Exploratory Data Visualization for Time-Series and Longitudinal Data

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# Exploratory Data Visualization for Time-Series and Longitudinal Data

Christian Testa 
<a href='mailto:ctesta@hsph.harvard.edu'><svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#F92672;" xmlns="http://www.w3.org/2000/svg"> <path d="M464 64H48C21.49 64 0 85.49 0 112v288c0 26.51 21.49 48 48 48h416c26.51 0 48-21.49 48-48V112c0-26.51-21.49-48-48-48zm0 48v40.805c-22.422 18.259-58.168 46.651-134.587 106.49-16.841 13.247-50.201 45.072-73.413 44.701-23.208.375-56.579-31.459-73.413-44.701C106.18 199.465 70.425 171.067 48 152.805V112h416zM48 400V214.398c22.914 18.251 55.409 43.862 104.938 82.646 21.857 17.205 60.134 55.186 103.062 54.955 42.717.231 80.509-37.199 103.053-54.947 49.528-38.783 82.032-64.401 104.947-82.653V400H48z"></path></svg> ctesta@hsph.harvard.edu</a> 
<a style='color: #55acee;' href='https://fediscience.org/@ctesta'><svg viewBox="0 0 448 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#55acee;" xmlns="http://www.w3.org/2000/svg"> <path d="M433 179.11c0-97.2-63.71-125.7-63.71-125.7-62.52-28.7-228.56-28.4-290.48 0 0 0-63.72 28.5-63.72 125.7 0 115.7-6.6 259.4 105.63 289.1 40.51 10.7 75.32 13 103.33 11.4 50.81-2.8 79.32-18.1 79.32-18.1l-1.7-36.9s-36.31 11.4-77.12 10.1c-40.41-1.4-83-4.4-89.63-54a102.54 102.54 0 0 1-.9-13.9c85.63 20.9 158.65 9.1 178.75 6.7 56.12-6.7 105-41.3 111.23-72.9 9.8-49.8 9-121.5 9-121.5zm-75.12 125.2h-46.63v-114.2c0-49.7-64-51.6-64 6.9v62.5h-46.33V197c0-58.5-64-56.6-64-6.9v114.2H90.19c0-122.1-5.2-147.9 18.41-175 25.9-28.9 79.82-30.8 103.83 6.1l11.6 19.5 11.6-19.5c24.11-37.1 78.12-34.8 103.83-6.1 23.71 27.3 18.4 53 18.4 175z"></path></svg> @ctesta</a> 
<a style='color: #636e72;' href='https://github.com/ctesta01'><svg viewBox="0 0 496 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#636e72;" xmlns="http://www.w3.org/2000/svg"> <path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"></path></svg> ctesta01</a>

</div>

<img src="images/osha_covid.png" alt="Research Figure Showing correlations in COVID and OSHA complaints"/>
Hanage, W.P., Testa, C., Chen, J.T. et al. [COVID-19: US federal accountability for entry, spread, and inequities—lessons for the future.](https://link.springer.com/article/10.1007/s10654-020-00689-2) Eur J Epidemiol 35, 995–1006 (2020). https://doi.org/10.1007/s10654-020-00689-2

---
# Motivations

.pull-left[

> "The greatest value of a picture is when it forces us to notice what we never expected to see." –John Tukey

> "There's no sense in being precise when you don't even know what you're talking about."
-von Neumann

> "Chaos is the beginning, simplicity is the end."
-M.C. Escher

]

.pull-right[

![](images/EleanorLutz.png)
]

---
# Aims

- Learn how to use data manipulation tools such as `dplyr` and `tidyr`
- Learn how to use `ggplot2`, a powerful, flexible framework for visualizing data in R
- Learn where to find more resources
 
<img src='images/R-for-Data-Science.jpg'/ height='30%'>

---
# Before we get started

There are some packages you'll want to make sure you have installed.

``` r
install.packages("tidyverse")
```

``` r
library(tidyverse, quietly = F, warn.conflicts = T)
```

```
## Warning: package 'ggplot2' was built under R version 4.5.2
```

```
## Warning: package 'purrr' was built under R version 4.5.2
```

```
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 4.0.1 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.2.1 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
```

---
# Example Data Set

``` r
df <- readr::read_csv("example_data/example_dataset_1.csv")
```

```
## Rows: 400 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): strata, gender
## dbl (4): X2005, X2010, X2015, X2020
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
```

Why use `readr::read_csv`?
- Reports on the assumed column types, with options to override
- Loads faster
- Loads into a tibble, which are faster, better data.frames.
  
---
# Check out the data

``` r
knitr::kable(head(df,4))
```

<table>
 <thead>
 <tr>
 <th style="text-align:left;"> strata </th>
 <th style="text-align:left;"> gender </th>
 <th style="text-align:right;"> X2005 </th>
 <th style="text-align:right;"> X2010 </th>
 <th style="text-align:right;"> X2015 </th>
 <th style="text-align:right;"> X2020 </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> tf </td>
 <td style="text-align:right;"> 19.156922 </td>
 <td style="text-align:right;"> 20.631165 </td>
 <td style="text-align:right;"> 21.493840 </td>
 <td style="text-align:right;"> 29.69722 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> tm </td>
 <td style="text-align:right;"> 4.297244 </td>
 <td style="text-align:right;"> 6.084038 </td>
 <td style="text-align:right;"> 7.552159 </td>
 <td style="text-align:right;"> 11.48248 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> tf </td>
 <td style="text-align:right;"> 21.891182 </td>
 <td style="text-align:right;"> 25.275057 </td>
 <td style="text-align:right;"> 24.650326 </td>
 <td style="text-align:right;"> 31.78028 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> tf </td>
 <td style="text-align:right;"> 27.130995 </td>
 <td style="text-align:right;"> 33.205461 </td>
 <td style="text-align:right;"> 37.978683 </td>
 <td style="text-align:right;"> 37.23926 </td>
 </tr>
</tbody>
</table>

Note that: 
- Data is in a wide format
- Column names need cleaning
- We have groups of participants

---
# Examine the categorical variables

Let's check what those groups are:

``` r
unique(df$strata)
```

```
## [1] "A" "B" "C" "D"
```

``` r
table(df$gender) 
```

```
## 
##  cm  nb  tf  tm 
##   7  38 213 142
```

---
# We may want to give the gender data labels
.tiny[

``` r
df$gender <- factor(
 df$gender,
 labels = c('Transgender Female',
 'Transgender Male',
 'Gender Nonbinary, Fluid, or other identity',
 'Cisgender Male',
 'Cisgender Female'),
 levels = c('tf', 'tm', 'nb', 'cm', 'cf'))
knitr::kable(table(Gender = df$gender))
```

<table>
 <thead>
 <tr>
 <th style="text-align:left;"> Gender </th>
 <th style="text-align:right;"> Freq </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:left;"> Transgender Female </td>
 <td style="text-align:right;"> 213 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Transgender Male </td>
 <td style="text-align:right;"> 142 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Gender Nonbinary, Fluid, or other identity </td>
 <td style="text-align:right;"> 38 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Cisgender Male </td>
 <td style="text-align:right;"> 7 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Cisgender Female </td>
 <td style="text-align:right;"> 0 </td>
 </tr>
</tbody>
</table>
]

---
# Summarize quantitative variables
--

``` r
df |> select(-c(strata, gender)) |>
  summary()
```

```
##      X2005            X2010             X2015             X2020       
##  Min.   : 0.000   Min.   :  0.000   Min.   :  0.000   Min.   :  0.00  
##  1st Qu.: 5.444   1st Qu.:  7.921   1st Qu.:  7.141   1st Qu.: 10.08  
##  Median :19.339   Median : 18.814   Median : 18.637   Median : 19.02  
##  Mean   :16.304   Mean   : 17.993   Mean   : 17.643   Mean   : 19.72  
##  3rd Qu.:23.030   3rd Qu.: 25.386   3rd Qu.: 25.071   3rd Qu.: 28.73  
##  Max.   :92.853   Max.   :102.787   Max.   :101.694   Max.   :105.50
```
--
... Let's break this one down.

---

# Intro to `|>` and dplyr

``` r
df |> select(-c(strata, gender)) |>
  summary()
```

`|>`, the pipe operator, is built into R since version 4.1.0 (released May 2021).

--
`x |> f()` is equivalent to `f(x)` 
`x |> f(y)` is equivalent to `f(x,y)`

--
`df |> select(-c(strata, gender))` is equivalent to `select(df, -c(strata, gender))`

--
Read `x |> f(y)` as "`x` gets passed to `f` with additional 
argument `y`."

--
Using pipes helps to:
 1. chain several commands together,
 2. without creating unnecessarily nested one-liners, e.g. 
 `summary(select(df, -strata))`

---
# What's the deal with select?

``` r
df |> select(-c(strata, gender)) |>
  summary()
```

`select` is the command for subsetting the columns of a data.frame or 
tibble.

--
Notice that `strata` and `gender` are not in quotes. This is because `dplyr` and
many of the functions in the tidyverse use tidy-evaluation, which allows users
to reference column names of data.frames and tibbles as if they are variables
within tidyverse functions.

--
The minus sign is saying that we want to remove strata and gender, or
equivalently to select all of the columns except for strata and gender.

---
# Let's add participant ID numbers

``` r
df <- df |>
 mutate(id = row_number()) |>
 select(id, everything())

knitr::kable(head(df,3))
```

<table>
 <thead>
 <tr>
 <th style="text-align:right;"> id </th>
 <th style="text-align:left;"> strata </th>
 <th style="text-align:left;"> gender </th>
 <th style="text-align:right;"> X2005 </th>
 <th style="text-align:right;"> X2010 </th>
 <th style="text-align:right;"> X2015 </th>
 <th style="text-align:right;"> X2020 </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:right;"> 1 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> Transgender Female </td>
 <td style="text-align:right;"> 19.156922 </td>
 <td style="text-align:right;"> 20.631165 </td>
 <td style="text-align:right;"> 21.493840 </td>
 <td style="text-align:right;"> 29.69722 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 2 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> Transgender Male </td>
 <td style="text-align:right;"> 4.297244 </td>
 <td style="text-align:right;"> 6.084038 </td>
 <td style="text-align:right;"> 7.552159 </td>
 <td style="text-align:right;"> 11.48248 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 3 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> Transgender Female </td>
 <td style="text-align:right;"> 21.891182 </td>
 <td style="text-align:right;"> 25.275057 </td>
 <td style="text-align:right;"> 24.650326 </td>
 <td style="text-align:right;"> 31.78028 </td>
 </tr>
</tbody>
</table>

`everything()` is part of the `tidyselect` package and system which 
helps with the programmatic selection of columns and offers other 
helpful functions like `starts_with` or `contains`.

---
# Referring to the left-hand-side

Some functions were written before the `tidyverse` became ubiquitous, 
and don't follow the convention that `data` should be the first argument.

One example is the `lm` function.

In situations like that, you can still pipe data into those functions, but
you need to use `_` to refer to the left-hand-side like this:

``` r
df |> filter(strata == 'A') |> 
  lm(formula = X2005 ~ gender, data = _) |> 
  coef()
```

```
##                                      (Intercept) 
##                                         23.96619 
##                           genderTransgender Male 
##                                        -18.73016 
## genderGender Nonbinary, Fluid, or other identity 
##                                        -18.65744
```

---
# A note about `|>` vs `%>%`

`|>` is a pipe now built into base R starting from version 4.1.0.

Basically the main differences is that `%>%` uses 
the `.` notation to refer to the left-hand-side of the pipe, 
while `|>` uses `_` instead. There are some subtle differences
in what you can do with the left-hand-side (like `%>%` allows you to use `.`
inside nested function calls
while `|>` does not).

In many regards, the two pipes are similar, and you will start to 
see more and more code using `|>` because it is new and faster than `%>%`.

Also, debugging is a bit easier with `|>`.

.pull-left[
<a href="https://www.tidyverse.org/blog/2023/04/base-vs-magrittr-pipe/"><img src="images/pipe_article.png" width='300px' /> </a>
]

.pull-right[
Read more here: <https://www.tidyverse.org/blog/2023/04/base-vs-magrittr-pipe/>
]

---

# Let's convert to a tidy format

``` r
df <- df |> tidyr::pivot_longer(
 cols = starts_with('X'),
 names_to = 'year',
 values_to = 'rate')

knitr::kable(head(df, 4))
```

<table>
 <thead>
 <tr>
 <th style="text-align:right;"> id </th>
 <th style="text-align:left;"> strata </th>
 <th style="text-align:left;"> gender </th>
 <th style="text-align:left;"> year </th>
 <th style="text-align:right;"> rate </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:right;"> 1 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> Transgender Female </td>
 <td style="text-align:left;"> X2005 </td>
 <td style="text-align:right;"> 19.15692 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> Transgender Female </td>
 <td style="text-align:left;"> X2010 </td>
 <td style="text-align:right;"> 20.63117 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> Transgender Female </td>
 <td style="text-align:left;"> X2015 </td>
 <td style="text-align:right;"> 21.49384 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> Transgender Female </td>
 <td style="text-align:left;"> X2020 </td>
 <td style="text-align:right;"> 29.69722 </td>
 </tr>
</tbody>
</table>

---
# Get rid of "X"

``` r
df <- df |>
 mutate(year = stringr::str_remove(year, "X"))

knitr::kable(head(df, 4))
```

<table>
 <thead>
 <tr>
 <th style="text-align:right;"> id </th>
 <th style="text-align:left;"> strata </th>
 <th style="text-align:left;"> gender </th>
 <th style="text-align:left;"> year </th>
 <th style="text-align:right;"> rate </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:right;"> 1 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> Transgender Female </td>
 <td style="text-align:left;"> 2005 </td>
 <td style="text-align:right;"> 19.15692 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> Transgender Female </td>
 <td style="text-align:left;"> 2010 </td>
 <td style="text-align:right;"> 20.63117 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> Transgender Female </td>
 <td style="text-align:left;"> 2015 </td>
 <td style="text-align:right;"> 21.49384 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> Transgender Female </td>
 <td style="text-align:left;"> 2020 </td>
 <td style="text-align:right;"> 29.69722 </td>
 </tr>
</tbody>
</table>

---
# Now we can do some plotting

.pull-left[

``` r
ggplot(data = df,  
 aes(x = year, y = rate)) + 
   geom_point()
```

]

.pull-right[
<img src="longitudinal_eda_files/figure-html/unnamed-chunk-19-1.png" width="100%" />
]

---
# Now let's try geom_line

.pull-left[

``` r
ggplot(data = df,  
 aes(x = year, y = rate,
*    group = id)) +
   geom_line()
```

]

.pull-right[
<img src="longitudinal_eda_files/figure-html/unnamed-chunk-20-1.png" width="100%" />
]

---
# Adding color

.pull-left[

``` r
ggplot(data = df,  
 aes(x = year, y = rate,
     group = id,
*    color = strata)) +
*  geom_line(alpha=0.5)
```

]

.pull-right[
<img src="longitudinal_eda_files/figure-html/unnamed-chunk-21-1.png" width="100%" />
]

---
# Facet Wrapping

.tiny[

``` r
ggplot(data = df,  aes(x = year, y = rate, group = id, color = strata)) +
  geom_line(alpha=0.5) + 
* facet_wrap(~strata)
```
]

---
# Facet Grid

.tiny[

``` r
ggplot(data = df,  aes(x = year, y = rate, group = id, color = strata)) +
  geom_line(alpha=0.5) + 
* facet_grid(gender~strata) +
  ggtitle("Different strata had different trajectories")
```
]

---
# Could we look cross-sectionally for a moment?
.tiny[

``` r
df |> filter(year == 2010) |> 
  ggplot(aes(x = rate, fill = gender)) + 
  geom_density(position = 'identity', alpha = 0.5) + 
  facet_grid(.~strata) + 
  ggtitle(
    "Differences in gender distribution of outcome rate at 2010 across strata.")
```

<img src="longitudinal_eda_files/figure-html/unnamed-chunk-24-1.png" width="864" />
]

---
# That suggests a useful indicator to us

``` r
df$gender_tf_lgl <- df$gender == 'Transgender Female'
table(df$gender_tf_lgl)
```

```
## 
## FALSE  TRUE 
##   748   852
```

---
# Using Stat Summaries

.tiny[

``` r
ggplot(data = df,  aes(x = year, y = rate, group = id, color = gender_tf_lgl)) +
  geom_line(alpha=0.5) + 
  facet_wrap(~strata) + 
  stat_summary(aes(group = interaction(strata, gender_tf_lgl)), fun = mean, geom='line', color = 'black') + 
  stat_summary(aes(group = interaction(strata, gender_tf_lgl), shape=gender_tf_lgl), fun = mean, geom='point', size=2, color = 'black') + 
  labs(shape = 'Gender (Transgender Female vs. all others) and Strata\nLevel Average', color = 'Gender') +
  ggtitle("Transgender women have higher rates")
```
]

---
# Another way using boxplots

.tiny[

``` r
ggplot(data = df,  aes(x = year, y = rate, color = gender)) +
  geom_boxplot(alpha=0.5) + 
  stat_summary(aes(group = interaction(strata, gender), shape=''), position = position_dodge(width=0.75), 
               fun = mean, geom='point', color = 'grey10', alpha=0.8) +
  facet_wrap(~strata) + 
  labs(color = "Gender", shape = "Gender + Strata\nLevel Average") + 
  ggtitle("Boxplots allow us to see the interquartile range clearly")
```
]

---
# Using `geom_ribbon`

.tiny[

``` r
df |> group_by(strata, gender_tf_lgl, year) |>
  summarize(
    percentile_97.5 = quantile(rate, 0.975),
    percentile_2.5 = quantile(rate, 0.025),
    mean = mean(rate),
    .groups = 'keep') |>
  
  ggplot(aes(x = year, y = mean, ymax = percentile_97.5, 
       ymin = percentile_2.5, group = gender_tf_lgl, fill = gender_tf_lgl, color = gender_tf_lgl)) +
    geom_ribbon(alpha=0.5, linewidth = 0) +
    geom_line(aes(linetype='')) +
    facet_wrap(~strata) + 
    scale_color_manual(values = c('tm' = '#2980b9', 'F' = '#c0392b')) + 
    labs(linetype = 'Gender+Strata\nLevel Average', fill = 'Gender', color = 'Gender (Transgender Women vs. any other gender)', y = 'Rate') + 
    ggtitle(paste0("The difference between transgender women and others was consistent over time"))
```
]

---
# Using plotly for interactive graphics

.tiny[

``` r
suppressMessages(library(plotly))
ggplotly() |> layout(width = 8, height = 3.5)
```

<div class="plotly html-widget html-fill-item" id="htmlwidget-d0247fd694595fc3cb66" style="width:504px;height:504px;"></div>
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]

```
## Warning: No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
## No shared levels found between `names(values)` of the manual scale and the
## data's colour values.
```

```
## Warning: Specifying width/height in layout() is now deprecated.
## Please specify in ggplotly() or plot_ly()
```

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---
# Widening Data for Correlation Analysis

Before we can look at correlation across the years, we need 
to widen the dataframe (similar to how it was originally formatted).

``` r
df_wide <- df |>
 tidyr::pivot_wider(id_cols = c(id, strata, gender_tf_lgl),
 names_from = year,
 values_from = rate)
knitr::kable(head(df_wide, 3))
```

<table>
 <thead>
 <tr>
 <th style="text-align:right;"> id </th>
 <th style="text-align:left;"> strata </th>
 <th style="text-align:left;"> gender_tf_lgl </th>
 <th style="text-align:right;"> 2005 </th>
 <th style="text-align:right;"> 2010 </th>
 <th style="text-align:right;"> 2015 </th>
 <th style="text-align:right;"> 2020 </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:right;"> 1 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> TRUE </td>
 <td style="text-align:right;"> 19.156922 </td>
 <td style="text-align:right;"> 20.631165 </td>
 <td style="text-align:right;"> 21.493840 </td>
 <td style="text-align:right;"> 29.69722 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 2 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> FALSE </td>
 <td style="text-align:right;"> 4.297244 </td>
 <td style="text-align:right;"> 6.084038 </td>
 <td style="text-align:right;"> 7.552159 </td>
 <td style="text-align:right;"> 11.48248 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 3 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> TRUE </td>
 <td style="text-align:right;"> 21.891182 </td>
 <td style="text-align:right;"> 25.275057 </td>
 <td style="text-align:right;"> 24.650326 </td>
 <td style="text-align:right;"> 31.78028 </td>
 </tr>
</tbody>
</table>

---
# Correlation Plot

.tiny[

``` r
# install.packages(GGally)
library(GGally)
ggpairs(df_wide, aes(color = strata, alpha=0.25), columns = c('2005', '2010', '2015', '2020'), progress=F)
```
]

---
# Bivariate Pairs Plots

``` r
ggbivariate(df, outcome = 'gender', explanatory = 'strata') + 
  theme(legend.position = 'bottom') + 
  ggtitle("Strata were about evenly split across gender")
```

---
<img src="images/tt_logo.png" align='right' width='30%'
 style='padding: 0px;'>

# Where you can learn more

For data manipulation and visualization:

<img src="images/ggplot2-cheatsheet.png" align='right' width='30%'
 style='padding: 0px;'>
 
- R for Data Science, by Garrett Grolemund and Hadley Wickham, [r4ds.had.co.nz](https://r4ds.had.co.nz)
- The ggplot2 Website, [ggplot2.tidyverse.org](https://ggplot2.tidyverse.org)
- The [RStudio Cheatsheets](https://rstudio.com/resources/cheatsheets/) (I suggest starting with [dplyr](https://github.com/rstudio/cheatsheets/raw/master/data-transformation.pdf) and [ggplot2](https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf))
- Watch the [TidyTuesday tutorials on YouTube](https://www.youtube.com/results?search_query=tidytuesday) or check out [TidyTuesday 
 on GitHub](https://github.com/rfordatascience/tidytuesday/)
 
 
 
--
For longitudinal data analysis: 
- [Applied Longitudinal Analysis by Garrett Fitzmaurice, Nan Laird, and James Ware](https://content.sph.harvard.edu/fitzmaur/ala2e/)
- [Marie Davidian's Slides on Modeling and Analysis of Longitudinal Data](https://www4.stat.ncsu.edu/~davidian/enar06_handout.pdf)
- [Patrick Hagearty's notes on Longitudinal Data Analysis](https://faculty.washington.edu/heagerty/Courses/VA-longitudinal/private/LDAchapter.pdf) (fairly technical)
- Longitudinal Data Analysis: Autoregressive Linear Mixed Effects Models, by Ikuko FunatogawaTakashi Funatogawa (very technical)

---
# How to take your longitudinal analysis further

To me, the first question is: what would it take to do a 
causal longitudinal analysis?  But that can be complicated.

.pull-left[

Without going fully causal, there are a lot of descriptive
longitudinal statistical data analysis tools available.

<ul>
You can use models to make statistical inferences about your data. 
Models for longitudinal data often include the following features: 
<li> Auto-regression</li>
<li> Multi-level or random effects design</li>
<li> Generalized Linear Models</li>
<li> Treatment of missing data</li>
</ul>

]

.pull-right[
<img src='images/multilevel-reaction.png'/>

`library(lme4)` 
`lmer(Reaction ~ Days + (Days|Subject), sleepstudy)`

]

---
# How to take your longitudinal analysis further (part 2)

Define your scientific question(s) of interest first!

Are you interested in a marginal mean?

Are you interested in subject-specific rates of increase?

* Is your hypothesis that heterogeneity in individual slopes exists? 
  * Or that you could predict them from certain baseline covariates? 
* Is your hypothesis about a covariate predicting worsening/improving
trends?

---
# How can you do a good job definining your (longitudinal) scientific question of interest?

Extend existing frameworks:

- Is there an equity gap widening or shrinking? 
  - Is there a lagged trend? 
  - Do the trajectories cluster?
  - Are trajectories' features explained by baseline predictors?

---
# Get More Inspiration

.pull-left[
* [Georgios Karamanis](https://karaman.is/)
* [Cedric Scherer](https://cedricscherer.com)
* [Eleanor Lutz](https://eleanorlutz.com/)
* [Flowing Data](https://flowingdata.com/)
<img src="images/Kaashoek.png" alt="A figure showing effectiveness of statewide interventions as an effect modifier" />
Kaashoek J, Testa C, Chen JT, Stolerman LM, Krieger N, Hanage WP, et al. [The evolving roles of US political partisanship and social vulnerability in the COVID-19 pandemic from February 2020–February 2021.](https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0000557) PLOS Global Public Health. 2022 

]

.pull-right[
<img src="images/EscalatingDrought.jpg" height='500px' alt='A figure on drought from Cedric Scherer' />
]

---
# Find this talk on my github

---
# Image Credits

<ul>
<li>R for Data Science: https://www.dataoptimal.com/wp-content/uploads/R-for-Data-Science.jpg </li>
<li>Magrittr Logo: https://magrittr.tidyverse.org/logo.png</li>
<li>Tidy Tuesday Logo: https://github.com/rfordatascience/tidytuesday/</li>
<li>Escalating Drought: https://www.scientificamerican.com/article/climate-change-drives-escalating-drought/</li>
</ul>
 
</div>