Importing & Cleaning Data with R

Importing data into R is the easiest way to start your analysis. Data can come in many formats. These formats include text files, statistical software files, as well as databases or HTML data. Before you begin the actual analysis, it is crucial to choose the right approach. This course will show you how to read text files and.csv files using R. Next you will learn about data.table and reader packages which allow you to import flat file data quickly and efficiently. Next, you will learn how to open.xls file using readxl and gdata in R.

This course will provide a deeper understanding of the various data formats. This course will teach you how to import data into relational databases and how to use data from the internet. Practical experience will also be gained with data import using statistical software packages such as SAS, STATA, and SPSS.

Data scientists spend around 80% of their time cleaning and manipulating data and 20% on analysing it. Spending time cleaning data is crucial as it can lead to incorrect conclusions.

This course will show you how to clean up data. This course will teach you how to use R to identify errors in data and correct them with fuzzy string matching and data transformation. Learn how to work with real data such as restaurant reviews and bike-share trips. This will allow you to sharpen your skills and gain amazing insights from raw data quickly.

It can be overwhelming to deal with complex and confusing data sets. To organize this data, tidyr can be used to make it neater. The rows will contain the accessible values from column names. JSON files will convert to data frames. The missing values will not be lost again. These techniques can be used to analyze a wide range of data sets. This will reveal how many dogs were sent into space by the Soviet Union, and which bird is most popular in New Zealand. Any dataset can be transformed using the tidyr package in your tidyverse toolkit. This will allow you to easily complete the rest of your analysis in clean formats.

This project will require you to use your data cleaning and importing skills to gain insights into the New York City Airbnb market. To answer questions about New York's Airbnb market, you will need to import data from different file types. To extract the correct information from the data, you will need to use your date manipulation and string cleaning skills. Data scientists use these packages and tools every day, as a lot of the data in the world is stored in unclean formats that are not ready for analysis.

When working with R, it's essential to know how to prepare your data. This track will teach you how to import data from many sources including.csv and.xls. The skills you will need to prepare your data for analysis include converting data types, filling out missing values and using fuzzy string matching. You'll be able to use your skills in real-world situations such as restaurant reviews and customer asset portfolios. This track will help you gain data preparation skills that can be used to clean up your data.