Let's start at the very beginning

A very good place to start

Jen Richmond · R-Ladies Sydney

https://jennyslides.netlify.app/byod/

The Plan

Why R?
Navigating RStudio
Getting your data in
Cleaning it up a little
Maybe....probably not... summary stats/plotting

You will need a laptop and access to wifi, but we will use RStudio Cloud, so don't worry about installation.

Image credit: Allison Horst @allison_horst from R-Ladies Santa Barbara

]

Disclaimer

Today we are really unlikely to know the answers to all your questions, but we can help you google!

Google is every R user's best friend, even Hadley Wickham.

1. Why R?

Lets introduce ourselves, why do you want to learn R?

Why I R...

Reproducibility

One stop shop

Equity

Community

Fun!

Image credit Darren Dahly @statsepi

Warning: You will want to quit

Image credit Dani Navarro Workflow

Navigating RStudio

Link to RStudio Cloud Workspace

https://rstudio.cloud/project/637830

Your turn 1: Set up RStudio Cloud

Go to this link https://rstudio.cloud/project/637830
Sign up for an RStudio Cloud account
Wait for project to chug
Click Save a Permanent Copy

03:00

Think of R-Studio as your kitchen

Image credit to Jessica Ward from @RLadiesNCL

Your turn 2: Let's start by redecorating

From the top menu choose

Tools
Global Options
Appearance

Then try out a few Editor themes and choose one you like.

04:00

What questions do you have ?

Packages

A package is a group functions that some kind person has written, tested, bundled together and given away for everyone to use.

To install packages, type into your console. Just do this once.

To use a package, load it using the library() function. You need to do this every time you want to use the functions in a package.

install.packages("packagename")

library(packagename)

What is the tidyverse?

The tidyverse is a mega-package (i.e. a package of other packages) designed to make data wrangling and visualisation easier.

library(tidyverse)

is the same as...

library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(purrr)
library(tibble)
library(stringr)
library(forcats)

It's a good idea to put library(tidyverse) at the top of every analysis document. You will probably need it.

Your turn 3: Install some packages

Step 1. In your console, install the following packages...

janitor
here
beepr
skimr

install.packages("packagename")

Step 2. Then open the Rmd_demo_babynames.Rmd file and find the grey "chunk" that has library(tidyverse) in it. Add these below to load the following packages.

library(janitor) 
library(here)
library(beepr)
library(skimr)

Run the code in the chunk using the little green arrow on the right

04:00

What questions do you have ?

An aside about RMarkdown

RMarkdown documents (or .Rmd files) are key to reproducible analysis.

notes/explanations + "chunks" of R code
"knit" the document (notes + code + output) into a nice format that other people (who may not know R) can access.

Tricks and tips

Insert a chunk using shortcut Alt-Cmd-I
Hash to get levels of heading
Asterisks to get bold and italics

Image credit: Allison Horst @allison_horst from R-Ladies Santa Barbara

Your turn 4: "Knit" your RMarkdown document

Change a few things about the text in the demo.Rmd doc

Change the title at the top
Add a few headings using #
Make words bold by putting them in double asterik

Then choose "Knit" and check out the output.

via GIPHY

02:00

What questions do you have ?

https://jennyslides.netlify.app/byod/
Getting data in22

Where is the data?

The here package will help avoid file path drama. You can use it to tell R where you data is, relative to the top level of your project folder.

We can use the read_csv() from readr and here() to read a .csv file.

Image credit: Allison Horst @allison_horst from @RLadiesSB

Template code

my_data_in_r <- read_csv(here("data_folder", "filename.csv"))

Your turn 5: Read in the babynames data

Step 1: Make a new chunk (Cmd-Option-I)
Step 2: Copy the template code

my_data_in_r <- read_csv(here("data_folder", "filename.csv"))

Step 3: Adapt the template to read in the SAbabynames.csv data
Step 4: Play with the code in each chunk to plot...
- How popular is your name?
- How popular is your name relative to your neighbours?
Step 5: Knit your document (** use the cog to change the display option to Show Code and Output)

10:00

What questions do you have ?

Before we break... a key idea

Image credit Dani Navarro Tidyverse for Beginners

https://jennyslides.netlify.app/byod/
Time for a break...Next up.... your own data
05:00
27

Your turn 6: Lets get YOUR data in

Step 1: Upload your data into the data folder
Step 2: Make a new RMarkdown document (File-NewFile-RMarkdown), save as my_analysis.Rmd
Step 3: Make a new chunk (Cmd-Option-I) and load packages using library()
Step 4: Make another new chunk, copy the template code

my_data_in_R <- read_csv(here("data_folder", "filename.csv"))

Step 5: Adapt the template to match your datafile
Step 6: Run the chunk to read your data in
Step 7: Have a look at your data, any obvious problems?

05:00

YAY your data is in R!

But its probably "dirty".

Maybe...

... the variable names are weird and inconsistent

... you regret your choice of variable names

... you don't need all of those variables

Image credit: Allison Horst @allison_horst from R-Ladies Santa Barbara

clean_names() from `janitor`

Problem 1: all of the variable names are weird and inconsistent

better_data_in_R <- your_data_in_R %>%
               clean_names()

rename() from `dplyr`

Problem 2: you regret your variable names

betterdata <- yourdata %>%
  rename(newname1 = oldname1, newname2 = oldname2)

NOTE: I can never remember the order of the names in this function. Is it old = new? or new = old? I have resorted to a stupid mnemonic... it happens to be alphabetical (i.e. L-M-New = Old)

select() from `dplyr`

Problem 3: you don't need all of those variables

Keep just the columns you name

better_data_in_R <- your_data_in_R %>%
  select(age, gender, score)

Keep columns 1 thru 5, and 8 thru 12

better_data_in_R <- your_data_in_R %>%
  select(1:5, 8:12)

Use - to drop named variable, keeping everything else

better_data_in_R <- your_data_in_R %>%
  select(-date)

Your turn 7: Clean up your own data

Use clean_names() if your variable names are weird

better_data_in_R <- your_data_in_R %>%
               clean_names()

Use select() to pare down to just the variables you need

better_data_in_R <- your_data_in_R %>%
  select(age, gender, score)

Use rename() to give your variables more useful names

better_data_in_R <- your_data_in_R %>%
  rename(newname1 = oldname1)

15:00

What questions do you have ?

Well done! Your data is in and a little cleaner.

I think we are going to get to here, the rest of these slides cover...

how to make your data tidy (i.e. convert it from wide format into long format)
how to use summary(), skim(), and group_by() + summarise() to get summary stats
the basics of ggplot

via GIPHY

Recommended learning resources

Until next time....

via GIPHY

https://jennyslides.netlify.app/byod/
Keep working through these slides to learn aboutpivot_longer() and pivot_wider()summary(), skim(), group_by() %>% summarise()ggplot()38

Your data is clean-er, but is it "tidy"?

Three rules for tidy data:

Each variable must have its own column.
Each observation must have its own row.
Each value must have its own cell.

Image credit: R4DS

Read more about tidy data Wickham (2014)

Wide to Long

pivot_longer() from `tidyr`

pivot_longer wants to know 3 things

names_to = what you want to call the new column containing the names of the columns
values_to = what you want to call the new column containing the values
the range of columns that you want to make long (col1:col6)

longdata <- wide %>% 
            pivot_longer(names_to = "new_names_col", 
                    values_to = "new_values_col", col1:col6)

https://jennyslides.netlify.app/byod/
My turn: Make favourite things long42

Your turn 8: make your wide data long

Take a look at your data and work out ...

which columns need to become long
what the values column should be called
what the "names" column should be called

longdata <- wide %>% 
      pivot_longer(names_to = "new_names_col", 
                  values_to = "new_values_col", col1:col6)

15:00

Before we break... reminding you about a key idea

The pipe %>% allows you to string together a series of functions and accomplish a LOT in just a few lines of code

ready_to_plot <- raw_data %>%
  select(Just, The, Columns, You, Want) %>%
  rename(just = Just, the = The, columns = Columns, you = You, want = Want) %>%
  pivot_longer(names_to = "condition", values = "response", columns:want)

https://jennyslides.netlify.app/byod/
Time for a break...Next up.... summary stats & plotting
10:00
45

Get a quick summary of...

... the structure of your data

head(yourdata)
str(yourdata)
length(yourdata)
glimpse(yourdata)
names(yourdata)

Get some quick summary stats

There are lots of ways to get summary stats in R.

base R

summary(yourdata)

`skimr` package

skim(yourdata)

`dplyr` package

summary_stats <- yourdata %>%
  group_by(something_interesting) %>%
  summarise(mean = mean(resp), 
            stdev = sd(resp), 
            n = n(), 
            se = stdev/sqrt(n))

https://jennyslides.netlify.app/byod/
My turn: summarise my favourite things48

Your turn 9: Get some summary stats from your data

Try out summary() or skim(), or create a dataframe using this code template

summary_stats <- yourdata %>%
  group_by(something_interesting) %>%
  summarise(mean = mean(resp), 
            stdev = sd(resp), 
            n = n(), 
            se = stdev/sqrt(n))

Heads Up: do you need na.rm = TRUE?

10:00

What questions do you have ?

https://jennyslides.netlify.app/byod/
We made it! Lets make a ggplot!51

The grammar of graphics

ggplot wants to know 3 things

what data you want to plot
the "aesthetics" i.e. which variables you want on the x and y, what things look like (colour/shape/fill)
which "geom" you want to use (point, line, col, histogram, violin)

ggplot(data, aes(x = __, y = __)) +
  geom_point()

WATCH OUT! You can pipe data into ggplot (and combine with dplyr functions) but within ggplot you need to ADD LAYERS with +

data %>%
  filter(interesting_variable > z) %>%
  ggplot(aes(x = __, y = __)) +
  geom_point()

https://jennyslides.netlify.app/byod/
My turn: plot favourite things53

Your turn 10: Plot your data

ggplot(data, aes(x = __, y = __)) +
  geom_point()

data %>%
  filter(interesting_variable > z) %>%
  ggplot(aes(x = __, y = __, colour = condition)) +
  geom_point() +
  facet_wrap(~ group)
  ggsave("your_first_ggplot.png")

Image credit: Allison Horst @allison_horst from R-Ladies Santa Barbara

10:00

Google: How to I add/get rid of __ ggplot in R?

1. error bars

2. axis labels

3. a title

4. legend

5. colour ....

Or check out the R Graph Gallery for inspiration and code

The End....

via GIPHY

https://jennyslides.netlify.app/byod/
57

↑, ←, Pg Up, k	Go to previous slide
↓, →, Pg Dn, Space, j	Go to next slide
Home	Go to first slide
End	Go to last slide
Number + Return	Go to specific slide
b / m / f	Toggle blackout / mirrored / fullscreen mode
c	Clone slideshow
p	Toggle presenter mode
t	Restart the presentation timer
?, h	Toggle this help

Let's start at the very beginning

A very good place to start

Jen Richmond · R-Ladies Sydney

https://jennyslides.netlify.app/byod/

The Plan

Disclaimer

Today we are really unlikely to know the answers to all your questions, but we can help you google!

Google is every R user's best friend, even Hadley Wickham.

1. Why R?

Why I R...

Reproducibility

One stop shop

Equity

Community

Fun!

Warning: You will want to quit

Navigating RStudio

Your turn 1: Set up RStudio Cloud

Think of R-Studio as your kitchen

Your turn 2: Let's start by redecorating

What questions do you have ?

Packages

What is the tidyverse?

Your turn 3: Install some packages

What questions do you have ?

An aside about RMarkdown

Tricks and tips

Your turn 4: "Knit" your RMarkdown document

What questions do you have ?

Getting data in

Where is the data?

Template code

Your turn 5: Read in the babynames data

What questions do you have ?

Before we break... a key idea

Time for a break...

Next up.... your own data

Your turn 6: Lets get YOUR data in

YAY your data is in R!

But its probably "dirty".

Maybe...

... the variable names are weird and inconsistent

... you regret your choice of variable names

... you don't need all of those variables

clean_names() from janitor

Problem 1: all of the variable names are weird and inconsistent

rename() from dplyr

Problem 2: you regret your variable names

select() from dplyr

Problem 3: you don't need all of those variables

Your turn 7: Clean up your own data

What questions do you have ?

Well done! Your data is in and a little cleaner.

I think we are going to get to here, the rest of these slides cover...

Recommended learning resources

RYouWithMe (online course)

R for Data Science (free e-book)

RStudio Education site (lots of great resources)

Until next time....

Keep working through these slides to learn about

pivot_longer() and pivot_wider()

summary(), skim(), group_by() %>% summarise()

ggplot()

Your data is clean-er, but is it "tidy"?

Three rules for tidy data:

Wide to Long

pivot_longer() from tidyr

pivot_longer wants to know 3 things

My turn: Make favourite things long

Your turn 8: make your wide data long

Before we break... reminding you about a key idea

The pipe %>% allows you to string together a series of functions and accomplish a LOT in just a few lines of code

Time for a break...

Next up.... summary stats & plotting

Get a quick summary of...

... the structure of your data

Get some quick summary stats

base R

skimr package

dplyr package

clean_names() from `janitor`

rename() from `dplyr`

select() from `dplyr`

pivot_longer() from `tidyr`

`skimr` package

`dplyr` package