title

author

date

output

Learn-R-tutorial

R-Ladies-Vancouver

April 2, 2018

html_document

keep_md
true

Overview

Notes from a hands-on 3 hour workshop for participants with no prior knowledge of programming skills.

Learning materials used in this tutorial were adapted form:

Intro into R by Research Bazar
Software Carpentry - "R for reproducible scientific analysis"
2-hour intro into R by Mine Cetinkaya-Rundel

Required

a laptop
R and R studio installed. Click here for instructions.
we will also need be installing the dplyr and ggplot2packages during the tutorial

Other resources

Slides
The open-source dataset with passanger onboard a Titanic from Kaggle website. Download the csv file with the dataset here. We will use this learn dplyr and ggplot2 package

Topics covered

RStudio orientation & good practices for data analysis
Basics of coding in R
Working with functions and packages
Loading and inspecting the titanic.csv dataset
Basics of data wrangling with the dplyr package
Basics of data plotting with the ggplot2 package

What is R and RStudio?

What is R and why learn it?

one of the leading languages in statistics and data science
open-source and free
powerfull for data wrangling and visualization
large library of tools (packages and functions) for diverse applications (e.g. Bioconductor for genomic research, rOpenGov for social sciences)
increasing number of organizations use R, find out more in this StackOverflow post
with a programming language such as R you can document the process of your data analysis, making it easier for you or another user to reproduce. This is challenging using tools such as an excell spreadsheet, where calculations are hidden in individual cells.
R has a highly active, supportive and fast-growing user and developer community.

What is RStudio?

powerful and productive user interface for R
free and open-source.

Data analysis workflow

As adapted from R for Data Science by Hadley Wickham, the maker of the many popular R packages such as dplyr and ggplot2 packages we will be working with today

RStudio orientation & best practices for data analysis

This is what you should see when you launch RStudio:

the interactive console (left), a version of R you are working with will be printed there every time you start a new session
Environment/History (top right)
Files/Plots/Packages/Help/Viewer

Setting yourself up for reproducible R work

Before we begin producing R code, we need to familiarize ourselves with where R work is going to be located on our computer.To find out, type getwd() into the console. This will prompt R to print out a path to a location on your computer where any scripts, plots, data you generate will be saved.

getwd()

## [1] "C:/Users/Ania/R_coding/RLadies-Van-Github/April2018-Learn-R-Beginner"

It is a good practice to keep all of your data analysis for a particular project in a single location. RStudio comes with a built-in framework called R projects. To create a new project in RStudio:

Click the “File” menu button, then “New Project”.
Click “New Directory”.
Click “Empty Project”.
Type in the name of the directory to store your project
Click the “Create Project” button.

A recommendation on how to organize your R project from Good Enough Practices for Scientific Computing as summarized by Software Carpentry

Put each project in its own directory, which is named after the project.
Put text documents associated with the project in the doc directory.
Put raw data and metadata in the data directory, and files generated during cleanup and analysis in a results directory.
Put source for the project’s scripts and programs in the src directory, and programs brought in from elsewhere or compiled locally in the bin directory.
Name all files to reflect their content or function.

Challenge

Create an Rproject called “R-beginner-workshop". Click File>New Project>New directory>Empty project
Create a folder called "data"
Put the titanic.csv into folder data
Create a folder called "scr"

Basics of R coding

You can clear your console with a command ctr + L

We can use R to do simple arithmetic:

division /
multiple *
addition +
exponent ^
subtraction -

For instance, type the following in your console:

5+5

## [1] 10

10/5

## [1] 2

The answer to this is printed below the command, preceded by [1]. Right now, none of our work exists anywhere besides the console.

To create a script:

Click File
Click New File
Click R script
Give your script a name

Challenge

Create a script called “R-beginner-code” in your current project and put it in scr folder

We can annotate a script with comments using the #. If you precede anything with this sign, R will ignore it. For instance

# to do a addition in R, we do the following
5+5

## [1] 10

To store results or data we need to assign it a name, using assignment operator <-

# now I tell R to store my height

height_cm <- 172

Now, position your cursor at the line of the above command and press CTRL + ENTER (Windows) or COMMAND + ENTER (Mac). In the environment (top right) you should see a variable height_cm and its value appear

It is not always possible to view what is stored in a variable by looking at your environment. To find out what value is stored in the variable height_cm simply type it in the new line of our script and run CTRL + ENTER/COMMAND + ENTER.

# what is stored in height_cm?

height_cm

## [1] 172

Note, R is case sensitive. If we were to type Height_cm we would get an error. Try it!

# what is stored in height_cm?

# Height_cm

Say you wanted to convert your heigh to inches.

# what is my height in inches?

height_cm * 0.39

## [1] 67.08

# oh wait, I want to store this value!

height_inches <- height_cm * 0.39

# what is it again?

height_inches

## [1] 67.08

If we wanted to clear the environment of the variables we have 2 options:

rm(list = ls())
start a new R session - essentially a refresh button. In RStudio click "Session" > "Restart R"

Challenge

Create a variable called mass_kg with a value of 50
Convert mass_kg to mass in pounds (multiply by 2.2) and store the answer in mass_lbs

What are functions and packages?

Functions:

fundamental building blocks of R
a list of "build-in"R functions (come with installation) can be found here
functions are used by first specifying first their name followed by (), into which we put arguments that a given function takes.

log() is an example of a function to compute a logarithm.

You can also get information on how to use a function in R studio by typing

?log()

This will promt a Help page in the bottom right pane to appear with description of a function, what arguments a given function uses and some examples of how to apply it.

# find out log of height_cm
log(height_cm)

## [1] 5.147494

# find out exp of height_cm
exp(height_cm)

## [1] 4.996327e+74

Objects can store more than one value, and this value can be

numeric (e.g. 1,2, -3, .2)
character (e.g."vancouver","victoria","nanaimo")
locaical (TRUE or FALSE)

We will see shortly that an object can also be a large dataset with a variety of data types.

# an object with few numeric values
heights_cm <- c(172, 143, 152, 180)

mean(heights_cm)

## [1] 161.75

sd(heights_cm)

## [1] 17.17314

length(heights_cm)

## [1] 4

Packages:

Packages are the fundamental units of reproducible R code. They include reusable R functions, the documentation that describes how to use them, and (often) sample data. (From: http://r-pkgs.had.co.nz)
Packages need to be installed once only, therefore we don't put these commands into the script. To install a package we use a command install.packages("package-name"). Today, we will be working with two packages called the dplyr for data wrangling and ggplot2for data visualization
Packages need to be loaded everytime we start a new R session. Typically this command goes on top of a script. To do so we use library("package-name") command

Challenge

Install the dplyr and ggplot2 packages.

Type the following into console.

# install.packages("ggplot2")
# install.packages("dplyr")

Now load these packages.

library("ggplot2")
library("dplyr")

## Warning: package 'dplyr' was built under R version 3.4.3

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Loading and inspecting the titanic.csv dataset

To load a csv file into RStudio we will use function read.csv()and assign our dataset a name (create an object). As a result, what will be created in the R environment is known as a DATAFRAME (a type of data structure in R) which has:

variables of a data set as columns
observations as rows
and is rectangular

library("dplyr")
library("ggplot2")

titanic <- read.csv("Data/titanic.csv")

# tibble: a refined print method that shows only first 10 rows and all columns fit the screen

titanic <- as_tibble(titanic)

Before we dive into extracting information from this dataset, it is important to know what variables we are working with.

There are several functions that will provide information about the structure of the dataframe: str(), head() (part of base R) or glimpse()(part of DPLYR package).

Note the differences in the output in your R console when you run these three functions.

head(titanic)

## # A tibble: 6 x 12
##   PassengerId Survived Pclass Name    Sex     Age SibSp Parch Ticket  Fare
##         <int>    <int>  <int> <fct>   <fct> <dbl> <int> <int> <fct>  <dbl>
## 1           1        0      3 Braund~ male    22.     1     0 A/5 2~  7.25
## 2           2        1      1 Cuming~ fema~   38.     1     0 PC 17~ 71.3 
## 3           3        1      3 Heikki~ fema~   26.     0     0 STON/~  7.92
## 4           4        1      1 Futrel~ fema~   35.     1     0 113803 53.1 
## 5           5        0      3 Allen,~ male    35.     0     0 373450  8.05
## 6           6        0      3 Moran,~ male    NA      0     0 330877  8.46
## # ... with 2 more variables: Cabin <fct>, Embarked <fct>

glimpse(titanic)

## Observations: 891
## Variables: 12
## $ PassengerId <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,...
## $ Survived    <int> 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0,...
## $ Pclass      <int> 3, 1, 3, 1, 3, 3, 1, 3, 3, 2, 3, 1, 3, 3, 3, 2, 3,...
## $ Name        <fct> Braund, Mr. Owen Harris, Cumings, Mrs. John Bradle...
## $ Sex         <fct> male, female, female, female, male, male, male, ma...
## $ Age         <dbl> 22, 38, 26, 35, 35, NA, 54, 2, 27, 14, 4, 58, 20, ...
## $ SibSp       <int> 1, 1, 0, 1, 0, 0, 0, 3, 0, 1, 1, 0, 0, 1, 0, 0, 4,...
## $ Parch       <int> 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 1, 0, 0, 5, 0, 0, 1,...
## $ Ticket      <fct> A/5 21171, PC 17599, STON/O2. 3101282, 113803, 373...
## $ Fare        <dbl> 7.2500, 71.2833, 7.9250, 53.1000, 8.0500, 8.4583, ...
## $ Cabin       <fct> , C85, , C123, , , E46, , , , G6, C103, , , , , , ...
## $ Embarked    <fct> S, C, S, S, S, Q, S, S, S, C, S, S, S, S, S, S, Q,...

str(titanic)

## Classes 'tbl_df', 'tbl' and 'data.frame':	891 obs. of  12 variables:
##  $ PassengerId: int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Survived   : int  0 1 1 1 0 0 0 0 1 1 ...
##  $ Pclass     : int  3 1 3 1 3 3 1 3 3 2 ...
##  $ Name       : Factor w/ 891 levels "Abbing, Mr. Anthony",..: 109 191 358 277 16 559 520 629 417 581 ...
##  $ Sex        : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
##  $ Age        : num  22 38 26 35 35 NA 54 2 27 14 ...
##  $ SibSp      : int  1 1 0 1 0 0 0 3 0 1 ...
##  $ Parch      : int  0 0 0 0 0 0 0 1 2 0 ...
##  $ Ticket     : Factor w/ 681 levels "110152","110413",..: 524 597 670 50 473 276 86 396 345 133 ...
##  $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ...
##  $ Cabin      : Factor w/ 148 levels "","A10","A14",..: 1 83 1 57 1 1 131 1 1 1 ...
##  $ Embarked   : Factor w/ 4 levels "","C","Q","S": 4 2 4 4 4 3 4 4 4 2 ...

It is also possible to view a dataframe in a spreadsheet-like view. Click on the titanic in your Environment to chek it out.

What is in the titanic dataset?

Variable	Definition	Key
survival	Survival	0 = No, 1 = Yes
pclass	Ticket class	1 = 1st, 2 = 2nd, 3 = 3rd
sex	Sex
Age	Age in years
sibsp	# of siblings / spouses aboard the Titanic
parch	# of parents / children aboard the Titanic
ticket	Ticket number
fare	Passenger fare
cabin	Cabin number
embarked	Port of Embarkation C = Cherbourg, Q = Queenstown, S = Southampton

Other usefull commands to explore a dataset:

names(titanic) # lists the variable names

##  [1] "PassengerId" "Survived"    "Pclass"      "Name"        "Sex"        
##  [6] "Age"         "SibSp"       "Parch"       "Ticket"      "Fare"       
## [11] "Cabin"       "Embarked"

dim(titanic) # dimentions of the dataset, it has 891 observations by 12 columns

## [1] 891  12

nrow(titanic) # number of observations

## [1] 891

summary(titanic) # gives me summary statistics for every column/variable

##   PassengerId       Survived          Pclass     
##  Min.   :  1.0   Min.   :0.0000   Min.   :1.000  
##  1st Qu.:223.5   1st Qu.:0.0000   1st Qu.:2.000  
##  Median :446.0   Median :0.0000   Median :3.000  
##  Mean   :446.0   Mean   :0.3838   Mean   :2.309  
##  3rd Qu.:668.5   3rd Qu.:1.0000   3rd Qu.:3.000  
##  Max.   :891.0   Max.   :1.0000   Max.   :3.000  
##                                                  
##                                     Name         Sex           Age       
##  Abbing, Mr. Anthony                  :  1   female:314   Min.   : 0.42  
##  Abbott, Mr. Rossmore Edward          :  1   male  :577   1st Qu.:20.12  
##  Abbott, Mrs. Stanton (Rosa Hunt)     :  1                Median :28.00  
##  Abelson, Mr. Samuel                  :  1                Mean   :29.70  
##  Abelson, Mrs. Samuel (Hannah Wizosky):  1                3rd Qu.:38.00  
##  Adahl, Mr. Mauritz Nils Martin       :  1                Max.   :80.00  
##  (Other)                              :885                NA's   :177    
##      SibSp           Parch             Ticket         Fare       
##  Min.   :0.000   Min.   :0.0000   1601    :  7   Min.   :  0.00  
##  1st Qu.:0.000   1st Qu.:0.0000   347082  :  7   1st Qu.:  7.91  
##  Median :0.000   Median :0.0000   CA. 2343:  7   Median : 14.45  
##  Mean   :0.523   Mean   :0.3816   3101295 :  6   Mean   : 32.20  
##  3rd Qu.:1.000   3rd Qu.:0.0000   347088  :  6   3rd Qu.: 31.00  
##  Max.   :8.000   Max.   :6.0000   CA 2144 :  6   Max.   :512.33  
##                                   (Other) :852                   
##          Cabin     Embarked
##             :687    :  2   
##  B96 B98    :  4   C:168   
##  C23 C25 C27:  4   Q: 77   
##  G6         :  4   S:644   
##  C22 C26    :  3           
##  D          :  3           
##  (Other)    :186

Data wrangling with the `dplyr` package

The dplyr package is based on the concepts of functions as verbs that manipulate data frames, the core of dplyr lies in the 5 main verbs:

filter(): pick rows matching criteria
select(): pick columns by name
group_by(): group rows based on columns
summarise(): reduce groups to values
mutate(): add new variables

Each one of these functions can be performed individually but the real power comes in when we string them together using the pipe (%>%) operator. This allows us to perform multiple operations in one hit and return a single tibble with our results at the end.

Let's explore some of these functions!

select()

Use the select() function to extract specific variables from a dataframe. You can store variables of interest extracted with select() in new tibble should you want to have a small subset of the data to work on seperately.

Here's an example.

# this 'pipes' the titanic dataset to the select command and will print only the age, sex and survived columns
titanic %>%
  select(Age, Sex, Survived)

## # A tibble: 891 x 3
##      Age Sex    Survived
##    <dbl> <fct>     <int>
##  1   22. male          0
##  2   38. female        1
##  3   26. female        1
##  4   35. female        1
##  5   35. male          0
##  6   NA  male          0
##  7   54. male          0
##  8    2. male          0
##  9   27. female        1
## 10   14. female        1
## # ... with 881 more rows

I am happy with the output, now let's create a seperate object containing just these 3 columns (aka variables)

gender_survival <- titanic %>%
   select(Age, Sex, Survived)

There are various ways to use select() function. For instance, we can extract variables that start or end with certain letters or strings.

# select columns that begin with A 
   titanic %>%
   select(starts_with("A"))

## # A tibble: 891 x 1
##      Age
##    <dbl>
##  1   22.
##  2   38.
##  3   26.
##  4   35.
##  5   35.
##  6   NA 
##  7   54.
##  8    2.
##  9   27.
## 10   14.
## # ... with 881 more rows

# select columns that end with s
   titanic %>%
   select(ends_with("s"))

## # A tibble: 891 x 1
##    Pclass
##     <int>
##  1      3
##  2      1
##  3      3
##  4      1
##  5      3
##  6      3
##  7      1
##  8      3
##  9      3
## 10      2
## # ... with 881 more rows

Challenge

Create a subset of titanic dataframe called passanger_info containing Name,Age,Sex

filter()

The function filter() extracts data row wise.

An example: Column (variable) Sex contains two categories: females and males. Let's extract all of the information (all variables) contained in the titanic dataset but only for females

# extract all of the variables but only for females
titanic %>%
  filter(Sex == "female")

## # A tibble: 314 x 12
##    PassengerId Survived Pclass Name   Sex     Age SibSp Parch Ticket  Fare
##          <int>    <int>  <int> <fct>  <fct> <dbl> <int> <int> <fct>  <dbl>
##  1           2        1      1 Cumin~ fema~   38.     1     0 PC 17~ 71.3 
##  2           3        1      3 Heikk~ fema~   26.     0     0 STON/~  7.92
##  3           4        1      1 Futre~ fema~   35.     1     0 113803 53.1 
##  4           9        1      3 Johns~ fema~   27.     0     2 347742 11.1 
##  5          10        1      2 Nasse~ fema~   14.     1     0 237736 30.1 
##  6          11        1      3 Sands~ fema~    4.     1     1 PP 95~ 16.7 
##  7          12        1      1 Bonne~ fema~   58.     0     0 113783 26.6 
##  8          15        0      3 Vestr~ fema~   14.     0     0 350406  7.85
##  9          16        1      2 "Hewl~ fema~   55.     0     0 248706 16.0 
## 10          19        0      3 Vande~ fema~   31.     1     0 345763 18.0 
## # ... with 304 more rows, and 2 more variables: Cabin <fct>,
## #   Embarked <fct>

We can also do multiple levels of filtering.

# extract all of the variables but only or females aged 20 and up
titanic %>% 
  filter(Sex == "female", Age > 20)

## # A tibble: 184 x 12
##    PassengerId Survived Pclass Name   Sex     Age SibSp Parch Ticket  Fare
##          <int>    <int>  <int> <fct>  <fct> <dbl> <int> <int> <fct>  <dbl>
##  1           2        1      1 Cumin~ fema~   38.     1     0 PC 17~ 71.3 
##  2           3        1      3 Heikk~ fema~   26.     0     0 STON/~  7.92
##  3           4        1      1 Futre~ fema~   35.     1     0 113803 53.1 
##  4           9        1      3 Johns~ fema~   27.     0     2 347742 11.1 
##  5          12        1      1 Bonne~ fema~   58.     0     0 113783 26.6 
##  6          16        1      2 "Hewl~ fema~   55.     0     0 248706 16.0 
##  7          19        0      3 Vande~ fema~   31.     1     0 345763 18.0 
##  8          26        1      3 Asplu~ fema~   38.     1     5 347077 31.4 
##  9          41        0      3 Ahlin~ fema~   40.     1     0 7546    9.48
## 10          42        0      2 Turpi~ fema~   27.     1     0 11668  21.0 
## # ... with 174 more rows, and 2 more variables: Cabin <fct>,
## #   Embarked <fct>

The trick with the filter() option is that we always want each statement to return a True or a False.

Challenge

Create a new object called 'males' containing only male passengers and only the columns Age, Sex, Name and Fare. What dimensions does your object have?

group_by() and summarize()

So far we learnt how to extract pieces of data from the titanic dataset, but we still have lots of rows of data. How do we summarize it so that we can find out something useful, say the average age of people who survived the tragedy? This is where two powerful functions come in: group_by and summarize().

To perform operations on the titanic dataset, we first need to specify what variable we want to summarize with the group_byfunction.

In the example below we specify that the operation will be grouped by Sex for the titanic data. Because we have 2 categories in Sex, the female and male, any calculations we perform after the group_by will produce one value for females and one value for males.

titanic %>%
  group_by(Sex)

## # A tibble: 891 x 12
## # Groups:   Sex [2]
##    PassengerId Survived Pclass Name   Sex     Age SibSp Parch Ticket  Fare
##          <int>    <int>  <int> <fct>  <fct> <dbl> <int> <int> <fct>  <dbl>
##  1           1        0      3 Braun~ male    22.     1     0 A/5 2~  7.25
##  2           2        1      1 Cumin~ fema~   38.     1     0 PC 17~ 71.3 
##  3           3        1      3 Heikk~ fema~   26.     0     0 STON/~  7.92
##  4           4        1      1 Futre~ fema~   35.     1     0 113803 53.1 
##  5           5        0      3 Allen~ male    35.     0     0 373450  8.05
##  6           6        0      3 Moran~ male    NA      0     0 330877  8.46
##  7           7        0      1 McCar~ male    54.     0     0 17463  51.9 
##  8           8        0      3 Palss~ male     2.     3     1 349909 21.1 
##  9           9        1      3 Johns~ fema~   27.     0     2 347742 11.1 
## 10          10        1      2 Nasse~ fema~   14.     1     0 237736 30.1 
## # ... with 881 more rows, and 2 more variables: Cabin <fct>,
## #   Embarked <fct>

Not much will happen when running group_by by itself, although in the console above the dataframe that was printed we can see a new line #Groups: Sex [2]. To perform an operation we need to use group_by in conjunction with summarise()

With summarize we can use the functions we explored at the beginning of the workshop (mean,sd,median,n()) or we can perform calculations like we would do in a cell of an excel spreadhseet.

To calculate the mean age for females and males in the titanic dataset:

Group your data by variable Sex
Define what to average with the mean() function and put this inside the summarize()
Note, there are some missing values (NA's) in the variable Age, we must tell R to ignore NA's in those calculations using na.rm = TRUE (i.e. na remove = yes)

titanic %>%
  group_by(Sex) %>%
  summarize(mean(Age, na.rm = TRUE))

## # A tibble: 2 x 2
##   Sex    `mean(Age, na.rm = TRUE)`
##   <fct>                      <dbl>
## 1 female                      27.9
## 2 male                        30.7

Now, let's find out the average age of females and males that survived (1) and died (0) by adding Survived into the groub_by()

titanic %>%
  group_by(Sex, Survived) %>%
  summarize(mean_age = mean(Age, na.rm = TRUE))

## # A tibble: 4 x 3
## # Groups:   Sex [?]
##   Sex    Survived mean_age
##   <fct>     <int>    <dbl>
## 1 female        0     25.0
## 2 female        1     28.8
## 3 male          0     31.6
## 4 male          1     27.3

Just like with groub_by() where we can have many variables, we can apply many operations in summarize.

titanic %>%
  group_by(Sex, Survived) %>%
  summarize(mean_age = mean(Age, na.rm = TRUE),
            stdev = sd(Age, na.rm = TRUE),
            num_of_obs = n())

## # A tibble: 4 x 5
## # Groups:   Sex [?]
##   Sex    Survived mean_age stdev num_of_obs
##   <fct>     <int>    <dbl> <dbl>      <int>
## 1 female        0     25.0  13.6         81
## 2 female        1     28.8  14.2        233
## 3 male          0     31.6  14.1        468
## 4 male          1     27.3  16.5        109

Challenge 2

Calculate mean, stdev and number of observations for variables Sex in conjunction with Passanger class. Hint!: follow the example we just did.
Calculate the average Survived value per Pclass and Sex. Which combination of Pclass and Sex had the highest Survived value and which combination had the lowest?

`mutate()`

The mutate() function allows you to create brand new variables by performing operations on the existing data in the dataframe. For example, let's create a new variable called 'status' that combines the information of the Pclass variable with the age variable.

titanic %>%
  mutate(status = Pclass*Age)

## # A tibble: 891 x 13
##    PassengerId Survived Pclass Name   Sex     Age SibSp Parch Ticket  Fare
##          <int>    <int>  <int> <fct>  <fct> <dbl> <int> <int> <fct>  <dbl>
##  1           1        0      3 Braun~ male    22.     1     0 A/5 2~  7.25
##  2           2        1      1 Cumin~ fema~   38.     1     0 PC 17~ 71.3 
##  3           3        1      3 Heikk~ fema~   26.     0     0 STON/~  7.92
##  4           4        1      1 Futre~ fema~   35.     1     0 113803 53.1 
##  5           5        0      3 Allen~ male    35.     0     0 373450  8.05
##  6           6        0      3 Moran~ male    NA      0     0 330877  8.46
##  7           7        0      1 McCar~ male    54.     0     0 17463  51.9 
##  8           8        0      3 Palss~ male     2.     3     1 349909 21.1 
##  9           9        1      3 Johns~ fema~   27.     0     2 347742 11.1 
## 10          10        1      2 Nasse~ fema~   14.     1     0 237736 30.1 
## # ... with 881 more rows, and 3 more variables: Cabin <fct>,
## #   Embarked <fct>, status <dbl>

Advanced dplyr Challenge

Calculate the average Survived value for a group of 20 randomly selected females from each Pclass group. Then arrange the classes in descending order. Hint: Use the dplyr functions arrange() and sample_n(), they have similar syntax to other dplyr functions. Look at the help!

Data plotting with the GGPLOT2 package

Ggplot2 is built on the Grammar of Graphics. It is a package created by Hadley Wickham.

The building blocks of a ggplot include:

a dataset
an aesthetic (position (x and y), color, shape, size, linetype, fill)
a set of geoms--they define how data will be plotted (e.g geom_point is for scatterplots, while geom_bar is for bar plots)
statistical transformation (optional)
scales
a coordinate system
facetting (for creating subplots)

There is a handy cheatsheet for using ggplot2.

And a crazy amount of graphs and options you can customise - check it out!

Examples of GEOMS:

geom_point() - will create a scatterplot
geom_bar() - will create a bar plot
geom_boxplot() - you get the idea...
geom_line()
geom_errorbar()
geom_dotplot()

Scatter plot example

Below we make a simple scatterplot of Age versus Fare and colour the points by the passenger's sex.

titanic %>%
  ggplot(aes( x = Age, y = Fare, color = Sex )) + 
  geom_point()

## Warning: Removed 177 rows containing missing values (geom_point).

Dplyr + ggplot

We can mix in some dplyr verbs we learnt earlier to zoom in on a data of interest within our graph.

titanic %>%
  filter(Fare < 200) %>%
  ggplot(aes(x = Age, y = Fare, color = Sex)) + 
  geom_point()

## Warning: Removed 175 rows containing missing values (geom_point).

Bar plots

Creating bar plots : geom_bar()

titanic %>%
  ggplot(aes(x = Pclass, fill = Sex)) +
  geom_bar(position = "dodge")

Avoiding overplotting

Visualizing data points with geom_jitter()

titanic %>%
  ggplot(aes(x = Pclass, y = Age, color = Sex)) +
  geom_jitter()

## Warning: Removed 177 rows containing missing values (geom_point).

You can also try geom_hex(), geom_count() or geom_bin2d().

Facetting

Creating multiple plots using facet_grid()

titanic %>%
  ggplot(aes(x= Pclass, y = Age, color = Sex)) +
  geom_jitter() +
  facet_grid(~Survived)

## Warning: Removed 177 rows containing missing values (geom_point).

Useful Resources: local and remote places to practice R

Cheatsheets: R for data science:

for various R tools - RStudio cheatsheets
RStudio - IDE
data import
dplyr
ggplot2

R-Ladies resources: global repository

Stay tunes for future beginner R meetups with R-Ladies Vancouver!
Ecoscope offers a variety of R worshops for all levels Ecoscope - next one is on reproducible research check out tickets here
Check out a series of workshops on Intro into Data Science by Women Who Code Vancouver

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Learn-R-tutorial.md

Learn-R-tutorial.md

Overview

What is R and RStudio?

Data analysis workflow

RStudio orientation & best practices for data analysis

Setting yourself up for reproducible R work

Basics of R coding

What are functions and packages?

Loading and inspecting the titanic.csv dataset

Data wrangling with the `dplyr` package

select()

Challenge

filter()

group_by() and summarize()

`mutate()`

Data plotting with the GGPLOT2 package

Scatter plot example

Dplyr + ggplot

Bar plots

Avoiding overplotting

Facetting

Useful Resources: local and remote places to practice R

Files

Learn-R-tutorial.md

Latest commit

History

Learn-R-tutorial.md

File metadata and controls

Overview

What is R and RStudio?

Data analysis workflow

RStudio orientation & best practices for data analysis

Setting yourself up for reproducible R work

Basics of R coding

What are functions and packages?

Loading and inspecting the titanic.csv dataset

Data wrangling with the dplyr package

select()

Challenge

filter()

group_by() and summarize()

mutate()

Data plotting with the GGPLOT2 package

Scatter plot example

Dplyr + ggplot

Bar plots

Avoiding overplotting

Facetting

Useful Resources: local and remote places to practice R

Data wrangling with the `dplyr` package

`mutate()`