R code chunk options
You've been introduced to some R code chunk options in the video. R code chunks can also be named. This makes sense in large documents, especially if there is an error inside a chunk. While knitting a document in RStudio, for example, the error can then be located in an easier fashion. Some chunks in your report have been named so you get the idea behind that concept. Chunk options are added after the name of the chunk and a comma, like so: {r name, option = value}
Probably among the most used chunk options are the ones for controlling the display of the output. By default, all output is shown in RMarkdown documents, i.e. messages, warnings and errors. By specifying options like echo = FALSE, you can suppress the display of the R code itself, for example, while the code itself is still evaluated. Refer to the video for more display options.
Remember to separate your chunk name and chunk options with a comma to prevent your session from crashing.
This exercise is part of the course
Communicating with Data in the Tidyverse
Exercise instructions
- On line 14, suppress messages in the first R code chunk with the
messageoption, such that the output that is sometimes generated when loading packages is not shown. - On line 64, the chunk
defining_a_themeenriches your report with a custom plot, however the code behind it is not so important for the readers of your report.- Hide all the code, but let R evaluate it nonetheless (if you don't remember the chunk option, re-watch the video).
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
{"my_document.Rmd":"---\ntitle: \"The reduction in weekly working hours in Europe\" \nsubtitle: \"Looking at the development between 1996 and 2006\"\nauthor: \"Insert your name here\"\noutput: html_document\n---\n\n## Summary \n\nThe **International Labour Organization (ILO)** has many [data sets](http://www.ilo.org/global/statistics-and-databases/lang--en/index.htm) on working conditions. For example, one can look at how weekly working hours have been decreasing in many countries of the world, while monetary compensation has risen. In this report, *the reduction in weekly working hours* in European countries is analysed, and a comparison between 1996 and 2006 is made. All analysed countries have seen a decrease in weekly working hours since 1996 – some more than others.\n\n## Preparations \n\n```{r loading_packages}\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(forcats)\n```\n\n## Analysis\n\n### Data\n\nThe herein used data can be found in the [statistics database of the ILO](http://www.ilo.org/ilostat/faces/wcnav_defaultSelection;ILOSTATCOOKIE=ZOm2Lqrr-OIuzxNGn2_08bNe9AmHQ1kUA6FydqyZJeIudFLb2Yz5!1845546174?_afrLoop=32158017365146&_afrWindowMode=0&_afrWindowId=null#!%40%40%3F_afrWindowId%3Dnull%26_afrLoop%3D32158017365146%26_afrWindowMode%3D0%26_adf.ctrl-state%3D4cwaylvi8_4). For the purpose of this course, it has been slightly preprocessed.\n\n```{r loading_data}\nload(url(\"http://s3.amazonaws.com/assets.datacamp.com/production/course_5807/datasets/ilo_data.RData\"))\n```\n\n```{r generating_summary_statistics}\n# Some summary statistics\nilo_data %>%\n group_by(year) %>%\n summarize(mean_hourly_compensation = mean(hourly_compensation),\n mean_working_hours = mean(working_hours))\n```\n\nAs can be seen from the above table, the average weekly working hours of European countries have been descreasing since 1980.\n\n### Preprocessing\n\nThe data is now filtered so it only contains the years 1996 and 2006 – a good time range for comparison. \n\n```{r}\nilo_data <- ilo_data %>%\n filter(year == \"1996\" | year == \"2006\")\n \n# Reorder country factor levels\nilo_data <- ilo_data %>%\n # Arrange data frame first, so last is always 2006\n arrange(year) %>%\n # Use the fct_reorder function inside mutate to reorder countries by working hours in 2006\n mutate(country = fct_reorder(country,\n working_hours,\n last))\n``` \n\n### Results\n\nIn the following, a plot that shows the reduction of weekly working hours from 1996 to 2006 in each country is produced.\n\nFirst, a custom theme is defined.\n\n```{r defining_a_theme}\n# Better to define your own function than to always type the same stuff\ntheme_ilo <- function(){\n theme_minimal() +\n theme(\n text = element_text(family = \"Bookman\", color = \"gray25\"),\n plot.subtitle = element_text(size = 12),\n plot.caption = element_text(color = \"gray30\"),\n plot.background = element_rect(fill = \"gray95\"),\n plot.margin = unit(c(5, 10, 5, 10), units = \"mm\")\n )\n}\n``` \n\nThen, the plot is produced. \n\n```{r}\n# Compute temporary data set for optimal label placement\nmedian_working_hours <- ilo_data %>%\n group_by(country) %>%\n summarize(median_working_hours_per_country = median(working_hours)) %>%\n ungroup()\n\n# Have a look at the structure of this data set\nstr(median_working_hours)\n\n# Plot\nggplot(ilo_data) +\n geom_path(aes(x = working_hours, y = country),\n arrow = arrow(length = unit(1.5, \"mm\"), type = \"closed\")) +\n # Add labels for values (both 1996 and 2006)\n geom_text(\n aes(x = working_hours,\n y = country,\n label = round(working_hours, 1),\n hjust = ifelse(year == \"2006\", 1.4, -0.4)\n ),\n # Change the appearance of the text\n size = 3,\n family = \"Bookman\",\n color = \"gray25\"\n ) +\n # Add labels for country\n geom_text(data = median_working_hours,\n aes(y = country,\n x = median_working_hours_per_country,\n label = country),\n vjust = 2,\n family = \"Bookman\",\n color = \"gray25\") +\n # Add titles\n labs(\n title = \"People work less in 2006 compared to 1996\",\n subtitle = \"Working hours in European countries, development since 1996\",\n caption = \"Data source: ILO, 2017\"\n ) +\n # Apply your theme \n theme_ilo() +\n # Remove axes and grids\n theme(\n axis.ticks = element_blank(),\n axis.title = element_blank(),\n axis.text = element_blank(),\n panel.grid = element_blank(),\n # Also, let's reduce the font size of the subtitle\n plot.subtitle = element_text(size = 9)\n ) +\n # Reset coordinate system\n coord_cartesian(xlim = c(25, 41))\n```\n\n\n"}