Aggiungere opzioni dell'indice
Quando toc_float è incluso, l'indice (table of contents) appare sul lato sinistro del documento e rimane visibile mentre chi legge scorre il contenuto. Per impostazione predefinita, mostra l'intestazione più grande, si espande man mano che si procede nella lettura del report o si interagisce con l'indice per navigare in un'altra sezione e anima lo scorrimento della pagina durante la navigazione del report.
In questo esercizio, aggiungerai toc_float e modificherai queste impostazioni usando i campi collapsed e smooth_scroll, così che l'intero indice rimanga sempre visibile e lo scorrimento di pagina non sia animato.
Questo esercizio fa parte del corso
Reportistica con R Markdown
Istruzioni dell'esercizio
- Sotto il campo
toc, aggiungitoc_floate i due punti alla fine del campo. - Su una nuova riga, aggiungi un ulteriore rientro e il campo
collapsed, in modo che l'intero indice rimanga visibile per tutto il report. - Aggiungi un'altra riga e inserisci il campo
smooth_scrollin modo che lo scorrimento della pagina non sia animato.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
{"investment_report.Rmd":"---\ntitle: \"Investment Report\"\noutput: \n html_document:\n toc: true\n toc_depth: 3\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"}