{"id":7644,"date":"2018-10-14T14:57:44","date_gmt":"2018-10-14T14:57:44","guid":{"rendered":"https:\/\/www.datanovia.com\/en\/?post_type=dt_lessons&p=7644"},"modified":"2018-10-21T08:26:28","modified_gmt":"2018-10-21T06:26:28","slug":"data-preparation-and-r-packages-for-cluster-analysis","status":"publish","type":"dt_lessons","link":"https:\/\/www.datanovia.com\/en\/lessons\/data-preparation-and-r-packages-for-cluster-analysis\/","title":{"rendered":"Data Preparation and R Packages for Cluster Analysis"},"content":{"rendered":"
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In this chapter, we start by presenting the data format and preparation<\/strong> for cluster<\/strong> analysis. Next, we introduce two main R packages - cluster<\/strong> and factoextra<\/strong> - for computing and visualizing clusters.<\/p>\n

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Related Book <\/a><\/h4>

Practical Guide to Cluster Analysis<\/p><\/div>\n

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Data preparation<\/h2>\n

To perform a cluster analysis in R, generally, the data should be prepared as follow:<\/p>\n

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  1. Rows are observations (individuals) and columns are variables<\/li>\n
  2. Any missing value in the data must be removed or estimated.<\/li>\n
  3. The data must be standardized (i.e., scaled) to make variables comparable. Recall that, standardization consists of transforming the variables such that they have mean zero and standard deviation one. Read more about data standardization in chapter @ref(clustering-distance-measures).<\/li>\n<\/ol>\n

    Here, we\u2019ll use the built-in R data set \u201cUSArrests\u201d, which contains statistics in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. It includes also the percent of the population living in urban areas.<\/p>\n

    data(\"USArrests\")  # Load the data set\r\ndf <- USArrests    # Use df as shorter name<\/code><\/pre>\n
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    1. To remove any missing value that might be present in the data, type this:<\/li>\n<\/ol>\n
      df <- na.omit(df)<\/code><\/pre>\n
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      1. As we don\u2019t want the clustering algorithm to depend to an arbitrary variable unit, we start by scaling\/standardizing the data using the R function scale<\/em>():<\/li>\n<\/ol>\n
        df <- scale(df)\r\nhead(df, n = 3)<\/code><\/pre>\n
        ##         Murder Assault UrbanPop     Rape\r\n## Alabama 1.2426   0.783   -0.521 -0.00342\r\n## Alaska  0.5079   1.107   -1.212  2.48420\r\n## Arizona 0.0716   1.479    0.999  1.04288<\/code><\/pre>\n<\/div>\n
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        Required R Packages<\/h2>\n
        In this book, we\u2019ll use mainly the following R packages:<\/p>\n