├── img └── tidyverse2.png ├── FB_Live_tidyverse.Rproj ├── .gitignore ├── LICENSE ├── README.md ├── FB_tidy_NB.Rmd ├── FB_tidy_NB_soln.Rmd └── data └── train.csv /img/tidyverse2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datacamp/datacamp_facebook_live_intro_to_tidyverse/master/img/tidyverse2.png -------------------------------------------------------------------------------- /FB_Live_tidyverse.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 | -------------------------------------------------------------------------------- /.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 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 DataCamp 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 | # IMPORTANT 2 | 3 | **If you're planning to code along, make sure to clone, download, or re-pull this repository on the morning of Thursday April 5. All edits will be completed by end of day ET Wednesday April 4.** 4 | 5 | # Introduction to the Tidyverse: Survivors of the Titanic, the Tidy Way 6 | DataCamp Facebook Live Code Along Session 5: Learn how to to use tidy tools in R, such as `dplyr` and `ggplot2`, to intuitively explore & analyze your data. 7 | 8 | with DataCamp's very own Hugo Bowne-Anderson. Follow him on twitter [@hugobowne](https://twitter.com/hugobowne). 9 | 10 | ## Description 11 | 12 | In this live code-along session, you'll learn the basic principles of analyzing your data using R and the tidyverse. You'll code along with Hugo to explore the Titanic dataset and discover how you can use packages (i.e. sets of tools for doing stuff with data) in the tidyverse to get from data to insight as intuitively and efficiently as possible. But seriously: if you want to filter your data, you use the verb (or function) `filter`; if you wish to arrange it in a particular order, you use `arrange`. The tidyverse set of tools is a revolution that consists of computational tools that intuitively mirror how we all think about data. Be part of the revolution. Join us code along to use these tools to explore the Titanic dataset, which consists of observations of passengers on the infamous ship, many of their characteristics and whether they survived or not. 13 | 14 | ## Prerequisites 15 | 16 | None. I have always found that the most important and beneficial prerequisite is a will to learn new things so if you have this quality, you'll definitely get something out of this code-along session. 17 | 18 | Also, if you'd like to watch and **not** code along, you'll also have a great time and these notebooks will be downloadable afterwards also. 19 | 20 | ## Installation 21 | 22 | 1. Install R from [here](https://cloud.r-project.org/). 23 | 2. Install RStudio from [here](http://www.rstudio.com/ide/download/). 24 | 3. Follow Jenny Bryan's instructions [here](https://jennybc.github.io/2014-05-12-ubc/r-setup.html) to test your set-up (note: it is not necessary to establish communication between RStudio and Git for this session). 25 | 4. Install all the packages in the [tidyverse](https://www.tidyverse.org/packages/) by running `install.packages("tidyverse")` in RStudio. 26 | 27 | ## Clone the repository 28 | 29 | To get set up for this live coding session, clone this repository. You can do so by executing the following in your terminal: 30 | 31 | ``` 32 | git clone https://github.com/datacamp/datacamp_facebook_live_intro_to_tidyverse 33 | ``` 34 | 35 | Alternatively, you can download the zip file of the repository at the top of the main page of the repository. If you prefer not to use git or don't have experience with it, this a good option. 36 | 37 | ## License 38 | 39 | The code in this repository is released under the MIT license. Read more at [the Open Source Initiative](https://opensource.org/licenses/MIT). All text remains the Intellectual Property of DataCamp. If you wish to reuse, adapt or remix, get in touch with me at hugo at datacamp com to request permission. 40 | -------------------------------------------------------------------------------- /FB_tidy_NB.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Survivors of the Titanic, the Tidy Way" 3 | output: 4 | html_document: 5 | df_print: paged 6 | html_notebook: default 7 | --- 8 | 9 | Welcome to our [R Markdown](http://rmarkdown.rstudio.com) Notebook for our Facebook Live session on Introduction to the Tidyverse. When you execute code within the notebook, the results appear beneath the code. 10 | 11 | You can execute chunks by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter* or *Ctrl+Shift+Enter* on Windows. 12 | 13 | The core tidyverse includes the packages that you're likely to use in everyday data analyses, such as [ggplot2](http://ggplot2.tidyverse.org/) for data visualization and [dplyr](http://dplyr.tidyverse.org/) for data wrangling. You'll focus on these two in this session. 14 | 15 | FYI, A package is essentially a set of tools for doing stuff with data. 16 | 17 | For more on the Tidyverse, check out David Robinson's [Introduction to Tidyverse course](https://www.datacamp.com/courses/introduction-to-the-tidyverse) on DataCamp and the [Learn the Tidyverse resources](https://www.tidyverse.org/learn/). 18 | 19 | Also check out [this week's episode of DataFramed](https://www.datacamp.com/community/podcast/data-nerd), a conversation with Mara Averick, Tidyverse dev. advocate at RStudio. 20 | 21 | ![A Tidy Data Workflow](img/tidyverse2.png) 22 | 23 | Install the tidyverse, if you haven't already: 24 | 25 | ```{r message = FALSE} 26 | # Install the tidyverse 27 | # install.packages("tidyverse") 28 | ``` 29 | 30 | Now that you've installed the tidyverse, it's time to load your data and check out some of the observations. 31 | 32 | In this code along session, you'll be exploring the [Titanic dataset](https://www.kaggle.com/c/titanic), where each observation is a person and each variable is a feature such as "Name", "Age" and "Survived" (or not). 33 | 34 | Load your data: 35 | 36 | ```{r message = FALSE, warning=FALSE} 37 | # Import the Tidyverse 38 | 39 | # Import data 40 | 41 | # Check out the first several observations of your dataframe 42 | 43 | ``` 44 | 45 | What are all of these variables? See [dataset description](https://www.kaggle.com/c/titanic/data) for more details. 46 | 47 | To get an overview of your data, use the function "summary": 48 | 49 | 50 | ```{r message = FALSE} 51 | # Summarize titanic 52 | 53 | ``` 54 | 55 | Now do the same using a pipe, one of the handiest tools in the tidyverse: 56 | 57 | ```{r message = FALSE} 58 | # Summarize titanic using a pipe 59 | 60 | 61 | ``` 62 | 63 | Do the same after dropping observations that have missing values (hint: you can concatenate pipes): 64 | 65 | ```{r message = FALSE} 66 | # Summarize titanic after dropping na 67 | 68 | ``` 69 | 70 | You may have noticed stylistic consistency in the code that we've written above. That's because we're adhering to a style guide. In data science *and* programming/coding in general, it's important to get accustomed to using a style guide asap. As Hadley Wickham puts it in the [tidyverse style guide](http://style.tidyverse.org/index.html), 71 | 72 | > Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread. 73 | 74 | **Recap:** 75 | 76 | * You've imported your data & checked it out 77 | * You've learnt about the pipe operator `%>%` 78 | 79 | **Up next:** 80 | 81 | * Data wrangling with `dplyr` to **filter** your data, **arrange** it and create new features by **mutating** old ones. 82 | 83 | If you're enjoying this session, retweet or share on FB now and follow us on Twitter: [hugobowne](https://twitter.com/hugobowne) & [DataCamp](https://twitter.com/DataCamp). 84 | 85 | 86 | ## Wrangle your data 87 | 88 | Now it's time to explore your data and get some initial insight into the dataset. You'll be using `dplyr` **verbs** such as `filter`, `arrange` and `mutate`, which do exactly what they say. 89 | 90 | Let's sat that we wanted to choose a particular set of observations, say, those for which the "Sex" was 'female'. `dplyr` allows us to do intuitively and in a language that mirrors how we think and talk about data. 91 | 92 | The `filter` verb chooses only the observations that match the condition. See it in action: 93 | 94 | ```{r message = FALSE} 95 | # Filter to get all "male" rows 96 | 97 | ``` 98 | 99 | **What can you see here?** Any ideas about whether men are likely to have survived or not? 100 | 101 | **Note**: 102 | 103 | * you can read `dplyr` code like a sentence: take your data and then (`%>%`) filter it according the condition that the sex is male. 104 | * the above does **not** alter the original dataframe. 105 | * `filter(Sex = "male")` is a common error (I've made it); there's a cool [tidyverse error guide](http://style.tidyverse.org/error-messages.html). 106 | 107 | ```{r message = FALSE} 108 | # Filter to get all "female" rows 109 | 110 | ``` 111 | 112 | **What can you see here?** Any ideas about whether women are likely to have survived or not? 113 | 114 | Lets say you wanted to `arrange` your observations by increaing Fare to see if you can notice any trends. You can use the **verb** `arrange` to achieve this: 115 | 116 | ```{r message = FALSE} 117 | # Arrange by increasing Fare 118 | 119 | ``` 120 | 121 | **What can you see here?** 122 | 123 | You can also `arrange` by decreasing Fare: 124 | 125 | ```{r message = FALSE} 126 | # Arrange by decreasing Fare 127 | 128 | ``` 129 | 130 | **What can you see here?** 131 | 132 | Sometimes you may wish to create new variables. We know that the variable "Parch" is the number of parents and children while "SibSp" is the number of siblings and spouses. We can add these together to get a new variable "FamSize". To do so, you `mutate` the original variables into the new one. 133 | 134 | ```{r message = FALSE} 135 | # Create new column FamSize (size of family) 136 | 137 | ``` 138 | 139 | **Note:** 140 | 141 | * `mutate()` can be used to create new columns but also modify existing columns in much the same way that a mutation may from a biological perspective. It's not exactly right but a nice way to provide context for verb choice. 142 | 143 | Is it possible that largeer family have a lower rate of survival? 144 | 145 | To test this hypothesis, create a new variable "FamSize" as the sum of "Parch" and "SibSp" as above, then arrange by decreasing "FamSize": 146 | 147 | ```{r message = FALSE} 148 | # Create new column FamSize (size of family) 149 | # Arrange by decreasing FamSize 150 | 151 | ``` 152 | 153 | As zeroes and ones don't often say a great deal, mutate the values of the "Survived" variable to strings "No" and "Yes" (and create new dataframe!): 154 | 155 | ```{r message = FALSE} 156 | # Turn numerical values of Survived column to "No" & "Yes" (new dataframe) 157 | 158 | ``` 159 | 160 | **Recap:** 161 | 162 | * You've imported your data & checked it out 163 | * You've learnt about the pipe operator `%>%` 164 | * You can now use `dplyr` to wrangle your data: `filter` it, `arrange` it and create new features by `mutating` old ones. 165 | 166 | All of this and more is covered in Chapter 1 of David Robinson's [Introduction to Tidyverse course](https://www.datacamp.com/courses/introduction-to-the-tidyverse) on DataCamp. 167 | 168 | **Up next:** 169 | 170 | * You'll learn how to easily and intuitively plot your data using `ggplot2`. 171 | 172 | If you're enjoying this session, retweet or share on FB now and follow us on Twitter: [hugobowne](https://twitter.com/hugobowne) & [DataCamp](https://twitter.com/DataCamp). 173 | 174 | ## Plotting your data 175 | 176 | To plot your data using `ggplot2`, you specify three things: 177 | 178 | * Your data 179 | * Your aesthetics (e.g. what's on the x-axis) 180 | * Your layers (e.g. barplot, scatterplot) 181 | 182 | 183 | First you'll plot a barplot of Sex to see how many males and females were recorded aboard the Titanic: 184 | 185 | ```{r message = FALSE} 186 | # Plot barplot of passenger Sex 187 | 188 | ``` 189 | 190 | **What can you see here?** 191 | 192 | **Note:** 193 | 194 | * the `aes()` function is really a way to map the `aes`thetics of the plot to the variables in the data. See [Modern Dive](http://moderndive.com/3-viz.html#grammarofgraphics) by Chester Ismay & Albert Y. Kim for more. 195 | 196 | Now it's time for some scatter plots. Is the Age of any given passenger correlated with the Fare that they paid? 197 | 198 | ```{r message = FALSE} 199 | # Scatter plot of Age vs Fare 200 | 201 | ``` 202 | 203 | **What can you see here?** 204 | 205 | **Note:** 206 | 207 | * You can read your `ggplot2` code like a sentence, just like your `dplyr` code above: "We take the data as titanic and we map Age to the x axis and Fare to the y axis, adding points on as the layer of the plot." 208 | 209 | 210 | Let's take the previous plot and color each point by Sex, to see any correlations between Sex, Age and Fare: 211 | 212 | ```{r message = FALSE} 213 | # Scatter plot of Age vs Fare colored by Sex 214 | 215 | ``` 216 | 217 | **What can you see here?** 218 | 219 | Visualizing three variables on one plot is pretty cool, but what if wanted to throw the Survived variable into the mix to see if there are any apparent trends? 220 | 221 | Faceting is a way to produce multiple plots simultaneously: 222 | 223 | ```{r message = FALSE} 224 | # Scatter plot of Age vs Fare colored by Sex faceted by Survived 225 | 226 | ``` 227 | 228 | **What can you see here?** 229 | 230 | Now let's redo our bar plot of passenger "Sex" and this time fill our bars according to "Survived": 231 | 232 | ```{r message = FALSE} 233 | # Plot barplot of passenger Sex & fill according to Survival 234 | 235 | ``` 236 | 237 | **What can you see here?** 238 | 239 | 240 | **Recap:** 241 | 242 | * You've imported your data & checked it out 243 | * You've learnt about the pipe operator `%>%` 244 | * You can now use `dplyr` to wrangle your data: `filter` it, `arrange` it and create new features by `mutating` old ones. 245 | * You've learnt how to easily and intuitively plot your data using `ggplot2`. 246 | 247 | All of this and more is covered in Chapters 1 & 2 of David Robinson's [Introduction to Tidyverse course](https://www.datacamp.com/courses/introduction-to-the-tidyverse) on DataCamp. 248 | 249 | **Up next:** 250 | 251 | * Learn how to summarise your data across many dimensions, such as "Sex". 252 | 253 | 254 | 255 | If you're enjoying this session, retweet or share on FB now and follow us on Twitter: [hugobowne](https://twitter.com/hugobowne) & [DataCamp](https://twitter.com/DataCamp). 256 | 257 | 258 | ## Summarizing and grouping your data 259 | 260 | Use the `summarise` verb to find out the mean fare paid: 261 | 262 | ```{r message = FALSE} 263 | # Check out mean Fare 264 | 265 | ``` 266 | 267 | Use the `summarise` verb to find out the median fare paid: 268 | 269 | ```{r message = FALSE} 270 | # Check out mean Fare 271 | 272 | ``` 273 | 274 | Use the `filter` and `summarise` verbs to find out the mean fare paid among men: 275 | 276 | ```{r message = FALSE} 277 | # Check out mean Fare for men 278 | 279 | ``` 280 | 281 | Use the `filter` and `summarise` verbs to find out the mean fare paid among women: 282 | 283 | ```{r message = FALSE} 284 | # Check out mean Fare for women 285 | 286 | ``` 287 | 288 | Use the `filter` and `summarise` verbs to find out the mean fare paid among women **and** how many women survived: 289 | 290 | ```{r message = FALSE} 291 | # Check out mean Fare & number of survivors for women 292 | 293 | ``` 294 | 295 | Use the `group_by` and `summarise` verbs to find the mean fare and number of survivors as a function of sex: 296 | 297 | ```{r message = FALSE} 298 | # Check out mean Fare & number of survivors grouped by Sex 299 | 300 | ``` 301 | 302 | Use the `group_by` and `summarise` verbs to find the mean fare and proportion of survivors as a function of sex: 303 | 304 | 305 | ```{r message = FALSE} 306 | # Check out mean Fare & proportion of survivors grouped by Sex 307 | 308 | ``` 309 | 310 | **Recap:** 311 | 312 | * You've imported your data & checked it out 313 | * You've learnt about the pipe operator `%>%` 314 | * You can now use `dplyr` to wrangle your data: `filter` it, `arrange` it and create new features by `mutating` old ones. 315 | * You've learnt how to easily and intuitively plot your data using `ggplot2`. 316 | * You've learnt how to summarise your data across many dimensions, such as "Sex". 317 | 318 | All of this and more is covered in Chapters 1 through 3 of David Robinson's [Introduction to Tidyverse course](https://www.datacamp.com/courses/introduction-to-the-tidyverse) on DataCamp. And there's even more tidyverse gold in Chapter 4. 319 | 320 | 321 | If you enjoyed this session, retweet or share on FB now and follow us on Twitter: [hugobowne](https://twitter.com/hugobowne) & [DataCamp](https://twitter.com/DataCamp). 322 | 323 | -------------------------------------------------------------------------------- /FB_tidy_NB_soln.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Survivors of the Titanic, the Tidy Way" 3 | output: 4 | html_document: 5 | df_print: paged 6 | html_notebook: default 7 | --- 8 | 9 | Welcome to our [R Markdown](http://rmarkdown.rstudio.com) Notebook for our Facebook Live session on Introduction to the Tidyverse. When you execute code within the notebook, the results appear beneath the code. 10 | 11 | You can execute chunks by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Cmd+Shift+Enter* or *Ctrl+Shift+Enter* on Windows. 12 | 13 | The core tidyverse includes the packages that you're likely to use in everyday data analyses, such as [ggplot2](http://ggplot2.tidyverse.org/) for data visualization and [dplyr](http://dplyr.tidyverse.org/) for data wrangling. You'll focus on these two in this session. 14 | 15 | FYI, A package is essentially a set of tools for doing stuff with data. 16 | 17 | For more on the Tidyverse, check out David Robinson's [Introduction to Tidyverse course](https://www.datacamp.com/courses/introduction-to-the-tidyverse) on DataCamp and the [Learn the Tidyverse resources](https://www.tidyverse.org/learn/). 18 | 19 | Also check out [this week's episode of DataFramed](https://www.datacamp.com/community/podcast/data-nerd), a conversation with Mara Averick, Tidyverse dev. advocate at RStudio. 20 | 21 | ![A Tidy Data Workflow](img/tidyverse2.png) 22 | 23 | Install the tidyverse, if you haven't already: 24 | 25 | ```{r message = FALSE} 26 | # Install the tidyverse 27 | # install.packages("tidyverse") 28 | ``` 29 | 30 | Now that you've installed the tidyverse, it's time to load your data and check out some of the observations. 31 | 32 | In this code along session, you'll be exploring the [Titanic dataset](https://www.kaggle.com/c/titanic), where each observation is a person and each variable is a feature such as "Name", "Age" and "Survived" (or not). 33 | 34 | Load your data: 35 | 36 | ```{r message = FALSE, warning=FALSE} 37 | # Import the Tidyverse 38 | library(tidyverse) 39 | 40 | # Import data 41 | passengers <- read.csv("data/train.csv") 42 | 43 | # Check out the first several observations of your dataframe 44 | passengers 45 | ``` 46 | 47 | What are all of these variables? See [dataset description](https://www.kaggle.com/c/titanic/data) for more details. 48 | 49 | To get an overview of your data, use the function "summary": 50 | 51 | 52 | ```{r message = FALSE} 53 | # Summarize titanic 54 | summary(passengers) 55 | ``` 56 | 57 | Now do the same using a pipe, one of the handiest tools in the tidyverse: 58 | 59 | ```{r message = FALSE} 60 | # Summarize titanic using a pipe 61 | passengers %>% 62 | summary() 63 | 64 | ``` 65 | 66 | Do the same after dropping observations that have missing values (hint: you can concatenate pipes): 67 | 68 | ```{r message = FALSE} 69 | # Summarize titanic after dropping na 70 | passengers %>% 71 | drop_na() %>% 72 | summary() 73 | ``` 74 | 75 | You may have noticed stylistic consistency in the code that we've written above. That's because we're adhering to a style guide. In data science *and* programming/coding in general, it's important to get accustomed to using a style guide asap. As Hadley Wickham puts it in the [tidyverse style guide](http://style.tidyverse.org/index.html), 76 | 77 | > Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread. 78 | 79 | **Recap:** 80 | 81 | * You've imported your data & checked it out 82 | * You've learnt about the pipe operator `%>%` 83 | 84 | **Up next:** 85 | 86 | * Data wrangling with `dplyr` to **filter** your data, **arrange** it and create new features by **mutating** old ones. 87 | 88 | If you're enjoying this session, retweet or share on FB now and follow us on Twitter: [hugobowne](https://twitter.com/hugobowne) & [DataCamp](https://twitter.com/DataCamp). 89 | 90 | 91 | ## Wrangle your data 92 | 93 | Now it's time to explore your data and get some initial insight into the dataset. You'll be using `dplyr` **verbs** such as `filter`, `arrange` and `mutate`, which do exactly what they say. 94 | 95 | Let's sat that we wanted to choose a particular set of observations, say, those for which the "Sex" was 'female'. `dplyr` allows us to do intuitively and in a language that mirrors how we think and talk about data. 96 | 97 | The `filter` verb chooses only the observations that match the condition. See it in action: 98 | 99 | ```{r message = FALSE} 100 | # Filter to get all "male" rows 101 | passengers %>% 102 | filter(Sex == "male") 103 | ``` 104 | 105 | **What can you see here?** Any ideas about whether men are likely to have survived or not? 106 | 107 | **Note**: 108 | 109 | * you can read `dplyr` code like a sentence: take your data and then (`%>%`) filter it according the condition that the sex is male. 110 | * the above does **not** alter the original dataframe. 111 | * `filter(Sex = "male")` is a common error (I've made it); there's a cool [tidyverse error guide](http://style.tidyverse.org/error-messages.html). 112 | 113 | ```{r message = FALSE} 114 | # Filter to get all "female" rows 115 | passengers %>% 116 | filter(Sex == "female") 117 | ``` 118 | 119 | **What can you see here?** Any ideas about whether women are likely to have survived or not? 120 | 121 | Lets say you wanted to `arrange` your observations by increaing Fare to see if you can notice any trends. You can use the **verb** `arrange` to achieve this: 122 | 123 | ```{r message = FALSE} 124 | # Arrange by increasing Fare 125 | passengers %>% 126 | arrange(Fare) 127 | ``` 128 | 129 | **What can you see here?** 130 | 131 | You can also `arrange` by decreasing Fare: 132 | 133 | ```{r message = FALSE} 134 | # Arrange by decreasing Fare 135 | passengers %>% 136 | arrange(desc(Fare)) 137 | ``` 138 | 139 | **What can you see here?** 140 | 141 | Sometimes you may wish to create new variables. We know that the variable "Parch" is the number of parents and children while "SibSp" is the number of siblings and spouses. We can add these together to get a new variable "FamSize". To do so, you `mutate` the original variables into the new one. 142 | 143 | ```{r message = FALSE} 144 | # Create new column FamSize (size of family) 145 | passengers %>% 146 | mutate(FamSize = Parch + SibSp) 147 | ``` 148 | 149 | **Note:** 150 | 151 | * `mutate()` can be used to create new columns but also modify existing columns in much the same way that a mutation may from a biological perspective. It's not exactly right but a nice way to provide context for verb choice. 152 | 153 | Is it possible that largeer family have a lower rate of survival? 154 | 155 | To test this hypothesis, create a new variable "FamSize" as the sum of "Parch" and "SibSp" as above, then arrange by decreasing "FamSize": 156 | 157 | ```{r message = FALSE} 158 | # Create new column FamSize (size of family) 159 | # Arrange by decreasing FamSize 160 | passengers %>% 161 | mutate(FamSize = Parch + SibSp) %>% 162 | arrange(desc(FamSize)) 163 | ``` 164 | 165 | As zeroes and ones don't often say a great deal, mutate the values of the "Survived" variable to strings "No" and "Yes" (and create new dataframe!): 166 | 167 | ```{r message = FALSE} 168 | # Turn numerical values of Survived column to "No" & "Yes" (new dataframe) 169 | passengers1 <- passengers %>% 170 | mutate(Survived = ifelse(Survived == 0, "No", "Yes")) 171 | passengers1 172 | ``` 173 | 174 | **Recap:** 175 | 176 | * You've imported your data & checked it out 177 | * You've learnt about the pipe operator `%>%` 178 | * You can now use `dplyr` to wrangle your data: `filter` it, `arrange` it and create new features by `mutating` old ones. 179 | 180 | All of this and more is covered in Chapter 1 of David Robinson's [Introduction to Tidyverse course](https://www.datacamp.com/courses/introduction-to-the-tidyverse) on DataCamp. 181 | 182 | **Up next:** 183 | 184 | * You'll learn how to easily and intuitively plot your data using `ggplot2`. 185 | 186 | If you're enjoying this session, retweet or share on FB now and follow us on Twitter: [hugobowne](https://twitter.com/hugobowne) & [DataCamp](https://twitter.com/DataCamp). 187 | 188 | ## Plotting your data 189 | 190 | To plot your data using `ggplot2`, you specify three things: 191 | 192 | * Your data 193 | * Your aesthetics (e.g. what's on the x-axis) 194 | * Your layers (e.g. barplot, scatterplot) 195 | 196 | 197 | First you'll plot a barplot of Sex to see how many males and females were recorded aboard the Titanic: 198 | 199 | ```{r message = FALSE} 200 | # Plot barplot of passenger Sex 201 | ggplot(passengers, aes(x = Sex)) + 202 | geom_bar() 203 | ``` 204 | 205 | **What can you see here?** 206 | 207 | **Note:** 208 | 209 | * the `aes()` function is really a way to map the `aes`thetics of the plot to the variables in the data. See [Modern Dive](http://moderndive.com/3-viz.html#grammarofgraphics) by Chester Ismay & Albert Y. Kim for more. 210 | 211 | Now it's time for some scatter plots. Is the Age of any given passenger correlated with the Fare that they paid? 212 | 213 | ```{r message = FALSE} 214 | # Scatter plot of Age vs Fare 215 | ggplot(passengers, aes(x = Age, y = Fare)) + 216 | geom_point() 217 | ``` 218 | 219 | **What can you see here?** 220 | 221 | **Note:** 222 | 223 | * You can read your `ggplot2` code like a sentence, just like your `dplyr` code above: "We take the data as titanic and we map Age to the x axis and Fare to the y axis, adding points on as the layer of the plot." 224 | 225 | 226 | Let's take the previous plot and color each point by Sex, to see any correlations between Sex, Age and Fare: 227 | 228 | ```{r message = FALSE} 229 | # Scatter plot of Age vs Fare colored by Sex 230 | ggplot(passengers %>% drop_na(), aes(x = Age, y = Fare, color = Sex)) + 231 | geom_point() 232 | ``` 233 | 234 | **What can you see here?** 235 | 236 | Visualizing three variables on one plot is pretty cool, but what if wanted to throw the Survived variable into the mix to see if there are any apparent trends? 237 | 238 | Faceting is a way to produce multiple plots simultaneously: 239 | 240 | ```{r message = FALSE} 241 | # Scatter plot of Age vs Fare colored by Sex faceted by Survived 242 | ggplot(passengers1, aes(x = Age, y = Fare, color = Sex)) + 243 | geom_point() + 244 | facet_grid(~Survived) 245 | ``` 246 | 247 | **What can you see here?** 248 | 249 | Now let's redo our bar plot of passenger "Sex" and this time fill our bars according to "Survived": 250 | 251 | ```{r message = FALSE} 252 | # Plot barplot of passenger Sex & fill according to Survival 253 | ggplot(passengers1, aes(x = Sex, fill = Survived)) + 254 | geom_bar() 255 | ``` 256 | 257 | **What can you see here?** 258 | 259 | 260 | **Recap:** 261 | 262 | * You've imported your data & checked it out 263 | * You've learnt about the pipe operator `%>%` 264 | * You can now use `dplyr` to wrangle your data: `filter` it, `arrange` it and create new features by `mutating` old ones. 265 | * You've learnt how to easily and intuitively plot your data using `ggplot2`. 266 | 267 | All of this and more is covered in Chapters 1 & 2 of David Robinson's [Introduction to Tidyverse course](https://www.datacamp.com/courses/introduction-to-the-tidyverse) on DataCamp. 268 | 269 | **Up next:** 270 | 271 | * Learn how to summarise your data across many dimensions, such as "Sex". 272 | 273 | 274 | 275 | If you're enjoying this session, retweet or share on FB now and follow us on Twitter: [hugobowne](https://twitter.com/hugobowne) & [DataCamp](https://twitter.com/DataCamp). 276 | 277 | 278 | ## Summarizing and grouping your data 279 | 280 | Use the `summarise` verb to find out the mean fare paid: 281 | 282 | ```{r message = FALSE} 283 | # Check out mean Fare 284 | passengers %>% 285 | summarise(meanFare = mean(Fare)) 286 | ``` 287 | 288 | Use the `summarise` verb to find out the median fare paid: 289 | 290 | ```{r message = FALSE} 291 | # Check out mean Fare 292 | passengers %>% 293 | summarise(medianFare = median(Fare)) 294 | ``` 295 | 296 | Use the `filter` and `summarise` verbs to find out the mean fare paid among men: 297 | 298 | ```{r message = FALSE} 299 | # Check out mean Fare for men 300 | passengers %>% 301 | filter(Sex == "male") %>% 302 | summarise(meanFare = mean(Fare)) 303 | 304 | ``` 305 | 306 | Use the `filter` and `summarise` verbs to find out the mean fare paid among women: 307 | 308 | ```{r message = FALSE} 309 | # Check out mean Fare for women 310 | passengers %>% 311 | filter(Sex == "female") %>% 312 | summarise(meanFare = mean(Fare)) 313 | ``` 314 | 315 | Use the `filter` and `summarise` verbs to find out the mean fare paid among women **and** how many women survived: 316 | 317 | ```{r message = FALSE} 318 | # Check out mean Fare & number of survivors for women 319 | passengers %>% 320 | filter(Sex == "female") %>% 321 | summarise(meanFare = mean(Fare), numSurv = sum(Survived)) 322 | ``` 323 | 324 | Use the `group_by` and `summarise` verbs to find the mean fare and number of survivors as a function of sex: 325 | 326 | ```{r message = FALSE} 327 | # Check out mean Fare & number of survivors grouped by Sex 328 | passengers %>% 329 | group_by(Sex) %>% 330 | summarise(meanFare = mean(Fare), numSurv = sum(Survived)) 331 | ``` 332 | 333 | Use the `group_by` and `summarise` verbs to find the mean fare and proportion of survivors as a function of sex: 334 | 335 | 336 | ```{r message = FALSE} 337 | # Check out mean Fare & proportion of survivors grouped by Sex 338 | passengers %>% 339 | group_by(Sex) %>% 340 | summarise(meanFare = mean(Fare), numSurv = sum(Survived)/n()) 341 | ``` 342 | 343 | **Recap:** 344 | 345 | * You've imported your data & checked it out 346 | * You've learnt about the pipe operator `%>%` 347 | * You can now use `dplyr` to wrangle your data: `filter` it, `arrange` it and create new features by `mutating` old ones. 348 | * You've learnt how to easily and intuitively plot your data using `ggplot2`. 349 | * You've learnt how to summarise your data across many dimensions, such as "Sex". 350 | 351 | All of this and more is covered in Chapters 1 through 3 of David Robinson's [Introduction to Tidyverse course](https://www.datacamp.com/courses/introduction-to-the-tidyverse) on DataCamp. And there's even more tidyverse gold in Chapter 4. 352 | 353 | 354 | If you enjoyed this session, retweet or share on FB now and follow us on Twitter: [hugobowne](https://twitter.com/hugobowne) & [DataCamp](https://twitter.com/DataCamp). 355 | 356 | -------------------------------------------------------------------------------- /data/train.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 2 | 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S 3 | 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C 4 | 3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S 5 | 4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S 6 | 5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S 7 | 6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q 8 | 7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S 9 | 8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S 10 | 9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S 11 | 10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C 12 | 11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S 13 | 12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S 14 | 13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S 15 | 14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S 16 | 15,0,3,"Vestrom, Miss. 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