Jupyter Guide

To get started, open the file on the right entitled "Step 1...". You'll complete each task for Step 1 in that Jupyter Notebook file. Remember, you must run the cells (ctrl/cmd(⌘) + Enter) for each task before moving onto the next task in the Jupyter Notebook. Continue until you have completed all tasks in this step. Then when you are ready to move onto the next step, you'll come back and click on the file for the next step until you have completed all tasks in all steps of the lab.

Importing and Manipulating Text Files

To review the concepts covered in this step, please refer to the Importing Text Files in R module of the Importing Formatted Text Files: R Playbook course.

Understanding how to import and manipulate text files is crucial because it lays the foundation for data analysis in R. This step covers the basics of reading text files, including using various arguments in the read.table function to customize the import process.

Dive into the world of text files with R! Your goal is to practice importing text files into R and manipulating the imported data to fit your analysis needs. You'll use the read.table function, exploring its various arguments such as header, sep, skip, and stringsAsFactors to import a sample text file. After importing, you'll practice subsetting the data by reading specific lines using the skip and nrows arguments. This hands-on experience will solidify your understanding of handling text data in R.

Task 1.1: Importing a Text File

Start by importing the employee_data.txt file using the read.table function. The file is space separated. Make sure to include the header argument to specify if the first line of the file should be treated as the column names.

data <- read.table(file='employee_data.txt', header=TRUE, sep=' ')
data

Task 1.2: Customizing the Separator

Now import the employee_data_pipe_separated.txt file. This data is separated with a pipe character |. Customize the import process by specifying a new column separator using the sep argument in the read.table function.

data <- read.table(file='employee_data_pipe_separated.txt', header=TRUE, sep='|')
data

Task 1.3: Skipping Rows and Reading Specific Lines

With employee_data.txt, practice subsetting the data by reading specific lines. Use the skip argument to ignore the first line of the file (the header), and nrows to read two lines after skipping.

data <- read.table(file='employee_data.txt', header=FALSE, sep=' ', skip=1, nrows=2)
data

Task 1.4: Handling Strings as Factors

Explore how to handle strings in your imported data. For columns that are strings, import them as factors.

data <- read.table(file='employee_data.txt', header=TRUE, sep=' ', stringsAsFactors=TRUE)
data

Mastering CSV File Imports

To review the concepts covered in this step, please refer to the Importing CSV Files in R module of the Importing Formatted Text Files: R Playbook course.

Mastering the import of CSV files is important because CSV is one of the most common data formats in data analysis. This step focuses on using the read.csv function and its arguments to effectively import and preprocess CSV data in R.

Embark on a journey to master CSV file imports in R! Your mission is to utilize the read.csv function to import a CSV file into an R data frame. Pay special attention to handling missing values using the na.strings argument and selecting specific columns and defining their data types with the colClasses argument. This practice will enhance your ability to work with one of the most prevalent data formats in data analysis.

Task 2.1: Importing a CSV File

Import a CSV file named data.csv into an R data frame. Use the read.csv function to accomplish this task. Ensure you visualize the imported data by printing the first few rows of the data frame.

# Import the CSV file
data <- read.csv('employee_data.csv')
# Print the first few rows of the data frame
head(data)

Task 2.2: Handling Missing Values

In employee_data.csv, some values are coded as NaN, which implies a missing value. Modify the previous task to properly import these values in the CSV file. Then verify these values were properly coded as NA in R, rather than as a character string.

# Import the CSV file with missing values handled
data <- read.csv('employee_data.csv', na.strings = 'NaN')
# Print the data
head(data)
# Verify NAs were handled properly
is.na(data)

Task 2.3: Selecting Specific Columns and Defining Their Data Types

Now, import the same CSV file but only select the columns ID and Salary, and define their data types as character and double respectively.

# Import the CSV file selecting specific columns
data <- read.csv('employee_data.csv', na.strings = 'NaN', 
                 colClasses = c(ID = 'character', Salary = 'double', Name='NULL', Department='NULL'))
# Print the first few rows of the data frame
head(data)

Delving into Delimited Files and Dataframe Searches

To review the concepts covered in this step, please refer to the Importing Delimited Files in R module of the Importing Formatted Text Files: R Playbook course.

Delving into delimited files and understanding dataframe searches is essential because it expands your data import capabilities and enhances your data manipulation skills in R. This step combines importing delimited files with searching dataframes using which.max and which.min functions. This exercise will broaden your data handling skills and introduce you to more complex data analysis techniques in R.

Task 3.1: Importing a Tab-Delimited File

Import the tab-delimited file named employee_data_tab_separated.txt into R using the read.delim function. Store the imported data in a variable named data_df. After importing, display the first few rows of the dataframe to ensure it's loaded correctly.

data_df <- read.delim('employee_data_tab_separated.txt')
head(data_df)

Task 3.2: Finding the Maximum Value in a Column

Find the index of the maximum value in the Salary column of the data_df dataframe. Store the index in a variable named max_index and print it.

max_index <- which.max(data_df$Salary)
print(max_index)

Task 3.3: Finding the Minimum Value in a Column

Find the index of the minimum value in the Salary column of the data_df dataframe. Store the index in a variable named min_index and print it.

min_index <- which.min(data_df$Salary)
print(min_index)

Converting JSON Data for R Analysis

To review the concepts covered in this step, please refer to the Importing JSON Files in R module of the Importing Formatted Text Files: R Playbook course.

Learning to convert JSON data into a format that R can analyze is crucial because JSON is a widely used data format in web applications. This step focuses on importing JSON data and converting it into an R list or data frame for analysis.

Step into the world of JSON data with R! Your task is to practice importing JSON data using the fromJSON function from the rjson package. After importing, you'll convert the JSON data into an R list and then into a data frame. This exercise will equip you with the skills to handle JSON data, a common format in web-based data sources.

Task 4.1: Import JSON Data from a Local File

Your initial task is to import JSON data from a local file. You'll import the employee_data.json file which is located in the current working directory. Store the imported data in a variable named json_data and print it to verify that the data was imported.

# Load the rjson R package
library(rjson)

# Import the data
json_data <- fromJSON(file = 'employee_data.json')
print(json_data)

Task 4.2: Convert JSON Data to a Data Frame

The fromJSON function imports the data as a list of lists. Transform the JSON data into a matrix, and from a matrix into an R data frame. Store the resulting data frame in a variable named df_data and print it.

# Convert to a matrix
matrix_data <- do.call(rbind, json_data)

# Convert the matrix to a data frame
df_data <- as.data.frame(data_matrix)
print(df_data)

Importing and Analyzing XML Data

To review the concepts covered in this step, please refer to the Importing XML Files in R module of the Importing Formatted Text Files: R Playbook course.

Importing and analyzing XML data is important because XML is frequently used in data exchange and storage. This step covers importing XML data into R and converting it into a usable format for analysis. This hands-on experience will prepare you to work with XML data, enhancing your data import and analysis capabilities in R.

Task 5.1: Load the XML Package

Load the XML package, which comes pre-installed in this R environment. This package provides functions necessary for importing and processing XML files.

# Load the XML package into R
library(XML)

Task 5.2: Import XML Data from a Local File

Import the file employee_data.xml into R as a data frame. Print the data frame to verify it imported correctly.

# Import the data
xml_data <- xmlToDataFrame('employee_data.xml')
xml_data