Gain insights into data manipulation, analysis, and visualization with R. Learn statistical concepts, create insightful visualizations, and empower decision-making to excel in data-centric roles.

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This course delves into data manipulation, analysis, and visualization with R. It demonstrates the power of R as open-source statistical software, unlocking valuable insights for decision-making across diverse fields.

You'll begin by mastering the basics of R programming, fundamental data structures in R, and file management. You will learn fundamental statistical concepts such as hypothesis testing and regression analysis. You will also learn to leverage well-structured data for creating insightful visualizations using R's robust graphics libraries.

After completing this course, you'll confidently approach data-driven challenges. With proficiency in R, you can efficiently manipulate and analyze data, positioning yourself as a sought-after asset in data-centric roles. Your ability to interpret statistical results and create compelling data visualizations will empower improved decision-making, paving the way for new opportunities for career growth and success across various industries.

Introduction to Data Analysis and Visualization with R

# Compare solutions
Please compare your code to the one given below. R is a flexible programming language. We can find the solution using extra functions or by following the steps in a different order. Please keep in mind that this solution is only one of many. Feel free to play with the code and try various methods. Here is our solution to the challenge in the previous lesson:

She saw Sheriff's shoes on the sofa.
But was she so sure those were Sheriff's shoes she saw?

library(stringr)    # Call the libraries
library(readr)      # Call the libraries
create_matrix <- function(x){ 
  # Code between the lines 
  # ------
  text <- read_file('hello.txt')   # Read the file
  my_text <- str_to_lower(text)   # Lowercase the text
  my_text <- str_replace_all(my_text,'\n','')  # Replace all newline characters with nothing-delete-
  my_text <- str_split(my_text,' ')  # Split the text where blanks are
  my_text <- my_text[[1]]      # Select the first underlying element
  words <- my_text[str_detect(my_text,'sh')]   # Detect all words involving 'sh'
  result <- str_length(words)  # Calculate the lengths of words
  # ------
  #Please name the desired output as 'result'
  cat(result)
}
create_matrix(text)

## Code explanation 

**Lines 1–2:** We call the libraries needed for the challenge.

**Line 4:** We create the test function.

**Line 6:** We read the text file using the `read_file()` function from the `readr` library.


**Line 7:** We convert the text into lowercase so that the words with the same spellings but with different letter cases are identical. We can work on the text in its lowercase form.

**Line 8:** We replace the newline creator characters `\n` with nothing (that is, we delete the newline creator characters).

**Line 9:** We split the text object by the blanks. This allows us to isolate each word. This way, we can work on them separately. 

**Line 10:** We select the first object of the list using double brackets (`[[ ]]`). Double brackets help us choose the underlying elements, which is the appropriate format for processing. 

**Line 11:** Now that the underlying vector is accessed, we use the `str_detect()` function to find the words that include the `sh` characters. Remember that `str_detect()` returns logical values. Masking the original vector that contains the words with the detect object that includes logical elements only returns the words with `sh` characters. We name this object `words`.

**Line 12:** Finally, we calculate the length of each word using the `str_length()` function and name it 
`result`.
  
**Line 15:** We concatenate and print the result.



See one of several solutions to the "Calculate String Length" challenge and compare your solution to it.

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Uber Data Analysis Using the R Language

Conclusion

Evaluation

Netflix Shows

Solution: Calculate String Length

Compare solutions