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#Principle app data visualization pdf#
#Principle app data visualization code#

Cheng (2014) decomposes the graph and provides some simpler visualizations she also provides the following background: Further, vertical lines linking the temperature display to the number of troops indicate the often perilous river crossings which further decimated Napoleon’s troops).

The visualization depicts the size, latitude, and longitude of Napoleon’s army as they moved towards (tan line) then away (black line) from Moscow temperature during the retreat is plotted as well. 22.2 the law of unintended consequencesįig 6.2: Minard’s display of Napoleon’s catastrophic assault on Moscow, 1812.22 some ethical concerns for the data scientist.21.6 a postscript: The Tidymodels packages.21.5 compensatory versus non-compensatory problems.21.4.1 resampling: beyond test, training, and validation samples.21 machine learning: chihuahuas vs muffins, and other distinctions and ideas.20.2.5 avoiding capitalization on chance (again).20.2 another approach to classification: k-nearest neighbor.20.1.1 applying the model to the test data.20.1 from regression to classification: selection of a threshold.19.3.1 applying logistic regression analysis to the training data.19.3 an example of cross-validated linear regression.19.2.1 splitting the data into training and test subsamples.18.2.2 Warning: there are two regression lines.18.2 correlations based on small samples are unstable: A Monte Carlo demonstration.14.3.1 combining the song titles with our US artists.14.3 drowning in the sea of songs (with apologies to Artist # ARIVOIM1187B990643).14.2.1 applying the function to the music data.14.2 working with geodata: a function to get US states from latitude/longitude.

13.3 Make/extract/combine your own data.13 finding, exploring, and cleaning data.11.3 R markdown documents integrate rationale, script, and results.11.2 projects are directories containing related scripts.11 literate programming with R markdown.10.2 answers to the reproducibility crisis III: Pre-registration.10.1.2 keep a log of every step of every analysis in R markdown or Jupyter notebooks.10.1 Answers to the reproducibility crisis.10 reproducibility and the replication crisis.9.3 continuous probability distributions.9.2.1 keeping conditional probabilities straight.8.2 plotting confirmed cases (Feb-Mar 2020).8.1.9 adding recovered cases (code from Feb, data through Mar 2020).

#Principle app data visualization code#

8.1.3 eleven months later: the code still runs!.

8.1.2 cleaning (wrangling, munging) the data (Feb 2020).

8.1 tracking the Novel Coronavirus (from Feb 2020).

6.7.2 telling the truth when the truth is unclear.

6.7 the psychology of data visualization.

6.6.2 poor design can be a tool to deceive.

6.6.1 poor design leads to an uninformed or misinformed world.

5.6 Code along with a tidy webinar, or attend a virtual conference.

5.5 Read Peng’s text and/or watch the associated videos.

5.2.2 Play with and explore the movies data.

#Principle app data visualization pdf#

5.2.1 Create a new R Markdown document and knit it to a PDF or a Word doc.

2.5 discussion: who deserves a good grade?.

2.3 setting up your machine: some basic tools.

2.2 some best practices for spreadsheets.

2.1 are you already a programmer and statistician?.

1.6 discussion: what will you do with data science?.

1.2 the incompleteness of the data science Venn diagram.

1.1 type C data science = data science for the liberal arts.