Dodawanie parametru do raportu
W tym ćwiczeniu dodasz parametr country do raportu i zmodyfikujesz istniejący kod tak, aby móc tworzyć nowe raporty o projektach inwestycyjnych dla dowolnego kraju uwzględnionego w danych investment_services_projects.
To ćwiczenie jest częścią kursu
Raportowanie z R Markdown
Instrukcje do ćwiczenia
- Poniżej pola
datew nagłówku YAML dodaj sekcję parametrów za pomocąparams, dodaj parametrcountryi jako wartość tego parametru podajBrazil. - Znajdź wszystkie wywołania
filter()z"Brazil"w dokumencie i zastąp je odwołaniem do parametrucountry. - We fragmencie kodu
brazil-investment-projectszmień nazwę fragmentu nacountry-investment-projects, a obiektbrazil_investment_projectsprzemianuj nacountry_investment_projects. - We fragmencie kodu
brazil-investment-projects-2018zmień nazwę fragmentu nacountry-investment-projects-2018, a obiektbrazil_investment_projects_2018oraz wszystkie odwołania do niego w tekście przemianuj nacountry_investment_projects_2018. - Usuń frazę „in Brazil" z tytułów wykresów w raporcie.
Interaktywne ćwiczenie praktyczne
Spróbuj tego ćwiczenia, uzupełniając ten przykładowy kod.
{"investment_report.Rmd":"---\ntitle: \"Investment Report\"\noutput: \n html_document:\n toc: true\n toc_float: true\ndate: \"`r format(Sys.time(), '%d %B %Y')`\"\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## 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 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}\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}\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"}