README.md

In melissakey/classCleaner: Tools to Identify Outliers in grouped (class) Data

classCleaner

The goal of classCleaner is to …

Installation

classCleaner is currently only available on Github, where it can be downloaded at

# install.packages("devtools")
devtools::install_github("melkey/classCleaner")

Example

Here’s a simple example of classCleaner on the Congressional Voting Records Data Set from the UCI Machine Learning Repository. Although classCleaner does not require any tidyverse package, it’s included to clean and present the data.

The data consists of (simplified) voting records for each member of the US House of Representatives on 16 key votes (identified by the CQA) in 1984. Each Representative’s vote is summarized as “y” (voted for), “n” (voted against), and “?” (voted present or did not vote). We use classCleaner to identify which party members tended to vote in a manner different from their political party.

library(classCleaner)
library(tidyverse)

## load example data set

votes <- read_csv(
  "https://archive.ics.uci.edu/ml/machine-learning-databases/voting-records/house-votes-84.data",
  col_types = cols(),
  col_names = c('party', paste0("v", 1:16))
) %>%
  print()
#> # A tibble: 435 x 17
#>    party v1    v2    v3    v4    v5    v6    v7    v8    v9    v10   v11   v12  
#>    <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#>  1 repu~ n     y     n     y     y     y     n     n     n     y     ?     y    
#>  2 repu~ n     y     n     y     y     y     n     n     n     n     n     y    
#>  3 demo~ ?     y     y     ?     y     y     n     n     n     n     y     n    
#>  4 demo~ n     y     y     n     ?     y     n     n     n     n     y     n    
#>  5 demo~ y     y     y     n     y     y     n     n     n     n     y     ?    
#>  6 demo~ n     y     y     n     y     y     n     n     n     n     n     n    
#>  7 demo~ n     y     n     y     y     y     n     n     n     n     n     n    
#>  8 repu~ n     y     n     y     y     y     n     n     n     n     n     n    
#>  9 repu~ n     y     n     y     y     y     n     n     n     n     n     y    
#> 10 demo~ y     y     y     n     n     n     y     y     y     n     n     n    
#> # ... with 425 more rows, and 4 more variables: v13 <chr>, v14 <chr>,
#> #   v15 <chr>, v16 <chr>

ClassCleaner, requires a matrix of pairwise distances between the Representatives. To do this, we recode the v1 - v16, with y -> 1, n -> 0, and ? -> 0.5, and use Manhattan (“city-block”) distance to calculate the distance between Representatives.

votes <- votes %>%
  mutate_at(vars(v1:v16), 
    ~ recode(., 
      'y' = 1,
      'n' = 0,
      '?' = .5
    )
  ) %>%
  print()
#> # A tibble: 435 x 17
#>    party    v1    v2    v3    v4    v5    v6    v7    v8    v9   v10   v11   v12
#>    <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 repu~   0       1     0   1     1       1     0     0     0     1   0.5   1  
#>  2 repu~   0       1     0   1     1       1     0     0     0     0   0     1  
#>  3 demo~   0.5     1     1   0.5   1       1     0     0     0     0   1     0  
#>  4 demo~   0       1     1   0     0.5     1     0     0     0     0   1     0  
#>  5 demo~   1       1     1   0     1       1     0     0     0     0   1     0.5
#>  6 demo~   0       1     1   0     1       1     0     0     0     0   0     0  
#>  7 demo~   0       1     0   1     1       1     0     0     0     0   0     0  
#>  8 repu~   0       1     0   1     1       1     0     0     0     0   0     0  
#>  9 repu~   0       1     0   1     1       1     0     0     0     0   0     1  
#> 10 demo~   1       1     1   0     0       0     1     1     1     0   0     0  
#> # ... with 425 more rows, and 4 more variables: v13 <dbl>, v14 <dbl>,
#> #   v15 <dbl>, v16 <dbl>

dist_mat <- votes %>% 
  as.data.frame(select(., -1)) %>%
  as.matrix() %>%
  dist(method = "manhattan") %>%
  as.matrix()

Running classCleaner on this produces

cc_result <- classCleaner(dist_mat, votes$party)

print(cc_result)
#> classCleaner was applied to 2 of 2 classes
#> A total of 362 instances were retained out of 435 
#>            # Instances # Retained    %
#> democrat           267        217 81.3
#> republican         168        145 86.3

melissakey/classCleaner documentation built on Feb. 11, 2022, 3:33 a.m.