Importing and manipulating Data

First make sure there is a folder called data in your wd

Let us download the gapminder_data.csv dataset into our project

download.file(url="https://raw.githubusercontent.com/cambiotraining/reproducibility-training/master/data/gapminder_data.csv", destfile="data/gapminder_data.csv")

Start to load the dataset

# Load all libraries
library(tidyverse) # use for data manipulation and visualisation
library(kableExtra) # used for kbl()
library(rmarkdown) # used for paged_table function
library(ggpubr) #used for ggarrange function

# read file into R 
pop_data = read_csv("data/gapminder_data.csv")

# create a table with data from Euro countries in 2007 - ordered by life expectancy 
euro_data_tbl = pop_data %>% 
  # Filter by continent and year 
  filter(continent == "Europe" & year == 2007) %>% 
  # remove continent and year (since they are all the same)
  select(-continent, -year) %>% 
  # arrange by life expecntancy 
  arrange(desc(lifeExp)) %>% 
  # Rename columns names to how we want them 
  rename(Country = country, 
         "Population size" = pop, 
         "Life Expectancy" = lifeExp,
         "GDP" = gdpPercap)

Tables in R Markdown

We will use the kableExtra package to design some tables. The vignette for kableExtra can be found here

The results in euro_data_tbl are displayed in the table below:

euro_data_tbl %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = c("striped", full_width = F)) %>% 
                  scroll_box(width = "100%", height = "200px" )

Country	Population size	Life Expectancy	GDP
Iceland	301931	81.757	36180.789
Switzerland	7554661	81.701	37506.419
Spain	40448191	80.941	28821.064
Sweden	9031088	80.884	33859.748
France	61083916	80.657	30470.017
Italy	58147733	80.546	28569.720
Norway	4627926	80.196	49357.190
Austria	8199783	79.829	36126.493
Netherlands	16570613	79.762	36797.933
Greece	10706290	79.483	27538.412
Belgium	10392226	79.441	33692.605
United Kingdom	60776238	79.425	33203.261
Germany	82400996	79.406	32170.374
Finland	5238460	79.313	33207.084
Ireland	4109086	78.885	40675.996
Denmark	5468120	78.332	35278.419
Portugal	10642836	78.098	20509.648
Slovenia	2009245	77.926	25768.258
Czech Republic	10228744	76.486	22833.309
Albania	3600523	76.423	5937.030
Croatia	4493312	75.748	14619.223
Poland	38518241	75.563	15389.925
Bosnia and Herzegovina	4552198	74.852	7446.299
Slovak Republic	5447502	74.663	18678.314
Montenegro	684736	74.543	9253.896
Serbia	10150265	74.002	9786.535
Hungary	9956108	73.338	18008.944
Bulgaria	7322858	73.005	10680.793
Romania	22276056	72.476	10808.476
Turkey	71158647	71.777	8458.276

A better way to display long tables is using the function paged_table() in the rmarkdown library can be used to do this

paged_table(euro_data_tbl)

Adding images

Adding images is straightforward and does not require using a specific R Markdown function.

Create a new dataset euro_data_fig by filtering the pop_data tibble to contain only data from Europe. Draw a plot to display the lifeExp on the y axis and year on the x axis. Use geom_violin() to draw this as a violin plot to show the distrubution of the data across each year and save it in a 1euro_plot variable.

euro_plot = pop_data %>% 
  filter(continent == "Europe") %>% 
  select(-continent) %>% 
  # Add factor() to ensure year is treated as discrete 
  ggplot(aes(x = factor(year), y = lifeExp)) +
  # use geom_violin
  geom_violin() +
  # plot the median as a point on the violin 
  stat_summary(fun = median, geom = "point") +
  ylim(40, 85)

# display plot 
euro_plot

Create a new dataset uk_data_fig by filtering the pop_data tibble to contain only data from the United Kingdom. Draw a scatter plot to display the lifeExp on the y axis and year on the x axis and save it in a uk_plot variable. Draw the euro_plot created in the previous challenge next to a uk_plot using the ggarrange() function. Label the plots A and B respectively.

uk_plot = pop_data %>% 
  # filter for United Kingdom 
  filter(country == "United Kingdom") %>%
  # use mutate to convert year to factor 
  mutate(year = as_factor(year)) %>% 
  # Plot a scatterplot with geom_point
  ggplot(aes(x = year, y = lifeExp)) +
  geom_point() +
  ylim(40, 85)

# display plot 
uk_plot

ggarrage() is a function present in the ggpubr pacakge that can place two or more figures next to each other

# Use ggarrange to arrange the uk_plot and euro_plot 
ggarrange(uk_plot, euro_plot, ncol = 2, nrow = 1, labels = c("A", "B"))

Send to Github

First render files to publish files in Github using the follwoing commands

rmarkdown::render("inserting-code-in-rmarkdown.Rmd") rmarkdown::render("index.Rmd")

To download git first go to here

Then click Tools > Version Control > Project set up Git
If asked whether you want to initialise a new Git repository for this project, click Yes.
If asked whether RStudio needs to be restarted, click Yes.
Next we need to configure Git. Go to Tools > Shell. This should take you to the Terminal window in RStudio. Make sure you are first in the working directory of your project (use getwd() to check that). Then set the Git username and email using the following two commands:

git config --global user.name "yourGitHubUsername"
git config --gloabl user.email "name@provider.com"

Next create a repository in GitHub.
After you click on the Create repository button, a Quick setup page is displayed which provides code for different scenarios. Copy the first line of the “…or push an existing repository from the command line” section and run it in the RStudio terminal. The line of code should look something like this: git remote add origin https://github.com/Another-Goodman/R-Markdown-Basics.git
From the Git tab in RStudio, select the files you would like to commit to GitHub.
Press the Commit button, enter a commit message and then press the Commit button in the new pop-up commit window.

Inserting R Code in Markdown

Richard Goodman

11 December 2020

Importing and manipulating Data

Tables in R Markdown

Adding images

Send to Github