R and Data Mining. SEMATECH e-Handbook of Statistical Methods.
R Spatial Tips. R. Library(stringr) [1] "1 Introduction" [3] "Climate projections of the Intergovernmental Panel on Climate Change (IPCC) forecast a general increase of seasonal temperatures in the present century across the temperate zone, aggravated by decreasing amounts of summer rainfall in certain regions at lower latitudes (Christensen et al. 2007). These changes imply serious ecological consequences, especially in biome transition zones (Fischlin et al. 2007). Due to their economic importance, as well as their major contribution to supporting, regulating and cultural ecosystem services, predicted changes and shifts in temperate forest ecosystems receive wide public attention. It’s no surprise that dominant forest tree species are frequently modelled in bioclimatic impact studies (e.g., Sykes et al. 1996; Iverson, Prasad 2001; Rehfeldt et al. 2003; Ohlemüller et al. 2006).
Extr1 <- unlist(str_extract_all(txt, pattern = "\\(.*? Extr2 <- extr1[grep("[0-9]{4}", extr1)] (str_extract(extr2, "[A-Z].*[0-9]")) Visualizing Tables with plot.table. Home > R > Visualizing Tables with plot.table plot.table function in the Systematic Investor Toolbox is a flexible table drawing routine. plot.table has a simple interface and takes following parameters: plot.matrix – matrix with data you want to plotsmain – text to draw in (top, left) cell; default value is blank stringhighlight – Either TRUE/FALSE to indicate if you want to color each cell based on its numeric value Or a matrix with colors for each cellcolorbar – TRUE/FALSE flag to indicate if you want to draw colorbar Here is a few examples how you can use plot.table function to create summary reports. First, let’s load Systematic Investor Toolbox: To create basic plot.table: To create plot.table with colorbar: Next, I want to show a more practical example of plot.table function.
I will show more examples of plot.table in the future posts. To view the complete source code for this example, please have a look at the plot.table.test() function in plot.table.r at github. Like this: Polygon Overlay Analysis. Download data and R Code for this example Project Requirement: Polygon Overlay operations determine the spatial coincidence (if any) of two polygon data layers, or between polygon and point layer, usually creating a new data layer in the process. Polygon overlay techniques are often used by field scientists to explore the relationships between spatial attributes, stored as layers in a geophysical data model. Examples of polygon data are: species geographic range ranges, biomes, and watersheds. Examples of point data are: species sightings, breeding pair nest locations, or measurement stations. Three useful (and widely used) polygon overlay operations are: Intersection (logical AND): The common or shared area between two overlapping polygons.
Union (logical OR): The combined areas of two possibly overlapping polygons. Point-in-Polygon (logical AND): Between a point and polygon layer, the subset of points located within the polygon boundary. 1) What is the area of each Species Range? Workflow: