Introduction to Data Visualization for Meta-Analysis

# Introduction to Data Visualization for Meta-Analysis
## with tidymeta and ggplot2
### Malcolm Barrett Install R: <a href="http://bit.ly/pm605_r">bit.ly/pm605_r</a> Handout: <a href="http://bit.ly/pm605_tut">bit.ly/pm605_tut</a>
### 04/23/2018

---

# Data Visualization with R
## ggplot2 and the tidyverse are friendly and consistent tools for data analysis and visualization

---
class: inverse-ns, center

# Data Visualization with R
## ggplot2 and the tidyverse are friendly and consistent tools for data analysis and visualization
## Better plots are better communication

---
class: inverse-ns, center

# Data Visualization with R
## ggplot2 and the tidyverse are friendly and consistent tools for data analysis and visualization
## Better plots are better communication
## tidymeta makes it easy to manipulate and plot meta-analysis results

---

# Introduction to the Data 
## What's the impact of intrauterine device (IUD) use on risk of cervical cancer?

---

# Introduction to the Data 
## What's the impact of intrauterine device (IUD) use on risk of cervical cancer?
## 16 studies: 4,945 cases and 7,537 controls

---

# Introduction to the Data 
## What's the impact of intrauterine device (IUD) use on risk of cervical cancer?
## 16 studies: 4,945 cases and 7,537 controls
## Women who used IUDs were at a third less risk than those who didn't (OR 0.64)

---

```r
library(tidymeta)
*iud_cxca
```

```
## # A tibble: 16 x 26
## study_id study_name author es l95 u95 lnes lnl95 lnu95
## <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 Roura, 2016 Roura 0.600 0.300 1.20 -0.511 -1.20 0.182 
## 2 2 Lassise, 1991 Lassi… 0.800 0.500 1.20 -0.223 -0.693 0.182 
## 3 3 Li, 2000 Li 0.890 0.730 1.08 -0.117 -0.315 0.0770
## 4 4 Shields, 2004 Shiel… 0.500 0.300 0.820 -0.693 -1.20 -0.198 
## 5 5 Castellsague… Caste… 0.630 0.380 1.06 -0.462 -0.968 0.0583
## 6 6 Castellsague… Caste… 0.450 0.300 0.670 -0.799 -1.20 -0.400 
## 7 7 Brinton, 1990 Brint… 0.690 0.500 0.900 -0.371 -0.693 -0.105 
## 8 8 Parazzini, 1… Paraz… 0.600 0.300 1.10 -0.511 -1.20 0.0953
## 9 9 Williams, 19… Willi… 1.00 0.600 1.60 0. -0.511 0.470 
## 10 10 Hammouda, 20… Hammo… 0.300 0.100 0.500 -1.20 -2.30 -0.693 
## 11 11 Castellsague… Caste… 1.08 0.370 3.20 0.0770 -0.994 1.16 
## 12 12 Castellsague… Caste… 0.340 0.0500 2.56 -1.08 -3.00 0.940 
## 13 13 Castellsague… Caste… 0.870 0.340 2.23 -0.139 -1.08 0.802 
## 14 14 Castellsague… Caste… 0.490 0.190 1.23 -0.713 -1.66 0.207 
## 15 15 Castellsague… Caste… 0.240 0.0900 0.660 -1.43 -2.41 -0.416 
## 16 16 Celentano, 1… Celen… 0.500 0.170 1.47 -0.693 -1.77 0.385 
## # ... with 17 more variables: selnes <dbl>, group <fct>, case_num <dbl>,
## # control_num <dbl>, start_recruit <dbl>, stop_recruit <dbl>,
## # pub_year <dbl>, numpap <dbl>, ses <dbl>, gravidity <dbl>,
## # lifetimepart <dbl>, coitarche <dbl>, hpvstatus <dbl>, smoking <dbl>,
## # location <chr>, aair <dbl>, hpvrate <dbl>
```

---

# Five variables from `iud_cxca` we'll use

## `study_name`

## `lnes`

## `selnes`

## `group`

## `pub_year`

---

# Five variables from `iud_cxca` we'll use

## <code>study_name</code> = Author + study year

## `lnes`

## `selnes`

## `group`

## `pub_year`

---

# Five variables from `iud_cxca` we'll use

## `study_name`

## <code>lnes</code> = ln(Odds Ratio)

## `selnes`

## `group`

## `pub_year`

---

# Five variables from `iud_cxca` we'll use

## `study_name`

## `lnes`

## <code>selnes</code> = SE of ln(OR)

## `group`

## `pub_year`

---

# Five variables from `iud_cxca` we'll use

## `study_name`

## `lnes`

## `selnes`

## <code>group</code> = Study design

## `pub_year`

---

# Five variables from `iud_cxca` we'll use

## `study_name`

## `lnes`

## `selnes`

## `group`

## <code>pub_year</code> = Publication year

---
class: inverse-ns, center, middle

# Meta-Analysis Plot Types

---
class: inverse-ns, center, middle

# Meta-Analysis Plot Types
## Forest Plot

---

## `forest_plot()`

---
class: inverse-ns, center, middle

# Meta-Analysis Plot Types
## Forest Plot
## Funnel Plot

---
## `funnel_plot()`
<img src="ma_workshop_files/figure-html/unnamed-chunk-3-1.png" width="75%" style="display: block; margin: auto;" />

---
class: inverse-ns, center, middle

# Meta-Analysis Plot Types
## Forest Plot
## Funnel Plot
## Influence/Sensitivity Plot

---
## `influence_plot()`
<img src="ma_workshop_files/figure-html/unnamed-chunk-4-1.png" width="60%" style="display: block; margin: auto;" />

---

# Meta-Analysis Plot Types
## Forest Plot
## Funnel Plot
## Influence/Sensitivity Plot
## Cumulative Plot

---
## `cumulative_plot()`
<img src="ma_workshop_files/figure-html/unnamed-chunk-5-1.png" width="60%" style="display: block; margin: auto;" />

---

# A Crash Course in the Tidyverse

---
background-image: url(http://hexb.in/hexagons/ggplot2.png)
background-position: 90% 10%

# ggplot2: Elegant Data Visualizations in R

---
background-image: url(http://hexb.in/hexagons/ggplot2.png)
background-position: 90% 10%

# ggplot2: Elegant Data Visualizations in R

## Based on a Grammar of Graphics

---
background-image: url(http://hexb.in/hexagons/ggplot2.png)
background-position: 90% 10%

# ggplot2: Elegant Data Visualizations in R

## Based on a Grammar of Graphics
## Data is mapped to aesthetics; Statistics and plot are linked

---
background-image: url(http://hexb.in/hexagons/ggplot2.png)
background-position: 90% 10%

# ggplot2: Elegant Data Visualizations in R

## Based on a Grammar of Graphics
## Data is mapped to aesthetics; Statistics and plot are linked
## Sensible defaults; Infinitely extensible

---

```r
library(ggplot2)
p <- ggplot(iud_cxca, aes(case_num + control_num, lnes, color = group))
p
```

<img src="ma_workshop_files/figure-html/unnamed-chunk-6-1.png" style="display: block; margin: auto;" />
---

```r
library(ggplot2)
p <- p + geom_point()
p
```

---

```r
p <- p + geom_smooth(method = "lm", se = FALSE)
p
```

---

```r
p + 
  labs(title = "The Effect of Sample Size on Estimate",
      x = "Sample Size",
      y = "ln(Odds Ratio)") + 
  scale_color_discrete(name = "Study Design") +
  theme_minimal() + 
  theme(text = element_text(size = 16))
```

---

# Tidy Data is Easier to Plot
![](http://garrettgman.github.io/images/tidy-1.png)</div>

---

# Tidy Data is Easier to Plot
![](http://garrettgman.github.io/images/tidy-1.png)</div>
### .medium[Each column is a single variable]

---

# Tidy Data is Easier to Plot
![](http://garrettgman.github.io/images/tidy-1.png)</div>
### .medium[Each column is a single variable]
### .medium[Each row is a single observation]

---

# Tidy Data is Easier to Plot
![](http://garrettgman.github.io/images/tidy-1.png)</div>
### .medium[Each column is a single variable]
### .medium[Each row is a single observation]
### .medium[Each cell is a value]

---

# Our Tidy Tools

---

# Our Tidy Tools

.pull-left[
### <code>%>%</code>: passes the results of one function to the next
### `mutate()`
### `arrange()`
### `group_by()`
### `tidy()` 
]

# Our Tidy Tools

.pull-left[
### `%>%`
### <code>mutate()</code>: changes or creates a new variable
### `arrange()`
### `group_by()`
### `tidy()` 
]

# Our Tidy Tools

.pull-left[
### `%>%`
### `mutate()`
### <code>arrange()</code>: sorts a data set by a variable
### `group_by()`
### `tidy()` 
]

# Our Tidy Tools

.pull-left[
### `%>%`
### `mutate()`
### `arrange()`
### <code>group_by()</code>: groups a data set by a variable
### `tidy()` 
]

# Our Tidy Tools

.pull-left[
### `%>%`
### `mutate()`
### `arrange()`
### `group_by()`
### <code>tidy()</code>: tidies statistical results
]

---
# Tidy Meta-Analysis
## <code>meta_analysis()</code>

---

# Tidy Meta-Analysis
## <code>meta_analysis()</code>

```r
ma <- iud_cxca %>% 
 group_by(group) %>% 
 meta_analysis(yi = lnes, sei = selnes, slab = study_name, exponentiate = TRUE)

ma
```

```
## # A tibble: 21 x 11
## group study type estimate std.error statistic p.value conf.low
## <fct> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Nested … Roura, 2… study 0.600 0.354 -1.44 NA 0.300
## 2 Nested … Subgroup… summ… 0.600 0.354 -1.44 0.149 0.300
## 3 Populat… Lassise,… study 0.800 0.223 -0.999 NA 0.516
## 4 Populat… Li, 2000 study 0.890 0.0999 -1.17 NA 0.732
## 5 Populat… Shields,… study 0.500 0.257 -2.70 NA 0.302
## 6 Populat… Castells… study 0.630 0.262 -1.77 NA 0.377
## 7 Populat… Castells… study 0.450 0.205 -3.90 NA 0.301
## 8 Populat… Subgroup… summ… 0.655 0.146 -2.90 0.00374 0.492
## 9 Clinic-… Brinton,… study 0.690 0.150 -2.47 NA 0.514
## 10 Clinic-… Parazzin… study 0.600 0.331 -1.54 NA 0.313
## # ... with 11 more rows, and 3 more variables: conf.high <dbl>,
## # meta <list>, weight <dbl>
```

---
# Forest Plot
## <code>forest_plot()</code>

```r
ma %>% 
  forest_plot(group = group)
```

---
# Forest Plot
## `forest_plot()`

```r
ma %>% 
  forest_plot(group = group)
```

## <code>text_table()</code>

---
# Forest Plot
## `forest_plot()`

```r
ma %>% 
  forest_plot(group = group)
```

## <code>text_table()</code>

```r
ma %>% 
  text_table(group = group, "Weights" = weight)
```

---

# patchwork: Compose ggplots

.pull-right[
![](https://raw.githubusercontent.com/thomasp85/patchwork/master/man/figures/logo.png)
]

---

# patchwork: Compose ggplots

.pull-right[
![](https://raw.githubusercontent.com/thomasp85/patchwork/master/man/figures/logo.png)
]

---

# patchwork: Compose ggplots

```r
library(patchwork)

forest_plot() + text_table()
```
]

.pull-right[
![](https://raw.githubusercontent.com/thomasp85/patchwork/master/man/figures/logo.png)
]

---
# Funnel Plot
## <code>funnel_plot()</code>

```r
ma %>% 
  funnel_plot(log_summary = TRUE)
```

---
# Influence Plot
## <code>sensitivity()</code>

```r
ma %>% 
  sensitivity(exponentiate = TRUE) 
```

---
# Influence Plot
## `sensitivity()`

```r
ma %>% 
  sensitivity(exponentiate = TRUE) 
```

## <code>influence_plot()</code>

---

# Influence Plot
## `sensitivity()`

```r
ma %>% 
  sensitivity(exponentiate = TRUE) 
```

## <code>influence_plot()</code>

```r
ma %>% 
  sensitivity(exponentiate = TRUE) %>% 
  influence_plot()
```

---
# Cumulative Plot
## <code>cumulative()</code>

```r
ma %>% 
  arrange(desc(weight)) %>% 
  cumulative(exponentiate = TRUE)
```

---

# Cumulative Plot
## `cumulative()`

```r
ma %>% 
  arrange(desc(weight)) %>% 
  cumulative(exponentiate = TRUE)
```

## <code>cumulative_plot()</code>

---

# Cumulative Plot
## `cumulative()`

```r
ma %>% 
  arrange(desc(weight)) %>% 
  cumulative(exponentiate = TRUE)
```

## <code>cumulative_plot()</code>

```r
ma %>% 
  arrange(desc(weight)) %>% 
  cumulative(exponentiate = TRUE) %>% 
  cumulative_plot(sum_lines = FALSE)
```

---
# Importing Stata data, saving ggplots

---

# Importing Stata data, saving ggplots
## haven: <code>read_dta()</code>

---

# Importing Stata data, saving ggplots
## haven: <code>read_dta()</code>

```r
library(haven)
data <- read_dta("stata_data.dta")
```

---

# Importing Stata data, saving ggplots
## haven: `read_dta()`

```r
library(haven)
data <- read_dta("stata_data.dta")
```

## ggplot2: <code>ggsave()</code>

---

# Importing Stata data, saving ggplots
## haven: `read_dta()`

```r
library(haven)
data <- read_dta("stata_data.dta")
```

## ggplot2: <code>ggsave()</code>

```r
library(ggplot2)
p <- forest_plot(ma, group = group)
ggsave(p, "forest_plot.png", dpi = 320, height = 8)
```

---

# tidymeta

---

# tidymeta

## <code>meta_analysis()/your_favorite_function() + tidy()</code>

---

# tidymeta

## `meta_analysis()/your_favorite_function() + tidy()`
## <code>forest_plot()/text_table()</code>

---

# tidymeta

## `meta_analysis()/your_favorite_function() + tidy()`
## `forest_plot()/text_table()`
## <code>sensitivity()/influence_plot()</code>

---

# tidymeta

## `meta_analysis()/your_favorite_function() + tidy()`
## `forest_plot()/text_table()`
## `sensitivity()/influence_plot()`
## <code>cumulative()/cumulative_plot()</code>

---
class: inverse, center

# Resources
## [R for Data Science](http://r4ds.had.co.nz/): A comprehensive but friendly introduction to the tidyverse. Free online.
## [DataCamp](https://www.datacamp.com/): ggplot2 courses and tidyverse courses
## [ggplot2: Elegant Graphics for Data Analysis](https://smile.amazon.com/ggplot2-Elegant-Graphics-Data-Analysis/dp/331924275X/ref=sr_1_2?ie=UTF8&qid=1524362742&sr=8-2&keywords=ggplot2): The official ggplot2 book

---

![](https://media.giphy.com/media/3oz8xIsloV7zOmt81G/giphy.gif)

### [github.com/malcolmbarrett/tidymeta](https://github.com/malcolmbarrett/tidymeta)
### [github.com/malcolmbarrett/ma_viz_workshop](https://github.com/malcolmbarrett/ma_viz_workshop)

Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan).