Module # 8 Input/Output, string manipulation and plyr package

> # Load package

> library(plyr)
> 
> # Step 1: Import the dataset
> students <- read.table("Assignment 6 Dataset.txt", header = TRUE, sep = ",")
> 
> # Mean Grade using Sex as the category
> students_gendered_mean <- ddply(students, "Sex", transform,
+ Grade.Average = mean(Grade))
> 
> # View result
> students_gendered_mean
        Name Age    Sex Grade Grade.Average
1      Lauri  21 Female    90       86.9375
2     Leonie  21 Female    91       86.9375
3    Sherlyn  22 Female    85       86.9375
4    Mikaela  20 Female    69       86.9375
5       Aiko  24 Female    97       86.9375
6   Tiffaney  21 Female    78       86.9375
7     Corina  23 Female    81       86.9375
8  Petronila  23 Female    98       86.9375
9     Alecia  20 Female    87       86.9375
10   Shemika  23 Female    97       86.9375
11    Fallon  22 Female    90       86.9375
12   Deloris  21 Female    67       86.9375
13    Randee  23 Female    91       86.9375
14     Eboni  20 Female    84       86.9375
15   Delfina  19 Female    93       86.9375
16 Ernestina  19 Female    93       86.9375
17      Raul  25   Male    80       80.2500
18    Booker  18   Male    83       80.2500
19   Raphael  23   Male    91       80.2500
20      Milo  19   Male    67       80.2500
> 
> # Write output to a file
> write.table(students_gendered_mean, "Students_Gendered_Mean.csv",
+ sep = ",", row.names = FALSE)
> 
> # Step 2: Create a new dataset with names containing i
> i_students <- subset(students, grepl("i", Name, ignore.case = TRUE))
> 
> # View filtered data
> i_students
        Name Age    Sex Grade
3      Lauri  21 Female    90
4     Leonie  21 Female    91
6    Mikaela  20 Female    69
8       Aiko  24 Female    97
9   Tiffaney  21 Female    78
10    Corina  23 Female    81
11 Petronila  23 Female    98
12    Alecia  20 Female    87
13   Shemika  23 Female    97
15   Deloris  21 Female    67
17     Eboni  20 Female    84
18   Delfina  19 Female    93
19 Ernestina  19 Female    93
20      Milo  19   Male    67
> 
> # Step 3: Write filtered dataset to CSV
> write.csv(i_students, "i_students.csv", row.names = FALSE)

For this assignment, I imported the Assignment 6 dataset into R and used the plyr package to analyze the data. Using the ddply() function, I calculated the mean grade for students grouped by sex. The results showed that the average grade for female students was 86.94, while the average grade for male students was 80.25. After calculating the averages, I created a new dataset by filtering the original data to include only student names that contained the letter “i” (case insensitive). This subset of the data was then written to a CSV file for further use. These steps demonstrate how R can be used to import datasets, perform grouped statistical analysis, filter observations, and export results to external files.