Introduction to R and RStudio

Figure 1

RStudio layout

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RStudio layout with .R file open

Project Management With RStudio

Figure 1

Screenshot of file manager demonstrating bad project organisation

Seeking Help

Data Structures

Exploring Data Frames

Subsetting Data

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Inequality testing

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Inequality testing: results of recycling

Control Flow

Creating Publication-Quality Graphics with ggplot2

Figure 1

Blank plot, before adding any mapping aesthetics to ggplot().

Figure 2

Plotting area with axes for a scatter plot of life expectancy vs GDP, with no data points visible.

Figure 3

Scatter plot of life expectancy vs GDP per capita, now showing the data points.

Figure 4

Binned scatterplot of life expectancy versus year showing how life expectancy has increased over time
Binned scatterplot of life expectancy versus year showing how life expectancy has increased over time

Figure 5

Binned scatterplot of life expectancy vs year with color-coded continents showing value of 'aes' function
Binned scatterplot of life expectancy vs year with color-coded continents showing value of ‘aes’ function

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Figure 10

Scatter plot of life expectancy vs GDP per capita with a trend line summarising the relationship between variables. The plot illustrates the possibilities for styling visualisations in ggplot2 with data points enlarged, coloured orange, and displayed without transparency.

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Figure 12

Scatterplot of GDP vs life expectancy showing logarithmic x-axis data spread
Scatterplot of GDP vs life expectancy showing logarithmic x-axis data spread

Figure 13

Scatter plot of life expectancy vs GDP per capita with a blue trend line summarising the relationship between variables, and gray shaded area indicating 95% confidence intervals for that trend line.

Figure 14

Scatter plot of life expectancy vs GDP per capita with a trend line summarising the relationship between variables. The blue trend line is slightly thicker than in the previous figure.

Figure 15

Scatter plot of life expectancy vs GDP per capita with a trend line summarising the relationship between variables. The plot illustrates the possibilities for styling visualisations in ggplot2 with data points enlarged, coloured orange, and displayed without transparency.

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Vectorization

Figure 1

Scatter plot showing populations in the millions against the year for China, India, and Indonesia, countries are not labeled.

Figure 2

Scatter plot showing populations in the millions against the year for China, India, and Indonesia, countries are not labeled.

Functions Explained

Writing Data

Data Frame Manipulation with dplyr

Figure 1

Diagram illustrating use of select function to select two columns of a data frame If we want to remove one column only from the gapminder data, for example, removing the continent column.

Figure 2

Diagram illustrating how the group by function oraganizes a data frame into groups

Figure 3

Diagram illustrating the use of group by and summarize together to create a new variable

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Data Frame Manipulation with tidyr

Figure 1

Diagram illustrating the difference between a wide versus long layout of a data frame

Figure 2

Diagram illustrating the wide format of the gapminder data frame

Figure 3

Diagram illustrating how pivot longer reorganizes a data frame from a wide to long format

Figure 4

Diagram illustrating the long format of the gapminder data

Producing Reports With knitr

Figure 1

Screenshot of the New R Markdown file dialogue box in RStudio

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RStudio versions 1.4 and later include visual markdown editing mode. In visual editing mode, markdown expressions (like **bold words**) are transformed to the formatted appearance (bold words) as you type. This mode also includes a toolbar at the top with basic formatting buttons, similar to what you might see in common word processing software programs. You can turn visual editing on and off by pressing the Icon for turning on and off the visual editing mode in RStudio, which looks like a pair of compasses button in the top right corner of your R Markdown document.

Writing Good Software