Como organizar gráficos superpuestos en R

Como es bien sabido, la organización de datos es fundamental para la organización de toma de decisiones y el entendimiento de los diferentes tipos de realidades( dependiendo el tema),  y es por ello que con R studio se ha empezado a implementar diferentes tipos de gráficas:

i) Tipo Pastel: Estos gráficos estadístico muestra proporciones, porcentajes y divisiones, es muy popular porque sirve para comparación entre métricas de poblaciones o fenómenos estudiados. Según varios estudios, este gráfico es poco recomendado  debido a la apreciación humana a las longitudes.

Supongamos el siguiente comando sobre debilidad política ( institucional) para tener en cuenta una aplicación en R. Hablaremos de dictaduras y quiebres a la ley por orden de importancia, supongamos el siguiente cuadro

Fuente: Daniel Jimenez. Manual para Econometria con R. (2015) Free.

Fuente: Daniel Jimenez 2015

En el siguiente blog desarrollaremos otro estilo de gráficas, y como superponerlas.

Recomendado sobre escritura de textos academicos

Hola a todos, en estos días se hace importante tanto para la investigación como para la academia, la escritura, que a veces es tan complicada como poética, los extremos, lo sé , por ello le recomendamos el curso de como elaborar textos académicos para humanidades y ciencias sociales, descrito en el siguiente enlace : https://www.miriadax.net/web/general-navigation/cursos

What is R?

I did miss for long time, but the job.......... u know!!
In this opportunity , i wanna talk about R, but in words of their own website! ( Not my words) because in essential , http://www.r-project.org, have a vision more  accurate.

What is R?Introduction to RR is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

One of R's strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

R is available as Free Software under the terms of the Free Software Foundation's GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

The R environmentR is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes

• an effective data handling and storage facility,
• a suite of operators for calculations on arrays, in particular matrices,
• a large, coherent, integrated collection of intermediate tools for data analysis,
• graphical facilities for data analysis and display either on-screen or on hardcopy, and
• a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.

The term "environment" is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.

R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.

Many users think of R as a statistics system. We prefer to think of it of an environment within which statistical techniques are implemented. R can be extended (easily) via packages. There are about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet sites covering a very wide range of modern statistics.

R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hardcopy.


taken:http://www.r-project.org