Specify packages in the first code chunk

We've copied the results of your analysis done in the second chapter to another RMarkdown report, shown here on the right. It uses your structure from the previous exercises, and has R code chunks added to the respective sections.

However, something is missing: The information about the software you've used. Specifying the packages that are needed for a certain analysis is a necessity if you want to ensure reproducibility, so that others can reuse your work.

Remember to specify your R code chunk as follows!

```{r}
# Some code

```

This exercise is part of the course

Communicating with Data in the Tidyverse

View Course

Exercise instructions

First of all, get familiar with the structure of the new RMarkdown document you're going to work with from now on.
In the "Preparations" section, add a new R code chunk and delete the placeholder text. In this code chunk, load the Tidyverse libraries dplyr, ggplot2 and forcats.

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\nThis is where you have to load the necessary R packages.\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}\nload(url(\"http://s3.amazonaws.com/assets.datacamp.com/production/course_5807/datasets/ilo_data.RData\"))\n```\n\n```{r}\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}\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"}

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