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<!DOCTYPE html>
<html>
<head>
<title>methodsR</title>
<meta charset="utf-8">
<meta name="author" content="Michael Battaglia" />
<meta name="date" content="2018-06-26" />
<link rel="stylesheet" href="mc-xaringan.css" type="text/css" />
<link rel="stylesheet" href="mc-xaringan-fonts.css" type="text/css" />
</head>
<body>
<textarea id="source">
class: center, middle, inverse, title-slide
# methodsR
### Michael Battaglia
### June 26, 2018
---
layout: true
background-color: #fafaef
<div class="my-footer"><img src="mc_logo_rectangle.png" style="height: 30px;"/></div>
---
# Install Package
```r
creds <- git2r::cred_ssh_key("~/../.ssh/id_rsa.pub",
"~/../.ssh/id_rsa")
devtools::install_git("git@gitlab.com:methodsconsultants/r-package/methods-r-package.git",
credentials = creds,
upgrade_dependencies = FALSE)
```
---
# Read in Example Data
Made up dataset, which includes a few corner cases. The "treatment" is whether the person had ice cream or candy.
```r
file <- system.file("extdata", "example-treatment-data.csv", package = "methodsR")
dat <- read.csv(file) %>%
as_data_frame()
dat %>%
tail(5) %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> gender </th>
<th style="text-align:right;"> age </th>
<th style="text-align:right;"> sugar_factor </th>
<th style="text-align:left;"> treatment </th>
<th style="text-align:left;"> happiness </th>
<th style="text-align:left;"> happy </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> male </td>
<td style="text-align:right;"> 13 </td>
<td style="text-align:right;"> 0.3999984 </td>
<td style="text-align:left;"> candy </td>
<td style="text-align:left;"> sad </td>
<td style="text-align:left;"> no </td>
</tr>
<tr>
<td style="text-align:left;"> male </td>
<td style="text-align:right;"> 7 </td>
<td style="text-align:right;"> 0.5366504 </td>
<td style="text-align:left;"> candy </td>
<td style="text-align:left;"> sad </td>
<td style="text-align:left;"> no </td>
</tr>
<tr>
<td style="text-align:left;"> male </td>
<td style="text-align:right;"> 67 </td>
<td style="text-align:right;"> 0.9412179 </td>
<td style="text-align:left;"> candy </td>
<td style="text-align:left;"> NA </td>
<td style="text-align:left;"> NA </td>
</tr>
<tr>
<td style="text-align:left;"> male </td>
<td style="text-align:right;"> 75 </td>
<td style="text-align:right;"> 0.7966411 </td>
<td style="text-align:left;"> ice cream </td>
<td style="text-align:left;"> happy </td>
<td style="text-align:left;"> Yes </td>
</tr>
<tr>
<td style="text-align:left;"> female </td>
<td style="text-align:right;"> 77 </td>
<td style="text-align:right;"> 0.2233713 </td>
<td style="text-align:left;"> candy </td>
<td style="text-align:left;"> sad </td>
<td style="text-align:left;"> no </td>
</tr>
</tbody>
</table>
---
# Package Hierarchy
- `all_summary`
- `cat_summary`
- `mean_sd_cont`
- `median_IQR_cont`
---
# Package Functions
- `all_summary`:
- Highest level function, only need to use this function
- Combines outputs of `cat_summary` and `mean_sd_cont` into one table
- `cat_summary`:
- Makes summary table of categorical variables
- Calculates count and percentage of each variable
- P-value from chi-squared/fisher
- `mean_sd_cont`:
- Makes summary tables of continuous variables
- Produces mean and standard deviation for each variable
- P-value from t-test
- `median_IQR_cont`:
- Summarizes continuous variables
- Calculates median and IQR
- P-value from Mann-Whitney-Wilcoxon
---
# Goal
The goal of these data summary functions are to make it as easy as possible to make tables like these:
<p align="center">
<img src="table-1.png">
</p>
---
# Goal
<p align="center">
<img src="table-2.png">
</p>
---
# Continuous Variables Summary
## Simple Univariate Tables
Simple summary statistics for continuous variables is easy, so lets start here. `age` and `sugar_factor` are the continuous variables.
```r
cont_variables <- c("age", "sugar_factor")
mean_na <- partial(mean, na.rm = TRUE)
sd_na <- partial(sd, na.rm = TRUE)
dat %>%
select(cont_variables) %>%
summarise_all(funs("Mean" = mean_na, "SD" = sd_na)) %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:right;"> age_Mean </th>
<th style="text-align:right;"> sugar_factor_Mean </th>
<th style="text-align:right;"> age_SD </th>
<th style="text-align:right;"> sugar_factor_SD </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:right;"> 42.32525 </td>
<td style="text-align:right;"> 0.4878811 </td>
<td style="text-align:right;"> 22.11026 </td>
<td style="text-align:right;"> 0.2956023 </td>
</tr>
</tbody>
</table>
---
## Simple Univariate Tables
```r
dat %>%
select(cont_variables) %>%
summarise_all(funs("Mean" = mean_na, "SD" = sd_na)) %>%
gather(var, val) %>%
separate(var, c("var", "mean_sd"), "_(?!.*_)") %>%
spread(key = mean_sd, value = val) %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> var </th>
<th style="text-align:right;"> Mean </th>
<th style="text-align:right;"> SD </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> age </td>
<td style="text-align:right;"> 42.3252525 </td>
<td style="text-align:right;"> 22.1102608 </td>
</tr>
<tr>
<td style="text-align:left;"> sugar_factor </td>
<td style="text-align:right;"> 0.4878811 </td>
<td style="text-align:right;"> 0.2956023 </td>
</tr>
</tbody>
</table>
---
## Simple Univariate Tables
```r
dat %>%
select(cont_variables) %>%
summarise_all(funs("Mean" = mean_na, "SD" = sd_na)) %>%
gather(Variable, val) %>%
separate(Variable, c("Variable", "mean_sd"), "_(?!.*_)") %>%
spread(key = mean_sd, value = val) %>%
mutate(`Mean (SD)` = paste0(round(Mean, 3), " (", round(SD, 3), ")")) %>%
select(-Mean, -SD) %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> Variable </th>
<th style="text-align:left;"> Mean (SD) </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> age </td>
<td style="text-align:left;"> 42.325 (22.11) </td>
</tr>
<tr>
<td style="text-align:left;"> sugar_factor </td>
<td style="text-align:left;"> 0.488 (0.296) </td>
</tr>
</tbody>
</table>
---
## `mean_sd_cont`
This is almost exactly what `mean_sd_cont` does when a treatment variable is not provided.
```r
dat %>%
mean_sd_cont(cont_variables = c("age", "sugar_factor")) %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> Variable </th>
<th style="text-align:left;"> Mean (SD) </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> age </td>
<td style="text-align:left;"> 42.325 (22.11) </td>
</tr>
<tr>
<td style="text-align:left;"> sugar_factor </td>
<td style="text-align:left;"> 0.488 (0.296) </td>
</tr>
</tbody>
</table>
---
## `mean_sd_cont`
Also if variables are not provided, it will infer the variables from column types.
```r
dat %>%
mean_sd_cont() %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> Variable </th>
<th style="text-align:left;"> Mean (SD) </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> age </td>
<td style="text-align:left;"> 42.325 (22.11) </td>
</tr>
<tr>
<td style="text-align:left;"> sugar_factor </td>
<td style="text-align:left;"> 0.488 (0.296) </td>
</tr>
</tbody>
</table>
---
## `median_IQR_cont`
`median_IQR_cont` works in exactly the same way.
```r
dat %>%
median_IQR_cont() %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> Variable </th>
<th style="text-align:left;"> Median (IQR) </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> age </td>
<td style="text-align:left;"> 41 (37) </td>
</tr>
<tr>
<td style="text-align:left;"> sugar_factor </td>
<td style="text-align:left;"> 0.469 (0.525) </td>
</tr>
</tbody>
</table>
---
## Split by Treatment
Presenting a table of summary statistics split by a treatment variable, along with p-values is a common task.
```r
summary_tab <- dat %>%
select(cont_variables, treatment) %>%
group_by(treatment) %>%
summarise_all(funs("Mean" = mean_na, "SD" = sd_na)) %>%
gather(Variable, val, -treatment) %>%
separate(Variable, c("Variable", "mean_sd"), "_(?!.*_)") %>%
spread(key = mean_sd, value = val) %>%
mutate(`Mean (SD)` = paste0(round(Mean, 3), " (", round(SD, 3), ")")) %>%
select(-Mean, -SD) %>%
spread(treatment, `Mean (SD)`)
summary_tab %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> Variable </th>
<th style="text-align:left;"> candy </th>
<th style="text-align:left;"> ice cream </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> age </td>
<td style="text-align:left;"> 42.26 (22.616) </td>
<td style="text-align:left;"> 42.394 (21.611) </td>
</tr>
<tr>
<td style="text-align:left;"> sugar_factor </td>
<td style="text-align:left;"> 0.456 (0.3) </td>
<td style="text-align:left;"> 0.522 (0.287) </td>
</tr>
</tbody>
</table>
---
## Split by Treatment
```r
t_test_p(df = dat, var = "age", treatment = "treatment")
```
```
## [1] 0.9461444
```
```r
summary_tab %>%
mutate(`P Value` = purrr::map_dbl(Variable, partial(t_test_p, df = dat, treatment = "treatment"))) %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> Variable </th>
<th style="text-align:left;"> candy </th>
<th style="text-align:left;"> ice cream </th>
<th style="text-align:right;"> P Value </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> age </td>
<td style="text-align:left;"> 42.26 (22.616) </td>
<td style="text-align:left;"> 42.394 (21.611) </td>
<td style="text-align:right;"> 0.9461444 </td>
</tr>
<tr>
<td style="text-align:left;"> sugar_factor </td>
<td style="text-align:left;"> 0.456 (0.3) </td>
<td style="text-align:left;"> 0.522 (0.287) </td>
<td style="text-align:right;"> 0.0127392 </td>
</tr>
</tbody>
</table>
---
## `mean_sd_cont`
`mean_sd_cont` will produce a treatment-split table if a treatment column is specified.
```r
dat %>%
mean_sd_cont(treatment = "treatment", cont_variables = c("age", "sugar_factor")) %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> Variable </th>
<th style="text-align:left;"> candy </th>
<th style="text-align:left;"> ice cream </th>
<th style="text-align:left;"> P Value </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> age </td>
<td style="text-align:left;"> 42.26 (22.616) </td>
<td style="text-align:left;"> 42.394 (21.611) </td>
<td style="text-align:left;"> 0.946 </td>
</tr>
<tr>
<td style="text-align:left;"> sugar_factor </td>
<td style="text-align:left;"> 0.456 (0.3) </td>
<td style="text-align:left;"> 0.522 (0.287) </td>
<td style="text-align:left;"> 0.013 </td>
</tr>
</tbody>
</table>
---
## `mean_sd_cont`
```r
dat %>%
mean_sd_cont(treatment = "treatment") %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> Variable </th>
<th style="text-align:left;"> candy </th>
<th style="text-align:left;"> ice cream </th>
<th style="text-align:left;"> P Value </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> age </td>
<td style="text-align:left;"> 42.26 (22.616) </td>
<td style="text-align:left;"> 42.394 (21.611) </td>
<td style="text-align:left;"> 0.946 </td>
</tr>
<tr>
<td style="text-align:left;"> sugar_factor </td>
<td style="text-align:left;"> 0.456 (0.3) </td>
<td style="text-align:left;"> 0.522 (0.287) </td>
<td style="text-align:left;"> 0.013 </td>
</tr>
</tbody>
</table>
---
## `mean_sd_cont`
By default it will round p-values, you can get the raw p-value by setting `round_p = FALSE`.
```r
dat %>%
mean_sd_cont(treatment = "treatment", round_p = FALSE) %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> Variable </th>
<th style="text-align:left;"> candy </th>
<th style="text-align:left;"> ice cream </th>
<th style="text-align:right;"> P Value </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> age </td>
<td style="text-align:left;"> 42.26 (22.616) </td>
<td style="text-align:left;"> 42.394 (21.611) </td>
<td style="text-align:right;"> 0.9461444 </td>
</tr>
<tr>
<td style="text-align:left;"> sugar_factor </td>
<td style="text-align:left;"> 0.456 (0.3) </td>
<td style="text-align:left;"> 0.522 (0.287) </td>
<td style="text-align:right;"> 0.0127392 </td>
</tr>
</tbody>
</table>
---
## `median_IQR_cont`
`median_IQR_cont` works similarly. The statistics are the median and IQR, and the p-value comes from the Mann-Whitney-Wilcoxon test.
```r
dat %>%
median_IQR_cont(treatment = "treatment") %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> Variable </th>
<th style="text-align:left;"> candy </th>
<th style="text-align:left;"> ice cream </th>
<th style="text-align:left;"> P Value </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> age </td>
<td style="text-align:left;"> 42 (40) </td>
<td style="text-align:left;"> 41 (35) </td>
<td style="text-align:left;"> 0.888 </td>
</tr>
<tr>
<td style="text-align:left;"> sugar_factor </td>
<td style="text-align:left;"> 0.438 (0.542) </td>
<td style="text-align:left;"> 0.517 (0.478) </td>
<td style="text-align:left;"> 0.012 </td>
</tr>
</tbody>
</table>
---
# Categorical Variable Summary
## Univariate Summary Table
```r
cat_variables <- c("gender", "happiness", "happy")
dat <- dat %>%
mutate_if(is.factor, as.character)
```
```r
dat %>%
count(gender) %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> gender </th>
<th style="text-align:right;"> n </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> female </td>
<td style="text-align:right;"> 234 </td>
</tr>
<tr>
<td style="text-align:left;"> male </td>
<td style="text-align:right;"> 266 </td>
</tr>
</tbody>
</table>
---
## Univariate Summary Table
```r
dat %>%
count(gender) %>%
mutate(`N (%)` = methodsR:::proportions(n)) %>%
select(-n) %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> gender </th>
<th style="text-align:left;"> N (%) </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> female </td>
<td style="text-align:left;"> 234 (46.8%) </td>
</tr>
<tr>
<td style="text-align:left;"> male </td>
<td style="text-align:left;"> 266 (53.2%) </td>
</tr>
</tbody>
</table>
---
## Univariate Summary Table
```r
dat %>%
count(gender) %>%
mutate(`N (%)` = methodsR:::proportions(n)) %>%
select(-n) %>%
methodsR:::add_spacing(is_treatment = FALSE) %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> Variable </th>
<th style="text-align:left;"> Label </th>
<th style="text-align:left;"> N (%) </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> gender </td>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> female </td>
<td style="text-align:left;"> 234 (46.8%) </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> male </td>
<td style="text-align:left;"> 266 (53.2%) </td>
</tr>
</tbody>
</table>
---
## Univariate Summary Table
To do this for all variables, we can wrap this code in a function and iterate over all variables.
```r
simple_one_variable <- function(df, var) {
var <- sym(var)
df %>%
count(!!var) %>%
filter(!is.na(!!var)) %>%
mutate(`N (%)` = methodsR:::proportions(n)) %>%
select(-n) %>%
methodsR:::add_spacing(is_treatment = FALSE)
}
```
---
## Univariate Summary Table
```r
cat_variables %>%
map_dfr(partial(simple_one_variable, df = dat)) %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> Variable </th>
<th style="text-align:left;"> Label </th>
<th style="text-align:left;"> N (%) </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> gender </td>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> female </td>
<td style="text-align:left;"> 234 (46.8%) </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> male </td>
<td style="text-align:left;"> 266 (53.2%) </td>
</tr>
<tr>
<td style="text-align:left;"> happiness </td>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> happy </td>
<td style="text-align:left;"> 366 (77.71%) </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> sad </td>
<td style="text-align:left;"> 105 (22.29%) </td>
</tr>
<tr>
<td style="text-align:left;"> happy </td>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> no </td>
<td style="text-align:left;"> 105 (22.29%) </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> yes </td>
<td style="text-align:left;"> 91 (19.32%) </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> Yes </td>
<td style="text-align:left;"> 275 (58.39%) </td>
</tr>
</tbody>
</table>
---
## `cat_summary`
`cat_summary` provides this functionality, along with additional features such as convering yes/no and 0/1 variables to binary.
```r
dat %>%
cat_summary(cat_variables = c("gender", "happiness", "happy")) %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> Variable </th>
<th style="text-align:left;"> Label </th>
<th style="text-align:left;"> N (%) </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> gender </td>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> female </td>
<td style="text-align:left;"> 234 (46.8%) </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> male </td>
<td style="text-align:left;"> 266 (53.2%) </td>
</tr>
<tr>
<td style="text-align:left;"> happiness </td>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> happy </td>
<td style="text-align:left;"> 366 (77.71%) </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> sad </td>
<td style="text-align:left;"> 105 (22.29%) </td>
</tr>
<tr>
<td style="text-align:left;"> happy </td>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> 366 (77.71%) </td>
</tr>
</tbody>
</table>
---
## `cat_summary`
```r
dat %>%
cat_summary() %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> Variable </th>
<th style="text-align:left;"> Label </th>
<th style="text-align:left;"> N (%) </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> gender </td>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> female </td>
<td style="text-align:left;"> 234 (46.8%) </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> male </td>
<td style="text-align:left;"> 266 (53.2%) </td>
</tr>
<tr>
<td style="text-align:left;"> treatment </td>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> candy </td>
<td style="text-align:left;"> 257 (51.4%) </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> ice cream </td>
<td style="text-align:left;"> 243 (48.6%) </td>
</tr>
<tr>
<td style="text-align:left;"> happiness </td>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> happy </td>
<td style="text-align:left;"> 366 (77.71%) </td>
</tr>
<tr>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> sad </td>
<td style="text-align:left;"> 105 (22.29%) </td>
</tr>
<tr>
<td style="text-align:left;"> happy </td>
<td style="text-align:left;"> </td>
<td style="text-align:left;"> 366 (77.71%) </td>
</tr>
</tbody>
</table>
---
## Summary Table Split by Treatment
```r
dat %>%
select(gender, treatment) %>%
group_by(treatment) %>%
count(gender) %>%
spread(treatment, n) %>%
mutate_at(vars(-gender), funs(methodsR:::proportions)) %>%
kable()
```
<table>
<thead>
<tr>
<th style="text-align:left;"> gender </th>
<th style="text-align:left;"> candy </th>
<th style="text-align:left;"> ice cream </th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;"> female </td>
<td style="text-align:left;"> 124 (48.25%) </td>
<td style="text-align:left;"> 110 (45.27%) </td>
</tr>
<tr>
<td style="text-align:left;"> male </td>
<td style="text-align:left;"> 133 (51.75%) </td>
<td style="text-align:left;"> 133 (54.73%) </td>
</tr>
</tbody>
</table>
---
## Summary Table Split by Treatment
```r
p_val <- dat %>%
chi_fisher_p("gender", "treatment") %>%
round(3)
dat %>%
select(gender, treatment) %>%
group_by(treatment) %>%