添加表格
之前，您已将报告中使用的数据集直接打印到报告里，便于读者自行查看数据。现在，您将为 investment_region_summary 创建一个表格，以更清晰地呈现这些信息。investment_region_summary 提供了 2012 至 2018 财政年度各地区的投资总额。
本练习是课程的一部分
使用 R Markdown 生成报告
练习说明
- 在第
15行的代码中，在ggplot2包之后加载knitr包（位于数据代码区块中）。 - 在从第
44行开始的tables代码区块中，为investment_region_summary数据创建一个表格，列名为Region和Dollars in Millions，并将两列都设置为居中对齐。 - 为该表格添加如下标题（caption）：
"Table 1.1 The total investment summary for each region for the 2012 to 2018 fiscal years."
交互式实操练习
通过完成这段示例代码来试试这个练习。
{"investment_report.Rmd":"---\ntitle: \"Investment Report\"\ndate: \"`r format(Sys.time(), '%d %B %Y')`\"\noutput: html_document\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# Load the knitr package here\n\ninvestment_annual_summary <- read_csv(\"https://assets.datacamp.com/production/repositories/5756/datasets/d0251f26117bbcf0ea96ac276555b9003f4f7372/investment_annual_summary.csv\")\ninvestment_region_summary <- read_csv(\"https://assets.datacamp.com/production/repositories/5756/datasets/52f5414f6504e0503e86eb1043afa9b3d157fab2/investment_region_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### Investment Annual Summary\nThe `investment_annual_summary` dataset provides a summary of the dollars in millions provided to each of the following regions for each fiscal year, from 2012 to 2018:\n\n1. East Asia and the Pacific \n2. Europe and Central Asia \n3. Latin America and the Caribbean\n4. Middle East and North Africa \n5. South Asia \n6. Sub-Saharan Africa\n\n```{r investment-annual-summary, out.width = '85%', fig.cap = 'Figure 1.1 The Investment Annual Summary for each region for 2012 to 2018.'}\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```{r tables}\n___(___, col.names = c(___), align = ___, caption = ___)\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.\n\n```{r brazil-investment-projects, out.width = '95%', fig.cap = 'Figure 1.2 The Investment Services Projects in Brazil from 2012 to 2018.'}\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`. \n\n```{r brazil-investment-projects-2018, out.width = '95%', fig.cap = 'Figure 1.3 The Investment Services Projects in Brazil in 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"}