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

Input/Output, string manipulation and plyr package

Part 1

First, importing the dataset into R using read.table() and specified that the file is comma-separated.

Input:

students <- read.table(file.choose(), header = TRUE, sep = ",")

students

Output:

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
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

Next, using the plyr package and the ddply() function to calculate the average grade grouped. 
Input:
students_gendered_mean <- ddply(students, "Sex", summarise,
                                Grade_Average = mean(Grade))

students_gendered_mean

Output:
     Sex Grade_Average
1 Female       86.9375
2   Male       80.2500


Finally, I exported the results to a file.

write.table(students_gendered_mean,
            "Students_Gendered_Mean.txt",
            row.names = FALSE)



Part 2
Filtering the dataset using subset() and grepl() to select students whose names contain the letter i.

Input:

 i_students <- subset(students, grepl("i", students$Name, ignore.case = TRUE)) 
 i_students


Output:


        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

The filtered dataset was written to a CSV file. 


write.csv(i_students,
          "Students_with_i.csv",
          row.names = FALSE)