Anpassa rapportens stil

Nu när du har lärt dig hur du anpassar stilen på din rapport är det dags att lägga till specifika teckensnitt och färger i din befintliga rapport.

Den här övningen är en del av kursen

Rapportering med R Markdown

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Övningsinstruktioner

Lägg till body med klammerparenteser för att ändra dokumentets bakgrund, och pre med klammerparenteser för att ändra kodblock – allt inom <style>- och </style>-taggarna.
Inom klammerparenteserna för body: ange #708090 som textfärg med color, Calibri som font-family och #F5F5F5 som background-color.
Inom klammerparenteserna för pre: ange #708090 för color och #F8F8FF för background-color.

Interaktiv övning med praktiskt arbete

Testa den här övningen genom att slutföra den här exempelkoden.

{"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"}

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