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 the answers
In this lesson, we'll go through the challenge and explain the tasks required from us one by one. Check out the solution provided in the container below. Please note that there is not just one correct answer. We can still reach the same result by taking different paths.

# We use the 'patients' dataset in this exercise
library(dplyr)
print(head(patients))
print('----------The details of the dataset: -----')
print(str(patients))
print('--------- The result: ----------')
# First we define the average BMI for females and males by ignoring the null values
avg_f =  mean(patients[patients$gender=='Female',]$BMI,na.rm = TRUE) # Average BMI for female patients
avg_m =  mean(patients[patients$gender=='Male',]$BMI,na.rm = TRUE)   # Average BMI for male patients

result <- patients %>% mutate(BMI = ifelse(is.na(BMI)&gender=='Female',avg_f,BMI))%>% #  Impute null values with female average
  mutate(BMI = ifelse(is.na(BMI)&gender=='Male',avg_m,BMI))%>%  #  Impute null values with male average
  mutate(fasting_glucose=as.double(fasting_glucose))%>%     # Convert the character data type into double data type
  mutate(glucose_diff = abs((fasting_glucose - mean(fasting_glucose))/sd(fasting_glucose)))%>%  #  Calculate the Z-score for each fasting glucose value and create a new column
  mutate(avg_bp = (high_bp + low_bp)/2)%>%  # Create a new column for the average blood pressure
  mutate(in_danger= ifelse(avg_bp>=100,TRUE,FALSE))%>%  # Create a binary column stating that the patient is in danger if the average blood pressure is over 110
  select(c(id,gender,BMI,fasting_glucose,glucose_diff,avg_bp,in_danger))%>% # Select the columns desired by the managers.
  filter(glucose_diff >= 2 & BMI >= 25 & in_danger == TRUE)  # Filter the patients that carry danger in the given measurements.
print(result)   # Check the patients who are in danger
print('-------- The number of rows: -------')
result<- result%>%nrow()  # Find the number of patients who needs to be care.
print(result)

- **Line 5:** We check the data types of the columns. Notice that the `fasting_glucose` column is in character data type even though the characters are numeric. 

- **Lines 8–9:** We calculate the average `BMI` values for each sex, filtering the data frame by age first and calculating the mean values. We use the `na.rm` argument to omit the missing values.

- **Line 11:** We first change the null `BMI` values of female patients with the female average. We utilize the `mutate()` function to create new columns and modify data types.

- **Line 12:** We do the same thing for male patients. 

- **Line 13:** We convert the data type of the `fasting_glucose` column into the double data type.

- **Line 14:** We create a new column named `glucose_diff`. This column shows the standardized/z-score  version of the glucose levels. We calculate it by subtracting each value from the mean and dividing the outcome by the standard deviation of the `fasting_glucose` column. 

- **Line 15:** We create the `avg_bp` column by taking the average of the `high_bp` and the `low_bp` values.

- **Line 16:** We create another column named `in_danger`, which labels patients with `avg_bp` over 100 as `TRUE` and `FALSE` otherwise.

- **Line 17:** We utilize the `select()` function to separate these columns from the data frame: `id`, `sex`, `BMI`, `fasting_glucose`, `glucose_diff`, `avg_bp`, and `in_danger`.


- **Line 18:** We filter the rows with the `glucose_diff` equal to or bigger than 2, 
`BMI` equal to or bigger than 25,
and `in_danger` equal to `TRUE`.

- **Line 21:** We calculate the number of rows in the filtered data. 







Use data filtering, aggregation, and modification techniques to complete the challenge.

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: Help Patients

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