---
title: 'SOCI424: Affiliation and fields 2'
author:
- (your name and role here)
- (your name and role here)
output:
  html_document: default
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
# load the 'igraph' package
library(igraph)
```

# In Pairs:

## Part 1: Get the data

### Cultural tastes
Today's data will be a simplified version of the [survey](https://tinyurl.com/tastesonline) of online (social) media use from your fellow students. The data is presented as an affiliation matrix, with each row representing a survey response, and the columns representing students' preferences for platforms and content. The affiliation network is available at <https://soci424.netlify.app/data/cultural_tastes.csv>).

-   *Use R's `read.csv()` and `as.matrix()` functions to load the data and convert it to a `matrix` object. Store this as variable named `amat`.*

```{r 1_1}
# (your code here)
```

-   *Examine `amat` visually. How many rows and columns does it have? What do the rows and columns represent?*

```{r 1_2}
# (your code here)
```



## Part 2: Bipartite network

In this section, you will create a bipartite (affiliation) network to explore the survey results.

-   *Use `graph_from_incidence_matrix()` to create a bipartite network from the affiliation matrix, and store the resulting graph to a variable named `anet`. This is the same process you used last week to generate a network of suffragette co-arrests. Plot the resulting network.*

```{r 2_1}
# (your code here)
```

-   *Use the data in the `type` vertex attribute to set the `color` attribute differently for respondents and their preferences Then, plot the network you just created again. Do you see any obvious patterns? How similar are students' online platform preferences? Are there any online platforms that tend to be 'clustered' together (i.e. they they tend to be used by the same people)?*

```{r 2_2}
# (your code here)
```

-   *Use the `bipartite_projection()` to create two new networks named `personnet` and `preferencenet`, representing each of these projections.*

```{r 2_3}
# (your code here)
```

-   *Plot each of the projections from the previous step. What kinds of interpersonal structures can you see? What kind of structure can you infer between online platforms based on student preferences?*

```{r 2_4}
# (your code here)
```



## Part 3: Networks vs fields

In this section, you will compare the results you generated in Part 2 above with the results from the correspondence analysis demonstrate in class (see <https://soci424.netlify.app/data/cultural_tastes_ca_resps.pdf> for the respondent plot, <https://soci424.netlify.app/data/cultural_tastes_ca_tastes.pdf> for the tastes plot, and <https://soci424.netlify.app/data/cultural_tastes_ca_both.pdf> for the overlay. The code that created the plots is available at <https://soci424.netlify.app/worksheets/MCA_cultural_tastes_online.Rmd>)

-   *Affiliation networks, like correspondence analysis, characterize the relations between actors in terms of the similarity the social and cultural _orientations_ of those actors. When projecting a two-mode affiliation network into a one-mode actor–actor network (as you did above), the ties between those actors measure similarity in their engagement with events or institutions. Compare your bipartite projection from part 2 to the respondent mapping in the above figures. Do the two analyses suggest similar structures among your classmates?*

  * _(your response here)_

-   *How do the two representations of the survey data (the affiliation network projection and the correspondence analysis) differ in their conceptions of a _relation_ between people and between cultural objects? According to John Mohr, "for Bourdieu social network methods appeared to be limited to capturing only flat, life-less, one-dimensional expressions of social structure" (2013, 117). Compared to the correspondence analysis, do you think this quote describes network representation you created above?*

  * _(your response here)_

-   *Overall, is there any aspect of the cultural consumption of your classmates that is apparent in the correspondence analysis that is not apparent in the affiliation network analysis? Conversely, does the affiliation network analysis reveal anything about the social structures of the class that the correspondence analysis misses?*

  * _(your response here)_






