clust431

The goal of clust431 is to provide two main functions: k_means(), which performs clustering on a given dataset using a basic k means algorithm hclust(), which …

Installation

You can install the released version of clust431 from GitHub with:

install_github("josh-rowe/lab-7-clustering-methods-josh-rowe")

Functions

k_means()

Inputs

dat a dataframe where the first two columns are x and y coordinates

k a number describing the number of clusters to search for

pca a boolean which allows the user to have k_means() perform pca on the data before running the k means algorithm (defaults to FALSE)

max_iter a number describing the maximum number of iterations for k_means() to run through if convergence is not met (defaults to 100)

Outputs

A list with two elements:

groups a dataframe with the x and y values of the original data and a factor which contains the grouping assignment of each observation

tss the total sum of squares of the final grouping assignment

Example

This is a basic example which shows you how to solve a common problem:

library(clust431)

dat <- data.frame(x = c(1, 2, 3, 10, 11, 12, 20, 21, 22),
                  y = c(1, 2, 3, 10, 11, 12, 20, 21, 22))

dat_groups <- k_means(dat = dat, k = 3)

hier_clust()

Inputs

data a numeric data frame

cluster a number describing the number of clusters to search for

Outputs

A data frame with two columns and a number of rows corresponding to the inputted data frame:

index index row number corresponding to the inputted data set

clusters cluster assignment for index

hier_clust() Example

This is a basic example which shows you how to solve a common problem:

library(clust431)

data <- iris %>% dplyr::select(-Species)
cluster <- 3

hierarchical <- hier_clust(data, cluster)
tail(hierarchical)
#>     index clusters
#> 145   145        3
#> 146   146        3
#> 147   147        3
#> 148   148        3
#> 149   149        3
#> 150   150        2
table(hierarchical$clusters)
#> 
#>  1  2  3 
#> 50 51 49

What is special about using README.Rmd instead of just README.md? You can include R chunks like so:

summary(cars)
#>      speed           dist       
#>  Min.   : 4.0   Min.   :  2.00  
#>  1st Qu.:12.0   1st Qu.: 26.00  
#>  Median :15.0   Median : 36.00  
#>  Mean   :15.4   Mean   : 42.98  
#>  3rd Qu.:19.0   3rd Qu.: 56.00  
#>  Max.   :25.0   Max.   :120.00

You’ll still need to render README.Rmd regularly, to keep README.md up-to-date.

You can also embed plots, for example:

In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub!