보고서에 여러 파라미터 추가하기
이전에 investment_services_projects 데이터에 포함된 모든 국가에 대해 투자 프로젝트 정보를 요약하는 새 보고서를 만들 수 있도록 country 파라미터를 추가하셨죠. 이제 회계연도(fiscal year)용 파라미터를 추가하고 기존 코드를 수정해, investment_services_projects 데이터에서 임의의 국가와 회계연도에 대한 투자 프로젝트 보고서를 생성할 수 있도록 하겠습니다.
이 연습은 강의의 일부입니다
R Markdown으로 보고서 만들기
연습 안내
- 회계연도용
fy파라미터를 추가하고 회계연도를2018로 지정하세요. - 2018 회계연도의 시작과 종료 날짜를 위해
year_start와year_end파라미터를 추가하고, 각각2017-07-01(yearstart)과2018-06-30(yearend)으로 설정하세요. filter()의64행과65행에서 날짜 참조를year_start와year_end파라미터 참조로 바꾸세요.country-investment-projects-2018코드 청크에서 코드 청크 이름을country-annual-investment-projects로 바꾸고, 텍스트 내 객체 이름과 해당 참조도country_annual_investment_projects로 변경하세요.
실습형 인터랙티브 연습
이 예제를 이 샘플 코드를 완성하여 풀어보세요.
{"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"}