Curso Capacitación en R

# Curso Capacitación en R

## Sesión 1

## Reglas del juego y nociones iniciales de R y R Studio

### Mayo 2025

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Bienvenidos a la primera sesión del curso **Introducción a R**. 😄

Mi nombre es **Nicolás Olivero** y seré su tutor en este curso.

Por favor, cualquier duda, sugerencia, comentario y/o crítica enviar un correo a **nolivero@superdesalud.gob.cl** o hablarme por Teams.

.medium-par[*Entregar las nociones básicas en materia de procesos de .red[carga], .red[limpieza], .red[manipulación] y .red[visualización] de bases de datos mediante la utilización de las librerías más relevantes como **dplyr**, **ggplot2** y **R Markdown.** *]

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# Reglas del juego

Carácteristicas del curso:

- 9 sesiones de 1 hora y media los días jueves de 11 a 12:30 horas.

- Las clases utilizarán una presentación y si es que realizamos ejercicios se compartirá el script correspondiente.

- Al final de todas las clases (con excepción de la primera) se entregará una tarea que debe ser entregar antes de la siguiente clase. 
--

- Se puede faltar un máximo de 1 clase sin justificación y 2 con justificación (correo con copia a jefatura). Tres inasistencias significará reprobación.

- La principal ventaja de tomar este curso es que tiene como objetivo que cada uno de ustedes sea capaz de **automatizar** algun proceso asociado en su trabajo del día a día. A esto lo llamaremos **Proyecto en R**.

- Dado lo anterior, para poder aprobar el curso se deben cumplir **tres** condiciones: cumplir la asistencia requerida, aprobar en promedio las tareas entregadas y realizar el proyecto en R.

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# Proyecto en R

El proyecto en R tiene dos objetivos:

1. *Forzar* a que mantengan el uso constante de R para la resolución de tareas.

2. Demostrar en la práctica cómo la utilización de R permite generar procesos más eficientes de trabajo.

Tendrán desde hoy hasta la próxima semana para definir un proyecto donde se deben especificar las siguientes características:

- Breve descripción de la tarea y cómo actualmente se realiza.

- Cantidad de tiempo que requiere esta tarea.

- Propuesta de solución (a grandes rasgos).

- Luego de generar la propuesta de solución (al final del curso) calcular la ganancia en productividad.

.medium-par[**Consejo:** A pesar que este es un curso introductorio... ¡sean **ambiciosos** con su proyecto! 😎]

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# ¿Qué es R?

**R** es un lenguaje de programación con enfoque al análisis estadístico desarrollado por investigadores de la Universidad de Auckland en 1993.

## ¿Por qué (utilizar) R?

- Es gratuito, de código abierto y está disponible en todas las plataformas principales. Por lo tanto, si realiza su análisis en R, cualquiera puede replicarlo fácilmente, sin importar dónde viva o cuánto gane.

- **R** tiene una comunidad diversa y acogedora tanto online como reuniones presenciales.

- Existe una cantidad **enorme** de paquetes para modelado estadístico, aprendizaje de máquinas, visualización, e importanción y manipulación de datos. Cualquier modelo o gráfico que se busque hacer, **seguramente** alguien ya ha tratado de hacerlo y se puede aprender de ellos.

- Potentes herramientas para la comunicación y difusión de resultados como **R Markdown** y **Shiny**.

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## ¿Por qué (utilizar) R? (continuación)

- Herramientas de vanguardia donde investigadores de diversos ámbitos pueden publicar paquetes en R acompañando sus artículos y papers.

- **R** cuenta con un IDE (*integrated development environment*) construido a su medida (R Studio), facilitando su uso y manejo.

En mi visión personal:

- **R** es un lenguaje que cumple con todos los requisitos que necesita un analista para manejar profundamente datos e información.

- Posee una curva de aprendizaje muy abordable. No se necesita ser ni informático ni tener experiencia previa en programación para aprenderlo.

- Las herramientas de visualización y de difusión de información en R son de **muy alto nivel**.

Pero... **mejor mostrarles algunos casos para que me crean** 😇

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# Ejemplos de visualización

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# Más visualización...

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# Maaaaaás visualización...

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---

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# Reportería y difusión

- <https://pkgs.rstudio.com/flexdashboard/articles/examples.html>

- <https://www.ine.gob.cl/docs/default-source/índice-de-precios-al-consumidor/boletines/español/2025/boletin-índice-de-precios-al-consumidor-enero-2025.pdf?sfvrsn=47290d68_4>

- <https://www.ine.gob.cl/docs/default-source/ocupacion-y-desocupacion/boletines/2025/nacional/ene-nacional-318.pdf?sfvrsn=51dfaa0f_5>

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# Herramientas de productividad

- <https://cran.r-project.org/web/packages/tesseract/vignettes/intro.html#Read_from_PDF_files>

- Clasificación automatizada

- Script de limpieza y/o unión de diferentes bases

- Generación automatizada de presentaciones
--
 (*esta presentación está hecha en R* 😸)

- Otros

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---

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# Interfaz R Studio

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# Formas de interactuar en R 
 
--

¿Se les ocurren razones para utilizar .pur[scripts] en vez de la consola?

Para ejecutar código en *scripts* se puede utilizar el *shortcut* .pur[Ctrl + Enter].

---

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# Primera interacción: R como calculadora

``` r
10 + 7 # adición
```

```
## [1] 17
```
--

``` r
8 * 7 # multiplicación
```

```
## [1] 56
```
--

``` r
25 / 5 # división
```

```
## [1] 5
```
--

``` r
6 + 5 * 4 + 3     # sin utilizar paréntesis
```

```
## [1] 29
```

``` r
(6 + 5) * (4 + 3) # utilizando paréntesis
```

```
## [1] 77
```

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# Operadores aritméticos 1

``` r
3 + 4      # adición
```

```
## [1] 7
```

``` r
5 - 2      # sustracción
```

```
## [1] 3
```

``` r
7 * 7      # multiplicación
```

```
## [1] 49
```

``` r
56 / 8     # división
```

```
## [1] 7
```

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# Operadores aritméticos 2

``` r
4^3        # Exponenciación 1
```

```
## [1] 64
```

``` r
4**3       # Exponenciación 2
```

```
## [1] 64
```

``` r
56%%5      # ¿Qué hace?
```

```
## [1] 1
```

``` r
10%/%4     # ¿Qué hace?
```

```
## [1] 2
```

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# Operadores de comparación 1

``` r
3 == 3        # igualdad
```

```
## [1] TRUE
```

``` r
3 != 3        # no igualdad
```

```
## [1] FALSE
```

``` r
10 > 14       # mayor a
```

```
## [1] FALSE
```

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# Operadores de comparación 2

``` r
10 < 14 # menor a
```

```
## [1] TRUE
```

``` r
20 >= 19      # mayor o igual a
```

```
## [1] TRUE
```

``` r
19 <= 19 # menor o igual a
```

```
## [1] TRUE
```

---

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# Operadores lógicos

``` r
TRUE | TRUE       # O
```

```
## [1] TRUE
```

``` r
TRUE | FALSE      # O
```

```
## [1] TRUE
```

``` r
TRUE & TRUE       # Y
```

```
## [1] TRUE
```

``` r
TRUE & FALSE      # Y
```

```
## [1] FALSE
```

``` r
!FALSE             # NO
```

```
## [1] TRUE
```

---

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# Clases básicas de datos

1. `numeric`: cualquier número real, ie. .pur[1], .pur[3], .pur[4.5], `\(\pi\)`, etc.

--
2. `character`: cadena de caracteres o caracteres individuales, ie. .pur["Hola"], .pur["HoL"], .pur["H"], .pur["Hola buenos días"], .pur["78"] etc.

--
3. `integer`: cualquier número **entero**, ie. .pur[1], .pur[3], .pur[100], .pur[5420], etc.

--
4. `factor`: variables categóricas/cualitativas. Veremos su uso en próximas sesiones.

--
5. `logical`: variables booleanas/lógicas. Pueden tomar los valores .pur[TRUE] o .pur[FALSE].

--
6. `Date / POSIXct`: variables que representan tiempo y fechas de calendario. Pueden tomar los valores como .pur["2025-05-07"] o .pur["2025-05-04 16:11:54 -04"].

---

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# Conociendo la primera función: class()

La función `class()` recibe un tipo de dato y entrega la clase que lo describe. Por ejemplo:

``` r
class(2)
```

```
## [1] "numeric"
```

``` r
class("Lucas")
```

```
## [1] "character"
```

Las funciones incluidas en R tanto como las funciones incluidas en paquetes del repositorio CRAN incluyen documentación asociada para entender como funcionan.

La forma de acceder a esta documentación es mediante el uso del símbolo `?` antes de la función o utilizando la función `help()`.

---

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# Conociendo la primera función: class()

``` r
?class()
```

``` r
help("class")
```

---

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# IMPORTANTE: operador de asignación

Todo en .pur[R] es un .pur[objeto] y estos pueden ser prácticamente cualquier cosa, desde un número, letras, palabras, o incluso bases de datos enteras, gráficos complejos, summary/resumen/output de análisis estadístico, etc.

El operador de asignación está compuesto por el símbolo *menor a* `<` y un guión `-`, es decir, `<-`.

``` r
a <- 4
```

Asignando variables a estos objetos podemos realizar las operaciones vistas anteriormente.

``` r
b <- 5

c <- a + b

c
```

```
## [1] 9
```

¿Qué está ocurriendo en la parte de ambiente de R Studio cuando asignan objetos?
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# Juguemos un poco con asignaciones

.can-edit.key-likes[

]
]

.can-edit.key-dislikes[

]
]

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# Librerías y repositorios

Actualmente existen más de 22406 paquetes disponibles en el repositorio de paquetes .pur[CRAN] creado por usuarios individuales, instituciones, investigadores, etc.

En el transcurso de la capacitación utilizaremos distintos paquetes o librerías que nos aportarán con una mayor cantidad y calidad de funciones y herramientas que el .pur[R base] no posee.

Para instalar una determinada librería se utiliza la función `install.packages()` de la siguiente manera:

``` r
install.packages("tidyverse") # para instalar se deben utilizar las comillas!
```

Si todo funciona bien, veremos la librería en la pestaña .pur[packages] en la zona de utilidades de R Studio. ¿La encuentran?

Pero... luego de instalar, ¿esto quiere decir que ya podemos utilizar las funcionalidades de la librería? 
--
.red[**NO**]. 
--
Para utilizar o cargar una librería se debe correr el comando o función `library()` de la siguiente manera:

``` r
library(tidyverse) # para cargar una librería ya no necesitamos las comillas!
```

---

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# Consejos finales

- No se asusten si al correr algún código ven muchas cosas .red[rojas]. ¡Equivocarse es lo mejor que les puede pasar para así aprender y saber efectivamente lo que sucede dentro del programa!

- No ocupen Chatgpt u otro software de inteligencia artificial como primera opción. Es mejor googlear, buscar y leer la documentación y a otras personas antes que directamente preguntarle a una IA. Las IA's aún se equivocan mucho y puede que les entreguen un código que funcione, pero puede que no sea necesariamente el más eficiente o utilice funciones engorrosas y equívocas.

- Traten de al menos 10 minutos todos los días abrir un archivo en R Studio. Piensen que les gustaría hacer con él o qué requieren procesar o analizar. Traten de hacerlo, equivóquense y durante el curso vamos solucionando esas dudas.

- ¡Pásenlo bien y disfruten el curso! La idea es que aprendan **R**, lo utilicen y puedan hacer más eficiente algún proceso de su día a día y así tener más tiempo para dedicarle a otras cosas.
--
💻
--
👨‍👩‍👦‍👦 
--
⚽😴
--
🍻