population density map us by census tract

#Population density map us by census tract series

This helps greatly with exploratory data analysis tasks where the primary audience is the analyst exploring the dataset. The attractive defaults of ggplot2 visualizations allow for the creation of legible graphics with little to no customization. The example that follows generates a scatter plot to visualize the relationship between county median age and county median household income in Georgia. This requires specification of two columns in the call to aes() as opposed to the single column used in the univariate distribution visualization examples. Scatter plots are implemented in ggplot2 with the geom_point() function, which plots points on a chart relative to X and Y values for observations in a dataset. The resulting plot then gives the analyst a sense of the nature of the relationship between the two variables. For two numeric variables, a common exploratory chart is a scatter plot, which maps values in one column to the X-axis and values in another column to the Y-axis. The optional call to options(scipen = 999) instructs R to avoid using scientific notation in its output, including on the ggplot2 tick labels.Ĥ.1.2 Visualizing multivariate relationships with scatter plotsĪs part of the exploratory data analysis process, analysts will often want to visualize interrelationships between Census variables along with the univariate data distributions discussed above. This first example uses ggplot2 and its geom_histogram() function to generate such a histogram of median household income by county in Georgia. One of the first exploratory graphics an analyst will want to produce when examining a new dataset is a histogram, which characterizes the distribution of values in a column through varying lengths of bars.

Layers are linked to the ggplot object by using the + operator. For example, geom_bar() will create a bar chart, geom_line() a line chart, geom_point() a point plot, and so forth. Essential to the plot is a geom, which specifies one of many chart types available in ggplot2.

#Population density map us by census tract series

Within the aes() function, a user can specify a series of mappings onto either the data axes or other characteristics of the plot, such as element fill or color.Īfter initializing the ggplot object, users can layer plot elements onto the plot object. Ggplot2 visualizations are initialized with the ggplot() function, to which a user commonly supplies a dataset and an aesthetic, defined with the aes() function. Finally, the chapter introduces the plotly package for interactive visualization, which can be used to convert ggplot2 objects to interactive web graphics. Chart types explored in this chapter include basic chart types faceted, or “small multiples” plots population pyramids margin of error plots for ACS data and advanced visualizations using extensions to ggplot2. This chapter includes several examples of how R users can visualize data from the US Census Bureau using ggplot2. It has powerful capacity for basic charts, group-wise comparisons, and advanced chart types such as maps. Ggplot2 is an ideal package for visualization of US Census data, especially when obtained in tidy format by the tidycensus package. ggplot2 allows R users to visualize data using a layered grammar of graphics approach, in which plot objects are initialized upon which the R user layers plot elements. Originally developed by RStudio chief scientist Hadley Wickham, ggplot2 is a widely-used visualization framework by R developers, accounting for 70,000 downloads per day in June 2021 from the RStudio CRAN mirror. The core visualization package within the tidyverse suite of packages is ggplot2 ( Wickham 2016).

Construction both affects the economy and is in turn affected by it.4 Exploring US Census data with visualization The value of the construction is also a major economic component in terms of the dollars created by industry within any area, primarily in new job creation and in the multiplier effect of those wages and salaries. If the demand for new homes is strong, this demand points to the area's growth in population, employment and earnings. The value of any construction - building or non-building - is a strong indicator of the health of the area's economy.