Module 8

 This assignment shows how to load data, calculate averages by groups, find specific patterns in text, and save results to files.


Part 1: Load the Data

library(plyr)

> student6 = read.table(file.choose(), header = TRUE, stringsAsFactors = FALSE)

> str(student6)

'data.frame': 20 obs. of  1 variable:

 $ Name.Age.Sex.Grade: chr  "Raul,25,Male,80" "Booker,18,Male,83" "Lauri,21,Female,90" "Leonie,21,Female,91" ...

> head(student6)

    Name.Age.Sex.Grade

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

What it does: Loads the student data file into R and checks that it imported correctly with 20 students and their information.


Part 2: Calculate Average Grades

 #Step 2

> student6 = read.table(file.choose(), header = TRUE, stringsAsFactors = FALSE, sep = ",")

> str(student6)

'data.frame': 20 obs. of  4 variables:

 $ Name : chr  "Raul" "Booker" "Lauri" "Leonie" ...

 $ Age  : int  25 18 21 21 22 20 23 24 21 23 ...

 $ Sex  : chr  "Male" "Male" "Female" "Female" ...

 $ Grade: int  80 83 90 91 85 69 91 97 78 81 ...

> head(student6)

     Name Age    Sex Grade

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

> gender_mean = ddply(student6, "Sex", summarise, GradeAverage = mean(Grade, na.rm = TRUE))

> gender_mean

     Sex GradeAverage

1 Female      86.9375

2   Male      80.2500

What it does: Calculates the average grade separately for males and females using the plyr package.


Part 3: Save Averages to File

#stp 3

> write.table(gender_mean, file = "gender_mean.txt", sep = "\t", row.names = FALSE)

> list.files(pattern = "gender_mean.txt")

[1] "gender_mean.txt"

What it does: Saves the average grades to a text file so you can open it later or share it with others.


Part 4: Find Names with Letter i

#step 4

> i_students = subset(student6, grepl("[iI]", student6$Name))

> 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

What it does: Finds all students who have the letter i anywhere in their name, whether uppercase or lowercase.


Part 5: Save Just the Names

> #step 5

> write.csv(i_students$Name, file = "i_students.csv", row.names = FALSE, quote = FALSE)

What it does: Creates a CSV file with just the names of students who have i in their name.


Part 6: Save Complete Filtered Data

#Step 6

> write.csv(i_students, file = "i_students_full.csv", row.names = FALSE)

> 

> 

> 

> 

> 

> View(student6)

> View(i_students)

> View(student6)

> View(i_students)

> View(gender_mean)

> list.files(pattern = "*.txt|*.csv")

[1] "gender_mean.txt"     "i_students_full.csv" "i_students.csv"     

What it does: Saves all the information about students with i in their names and confirms all three files were created successfully.





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