tut17

100%

✓ Published0🌍 Public

Last edited Feb 13, 2020

Created on Feb 13, 2020

This example builds a classification-ready dataset from income survey data, showing how raw income brackets are recoded into binary “Under 30k” versus “Over 30k” categories. It then splits the cleaned data into training and test sets using base R’s `sample` function. The code uses `dplyr`’s `mutate`, `if_else`, and `mutate_if` for transformations, along with `forcats::fct_explicit_na` to label missing factors as “Unknown.” The data is sourced from a remote CSV via `read.csv` and processed entirely in R, with no visualization rendering or plotting involved.

AI-generated description

Similar vizzes

Loading thumbnail…

tut21

This example shows a supervised machine learning workflow using the random forest algorithm to predict income categories from a dataset of demographic features. The code builds a random forest model with 500 trees and three variables considered at each split, using the `randomForest` function from the R randomForest package. After training on a designated training set, it generates predictions and compares them against actual income labels using a confusion matrix, reporting a classification accuracy of approximately 77.6%. The visualization itself is not graphical; instead, it presents the model’s performance metrics as tabular and numeric output.

BBenHeubl

73% match

Loading thumbnail…

tut14

This example visualizes the relative importance of different variables in predicting income levels using a random forest model. The code loads an income dataset from GitHub, shuffles rows, and handles missing factor values before training the model with `randomForest`. It then relies on `ggRandomForests` to plot variable importance scores, showing which predictors most strongly influence income classification. The visual output highlights the ranked contribution of each feature, with the model trained on the processed data frame.

BBenHeubl

70% match

Loading thumbnail…

Tut3

This visualization demonstrates a machine-learning workflow for splitting a Brexit-related dataset into training and testing subsets. It shows how 70 percent of the data is randomly sampled for model training, with the remaining 30 percent held out for validation. The code uses base R’s `sample()` function and `nrow()` to compute indices, then applies bracket indexing to partition the data frame. The example highlights a common data-preparation step, with the split proportion clearly visible in the sampling logic. No plotting or rendering libraries are used; the focus is purely on the data-splitting mechanism.

BBenHeubl

69% match

Loading thumbnail…

tut19

This visualization demonstrates the accuracy of a decision tree model in predicting the `INCOME` category from a training dataset. It shows a confusion matrix and the computed accuracy rate of approximately 73.4%, comparing predicted versus actual income labels. The R code uses the `caret` package’s `train()` function with the `"rpart"` method to build the model, then evaluates it with the `table()` and `mean()` functions. The script also loads `e1071`, though the visible steps primarily rely on caret and rpart for classification and validation.

BBenHeubl

69% match

Loading thumbnail…

tut16

This example calculates the mode occupation for each income group using a custom `getmode` function defined in R. The code groups the income dataset by the `INCOME` variable and summarizes it with the mode of `OCCUPATION`, producing a table of the most common occupation per income bracket. It relies on base R functions like `unique`, `tabulate`, and `match`, along with the dplyr verbs `group_by` and `summarise`. The result displays the categorical relationship between income levels and predominant job types, without any graphical output or rendering library.

BBenHeubl

68% match

Loading thumbnail…

Tut1

This example loads survey data from a CSV file hosted on DataCandy and examines its structure using the R programming language. It shows the initial data inspection step, displaying that the dataset contains 375 observations across 32 variables. The code calls the `read.csv` function to import the data and then uses the `str` function from base R to display the structure. It also employs `View` from the RStudio IDE to open the data in a tabular viewer. The example uses the `tidyverse` package collection, though the visible operations rely on base R functions for data reading and inspection.

BBenHeubl

61% match

Loading thumbnail…

tut8

This example shows a machine-learning workflow for predicting Brexit referendum outcomes, comparing predicted versus actual Leave votes in a test dataset. It uses R to fit a final classification model, then applies the `predict` function to generate class predictions on held-out test data. The visualization or summary aspect centers on computing `base_accuracy`, the mean agreement between predicted and observed `Percent.Leave` values. The code relies on base R modeling and prediction APIs, with no plotting libraries, instead focusing on quantitative accuracy assessment rather than graphical output.

BBenHeubl

59% match

Loading thumbnail…

Tut2

This example demonstrates how to train a decision tree classification model using the R `rpart` package. The code defines a model that predicts a target `Column_of_interest` from all other variables in a user-supplied dataframe, using the `method = "class"` setting for categorical outcomes. It also incorporates a parameter for an optimal complexity parameter (`cp`) to control tree pruning, which is passed through `rpart.control`. The snippet does not include any data or visualization, serving instead as a template for building the underlying statistical model.

BBenHeubl

59% match