README.Rmd

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output: github_document
---

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knitr::opts_chunk$set(
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# fec16 <img src="data-raw/Sticker/hex_fec16.png" align="right" height=140/>

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**fec16** contains relational data from the Federal Election Commission website pertaining to candidates and committees for the United States 2015-2016 election cycle. Additionally, result of the 2016 general election and contribution data both from committees and individuals are included. 


## Installation

`fec16` is hosted on GitHub and call be installed by running the following:

```{r, eval=FALSE}
devtools::install_github("ranawg/fec16")
```

```{r}
library(fec16)
```


## Data

- `candidates`: all candidates registered with the FEC during the 2015-2016 election cycle
- `committees`: all committees registered with the FEC during the 2015-2016 election cycle
- `results`: the results of the 2016 general presidential election
- `individuals`: a sample of 5000 individual contributions to candidates/committees during the primary and general 2016 elections
- `committee_contributions`: total contributions, aggregated by candidate, from committees

## Examples

### Data Wrangling 

`fec16` can be used to summarize data in order see how many candidates are running for elections (in all offices) for the two major parties:
```{r example, message = FALSE}
library(fec16)
library(tidyverse)

candidates %>%
  filter(cand_pty_aff == "REP" | cand_pty_aff == "DEM") %>%
  group_by(cand_pty_aff) %>%
  summarise(size = n())
```

#### Joining Data 

We can join any of the datasets using `cand_id`. Each dataset with the exception of the `individuals` dataset contains a possible joining key: `cand_id`.

Here is an example of calculating how many candidates are in each of the two major parties: Democratic (DEM) and Republican (REP), based on their committee type:
```{r message=FALSE, warning=FALSE}
cand_cmte <- candidates %>%
  full_join(committees, by = "cand_id") %>%
  filter(cand_pty_aff == "REP" | cand_pty_aff == "DEM") %>%
  group_by(cand_pty_aff, committee_type) %>%
  summarise(n = n()) %>%
  drop_na(committee_type)
head(cand_cmte)
```

### Data Visualization 

And extending that to create a visualization to see the results easily.
```{r message=FALSE, warning=FALSE}
ggplot(cand_cmte, aes(x = committee_type, y = n, fill = cand_pty_aff)) + 
  geom_col(position = "dodge") +
  labs(title = "Bar Chart of Total Committees by Type and Party", 
       x = "Committee Type", y = "Count", fill = "Candidate Party Affiliation")
```


## Contributors
 - [Irene Ryan](https://github.com/ireneryan)
 - [Marium Tapal](https://github.com/mariumtapal)
 - [Rana Gahwagy](https://github.com/ranawg)