README.Rmd

---
title: "MarkovifyR"
output: github_document
---


`markovifyR` : R wrapper for Markovify

Ref: <https://github.com/jsvine/markovify>

>_"Markovify is a simple, extensible Markov chain generator. Right now, its main use is for building Markov models of large corpora of text, and generating random sentences from that."_

This package requires Python and markovify to be installed.

To install markovify in R you can run:

```{r eval = F}
system("pip install markovify")
```

The following functions are implemented:

- `generate_markovify_model:` Generates a markov model
- `markovify_text`: Generates text from a markov model
- `generate_sentence_starting_with`: Generates text, if possible, with your specified start word
- `generate_start_words`: Produces a data frame with the starting words for each input sentence
-

### Installation

```{r eval=FALSE}
devtools::install_github("abresler/markovifyR")
```

```{r message=FALSE, warning=FALSE, error=FALSE}
options(width=120)
```

### Usage

```{r message=FALSE, warning=FALSE, error=FALSE}
library(markovifyR)
library(dplyr)
```

### Generate New Peter Linneman "Life Lessons""

Here we are going to showcase how to use the package to create new [Life Lessons](asbcllc.com/reflections/peter_linneman/) from my favorite professor from college Peter Linneman.

#### Step 1 -- Build the Corpus

```{r}
data("linneman_lessons")

lessons <-
  linneman_lessons %>% 
  pull(textLesson)

lessons %>% str()
```

#### Step 2 -- Build the Model

```{r}
markov_model <-
  generate_markovify_model(
    input_text = lessons,
    markov_state_size = 2L,
    max_overlap_total = 25,
    max_overlap_ratio = .85
  )

```


### Step 3 -- Generate the Text
```{r}
markovify_text(
  markov_model = markov_model,
  maximum_sentence_length = NULL,
  output_column_name = 'textLinnemanBot',
  count = 25,
  tries = 100,
  only_distinct = TRUE,
  return_message = TRUE
)

```

#### Step 4 -- Other Features

Generate random sentence starting with.

```{r}
markovify_text(
  markov_model = markov_model,
  maximum_sentence_length = NULL,
  start_words = c("The", "You", "Life"),
  output_column_name = 'textLinnemanBot',
  count = 25,
  tries = 100,
  only_distinct = TRUE,
  return_message = TRUE
)
```

Look at corpus start-words


```{r}
generate_start_words(markov_model = markov_model)
```