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Visualisera alla projekt för ett land och år

Nu ska du skapa ett linjediagram med de data som filtrerades fram för alla projekt i Brasilien under räkenskapsåret 2018. I tidigare övningar lades etiketterna till åt dig. Den här gången får du öva på att lägga till egna etiketter som visas när du knittar rapporten.

Den här övningen är en del av kursen

Rapportering med R Markdown

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Övningsinstruktioner

  • Skapa ett punktdiagram av data i brazil_investment_projects_2018 i kodblocket brazil-investment-projects-2018.
  • Lägg till titeln "Investment Services Projects in Brazil in 2018" i diagrammet.
  • Märk x-axeln "Date Disclosed" och y-axeln "Total IFC Investment in Dollars in Millions".

Interaktiv övning med praktiskt arbete

Testa den här övningen genom att slutföra den här exempelkoden.

{"investment_report.Rmd":"---\ntitle: \"Investment Report\"\ndate: \"`r format(Sys.time(), '%d %B %Y')`\"\noutput: html_document\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\n\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\n\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```{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\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(___, aes(x = date_disclosed, y = total_investment, color = status)) +\n  geom_point() +\n  labs(\n    title = ___,\n    x = ___,\n    y = ___\n  ) \n```\n\n\n"}
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