06-Creating-a-Graph-of-Retweet-Relationships.Rmd

# Creating a Graph of Retweet Relationships

## Problem

You want to construct and analyze a graph data structure of retweet relationships for a set of query results.

## Solution

Query for the topic, extract the retweet origins, and then use `igraph` to construct a graph to analyze.

## Discussion

Recipes 4 and 5 introduced and expanded on searching Twitter plus looking for retweets. The [`igraph`](http://igraph.org/r/) package can be used to capture and analyze details of relationships across retweets. We'll focus on just examining the Twitter user pair relationships.

Let's get a larger sample this time --- 1,500 tweets in `#rstats`. We can use the technique from the previous recipe and:

- find the retweets (using the API-provided data)
- expand out all the mentioned screen names
- create an `igraph` graph object
- look at some summary statistics for the graph

```{r 06_lib, message=FALSE, warning=FALSE}
library(rtweet)
library(igraph)
library(hrbrthemes)
library(tidyverse)
```
```{r include=FALSE, echo=FALSE}
extrafont::loadfonts(quiet=TRUE)
```
```{r 06_rstats, message=FALSE, warning=FALSE, cache=TRUE}
rstats <- search_tweets("#rstats", n=1500)

filter(rstats, retweet_count > 0) %>% 
  select(screen_name, mentions_screen_name) %>%
  unnest(mentions_screen_name) %>% 
  filter(!is.na(mentions_screen_name)) %>% 
  graph_from_data_frame() -> rt_g
```

You can reference the [`igraph` `print()` and `summary()` functions](http://igraph.org/r/doc/print.igraph.html) for more information on the output of `summary()` but output from the following line shows that the graph is `D`irected with `N`amed vertices and it has `r scales::comma(length(V(rt_g)))` vertices and `r scales::comma(length(E(rt_g)))` edges.

```{r 06_sum, message=FALSE, warning=FALSE, cache=TRUE}
summary(rt_g)
```

We'll produce more visualizations in the next recipe, but the _degree_ of graph vertices is one of the most fundamental properties of a graph and it's much nicer to see the degree distribution than stare at a wall of numbers:

```{r 06_gg, message=FALSE, warning=FALSE, cache=TRUE}
ggplot(data_frame(y=degree_distribution(rt_g), x=1:length(y))) +
  geom_segment(aes(x, y, xend=x, yend=0), color="slateblue") +
  scale_y_continuous(expand=c(0,0), trans="sqrt") +
  labs(x="Degree", y="Density (sqrt scale)", title="#rstats Retweet Degree Distribution") +
  theme_ipsum_rc(grid="Y", axis="x")
```

## See Also

- [`igraph`](http://igraph.org/)