[R] Clean the data before analysis

Steps you might take when preparing a new dataset for analysis:

1. Explore and Understand the Data:

   • Examine the structure of the dataset using functions like str() and summary().
   • Identify the types of variables present (numeric, character, factor) and understand the nature of the data.

2. Handle Missing Values:

   • Check for missing values and decide on an appropriate strategy for handling them, such as imputation or removal.

3. Convert Data Types:

   • Use functions like as.numeric(), as.character(), and as_factor() to convert variables to the appropriate data types.

4. Ensure Consistency:

   • Make sure variables are consistently represented across different datasets. For example, if a variable is categorical, ensure it is stored as a factor.

5. Handle Categorical Variables:

   • If dealing with categorical variables, consider using factors and ensure that the levels are appropriately defined.

6. Prepare Data for Analysis:

   • Perform any necessary data transformations, scaling, or normalization depending on the requirements of your analysis.

7. Check Compatibility with Functions:

   • Verify that the functions you plan to use for analysis are compatible with the data types and structures in your dataset.

8. Document Your Steps:

   • Keep a record of the steps you took to preprocess the data, as this documentation is crucial for transparency and reproducibility.

Packages: psych, hmisc, dplyr  are needed for this time

install.packages(c("psych", "hmisc", "dplyr"))

library(psych)
library(hmisc)
library(dplyr)

 

psych Package:

• Purpose:

  • Primarily used for psychological and educational measurement.
  • Provides functions for descriptive statistics, factor analysis, and reliability analysis.

        

# Descriptive statistics
describe(your_data_frame)

# Factor analysis
fa(your_data_frame)

# Reliability analysis
alpha(your_data_frame)

hmisc Package:

• Purpose:

  • Stands for "Harrell Miscellaneous."
  • Offers a variety of functions for data analysis, especially related to regression models and data manipulation.

  • # Creating a summary table
    summary(your_regression_model)
    
    # Imputing missing values
    impute(your_data_frame)
    
    # Creating a frequency table
    table(your_data_frame$variable)
    

    dplyr Package:

  • Purpose:

    • Essential for data manipulation tasks.
    • Provides a set of functions for selecting, filtering, transforming, and summarizing data.

Example Functions (Recap):

# Selecting specific variables
select(your_data_frame, variable1, variable2)

# Filtering data
filter(your_data_frame, variable1 > 10)

# Transforming data
mutate(your_data_frame, new_variable = variable1 + variable2)

# Arranging data
arrange(your_data_frame, variable1)

# Summarizing data
summarize(your_data_frame, mean_variable1 = mean(variable1))

forcats Package:

To transform the variable Golf_trainer$worker into a factor variable using as_factor from the forcats package (a part of the tidyverse), you would need to follow these steps:

1. Install and Load the Required Packages:

   install.packages("tidyverse")
   library(tidyverse)
   

   # Assuming Golf_trainer is your data frame
   Golf_trainer$worker <- as_factor(Golf_trainer$worker)
   

   This will convert the worker variable in the Golf_trainer data frame into a factor using the as_factor function.

2. Categorical Data Representation:

   • Factors are useful for representing categorical variables, especially when dealing with nominal or ordinal data.
   • They enable efficient storage and manipulation of categorical information.

3. Statistical Modeling:

   • Many statistical models and analyses in R expect categorical variables to be represented as factors.
   • Factors facilitate the creation of dummy variables for regression modeling.

4. Levels for Ordinal Data:

   • Factors can have predefined levels, which is beneficial for ordinal data where the order matters.

 

Now, regarding as.numeric and as.characte

• as.numeric:

  • It is used to coerce a variable into a numeric type.
  • Useful when you have a character or factor variable that represents numeric values, and you want to perform arithmetic operations on it.

  • Mathematical Operations:

    • Numeric data types are essential for mathematical operations, including arithmetic calculations and statistical analyses.
    • They allow for quantitative measurements and numerical computations.

  • Statistical Analysis:

    • Many statistical tests and models require numeric input, such as regression analysis, t-tests, and correlation analysis.

  • Plotting:

    • Numeric variables are often used for creating meaningful visualizations like scatter plots, histograms, and box plots.

Example

numeric_vector <- as.numeric(character_vector)

as.character:        

• It is used to coerce a variable into a character type.
• Useful when you want to treat numeric or factor variables as characters, such as when creating new variable names or combining strings.

• Textual Data Representation:

  • Character data types are used for storing textual information, such as names, labels, and descriptive text.
  • Useful for variables with non-numeric identifiers.

• String Manipulation:

  • Character data types support string manipulation functions, making it easy to modify and extract parts of text.

• Plot Labels and Annotations:

  • Often used for labeling axes, legends, and other annotations in plots.

Notice: 

        before transforming a variable, screen it to compare before and after transformation

        Do not over write, but create a new variable you can eventually delete.

for example: the year_of_birth maybe changed during the process.

how to code the non answer with na_if?

In R, the na_if() function is part of the dplyr package and is used to replace specified values with NA (missing values). If you want to replace specific non-answer values in your dataset with NA, you can use na_if().

library(dplyr)

# Assuming your data frame is named "your_data" and the non-answer value is -999
your_data <- your_data %>%
  mutate_all(~na_if(., -999))

In this example, mutate_all() is used to apply the na_if() function to all columns in your data frame. It replaces all occurrences of the specified non-answer value (-999 in this case) with NA.

You also can use na_if() in the way :

Modified_object <- na_if(original_object, specific_value)

Here, original_object is the vector or column you want to modify, and specific_value is the value you want to replace with NA in that object.

Here's a simple example:

# Create a vector with some specific values
original_vector <- c(10, 20, 30, 40, 10, 50)

# Use na_if to replace occurrences of 10 with NA
modified_vector <- na_if(original_vector, 10)

# Print the modified vector
print(modified_vector)

Indeed, the droplevels() function in R is often used in conjunction with factors. When you manipulate data and create subsets, factors might retain levels that are no longer present in the subset. droplevels() helps remove those unused levels, making your factor more efficient and reflective of the actual data.

Here's an example using both na_if() and droplevels():

library(dplyr)

# Assuming your_data is a data frame and column_name is the column you want to modify
your_data <- your_data %>%
  mutate(column_name = na_if(column_name, specific_value)) %>%
  droplevels()

The %>% symbol in R represents the pipe operator, and it is part of the tidyverse, particularly associated with the dplyr package. It is used for creating pipelines in which the output of one function becomes the input of the next. This can make your code more readable and expressive.

# Example with factors and NAs
original_factor <- factor(c("A", "B", "A", NA, "B"))

# Check levels before droplevels
levels(original_factor)  # Output: [1] "A" "B" NA

# Use droplevels
modified_factor <- droplevels(original_factor)

# Check levels after droplevels
levels(modified_factor)  # Output: [1] "A" "B"

In this example, even though original_factor has an NA level, using droplevels() on it results in a factor with only levels "A" and "B." However, the NA level is still present in the modified factor; it's just that it's not shown in the levels. 

Reordering levels of a factor variable.

Reordering levels of a factor variable can be done using the factor() function or the reorder() function in R. Here's how you can use both approaches:

# Example factor variable
original_factor <- factor(c("Low", "Medium", "High", "Low", "High"))

# Reordering levels
reordered_factor <- factor(original_factor, levels = c("Low", "Medium", "High"))

# Checking the levels
levels(reordered_factor)

# Example factor variable
original_factor <- factor(c("Low", "Medium", "High", "Low", "High"))

# Reordering levels with reorder()
reordered_factor <- reorder(original_factor, levels = c("Low", "Medium", "High"))

# Checking the levels
levels(reordered_factor)

To merge or recode levels of a factor variable in R,

you can use the recode() function from the dplyr package. The recode() function allows you to replace specific values with new values, effectively merging levels.

library(dplyr)

# Example factor variable
original_factor <- factor(c("Low", "Medium", "High", "Low", "High"))

# Recode levels (merge "Low" and "Medium" into "Low_Medium")
recoded_factor <- recode(original_factor, "Low" = "Low_Medium", "Medium" = "Low_Medium")

# Checking the levels
levels(recoded_factor)