Meerdere parameters aan het rapport toevoegen
Eerder heb je een parameter voor country toegevoegd om nieuwe rapporten te maken die informatie samenvatten over de investeringsprojecten voor elke country die in de gegevens investment_services_projects voorkomt. Nu voeg je parameters toe voor het fiscale jaar en pas je de bestaande code aan, zodat je nieuwe rapporten kunt maken over de investeringsprojecten voor elke country en elk fiscaal jaar uit de gegevens investment_services_projects.
Deze oefening maakt deel uit van de cursus
Rapporteren met R Markdown
Oefeninstructies
- Voeg een parameter
fytoe voor het fiscale jaar en zet2018als het fiscale jaar. - Voeg parameters toe voor de datums
year_startenyear_end, met2017-07-01vooryear_starten2018-06-30vooryear_endvan het fiscale jaar 2018. - Vervang dataverwijzingen in de
filter()op regels64en65door verwijzingen naar de parametersyear_startenyear_end. - Hernoem in de codechunk
country-investment-projects-2018de codechunk naarcountry-annual-investment-projectsen hernoem de objectnaam en objectnaamverwijzingen in de tekst naarcountry_annual_investment_projects.
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
{"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"}