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Especificar cabeceras y secciones numéricas

Ahora que has añadido un índice, modificarás cómo aparece en el informe y qué información incluye. Utilizarás toc_depth para especificar la profundidad de los encabezados que se incluirán en el índice y number_sections para añadir numeración de sección a los encabezados del informe.

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Instrucciones del ejercicio

  • Debajo del campo toc, especifica una profundidad de cabecera de 2 para el índice de contenidos utilizando toc_depth.
  • Debajo del campo toc_depth, añade secciones numeradas al documento utilizando number_sections.
  • Modifica las cabeceras del documento eliminando una almohadilla de cada cabecera, de modo que la cabecera más grande empiece con una sola almohadilla y la numeración de las secciones empiece con un 1.

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{"investment_report.Rmd":"---\ntitle: \"Investment Report\"\noutput: \n  html_document:\n    toc: true\ndate: \"`r format(Sys.time(), '%d %B %Y')`\"\n---\n\n```{r setup, include = FALSE}\nknitr::opts_chunk$set(fig.align = 'center', echo = TRUE)\n```\n\n```{r data, include = FALSE}\nlibrary(readr)\nlibrary(dplyr)\nlibrary(ggplot2)\n\ninvestment_annual_summary <- read_csv(\"https://assets.datacamp.com/production/repositories/5756/datasets/d0251f26117bbcf0ea96ac276555b9003f4f7372/investment_annual_summary.csv\")\ninvestment_services_projects <- read_csv(\"https://assets.datacamp.com/production/repositories/5756/datasets/bcb2e39ecbe521f4b414a21e35f7b8b5c50aec64/investment_services_projects.csv\")\n```\n\n## Datasets \n\n### Investment Annual Summary\nThe `investment_annual_summary` dataset provides a summary of the dollars in millions provided to each region for each fiscal year, from 2012 to 2018.\n```{r investment-annual-summary}\nggplot(investment_annual_summary, aes(x = fiscal_year, y = dollars_in_millions, color = region)) +\n  geom_line() +\n  labs(\n    title = \"Investment Annual Summary\",\n    x = \"Fiscal Year\",\n    y = \"Dollars in Millions\"\n  )\n```\n\n### Investment Projects in Brazil\nThe `investment_services_projects` dataset provides information about each investment project from 2012 to 2018. Information listed includes the project name, company name, sector, project status, and investment amounts. Projects that do not have an associated investment amount are excluded from the plot.\n\n```{r brazil-investment-projects}\nbrazil_investment_projects <- investment_services_projects %>%\n  filter(country == \"Brazil\") \n\nggplot(brazil_investment_projects, aes(x = date_disclosed, y = total_investment, color = status)) +\n  geom_point() +\n  labs(\n    title = \"Investment Services Projects in Brazil\",\n    x = \"Date Disclosed\",\n    y = \"Total IFC Investment in Dollars in Millions\"\n  )\n```\n\n### Investment Projects in Brazil in 2018\nThe `investment_services_projects` dataset was filtered below to focus on information about each investment project from the 2018 fiscal year, and is referred to as `brazil_investment_projects_2018`. Projects that do not have an associated investment amount are excluded from the plot.\n\n```{r brazil-investment-projects-2018}\nbrazil_investment_projects_2018 <- investment_services_projects %>%\n  filter(country == \"Brazil\",\n         date_disclosed >= \"2017-07-01\",\n         date_disclosed <= \"2018-06-30\") \n\nggplot(brazil_investment_projects_2018, aes(x = date_disclosed, y = total_investment, color = status)) +\n  geom_point() +\n  labs(\n    title = \"Investment Services Projects in Brazil in 2018\",\n    x = \"Date Disclosed\",\n    y = \"Total IFC Investment in Dollars in Millions\"\n  ) \n```\n\n\n"}
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