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>

October 25th, 2024
</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

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

> "Visualization is often used for evil - twisting insignificant data changes
and making them look meaningful. Don't do that crap if you want to be my friend.
Present results clearly and honestly. If something isn't working - those
reviewing results need to know." —John Tukey

---
# 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'/>

---
# 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)
```

```
## ── 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 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1 
## ── 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;"> M </td>
 <td style="text-align:right;"> 10.509583 </td>
 <td style="text-align:right;"> 15.796463 </td>
 <td style="text-align:right;"> 14.918578 </td>
 <td style="text-align:right;"> 25.42652 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> M </td>
 <td style="text-align:right;"> 19.413696 </td>
 <td style="text-align:right;"> 25.082179 </td>
 <td style="text-align:right;"> 25.200332 </td>
 <td style="text-align:right;"> 30.43139 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> M </td>
 <td style="text-align:right;"> 12.475157 </td>
 <td style="text-align:right;"> 19.262194 </td>
 <td style="text-align:right;"> 19.930757 </td>
 <td style="text-align:right;"> 20.63809 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> F </td>
 <td style="text-align:right;"> 1.284895 </td>
 <td style="text-align:right;"> 5.251643 </td>
 <td style="text-align:right;"> 8.746856 </td>
 <td style="text-align:right;"> 10.24585 </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) 
```

```
## 
##   F   M 
## 210 190
```

---
# Summarize quantitative variables
--

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

```
##      X2005            X2010            X2015            X2020       
##  Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
##  1st Qu.: 5.217   1st Qu.: 6.765   1st Qu.: 6.135   1st Qu.: 7.621  
##  Median : 9.292   Median :10.592   Median :10.280   Median :12.065  
##  Mean   : 9.537   Mean   :11.260   Mean   :10.677   Mean   :12.678  
##  3rd Qu.:12.851   3rd Qu.:15.073   3rd Qu.:14.331   3rd Qu.:17.638  
##  Max.   :34.977   Max.   :34.707   Max.   :33.236   Max.   :39.052
```
--
... Let's break this one down.

---

# Intro to `%>%` and dplyr

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

`%>%`, the pipe operator, comes from the `magrittr` package, but is also included 
in `dplyr`.

--
`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 = 1:nrow(.)) %>% 
 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;"> M </td>
 <td style="text-align:right;"> 10.50958 </td>
 <td style="text-align:right;"> 15.79646 </td>
 <td style="text-align:right;"> 14.91858 </td>
 <td style="text-align:right;"> 25.42652 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 2 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> M </td>
 <td style="text-align:right;"> 19.41370 </td>
 <td style="text-align:right;"> 25.08218 </td>
 <td style="text-align:right;"> 25.20033 </td>
 <td style="text-align:right;"> 30.43139 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 3 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> M </td>
 <td style="text-align:right;"> 12.47516 </td>
 <td style="text-align:right;"> 19.26219 </td>
 <td style="text-align:right;"> 19.93076 </td>
 <td style="text-align:right;"> 20.63809 </td>
 </tr>
</tbody>
</table>

--
Note the use of `.` here, which refers to the argument 
passed using `%>%`.

--
Equivalently, we could have written `mutate(df, id = 1:nrow(df))`.

--
`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`.

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

`|>` 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 `%>%` supports `.$var` 
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 
hopefully faster than `%>%`.

.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;"> M </td>
 <td style="text-align:left;"> X2005 </td>
 <td style="text-align:right;"> 10.50958 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> M </td>
 <td style="text-align:left;"> X2010 </td>
 <td style="text-align:right;"> 15.79646 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> M </td>
 <td style="text-align:left;"> X2015 </td>
 <td style="text-align:right;"> 14.91858 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> M </td>
 <td style="text-align:left;"> X2020 </td>
 <td style="text-align:right;"> 25.42652 </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;"> M </td>
 <td style="text-align:left;"> 2005 </td>
 <td style="text-align:right;"> 10.50958 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> M </td>
 <td style="text-align:left;"> 2010 </td>
 <td style="text-align:right;"> 15.79646 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> M </td>
 <td style="text-align:left;"> 2015 </td>
 <td style="text-align:right;"> 14.91858 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 1 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> M </td>
 <td style="text-align:left;"> 2020 </td>
 <td style="text-align:right;"> 25.42652 </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-17-1.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-18-1.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-19-1.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")
```
]

---
# Using Stat Summaries

.tiny[

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

---
# 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, 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, fill = gender, color = gender)) +
    geom_ribbon(alpha=0.5, linewidth = 0) +
    geom_line(aes(linetype='')) +
    facet_wrap(~strata) + 
    scale_color_manual(values = c('M' = '#2980b9', 'F' = '#c0392b')) + 
    labs(linetype = 'Gender+Strata\nLevel Average', fill = 'Gender', color = 'Gender', y = 'Rate') + 
    ggtitle(paste0("The difference between men and women 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-overflow-hidden html-fill-item" id="htmlwidget-53ff0a83ed51c6bb774c" style="width:504px;height:504px;"></div>
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]

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

<div class="plotly html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-025ae05043553a8129bf" style="width:100%;height:504px;"></div>
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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),
 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 </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;"> M </td>
 <td style="text-align:right;"> 10.50958 </td>
 <td style="text-align:right;"> 15.79646 </td>
 <td style="text-align:right;"> 14.91858 </td>
 <td style="text-align:right;"> 25.42652 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 2 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> M </td>
 <td style="text-align:right;"> 19.41370 </td>
 <td style="text-align:right;"> 25.08218 </td>
 <td style="text-align:right;"> 25.20033 </td>
 <td style="text-align:right;"> 30.43139 </td>
 </tr>
 <tr>
 <td style="text-align:right;"> 3 </td>
 <td style="text-align:left;"> A </td>
 <td style="text-align:left;"> M </td>
 <td style="text-align:right;"> 12.47516 </td>
 <td style="text-align:right;"> 19.26219 </td>
 <td style="text-align:right;"> 19.93076 </td>
 <td style="text-align:right;"> 20.63809 </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

.pull-left[

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

]

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

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

]

---
# 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' />
]

---
# Join the R User Group! &nbsp; rug-at-hdsi.org

.pull-left[
<img src="images/rug.png" alt='screenshot of our RUG YouTube'> 
Join at <https://rug-at-hdsi.org> and check out our [YouTube](https://www.youtube.com/@rusergroupatharvarddatasci7232/videos)
]

---
# 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>
 
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