48 | 49 | 50 | 51 | ## Let's start 52 | 53 | https://tinyurl.com/IBID-R-intro 54 | -------------------------------------------------------------------------------- /data/data2.csv: -------------------------------------------------------------------------------- 1 | pathotype,strain 2 | HFEC,5527 3 | HFEC,5528 4 | HFEC,5529 5 | HFEC,5531 6 | HFEC,5532 7 | HFEC,5535 8 | HFEC,5536 9 | HFEC,5537 10 | HFEC,5538 11 | HFEC,5539 12 | HFEC,5540 13 | HFEC,5541 14 | HFEC,5542 15 | HFEC,5543 16 | HFEC,5544 17 | HFEC,5545 18 | HFEC,5547 19 | HFEC,5548 20 | HFEC,5549 21 | HFEC,5551 22 | HFEC,5553 23 | HFEC,5554 24 | HFEC,5555 25 | HFEC,5556 26 | UPEC,5228 27 | UPEC,5229 28 | UPEC,5231 29 | UPEC,5232 30 | UPEC,5233 31 | UPEC,5235 32 | UPEC,5237 33 | UPEC,5238 34 | UPEC,5239 35 | UPEC,5240 36 | UPEC,5241 37 | UPEC,5242 38 | UPEC,5243 39 | UPEC,5245 40 | UPEC,5246 41 | UPEC,5247 42 | UPEC,5248 43 | UPEC,5249 44 | UPEC,5250 45 | UPEC,5253 46 | UPEC,5254 47 | UPEC,5255 48 | UPEC,5256 49 | UPEC,5257 50 | ETEC,5200 51 | ETEC,5201 52 | ETEC,5202 53 | ETEC,5203 54 | ETEC,5204 55 | ETEC,5205 56 | ETEC,5206 57 | ETEC,5286 58 | ETEC,5287 59 | ETEC,5288 60 | ETEC,5637 61 | ETEC,5638 62 | ETEC,5640 63 | ETEC,5641 64 | ETEC,5642 65 | ETEC,5680 66 | ETEC,5682 67 | ETEC,5686 68 | ETEC,5687 69 | ETEC,5688 70 | ETEC,5689 71 | ETEC,5690 72 | ETEC,5691 73 | ETEC,5692 74 | EPEC,5207 75 | EPEC,5208 76 | EPEC,5209 77 | EPEC,5210 78 | EPEC,5211 79 | EPEC,5212 80 | EPEC,5263 81 | EPEC,5267 82 | EPEC,5268 83 | EPEC,5281 84 | EPEC,5282 85 | EPEC,5285 86 | EPEC,5289 87 | EPEC,5291 88 | EPEC,5292 89 | EPEC,5631 90 | EPEC,5632 91 | EPEC,5633 92 | EPEC,5651 93 | EPEC,5652 94 | EPEC,5653 95 | EPEC,5654 96 | EPEC,5655 97 | EPEC,5656 98 | EAEC,5213 99 | EAEC,5214 100 | EAEC,5215 101 | EAEC,5216 102 | EAEC,5260 103 | EAEC,5264 104 | EAEC,5265 105 | EAEC,5266 106 | EAEC,5269 107 | EAEC,5270 108 | EAEC,5271 109 | EAEC,5272 110 | EAEC,5275 111 | EAEC,5276 112 | EAEC,5277 113 | EAEC,5278 114 | EAEC,5279 115 | EAEC,5280 116 | EAEC,5634 117 | EAEC,5635 118 | EAEC,5636 119 | EAEC,5639 120 | EAEC,5643 121 | EAEC,5644 122 | SAEC,5147 123 | SAEC,5148 124 | SAEC,5149 125 | SAEC,5150 126 | SAEC,5151 127 | SAEC,5152 128 | SAEC,5153 129 | SAEC,5155 130 | SAEC,5156 131 | SAEC,5157 132 | SAEC,5158 133 | SAEC,5159 134 | SAEC,5160 135 | SAEC,5161 136 | SAEC,5162 137 | SAEC,5163 138 | SAEC,5164 139 | SAEC,5165 140 | SAEC,5166 141 | SAEC,5167 142 | SAEC,5168 143 | SAEC,5169 144 | SAEC,5170 145 | SAEC,5171 146 | CAEC,5180 147 | CAEC,5181 148 | CAEC,5182 149 | CAEC,5183 150 | CAEC,5184 151 | CAEC,5185 152 | CAEC,5186 153 | CAEC,5187 154 | CAEC,5188 155 | CAEC,5189 156 | CAEC,5190 157 | CAEC,5191 158 | CAEC,5192 159 | CAEC,5193 160 | CAEC,5194 161 | CAEC,5195 162 | CAEC,5196 163 | CAEC,5198 164 | CAEC,5199 165 | AFEC,5559 166 | AFEC,5560 167 | AFEC,5561 168 | AFEC,5562 169 | AFEC,5563 170 | AFEC,5564 171 | AFEC,5565 172 | AFEC,5566 173 | AFEC,5567 174 | AFEC,5568 175 | AFEC,5569 176 | AFEC,5570 177 | AFEC,5571 178 | AFEC,5572 179 | AFEC,5573 180 | AFEC,5574 181 | AFEC,5575 182 | AFEC,5576 183 | AFEC,5577 184 | AFEC,5578 185 | AFEC,5579 186 | AFEC,5580 187 | AFEC,5581 188 | AFEC,5582 189 | -------------------------------------------------------------------------------- /2018/data/data2.csv: -------------------------------------------------------------------------------- 1 | pathotype,strain 2 | HFEC,5527 3 | HFEC,5528 4 | HFEC,5529 5 | HFEC,5531 6 | HFEC,5532 7 | HFEC,5535 8 | HFEC,5536 9 | HFEC,5537 10 | HFEC,5538 11 | HFEC,5539 12 | HFEC,5540 13 | HFEC,5541 14 | HFEC,5542 15 | HFEC,5543 16 | HFEC,5544 17 | HFEC,5545 18 | HFEC,5547 19 | HFEC,5548 20 | HFEC,5549 21 | HFEC,5551 22 | HFEC,5553 23 | HFEC,5554 24 | HFEC,5555 25 | HFEC,5556 26 | UPEC,5228 27 | UPEC,5229 28 | UPEC,5231 29 | UPEC,5232 30 | UPEC,5233 31 | UPEC,5235 32 | UPEC,5237 33 | UPEC,5238 34 | UPEC,5239 35 | UPEC,5240 36 | UPEC,5241 37 | UPEC,5242 38 | UPEC,5243 39 | UPEC,5245 40 | UPEC,5246 41 | UPEC,5247 42 | UPEC,5248 43 | UPEC,5249 44 | UPEC,5250 45 | UPEC,5253 46 | UPEC,5254 47 | UPEC,5255 48 | UPEC,5256 49 | UPEC,5257 50 | ETEC,5200 51 | ETEC,5201 52 | ETEC,5202 53 | ETEC,5203 54 | ETEC,5204 55 | ETEC,5205 56 | ETEC,5206 57 | ETEC,5286 58 | ETEC,5287 59 | ETEC,5288 60 | ETEC,5637 61 | ETEC,5638 62 | ETEC,5640 63 | ETEC,5641 64 | ETEC,5642 65 | ETEC,5680 66 | ETEC,5682 67 | ETEC,5686 68 | ETEC,5687 69 | ETEC,5688 70 | ETEC,5689 71 | ETEC,5690 72 | ETEC,5691 73 | ETEC,5692 74 | EPEC,5207 75 | EPEC,5208 76 | EPEC,5209 77 | EPEC,5210 78 | EPEC,5211 79 | EPEC,5212 80 | EPEC,5263 81 | EPEC,5267 82 | EPEC,5268 83 | EPEC,5281 84 | EPEC,5282 85 | EPEC,5285 86 | EPEC,5289 87 | EPEC,5291 88 | EPEC,5292 89 | EPEC,5631 90 | EPEC,5632 91 | EPEC,5633 92 | EPEC,5651 93 | EPEC,5652 94 | EPEC,5653 95 | EPEC,5654 96 | EPEC,5655 97 | EPEC,5656 98 | EAEC,5213 99 | EAEC,5214 100 | EAEC,5215 101 | EAEC,5216 102 | EAEC,5260 103 | EAEC,5264 104 | EAEC,5265 105 | EAEC,5266 106 | EAEC,5269 107 | EAEC,5270 108 | EAEC,5271 109 | EAEC,5272 110 | EAEC,5275 111 | EAEC,5276 112 | EAEC,5277 113 | EAEC,5278 114 | EAEC,5279 115 | EAEC,5280 116 | EAEC,5634 117 | EAEC,5635 118 | EAEC,5636 119 | EAEC,5639 120 | EAEC,5643 121 | EAEC,5644 122 | SAEC,5147 123 | SAEC,5148 124 | SAEC,5149 125 | SAEC,5150 126 | SAEC,5151 127 | SAEC,5152 128 | SAEC,5153 129 | SAEC,5155 130 | SAEC,5156 131 | SAEC,5157 132 | SAEC,5158 133 | SAEC,5159 134 | SAEC,5160 135 | SAEC,5161 136 | SAEC,5162 137 | SAEC,5163 138 | SAEC,5164 139 | SAEC,5165 140 | SAEC,5166 141 | SAEC,5167 142 | SAEC,5168 143 | SAEC,5169 144 | SAEC,5170 145 | SAEC,5171 146 | CAEC,5180 147 | CAEC,5181 148 | CAEC,5182 149 | CAEC,5183 150 | CAEC,5184 151 | CAEC,5185 152 | CAEC,5186 153 | CAEC,5187 154 | CAEC,5188 155 | CAEC,5189 156 | CAEC,5190 157 | CAEC,5191 158 | CAEC,5192 159 | CAEC,5193 160 | CAEC,5194 161 | CAEC,5195 162 | CAEC,5196 163 | CAEC,5198 164 | CAEC,5199 165 | AFEC,5559 166 | AFEC,5560 167 | AFEC,5561 168 | AFEC,5562 169 | AFEC,5563 170 | AFEC,5564 171 | AFEC,5565 172 | AFEC,5566 173 | AFEC,5567 174 | AFEC,5568 175 | AFEC,5569 176 | AFEC,5570 177 | AFEC,5571 178 | AFEC,5572 179 | AFEC,5573 180 | AFEC,5574 181 | AFEC,5575 182 | AFEC,5576 183 | AFEC,5577 184 | AFEC,5578 185 | AFEC,5579 186 | AFEC,5580 187 | AFEC,5581 188 | AFEC,5582 189 | -------------------------------------------------------------------------------- /readme.md: -------------------------------------------------------------------------------- 1 | # Data Science Workshop - Introduction to Data Science in [R](http://cran.us.r-project.org/) 2 | 3 | This introductory workshop provides training in data mining with the [R](http://cran.us.r-project.org/) environment. Examples presented in the workshop will focus on applications in natural and life sciences. [R](http://cran.us.r-project.org/) is a quickly developing tool for data analysis, embraced both by academia and industry (Google, Microsoft or Facebook). This workshop will introduce attendees to key concepts of data science and give a hands-on experience without requiring a previous knowledge of [R](http://cran.us.r-project.org/). 4 | 5 | 6 | The workshop is part of the two conferences: 7 | 8 | * [International Biotech Innovation Days](https://www.healthcapital.de/en/events/event/ibid-2020-international-biotech-innovation-days/), which will take place from the 27 - 29 October 2020 remotly at the [Brandenburg University of Technology (BTU) Cottbus – Senftenberg](https://www.b-tu.de/en/) (Campus Senftenberg) and 9 | * the [WhyR annual meeting of Polish R community](https://2020.whyr.pl/). 10 | 11 | The workshop will cover fundamental topics including: 12 | 13 | - getting to know [R](http://cran.us.r-project.org/) packages and R-related tools for data science 14 | - data manipulation (import of data, summarizing and transforming large volumes of data), 15 | - data visualization (graphical exploratory analysis, visualization of predictive models). 16 | 17 | Exercises from this workshop employ data from life sciences, with a special focus on biomedical sciences and bioinformatics. Despite that fact, skills mastered during this event can be easily transferred to other applications. 18 | 19 | 20 | ## Target audience: 21 | 22 | The course is tailored for attendees with little to no experience with [R](http://cran.us.r-project.org/). Any programming experience is welcomed, but not required to participate in the workshop. Topics touched during the workshop stuits equally needs of participants from industry and research. 23 | 24 | ## Requirements: 25 | 26 | To participate in this course you are required to bring your own laptop, with a recent version of [R](http://cran.us.r-project.org/) ([http://cran.us.r-project.org/](http://cran.us.r-project.org/)) and [Rstudio](https://www.rstudio.com/products/rstudio/download/) ([https://www.rstudio.com/products/rstudio/download/](https://www.rstudio.com/products/rstudio/download/)). To install necessary packages and download data run in [R](http://cran.us.r-project.org/) the following script: 27 | 28 | ``` 29 | source("https://raw.githubusercontent.com/michbur/IBID-R-introduction/master/install_packages.R") 30 | ``` 31 | ## Instructors: 32 | 33 | [Michał Burdukiewicz](https://www.researchgate.net/profile/Michal_Burdukiewicz) is a bioinformatician at Institute of Biochemistry and Biophysics Polish Academy of Sciences with 14 years of experience with R. He is working on machine learning models for prediction of protein properties. He is passionate about open-source scientific software, Michał established Wrocław [R](http://cran.us.r-project.org/) User Group and organizes Why R? conferences. Michał is author of several research papers, [R](http://cran.us.r-project.org/) software packages and R-based web-server applications. 34 | 35 | [Dominik Rafacz](https://www.researchgate.net/profile/Dominik_Rafacz2) is a student at Warsaw University of Technology at field of Data Science. [R](http://cran.us.r-project.org/), Java and C++ developer, interested especially in designing efficient tools and refining existing ones. He loves to write clean software with attention to comfort of use and maintenance. He takes his first steps in bioinformatics. Hobbistically interested in board games, both in terms of recreation and in terms of strategy analysis and optimization. 36 | 37 | -------------------------------------------------------------------------------- /2018/readme.md: -------------------------------------------------------------------------------- 1 | ![Produced by IBID 2018 and WhyR 2018](https://raw.githubusercontent.com/michbur/IBID-R-introdution/master/logo_IBID_kolko.png) 2 | 3 | # Data Science Workshop - Introduction to Data Science in [R](http://cran.us.r-project.org/) - 4 | 5 | This introductory workshop on the **23. May 2018** provides training in data mining with the [R](http://cran.us.r-project.org/) environment. Examples presented in the workshop will focus on applications in natural and life sciences. [R](http://cran.us.r-project.org/) is a quickly developing tool for data analysis, embraced both by academia and industry (Google, Microsoft or Facebook). This workshop will introduce attendees to key concepts of data science and give a hands-on experience without requiring a previous knowledge of [R](http://cran.us.r-project.org/). 6 | 7 | 8 | The workshop is part of the two conferences: 9 | 10 | * [International Biotech Innovation Days](https://ibid-2018.b2match.io/), wich will take place from the 23 - 25 May 2018 in [Senftenberg, Germany](https://en.wikipedia.org/wiki/Senftenberg) at the [Brandenburg University of Technology (BTU) Cottbus – Senftenberg](https://www.b-tu.de/en/) (Campus Senftenberg) and 11 | * the [WhyR annual meeting of Polish R community](http://whyr2018.pl/). 12 | 13 | Since the [IBID 2018](https://ibid-2018.b2match.io/) is an **open access conference**, participation is **free of charge**. 14 | 15 | The workshop will cover fundamental topics including: 16 | 17 | - getting to know [R](http://cran.us.r-project.org/) packages and R-related tools for data science 18 | - data manipulation (import of data, summarizing and transforming large volumes of data), 19 | - data visualization (graphical exploratory analysis, visualization of predictive models), 20 | - machine learning (feature selection, tuning of predictors). 21 | 22 | Exercises from this workshop employ data from life sciences, with a special focus on biomedical sciences and bioinformatics. Despite that fact, skills mastered during this event can be easily transferred to other applications. 23 | 24 | 25 | ## Target audience: 26 | 27 | The course is tailored for attendees with little to no experience with [R](http://cran.us.r-project.org/). Any programming experience is welcomed, but not required to participate in the workshop. Topics touched during the workshop stuits equally needs of participants from industry and research. 28 | 29 | ## Requirements: 30 | 31 | To participate in this course you are required to bring your own laptop, with a recent version of [R](http://cran.us.r-project.org/) ([http://cran.us.r-project.org/](http://cran.us.r-project.org/)) and [Rstudio](https://www.rstudio.com/products/rstudio/download/) ([https://www.rstudio.com/products/rstudio/download/](https://www.rstudio.com/products/rstudio/download/)). To install necessary packages and download data run in [R](http://cran.us.r-project.org/) the following script: 32 | 33 | ``` 34 | source("https://raw.githubusercontent.com/michbur/IBID-R-introdution/master/install_packages.R") 35 | ``` 36 | 37 | ## Registration: 38 | 39 | Although the event is **free of charge**, participants must register under 40 | 41 | [https://ibid-2018.b2match.io/signup](https://ibid-2018.b2match.io/signup), 42 | 43 | as available places are limited and information is required for certified participation. Participation in the free IBID conference is not necessary but recommended for those who are interrested in latest findings of medical siences and bioinformatics. 44 | 45 | [**Register here**](URL) 46 | 47 | [**The registration is open until 21 May 2018.**](https://ibid-2018.b2match.io/signup) 48 | 49 | 50 | > Limited number of participants: 21 of 30 places left 51 | 52 | ## Instructors: 53 | 54 | [Michał Burdukiewicz](https://www.researchgate.net/profile/Michal_Burdukiewicz) is a bioinformatician at [University of Wrocław](https://international.uni.wroc.pl/en/s3.php) with 10 years of experience with R. As part of his PhD research he is working on bioinformatical tools that can be used for prediction of amyloidogenicity. He is passionate about open-source scientific software, Michał established Wrocław [R](http://cran.us.r-project.org/) User Group and organizes [WhyR 2018](http://whyr2018.pl/) conference. Michał is author of several research papers, [R](http://cran.us.r-project.org/) software packages and R-based web-server applications. 55 | 56 | [Stefan Rödiger](https://www.researchgate.net/profile/Stefan_Roediger) studied pharma-biotechnology and forensics and holds a doctoral degree in medical sciences. He is group leader at the [Brandenburg University of Technology Cottbus – Senftenberg](https://www.b-tu.de/en/), where he works in the field of medical bioinformatics and bioanalytics. He has more than 13 years of experience with [R](http://cran.us.r-project.org/). He is passionate about open-source scientific software and contributed to the [R](http://cran.us.r-project.org/) graphical user interface and integrated development environment [RKWard](https://rkward.kde.org/). Stefan is author of several research papers, [R](http://cran.us.r-project.org/) software packages and contributed to web-server applications. 57 | -------------------------------------------------------------------------------- /dplyr.R: -------------------------------------------------------------------------------- 1 | # https://github.com/michbur/IBID-R-introdution/blob/master/dplyr.R 2 | 3 | library(dplyr) 4 | library(reshape2) 5 | 6 | # Data and data types 7 | 8 | # ctrl + enter to run the line in R 9 | dat <- read.csv("https://raw.githubusercontent.com/michbur/IBID-R-introdution/master/data/data1.csv") 10 | 11 | # readr package: read xls files 12 | 13 | class(dat) 14 | 15 | head(dat) 16 | 17 | colnames(dat) 18 | 19 | dat[["pathotype"]] 20 | class(dat[["pathotype"]]) 21 | 22 | dat[["LB_1"]] 23 | class(dat[["LB_1"]]) 24 | 25 | dat[["strain"]] 26 | class(dat[["strain"]]) 27 | 28 | # pipes 29 | 30 | dat[["strain"]] <- as.character(dat[["strain"]]) 31 | class(dat[["strain"]]) 32 | 33 | mutate(dat, strain = as.character(strain)) 34 | 35 | dat <- read.csv("https://raw.githubusercontent.com/michbur/IBID-R-introdution/master/data/data1.csv") %>% 36 | mutate(strain = as.character(strain)) 37 | 38 | dat[["strain"]] 39 | 40 | # pipe shortcut: ctrl+shift+m 41 | 42 | dat %>% 43 | mutate(mean_LB = mean(c(LB_1, LB_2, LB_3))) 44 | 45 | # Subsetting I: indices 46 | 47 | select(dat, M63_1, M63_2, M63_3) 48 | select(dat, M63_1) %>% head 49 | select_(dat, "M63_1") %>% head 50 | 51 | paste0("M63_", 1L:3) 52 | 53 | select_(dat, .dots = paste0("M63_", 1L:3)) %>% head 54 | 55 | # select selects columns 56 | 57 | slice(dat, 25L:27) 58 | 59 | # slice: extract rows 60 | 61 | # Task: Extract rows 50-57 but only for columns active, pathotype 62 | # and LB_1 63 | 64 | slice(dat, 50L:57) %>% 65 | select(active, pathotype, LB_1) 66 | 67 | 68 | # Melting data: long and wide formats 69 | 70 | melt(dat, variable.name = "medium") 71 | 72 | melt(dat, variable.name = "medium")[["medium"]] 73 | 74 | # separate between medium name and replicate ID 75 | 76 | melt(dat, variable.name = "medium")[["medium"]] %>% 77 | as.character 78 | 79 | melt(dat, variable.name = "medium")[["medium"]] %>% 80 | as.character %>% 81 | strsplit(split = "_") 82 | 83 | melt(dat, variable.name = "medium")[["medium"]] %>% 84 | as.character %>% 85 | strsplit("_") %>% 86 | sapply(first) 87 | 88 | melt(dat, variable.name = "medium") %>% 89 | mutate(medium2 = sapply(strsplit(as.character(medium), "_"), first)) 90 | 91 | x <- c(3, 5, 7, -1, -6) 92 | ifelse(x < 0, 0, x) 93 | 94 | # Task: Change everything in vector x larger than 4 to 4 95 | ifelse(x > 4, 4, x) 96 | 97 | melt(dat, variable.name = "medium") %>% 98 | mutate(medium = sapply(strsplit(as.character(medium), "_"), first), 99 | value = ifelse(value < 0, 0, value)) 100 | 101 | melt(dat, variable.name = "medium") %>% 102 | mutate(medium = sapply(strsplit(as.character(medium), "_"), first), 103 | value = ifelse(value < 0, 0, value)) %>% 104 | group_by(medium) %>% 105 | mutate(value = (max(value) - min(value))/max(value)) 106 | 107 | median_dat <- melt(dat, variable.name = "medium") %>% 108 | mutate(medium = sapply(strsplit(as.character(medium), "_"), first), 109 | value = ifelse(value < 0, 0, value)) %>% 110 | group_by(active, strain, medium) %>% 111 | summarise(value = median(value)) %>% 112 | ungroup 113 | 114 | # Subsetting II: conditions 115 | 116 | filter(median_dat, strain == "5160") 117 | 118 | filter(median_dat, strain == "5160", active %in% c("W1", "W2")) 119 | 120 | # Task 1: filter data for strain 5160 and medium LB 121 | 122 | filter(median_dat, strain == "5160", medium == "LB") 123 | filter(median_dat, strain == "5160", medium %in% "LB") 124 | filter(median_dat, strain %in% "5160", medium %in% "LB") 125 | 126 | # Task 2: filter data for strains 5160 and 5207, and medium LB 127 | filter(median_dat, strain %in% c("5160", "5207"), medium == "LB") 128 | 129 | # Task 3: filter data for strains: 5160, 5207 and 5213, and medium LB 130 | filter(median_dat, strain %in% c("5160", "5207", "5213"), 131 | medium == "LB") 132 | 133 | # Task 3: filter data for strains: 5160, 5207 and 5213, medium LB and 134 | # active W1 135 | 136 | filter(median_dat, strain %in% c("5160", "5207", "5213"), 137 | medium == "LB", 138 | active == "W1") 139 | 140 | filter(median_dat, strain != "5160") 141 | 142 | filter(median_dat, strain == "5160", !(active %in% c("W1", "W2"))) 143 | 144 | # Task: Select data for strain 5207 and NOT medium LB 145 | 146 | filter(median_dat, strain == "5207", medium != "LB") 147 | 148 | # Task: Select data for strain 5207 and NOT mediums LB and M63 149 | 150 | filter(median_dat, strain == "5207", !(medium %in% c("LB", "M63"))) 151 | 152 | filter(median_dat, strain == "5160", active == "W3") 153 | 154 | filter(median_dat, strain == "5160" & active == "W3") 155 | 156 | filter(median_dat, strain == "5160" | active == "W3") 157 | 158 | # Final task: filter data only for strain 5160 and active W2; 159 | # then select only strain, medium and value 160 | 161 | ungroup(median_dat) %>% 162 | filter(strain == "5160", active == "W2") %>% 163 | select(strain, medium, value) 164 | 165 | # Grouping 166 | 167 | group_by(median_dat, active, medium) %>% 168 | summarise(max_value = max(value)) 169 | 170 | # Task: Instead of maximum, compute minimum (min function) 171 | # for each level of active and medium 172 | 173 | group_by(median_dat, active, medium) %>% 174 | summarise(min_value = min(value)) 175 | 176 | group_by(median_dat, active, medium) %>% 177 | summarise(max_value = max(value), 178 | min_value = min(value)) 179 | 180 | # Task: Instead of maximum, compute median (median function) 181 | # for each level of active and medium 182 | 183 | group_by(median_dat, active, medium) %>% 184 | summarise(median_value = median(value)) 185 | 186 | # Task: Instead of maximum, compute median (median function) 187 | # for each level of active 188 | 189 | group_by(median_dat, active) %>% 190 | summarise(median_value = median(value)) 191 | 192 | group_by(median_dat, active) %>% 193 | filter(value == max(value)) 194 | 195 | # Task: Find all strains (grouped by active) that have minimum value 196 | 197 | group_by(median_dat, active) %>% 198 | filter(value == min(value)) 199 | 200 | # Task: Find all strains (grouped by medium) that have maximum value 201 | 202 | group_by(median_dat, medium) %>% 203 | filter(value == max(value)) 204 | 205 | group_by(median_dat, active) %>% 206 | mutate(value = value/max(value)) 207 | 208 | x <- data.frame(value = 1L:10, 209 | variable = c(rep("A", 5), rep("B", 5))) 210 | 211 | mutate(x, norm = value/max(value)) 212 | 213 | group_by(x, variable) %>% 214 | mutate(norm = value/max(value)) 215 | 216 | group_by(x, variable) %>% 217 | mutate(norm = (value - min(value))/(max(value) - min(value))) 218 | 219 | mutate(x, norm = (value - min(value))/(max(value) - min(value))) %>% 220 | group_by(variable) %>% 221 | mutate(norm_grouped = (value - min(value))/(max(value) - min(value))) 222 | 223 | # Task: Find all strains (grouped by active) that have value higher 224 | # than median value (median) 225 | 226 | group_by(median_dat, active) %>% 227 | filter(value > median(value)) 228 | 229 | # Task: Find all strains (grouped by active) that have value higher 230 | # than 0.3 231 | 232 | group_by(median_dat, active) %>% 233 | filter(value > 0.3) 234 | 235 | group_by(median_dat, active) %>% 236 | filter(value > 0.3) %>% 237 | summarise(count = length(value)) 238 | 239 | # Task: Find all strains (grouped by medium) that have value lower 240 | # than 0.05, select columns medium and strain, 241 | # and count number of strains for each medium 242 | 243 | group_by(median_dat, medium) %>% 244 | filter(value < 0.05) %>% 245 | select(medium, strain) %>% 246 | summarise(count = length(strain)) %>% 247 | arrange(desc(count)) 248 | 249 | 250 | # Merging 251 | 252 | dat_np <- select(dat, -pathotype) 253 | pathotype <- read.csv("https://raw.githubusercontent.com/michbur/IBID-R-introdution/master/data/data2.csv") %>% 254 | mutate(strain = as.character(strain)) 255 | 256 | final_dat <- inner_join(dat_np, pathotype, 257 | by = c("strain" = "strain")) 258 | 259 | # Final task: only for mediums LB and BHI count strains with value 260 | # larger than 0.1 for each active 261 | 262 | filter(median_dat, medium %in% c("LB", "BHI"), 263 | value > 0.1) %>% 264 | group_by(active) %>% 265 | summarise(count = length(strain)) 266 | 267 | 268 | # 1. Create a data.frame mean_dat of mean values only for replicates 1-2 for each 269 | # strain and active substance. 270 | # 2. In mean_dat find the minimum value for each medium and pathotype. 271 | # 3. Compute the median value for all pathotypes regardless of the active 272 | # 4. Count the number of strains for each pathotype. 273 | # 5. Find strains with the minimum value for each medium. 274 | 275 | melt(dat, variable.name = "medium") %>% 276 | mutate(medium2 = sapply(strsplit(as.character(medium), "_"), first), 277 | value = ifelse(value < 0, 0, value), 278 | rep_id = sapply(strsplit(as.character(medium), "_"), function(x) x[length(x)])) %>% 279 | filter(rep_id != "3") %>% 280 | group_by(active, strain, medium) %>% 281 | summarise(value = mean(value)) 282 | 283 | group_by(final_dat, pathotype) %>% 284 | summarise(n_strains = length(unique(strain))) 285 | 286 | group_by(final_dat, medium) %>% 287 | filter(value == min(value)) 288 | -------------------------------------------------------------------------------- /2018/dplyr.R: -------------------------------------------------------------------------------- 1 | # https://github.com/michbur/IBID-R-introdution/blob/master/dplyr.R 2 | 3 | library(dplyr) 4 | library(reshape2) 5 | 6 | # Data and data types 7 | 8 | # ctrl + enter to run the line in R 9 | dat <- read.csv("https://raw.githubusercontent.com/michbur/IBID-R-introdution/master/data/data1.csv") 10 | 11 | # readr package: read xls files 12 | 13 | class(dat) 14 | 15 | head(dat) 16 | 17 | colnames(dat) 18 | 19 | dat[["pathotype"]] 20 | class(dat[["pathotype"]]) 21 | 22 | dat[["LB_1"]] 23 | class(dat[["LB_1"]]) 24 | 25 | dat[["strain"]] 26 | class(dat[["strain"]]) 27 | 28 | # pipes 29 | 30 | dat[["strain"]] <- as.character(dat[["strain"]]) 31 | class(dat[["strain"]]) 32 | 33 | mutate(dat, strain = as.character(strain)) 34 | 35 | dat <- read.csv("https://raw.githubusercontent.com/michbur/IBID-R-introdution/master/data/data1.csv") %>% 36 | mutate(strain = as.character(strain)) 37 | 38 | dat[["strain"]] 39 | 40 | # pipe shortcut: ctrl+shift+m 41 | 42 | dat %>% 43 | mutate(mean_LB = mean(c(LB_1, LB_2, LB_3))) 44 | 45 | # Subsetting I: indices 46 | 47 | select(dat, M63_1, M63_2, M63_3) 48 | select(dat, M63_1) %>% head 49 | select_(dat, "M63_1") %>% head 50 | 51 | paste0("M63_", 1L:3) 52 | 53 | select_(dat, .dots = paste0("M63_", 1L:3)) %>% head 54 | 55 | # select selects columns 56 | 57 | slice(dat, 25L:27) 58 | 59 | # slice: extract rows 60 | 61 | # Task: Extract rows 50-57 but only for columns active, pathotype 62 | # and LB_1 63 | 64 | slice(dat, 50L:57) %>% 65 | select(active, pathotype, LB_1) 66 | 67 | 68 | # Melting data: long and wide formats 69 | 70 | melt(dat, variable.name = "medium") 71 | 72 | melt(dat, variable.name = "medium")[["medium"]] 73 | 74 | # separate between medium name and replicate ID 75 | 76 | melt(dat, variable.name = "medium")[["medium"]] %>% 77 | as.character 78 | 79 | melt(dat, variable.name = "medium")[["medium"]] %>% 80 | as.character %>% 81 | strsplit(split = "_") 82 | 83 | melt(dat, variable.name = "medium")[["medium"]] %>% 84 | as.character %>% 85 | strsplit("_") %>% 86 | sapply(first) 87 | 88 | melt(dat, variable.name = "medium") %>% 89 | mutate(medium2 = sapply(strsplit(as.character(medium), "_"), first)) 90 | 91 | x <- c(3, 5, 7, -1, -6) 92 | ifelse(x < 0, 0, x) 93 | 94 | # Task: Change everything in vector x larger than 4 to 4 95 | ifelse(x > 4, 4, x) 96 | 97 | melt(dat, variable.name = "medium") %>% 98 | mutate(medium = sapply(strsplit(as.character(medium), "_"), first), 99 | value = ifelse(value < 0, 0, value)) 100 | 101 | melt(dat, variable.name = "medium") %>% 102 | mutate(medium = sapply(strsplit(as.character(medium), "_"), first), 103 | value = ifelse(value < 0, 0, value)) %>% 104 | group_by(medium) %>% 105 | mutate(value = (max(value) - min(value))/max(value)) 106 | 107 | median_dat <- melt(dat, variable.name = "medium") %>% 108 | mutate(medium = sapply(strsplit(as.character(medium), "_"), first), 109 | value = ifelse(value < 0, 0, value)) %>% 110 | group_by(active, strain, medium) %>% 111 | summarise(value = median(value)) %>% 112 | ungroup 113 | 114 | # Subsetting II: conditions 115 | 116 | filter(median_dat, strain == "5160") 117 | 118 | filter(median_dat, strain == "5160", active %in% c("W1", "W2")) 119 | 120 | # Task 1: filter data for strain 5160 and medium LB 121 | 122 | filter(median_dat, strain == "5160", medium == "LB") 123 | filter(median_dat, strain == "5160", medium %in% "LB") 124 | filter(median_dat, strain %in% "5160", medium %in% "LB") 125 | 126 | # Task 2: filter data for strains 5160 and 5207, and medium LB 127 | filter(median_dat, strain %in% c("5160", "5207"), medium == "LB") 128 | 129 | # Task 3: filter data for strains: 5160, 5207 and 5213, and medium LB 130 | filter(median_dat, strain %in% c("5160", "5207", "5213"), 131 | medium == "LB") 132 | 133 | # Task 3: filter data for strains: 5160, 5207 and 5213, medium LB and 134 | # active W1 135 | 136 | filter(median_dat, strain %in% c("5160", "5207", "5213"), 137 | medium == "LB", 138 | active == "W1") 139 | 140 | filter(median_dat, strain != "5160") 141 | 142 | filter(median_dat, strain == "5160", !(active %in% c("W1", "W2"))) 143 | 144 | # Task: Select data for strain 5207 and NOT medium LB 145 | 146 | filter(median_dat, strain == "5207", medium != "LB") 147 | 148 | # Task: Select data for strain 5207 and NOT mediums LB and M63 149 | 150 | filter(median_dat, strain == "5207", !(medium %in% c("LB", "M63"))) 151 | 152 | filter(median_dat, strain == "5160", active == "W3") 153 | 154 | filter(median_dat, strain == "5160" & active == "W3") 155 | 156 | filter(median_dat, strain == "5160" | active == "W3") 157 | 158 | # Final task: filter data only for strain 5160 and active W2; 159 | # then select only strain, medium and value 160 | 161 | ungroup(median_dat) %>% 162 | filter(strain == "5160", active == "W2") %>% 163 | select(strain, medium, value) 164 | 165 | # Grouping 166 | 167 | group_by(median_dat, active, medium) %>% 168 | summarise(max_value = max(value)) 169 | 170 | # Task: Instead of maximum, compute minimum (min function) 171 | # for each level of active and medium 172 | 173 | group_by(median_dat, active, medium) %>% 174 | summarise(min_value = min(value)) 175 | 176 | group_by(median_dat, active, medium) %>% 177 | summarise(max_value = max(value), 178 | min_value = min(value)) 179 | 180 | # Task: Instead of maximum, compute median (median function) 181 | # for each level of active and medium 182 | 183 | group_by(median_dat, active, medium) %>% 184 | summarise(median_value = median(value)) 185 | 186 | # Task: Instead of maximum, compute median (median function) 187 | # for each level of active 188 | 189 | group_by(median_dat, active) %>% 190 | summarise(median_value = median(value)) 191 | 192 | group_by(median_dat, active) %>% 193 | filter(value == max(value)) 194 | 195 | # Task: Find all strains (grouped by active) that have minimum value 196 | 197 | group_by(median_dat, active) %>% 198 | filter(value == min(value)) 199 | 200 | # Task: Find all strains (grouped by medium) that have maximum value 201 | 202 | group_by(median_dat, medium) %>% 203 | filter(value == max(value)) 204 | 205 | group_by(median_dat, active) %>% 206 | mutate(value = value/max(value)) 207 | 208 | x <- data.frame(value = 1L:10, 209 | variable = c(rep("A", 5), rep("B", 5))) 210 | 211 | mutate(x, norm = value/max(value)) 212 | 213 | group_by(x, variable) %>% 214 | mutate(norm = value/max(value)) 215 | 216 | group_by(x, variable) %>% 217 | mutate(norm = (value - min(value))/(max(value) - min(value))) 218 | 219 | mutate(x, norm = (value - min(value))/(max(value) - min(value))) %>% 220 | group_by(variable) %>% 221 | mutate(norm_grouped = (value - min(value))/(max(value) - min(value))) 222 | 223 | # Task: Find all strains (grouped by active) that have value higher 224 | # than median value (median) 225 | 226 | group_by(median_dat, active) %>% 227 | filter(value > median(value)) 228 | 229 | # Task: Find all strains (grouped by active) that have value higher 230 | # than 0.3 231 | 232 | group_by(median_dat, active) %>% 233 | filter(value > 0.3) 234 | 235 | group_by(median_dat, active) %>% 236 | filter(value > 0.3) %>% 237 | summarise(count = length(value)) 238 | 239 | # Task: Find all strains (grouped by medium) that have value lower 240 | # than 0.05, select columns medium and strain, 241 | # and count number of strains for each medium 242 | 243 | group_by(median_dat, medium) %>% 244 | filter(value < 0.05) %>% 245 | select(medium, strain) %>% 246 | summarise(count = length(strain)) %>% 247 | arrange(desc(count)) 248 | 249 | 250 | # Merging 251 | 252 | dat_np <- select(dat, -pathotype) 253 | pathotype <- read.csv("https://raw.githubusercontent.com/michbur/IBID-R-introdution/master/data/data2.csv") %>% 254 | mutate(strain = as.character(strain)) 255 | 256 | final_dat <- inner_join(dat_np, pathotype, 257 | by = c("strain" = "strain")) 258 | 259 | # Final task: only for mediums LB and BHI count strains with value 260 | # larger than 0.1 for each active 261 | 262 | filter(median_dat, medium %in% c("LB", "BHI"), 263 | value > 0.1) %>% 264 | group_by(active) %>% 265 | summarise(count = length(strain)) 266 | 267 | 268 | # 1. Create a data.frame mean_dat of mean values only for replicates 1-2 for each 269 | # strain and active substance. 270 | # 2. In mean_dat find the minimum value for each medium and pathotype. 271 | # 3. Compute the median value for all pathotypes regardless of the active 272 | # 4. Count the number of strains for each pathotype. 273 | # 5. Find strains with the minimum value for each medium. 274 | 275 | melt(dat, variable.name = "medium") %>% 276 | mutate(medium2 = sapply(strsplit(as.character(medium), "_"), first), 277 | value = ifelse(value < 0, 0, value), 278 | rep_id = sapply(strsplit(as.character(medium), "_"), function(x) x[length(x)])) %>% 279 | filter(rep_id != "3") %>% 280 | group_by(active, strain, medium) %>% 281 | summarise(value = mean(value)) 282 | 283 | group_by(final_dat, pathotype) %>% 284 | summarise(n_strains = length(unique(strain))) 285 | 286 | group_by(final_dat, medium) %>% 287 | filter(value == min(value)) 288 | -------------------------------------------------------------------------------- /ggplot2.R: -------------------------------------------------------------------------------- 1 | # https://tinyurl.com/IBID-R-intro2 2 | # https://github.com/michbur/IBID-R-introdution/blob/master/ggplot2.R 3 | 4 | final_dat <- melt(dat, variable.name = "medium") %>% 5 | mutate(medium = sapply(strsplit(as.character(medium), "_"), first), 6 | value = ifelse(value < 0, 0, value)) %>% 7 | group_by(active, strain, medium) %>% 8 | summarise(value = median(value)) %>% 9 | inner_join(pathotype) %>% 10 | ungroup() 11 | 12 | library(ggplot2) 13 | 14 | # Dot plots 15 | 16 | ggplot(final_dat, aes(x = pathotype, y = value)) + 17 | geom_point() 18 | 19 | set.seed(1410) 20 | ggplot(final_dat, aes(x = pathotype, y = value)) + 21 | geom_point(position = "jitter") 22 | 23 | ggplot(final_dat, aes(x = pathotype, y = value)) + 24 | geom_point(position = "jitter") + 25 | facet_wrap(~ active) 26 | 27 | ggplot(final_dat, aes(x = pathotype, y = value)) + 28 | geom_point(position = "jitter") + 29 | facet_grid(medium ~ active) 30 | 31 | ggplot(final_dat, aes(x = pathotype, y = value)) + 32 | geom_point(position = "jitter") + 33 | facet_grid(medium ~ active, labeller = label_both) 34 | 35 | set.seed(1410) 36 | ggplot(final_dat, aes(x = pathotype, y = value)) + 37 | geom_point(position = "jitter") + 38 | facet_wrap(~ active + medium) 39 | 40 | # Task: Create a plot of relationship between medium and value and 41 | # split it using active and pathotype 42 | 43 | ggplot(final_dat, aes(x = medium, y = value)) + 44 | geom_point(position = "jitter") + 45 | facet_grid(pathotype ~ active, labeller = label_both) 46 | 47 | # Boxplots 48 | 49 | set.seed(1410) 50 | ggplot(final_dat, aes(x = pathotype, y = value)) + 51 | geom_boxplot() + 52 | facet_grid(medium ~ active) 53 | 54 | # Task: Create a boxplot only for active == "W1" and pathotype == "UPEC" 55 | 56 | filter(final_dat, active == "W1", pathotype == "UPEC") %>% 57 | ggplot(aes(x = pathotype, y = value)) + 58 | geom_boxplot() + 59 | facet_grid(medium ~ active) 60 | 61 | # Task: Create a boxplot only for active W1 and W3, on facets 62 | # active and pathotype, map medium to the X-axis 63 | 64 | filter(final_dat, active %in% c("W1", "W3")) %>% 65 | ggplot(aes(x = medium, y = value)) + 66 | geom_boxplot() + 67 | facet_grid(pathotype ~ active) 68 | 69 | # Task: Create a dotplot only for active W1 and W3, on facets 70 | # active and pathotype, map medium to the X-axis 71 | 72 | filter(final_dat, active %in% c("W1", "W3")) %>% 73 | ggplot(aes(x = medium, y = value)) + 74 | geom_point(position = "jitter") + 75 | facet_grid(pathotype ~ active) 76 | 77 | # Beeswarm charts - no more jitter 78 | # install.packages("ggbeeswarm") 79 | library(ggbeeswarm) 80 | 81 | ggplot(final_dat, aes(x = medium, y = value)) + 82 | geom_quasirandom() + 83 | facet_grid(pathotype ~ active, labeller = label_both) 84 | 85 | ggplot(final_dat, aes(x = pathotype, y = value, color = active)) + 86 | geom_quasirandom() + 87 | facet_wrap(~ medium) 88 | 89 | ggplot(final_dat, aes(x = pathotype, y = value, color = active)) + 90 | geom_point(position = "jitter") + 91 | facet_wrap(~ medium) 92 | 93 | ggplot(filter(final_dat, active != "W3"), aes(x = pathotype, y = value, color = active)) + 94 | geom_quasirandom() + 95 | facet_wrap(~ medium) 96 | 97 | # Task: Create a dotplot and beeswarm plot for data where we consider 98 | # only measurements with value higher than 0.1. X axis - medium, 99 | # facets: active 100 | 101 | filter(final_dat, value > 0.1) %>% 102 | ggplot(aes(x = medium, y = value)) + 103 | geom_point() + 104 | facet_wrap(~ active) + 105 | ggtitle("Dot plot") 106 | 107 | filter(final_dat, value > 0.1) %>% 108 | ggplot(aes(x = medium, y = value)) + 109 | geom_quasirandom(groupOnX = TRUE) + 110 | facet_wrap(~ active) + 111 | ggtitle("Beeswarm plot") 112 | 113 | # Customizing facets 114 | 115 | ggplot(final_dat, aes(x = pathotype, y = value, color = active)) + 116 | geom_point(position = "jitter") + 117 | facet_wrap(~ medium) 118 | 119 | ggplot(final_dat, aes(x = pathotype, y = value, color = active)) + 120 | geom_point(position = "jitter") + 121 | facet_wrap(~ medium, scales = "free_y") 122 | 123 | filter(final_dat, pathotype != "EAEC" | medium != "LB") %>% 124 | ggplot(aes(x = pathotype, y = value, color = active)) + 125 | geom_point(position = "jitter") + 126 | facet_wrap(~ medium, scales = "free_x") 127 | 128 | filter(final_dat, pathotype != "EAEC" | medium != "LB") %>% 129 | ggplot(aes(x = pathotype, y = value, color = active)) + 130 | geom_point(position = "jitter") + 131 | facet_wrap(~ medium, scales = "free") 132 | 133 | # Density plots 134 | 135 | ggplot(final_dat, aes(x = value)) + 136 | geom_density() 137 | 138 | ggplot(final_dat, aes(x = value)) + 139 | geom_density() + 140 | facet_wrap(~ medium) 141 | 142 | ggplot(final_dat, aes(x = value)) + 143 | geom_density() + 144 | facet_wrap(~ active) 145 | 146 | ggplot(final_dat, aes(x = value, fill = active)) + 147 | geom_density() + 148 | facet_wrap(~ medium) 149 | 150 | ggplot(final_dat, aes(x = value, fill = active)) + 151 | geom_density(alpha = 0.2) + 152 | facet_wrap(~ medium) 153 | 154 | ggplot(final_dat, aes(x = value, fill = active)) + 155 | geom_density(alpha = 0.2) + 156 | facet_grid(pathotype ~ medium) 157 | 158 | mutate(final_dat, active = factor(active, 159 | levels = c("W2", "W3", "W1"), 160 | labels = c("A1", "Helena Fisher", "W1"))) %>% 161 | ggplot(aes(x = value, fill = active)) + 162 | geom_density(alpha = 0.2) + 163 | facet_wrap(~ medium) 164 | 165 | 166 | # 1. Create a density plot for each pathotype and medium. 167 | 168 | ggplot(final_dat, aes(x = value, fill = medium)) + 169 | geom_density(alpha = 0.2) + 170 | facet_wrap(~ pathotype) 171 | 172 | ggplot(final_dat, aes(x = value)) + 173 | geom_density(alpha = 0.2) + 174 | facet_grid(medium ~ pathotype) 175 | 176 | # Bar plots 177 | 178 | thr_dat <- mutate(final_dat, thr = value > 0.07) 179 | 180 | ggplot(thr_dat, aes(x = thr)) + 181 | geom_bar() 182 | 183 | ggplot(thr_dat, aes(x = thr, fill = medium)) + 184 | geom_bar() 185 | 186 | ggplot(thr_dat, aes(x = thr, fill = medium)) + 187 | geom_bar(position = "stack") 188 | 189 | ggplot(thr_dat, aes(x = thr, fill = medium)) + 190 | geom_bar(position = "fill") 191 | 192 | ggplot(thr_dat, aes(x = thr, fill = medium)) + 193 | geom_bar(position = "stack") + 194 | coord_polar() 195 | 196 | ggplot(thr_dat, aes(x = medium, fill = thr)) + 197 | geom_bar(position = "fill") 198 | 199 | # Task: Using facets and bar charts show threshold data separately 200 | # for each active substance and medium. 201 | 202 | ggplot(thr_dat, aes(x = active, fill = thr)) + 203 | geom_bar(position = "fill") + 204 | facet_wrap(~ medium) 205 | 206 | # 2. Show on a barchart number of strains from each pathotype. 207 | 208 | thr_dat2 <- group_by(thr_dat, medium) %>% 209 | summarise(thr = mean(thr)) 210 | 211 | rbind(mutate(thr_dat2, thr_et = TRUE), 212 | mutate(thr_dat2, thr_et = FALSE, 213 | thr = 1 - thr)) %>% 214 | ggplot(aes(x = medium, y = thr, fill = thr_et, label = formatC(thr, 2))) + 215 | geom_bar(stat = "identity") + 216 | geom_text(vjust = 2) 217 | 218 | 219 | thr_dat <- mutate(final_dat, thr = value > 0.05) 220 | thr_dat2 <- group_by(thr_dat, medium) %>% 221 | summarise(thr = mean(thr)) 222 | rbind(mutate(thr_dat2, thr_et = TRUE), 223 | mutate(thr_dat2, thr_et = FALSE, 224 | thr = 1 - thr)) %>% 225 | ggplot(aes(x = medium, y = thr, fill = thr_et, label = formatC(thr, 2))) + 226 | geom_bar(stat = "identity") + 227 | geom_text(vjust = 2) 228 | 229 | mean_dat <- group_by(final_dat, active, medium, pathotype) %>% 230 | summarise(mean_value = mean(value), 231 | sd_value = sd(value)) 232 | 233 | ggplot(mean_dat, aes(x = pathotype, y = active, fill = mean_value)) + 234 | geom_tile(color = "black") + 235 | facet_wrap(~ medium) 236 | 237 | ggplot(mean_dat, aes(x = pathotype, y = mean_value, fill = medium)) + 238 | geom_bar(position = "dodge", stat = "identity") + 239 | facet_wrap(~ active, ncol = 1) 240 | 241 | ggplot(mean_dat, aes(x = pathotype, y = mean_value, fill = medium)) + 242 | geom_col(position = "dodge") + 243 | facet_wrap(~ active, ncol = 1) 244 | 245 | ggplot(mean_dat, aes(x = pathotype, y = mean_value, fill = medium)) + 246 | geom_col(position = "dodge") + 247 | geom_errorbar(aes(ymax = mean_value + sd_value, ymin = mean_value, color = medium), position = "dodge") + 248 | facet_wrap(~ active, ncol = 1) 249 | 250 | # Task: Using a bar chart compare median values for each medium, 251 | # pathotype and active. 252 | # Use median absolute deviation (mad()) as a dispersion measure. 253 | # mean() - median(); sd() - mad() 254 | 255 | median_dat <- group_by(final_dat, active, medium, pathotype) %>% 256 | summarise(median_value = median(value), 257 | mad_value = mad(value)) %>% 258 | mutate(upper = median_value + mad_value) 259 | 260 | ggplot(median_dat, aes(x = pathotype, y = median_value, fill = medium)) + 261 | geom_col(position = "dodge") + 262 | geom_errorbar(aes(ymax = upper, 263 | ymin = median_value, color = medium), 264 | position = "dodge") + 265 | facet_wrap(~ active, ncol = 1) 266 | 267 | # 2. Using a heat map compare median values for each medium and pathotype. 268 | 269 | ggplot(mean_dat, aes(x = pathotype, y = mean_value, fill = medium)) + 270 | geom_col(position = "dodge") + 271 | geom_errorbar(aes(ymax = mean_value + sd_value, ymin = mean_value, color = medium), position = "dodge") + 272 | facet_wrap(~ active, ncol = 1) + 273 | coord_flip() 274 | 275 | ggplot(mean_dat, aes(x = pathotype, y = mean_value, fill = medium)) + 276 | geom_col(position = "dodge") + 277 | geom_errorbar(aes(ymax = mean_value + sd_value, ymin = mean_value, color = medium), position = "dodge") + 278 | facet_wrap(~ active, nrow = 1) + 279 | coord_flip() 280 | 281 | p <- ggplot(mean_dat, aes(x = pathotype, y = mean_value, fill = medium)) + 282 | geom_col(position = "dodge") + 283 | geom_errorbar(aes(ymax = mean_value + sd_value, ymin = mean_value, color = medium), position = "dodge") + 284 | facet_wrap(~ active, ncol = 1) 285 | 286 | # Styling 287 | 288 | p + theme(axis.text.x = element_text(angle = 90, hjust = 1)) 289 | 290 | p + theme(axis.text.x = element_text(angle = 90, hjust = 1), 291 | legend.position = "bottom") 292 | 293 | my_theme <- theme(axis.text.x = element_text(angle = 90, hjust = 1), 294 | legend.position = "bottom") 295 | 296 | ggplot(thr_dat, aes(x = medium, fill = thr)) + 297 | geom_bar(position = "fill") + 298 | my_theme 299 | 300 | # 1. Create your own theme. See ?theme 301 | # 2. Try existing themes (for example theme_bw) 302 | 303 | ggplot(thr_dat, aes(x = medium, fill = thr)) + 304 | geom_bar(position = "fill") 305 | 306 | unique(thr_dat[["medium"]]) 307 | 308 | thr_dat2 <- mutate(thr_dat, 309 | medium = factor(medium, levels = c("LB", "BHI", "M63", "TSB"))) 310 | 311 | ggplot(thr_dat2, aes(x = medium, fill = thr)) + 312 | geom_bar(position = "fill") 313 | 314 | # Reverse the sequence of active (W3, W2, W1) and create a bar chart, 315 | # with the fraction of strains above threshold for each possible value 316 | # of active. 317 | 318 | ggplot(thr_dat, aes(x = medium, fill = thr)) + 319 | geom_bar(position = "fill") + 320 | scale_fill_discrete("Threshold") 321 | 322 | ggplot(thr_dat, aes(x = medium, fill = thr)) + 323 | geom_bar(position = "fill") + 324 | scale_fill_manual("Threshold", values = c("orange", "lightblue3")) 325 | 326 | ggplot(mean_dat, aes(x = pathotype, y = active, fill = mean_value, color = sd_value)) + 327 | geom_tile(color = "black") + 328 | geom_point() + 329 | facet_wrap(~ medium) + 330 | scale_color_continuous(low = "white", high = "black") 331 | 332 | ggplot(mean_dat, aes(x = pathotype, y = active, fill = mean_value, color = sd_value)) + 333 | geom_tile(color = "black") + 334 | geom_point() + 335 | facet_wrap(~ medium) + 336 | scale_color_continuous(low = "white", high = "black") + 337 | scale_fill_continuous(low = "blue", high = "red") 338 | 339 | ggplot(mean_dat, aes(x = pathotype, y = active, color = mean_value, size = sd_value)) + 340 | geom_point() + 341 | facet_wrap(~ medium) 342 | 343 | ggplot(mean_dat, aes(x = pathotype, y = active, color = mean_value, size = sd_value)) + 344 | geom_point() + 345 | facet_wrap(~ medium) + 346 | scale_size_continuous(range = c(5, 10)) 347 | 348 | # Task: Create a heatmap with gradient scale, 349 | # midpoint should be a median of mean_value 350 | 351 | ggplot(mean_dat, aes(x = pathotype, y = active, fill = mean_value, color = sd_value)) + 352 | geom_tile(color = "black") + 353 | facet_wrap(~ medium) + 354 | scale_fill_gradient2(low = "blue", high = "red", mid = "white", 355 | midpoint = (max(mean_dat[["mean_value"]]) - min(mean_dat[["mean_value"]]))/2) 356 | 357 | 358 | ggplot(final_dat, aes(x = value, fill = active)) + 359 | geom_density(alpha = 0.2) + 360 | facet_wrap( ~ medium) 361 | 362 | ggplot(final_dat, aes(x = value, fill = active)) + 363 | geom_density(alpha = 0.2) + 364 | facet_wrap( ~ medium) + 365 | coord_cartesian(xlim = c(0, 0.1)) 366 | 367 | # interactive plots 368 | 369 | library(plotly) 370 | ggplot(thr_dat, aes(x = medium, fill = thr)) + 371 | geom_bar(position = "fill") 372 | 373 | ggplotly(ggplot(thr_dat, aes(x = medium, fill = thr)) + 374 | geom_bar(position = "fill")) 375 | 376 | ggplotly(ggplot(final_dat, aes(x = pathotype, y = value, color = active)) + 377 | geom_point(position = "jitter") + 378 | facet_wrap(~ medium)) 379 | 380 | 381 | ggplotly(ggplot(final_dat, aes(x = pathotype, y = value, color = active)) + 382 | geom_quasirandom() + 383 | facet_wrap(~ medium)) 384 | -------------------------------------------------------------------------------- /2018/ggplot2.R: -------------------------------------------------------------------------------- 1 | # https://tinyurl.com/IBID-R-intro2 2 | # https://github.com/michbur/IBID-R-introdution/blob/master/ggplot2.R 3 | 4 | final_dat <- melt(dat, variable.name = "medium") %>% 5 | mutate(medium = sapply(strsplit(as.character(medium), "_"), first), 6 | value = ifelse(value < 0, 0, value)) %>% 7 | group_by(active, strain, medium) %>% 8 | summarise(value = median(value)) %>% 9 | inner_join(pathotype) %>% 10 | ungroup() 11 | 12 | library(ggplot2) 13 | 14 | # Dot plots 15 | 16 | ggplot(final_dat, aes(x = pathotype, y = value)) + 17 | geom_point() 18 | 19 | set.seed(1410) 20 | ggplot(final_dat, aes(x = pathotype, y = value)) + 21 | geom_point(position = "jitter") 22 | 23 | ggplot(final_dat, aes(x = pathotype, y = value)) + 24 | geom_point(position = "jitter") + 25 | facet_wrap(~ active) 26 | 27 | ggplot(final_dat, aes(x = pathotype, y = value)) + 28 | geom_point(position = "jitter") + 29 | facet_grid(medium ~ active) 30 | 31 | ggplot(final_dat, aes(x = pathotype, y = value)) + 32 | geom_point(position = "jitter") + 33 | facet_grid(medium ~ active, labeller = label_both) 34 | 35 | set.seed(1410) 36 | ggplot(final_dat, aes(x = pathotype, y = value)) + 37 | geom_point(position = "jitter") + 38 | facet_wrap(~ active + medium) 39 | 40 | # Task: Create a plot of relationship between medium and value and 41 | # split it using active and pathotype 42 | 43 | ggplot(final_dat, aes(x = medium, y = value)) + 44 | geom_point(position = "jitter") + 45 | facet_grid(pathotype ~ active, labeller = label_both) 46 | 47 | # Boxplots 48 | 49 | set.seed(1410) 50 | ggplot(final_dat, aes(x = pathotype, y = value)) + 51 | geom_boxplot() + 52 | facet_grid(medium ~ active) 53 | 54 | # Task: Create a boxplot only for active == "W1" and pathotype == "UPEC" 55 | 56 | filter(final_dat, active == "W1", pathotype == "UPEC") %>% 57 | ggplot(aes(x = pathotype, y = value)) + 58 | geom_boxplot() + 59 | facet_grid(medium ~ active) 60 | 61 | # Task: Create a boxplot only for active W1 and W3, on facets 62 | # active and pathotype, map medium to the X-axis 63 | 64 | filter(final_dat, active %in% c("W1", "W3")) %>% 65 | ggplot(aes(x = medium, y = value)) + 66 | geom_boxplot() + 67 | facet_grid(pathotype ~ active) 68 | 69 | # Task: Create a dotplot only for active W1 and W3, on facets 70 | # active and pathotype, map medium to the X-axis 71 | 72 | filter(final_dat, active %in% c("W1", "W3")) %>% 73 | ggplot(aes(x = medium, y = value)) + 74 | geom_point(position = "jitter") + 75 | facet_grid(pathotype ~ active) 76 | 77 | # Beeswarm charts - no more jitter 78 | # install.packages("ggbeeswarm") 79 | library(ggbeeswarm) 80 | 81 | ggplot(final_dat, aes(x = medium, y = value)) + 82 | geom_quasirandom() + 83 | facet_grid(pathotype ~ active, labeller = label_both) 84 | 85 | ggplot(final_dat, aes(x = pathotype, y = value, color = active)) + 86 | geom_quasirandom() + 87 | facet_wrap(~ medium) 88 | 89 | ggplot(final_dat, aes(x = pathotype, y = value, color = active)) + 90 | geom_point(position = "jitter") + 91 | facet_wrap(~ medium) 92 | 93 | ggplot(filter(final_dat, active != "W3"), aes(x = pathotype, y = value, color = active)) + 94 | geom_quasirandom() + 95 | facet_wrap(~ medium) 96 | 97 | # Task: Create a dotplot and beeswarm plot for data where we consider 98 | # only measurements with value higher than 0.1. X axis - medium, 99 | # facets: active 100 | 101 | filter(final_dat, value > 0.1) %>% 102 | ggplot(aes(x = medium, y = value)) + 103 | geom_point() + 104 | facet_wrap(~ active) + 105 | ggtitle("Dot plot") 106 | 107 | filter(final_dat, value > 0.1) %>% 108 | ggplot(aes(x = medium, y = value)) + 109 | geom_quasirandom(groupOnX = TRUE) + 110 | facet_wrap(~ active) + 111 | ggtitle("Beeswarm plot") 112 | 113 | # Customizing facets 114 | 115 | ggplot(final_dat, aes(x = pathotype, y = value, color = active)) + 116 | geom_point(position = "jitter") + 117 | facet_wrap(~ medium) 118 | 119 | ggplot(final_dat, aes(x = pathotype, y = value, color = active)) + 120 | geom_point(position = "jitter") + 121 | facet_wrap(~ medium, scales = "free_y") 122 | 123 | filter(final_dat, pathotype != "EAEC" | medium != "LB") %>% 124 | ggplot(aes(x = pathotype, y = value, color = active)) + 125 | geom_point(position = "jitter") + 126 | facet_wrap(~ medium, scales = "free_x") 127 | 128 | filter(final_dat, pathotype != "EAEC" | medium != "LB") %>% 129 | ggplot(aes(x = pathotype, y = value, color = active)) + 130 | geom_point(position = "jitter") + 131 | facet_wrap(~ medium, scales = "free") 132 | 133 | # Density plots 134 | 135 | ggplot(final_dat, aes(x = value)) + 136 | geom_density() 137 | 138 | ggplot(final_dat, aes(x = value)) + 139 | geom_density() + 140 | facet_wrap(~ medium) 141 | 142 | ggplot(final_dat, aes(x = value)) + 143 | geom_density() + 144 | facet_wrap(~ active) 145 | 146 | ggplot(final_dat, aes(x = value, fill = active)) + 147 | geom_density() + 148 | facet_wrap(~ medium) 149 | 150 | ggplot(final_dat, aes(x = value, fill = active)) + 151 | geom_density(alpha = 0.2) + 152 | facet_wrap(~ medium) 153 | 154 | ggplot(final_dat, aes(x = value, fill = active)) + 155 | geom_density(alpha = 0.2) + 156 | facet_grid(pathotype ~ medium) 157 | 158 | mutate(final_dat, active = factor(active, 159 | levels = c("W2", "W3", "W1"), 160 | labels = c("A1", "Helena Fisher", "W1"))) %>% 161 | ggplot(aes(x = value, fill = active)) + 162 | geom_density(alpha = 0.2) + 163 | facet_wrap(~ medium) 164 | 165 | 166 | # 1. Create a density plot for each pathotype and medium. 167 | 168 | ggplot(final_dat, aes(x = value, fill = medium)) + 169 | geom_density(alpha = 0.2) + 170 | facet_wrap(~ pathotype) 171 | 172 | ggplot(final_dat, aes(x = value)) + 173 | geom_density(alpha = 0.2) + 174 | facet_grid(medium ~ pathotype) 175 | 176 | # Bar plots 177 | 178 | thr_dat <- mutate(final_dat, thr = value > 0.07) 179 | 180 | ggplot(thr_dat, aes(x = thr)) + 181 | geom_bar() 182 | 183 | ggplot(thr_dat, aes(x = thr, fill = medium)) + 184 | geom_bar() 185 | 186 | ggplot(thr_dat, aes(x = thr, fill = medium)) + 187 | geom_bar(position = "stack") 188 | 189 | ggplot(thr_dat, aes(x = thr, fill = medium)) + 190 | geom_bar(position = "fill") 191 | 192 | ggplot(thr_dat, aes(x = thr, fill = medium)) + 193 | geom_bar(position = "stack") + 194 | coord_polar() 195 | 196 | ggplot(thr_dat, aes(x = medium, fill = thr)) + 197 | geom_bar(position = "fill") 198 | 199 | # Task: Using facets and bar charts show threshold data separately 200 | # for each active substance and medium. 201 | 202 | ggplot(thr_dat, aes(x = active, fill = thr)) + 203 | geom_bar(position = "fill") + 204 | facet_wrap(~ medium) 205 | 206 | # 2. Show on a barchart number of strains from each pathotype. 207 | 208 | thr_dat2 <- group_by(thr_dat, medium) %>% 209 | summarise(thr = mean(thr)) 210 | 211 | rbind(mutate(thr_dat2, thr_et = TRUE), 212 | mutate(thr_dat2, thr_et = FALSE, 213 | thr = 1 - thr)) %>% 214 | ggplot(aes(x = medium, y = thr, fill = thr_et, label = formatC(thr, 2))) + 215 | geom_bar(stat = "identity") + 216 | geom_text(vjust = 2) 217 | 218 | 219 | thr_dat <- mutate(final_dat, thr = value > 0.05) 220 | thr_dat2 <- group_by(thr_dat, medium) %>% 221 | summarise(thr = mean(thr)) 222 | rbind(mutate(thr_dat2, thr_et = TRUE), 223 | mutate(thr_dat2, thr_et = FALSE, 224 | thr = 1 - thr)) %>% 225 | ggplot(aes(x = medium, y = thr, fill = thr_et, label = formatC(thr, 2))) + 226 | geom_bar(stat = "identity") + 227 | geom_text(vjust = 2) 228 | 229 | mean_dat <- group_by(final_dat, active, medium, pathotype) %>% 230 | summarise(mean_value = mean(value), 231 | sd_value = sd(value)) 232 | 233 | ggplot(mean_dat, aes(x = pathotype, y = active, fill = mean_value)) + 234 | geom_tile(color = "black") + 235 | facet_wrap(~ medium) 236 | 237 | ggplot(mean_dat, aes(x = pathotype, y = mean_value, fill = medium)) + 238 | geom_bar(position = "dodge", stat = "identity") + 239 | facet_wrap(~ active, ncol = 1) 240 | 241 | ggplot(mean_dat, aes(x = pathotype, y = mean_value, fill = medium)) + 242 | geom_col(position = "dodge") + 243 | facet_wrap(~ active, ncol = 1) 244 | 245 | ggplot(mean_dat, aes(x = pathotype, y = mean_value, fill = medium)) + 246 | geom_col(position = "dodge") + 247 | geom_errorbar(aes(ymax = mean_value + sd_value, ymin = mean_value, color = medium), position = "dodge") + 248 | facet_wrap(~ active, ncol = 1) 249 | 250 | # Task: Using a bar chart compare median values for each medium, 251 | # pathotype and active. 252 | # Use median absolute deviation (mad()) as a dispersion measure. 253 | # mean() - median(); sd() - mad() 254 | 255 | median_dat <- group_by(final_dat, active, medium, pathotype) %>% 256 | summarise(median_value = median(value), 257 | mad_value = mad(value)) %>% 258 | mutate(upper = median_value + mad_value) 259 | 260 | ggplot(median_dat, aes(x = pathotype, y = median_value, fill = medium)) + 261 | geom_col(position = "dodge") + 262 | geom_errorbar(aes(ymax = upper, 263 | ymin = median_value, color = medium), 264 | position = "dodge") + 265 | facet_wrap(~ active, ncol = 1) 266 | 267 | # 2. Using a heat map compare median values for each medium and pathotype. 268 | 269 | ggplot(mean_dat, aes(x = pathotype, y = mean_value, fill = medium)) + 270 | geom_col(position = "dodge") + 271 | geom_errorbar(aes(ymax = mean_value + sd_value, ymin = mean_value, color = medium), position = "dodge") + 272 | facet_wrap(~ active, ncol = 1) + 273 | coord_flip() 274 | 275 | ggplot(mean_dat, aes(x = pathotype, y = mean_value, fill = medium)) + 276 | geom_col(position = "dodge") + 277 | geom_errorbar(aes(ymax = mean_value + sd_value, ymin = mean_value, color = medium), position = "dodge") + 278 | facet_wrap(~ active, nrow = 1) + 279 | coord_flip() 280 | 281 | p <- ggplot(mean_dat, aes(x = pathotype, y = mean_value, fill = medium)) + 282 | geom_col(position = "dodge") + 283 | geom_errorbar(aes(ymax = mean_value + sd_value, ymin = mean_value, color = medium), position = "dodge") + 284 | facet_wrap(~ active, ncol = 1) 285 | 286 | # Styling 287 | 288 | p + theme(axis.text.x = element_text(angle = 90, hjust = 1)) 289 | 290 | p + theme(axis.text.x = element_text(angle = 90, hjust = 1), 291 | legend.position = "bottom") 292 | 293 | my_theme <- theme(axis.text.x = element_text(angle = 90, hjust = 1), 294 | legend.position = "bottom") 295 | 296 | ggplot(thr_dat, aes(x = medium, fill = thr)) + 297 | geom_bar(position = "fill") + 298 | my_theme 299 | 300 | # 1. Create your own theme. See ?theme 301 | # 2. Try existing themes (for example theme_bw) 302 | 303 | ggplot(thr_dat, aes(x = medium, fill = thr)) + 304 | geom_bar(position = "fill") 305 | 306 | unique(thr_dat[["medium"]]) 307 | 308 | thr_dat2 <- mutate(thr_dat, 309 | medium = factor(medium, levels = c("LB", "BHI", "M63", "TSB"))) 310 | 311 | ggplot(thr_dat2, aes(x = medium, fill = thr)) + 312 | geom_bar(position = "fill") 313 | 314 | # Reverse the sequence of active (W3, W2, W1) and create a bar chart, 315 | # with the fraction of strains above threshold for each possible value 316 | # of active. 317 | 318 | ggplot(thr_dat, aes(x = medium, fill = thr)) + 319 | geom_bar(position = "fill") + 320 | scale_fill_discrete("Threshold") 321 | 322 | ggplot(thr_dat, aes(x = medium, fill = thr)) + 323 | geom_bar(position = "fill") + 324 | scale_fill_manual("Threshold", values = c("orange", "lightblue3")) 325 | 326 | ggplot(mean_dat, aes(x = pathotype, y = active, fill = mean_value, color = sd_value)) + 327 | geom_tile(color = "black") + 328 | geom_point() + 329 | facet_wrap(~ medium) + 330 | scale_color_continuous(low = "white", high = "black") 331 | 332 | ggplot(mean_dat, aes(x = pathotype, y = active, fill = mean_value, color = sd_value)) + 333 | geom_tile(color = "black") + 334 | geom_point() + 335 | facet_wrap(~ medium) + 336 | scale_color_continuous(low = "white", high = "black") + 337 | scale_fill_continuous(low = "blue", high = "red") 338 | 339 | ggplot(mean_dat, aes(x = pathotype, y = active, color = mean_value, size = sd_value)) + 340 | geom_point() + 341 | facet_wrap(~ medium) 342 | 343 | ggplot(mean_dat, aes(x = pathotype, y = active, color = mean_value, size = sd_value)) + 344 | geom_point() + 345 | facet_wrap(~ medium) + 346 | scale_size_continuous(range = c(5, 10)) 347 | 348 | # Task: Create a heatmap with gradient scale, 349 | # midpoint should be a median of mean_value 350 | 351 | ggplot(mean_dat, aes(x = pathotype, y = active, fill = mean_value, color = sd_value)) + 352 | geom_tile(color = "black") + 353 | facet_wrap(~ medium) + 354 | scale_fill_gradient2(low = "blue", high = "red", mid = "white", 355 | midpoint = (max(mean_dat[["mean_value"]]) - min(mean_dat[["mean_value"]]))/2) 356 | 357 | 358 | ggplot(final_dat, aes(x = value, fill = active)) + 359 | geom_density(alpha = 0.2) + 360 | facet_wrap( ~ medium) 361 | 362 | ggplot(final_dat, aes(x = value, fill = active)) + 363 | geom_density(alpha = 0.2) + 364 | facet_wrap( ~ medium) + 365 | coord_cartesian(xlim = c(0, 0.1)) 366 | 367 | # interactive plots 368 | 369 | library(plotly) 370 | ggplot(thr_dat, aes(x = medium, fill = thr)) + 371 | geom_bar(position = "fill") 372 | 373 | ggplotly(ggplot(thr_dat, aes(x = medium, fill = thr)) + 374 | geom_bar(position = "fill")) 375 | 376 | ggplotly(ggplot(final_dat, aes(x = pathotype, y = value, color = active)) + 377 | geom_point(position = "jitter") + 378 | facet_wrap(~ medium)) 379 | 380 | 381 | ggplotly(ggplot(final_dat, aes(x = pathotype, y = value, color = active)) + 382 | geom_quasirandom() + 383 | facet_wrap(~ medium)) 384 | -------------------------------------------------------------------------------- /data/data1.csv: -------------------------------------------------------------------------------- 1 | "active","pathotype","strain","LB_1","LB_2","LB_3","TSB_1","TSB_2","TSB_3","BHI_1","BHI_2","BHI_3","M63_1","M63_2","M63_3" 2 | "W1","HFEC",5527,0.0192,0.0307,0.015,0.012,0.028,0.0135,0.0203,0.0224,0.0315,0.0212,0.0712,0.0569 3 | "W1","HFEC",5528,0.008,0.0168,0.0094,0.0087,0.0291,0.0318,0.0131,0.0783,0.0231,0.016,0.1241,0.0166 4 | "W1","HFEC",5529,0.0178,0.0265,0.0189,0.0148,0.0221,0.0085,0.0209,0.0289,0.0379,0.0406,0.0966,0.1916 5 | "W1","HFEC",5531,0.0203,0.0668,0.0313,0.014,0.0302,0.048,0.0488,0.0476,0.0476,0.0093,0.0181,0.0084 6 | "W1","HFEC",5532,0.0919,0.0519,0.1426,0.1094,0.0172,0.096,0.1783,0.1611,0.3097,0.0299,0.0677,0.0153 7 | "W1","HFEC",5535,0.0055,0.046,0.0351,0.0209,0.0858,0.0394,0.0325,0.12,0.064,0.0138,0.0108,0.0148 8 | "W1","HFEC",5536,0.0135,0.0521,0.0361,0.0208,0.0286,0.0888,0.0281,0.055,0.0925,0.0103,0.0809,0.0659 9 | "W1","HFEC",5537,0.0167,0.0354,0.042,0.0214,0.0333,0.1124,0.0328,0.0443,0.0747,0.0309,0.1071,0.0503 10 | "W1","HFEC",5538,0.0201,0.1028,0.0207,0.0283,0.0269,0.0229,0.031,0.0974,0.0424,0.008,0.0082,0.0083 11 | "W1","HFEC",5539,0.0202,0.0604,0.0513,0.0457,0.0959,0.0415,0.0287,0.0584,0.04,0.208,0.0414,0.095 12 | "W1","HFEC",5540,0.0162,0.0466,0.0166,0.0212,0.0351,0.0238,0.0247,0.0571,0.0358,0.0111,0.0146,0.0146 13 | "W1","HFEC",5541,0.0425,0.0711,0.0501,0.0463,0.0598,0.0553,0.0475,0.0607,0.0811,0.0135,0.0131,0.0275 14 | "W1","HFEC",5542,0.0395,0.0715,0.0267,0.0288,0.1153,0.0317,0.0392,0.0642,0.0336,0.0308,0.027,0.0397 15 | "W1","HFEC",5543,0.0194,0.0342,0.0193,0.0131,0.0229,0.0303,0.0434,0.0647,0.0347,0.0098,0.0221,0.0202 16 | "W1","HFEC",5544,0.0286,0.0223,0.0174,0.0407,0.0135,0.0113,0.0413,0.0313,0.0201,0.0089,0.0094,0.0081 17 | "W1","HFEC",5545,0.0468,0.0472,0.0446,0.0319,0.0359,0.0837,0.2129,0.0865,0.0964,0.0479,0.1207,0.0295 18 | "W1","HFEC",5547,0.0426,0.0253,0.0407,0.0207,0.0214,0.0285,0.0444,0.019,0.0259,0.0636,0.0415,0.0381 19 | "W1","HFEC",5548,0.0259,0.1089,0.0417,0.0148,0.0266,0.0233,0.0538,0.0443,0.0339,0.1298,0.0685,0.014 20 | "W1","HFEC",5549,0.0473,0.0314,0.0352,0.0222,0.0108,0.0378,0.0568,0.0457,0.0384,0.0153,0.0445,0.0132 21 | "W1","HFEC",5551,0.1337,0.0859,0.0985,0.1378,0.0874,0.1073,0.1418,0.0751,0.1201,0.1075,0.0564,0.0232 22 | "W1","HFEC",5553,0.0426,0.0729,0.022,0.0402,0.0581,0.0582,0.0688,0.0687,0.0347,0.1664,0.0671,0.0287 23 | "W1","HFEC",5554,0.0335,0.0476,0.0281,0.045,0.0575,0.0677,0.0443,0.0696,0.0671,0.009,0.0187,0.0184 24 | "W1","HFEC",5555,0.0197,0.069,0.021,0.0471,0.0564,0.0429,0.0357,0.089,0.0445,0.0103,0.0157,0.0176 25 | "W1","HFEC",5556,0.0232,0.0877,0.0157,0.0303,0.043,0.0151,0.0367,0.0981,0.029,0.1391,0.1483,0.0659 26 | "W1","UPEC",5228,0.033,0.0321,0.0172,0.0297,0.0339,0.0131,0.0341,0.0327,0.0288,0.017,0.1651,0.0313 27 | "W1","UPEC",5229,0.0171,0.0194,0.006,0.0273,0.0257,0.0118,0.0436,0.0471,0.0101,-0.0012,0.008,0.0035 28 | "W1","UPEC",5231,0.0286,0.0234,0.0102,0.0379,0.0212,0.0118,0.0342,0.0426,0.0118,0.0322,0.3505,0.053 29 | "W1","UPEC",5232,0.04,0.0215,0.0153,0.0266,0.0209,0.0118,0.0352,0.0276,0.0247,-0.0026,0.0126,0.004 30 | "W1","UPEC",5233,0.0943,0.0214,0.0205,0.0578,0.015,0.0197,0.1456,0.0251,0.0292,0.174,0.1002,0.0476 31 | "W1","UPEC",5235,0.0833,0.0227,0.0228,0.0753,0.0236,0.0289,0.1404,0.0831,0.0573,0.0192,0.0241,0.0202 32 | "W1","UPEC",5237,0.0579,0.0269,0.0115,0.0308,0.0266,0.0175,0.0923,0.0529,0.0341,0.017,0.0205,0.0073 33 | "W1","UPEC",5238,0.054,0.0276,0.0093,0.0889,0.0347,0.0088,0.0683,0.0329,0.0123,0.0307,0.0509,0.0474 34 | "W1","UPEC",5239,0.1013,0.0565,0.0207,0.1078,0.0403,0.0323,0.1208,0.0505,0.0315,0.015,0.0165,0.0123 35 | 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