In rotterp/RTutorMachineLearningAndCreditDefault: Machine Learning and Credit Default: An Interactive Analysis in R

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title: "Machine Learning And Credit Default: An Interactive Analysis In R" author: "Patrick Rotter" date: December 26, 2017 output: word_document

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# Load libraries and source extra code
library(caret)
library(Ckmeans.1d.dp)
library(dplyr)
library(dygraphs)
library(ggplot2)
library(geojson)
library(geojsonio)
library(glmnet)
library(htmltools)
library(leaflet)
library(plotly)
library(pROC)
library(randomForest)
library(rattle)
library(RColorBrewer)
library(rpart)
library(stargazer)
library(stats)
library(tidyr)
library(tidytext)
library(widyr)
library(wordcloud)
library(xgboost)
library(RTutor)
source("complements.R")

# render data frames similar to the RTutor browser
RTutor::set.knit.print.opts(html.data.frame = TRUE, table.max.rows = 25, round.digits = 8, signif.digits = 8)

# continue knitting even if there is an error
knitr::opts_chunk$set(error = TRUE) 

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library(dplyr)

data %>%
  # Filter for loans between 2007 and 2011
  filter(period == 1) %>%
  # Drop all variables except sub_grade
  select(sub_grade) %>%
  # Return the distribution of loans subject to
  # the different loan grades
  table()

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rotterp/RTutorMachineLearningAndCreditDefault documentation built on May 19, 2019, 1:47 a.m.