Philippe J. S. De Brouwer - The Big R-Book

3 Chapter 19Table 19.1: Different levels of information value and their commonly accepted in ...

4 Chapter 22Table 22.1: The confusion matrix, where “pred.” refers to the predictions made b ...

5 Chapter 27Table 27.1: The decision matrix summarises the information that we have gathered ...

6 Chapter 30Table 30.1: An overview of the price dependency for call and put options. A plus ...Table 30.2: An overview of “the Greeks:” the most relevant derivatives of the op ...Table 30.3: Delta hedging of a hypothetical example where we only hedge our pos ...

7 Chapter 36Table 36.1: A comparision of flexdashboard and shinydashboard.

8 Chapter 37Table 37.1: Base R functions and their alternative in snow.

9 Appendix BTable B.1: Characterization of the nominal scale of Measurement .Table B.2: Characterization of the ordinal scale of measurement .

1 Chapter 2 Figure 2.1: A view on the steps in the scientific method for the data scient ... Figure 2.2: The role of data-science in a company is to take data and turn i ...

2 Chapter 3 Figure 3.1: An example showing the histogram of data generated from the norm ...

3 Chapter 4 Figure 4.1: The plot-function will result in a bar-chart for a factor-object ... Figure 4.2: The factor objects appear now in a logical order. Figure 4.3: The standard plot for a data frame in R shows each column printe ... Figure 4.4: The histogram of the CAD. Figure 4.5: A scatter-plot of one variable with another. Figure 4.6: The histogram of the most recent values of the CAD only.

4 Chapter 7 Figure 7.1: The sum of sine and cosine illustrated . Figure 7.2: A tibble plots itself like a data-frame . Figure 7.3: A linear model fit on generated data to illustrate the piping c ...

5 Chapter 8 Figure 8.1: A comparison between a set of random numbers drawn from the nor ... Figure 8.2: The same plot for the returns of the SP500 index seems acceptab ... Figure 8.3: A Q-Q plot is a good way to judge if a set of observations is n ... Figure 8.4: The probability to get maximum x tails when flipping a fair coi ...

6 Chapter 9 Figure 9.1: The plot-function will generate a scatter-plot for a vector. Not ... Figure 9.2: The plot-function will generate a scatter-plot of each column in ... Figure 9.3: Some plot characters. Most other characters will just plot thems ... Figure 9.4: A scatter-plot needs an x and a y variable. Figure 9.5: A line plot of the type b. Figure 9.6: A pie-chart in R. Figure 9.7: A standard bar-chart based on a vector. Figure 9.8: A bar-chart based on a matrix will produce stacked bars. Note ho ...Figure 9.9: A boxplot where the total of each bar equals 100%. Note how the ...Figure 9.10: Boxplots show information about the central tendency (median) a ...Figure 9.11: Violin plot as provided by the function vioplot from the packa ...Figure 9.12: Violin plot as traced by geom_violin provided by the library ...Figure 9.13: Violin plot as traced by geom_violin provided by the library ...Figure 9.14: A histogram in R is produced by the hist() function. Figure 9.15: In this histogram, the breaks are changed, and the y-axes is no ...Figure 9.16: Two line plots plotted by the function curve(). Figure 9.17: A colour mapping combined with a contour plot provides a nice i ...Figure 9.18: Heatmap for the “mtcars” data. Figure 9.19: Heatmap for the “mtcars” data with all columns rescaled ...Figure 9.20: The frequency of the ten most occurring words in this text. Not ...Figure 9.21: A word-could for the text of this book. This single image gives ...Figure 9.22: An illustration of six predefined colour schemes in R. This fig ...Figure 9.23: A visualisation of all built in colours in R. Note that the num ...Figure 9.24: Examples of discrete colour sets. The name of the colour-set is ...

7 Chapter 10Figure 10.1: The standard plot for a time series object for the returns of t ...Figure 10.2: The standard plot functionality of time series will keep the z- ...Figure 10.3: Afirst plot to show the data beforewe start. Thiswill allow us ...Figure 10.4: A forecast based on moving average. Figure 10.5: A backtest for our forecast. Figure 10.6: Optimal moving average forecast. Figure 10.7: Forecasting with an exponentially smoothed moving average. Figure 10.8: Holt exponentially smoothed moving average. Figure 10.9: Using the stl-function to decompose data in a seasonal part and ...Figure 10.10: TheHolt-Wintersmodel fits an exponential trend.Here we plot th ...Figure 10.11: The Holt-Winters model applied to the temperatures in Nottingh ...

8 Chapter 14Figure 14.1: The entity relationship (ER) diagram for our example, the libr ...Figure 14.2: The database scheme . Figure 14.3: Different join types illustrated. Note that the Venn-diagrams ...Figure 14.4: The improved database scheme that allows multiple authors to c ...

9 Chapter 15Figure 15.1: Histogram generated with data from the MySQL database .

10 Chapter 17Figure 17.1: Finally, we are able to make a plot of the tibble in a way that ...Figure 17.2: The standard plot function on a factored object with some value ...Figure 17.3: Maybe you would prefer to show this plot to the board meeting? ...Figure 17.4: A visualisation of how the age of customers impacted the satisf ...

11 Chapter 18Figure 18.1: The visualization of missing data with the function md.pattern ...

12 Chapter 19Figure 19.1: Two histograms of the same dataset. The histogram with less bin ...Figure 19.2: A plot of the fabricated dataset with the spending ratio in fun ...Figure 19.3: A simple aid to select binning borders is plotting a non-parame ...Figure 19.4: The underlying relation between spending probability for femal ...Figure 19.5: The dataset “as received from the customer service department” ...Figure 19.6: The data does not reveal much patterns for any of the variable ...

13 Chapter 20Figure 20.1: A visualization of the loadings of the principal components of ...Figure 20.2: The biplot of the datasetmtcars: all observation and dimension ...Figure 20.3: Visual aids to select the optimal number of factors.

14 Chapter 21Figure 21.1: A scatter-plot generated by the line “plot(survey$Height, surve ...Figure 21.2: A plot visualizing the linear regression model (the data in red ...Figure 21.3: Using the function abline() and cleaning up the titles. Figure 21.4: The results of the non-linear regression with nls(). This plot ...

15 Chapter 22Figure 22.1: The grey diamonds with red border are the data-points (not pass ...Figure 22.2: The ROC curve of a logistic regression. Figure 22.3: The ROC curve plotted with ggplot2. Figure 22.4: A plot of the accuracy in function of the cut-off (threshold) l ...Figure 22.5: The area under the curve (AUC) is the area A plus the area C. I ...Figure 22.6: The KS as the maximum distance between the cumulative distribut ...Figure 22.7: The KS as the maximum distance between the model and a pure ran ...Figure 22.8: The cost functions compared different cost structures. In plot ...