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

Github Link: https://github.com/rayhankhan-svg/r-programming-assignments


R Code: 

# Step 1: Import dataset

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


# View dataset

x


# Step 2: Install/load plyr and calculate mean Grade by Sex

install.packages("plyr")

library(plyr)


y <- ddply(x, "Sex", transform, Grade.Average = mean(Grade))


# View result

y


# Step 3: Write the mean result to a CSV-style file

write.table(y, "Sorted_Average", sep = ",", row.names = FALSE)


# Step 4: Filter names containing the letter i or I

newx <- subset(x, grepl("[iI]", x$Name))


# View filtered dataset

newx


# Step 5: Write the filtered subset to a CSV-style file

write.table(newx, "DataSubset", sep = ",", row.names = FALSE)


Output: 

> # Step 1: Import dataset

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

> 

> # View dataset

> x

        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

> 

> # Step 2: Install/load plyr and calculate mean Grade by Sex

> install.packages("plyr")

trying URL 'https://cran.rstudio.com/bin/macosx/big-sur-arm64/contrib/4.5/plyr_1.8.9.tgz'

Content type 'application/x-gzip' length 984753 bytes (961 KB)

==================================================

downloaded 961 KB



The downloaded binary packages are in

/var/folders/3k/3m0n90_x4pj426bkxvf2p01h0000gn/T//RtmpFal0Bh/downloaded_packages

> library(plyr)

> 

> y <- ddply(x, "Sex", transform, Grade.Average = mean(Grade))

> 

> # View result

> y

        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

> 

> # Step 3: Write the mean result to a CSV-style file

> write.table(y, "Sorted_Average", sep = ",", row.names = FALSE)

> 

> # Step 4: Filter names containing the letter i or I

> newx <- subset(x, grepl("[iI]", x$Name))

> 

> # View filtered dataset

> newx

        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 5: Write the filtered subset to a CSV-style file

> write.table(newx, "DataSubset", sep = ",", row.names = FALSE)

> 


Sorted_Average:

"Name","Age","Sex","Grade","Grade.Average"

"Lauri",21,"Female",90,86.9375

"Leonie",21,"Female",91,86.9375

"Sherlyn",22,"Female",85,86.9375

"Mikaela",20,"Female",69,86.9375

"Aiko",24,"Female",97,86.9375

"Tiffaney",21,"Female",78,86.9375

"Corina",23,"Female",81,86.9375

"Petronila",23,"Female",98,86.9375

"Alecia",20,"Female",87,86.9375

"Shemika",23,"Female",97,86.9375

"Fallon",22,"Female",90,86.9375

"Deloris",21,"Female",67,86.9375

"Randee",23,"Female",91,86.9375

"Eboni",20,"Female",84,86.9375

"Delfina",19,"Female",93,86.9375

"Ernestina",19,"Female",93,86.9375

"Raul",25,"Male",80,80.25

"Booker",18,"Male",83,80.25

"Raphael",23,"Male",91,80.25

"Milo",19,"Male",67,80.25


DataSubset:

"Name","Age","Sex","Grade"

"Lauri",21,"Female",90

"Leonie",21,"Female",91

"Mikaela",20,"Female",69

"Aiko",24,"Female",97

"Tiffaney",21,"Female",78

"Corina",23,"Female",81

"Petronila",23,"Female",98

"Alecia",20,"Female",87

"Shemika",23,"Female",97

"Deloris",21,"Female",67

"Eboni",20,"Female",84

"Delfina",19,"Female",93

"Ernestina",19,"Female",93

"Milo",19,"Male",67


Explanation: 

In order to get the mean grade for Module #8, I imported the Assignment 6 dataset into R and used the plyr tool using Sex as the category. After dividing the data by sex using the ddply() function, I created a new variable named Grade. I used the Grade column's mean to calculate the average. The average grade for female students was 86.9375, while the average grade for male students was 80.25, according to the data. I then used write.table() with comma separation to write this output to a file. I then created a new subset of the original dataset by filtering it so that only names with the letter i (including uppercase I) were included. To get those names, I used subset() in conjunction with grepl ("[iI]", x$Name). Students like Lauri, Leonie, Mikaela, Aiko, Corina, Petronila, Alecia, Shemika, Deloris, Eboni, Delfina, Ernestina, and Milo were included in the smaller dataset that resulted from this. Lastly, I created a second CSV-style file with this filtered subset. This assignment helped me practice importing data, using plyr to summarize grouped information, working with string matching, and exporting results to files.

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