https://github.com/bauer-alex/coalitions

Poll based calculation of coalition probabilities

https://github.com/bauer-alex/coalitions

Repository

Poll based calculation of coalition probabilities

https://github.com/bauer-alex/coalitions/blob/master/

# coalitions 

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## Overview

The package offers functions that calculate coalition probabilities in
multi-party elections, based on a survey results:

  - To get started, see our [workflow
    vignette](https://adibender.github.io/coalitions/articles/workflow.html)

  - See our [talk](https://adibender.netlify.com/talk/appliedr-meetup/)
    at the [Munich AppliedR
    MeetUp](https://www.meetup.com/de-DE/Applied-R-Munich/)

  - Check out our [interacitve shiny
    app](http://koala.stat.uni-muenchen.de/) on German (state and
    federal) elections/surveys

  - Updates are available on our [KOALA\_LMU twitter
    account](https://twitter.com/KOALA_LMU)\!

## Installation

```r
# To install from CRAN use:
install.packages("coalitions")

# To install the most current version from GitHub use:
devtools::install_github("adibender/coalitions")
```

## Usage

Detailed workflow is outlined in the
[workflow](https://adibender.github.io/coalitions/articles/workflow.html)
vignette.

A short overview is presented below.

### Scrape surveys

The wrapper `get_surveys()` which takes no arguments, downloads all
surveys currently available at
[wahlrecht.de](https://www.wahlrecht.de/umfragen) and stores them in a
nested `tibble`:

``` r
library(coalitions)
library(dplyr)
library(tidyr)
surveys <- get_surveys()
surveys
```

    ## # A tibble: 7 x 2
    ##   pollster   surveys
    ##         
    ## 1 allensbach 
    ## 2 emnid      
    ## 3 forsa      
    ## 4 fgw        
    ## 5 gms        
    ## 6 infratest  
    ## 7 insa       

Each row represents a polling agency and each row in the `surveys`
column again contains a nested `tibble` with survey results from
different time-points:

``` r
surveys %>%
    filter(pollster == "allensbach") %>%
    unnest()
```

    ## # A tibble: 42 x 6
    ##    pollster   date       start      end        respondents survey
    ##                                
    ##  1 allensbach 2018-02-23 2018-02-02 2018-02-15        1271 % unnest() %>% slice(1)
survey %>% unnest()
```

    ## # A tibble: 7 x 8
    ##   pollster   date       start      end        respondents party  percent
    ##                                   
    ## 1 allensbach 2018-02-23 2018-02-02 2018-02-15        1271 cdu      32.0
    ## 2 allensbach 2018-02-23 2018-02-02 2018-02-15        1271 spd      17.5
    ## 3 allensbach 2018-02-23 2018-02-02 2018-02-15        1271 greens   12.0
    ## 4 allensbach 2018-02-23 2018-02-02 2018-02-15        1271 fdp      11.0
    ## 5 allensbach 2018-02-23 2018-02-02 2018-02-15        1271 left      9.50
    ## 6 allensbach 2018-02-23 2018-02-02 2018-02-15        1271 afd      13.0
    ## 7 allensbach 2018-02-23 2018-02-02 2018-02-15        1271 others    5.00
    ## # ... with 1 more variable: votes 

### Calculate coalition probabilities

For each survey (row) we can calculate the coalition probabilities

``` r
survey %>% get_probabilities(nsim=1e4) %>% unnest()
```

    ## # A tibble: 6 x 4
    ##   pollster   date       coalition       probability
    ##                               
    ## 1 allensbach 2018-02-23 cdu                  0
    ## 2 allensbach 2018-02-23 cdu_fdp              0.0500
    ## 3 allensbach 2018-02-23 cdu_fdp_greens     100.0
    ## 4 allensbach 2018-02-23 spd                  0
    ## 5 allensbach 2018-02-23 left_spd             0
    ## 6 allensbach 2018-02-23 greens_left_spd      0

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