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在報告中加入多個參數

先前你已為 country 加入參數,以便從 investment_services_projects 資料建立新報告,彙整任何國家的投資專案資訊。現在,你會加入會計年度的參數,並調整既有程式碼,讓你可以從 investment_services_projects 資料為任何國家與會計年度建立投資專案的報告。

本練習屬於課程

使用 R Markdown 製作報告

檢視課程

練習說明

  • 新增會計年度的 fy 參數,並將會計年度列為 2018
  • year_startyear_end 日期新增參數,在 2018 會計年度中,year_start 使用 2017-07-01year_end 使用 2018-06-30
  • 將第 6465filter() 中的日期參照,改為參照 year_startyear_end 參數。
  • country-investment-projects-2018 程式碼區塊中,將程式碼區塊重新命名為 country-annual-investment-projects,並將文字中的物件名稱與其參照改為 country_annual_investment_projects

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 for Projects in `r params$country`\"\noutput: \n  html_document:\n    toc: true\n    toc_float: true\ndate: \"`r format(Sys.time(), '%d %B %Y')`\"\nparams:\n  country: Brazil\n---\n\n```{r setup, include = FALSE}\nknitr::opts_chunk$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$country` in 2018\nThe `investment_services_projects` dataset was filtered below to focus on information about each investment project from the 2018 fiscal year, and is referred to as `country_investment_projects_2018`. Projects that do not have an associated investment amount are excluded from the plot.\n```{r country-investment-projects-2018}\ncountry_investment_projects_2018 <- investment_services_projects %>%\n  filter(country == params$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\",\n    x = \"Date Disclosed\",\n    y = \"Total IFC Investment in Dollars in Millions\"\n  ) \n```\n\n\n"}
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