tidyverse for data analysis

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

Workshop materials are here:

https://kwondry.github.io/documentation/

Goals for this session

1. RStudio and the Quarto notebook

2. Loading and writing tabular data

3. Data wrangling and make plots with the tidyverse

4. Tables and statistics

Let’s take a poll

Go to the event on wooclap

Create a project

Positron

Let’s take a poll

Go to the event on wooclap

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 e.g. install.packages("tidyverse") 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("tidyverse") in a code chunk (usually in the first code cell of your document)

Quarto’s Code Chunk

You can quickly insert chunks like these into your file with

• the keyboard shortcut Ctrl + Alt + I (OS X: Shift + Command + I)
• the Add Chunk command in the editor toolbar
  
• or by typing the chunk delimiters

  ```{r}

  ```

Example chunk:

```{r}
10 + 5
```

Try writing a math expression in a code chunk, and hitting the button or Ctrl + Alt + Enter (OS X: Cmd + Option + Enter) to run the chunk.

Writing code: assigning variables

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.

• Google! Add “tidyverse” to search queries to get more relevant results.

• phind.com and chat.deepseek.com are good free AI services for getting help with code.

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()

05:00

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

Examples of coding

Quick live demo of doing some work in R

• Assigning variables
• Vectors are magic
• Functions and getting help

Elementary data manipulations

• Pick rows: filter()

• Pick columns: select()

• Sort rows: arrange()

• Count things: count()

• Make new columns: mutate()

How to 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("data/02_visit_clinical_measurements.csv")

But first: the pipe operator %>%

But first: the pipe operator %>%

%>% is pronounced “and then”

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()
```

Since R 4.1: Native pipe |>

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

Which to use? Native pipe or old-school pipe?

• |> is the future. If you can, use it.

• %>% works on older installations. It’s the safe choice for now.

We use %>% here because many people still run older R versions. Also, we’re old school.

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>                         <chr> 
 1 pid_01 placebo   non-smoker    26 grade 10-12, matriculated     Female
 2 pid_02 placebo   smoker        33 grade 10-12, matriculated     Female
 3 pid_03 placebo   smoker        30 post-secondary                Female
 4 pid_04 placebo   non-smoker    34 grade 10-12, not matriculated Female
 5 pid_05 treatment non-smoker    29 grade 10-12, matriculated     Female
 6 pid_06 placebo   smoker        34 post-secondary                Female
 7 pid_07 placebo   non-smoker    31 grade 10-12, not matriculated Female
 8 pid_08 placebo   smoker        30 grade 10-12, not matriculated Female
 9 pid_09 treatment non-smoker    35 grade 10-12, not matriculated Female
10 pid_10 treatment non-smoker    32 less than grade 9             Female
# 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>                         <chr> 
 1 pid_01 placebo non-smoker    26 grade 10-12, matriculated     Female
 2 pid_02 placebo smoker        33 grade 10-12, matriculated     Female
 3 pid_03 placebo smoker        30 post-secondary                Female
 4 pid_04 placebo non-smoker    34 grade 10-12, not matriculated Female
 5 pid_06 placebo smoker        34 post-secondary                Female
 6 pid_07 placebo non-smoker    31 grade 10-12, not matriculated Female
 7 pid_08 placebo smoker        30 grade 10-12, not matriculated Female
 8 pid_12 placebo non-smoker    31 grade 10-12, matriculated     Female
 9 pid_13 placebo non-smoker    32 post-secondary                Female
10 pid_14 placebo smoker        32 grade 10-12, matriculated     Female
# 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

Exercise

That’s enough slides for now time to try for yourself! Go to the module and go to Exercise 1.

20:00

Aesthetics - the elements of data visualization

Exercise 2

30:00

Get in groups of three. On excalidraw.com or pen and paper, create a visualization to answer the questions below. Check out the data visualization cheatsheet for inspiration. Think about values you’d have to compute, or if you have everything you need already in our joined_data table.

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")
```

Exercise

30:00

Time to try it yourself. Go to Exercise 3.

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

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

Let’s take a poll

Go to the event on wooclap

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") 
```

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>                         <chr> 
 1 pid_01 placebo   non-smoker    26 grade 10-12, matriculated     Female
 2 pid_02 placebo   smoker        33 grade 10-12, matriculated     Female
 3 pid_03 placebo   smoker        30 post-secondary                Female
 4 pid_04 placebo   non-smoker    34 grade 10-12, not matriculated Female
 5 pid_05 treatment non-smoker    29 grade 10-12, matriculated     Female
 6 pid_06 placebo   smoker        34 post-secondary                Female
 7 pid_07 placebo   non-smoker    31 grade 10-12, not matriculated Female
 8 pid_08 placebo   smoker        30 grade 10-12, not matriculated Female
 9 pid_09 treatment non-smoker    35 grade 10-12, not matriculated Female
10 pid_10 treatment non-smoker    32 less than grade 9             Female
# 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>                         <chr> 
 1 pid_01 placebo   non-smoker    26 grade 10-12, matriculated     Female
 2 pid_02 placebo   smoker        33 grade 10-12, matriculated     Female
 3 pid_03 placebo   smoker        30 post-secondary                Female
 4 pid_04 placebo   non-smoker    34 grade 10-12, not matriculated Female
 5 pid_05 treatment non-smoker    29 grade 10-12, matriculated     Female
 6 pid_06 placebo   smoker        34 post-secondary                Female
 7 pid_07 placebo   non-smoker    31 grade 10-12, not matriculated Female
 8 pid_08 placebo   smoker        30 grade 10-12, not matriculated Female
 9 pid_09 treatment non-smoker    35 grade 10-12, not matriculated Female
10 pid_10 treatment non-smoker    32 less than grade 9             Female
# 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

Exercise

That’s enough slides for now time to try for yourself! Go to the module and go to the second exercise.