使用參數建立新報告
你已經把參數加入文件。現在請使用 country 參數,從 investment_services_projects 資料為 Bangladesh 建立一份新的報告。
在 knit 報告之前,請先檢視並修改文件中的文字,確保最後 knit 出來的報告會反映參數中指定的國家。
本練習屬於課程
使用 R Markdown 製作報告
練習說明
- 將文件頁首中的
Brazil改為引用country參數。 - 在 YAML 頁首的
"Investment Report"標題欄位中加入country參數,讓檔案 knit 後,報告標題會顯示為「Investment Report for Projects in Bangladesh」。 - 使用
country參數,建立一個針對 Bangladesh 的新 Investment Report 檔案。
動手互動練習
試著完成這個範例程式碼,體驗一下這個練習。
{"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"}