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12.7.2 Cloning a repository using Jupyter.12.7.1 Generating a GitHub personal access token.12.7 Working with local repositories using Jupyter.12.6.3 Creating files on GitHub with the “Add file” menu.12.6.2 Editing files on GitHub with the pen tool.12.6.1 Creating a remote repository on GitHub.12.6 Working with remote repositories using GitHub.12.5.3 Pulling changes from a remote repository.12.5.2 Pushing changes to a remote repository.12.5.1 Committing changes to a local repository.12.3 What is version control, and why should I use it?.11.9 Exporting to a different file format.11.7.3 Summary of best practices for running a notebook.11.7.2 Best practices for including R packages in notebooks.
#JUPYTER NOTEBOOK ONLINE OUTPUT TO TEXT FILE CODE#
11.7.1 Best practices for executing code cells.11.7 Best practices for running a notebook.11 Combining code and text with Jupyter.10.5.3 Using the bootstrap to calculate a plausible range.10.4.2 Sampling distributions for means.10.4.1 Sampling distributions for proportions.8.5 Comparing simple linear and KNN regression.7.10 Strengths and limitations of KNN regression.7.6 Training, evaluating, and tuning the model.6.8.2 Finding a good subset of predictors.6.8.1 The effect of irrelevant predictors.6.5.4 Predict the labels in the test set.6.5 Evaluating accuracy with tidymodels.6 Classification II: evaluation & tuning.5.6 \(K\)-nearest neighbors with tidymodels.5.5.3 Summary of \(K\)-nearest neighbors algorithm.5.5.2 More than two explanatory variables.5.5 Classification with \(K\)-nearest neighbors.5.4.2 Describing the variables in the cancer data set.5 Classification I: training & predicting.4.5.5 Histograms: the Michelson speed of light data set.4.5.4 Bar plots: the island landmass data set.4.5.3 Axis transformation and colored scatter plots: the Canadian languages data set.4.5.2 Scatter plots: the Old Faithful eruption time data set.4.5.1 Scatter plots and line plots: the Mauna Loa CO \(_\) data set.4.5 Creating visualizations with ggplot2.3.11 Apply functions across columns within one row with rowwise and mutate.3.10 Apply functions across many columns with mutate and across.3.9.4 Calculating summary statistics on many columns.3.9.3 Calculating summary statistics for groups of rows.3.9.2 Calculating summary statistics when there are NAs.3.9.1 Calculating summary statistics on whole columns.3.9 Aggregating data with summarize and map.3.8.2 Using |> with more than two functions.3.8.1 Using |> to combine filter and select.3.6.6 Extracting rows above or below a threshold using > and.3.6.5 Extracting rows with values in a vector using %in%.3.6.4 Extracting rows satisfying at least one condition using |.3.6.3 Extracting rows satisfying multiple conditions using, or &.3.6.2 Extracting rows that do not have a certain value with !=.3.6.1 Extracting rows that have a certain value with =.3.5 Using select to extract a range of columns.3.4.3 Tidying up: using separate to deal with multiple delimiters.3.4.2 Tidying up: going from long to wide using pivot_wider.3.4.1 Tidying up: going from wide to long using pivot_longer.3.3.4 What does this have to do with data frames?.2.6.3 Why should we bother with databases at all?.2.6.2 Reading data from a PostgreSQL database.
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2.6.1 Reading data from a SQLite database.2.5 Reading tabular data from a Microsoft Excel file.2.4.6 Previewing a data file before reading it into R.2.4.5 Reading tabular data directly from a URL.2.4.4 read_delim as a more flexible method to get tabular data into R.2.4.3 read_tsv to read in tab-separated files.2.4.2 Skipping rows when reading in data.2.4.1 read_csv to read in comma-separated files.2.4 Reading tabular data from a plain text file into R.2 Reading in data locally and from the web.1.8.1 Using ggplot to create a bar plot.
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