보고서에 매개변수 추가하기
이번 연습에서는 보고서에 country 매개변수를 추가하고, 기존 코드를 수정해 investment_services_projects 데이터에 포함된 어떤 국가에 대해서도 투자 프로젝트 보고서를 새로 생성할 수 있도록 해 보겠습니다.
이 연습은 강의의 일부입니다
R Markdown으로 보고서 만들기
연습 안내
- YAML 헤더의
date필드 아래에params를 사용해 매개변수 섹션을 추가하고,country매개변수를 만든 뒤 해당 매개변수 안에 국가로Brazil을 지정하세요. - 문서 전체에서
filter()에 사용된"Brazil"을 확인해, 이를country매개변수 참조로 바꾸세요. brazil-investment-projects코드 청크의 이름을country-investment-projects로 바꾸고, 객체 이름brazil_investment_projects도country_investment_projects로 변경하세요.brazil-investment-projects-2018코드 청크의 이름을country-investment-projects-2018로 바꾸고, 객체brazil_investment_projects_2018과 본문에서의 모든 참조를country_investment_projects_2018로 변경하세요.- 보고서의 플롯 제목에서 "in Brazil"을 제거하세요.
실습형 인터랙티브 연습
이 예제를 이 샘플 코드를 완성하여 풀어보세요.
{"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')`\"\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 brazil-investment-projects}\nbrazil_investment_projects <- investment_services_projects %>%\n filter(country == \"Brazil\") \n\nggplot(brazil_investment_projects, aes(x = date_disclosed, y = total_investment, color = status)) +\n geom_point() +\n labs(\n title = \"Investment Services Projects in Brazil\",\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 `brazil_investment_projects_2018`. Projects that do not have an associated investment amount are excluded from the plot.\n\n```{r brazil-investment-projects-2018}\nbrazil_investment_projects_2018 <- investment_services_projects %>%\n filter(country == \"Brazil\",\n date_disclosed >= \"2017-07-01\",\n date_disclosed <= \"2018-06-30\") \n\nggplot(brazil_investment_projects_2018, aes(x = date_disclosed, y = total_investment, color = status)) +\n geom_point() +\n labs(\n title = \"Investment Services Projects in Brazil in 2018\",\n x = \"Date Disclosed\",\n y = \"Total IFC Investment in Dollars in Millions\"\n ) \n```\n\n\n"}