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How to analyze the ggplot2 tree diagram, in view of this problem, this article introduces the corresponding analysis and solution in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible method.

In August 2017, the R language update package silently added new geometric objects that support ggplot2 tree diagrams. From then on, tree diagrams were made in R language without the help of third-party packages.

The package is available in both an official release on Cran and a development version hosted on GitHub, and is installed as follows:

CRAN:

Install.package ("treemapify")

Github:

Devtools::install_github ("wilkox/treemapify")

GitHub Home Page:

Https://github.com/wilkox/treemapify

Load the expansion pack required for this article:

Library ("ggplot2")

Library ("treemapify")

Library ("tweenr")

Library ("gganimate")

Library ("RColorBrewer")

After installing the package, you will have an extra tree geometry object in your ggplot2-geom_treemap ().

In fact, I once shared a tree view case, but I used a third-party auxiliary package to make it, waiting for ggplot2 to come up with the tree view layer.

R language data Visualization-- TreeMap

This case uses the built-in dataset in the (treemapify) package:

Preview the dataset structure:

Str (G20)

Head (G20)

The data set describes the economic indicators of the countries participating in the 20 summit and contains five fields, namely, the global region, the name of the country (country), the GDP index (gdp_mil_usd) (which should be a secondary indicator), the human development index (hdi), and the degree of economic development (econ_classification).

Field name Typ

Region factor type

Country factor type

Gdp_mil_usd numeric (integer)

Hdi numeric (floating point)

Econ_classification factor type

The tree view is a kind of special graph without explicit coordinate system. Depending on the square algorithm, the total square of the sample is divided into a single rectangular square according to the proportion of the actual observed value to the total. Therefore, it needs at least one numerical variable as the input parameter.

A simple tree view:

Ggplot (G20, aes (area = gdp_mil_usd)) +

Geom_treemap ()

Because area simply defines the square size of a numeric variable, the fill color can be defined separately. But color can also be used as a separate expression of a numerical metric.

Ggplot (G20, aes (area = gdp_mil_usd)) +

Geom_treemap (fill= "steelblue")

Ggplot (G20, aes (area = gdp_mil_usd, fill = hdi)) +

Geom_treemap () +

Scale_fill_distiller (palette= "Greens")

Add tags:

The package author wrote the optimized text label function geom_treemap_text for the ggplot tree view (it was said at the beginning that the tree view went beyond the scope of the traditional three coordinate systems, there was no explicitly declared coordinate system, and the algorithm was so special that it was impossible to add tags using conventional geom_text ()).

Ggplot (G20, aes (area = gdp_mil_usd, fill = hdi, label = country)) +

Geom_treemap () +

Geom_treemap_text (fontface = "italic", colour = "red", place = "centre", grow = TRUE,alpha=.6) +

Scale_fill_distiller (palette= "Greens")

The place parameter controls the position of the label relative to the surrounding area in each square, while grow controls whether the label adapts to the size of the square.

Secondary subgroups (subgroups):

This package supports sub-grouping (technically called subgroups), which is widely used in practical scenarios. For example, when we observe the size of country metrics, we also want to obtain the overall metrics of the regions to which the country belongs. We can obtain two-dimensional information by adding secondary groups. By setting the subgroup parameter (a category variable) in the aesthetic mapping, the variable aggregation calculation of the subgroup can be completed automatically within the function, and the size of the secondary category can be shown with a frame line in the figure formation.

Ggplot (G20, aes (area = gdp_mil_usd, fill = hdi, label = country,subgroup = region)) +

Geom_treemap () +

Geom_treemap_subgroup_border () +

Geom_treemap_subgroup_text (place = "centre", grow = T, alpha = 0.5, colour = "black", fontface = "italic", min.size = 0) +

Geom_treemap_text (colour = "red", place = "topleft", reflow = Treco alpha. 5) +

Scale_fill_distiller (palette= "Greens")

The reflow parameter is used to control whether the label adapts to the size of the rectangular block. If the original size exceeds the rectangular block, it will automatically wrap.

Facet system:

When you feel that you can't get a good visual rendering with secondary grouping, geom_treemap also supports the fact_grid facet parameter in the ggplot function, which is the advantage of all ggplot2 extension functions and can inherit advanced graphic properties derived from ggplot2.

Ggplot (G20, aes (area = gdp_mil_usd, fill = region, label = country)) +

Geom_treemap () +

Geom_treemap_text (grow = T, reflow = T, colour = "black") +

Facet_wrap (~ econ_classification) +

Scale_fill_brewer (palette= "Blues") +

Labs (

Title = "The Gmur20 major economies"

Caption = "The area of each country is proportional to its relative GDP within the economic group (advanced or developing)"

Fill = "Region") +

Theme (legend.position = "bottom"

Plot.caption=element_text (hjust=0))

GIF animation stream:

With the help of the difference method provided by the tweened package, you can append the cross-section data at different time points and compose a group of animation frames that can be recognized by the gganimate function. The length of the change, the stay time of each frame and the number of frames are controlled by the parameters of tweenlength, statelength and nframes.

G20_alt

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