The goal of plotor is to generate Odds Ratio plots from logistic regression models.

You can install the development version of plotor from GitHub with:

```
# install.packages("devtools")
::install_github("craig-parylo/plotor") devtools
```

You can also install the latest released version from Cran with:

`install.packages("plotor")`

In this example we will explore the likelihood of surviving the Titanic disaster based on passenger economic status (class), sex, and age group.

In addition to `plotor`

the packages we will use include
`dplyr`

, `tidyr`

and `forcats`

for
general data wrangling, the `stats`

package to conduct the
logistic regression followed by `broom`

to tidy the output
and convert the results to Odds Ratios and confidence intervals, then
`ggplot2`

to visualise the plot.

```
library(plotor) # generates Odds Ratio plots
library(datasets) # source of example data
library(dplyr) # data wrangling
library(tidyr) # data wrangling - uncounting aggregated data
library(forcats) # data wrangling - handling factor variables
library(stats) # perform logistic regression using glm function
library(broom) # tidying glm model and producing OR and CI
library(ggplot2) # data visualisation
```

Start with getting the data from the datasets package.

```
<- datasets::Titanic |>
df as_tibble() |>
# convert counts to observations
filter(n > 0) |>
uncount(weights = n) |>
# convert categorical variables to factors.
# we specify an order for levels in Class and Survival, otherwise ordering
# in descending order of frequency
mutate(
Class = Class |>
fct(levels = c('1st', '2nd', '3rd', 'Crew')),
Sex = Sex |>
fct_infreq(),
Age = Age |>
fct_infreq(),
Survived = Survived |>
fct(levels = c('No', 'Yes'))
)
```

We now have a tibble of data containing four columns:

`Survived`

- our outcome variable describing whether the passenger survived`Yes`

or died`No`

,`Class`

- the passenger class, either`1st`

,`2nd`

,`3rd`

or`Crew`

,`Sex`

- the gender of the passenger, either`Male`

or`Female`

,`Age`

- whether the passenger was an`Adult`

or`Child`

.

We next conduct a logistic regression of survival (as a binary
factor: ‘yes’ and ‘no’) against the characteristics of passenger class,
sex and age group. For this we use the Generalised Linear Model function
(`glm`

) from the `stats`

package, specifying:

the family as ‘binomial’, and

the formula as survival being a function of

`Class`

,`Sex`

and`Age`

.

```
# conduct a logistic regression of survival against the other variables
<- glm(
lr data = df,
family = 'binomial',
formula = Survived ~ Class + Sex + Age
)
```

Finally, we can plot the Odds Ratio of survival using the
`plot_or`

function.

```
# using plot_or
plot_or(glm_model_results = lr)
```

This plot makes it clear that:

Children were 2.89 times more likely to survive than Adults,

Passengers in

`2nd`

,`3rd`

class as well as`Crew`

were all less likely to survive than those in`1st`

class,Women were 11.25 times more likely to survive than men.