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Importing Data and Working With Data in R (R Tutorial 1.6) Video Lecture | Mastering R Programming: For Data Science and Analytics - Database Management

51 videos
Video Timeline
Video Timeline
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00:07 How to read a dataset into R?
00:27 How to access the help menu in R
01:02 How to let R know that the first row of our data is the header?
01:14 How to let R know how the observations are separated?
02:03 How to specify the path to a file in R?
03:15 How to use Menu options in R Studio to import data into R
05:23 How to prepare the Excel data for importing into R
06:15 How to know the dimensions (the number of rows & columns) of the data in R?
06:35 How to see the first several rows of the data?
06:45 How to see the last several rows of the data in R?
07:18 How to check if the data was read correctly into R?
08:21 How to check the variable names in R?
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FAQs on Importing Data and Working With Data in R (R Tutorial 1.6) Video Lecture - Mastering R Programming: For Data Science and Analytics - Database Management

1. What is the purpose of importing data in R?
Ans. The purpose of importing data in R is to bring external data into R environment so that it can be manipulated, analyzed, and visualized using various R functions and packages.
2. How can I import data into R?
Ans. There are several ways to import data into R. Some common methods include using functions like read.csv() for importing CSV files, read.table() for importing text files, read_excel() for importing Excel files, and dbConnect() for connecting to databases and importing data from them.
3. Can I import data from databases in R?
Ans. Yes, R provides various packages such as RMySQL, RSQLite, and RPostgreSQL that allow you to connect to databases and import data directly into R. You can use functions like dbConnect(), dbGetQuery(), and dbReadTable() to interact with databases and import data from them.
4. How can I work with imported data in R?
Ans. Once the data is imported into R, you can perform various operations on it. You can manipulate the data using functions like subset(), merge(), and transform(). You can analyze the data by applying statistical functions and models, and visualize the data using plotting functions and libraries like ggplot2.
5. Are there any limitations or considerations when importing large datasets in R?
Ans. Yes, when importing large datasets in R, it is important to consider the memory limitations of your system. Importing large datasets can consume a significant amount of memory, so it is recommended to use functions like fread() from the data.table package or readr::read_csv() from the readr package, as they are optimized for reading large datasets efficiently. Additionally, you may need to use techniques such as data chunking or parallel processing to handle large datasets effectively.
51 videos
Video Timeline
Video Timeline
arrow
00:07 How to read a dataset into R?
00:27 How to access the help menu in R
01:02 How to let R know that the first row of our data is the header?
01:14 How to let R know how the observations are separated?
02:03 How to specify the path to a file in R?
03:15 How to use Menu options in R Studio to import data into R
05:23 How to prepare the Excel data for importing into R
06:15 How to know the dimensions (the number of rows & columns) of the data in R?
06:35 How to see the first several rows of the data?
06:45 How to see the last several rows of the data in R?
07:18 How to check if the data was read correctly into R?
08:21 How to check the variable names in R?
More
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