Objective:

This lab assignment aims to reinforce your understanding of data cleaning and descriptive analysis using the dplyr and psych packages in R. You will apply these concepts through practical exercises, focusing on using and stacking dplyr functions with the %>% operator.

Instructions:

  1. Complete each exercise by writing the necessary R code.

  2. Ensure you use the %>% operator to chain multiple dplyr functions together.

  3. Interpret the results for each exercise.

  4. Knit your R Markdown file to a PDF and submit it as per the submission instructions.

Homework Exercises:

Exercise 1: Cleaning Data with dplyr

Clean a dataset using various dplyr functions.

  1. Use the following dataset for the exercise:
data <- data.frame(
  participant_id = 1:10,
  reaction_time = c(250, 340, 295, NA, 310, 275, 325, 290, 360, NA),
  gender = c("M", "F", "F", "M", "M", "F", "M", "F", "M", "F"),
  accuracy = c(95, 87, 92, 88, 94, 91, 85, 89, 93, NA)
)

print(data)
##    participant_id reaction_time gender accuracy
## 1               1           250      M       95
## 2               2           340      F       87
## 3               3           295      F       92
## 4               4            NA      M       88
## 5               5           310      M       94
## 6               6           275      F       91
## 7               7           325      M       85
## 8               8           290      F       89
## 9               9           360      M       93
## 10             10            NA      F       NA

  1. Clean the dataset by performing the following steps:

    • Remove rows with missing values.

    • Rename the reaction_time column to response_time.

    • Create a new column performance_group based on accuracy (High if accuracy >= 90, otherwise Low).

    • Remove outliers from the response_time column.

    • Relevel the performance_group column to set “Low” as the reference level.

# Install the dplyr package (if not already installed)
if(!require(dplyr)){install.packages("dplyr", dependencies=TRUE)}
#Load Library
library(dplyr)
 remove_outliers <- function(data, column) {  
   # Calculate quartiles and IQR using tidy evaluation  
   Q1 <- quantile(pull(data, {{ column }}), 0.25, na.rm = TRUE)  
   Q3 <- quantile(pull(data, {{ column }}), 0.75, na.rm = TRUE)  
   IQR_val <- Q3 - Q1  
   lower_bound <- Q1 - 1.5 * IQR_val  
   upper_bound <- Q3 + 1.5 * IQR_val  
     
   # Filter rows based on the calculated bounds  
   data %>%  
     filter({{ column }} >= lower_bound,  
            {{ column }} <= upper_bound)  
 }  
#create cleaned_data
cleaned_data <- data %>%
  na.omit() %>% 
  rename(response_time = reaction_time) %>% 
  mutate(performance_group = ifelse(accuracy >= 90, "High", "Low")) %>% 
  remove_outliers(response_time) %>% 
  mutate(performance_group = relevel(factor(performance_group), ref = "Low"))

print(cleaned_data)
##   participant_id response_time gender accuracy performance_group
## 1              1           250      M       95              High
## 2              2           340      F       87               Low
## 3              3           295      F       92              High
## 4              5           310      M       94              High
## 5              6           275      F       91              High
## 6              7           325      M       85               Low
## 7              8           290      F       89               Low
## 8              9           360      M       93              High

Interpretation: Two rows of missing data were removed due to missing values. There were not outliers. A column was renamed response time from reaction time. Preformance group was added as a new column which categorized reaction times high to low.

Exercise 2: Generating Descriptive Statistics with psych

Generate descriptive statistics for a dataset.

  1. Use the following dataset for the exercise:
study_hours <- data.frame(
  participant_id = 1:10,
  hours = c(5, 6, 4, 7, 5, 3, 8, 6, 5, 7)
)
  1. Generate descriptive statistics using the describe() function from the psych package.
# Install the psych package (if not already installed)
if(!require(psych)){install.packages("psych", dependencies=TRUE)}
#load the psych package
library(psych)
# Generate descriptive statistics
describe(study_hours)
##                vars  n mean   sd median trimmed  mad min max range  skew
## participant_id    1 10  5.5 3.03    5.5    5.50 3.71   1  10     9  0.00
## hours             2 10  5.6 1.51    5.5    5.62 1.48   3   8     5 -0.08
##                kurtosis   se
## participant_id    -1.56 0.96
## hours             -1.18 0.48
  • Interpretation: The mean number of hours studied is 5.6 with a SD of 1.51. The median is 5.5, 0.1 off, likely no outliers. Study hours ranged from 3-8 with a -0,08 skew, therefore normal distribution.

Exercise 3: Visualizing Data with psych

Create graphical summaries of a dataset using the psych package.

  1. Use the following dataset for the exercise:
experiment_data <- data.frame(
  response_time = c(250, 340, 295, 310, 275, 325, 290, 360, 285, 310),
  accuracy = c(95, 87, 92, 88, 94, 91, 85, 89, 93, 90),
  age = c(23, 35, 29, 22, 30, 31, 27, 40, 24, 32)
)
  • Create a correlation plot using the corPlot() function.
# Create the correlation plot
corPlot(cor(experiment_data))

  • Interpretation: Describe the correlation coefficients displayed in the plot, indicating the strength and direction of relationships between variables.

  • Create pair panels using the pairs.panels() function.

# Create the pair panels
experiment_data %>% 
  pairs.panels()

Interpretation: Significant negative correlation between accuracy and response time. Significant positive correlation between age and response time. Small negative correlation between accuracy and age. For pair panels, response time, accuracy, and age are all evenly spread out.Shows the same correlations as the corPlot.