Richard Josue Alvarado Flores Taller Básico de R Markdown
__________________________
20-12-2025
1 1. ¿Qué es R Markdown?
R Markdown combina: - Texto explicativo - Código en R - Resultados (tablas y gráficos)
Objetivo taller: generar un reporte reproducible con Knit.
2 2. Estructura de un
.Rmd
Un .Rmd tiene: 1) YAML (arriba) 2) Texto en Markdown 3)
Chunks con código R
✅ Ejercicio 1 (5 min):
Cambia el title (arriba) por un título con tu nombre y
vuelve a hacer Knit.
3 3. Markdown esencial
3.1 3.1 Títulos y listas
✅ Ejercicio 2 (5–10 min):
Escribe una mini-lista con 3 objetivos personales del curso.
- TODO: Aprender R Markdown
- TODO: Generar reportes
- TODO: Automatizar analisis
3.2 3.2 Negrita y cursiva
✅ Ejercicio 3 (3 min):
Escribe una frase y resalta una palabra en negrita y
otra en cursiva.
Frase: Aprender R Markdown facilita el análisis reproducible.
4 4. Chunks (código en R)
4.1 4.1 Cargar datos de ejemplo
R incluye un dataset llamado mtcars.
# TODO: muestra las primeras 6 filas de mtcars
# Pista: usa head()
head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1✅ Ejercicio 4: Ejecuta este chunk y luego Knit.
summary(mtcars$mpg)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.40 15.43 19.20 20.09 22.80 33.90round(mean(mtcars$mpg), 2)## [1] 20.094.2 4.2 Resumen de una variable
summary(mtcars$mpg)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.40 15.43 19.20 20.09 22.80 33.90✅ Ejercicio 5: Knit y revisa el resultado.
4.3 4.3 Opciones de chunks
✅ Ejercicio 6 (exploración): 1) En el chunk de
arriba, agrega echo=FALSE dentro de las llaves. 2) Knit y
observa qué cambia.
Ejemplo:
```{{r resumen, echo=FALSE}}
…
``` r
knitr::kable(head(mtcars, 8))| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Mazda RX4 | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |
| Mazda RX4 Wag | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |
| Datsun 710 | 22.8 | 4 | 108.0 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |
| Hornet 4 Drive | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |
| Hornet Sportabout | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |
| Valiant | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 | 1 |
| Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.570 | 15.84 | 0 | 0 | 3 | 4 |
| Merc 240D | 24.4 | 4 | 146.7 | 62 | 3.69 | 3.190 | 20.00 | 1 | 0 | 4 | 2 |
5 5. Inline R (código dentro del texto)
✅ Ejercicio 7 (5 min):
Completa la frase usando inline R para mostrar la media de
mpg con 2 decimales:
La media de MPG es: r TODO
Pista: - mean(mtcars$mpg) - round( …, 2)
library(ggplot2)
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
labs(title = "Relación entre peso y rendimiento",
x = "Peso (wt)",
y = "MPG")
6 6. Tablas
✅ Ejercicio 8 (10 min):
Muestra una tabla con las primeras 8 filas usando
knitr::kable().
# TODO: knitr::kable(head(mtcars, 8))knitr::kable(head(mtcars, 8))| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Mazda RX4 | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |
| Mazda RX4 Wag | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |
| Datsun 710 | 22.8 | 4 | 108.0 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |
| Hornet 4 Drive | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |
| Hornet Sportabout | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |
| Valiant | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 | 1 |
| Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.570 | 15.84 | 0 | 0 | 3 | 4 |
| Merc 240D | 24.4 | 4 | 146.7 | 62 | 3.69 | 3.190 | 20.00 | 1 | 0 | 4 | 2 |
7 7. Gráficos
✅ Ejercicio 9 (15 min):
Crea un gráfico de dispersión entre wt (peso) y
mpg.
# TODO:
# 1) Instala/carga ggplot2 si hace falta
# 2) Crea un scatter plot con geom_point()
# Pista: ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point()install.packages("ggplot2")
library(ggplot2)
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point() +
labs(title = "Relación entre peso y rendimiento",
x = "Peso (wt)",
y = "MPG")
8 8. Actividad final (integrador)
Tu reporte final debe tener: - 2 secciones (con ##) - 2
chunks: - summary de mpg - un gráfico - 1 inline R - 1 tabla con
kable
8.1 8.1 Resultados numéricos
# TODO: summary(mtcars$mpg)summary(mtcars$mpg)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.40 15.43 19.20 20.09 22.80 33.908.2 8.2 Tabla
# TODO: knitr::kable(head(mtcars, 10))knitr::kable(head(mtcars, 10))| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Mazda RX4 | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |
| Mazda RX4 Wag | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |
| Datsun 710 | 22.8 | 4 | 108.0 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |
| Hornet 4 Drive | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |
| Hornet Sportabout | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |
| Valiant | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 | 1 |
| Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.570 | 15.84 | 0 | 0 | 3 | 4 |
| Merc 240D | 24.4 | 4 | 146.7 | 62 | 3.69 | 3.190 | 20.00 | 1 | 0 | 4 | 2 |
| Merc 230 | 22.8 | 4 | 140.8 | 95 | 3.92 | 3.150 | 22.90 | 1 | 0 | 4 | 2 |
| Merc 280 | 19.2 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.30 | 1 | 0 | 4 | 4 |
8.3 8.3 Gráfico
# TODO: gráfico con ggplot2ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_point(color = "steelblue") +
theme_minimal()
9 9. Checklist antes de entregar
## R version 4.5.2 (2025-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 10 x64 (build 19045)
##
## Matrix products: default
## LAPACK version 3.12.1
##
## locale:
## [1] LC_COLLATE=Spanish_Peru.utf8 LC_CTYPE=Spanish_Peru.utf8
## [3] LC_MONETARY=Spanish_Peru.utf8 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Peru.utf8
##
## time zone: America/Lima
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_4.0.1
##
## loaded via a namespace (and not attached):
## [1] vctrs_0.6.5 cli_3.6.5 knitr_1.51 rlang_1.1.6
## [5] xfun_0.55 S7_0.2.1 jsonlite_2.0.0 glue_1.8.0
## [9] labeling_0.4.3 htmltools_0.5.9 sass_0.4.10 scales_1.4.0
## [13] rmarkdown_2.30 grid_4.5.2 evaluate_1.0.5 jquerylib_0.1.4
## [17] fastmap_1.2.0 yaml_2.3.12 lifecycle_1.0.4 compiler_4.5.2
## [21] RColorBrewer_1.1-3 rstudioapi_0.17.1 farver_2.1.2 digest_0.6.39
## [25] R6_2.6.1 bslib_0.9.0 tools_4.5.2 withr_3.0.2
## [29] gtable_0.3.6 cachem_1.1.0