Pernyataan kode inline
Seperti yang Anda lihat di video, kode R tidak hanya bisa dimasukkan dalam bentuk chunk, tetapi juga sebagai pernyataan inline:
2 + 2 sama dengan `r 2 + 2` dan teks berlanjut di sini.
Pernyataan inline paling sering digunakan untuk menghitung satu nilai yang menyatu dengan teks di sekitarnya.
Dengan menggunakan pernyataan kode inline, tambahkan sebuah kalimat yang secara otomatis memberi tahu pembaca berapa banyak baris yang ada di himpunan data ILO.
Latihan ini adalah bagian dari kursus
Berkomunikasi dengan Data di Tidyverse
Petunjuk latihan
- Di bagian "Data", pada baris 30, lengkapi kalimat
"The loaded data contains..."untuk menyebutkan jumlah baris yang benar dalam himpunan datailo_data, dengan menggunakan fungsicount()dari Tidyverse. - Pastikan Anda membungkus kode R dengan simbol yang benar, seperti yang ditunjukkan di atas.
Latihan interaktif praktis
Cobalah latihan ini dengan menyelesaikan kode contoh berikut.
{"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, message = FALSE}\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\nThe loaded data contains ___ rows. \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```\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, echo = FALSE}\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"}