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R como herramienta de análisis y visualización de datos usando Inteligencia de Negocios y PowerBI

R as a data analysis and visualization tool using Business Intelligence and PowerBI

= ¹=

Edwin Fernando Mejía Peńafiel

<= /span>

https://orcid.org/0000-0001-6888-4621

<= /span>

Carrera de Estadística, Facultad de Ciencias, Escuela Superior Politécnica de Chimborazo, Riobamba, Ecuador.

efmejia@espoch.edu.ec, msmejiaedwinf@yahoo.com

=	Artículo de Investigación Científica y Tecnológi= ca Enviado: 14/05/2022 Revisado: 25/06/2022 Aceptado: 01/07/2022 Publicado:29/07/2022 DOI: https://doi.org/1= 0.33262/ap.v4i3.246
= =	=
Cítese:		Mejía Peńafiel, = E. F. (2022). R como herramienta de análisis y visualización de datos usando Inteligencia de Negocios y PowerBI. AlfaPublicaciones, 4(3), 186–208. https://doi.org/10.33262/ap= .v4i3.246
		*ALFA PUBLICACION= ES,* es una revista multidisciplinar, trimestral, que se publicará en soporte electrón= ico tiene como misión contribuir a la formación de profesionales competentes con visión humanística y crítica que sean capac= es de exponer sus resultados investigativos y científicos en la misma medida= que se promueva mediante su intervención cambios positivos en la sociedad.&nb= sp;https://alfapublicaciones.com= La revista es editada por la Editorial Ciencia Digital (Editorial de prestigio registra= da en la Cámara Ecuatoriana de Libro con No de Afiliación 663) www.celibro.org.ec=
		Esta revista está protegida bajo una licencia Creative Commons Attribution Non Commercial No Derivatives 4.0 Internatio= nal. Copia de la licencia: http://creativecommons.org/licenses/by-nc-nd/4.0/
Palabr= as claves: Prog= ramación, Programa, Metodología de programación, Estadística, Excel, PowerBI, Inteligencia de Negocios, Minería de datos.		Resumen Introdu= cción. El uso de R hoy en día en los ámbitos académic= os, estadísticos y de investigación es una herramienta con un potencial bueno= . Es importante, se puede aplicar en la ciencia básica como aplicada, dado que permite automatizar con complejidad cálculos estadísticos para poder real= izar el análisis de datos correspondiente. Objetivo. Realizar un dashbo= ard usando Excel y PowerBI con R aplicando Business Intelligence (BI) y Data = Warehouse (DW) en la presentación de informes para toma de decisiones con calidad. = Metodología. Esta investigación es descriptiva, usa la Metodología de Construcción de = un Algoritmo para Aprendizaje sistémico de estudiantes de primer semestre de TICs (Mejía et al., 2018), adaptada al uso de dashboard, la cual proporci= ona herramientas que permiten planificar e implementar un dashboard, para realizar scripts dentro del ámbito de trabajo de R. Dentro de esta metodología se insertan pasos adicionales para poder resolver mediante BI= y DW. Se realizaron pruebas con estudiantes de cuarto semestre de Estadísti= ca para determinar la resolución de problemas antes y después de usar un tip= o de software. Resultados. Los estudiantes al usar BI y DW sólo el 28,5= 7% llega a concluir de manera perfecta usando Excel, mientras que los estudiantes al usar BI llegan a un 85,71% de manera perfecta usando Power= BI, para concluir los ejercicios. Conclusión. Se concluye que bajo los parámetros de uso de Business Intelligence y Data Warehouse para la presentación de informes en la toma de decisiones los porcentajes favorec= en a estas técnicas proporcionadas por el investigador, este resultado en térm= inos estadísticos es muy favorable, dado que estas técnicas se basan en mejora= r la calidad de presentación de un informe para la web y para el gerente o presidente de una institución dentro del bienestar de la empresa. Los estudiantes con estas técnicas podrán desenvolverse de mejor manera en el ámbito profesional.
Keywor= ds: Programming, Program, Programming Methodology, Statistics, Excel, PowerBI, Business Intelligence, Data Mining.		Abstract<= /o:p> Introduction. The use of R today in the academic, statistical and research fields is a tool with good potential. It is important, it can be applied in basic and applied science since it allows complex statistical calculations to be automated to condu= ct the corresponding data analysis. Objective. Make a dashboard using Excel and PowerBI with R applying Business Intelligence (BI) and Data Warehouse (DW) in reporting for quality decision making. Methodology. = This research is descriptive, it uses the Methodology for the Construction of = an Algorithm for Systemic Learning of ICT students in the first semester (Me= jía et al, 2018), adapted to the use of a dashboard, which provides tools that allow planning and implementing a dashboard, to make scripts within the s= cope of work of R. Within this methodology additional steps are inserted to be able to solve through BI and DW. Tests were conducted with fourth-semester Statistics students to determine problem solving before and after using a type of software. Results. Students using BI and DW only 28.57% re= ach a perfect conclusion using Excel, while students using BI reach a perfect 85.71% using PowerBI, to complete the exercises. Conclusion. It is concluded that under the parameters of use of Business Intelligence and D= ata Warehouse for the presentation of reports in decision making, the percent= ages favor these techniques provided by the researcher, this result in statist= ical terms is very favorable, since these techniques are based on in improving= the quality of presentation of a report for the web and for the manager or president of an institution within the well-being of the company. Students with these techniques will be able to perform better in the professional field. &= nbsp;

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Introducción

Hoy en día es difícil imaginar que varios investigadores para presentar resultados estadísticos no lo hagan con la ay= uda de un software como R (Batanero & Diaz, 2011).

Cuando se usa un programa estadístico se debe darl= e la debida importancia, siempre en términos de la ciencia básica, así como tamb= ién en la aplicada, dado que tiene excelentes prestaciones para automatizar cálculos estadísticos dentro del análisis de datos. Los softwares de procesamiento estadístico que se tiene como base para una investigación científica ecuatoriana, como han sido los comerciales (Excel, Minitab, SPSS, Statgraphic, etc.), que se tienen que pagar por su licencia para poder usar= los. Dentro de este ámbito, existen un sin número de programas libres (PSPP, R, etc.) muy excelentes para poder realizar estas tareas de analizar datos en = una investigación propuesta (Avello & Seisdedo, 2017).

Para comprender el origen del lenguaje de programa= ción en R, es importante conocer algunos puntos históricos relevantes en su evolución. Fue creado en 1993 por los profesores e investigadores Robert Gentleman y Ross Ihaka (Hernández & Usuga, 2021).

Sin embargo, sus inicios se remontan al lenguaje previo llamado S, de John Chambers y sus colaboradores en los Laboratorios Bell, durante la década de los setenta. Cabe seńalar que desde 1995, el cód= igo fuente de R está disponible bajo licencia pública GNU para sistemas operati= vos Windows, MacOS y distribuciones Linux. Dicha licencia se mantiene con acceso libre por la Fundación R (R Foundation), y se puede ejecutar, copiar, distribuir, estudiar, modificar y mejorar sin restricción alguna (Vargas &a= mp; Mesa, 2021).

La Fundación R es una organización sin fines de lu= cro, concebida por los miembros del R

Development Core Team, cuyo objetivo es proporcion= ar un apoyo a R y las innovaciones en estadística computacional que requiera, asegurando un desarrollo continuo (R Core Team, 2020). En este manual se utiliza R para hablar del lenguaje de programación, más no la aplicación o paquete estadístico R. Esta aclaración tiene como excepción la descarga e i= nstalación del programa con el mismo nombre.

RStudio es el principal entorno de desarrollo integrado o IDE (Integrated Development Environment) para R, que es un soft= ware libre disponible para los sistemas operativos Windows, MacOS, y Linux (Equi= po RStudio, 2020).

Como puede apreciarse, una rápida mirada sobre la historia y evolución del concepto de recursión en su ámbito de aplicación original muestra que no parece haber un lugar destacado para Wittgenstein, = algo que, como veremos, no representa la verdadera aportación de este filósofo a= la Teoría de la Computabilidad.

El objetivo de es= te trabajo de investigación consiste en realizar un dashboard usando R y Power= BI con la aplicación de Business Intelligence y Data Warehouse para presentaci= ón de informes en la toma de decisiones con altísima calidad. Se realiza un an= álisis con un dashboard en Excel y el dashboard en PowerBI usando R. La investigac= ión realiza estos dos ejercicios con el mismo número de estudiantes dentro del semestre y se verifica la resolución de cada ejercicio.

Metodología

Se va a usar la Metodología de Construcción de un Algoritmo para el Aprendizaje sistémico de estudiantes de primer semestre de la asignatura de TICs (Mejía et al., 2018), que se observa en la figura 1 con pequeńos cambios en el uso de dashboard:

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Fig= ura 1=

Me<= span style=3D'font-size:12.0pt;line-height:115%;font-family:"Times New Roman",se= rif; color:black;letter-spacing:-.2pt;mso-ansi-language:ES-EC'>todo= logía para Dashboard usando R con PowerBI

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ANÁLISIS DEL SISTEMA

RECOLECCION DE INFO= RMACION

DISEŃO DEL SISTEMA

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DESARROLLO DEL SISTEMA

PRUEBAS

Fuen<= /b>te: Mejía et al. (2018)

Proyecto propuesto

Se pretende dar una solución para realizar un Dashboard usando programación estructurada basado en R con PowerBI a tra= vés de inteligencia de negocios junto con minería de datos. El uso de un programa dentro de R para este tipo de dashboard puede dar mejores resultados en la presentación, = para la toma de decisiones.

Business Intelligence (Inteligencia de Negocios)

La inteligencia de negocios (BI) combina análisis de negocios, minería de datos, visualización= de datos, herramientas e infraestructura de datos, y las prácticas recomendadas para ayudar a las organizaciones a tomar decisiones más basadas en los dato= s. En la práctica, sabes que tienes una inteligencia de negocios moderna cuando tienes una visión integral de los datos de tu organización y los utilizas p= ara impulsar el cambio, eliminar las ineficiencias y adaptarte rápidamente a los cambios del mercado o del suministro (Tableau, 2022).

Pr= ograma

Un programa es un conjunto de pasos lógicos escritos en un lenguaje de programación que nos permite realizar una tarea específica (CILSA, 2017).

Le= nguaje R

Es= te entorno es comúnmente utilizado para la computación estadística y gráfica, = ya que dispone de una amplia variedad de técnicas estadísticas (modelos lineal= es y no lineales, pruebas estadísticas clásicas, análisis de series de tiempo, clasificación, agrupamiento, etc.) y gráficas. Funciona en plataformas UNIX= y sistemas similares (incluidos FreeBSD y Linux), Windows y MacOS.

Su desarrollo actual es responsabilidad del R Development Core Team. Forma par= te de un proyecto colaborativo y abierto donde los usuarios pueden publicar paquetes que extienden su configuración básica (repositorio oficial de paquetes). Además, se puede descargar gratis a través del siguiente enlace: https://www.r-project.org/.

Da= shboard

Un= dashboard es una herramienta de gestión de la información que monitoriza, analiza y muestra de manera visual los indicadores clave de desempeńo (KPI), métricas= y datos fundamentales para hacer un seguimiento del estado de una empresa, un departamento, una campańa o un proceso específico. Contar con un panel de control centralizado es un enorme ahorro de tiempo. En lugar de tener que recopilar datos dispersos en diferentes fuentes y crear tus propios gráfico= s, los dashboards hacen todo el trabajo por ti. Solo tienes que invertir algo = de tiempo al principio para configurar las métricas y la forma de presentarlas= , y a partir de ese momento los informes se generan de manera automática. Como = se muestra en la figura 2, un dashboard creado con Data Studio por Cyberclick = (Ortiz, 2021).

Fig= ura 2

Ejemplo de dashboard<= /i>

Fuente: Ortiz (2021)

Po= werBI

Po= wer BI es un conjunto de herramientas que pone el conocimiento al alcance de to= dos y nos brinda acceder a nuestros datos de forma segura y rápida, generando grandes beneficios para nosotros y para nuestra empresa. Es un sistema predictivo, inteligente y de gran apoyo, capaz de traducir los datos (simpl= es o complejos) en gráficas, paneles o informes por sus cualidades como la capac= idad gráfica de presentación de la información, o la integración de Power Query:= el motor de extracción, transformación y carga (ETL) incluido en Excel (Clodde= d, 2022).

Po= wer BI es una solución de análisis empresarial basado en la nube, que permite u= nir diferentes fuentes de datos, analizarlos y presentar un análisis de estos a través de informes y paneles. Con Power BI se tiene de manera fácil acceso a datos dentro y fuera de la organización casi en cualquier dispositivo. Estos análisis pueden ser compartidos por diferentes usuarios de la misma organización; por lo que directivos, financieros, comerciales, etc., pueden disponer de la información del negocio en tiempo real (Clodded, 2022).=

Or= igen de datos

Po= wer BI permite conectar a cientos de orígenes de datos en la nube o entorno loc= al, creando informes con objetos integrados o creando objetos personalizados.

El acceso a los datos puede ser desde una tabla Excel, Salesforce, Dynamic CRM, Google Analytics, hasta complejas bases de datos (on-premise = o en la nube), información de servicios de Azure, etc., lo cual facilita tener t= oda la información en una única visualización (Clodded, 2022).

Por todo lo antes expuesto, esta herramienta nos permite de manera segura, tener análisis rápidos de nuestros datos, mejor colaboración en nuestros entornos= de trabajo, compartir análisis e informes en tiempo real en cualquier disposit= ivo.

Pa= ra nuestro problema usamos el instalador de PowerBI de la página de Microsoft,= tal como se muestra en la figura 3:

Fig= ura 3=

Instalador de PowerBI

Mi= crosoft Exel 365

Ex= cel es una herramienta muy eficaz para obtener información con significado a pa= rtir de grandes cantidades de datos. También funciona muy bien con cálculos sencillos y para realizar el seguimiento de casi cualquier tipo de informac= ión. La clave para desbloquear todo este potencial es la cuadrícula de las celda= s. Las celdas pueden contener números, texto o fórmulas. Los datos se escriben= en las celdas y se agrupan en filas y columnas. Esto permite sumar datos, ordenarlos y filtrarlos, ponerlos en tablas y crear gráficos muy visuales (Microsoft, 2022).

Us= aremos Excel que está dentro de office 365 la cual tenemos licencia dentro de la Escuela Superior Politécnica de Chimborazo.

Un= dashboard es una aplicación que usa business intelligence que posee una Base de datos (Excel) con toda la información que se desea presentar para r= esolver el problema planteado.

Ba= se de datos en Excel

En= la figura 4 se indica la base de datos con información de personas. En la Hoja= 1 se muestran los datos de estudiantes de cuarto ańo de estadística, con los cua= les se va a realizar nuestro dashboard usando inteligencia de negocios.

Fig= ura 4

Base de datos en Excel

Po= blación

La población que se va a usar para realizar los dos ejercicios tanto en Excel como en PowerBI es de 14 estudiantes, los mismos = que son de la asignatura de base de datos del cuarto semestre de la carrera de Estadística de la ESPOCH, Facultad de Ciencias.

Resultados

Es= quema a dar solución como prototipo en excel – Este dashboard que se va a realizar está compuesto por segmentación de datos el cual agrupa la información por CIUDAD y por SEXO, y las encadena de acuerdo con las tablas dinámicas creadas en Excel, en este informe realizado y desde el punto de vista de usuario que en este caso vie= ne hacer el docente, en su conjunto tiene varios objetos y que se muestra en varias presentaciones dentro de una misma hoja. En la figura 6 tenemos nues= tra base de datos en Excel, desde donde se inicia con la creación de una tabla dinámica, para esto se les indica a nuestros 14 estudiantes el proceso de c= ómo realizar primero esto, es decir, el proceso de inducción de conocimientos.<= i>

En la figura 5= se ha creado una tabla y un gráfico dinámicos desde la base de datos de la fig= ura 4. Aquí se indica por Ciudad, el número de estudiantes junto con su porcentaje= y un gráfico referente al mismo estudio:

Figura 5

Tabla dinámica de cantidad de estudiantes y porcen= taje por ciudad

Ahora generamos otra tabla dinámica por Sexo, indicando el número de estudiantes y su porcentaje, como se muestra e= n la figura 6:

Figura 6

Tabla dinámica de cantidad de estudiantes y porcen= taje por Sexo

Ahora procedemos a crear otra tabla dinámica con medidas de tendencia central de nuestros estudiantes con respe= cto a los parciales, como se muestra en la figura 7:

Figura 7

Tabla dinámica con medidas de tendencia central con parciales

Con las tablas y gráficos dinámicos creados en las anteriores figuras, procedemos a generar el dashboard= de la siguiente manera, como se muestra en la figura 8, para esto usamos segmentación de datos dentro de Excel y finalmente el dashboard crea= do tenemos de la siguiente manera:

Figura 8

Dashboard en Excel

Como se puede observar tenemos 2 tipos= de segmentación como son CIUDAD y SEXO. Los cuales permiten seleccionar datos a través de la tabla dinámica en Excel para visualizar información valiosa co= mo notas máximas y mínimas, promedio, etc. de los estudiantes y que en este ca= so permite tomar decisiones al docente.

Dashboard – PowerBI con R – Este dashboard que se va a realizar está compuesto por segmentación de datos el cual agrupa la información y las encadena de acuerdo al algoritmo que internamente maneja PowerBI, en este informe realizado y desde el punto de vista de usuario que= en este caso viene hacer el docente, en su conjunto tiene varios objetos y que <= /span>se muestra en varias presentaciones dentro de una misma hoja, los cuales muestr= an en las mismas basados en la segmentación antes dicha, algunas presentaciones se han plasmado de manera ideal para que muestre la información necesaria, que nos lleve a la toma de decisiones dentro de este ámbito de notas parciales dentro del semestre en curso. Además, se usa varios objetos de presentación basados en R para presentación de resultados usando programación. La metodología de construcc= ión del dashboard está bajo scripts de R, se tiene programación estructu= rada basada en paquetes y librerías de este lenguaje.

Importación de datos – Primero al ingresar a PowerBI, realizamos la importación de nuestros datos desde Excel. Tal cual como se muestra en la figura 9, se debe hacer clic en Inicio, obtener datos y se es= coge libro de Excel.

Ahora se debe indicar cual es el archi= vo que tiene esa información para proceder a importarlo, como se muestra en la figura:

Figura 9

Escoge la base de datos desde Excel<= span style=3D'font-size:12.0pt;line-height:115%;font-family:"Times New Roman",se= rif; mso-ansi-language:ES-EC'>

De esta manera se obtiene que la base = de datos se encuentra dentro de PowerBI.

Se= gmentación – Para poder agrupar las ciudades se = usará el siguiente objeto, que se inserta en la página para poder seleccionar y v= er los datos estadísticos que hace referencia.

En la figura10 se tiene el objeto y de donde se inserta:

Figura 10

Objeto Segmentación de datos en PowerBI=

La utilidad de la segmentación de los datos dentro de PowerBI es que automáticamente los otros objetos que se insertan se agrupan basados en lo = que se escoge en este objeto. Entonces, la búsqueda de la información correspon= derá a la manera y forma que nosotros tengamos referente con el mismo. En ambien= tes de prueba realizados nos ha dado buenos resultados con este informe diseńad= o a base de presentación de resultados para toma de decisiones dentro del ambie= nte de notas de parciales en una institución educativa.

In= fraestructura del proyecto – Para este proyecto utilizaremos la figura 11 en la cual se muestra como est= aría hecho nuestro dashboard. La idea principal es presentar una solución de inf= orme a través del uso de los diferentes objetos de PowerBI usando inteligencia de negocios junto con la programación en R, dando mayor fluidez a la forma de visualización

Figura 11

Sistemas interconectados del proyecto

Data Warehouse

Objetos estadísticos

Segmentación

Consultas extras

Datos procesados

Consulta datos

Base de datos

Script de R

Data Mining

de cada uno de los objetos y agrupando en base a lo que se hizo dentro de Exce= l en el anterior ejemplo y dependiendo siempre del conocimiento que se tenga respecto a R. El dashboard de la pagina1 como se observa en la figur= a11 está compuesto por 5 objetos principales: la segmentación en si por donde s= e va agrupar la información y el informe de resultados con las interfaces debidas   para   cada   uno de los datos estadísticos que se requiere, y la base de datos de donde se tomarán los datos y los diferentes almacenes de= datos para poder procesar la información y así poder obtener el informe deseado q= ue es visualizar un diagnóstico de los estudiantes respecto a notas dentro de = los parciales en una asignatura.

El= proceso del diseńo del dashboard dentro de PowerBI se debe seguir como se muestra en la anterior figura, para que se encuentren bien conectados los objetos con la base de datos, PowerBI usando inteligencia de negocios hace = de esto una manera muy fácil de presentar los resultados y además ańade un obj= eto extra que es el uso de R dentro del mismo para programar un script en base = a lo que se necesita dentro del mismo informe.

Ca= racterísticas del proyecto

El dashboard tiene las siguientes características que se muestran en la tabla 1:=

Tabla 1

Dashboard para docentes

Desempeńo

Características

Reflejo de una estructura

Cuando se diseńa un dashboard se debe tener de manera clara donde encajan los objetos.

Apoyo a diagnóstico

Debe apuntar a la probable fuente del problema. Ayudar de manera simple a ver qué es lo que pasa en el negocio.

Presenta simple datos          =

= Toda la información debe ser leída de manera clara y a tiempo.

Actualización continua      <= /p>

Tener actualizada la información siempre es crítica, pero hay que hacerla, para generar mejores prestaciones en los resultados.

Orientada acción específica

Pensar en el diseńo cuidadoso de la presentación de un informe hará del mismo que sea funcional.

Programación en R dentro de PowerBI

La instalación por defecto d= e R incluirá ciertos módulos, pero resultará imperativo el obtener funcionalida= des extras. Estas importaciones se pueden realizar directamente desde la consul= ta R que se está ejecutando a través de la utilización del comando:

install. package= s (‘[NOMBRE MODULO]’)

Todos los módulos de R disponibles pueden hallarse en Available CRAN Packages By Name

Problemas de permisos

Es frecuente encontrarse con= dificultades de permisos al querer instalar módulos de R dentro Power BI. Estos errores = en los permisos se deben a que la carpeta library (carpeta en donde se guardan= las referencias de los módulos importados) de R no está habilitada para escritu= ra por defecto. Para solucionar el incidente, debemos modificar la seguridad d= e la carpeta y habilitar la escritura de esta (Torteloro, 2022).

Módulos y sentencias

Las consultas R, a menudo incluyen sentencias de requiere, las cuales especifican que para ejecutar la porción de código contiguo es necesario utilizar uno o más módulos específi= cos.

En el siguiente ejemplo, se utiliza la sentencia install. package, para que se instale el paquete reque= rido por R para su ejecución, la importación del paquete readxl (paquete que habilita funcionalidades de manejo de Excel dentro de R). Y, además, el uso= del paquete ggplot2 para poder graficar lo que necesitamos en PowerBI. En la fi= gura12 se indica el script que se ejecuta para poder realizar gráficos de correlac= ión:

Figura 12

Script en R para realizar gráficos

=

Como se puede ver en RStudio usando R puedo grafic= ar una correlación referente a los dos parciales en mención usando el paquete = “performance analytics” y se usa los datos de las notas del primero y segundo parcial pa= ra poder realzar este cálculo.

Procesamiento de la información en PowerBI con R

Power BI ofrece un script de entrada donde se prog= rama las líneas de código de R, el uso de un data frame es decir una tabla dentr= o de R es tratado como un dataset, el cual PowerBI ya lo tiene insertado en el momento que nosotros importamos nuestra base de datos y puede ser accedido = solo invocándole como dataset. En la figura13 se muestra cómo se realiza el scri= pt de R dentro de PowerBI.

Figura 13

Script en R en PowerBI para realizar gráfico de correlación

En este paso, hemos convertido la tabla en un frame el cual está guardado en la variable dataset. El siguiente paso ser= á presentar la información de las entradas a través de la utilización del objeto correspondiente dentro de PowerBI. Como se muestra en la figura14:

Figura 14

Gráfico de correlación en PowerBI

Como se puede observar, se tiene una gráfica tal c= ual como se hizo en RStudio usando R dentro de PowerBI.

Figura 15

Script en R para gráfico de cajas

Una vez modeladas las entradas usando el objeto segmentación de datos para CIUDAD, especificamos el valor de retorno datase= t, de forma de que se auto genere el gráfico de correlación deseado, con las entradas modeladas y listas para ser utilizadas en el modelo.

Se realiza un diagrama de cajas dentro de R, tal c= ual como se observa en la figura16 y usando el paquete ggplot2.

Dentro de PowerBI usamos el objeto de R que nos permite generar el script necesario para poder plasmar el código de R y pod= er visualizar los resultados deseados. Como se muestra en la figura16:

Figura 16

Gráfico de cajas en PowerBI

Tabla 2

Resultados de la elaboración del trabajo por parte de los estudiante= s de cuarto semestre

Observación

Población

Cantidad Estudiant= es

Concluye

Cantidad Estudiant= es

No Concluye

Excel

14

4

28,57%

10

71,43%

PowerBI=

14

12

85,71%

2

14,29%

En la tabla 2 se observa los porcentajes de cómo se concluye el trabajo por parte de los estudiantes usando Excel y PowerBI. Los datos reflejan que el 28,57 % concluyen usando Excel y el 85,71 concluyen usando PowerBI.

En la figura 17 se muestra los porcentajes de como= los estudiantes concluyen o no el trabajo pedido que es el dashboard.

Figura 17

Estudiantes concluyen su trabajo del d= ashboard

Los estudiantes al usar Business Intelligence y Da= ta Warehouse sólo el 28,57% llega a concluir de manera perfecta usando Excel, mientras q= ue los estudiantes usando BI llegan a un 85,71% de manera perfecta usando Powe= rBI, para concluir los ejercicios.

Los estudiantes al usar Business Intelligence y Da= ta Warehouse el 71,43% NO llega a concluir de manera perfecta usando Excel, mientras que= los estudiantes usando BI, el 14,29% NO concluye de manera perfecta usando Powe= rBI.

Disc= usión

El dashboard creado en Excel se lo hizo manejando también la metodología, así como el dashboard creado en PowerBI usando R. C= omo se muestra en la figura18:

Figura 18

Dashboard en Excel (izquierda) y dashboard en PowerBI (derecha)=

En la parte izquierda se muestra el dashboard en E= xcel y en la parte derecha se muestra el dashboard en PowerBI. Como se puede observar se tiene similitud respecto a la forma de presentar la información. Pero la parte ejecutiva y un plus extra lo lleva el uso de R dentro de Powe= rBI para estos fines como es el grafico mejorado y presentación de resultados c= on mejor calidad.

El dashboard en Excel nos ayuda de manera clara y = en mayor tiempo de realización de este, la visualización de resultados de mane= ra fiable y correcta usando tablas dinámicas de calidad en cada fase del diseń= o, por supuesto se puede programar con visual basic dentro de su entorno, pero est= o lo haremos en un próximo artículo. A pesar de esto el diseńo y la presentación= de resultados está dando los frutos requeridos, ya que algunos docentes le gus= tan esta alternativa de presentación.

El dashboard en PowerBI nos ayuda también a presen= tar de manera clara y con un tiempo menor al de Excel, pero como sabemos tiene = el uso de R dentro del mismo software y eso ya hace que tenga un plus extra en cuanto a visualización de la información estadística para la toma de decisi= ones en ambientes muy complejos.

Se presentó un pequeńo problema y es que hay que s= aber configurar los totales y los data frame dentro de PowerBI, así como hay que saber realizar las respectivas tablas dinámicas dentro de Excel, los dos softwares tienen pro y tienen contra, pero nos llevan a una presentación de resultados con calidad. Hay que prever que cada data frame creado en R cump= la con sus términos deseados; por eso se tomó la situación de realizar data fr= ame únicos que son nuestros almacenes de datos dentro de PowerBI.

Como trabajos futuros se propone realizar estudios= más profundos con otros tipos de software como por ejemplo Python y en ambiente= s de trabajo sobre ventas en internet.

C<= /b>onclusiones

<= span style=3D'mso-list:Ignore'>•&nb= sp;        En este artículo = se ha realizado un análisis de los dashboard en Excel y PowerBI usando R para = la presentación de resultados con inteligencia de negocios basado en scripts p= ara programar el código necesario y visualizar los resultados deseados en el problema de notas de estudiantes de una asignatura de la carrera de Estadística dentro = de la ESPOCH.

<= span style=3D'mso-list:Ignore'>•&nb= sp;        El uso = de dashboard es de gran = ayuda para este tipo de toma de decisiones, pero hay que saber escoger el mejor prototipo, al momento de desarrollarlo.

<= span style=3D'mso-list:Ignore'>•&nb= sp;        Los problemas por donde hemos ido en este campo de la creación del dashboard fueron algunos, pero no complejos = ya que en el camino se fue encontrando las soluciones adecuadas.

<= ![if !supportLists]>•         Los estudiantes al usar Business Intel= ligence y Data Warehouse sólo el 28,57% llega a concluir de manera perfecta usando Excel, mientras que los estudiantes usando BI llegan a un 85,71% de manera perfecta usando PowerBI, para concluir los ejercicios.

<= ![if !supportLists]>•         Los estudiantes al usar Business Intel= ligence y Data Warehouse el 71,43% NO llega a concluir de manera perfecta usando Ex= cel, mientras que los estudiantes usando BI, el 14,29% NO concluye de manera perfecta usando PowerBI.

<= span style=3D'mso-list:Ignore'>•&nb= sp;        El dashboard en Excel en la medición respecto al tiempo es mayor (2 horas) y a la presentación de resultados es muy fácil y sencilla, en cambio en la construcción del dashboard en PowerBI se hizo en 1 hora con 15min y también de manera fácil. Con esto se obtiene que el dashbo= ard en PowerBI usando R se lo puede realizar en menos tiempo y con mayor detall= e.

•         La importancia radica que basado en el desarrollo de este dashboard, <= /span>el docente puede procesar alguna información y tomar la debida decisión como, por ejemplo, que estudiantes deben acudir a tutorías más días y que estudiantes pueden ayudar en las clases para ayudar con la asignatura. Considero que es un dashboard muy útil para que el docente pueda tomar decisiones.

Referencias Bibliográficas

Avello, R. & Seisdedo, A. (2017). El procesami= ento estadístico con R en la investigación científica. Medisur – Revista en Inte= rnet, 15(5), 583-586.

Batanero, C. & Diaz, C. (= 2011). Estadística con Proyectos. ReproDigital. Facultad de Ciencias.

CILSA. (2017). żQué es un pro= grama? Tecnología inclusiva.

<= span lang=3DES>https://desa= rrollarinclusion.cilsa.org/tecnologia-inclusiva/que-es-un-programa/#:~:text= =3DUn%20programa%20es%20un%20conjunto,cual%20interactuamos%20con%20la%20apl= icaci%C3%B3n.

Clodded, J. (2022). Qué es Po= wer BI. Deloitte Touche Tohmatsu Limited.

<= span lang=3DES>https://www2= .deloitte.com/es/es/pages/technology/articles/que-es-power-bi.html

Equipo RStudio. (2020). RStud= io: desarrollo integrado para R. RStudio, PBC, Boston, MA http://www.rstudio.com/

Hernández, F. & Usuga, O. (2021). Manual de R. https://fher= nanb.github.io/Manual-de-R/

Mejía, F., Rosero, R., Luna, = W. y Villa, E. (2018). Metodología de Construcción de un Algoritmo para el Aprendizaje Sistémico de Estudiantes de Primer Semestre de la Asignatura TICs. Ingeniería KnE, 3(9), 221–234. =

<= span lang=3DES>https://doi.= org/10.18502/keg.v3i9.365

Microsoft. (2022). Tareas bás= icas en Excel. Soporte de Microsoft. https://supp= ort.microsoft.com/es-es/office/tareas-b%C3%A1sicas-en-excel-dc775dd1-fa52-4= 30f-9c3c-d998d1735fca

Ortiz, D. (2021). żQué es un = dashboard y para qué se usa? Cyberclick Agent S.L. - R.M Barcelona, Tomo 32.063, Folio 52, Hoja B-201.583, Inscripción 1 NIF B-62084959.

          https://www.= cyberclick.es/numerical-blog/que-es-un-dashboard

R Core Team. (2020). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Tableau. (2022). żQué es la inteligencia de negoci= os? Tu guía para la BI y por qué es importante. https://www.tableau.com/es-mx/lea= rn/articles/business-intelligence

Torteloro, B. (2022). Referenciar R desde Power BI= .

https://www.compartimoss.com/revi= stas/numero-30/usando-r-script-en-power-bi/

Vargas, L. & Mesa, E. (20= 21). Introducción al análisis de datos con RStudio. Estudio 45-8 S.A.S

El artículo que se publica es de exclusiva responsabilidad de los autores y no necesariamente reflejan el pensamiento de la Revista Alfa Publicaciones.

El artículo queda en propiedad de la revista y, por tanto, su publicación parcial y/o total en otro medio tiene que ser autorizado por el director de la Revista Alfa Publicaciones.

=

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ISSN: 2773-7330

Vol. 4 No. 3. pp.= 186 – 208. julio-septiembre 2022

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                                      Vanguardia                             Página 186 | 1

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Desempeńo	Características
Reflejo de una estructura	Cuando se diseńa un dashboard se debe tener de manera clara donde encajan los objetos.
Apoyo a diagnóstico	Debe apuntar a la probable fuente del problema. Ayudar de manera simple a ver qué es lo que pasa en el negocio.
Presenta simple datos =	= Toda la información debe ser leída de manera clara y a tiempo.
Actualización continua <= /p>	Tener actualizada la información siempre es crítica, pero hay que hacerla, para generar mejores prestaciones en los resultados.
Orientada acción específica	Pensar en el diseńo cuidadoso de la presentación de un informe hará del mismo que sea funcional.

Observación	Población	Cantidad Estudiant= es	Concluye	Cantidad Estudiant= es	No Concluye
Excel	14	4	28,57%	10	71,43%
PowerBI=	14	12	85,71%	2	14,29%