R refresher (Brief review of Workshop I)

remixed from Claus O. Wilke’s SDS375 course and Andrew P. Bray’s quarto workshop




Workshop materials are at:

https://elsherbini.github.io/durban-data-science-for-biology/



Goals for this session

  1. RStudio and the Quarto notebook

  2. Data types and manipulations

  3. Review data visualization

  4. Data wrangling and make plots with the tidyverse

  5. Advanced table commands

  6. Plotting distributions with tidyverse and ggplot

Discussions: discord

Ask questions at #workshop-questions on https://discord.com/channels/1227264067899621427.

Stickies

Picture of a laptop with a red sticky note stuck to the top.

During an activity, place a yellow sticky on your laptop if you’re good to go and a pink sticky if you want help.

Image by Megan Duffy

Practicalities

WiFi:

Network: Southernsunconference

Network Password: S0uthernsun1

Getting on AWS & RStudio

To get on your instance, find the address associated with your name. Copy the link into your browser and it will take you to navigate to RStudio
IP addresses


Enter the login credentials

Username: genomics
Password: evomics2024

Create an R Project

  1. File -> New Project…

Create an R Project

  1. Click on New Directory

Create an R Project

  1. Navigate to the workshop_2 folder name your directory and click “Create Project”

Create an R Project

  1. You made a project! This creates a file for you with the .qmd extension

Create an R Project

  1. Switch from “visual” to “source” to see the plain-text version of this document.

Create an R Project

  1. Click on “Render” to ask Quarto to turn this plain-text document into an HTML page

Create an R Project

  1. Your default web-browser will open and show you the rendered document!

RStudio

What are the parts of RStudio?

The text editor

The console

The right panes

Installing and loading packages

Packages are a collection of functions and objects that are shared for free to use.

In the console, you can type install.packages("package_name") to install most R packages.

Sometimes R packages need to be installed a different way, and the documentation of the package will tell you how.

Then, to load a package, add library("package_name") in a code chunk (usually in the first code cell of your document)

Anatomy of a Document

  1. Code Cells
  2. Text
  3. Metadata

Quarto’s Code Chunk

```{r}
#| echo: false
rnorm(3)
```

Write a math expression in a chunk and press the green arrow at the top-right of the chunk.

Example: Figures from Code

```{r}
library(palmerpenguins)
library(ggplot2)

ggplot(penguins,
       aes(x = bill_length_mm,
           y = bill_depth_mm,
           col = island)) +
  geom_point()
```

Example: Figures from Code

```{r}
#| fig-width: 5
#| fig-height: 3

library(palmerpenguins)
library(ggplot2)

ggplot(penguins,
       aes(x = bill_length_mm,
           y = bill_depth_mm,
           col = island)) +
  geom_point()
```

Inline Elements: Text Formatting

Markdown

Markdown allows you to format text
with *emphasis* and **strong emphasis**.
You can also add superscripts^2^, 
subscripts~2~, and display code 
`verbatim`. Little known fact: you can 
also ~~strikethrough~~ text and present
it in [small caps]{.smallcaps}.

Output

Markdown allows you to format text with emphasis and strong emphasis. You can also add superscripts2, subscripts2, and display code verbatim. Little known fact: you can also strikethrough text and present it in small caps.

Markdown

You can embed [links with names](https://quarto.org/), direct urls
like <https://quarto.org/>, and links to 
[other places](#inline-elements-text-formatting) in the document. 
The syntax is similar for embedding an inline image:
![render icon](images/render-icon.png).


Output

You can embed links with names, direct urls like https://quarto.org/, and links to other places in the document. The syntax is similar for embedding an inline image: small icon of render arrow.

Metadata: YAML

“Yet Another Markup Language” or “YAML Ain’t Markup Language” is used to provide document level metadata …

[… in key-value pairs,]

[… that can nest,]

[… are fussy about indentation,]

[… and are kept between ---.]


---
format: 
  title: "Intro to R"
  author: "Yours Truly"
  html:
    toc: true
    code-fold: true
---

There are many options for front matter and configuring rendering.

Writing code

Assignment

You can use <- or = to assign values to variables

a <- 6
b = 8
c <- 5.44
d = TRUE
e = "hello world" 
e <- 'hello world' # same as double quote

We will use <- for all examples going forward.

Naming variables

A lot of R people use . inside variable names, but in most languages besides R this would be an error. It’s good practice these days to use the _ underscore if you want separation in your variable names.

r.people.sometimes.put.dots <- TRUE
dots.are.confusing <- "maybe"
just_use_underscores <- "please"

Functions

Functions are named bits of code that take parameters as input and return some output

library(tidyverse)
word_1 <- "hello"
word_2 <- "world"
str_c(word_1, word_2, sep = " ")
[1] "hello world"

str_c is a function that puts concatenates strings.

functions can have named parameters as well as positional parameters.
named parameters always take an = sign for assignment.

Getting help with functions

Type ?str_c in the console to get a help page. check out this guide on how to read the R help pages.

also try googling str_c R tidyverse to get help.

chatGPT and phind.com are really good at answering specific questions about R functions - not always correct but most of the time.

Value types in R

The type of the value can be

# numeric
c(1,2,3,4) 

# character
c("a","b","c","d")

# boolean
c(TRUE, FALSE)

# factor
c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun") %>% as_factor()

tibbles (aka data frames)

tibbles are the big reason R is great for working with tabular data.

A data frame is a rectangular collection of variables (in the columns) and observations (in the rows).

table_02
# A tibble: 132 × 6
   pid    time_point arm     nugent_score crp_blood    ph
   <chr>  <chr>      <chr>          <dbl>     <dbl> <dbl>
 1 pid_01 baseline   placebo            8      0.44   5.7
 2 pid_01 week_1     placebo            7      1.66   5.2
 3 pid_01 week_7     placebo            7      1.44   5.4
 4 pid_02 baseline   placebo            7      1.55   5.2
 5 pid_02 week_1     placebo            7      0.75   4.8
 6 pid_02 week_7     placebo            4      1.17   4.2
 7 pid_03 baseline   placebo            6      1.78   4.8
 8 pid_03 week_1     placebo           10      0.57   5.3
 9 pid_03 week_7     placebo            7      1.79   5.2
10 pid_04 baseline   placebo            5      1.76   4.8
# ℹ 122 more rows

Elementary data manipulations

  • Pick rows: filter()

  • Pick columns: select()

  • Sort rows: arrange()

  • Count things: count()

  • Make new columns: mutate()

Read in data

Data is often in tables, and the easiest way to store tabular data is in csv or tsv format.

csv - comma separated values
tsv - tab separated values

to read in data stored this way use read_csv(filename) or read_tsv(filename)

table_02 <- read_csv("02_visit_clinical_measurements_UKZN_workshop_2023.csv")

The pipe %>% feeds data into functions

```{r}
head(table_02)
```
# A tibble: 6 × 6
  pid    time_point arm     nugent_score crp_blood    ph
  <chr>  <chr>      <chr>          <dbl>     <dbl> <dbl>
1 pid_01 baseline   placebo            8      0.44   5.7
2 pid_01 week_1     placebo            7      1.66   5.2
3 pid_01 week_7     placebo            7      1.44   5.4
4 pid_02 baseline   placebo            7      1.55   5.2
5 pid_02 week_1     placebo            7      0.75   4.8
6 pid_02 week_7     placebo            4      1.17   4.2

The pipe %>% feeds data into functions

```{r}
# head(table_02)
table_02 %>%
  head()
```
# A tibble: 6 × 6
  pid    time_point arm     nugent_score crp_blood    ph
  <chr>  <chr>      <chr>          <dbl>     <dbl> <dbl>
1 pid_01 baseline   placebo            8      0.44   5.7
2 pid_01 week_1     placebo            7      1.66   5.2
3 pid_01 week_7     placebo            7      1.44   5.4
4 pid_02 baseline   placebo            7      1.55   5.2
5 pid_02 week_1     placebo            7      0.75   4.8
6 pid_02 week_7     placebo            4      1.17   4.2

The pipe %>% feeds data into functions

```{r}
ggplot(table_02, aes(crp_blood, ph, color = arm)) + geom_point()
```

The pipe %>% feeds data into functions

```{r}
table_02 %>%
  ggplot(aes(crp_blood, ph, color = arm)) + geom_point()
```

Picking rows or columns, and sorting

Pick rows from a table: filter()

Filter only placebo

```{r}
table_02 %>%
  filter(arm == "placebo")
```
# A tibble: 69 × 6
   pid    time_point arm     nugent_score crp_blood    ph
   <chr>  <chr>      <chr>          <dbl>     <dbl> <dbl>
 1 pid_01 baseline   placebo            8      0.44   5.7
 2 pid_01 week_1     placebo            7      1.66   5.2
 3 pid_01 week_7     placebo            7      1.44   5.4
 4 pid_02 baseline   placebo            7      1.55   5.2
 5 pid_02 week_1     placebo            7      0.75   4.8
 6 pid_02 week_7     placebo            4      1.17   4.2
 7 pid_03 baseline   placebo            6      1.78   4.8
 8 pid_03 week_1     placebo           10      0.57   5.3
 9 pid_03 week_7     placebo            7      1.79   5.2
10 pid_04 baseline   placebo            5      1.76   4.8
# ℹ 59 more rows

Filter out samples with ph < 4

```{r}
table_02 %>%
  filter(ph < 4)
```
# A tibble: 39 × 6
   pid    time_point arm       nugent_score crp_blood    ph
   <chr>  <chr>      <chr>            <dbl>     <dbl> <dbl>
 1 pid_05 week_1     treatment            3      0.19   3.2
 2 pid_05 week_7     treatment            2      0.45   3.5
 3 pid_09 week_1     treatment            3      0.27   3.6
 4 pid_10 week_1     treatment            0      0.01   3.5
 5 pid_10 week_7     treatment            1      2.87   2.9
 6 pid_11 week_1     treatment            1      0.1    3.3
 7 pid_15 week_1     treatment            3      0.84   3.4
 8 pid_15 week_7     treatment            3      0.68   3.5
 9 pid_16 week_1     treatment            0      0.03   3.7
10 pid_16 week_7     treatment            2      0.5    3.2
# ℹ 29 more rows

Pick columns from a table: select()

Pick columns pid, ph, and nugent

```{r}
table_02 %>%
  select(pid, ph, nugent_score)
```
# A tibble: 132 × 3
   pid       ph nugent_score
   <chr>  <dbl>        <dbl>
 1 pid_01   5.7            8
 2 pid_01   5.2            7
 3 pid_01   5.4            7
 4 pid_02   5.2            7
 5 pid_02   4.8            7
 6 pid_02   4.2            4
 7 pid_03   4.8            6
 8 pid_03   5.3           10
 9 pid_03   5.2            7
10 pid_04   4.8            5
# ℹ 122 more rows

Rename columns and subset with select

```{r}
table_02 %>%
  select(participant_id = pid, ph, nugent_score)
```
# A tibble: 132 × 3
   participant_id    ph nugent_score
   <chr>          <dbl>        <dbl>
 1 pid_01           5.7            8
 2 pid_01           5.2            7
 3 pid_01           5.4            7
 4 pid_02           5.2            7
 5 pid_02           4.8            7
 6 pid_02           4.2            4
 7 pid_03           4.8            6
 8 pid_03           5.3           10
 9 pid_03           5.2            7
10 pid_04           4.8            5
# ℹ 122 more rows

Sort the rows in a table: arrange()

Sort samples by ph ascending

```{r}
table_02 %>%
  arrange(ph)
```
# A tibble: 132 × 6
   pid    time_point arm       nugent_score crp_blood    ph
   <chr>  <chr>      <chr>            <dbl>     <dbl> <dbl>
 1 pid_31 week_7     treatment            2      1.36   2.8
 2 pid_10 week_7     treatment            1      2.87   2.9
 3 pid_28 baseline   treatment            3      0.67   2.9
 4 pid_26 week_1     treatment            0      0.11   3  
 5 pid_23 week_7     placebo              3      3.67   3.1
 6 pid_40 baseline   treatment            3      1.48   3.1
 7 pid_40 week_1     treatment            2      0.17   3.1
 8 pid_05 week_1     treatment            3      0.19   3.2
 9 pid_16 week_7     treatment            2      0.5    3.2
10 pid_37 week_7     treatment            2      0.7    3.2
# ℹ 122 more rows

Sort samples by ph, descending

```{r}
table_02 %>%
  arrange(desc(ph))
```
# A tibble: 132 × 6
   pid    time_point arm       nugent_score crp_blood    ph
   <chr>  <chr>      <chr>            <dbl>     <dbl> <dbl>
 1 pid_29 baseline   placebo              7      2.39   5.8
 2 pid_01 baseline   placebo              8      0.44   5.7
 3 pid_16 baseline   treatment            6      1.91   5.7
 4 pid_06 week_1     placebo              8      1.72   5.6
 5 pid_26 baseline   treatment            7      0.94   5.6
 6 pid_13 week_1     placebo              7      2.57   5.5
 7 pid_23 week_1     placebo              8      0.8    5.5
 8 pid_27 baseline   placebo              7      1.17   5.5
 9 pid_01 week_7     placebo              7      1.44   5.4
10 pid_04 week_7     placebo              7      5.68   5.4
# ℹ 122 more rows

Counting things

To demonstrate counting, let’s switch to table_01

```{r}
table_01
```
# A tibble: 44 × 6
   pid    arm       smoker       age education                     sex  
   <chr>  <chr>     <chr>      <dbl> <chr>                         <lgl>
 1 pid_01 placebo   non-smoker    26 grade 10-12, matriculated     FALSE
 2 pid_02 placebo   smoker        33 grade 10-12, matriculated     FALSE
 3 pid_03 placebo   smoker        30 post-secondary                FALSE
 4 pid_04 placebo   non-smoker    34 grade 10-12, not matriculated FALSE
 5 pid_05 treatment non-smoker    29 grade 10-12, matriculated     FALSE
 6 pid_06 placebo   smoker        34 post-secondary                FALSE
 7 pid_07 placebo   non-smoker    31 grade 10-12, not matriculated FALSE
 8 pid_08 placebo   smoker        30 grade 10-12, not matriculated FALSE
 9 pid_09 treatment non-smoker    35 grade 10-12, not matriculated FALSE
10 pid_10 treatment non-smoker    32 less than grade 9             FALSE
# ℹ 34 more rows

Counting things

```{r}
table_01 %>%
  count(smoker)
```
# A tibble: 2 × 2
  smoker         n
  <chr>      <int>
1 non-smoker    27
2 smoker        17

Counting things

```{r}
table_01 %>%
  count(arm, smoker)
```
# A tibble: 4 × 3
  arm       smoker         n
  <chr>     <chr>      <int>
1 placebo   non-smoker    12
2 placebo   smoker        11
3 treatment non-smoker    15
4 treatment smoker         6

Let’s take a poll

Go to the event on wooclap

M2. Does filter get rid of rows that match TRUE, or keep rows that match TRUE?

Use the pipe to build analysis pipelines

```{r}
table_01 %>%
  filter(arm == "placebo")
```
# A tibble: 23 × 6
   pid    arm     smoker       age education                     sex  
   <chr>  <chr>   <chr>      <dbl> <chr>                         <lgl>
 1 pid_01 placebo non-smoker    26 grade 10-12, matriculated     FALSE
 2 pid_02 placebo smoker        33 grade 10-12, matriculated     FALSE
 3 pid_03 placebo smoker        30 post-secondary                FALSE
 4 pid_04 placebo non-smoker    34 grade 10-12, not matriculated FALSE
 5 pid_06 placebo smoker        34 post-secondary                FALSE
 6 pid_07 placebo non-smoker    31 grade 10-12, not matriculated FALSE
 7 pid_08 placebo smoker        30 grade 10-12, not matriculated FALSE
 8 pid_12 placebo non-smoker    31 grade 10-12, matriculated     FALSE
 9 pid_13 placebo non-smoker    32 post-secondary                FALSE
10 pid_14 placebo smoker        32 grade 10-12, matriculated     FALSE
# ℹ 13 more rows

Use the pipe to build analysis pipelines

```{r}
table_01 %>%
  filter(age < 30) %>%
  select(pid, arm, smoker)
```
# A tibble: 12 × 3
   pid    arm       smoker    
   <chr>  <chr>     <chr>     
 1 pid_01 placebo   non-smoker
 2 pid_05 treatment non-smoker
 3 pid_15 treatment non-smoker
 4 pid_17 treatment non-smoker
 5 pid_21 treatment non-smoker
 6 pid_27 placebo   smoker    
 7 pid_30 placebo   non-smoker
 8 pid_31 treatment non-smoker
 9 pid_35 placebo   non-smoker
10 pid_36 placebo   non-smoker
11 pid_41 treatment smoker    
12 pid_44 treatment non-smoker

Use the pipe to build analysis pipelines

```{r}
table_01 %>%
  filter(age < 30) %>%
  select(pid, arm, smoker) %>%
  count(arm, smoker)
```
# A tibble: 4 × 3
  arm       smoker         n
  <chr>     <chr>      <int>
1 placebo   non-smoker     4
2 placebo   smoker         1
3 treatment non-smoker     6
4 treatment smoker         1

Adding new columns to a table

Make a new table column: mutate()

Example: C-reactive protein

The crp_blood column is in units of mg/L. What if you needed it in ug/ul? What’s the calculation?

```{r}
table_02 %>%
  select(pid, time_point, crp_blood)
```
# A tibble: 132 × 3
   pid    time_point crp_blood
   <chr>  <chr>          <dbl>
 1 pid_01 baseline        0.44
 2 pid_01 week_1          1.66
 3 pid_01 week_7          1.44
 4 pid_02 baseline        1.55
 5 pid_02 week_1          0.75
 6 pid_02 week_7          1.17
 7 pid_03 baseline        1.78
 8 pid_03 week_1          0.57
 9 pid_03 week_7          1.79
10 pid_04 baseline        1.76
# ℹ 122 more rows

Example: C-reactive protein

The crp_blood column is in units of mg/L. What if you needed it in ug/ul? What’s the calculation?

To get ug/L you would multiply by 1000. To get ug/ul you need to then divide by 1000000

```{r}
table_02 %>%
  select(pid, time_point, crp_blood)
```
# A tibble: 132 × 3
   pid    time_point crp_blood
   <chr>  <chr>          <dbl>
 1 pid_01 baseline        0.44
 2 pid_01 week_1          1.66
 3 pid_01 week_7          1.44
 4 pid_02 baseline        1.55
 5 pid_02 week_1          0.75
 6 pid_02 week_7          1.17
 7 pid_03 baseline        1.78
 8 pid_03 week_1          0.57
 9 pid_03 week_7          1.79
10 pid_04 baseline        1.76
# ℹ 122 more rows

Example: C-reactive protein

The crp_blood column is in units of mg/L. What if you needed it in ug/ul? What’s the calculation?

To get ug/L you would multiply by 1000. To get ug/ul you need to then divide by 1000000

```{r}
table_02 %>%
  select(pid, time_point, crp_blood) %>%
  mutate(crp_blood_ugul = crp_blood / 1000)
```
# A tibble: 132 × 4
   pid    time_point crp_blood crp_blood_ugul
   <chr>  <chr>          <dbl>          <dbl>
 1 pid_01 baseline        0.44        0.00044
 2 pid_01 week_1          1.66        0.00166
 3 pid_01 week_7          1.44        0.00144
 4 pid_02 baseline        1.55        0.00155
 5 pid_02 week_1          0.75        0.00075
 6 pid_02 week_7          1.17        0.00117
 7 pid_03 baseline        1.78        0.00178
 8 pid_03 week_1          0.57        0.00057
 9 pid_03 week_7          1.79        0.00179
10 pid_04 baseline        1.76        0.00176
# ℹ 122 more rows

Make multiple columns at once

```{r}
table_02 %>%
  select(pid, time_point, crp_blood) %>%
  mutate(crp_blood_ugul = crp_blood / 1000,
         crp_blood_ugl = crp_blood * 1000)
```
# A tibble: 132 × 5
   pid    time_point crp_blood crp_blood_ugul crp_blood_ugl
   <chr>  <chr>          <dbl>          <dbl>         <dbl>
 1 pid_01 baseline        0.44        0.00044           440
 2 pid_01 week_1          1.66        0.00166          1660
 3 pid_01 week_7          1.44        0.00144          1440
 4 pid_02 baseline        1.55        0.00155          1550
 5 pid_02 week_1          0.75        0.00075           750
 6 pid_02 week_7          1.17        0.00117          1170
 7 pid_03 baseline        1.78        0.00178          1780
 8 pid_03 week_1          0.57        0.00057           570
 9 pid_03 week_7          1.79        0.00179          1790
10 pid_04 baseline        1.76        0.00176          1760
# ℹ 122 more rows

Aesthetics - the elements of data visualization

Plots map data onto graphical elements.

Table 1: 02_visit_clinical_measurements_UKZN_workshop_2023.csv
pid time_point arm nugent_score crp_blood ph
pid_01 baseline placebo 8 0.44 5.7
pid_01 week_1 placebo 7 1.66 5.2
pid_01 week_7 placebo 7 1.44 5.4
pid_02 baseline placebo 7 1.55 5.2
pid_02 week_1 placebo 7 0.75 4.8
pid_02 week_7 placebo 4 1.17 4.2

pH mapped to y position

pH mapped to color

Commonly used aesthetics

Figure from Claus O. Wilke. Fundamentals of Data Visualization. O’Reilly, 2019

The same data values can be mapped to different aesthetics

Figure from Claus O. Wilke. Fundamentals of Data Visualization. O’Reilly, 2019

We can use many different aesthetics at once

Creating aesthetic mappings in ggplot

We define the mapping with aes()

```{r}
table_02 %>%
  ggplot(mapping = aes(x = time_point, y = ph, color = ph)) +
  geom_jitter()
```

We frequently omit argument names

Long form, all arguments are named:

```{r}
#| eval: false

ggplot(
  data= table_02,
  mapping = aes(x = time_point, y = ph, color = ph)
) +
  geom_jitter()
```

We frequently omit argument names

Abbreviated form, common arguments remain unnamed:

```{r}
#| eval: false

ggplot(table_02, aes(x = time_point, y = ph, color = ph)) +
  geom_jitter()
```

The geom determines how the data is shown

```{r}
ggplot(table_02, aes(x = time_point, y = ph, color = ph)) +
  geom_point()
```

The geom determines how the data is shown

```{r}
ggplot(table_02, aes(x = time_point, y = ph, color = ph)) +
  geom_boxplot()
```

The geom determines how the data is shown

```{r}
ggplot(table_02, aes(x = time_point, y = ph, color = ph)) +
  geom_jitter()
```

Different geoms have parameters for control

```{r}
ggplot(table_02, aes(x = time_point, y = ph, color = ph)) +
  geom_jitter(size=3)
```

Different geoms have parameters for control

```{r}
ggplot(table_02, aes(x = time_point, y = ph, color = ph)) +
  geom_jitter(size=3, width = 0.2)
```

Important: color and fill apply to different elements

color
Applies color to points, lines, text, borders

fill
Applies color to any filled areas

Many geoms have both color and fill aesthetics

```{r}
#| output-location: column
ggplot(
  data = table_02,
  mapping = aes(
    x = time_point,
    y = ph,
    color = time_point
  )
) + geom_boxplot()
```

Many geoms have both color and fill aesthetics

```{r}
#| output-location: column
ggplot(
  data = table_02,
  mapping = aes(
    x = time_point,
    y = ph,
    fill = time_point
  )
) + geom_boxplot()
```

Many geoms have both color and fill aesthetics

```{r}
#| output-location: column
ggplot(
  data = table_02,
  mapping = aes(
    x = time_point,
    y = ph,
    fill = time_point,
    color = time_point
  )
) + geom_boxplot()
```

Aesthetics can also be used as parameters in geoms

```{r}
#| output-location: column
ggplot(
  data = table_02,
  mapping = aes(
    x = time_point,
    y = ph
  )
) + geom_boxplot()
```

Aesthetics can also be used as parameters in geoms

```{r}
#| output-location: column
ggplot(
  data = table_02,
  mapping = aes(
    x = time_point,
    y = ph
  )
) + geom_boxplot(fill="orange")
```

Analyze subsets: group_by() and summarize()

Example application of grouping: Counting

Previously we used count, now we group the data

```{r}
table_01 %>%
  group_by(smoker)
```
# A tibble: 44 × 6
# Groups:   smoker [2]
   pid    arm       smoker       age education                     sex  
   <chr>  <chr>     <chr>      <dbl> <chr>                         <lgl>
 1 pid_01 placebo   non-smoker    26 grade 10-12, matriculated     FALSE
 2 pid_02 placebo   smoker        33 grade 10-12, matriculated     FALSE
 3 pid_03 placebo   smoker        30 post-secondary                FALSE
 4 pid_04 placebo   non-smoker    34 grade 10-12, not matriculated FALSE
 5 pid_05 treatment non-smoker    29 grade 10-12, matriculated     FALSE
 6 pid_06 placebo   smoker        34 post-secondary                FALSE
 7 pid_07 placebo   non-smoker    31 grade 10-12, not matriculated FALSE
 8 pid_08 placebo   smoker        30 grade 10-12, not matriculated FALSE
 9 pid_09 treatment non-smoker    35 grade 10-12, not matriculated FALSE
10 pid_10 treatment non-smoker    32 less than grade 9             FALSE
# ℹ 34 more rows

Example application of grouping: Counting

Previously we used count, now we group the data, and then summarise

```{r}
table_01 %>%
  group_by(smoker) %>%
  summarise(
    n = n() # n() returns the number of observations per group
    )
```
# A tibble: 2 × 2
  smoker         n
  <chr>      <int>
1 non-smoker    27
2 smoker        17

Example application of grouping: Counting

Now let’s group by multiple variables

```{r}
table_01 %>%
  group_by(smoker, arm)
```
# A tibble: 44 × 6
# Groups:   smoker, arm [4]
   pid    arm       smoker       age education                     sex  
   <chr>  <chr>     <chr>      <dbl> <chr>                         <lgl>
 1 pid_01 placebo   non-smoker    26 grade 10-12, matriculated     FALSE
 2 pid_02 placebo   smoker        33 grade 10-12, matriculated     FALSE
 3 pid_03 placebo   smoker        30 post-secondary                FALSE
 4 pid_04 placebo   non-smoker    34 grade 10-12, not matriculated FALSE
 5 pid_05 treatment non-smoker    29 grade 10-12, matriculated     FALSE
 6 pid_06 placebo   smoker        34 post-secondary                FALSE
 7 pid_07 placebo   non-smoker    31 grade 10-12, not matriculated FALSE
 8 pid_08 placebo   smoker        30 grade 10-12, not matriculated FALSE
 9 pid_09 treatment non-smoker    35 grade 10-12, not matriculated FALSE
10 pid_10 treatment non-smoker    32 less than grade 9             FALSE
# ℹ 34 more rows

Example application of grouping: Counting

Now let’s group by multiple variables, and summarise

```{r}
table_01 %>%
  group_by(smoker, arm) %>%
    summarise(
    n = n() # n() returns the number of observations per group
    )
```
# A tibble: 4 × 3
# Groups:   smoker [2]
  smoker     arm           n
  <chr>      <chr>     <int>
1 non-smoker placebo      12
2 non-smoker treatment    15
3 smoker     placebo      11
4 smoker     treatment     6

Example application of grouping: Counting

count(...) is a short-cut for group_by(...) %>% summarize(n = n()) group_by() and summarise()is the general method

```{r}
table_01 %>%
  count(smoker, arm)

table_01 %>%
  group_by(smoker, arm) %>%
  summarise(median_age = median(age))
```
# A tibble: 4 × 3
  smoker     arm           n
  <chr>      <chr>     <int>
1 non-smoker placebo      12
2 non-smoker treatment    15
3 smoker     placebo      11
4 smoker     treatment     6
# A tibble: 4 × 3
# Groups:   smoker [2]
  smoker     arm       median_age
  <chr>      <chr>          <dbl>
1 non-smoker placebo         31  
2 non-smoker treatment       30  
3 smoker     placebo         33  
4 smoker     treatment       33.5

You can make multiple summarise at once

```{r}
table_01 %>%
  group_by(smoker, arm) %>%
  summarise(
    n = n(),
    median_age = median(age)
    )
```
# A tibble: 4 × 4
# Groups:   smoker [2]
  smoker     arm           n median_age
  <chr>      <chr>     <int>      <dbl>
1 non-smoker placebo      12       31  
2 non-smoker treatment    15       30  
3 smoker     placebo      11       33  
4 smoker     treatment     6       33.5

Reshape: pivot_wider() and pivot_longer()

Reshaping example: Making a wide summary table

```{r}
table_01 %>%
  count(education, arm) %>%
  pivot_wider(names_from = arm, values_from = n)
```
# A tibble: 4 × 3
  education                     placebo treatment
  <chr>                           <int>     <int>
1 grade 10-12, matriculated           7         9
2 grade 10-12, not matriculated      11         7
3 less than grade 9                   2         4
4 post-secondary                      3         1

Reshaping example: Making a wide summary table

```{r}
education_wide <- table_01 %>%
  count(education, arm) %>%
  pivot_wider(names_from = arm, values_from = n)

education_wide %>%
  pivot_longer(-education, names_to = "arm", values_to = "n")
```
# A tibble: 8 × 3
  education                     arm           n
  <chr>                         <chr>     <int>
1 grade 10-12, matriculated     placebo       7
2 grade 10-12, matriculated     treatment     9
3 grade 10-12, not matriculated placebo      11
4 grade 10-12, not matriculated treatment     7
5 less than grade 9             placebo       2
6 less than grade 9             treatment     4
7 post-secondary                placebo       3
8 post-secondary                treatment     1

combining datasets: joins

We use joins to add columns from one table into another

There are different types of joins

The differences are all about how to handle when the two tables have different key values

left_join() - the resulting table always has the same key_values as the “left” table

right_join() - the resulting table always has the same key_values as the “right” table

inner_join() - the resulting table always only keeps the key_values that are in both tables

full_join() - the resulting table always has all key_values found in both tables

Left Join

left_join() - the resulting table always has the same key_values as the “left” table

inner_join

inner_join() - the resulting table always only keeps the key_values that are in both tables

table_a %>% inner_join(table_b)

Note, merging tables vertically is bind_rows(), not a join

table_a %>% bind_rows(table_b)

Visualizing distributions

Histograms and density plots

age sex class survived
0.17 female 3rd survived
0.33 male 3rd died
0.80 male 2nd survived
0.83 male 2nd survived
0.83 male 3rd survived
0.92 male 1st survived
1.00 female 2nd survived
1.00 female 3rd survived
1.00 male 2nd survived
1.00 male 2nd survived
1.00 male 3rd survived
1.50 female 3rd died
age sex class survived
1.5 female 3rd died
2.0 female 1st died
2.0 female 2nd survived
2.0 female 3rd died
2.0 female 3rd died
2.0 male 2nd survived
2.0 male 2nd survived
2.0 male 2nd survived
3.0 female 2nd survived
3.0 female 3rd survived
3.0 male 2nd survived
3.0 male 2nd survived
age sex class survived
3 male 3rd survived
3 male 3rd survived
4 female 2nd survived
4 female 2nd survived
4 female 3rd survived
4 female 3rd survived
4 male 1st survived
4 male 3rd died
4 male 3rd survived
5 female 3rd survived
5 female 3rd survived
5 male 3rd died

Histograms depend on the chosen bin width

Histograms and density plots in ggplot2

Making histograms with ggplot: geom_histogram()

```{r}
ggplot(titanic, aes(age)) +
  geom_histogram()
```

Setting the bin width

```{r}
ggplot(titanic, aes(age)) +
  geom_histogram(binwidth = 5)
```

Do you like where there bins are? What does the first bin say?

Always set the center as well, to half the bin_width

```{r}
ggplot(titanic, aes(age)) +
  geom_histogram(binwidth = 5, center=2.5)
```

Setting center 2.5 makes the bars start 0-5, 5-10, etc. instead of 2.5-7.5, etc. You could instead use the argument boundary=5 to accomplish the same behavior.

Making density plots with ggplot: geom_density()

```{r}
ggplot(titanic, aes(age)) +
  geom_density(fill = "skyblue")
```

Making density plots with ggplot: geom_density()

```{r}
ggplot(titanic, aes(age)) +
  geom_density()
```

without fill

Boxplots: Showing values along y, conditions along x

A boxplot is a crude way of visualizing a distribution.

How to read a boxplot

If you like density plots, consider violins

A violin plot is a density plot rotated 90 degrees and then mirrored.

How to read a violin plot

Making boxplots, violins, etc. in ggplot2

Making boxplots, violins, etc. in ggplot2

Plot type Geom Notes
boxplot geom_boxplot()
violin plot geom_violin()
strip chart geom_point() Jittering requires position_jitter()
sina plot geom_sina() From package ggforce
scatter-density plot geom_quasirandom() From package ggbeeswarm
ridgeline geom_density_ridges() From package ggridges

Examples: Boxplot

```{r}
ggplot(lincoln_temps, aes(x = month, y = mean_temp)) +
  geom_boxplot(fill = "skyblue") 
```

Examples: Violins

```{r}
ggplot(lincoln_temps, aes(x = month, y = mean_temp)) +
  geom_violin(fill = "skyblue") 
```

Examples: Strip chart (no jitter)

```{r}
ggplot(lincoln_temps, aes(x = month, y = mean_temp)) +
  geom_point(color = "skyblue") 
```

Examples: Strip chart (w/ jitter)

```{r}
ggplot(lincoln_temps, aes(x = month, y = mean_temp)) +
  geom_jitter(color = "skyblue") 
```

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