We provide practical tutorials on data mining, visualization and statistics for decision making.
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This article describes how to easily set ggplot axis ticks for both x and y axes. We’ll also explain how to rotate axis labels by specifying a rotation angle. In this R graphics tutorial, you will learn how to: Change the font style (size,...
This article describes how to easily set ggplot axis ticks for both x and y axes. We’ll also explain how to rotate axis labels by specifying a rotation angle. In this R graphics tutorial, you will learn how to: Change the font style (size,...
This article describes R functions for changing ggplot axis limits (or scales). We’ll describe how to specify the minimum and the maximum values of axes. Among the different functions available in ggplot2 for setting the axis range, the coord_cartesian() function is the most preferred,...
This article describes R functions for changing ggplot axis limits (or scales). We’ll describe how to specify the minimum and the maximum values of axes. Among the different functions available in ggplot2 for setting the axis range, the coord_cartesian() function is the most preferred,...
This article describes how to change ggplot axis labels (or axis title). This can be done easily using the R function labs() or the functions xlab() and ylab(). In this R graphics tutorial, you will learn how to: Remove the x and y axis...
This article describes how to change ggplot axis labels (or axis title). This can be done easily using the R function labs() or the functions xlab() and ylab(). In this R graphics tutorial, you will learn how to: Remove the x and y axis...
This article describes how to add and change a main title, a subtitle and a caption to a graph generated using the ggplot2 R package. We’ll show also how to center the title position, as well as, how to change the title font size...
This article describes how to add and change a main title, a subtitle and a caption to a graph generated using the ggplot2 R package. We’ll show also how to center the title position, as well as, how to change the title font size...
Cluster analysis is one of the important data mining methods for discovering knowledge in multidimensional data. The goal of clustering is to identify pattern or groups of similar objects within a data set of interest. Each group contains observations with similar profile according to...
Cluster analysis is one of the important data mining methods for discovering knowledge in multidimensional data. The goal of clustering is to identify pattern or groups of similar objects within a data set of interest. Each group contains observations with similar profile according to...
Clustering methods are used to identify groups of similar objects in a multivariate data sets collected from fields such as marketing, bio-medical and geo-spatial. They are different types of clustering methods, including: Partitioning methods Hierarchical clustering Fuzzy clustering Density-based clustering Model-based clustering In...
Clustering methods are used to identify groups of similar objects in a multivariate data sets collected from fields such as marketing, bio-medical and geo-spatial. They are different types of clustering methods, including: Partitioning methods Hierarchical clustering Fuzzy clustering Density-based clustering Model-based clustering In...
This article describes k-means clustering example and provide a step-by-step guide summarizing the different steps to follow for conducting a cluster analysis on a real data set using R software. We’ll use mainly two R packages: cluster: for cluster analyses and factoextra: for the...
This article describes k-means clustering example and provide a step-by-step guide summarizing the different steps to follow for conducting a cluster analysis on a real data set using R software. We’ll use mainly two R packages: cluster: for cluster analyses and factoextra: for the...
In R software, standard clustering methods (partitioning and hierarchical clustering) can be computed using the R packages stats and cluster. However the workflow, generally, requires multiple steps and multiple lines of R codes. This article describes some easy-to-use wrapper functions, in the factoextra R...
In R software, standard clustering methods (partitioning and hierarchical clustering) can be computed using the R packages stats and cluster. However the workflow, generally, requires multiple steps and multiple lines of R codes. This article describes some easy-to-use wrapper functions, in the factoextra R...
