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Ukrywanie ostrzeżeń

W poprzednim ćwiczeniu korzystałeś(-aś) z opcji collapse, dzięki której kod i komunikaty ostrzeżeń pojawiały się w tym samym bloku w wygenerowanym raporcie. W tym ćwiczeniu użyjesz opcji warning, aby ostrzeżenia nie pojawiały się w finalnym raporcie.

To ćwiczenie jest częścią kursu

Raportowanie z R Markdown

Zobacz kurs

Instrukcje do ćwiczenia

  • Korzystając z opcji warning, dodaj ją do bloków kodu, które generują wykres z ostrzeżeniem, tak aby komunikaty ostrzeżeń nie pojawiały się w pliku wyjściowym.

Interaktywne ćwiczenie praktyczne

Spróbuj tego ćwiczenia, uzupełniając ten przykładowy kod.

{"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')\n```\n\n```{r data, message = 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### 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, 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### 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, 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`. Projects that do not have an associated investment amount are excluded from the plot.\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"}
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