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

 

String Manipulation and the plyr Package

1) We have three important steps we need to cover for this week's assingent. Step #1: Import some data then, run the commend "mean" using Sex as the category (use plyr package for this operation). Last commend in this step: write the resulting output to a file. Step#2: Convert the data set to a dataframe for names whos' name contains the letter i, then create a new data set with those names, Write those names to a file separated by comma’s (CSV). Step#3: Write the filtered data set and convert it to CSV file. With all of that laid out lets write some code to make it all happen. 

> #Module 8 assignment: Input/Output, string manipulation and plyr package
> # Install necessary packages if not already installed
> install.packages("plyr")
> library(plyr)
> library(data.table)
> 
> # Step 1: Read the data from the file
> students <- fread("CENSORED/Assignment 6 Dataset.txt", header = TRUE, sep = ",")  # Using fread from data.table package
> print(students)
         Name   Age    Sex Grade
       <char> <int> <char> <int>
 1:      Raul    25   Male    80
 2:    Booker    18   Male    83
 3:     Lauri    21 Female    90
 4:    Leonie    21 Female    91
 5:   Sherlyn    22 Female    85
 6:   Mikaela    20 Female    69
 7:   Raphael    23   Male    91
 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
14:    Fallon    22 Female    90
15:   Deloris    21 Female    67
16:    Randee    23 Female    91
17:     Eboni    20 Female    84
18:   Delfina    19 Female    93
19: Ernestina    19 Female    93
20:      Milo    19   Male    67
         Name   Age    Sex Grade
> 
> # Calculate the mean grade for each sex category
> students_gendered_mean <- ddply(students, "Sex", summarise, Grade.Average = mean(Grade))
> 
> # Step 1: Write the output to a file
> write.table(students_gendered_mean, "Students_Gendered_Mean.txt", row.names = FALSE, sep = "\t")
> 
> # Step 2: Filter the dataset for names containing the letter "i"
> i_students <- subset(students, grepl("i", Name, ignore.case = TRUE))
> print(i_students)
         Name   Age    Sex Grade
       <char> <int> <char> <int>
 1:     Lauri    21 Female    90
 2:    Leonie    21 Female    91
 3:   Mikaela    20 Female    69
 4:      Aiko    24 Female    97
 5:  Tiffaney    21 Female    78
 6:    Corina    23 Female    81
 7: Petronila    23 Female    98
 8:    Alecia    20 Female    87
 9:   Shemika    23 Female    97
10:   Deloris    21 Female    67
11:     Eboni    20 Female    84
12:   Delfina    19 Female    93
13: Ernestina    19 Female    93
14:      Milo    19   Male    67
> 
> # Step 3: Write the filtered data to a CSV file
> write.csv(i_students, "i_students.csv", row.names = FALSE)

2) Just like that we have a new file, separated by commas with the names of students who have the letter "i" in them. Not much more to talk about this week.