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Raporu parametreyle özelleştirme

Bu egzersizde, rapora country için bir parametre ekleyecek ve mevcut kodu düzenleyerek investment_services_projects verilerinde yer alan herhangi bir ülke için yatırım projelerine dair yeni raporlar oluşturabileceksin.

Bu egzersiz

R Markdown ile Raporlama

kursunun bir parçasıdır
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Egzersiz talimatları

  • YAML başlığında date alanının altına params kullanarak parametreler için bir bölüm ekle, bir country parametresi tanımla ve country parametresinde ülke olarak Brazil belirt.
  • Belge genelinde "Brazil" için yapılan filter() kullanımını gözden geçir ve bunu country parametresine yapılan bir referansla değiştir.
  • brazil-investment-projects kod bloğunda, bloğun adını country-investment-projects olarak değiştir ve brazil_investment_projects nesnesini country_investment_projects olarak yeniden adlandır.
  • brazil-investment-projects-2018 kod bloğunda, bloğun adını country-investment-projects-2018 olarak değiştir ve brazil_investment_projects_2018 nesnesini ve metindeki tüm referanslarını country_investment_projects_2018 olarak yeniden adlandır.
  • Grafikteki başlıklardan "in Brazil" ifadesini kaldır.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

{"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"}
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