Hands-On Deep Learning with R

Preparing, preprocessing, and exploring data

Before we build a model, we need to first explore the input data to understand what is available for user-item recommendations. In this section, we will prepare, process, and explore the data, which includes users, items (games), and interactions (hours of gameplay), using the following steps:

1. First, let's load some R packages for preparing and processing our input data:

library(keras)library(tidyverse)library(knitr)

2. Next, let's load the data into R:

steamdata <- read_csv("data/steam-200k.csv", col_names=FALSE)

3. Let's inspect the input data using glimpse():

glimpse(steamdata)

This results in the following output:

4. Let's manually add column labels to organize this data:

colnames(steamdata) ...