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Tworzenie nowego raportu przy użyciu parametru

Skoro do dokumentu został dodany parametr, możesz teraz utworzyć nowy raport dla Bangladeszu na podstawie danych investment_services_projects, używając parametru country.

Przed kompilacją raportu przejrzyj treść dokumentu i wprowadź niezbędne zmiany, aby skompilowany raport odzwierciedlał kraj określony w parametrze.

To ćwiczenie jest częścią kursu

Raportowanie z R Markdown

Zobacz kurs

Instrukcje do ćwiczenia

  • Zastąp Brazil w nagłówkach dokumentu odwołaniem do parametru country.
  • Dodaj parametr country do pola tytułu "Investment Report" w nagłówku YAML, tak aby po kompilacji pliku tytuł raportu przyjął postać: "Investment Report for Projects in Bangladesh".
  • Używając parametru country, utwórz nowy plik raportu inwestycyjnego dla Bangladeszu.

Interaktywne ćwiczenie praktyczne

Spróbuj tego ćwiczenia, uzupełniając ten przykładowy kod.

{"investment_report.Rmd":"---\ntitle: \"Investment Report\"\noutput: \n  html_document:\n    toc: true\n    toc_float: true\ndate: \"`r format(Sys.time(), '%d %B %Y')`\"\nparams:\n  country: Brazil \n---\n\n```{r setup, include = FALSE}\nknitr::opts_chunk$set(fig.align = 'center', echo = TRUE)\n```\n\n```{r data, include = FALSE}\nlibrary(readr)\nlibrary(dplyr)\nlibrary(ggplot2)\n\ninvestment_annual_summary <- read_csv(\"https://assets.datacamp.com/production/repositories/5756/datasets/d0251f26117bbcf0ea96ac276555b9003f4f7372/investment_annual_summary.csv\")\ninvestment_services_projects <- read_csv(\"https://assets.datacamp.com/production/repositories/5756/datasets/bcb2e39ecbe521f4b414a21e35f7b8b5c50aec64/investment_services_projects.csv\")\n```\n\n## Datasets \n\n### Investment Annual Summary\nThe `investment_annual_summary` dataset provides a summary of the dollars in millions provided to each region for each fiscal year, from 2012 to 2018.\n```{r investment-annual-summary}\nggplot(investment_annual_summary, aes(x = fiscal_year, y = dollars_in_millions, color = region)) +\n  geom_line() +\n  labs(\n    title = \"Investment Annual Summary\",\n    x = \"Fiscal Year\",\n    y = \"Dollars in Millions\"\n  )\n```\n\n### Investment Projects in Brazil\nThe `investment_services_projects` dataset provides information about each investment project from 2012 to 2018. Information listed includes the project name, company name, sector, project status, and investment amounts. Projects that do not have an associated investment amount are excluded from the plot.\n\n```{r country-investment-projects}\ncountry_investment_projects <- investment_services_projects %>%\n  filter(country == params$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 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 `country_investment_projects_2018`. Projects that do not have an associated investment amount are excluded from the plot.\n```{r country-investment-projects-2018}\ncountry_investment_projects_2018 <- investment_services_projects %>%\n  filter(country == params$country,\n         date_disclosed >= \"2017-07-01\",\n         date_disclosed <= \"2018-06-30\")\n\nggplot(country_investment_projects_2018, aes(x = date_disclosed, y = total_investment, color = status)) +\n  geom_point() +\n  labs(\n    title = \"Investment Services Projects in 2018\",\n    x = \"Date Disclosed\",\n    y = \"Total IFC Investment in Dollars in Millions\"\n  ) \n```\n\n\n"}
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