Header und Inhaltsverzeichnis anpassen
In dieser Übung fügst du weitere Styles hinzu, indem du die Abschnitte für Inhaltsverzeichnis und Header in der Markdown-Datei anpasst.
Diese Übung ist Teil des Kurses
Berichten mit R Markdown
Anleitung zur Übung
- Füge innerhalb der Grenzen von
<style>und</style>, oberhalb desbody-Abschnitts, Abschnitte für#TOCund#headerhinzu. - Füge in die geschweiften Klammern von
#TOC#708090für die Textfarbe,Calibrifür die Schriftart,16pxfür diefont-sizeund#708090für dieborder-colorein. - Füge in die geschweiften Klammern von
#header#F08080für die Textfarbe,#F5F5F5für den Hintergrund,opacityvon0.6,Calibrifür die Schriftart und20pxfür diefont-sizeein.
Interaktive Übung
Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.
{"investment_report.Rmd":"---\ntitle: \"Investment Report for Projects in `r params$country`\"\noutput: \n html_document:\n toc: true\n toc_float: true\ndate: \"`r format(Sys.time(), '%d %B %Y')`\"\nparams:\n country: Brazil\n year_start: 2017-07-01\n year_end: 2018-06-30\n fy: 2018\n---\n\n\n\n```{r setup, include = FALSE}\nknitr::opts_chunk$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\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 `r params$country`\nThe `investment_services_projects` dataset provides information about each investment project from 2012 to 2018. Information listed includes the project name, company name, sector, project status, and investment amounts. Projects that do not have an associated investment amount are excluded from the plot.\n\n```{r country-investment-projects}\ncountry_investment_projects <- investment_services_projects %>%\n filter(country == params$country) \n\nggplot(country_investment_projects, aes(x = date_disclosed, y = total_investment, color = status)) +\n geom_point() +\n labs(\n title = \"Investment Services Projects\",\n x = \"Date Disclosed\",\n y = \"Total IFC Investment in Dollars in Millions\"\n )\n```\n\n### Investment Projects in `r params$country` in `r params$fy`\nThe `investment_services_projects` dataset was filtered below to focus on information about each investment project from the `r params$fy` fiscal year, and is referred to as `country_annual_investment_projects`. Projects that do not have an associated investment amount are excluded from the plot.\n```{r country-annual-investment-projects}\ncountry_annual_investment_projects <- investment_services_projects %>%\n filter(country == params$country,\n date_disclosed >= params$year_start,\n date_disclosed <= params$year_end) \n\nggplot(country_annual_investment_projects, aes(x = date_disclosed, y = total_investment, color = status)) +\n geom_point() +\n labs(\n title = \"Investment Services Projects\",\n x = \"Date Disclosed\",\n y = \"Total IFC Investment in Dollars in Millions\"\n ) \n```\n\n\n"}