Introducción a Rpara la Epidemiología Aplicada

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# Introducción a R para </br> Epidemiología Aplicada

### Caso práctico de ébola y limpieza de datos

[contact@appliedepi.org](mailto:contact@appliedepi.org)

---

# Objetivos y calendario

- Crear un nuevo proyecto de RStudio para el caso práctico de ébola
- Leer o importar los datos de una subcarpeta del proyecto utilizando `import()` y `here()`
- Familiarizarte con funciones de limpieza de datos {dplyr}
- Empezar a escribir un comando de limpieza utilizando el operador 'pipe' `%>%`

</br>

<div class="tabwid"><style>.cl-ec1f483a{}.cl-ec1b67b0{font-family:'Helvetica';font-size:11pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-ec1cf4a4{margin:0;text-align:left;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-ec1d06ba{width:1.033in;background-color:transparent;vertical-align: middle;border-bottom: 1.5pt solid rgba(102, 102, 102, 1.00);border-top: 1.5pt solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-ec1d06d8{width:2.825in;background-color:transparent;vertical-align: middle;border-bottom: 1.5pt solid rgba(102, 102, 102, 1.00);border-top: 1.5pt solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-ec1d06e2{width:1.033in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-ec1d0700{width:2.825in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-ec1d070a{width:1.033in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-ec1d0714{width:2.825in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-ec1d071e{width:1.033in;background-color:transparent;vertical-align: middle;border-bottom: 1.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-ec1d0728{width:2.825in;background-color:transparent;vertical-align: middle;border-bottom: 1.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-ec1d0732{width:1.033in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-ec1d073c{width:2.825in;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}</style><table data-quarto-disable-processing='true' class='cl-ec1f483a'><thead><tr style="overflow-wrap:break-word;"><th class="cl-ec1d06ba"><p class="cl-ec1cf4a4"><span class="cl-ec1b67b0">Time</span></p></th><th class="cl-ec1d06d8"><p class="cl-ec1cf4a4"><span class="cl-ec1b67b0">Topic</span></p></th></tr></thead><tbody><tr style="overflow-wrap:break-word;"><td class="cl-ec1d06e2"><p class="cl-ec1cf4a4"><span class="cl-ec1b67b0">10 minutos</span></p></td><td class="cl-ec1d0700"><p class="cl-ec1cf4a4"><span class="cl-ec1b67b0">Introducir el caso de estudio de ébola</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-ec1d070a"><p class="cl-ec1cf4a4"><span class="cl-ec1b67b0">20 minutos</span></p></td><td class="cl-ec1d0714"><p class="cl-ec1cf4a4"><span class="cl-ec1b67b0">Funciones para limpieza de datos</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-ec1d070a"><p class="cl-ec1cf4a4"><span class="cl-ec1b67b0">10 minutos</span></p></td><td class="cl-ec1d0714"><p class="cl-ec1cf4a4"><span class="cl-ec1b67b0">Demo de limpieza de datos</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-ec1d070a"><p class="cl-ec1cf4a4"><span class="cl-ec1b67b0">2 horas</span></p></td><td class="cl-ec1d0714"><p class="cl-ec1cf4a4"><span class="cl-ec1b67b0">Ejercicio</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-ec1d071e"><p class="cl-ec1cf4a4"><span class="cl-ec1b67b0">20 minutos</span></p></td><td class="cl-ec1d0728"><p class="cl-ec1cf4a4"><span class="cl-ec1b67b0">Devolución</span></p></td></tr></tbody><tfoot><tr style="overflow-wrap:break-word;"><td  colspan="2"class="cl-ec1d0732"><p class="cl-ec1cf4a4"><span class="cl-ec1b67b0">Toma las pausas que necesites durante el ejercicio</span></p></td></tr></tfoot></table></div>

???
Fíjate en las pausas de estiramiento.

---

# Orden del día

- **Proyectos RStudio** - un hogar para los datos y scripts de un análisis concreto

- Ejecutar comandos en **un script de R** usando comentarios

- Crear **objetos** con el operador de asignación **`<-`**

- Utilizar **funciones** como `max()`, `min()` y `paste()`

- Importar un conjunto de datos con **`import()`**

- Revisar un conjunto de datos con `skim()` y `summary()`

- Comprobar la **clase** de las columnas `class()`

---

# El caso de estudio: el brote de ébola

Los módulos 2 a 9 utilizarán datos de un brote simulado de ébola en Sierra Leona.

---

# Un nuevo proyecto de RStudio

El ejercicio te guiará para crear un nuevo **proyecto de RStudio** en la carpeta "intro\_curso/**ebola**/".

]

📁 intro\_curso

- 📁 modulo1
- 📂 covid
- **📁 ebola**
  - **ebola.Rproj**
  - 📂 datos
  - 📁 resultados
  - 📂 scripts

]

---

# Un nuevo Script de R

Escribirás un nuevo script de R llamado "ebola\_analisis.R" para guardar tus comandos.

El script se guardará en la subcarpeta "ebola/**scripts**/"

]

📁 intro\_curso

- 📂 modulo1
- 📁 covid
- 📁 **ebola**
  - **ebola.Rproj**
  - 📁 datos
  - 📁 resultados
  - 📂 **scripts**
    - **analisis\_ebola.R**

]

---

# Cargar paquetes

¿Cuál será tu primer comando en el nuevo script de R? ¿Qué función utilizará?

Utiliza **`pacman::p_load()`** para **cargar los paquetes** necesarios para el análisis

``` r
pacman::p_load(
     rio,          # para importar datos
     here,         # para rutas relativas para localizar arhivos
     skimr,        # para revisar los datos
     janitor,      # para limpieza de datos
     epikit,       # para crear categorías de edad
     tidyverse     # para manejo de datos y visualización
)
```

---

# Importar desde una subcarpeta

El listado de casos de ébola se guarda en la subcarpeta "**datos**/**brutos**/":

📁 **ebola**
- ebola.Rproj
- 📂 **datos**
  - 📂 limpios
  - 📁 **brutos**
    - **surveillance\_linelist\_20141201.csv**
- 📂 scripts
- 📂 resultados

`import()` espera un *ruta de archivo*: la ubicación o "dirección" de los datos

¿Funcionará este comando para importar el listado de casos de ébola?

``` r
import("listado_vigilancia_20141201.csv")
```

**No**, tienes que especificar en qué *subcarpeta* del proyecto se guardan los datos.

---

# Rutas de los archivos

**Evita** la ruta de archivo "absoluta" por ser frágil *(sólo funciona en un ordenador)*

``` r
import("C:/Users/Me/Docs/intro_course/ebola/datos/brutos/listado_vigilancia_20141201.csv")
```

**En un proyecto de RStudio** la ruta puede empezar en la carpeta raíz del proyecto

``` r
import("datos/brutos/listado_vigilancia_20141201.csv") # funciona casi en cualquier ordenador
```

**Utiliza `here()` para crear la ruta del archivo** sin barras

`here("datos", "brutos", "listado_vigilancia_bruto.csv")`

**El paso final** es colocar la ruta del archivo del comando `here()` *dentro* de `import()`

``` r
vig_bruta <- import(here("datos", "brutos", "listado_vigilancia_20141201.csv"))
```

*El operador de asignación `<-` guarda el conjunto de datos como un objeto con el nombre `vig_bruta`.*

???
Los enseñamos aquí() porque elimina la necesidad de manejar barras inclinadas, y es realmente útil cuando llegas a los informes automatizados.

---

# Los datos

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notifica","targets":21},{"name":"lat","targets":22},{"name":"lon","targets":23},{"name":"epilink","targets":24}],"order":[],"autoWidth":false,"orderClasses":false},"selection":{"mode":"multiple","selected":null,"target":"row","selectable":null}},"evals":[],"jsHooks":[]}</script>

???
La tabla muestra sólo las 25 primeras filas, para que se cargue más rápido.

---

# Demostración en vivo

## Nuevo proyecto de RStudio y script de R

---

## Limpieza de datos en R

---

# Datos limpios, datos desordenados

Ahora tus datos están importados. ¿Qué suele implicar la "limpieza" de un conjunto de datos?

]

- Preparar el análisis y la visualización
- Normalizar los nombres de las columnas
- Indexar filas y columnas
- Homogeneizar escritura
- Crear variables categóricas y calculadas
- Unir con otros datos
- Eliminar duplicados...

]

???
Pregunta a los participantes qué medidas toman para limpiar los conjuntos de datos

---

# El paquete {dplyr}

]

- El paquete más sencillo y versátil para la limpieza de datos

- Este paquete se instala como parte de un universo de paquetes de R, {tidyverse}

- {tidyverse} ha transformado R en los últimos 10 años

]

???
Tidyverse ha hecho que la codificación en R sea mucho más fácil de usar, intuitiva y accesible para los codificadores principiantes

---

# Base de datos de práctica

Vamos a utilizar una mini base de datos, **`vig_bruta`**, para practicar algunas funciones básicas de R.

|id_caso | edad|sexo |lab_conferma |inicio fecha | peso (kg)|
|:-------|----:|:----|:------------|:------------|---------:|
|694928  |   23|h    |FALSE        |11/9/2014    |        70|
|86340d  |    0|m    |TRUE         |10/30/2014   |        18|
|92d002  |   16|h    |TRUE         |8/16/2014    |        59|
|544bd1  |   10|m    |TRUE         |8/29/2014    |        39|
|544bd1  |   10|m    |TRUE         |8/29/2014    |        39|
|544bd1  |   10|m    |FALSE        |8/29/2014    |        39|

---

# Funciones para hoy

| Función | Utilidad                               | 
| ------- | -------------------------------------- |
| `filter()`        | filtrar **filas**            | 
| `select()`        | seleccionar **columnas**     | 
| `clean_names()`   | estandarizar los nombres de las columnas | 
| `rename()`        | renombrar columnas manualmente         | 
| `mutate()`        | crear y transformar columnas           | 
| `mdy()`, `dmy()`, `ymd()`      | indicar a R cómo entender las fechas      |

---

# Filtrar filas: `filter()`

``` r
filter(vig_bruta)
```

1er argumento: una base de datos

]

]

---

# Filtrar filas: `filter()`

``` r
filter(vig_bruta, edad < 18)
```

2º+ argumentos: pruebas lógicas para *mantener* las filas deseadas

]

|id_caso | edad|sexo |lab_conferma |inicio fecha | peso (kg)|
|:-------|----:|:----|:------------|:------------|---------:|
|86340d  |    0|m    |TRUE         |10/30/2014   |        18|
|92d002  |   16|h    |TRUE         |8/16/2014    |        59|
|544bd1  |   10|m    |TRUE         |8/29/2014    |        39|
|544bd1  |   10|m    |TRUE         |8/29/2014    |        39|
|544bd1  |   10|m    |FALSE        |8/29/2014    |        39|

]

---

# Filtrar filas: `filter()`

``` r
filter(vig_bruta, edad < 18, sexo == "f")
```

2º+ argumentos: pruebas lógicas para *mantener* las filas deseadas

]

|id_caso | edad|sexo |lab_conferma |inicio fecha | peso (kg)|
|:-------|----:|:----|:------------|:------------|---------:|
|86340d  |    0|m    |TRUE         |10/30/2014   |        18|
|544bd1  |   10|m    |TRUE         |8/29/2014    |        39|
|544bd1  |   10|m    |TRUE         |8/29/2014    |        39|
|544bd1  |   10|m    |FALSE        |8/29/2014    |        39|

]

---

# Filtrar filas: `filter()`

``` r
filter(vig_bruta, 
  edad < 18 & 
  (sexo == "m" | lab_conferma == TRUE)
)
```

*Las nuevas líneas y las sangrías no afectan al código*

La lógica puede volverse compleja utilizando:

- `&` (Y)
- `|` (O)
- Paréntesis

]

]

---

# Seleccionar columnas: `select()`

``` r
select(vig_bruta, ___) 
```

`select()` también espera una base de datos como primer argumento

]

]

---

# Seleccionar columnas: `select()`

``` r
select(vig_bruta, id_caso, edad)
```

Puedes proporcionar a `select()` los nombres de columnas a *mantener*

]

|id_caso | edad|
|:-------|----:|
|694928  |   23|
|86340d  |    0|
|92d002  |   16|
|544bd1  |   10|
|544bd1  |   10|
|544bd1  |   10|

]

---

# Seleccionar columnas: `select()`

``` r
select(vig_bruta, id_caso, edad, sexo)
```

Puedes proporcionar a `select()` los nombres de columnas a *mantener*

]

|id_caso | edad|sexo |
|:-------|----:|:----|
|694928  |   23|h    |
|86340d  |    0|m    |
|92d002  |   16|h    |
|544bd1  |   10|m    |
|544bd1  |   10|m    |
|544bd1  |   10|m    |

]

---

# Seleccionar columnas: `select()`

``` r
select(vig_bruta, -id_caso, -lab_conferma)
```

O puedes designar qué columnas *eliminar* con -.

]

| edad|sexo |inicio fecha | peso (kg)|
|----:|:----|:------------|---------:|
|   23|h    |11/9/2014    |        70|
|    0|m    |10/30/2014   |        18|
|   16|h    |8/16/2014    |        59|
|   10|m    |8/29/2014    |        39|
|   10|m    |8/29/2014    |        39|
|   10|m    |8/29/2014    |        39|

]

---

# ¿`filter()` *y* `select()`?

¡Sí! Utiliza el **%>%** para "pasar" datos de una función a otra.

Es como decir las palabras **"y entonces"**.

Una *secuencia* de limpieza típica contiene una serie de pasos enlazados

- Cambiar el nombre de las columnas
- Filtrar filas
- Seleccionar columnas
- Desduplicar
- Limpiar valores...

]

]

---

# Datos en la "tubería" (secuencia)

Anteriormente, el 1er argumento era la base de datos

`filter(`**vig\_raw**`, edad < 18)`

Usando tuberías, esto se escribe ahora como

**vig\_raw** `%>% filter(edad < 18)`

Puedes conectar los datos a través de *múltiples* funciones  
`vig_bruta`

---

# Datos en la "tubería" (secuencia)

Anteriormente, el 1er argumento era la base de datos

`filter(`**vig\_raw**`, edad < 18)`

Usando tuberías, esto se escribe ahora como

**vig\_raw** `%>% filter(edad < 18)`

Puedes conectar los datos de `vig_bruta` a través de *múltiples* funciones  
`vig_bruta` **%>%** `filter(edad < 18)`

---

# Datos en la "tubería" (secuencia)

Anteriormente, el 1er argumento era la base de datos

`filter(`**vig\_raw**`, edad < 18)`

Usando tuberías, esto se escribe ahora como

**vig\_raw** `%>% filter(edad < 18)`

Puedes conectar los datos de `vig_bruta` a través de *múltiples* funciones  
`vig_bruta` **%>%** `filter(edad < 18)` **%>%** `select(id_caso, edad, sexo)`

|id_caso | edad|sexo |
|:-------|----:|:----|
|86340d  |    0|m    |
|92d002  |   16|h    |
|544bd1  |   10|m    |
|544bd1  |   10|m    |
|544bd1  |   10|m    |

---