A. First, import the .txt file into R so you can process it. Keep in mind this is not a CSV file. You might have to open the file to see what you’re dealing with. Assign the resulting data frame to an object, df, that consists of three columns with humanreadable column names for each.
Import document
yob2016 <- read.csv("~/SMUDataScience/msds6306/Lesson5/Assignment/yob2016.txt", header=FALSE, sep=";")Update names
names(yob2016) = c("First Name", "Gender", "Amount Of Children")
names(yob2016)Changed yob2016 to PopularChildrensNames2016
PopularChildrensNames2016 = data.frame(yob2016)
PopularChildrensNames2016B. Display the summary & structure of df
Summary
summary(PopularChildrensNames2016)Output Summary
First.Name Gender Amount.Of.Children
Aalijah: 2 F:18758 Min. : 5.0
Aaliyan: 2 M:14111 1st Qu.: 7.0
Aamari : 2 Median : 12.0
Aarian : 2 Mean : 110.7
Aarin : 2 3rd Qu.: 30.0
Aaris : 2 Max. :19414.0
(Other):32857 Structure
str(PopularChildrensNames2016)Output Structure
data.frame: 32869 obs. of 3 variables:
First.Name: Factor w/ 30295 levels "Aaban","Aabha",..: 9317 22546 3770 26409 12019 20596 6185 339 9298 11222...
Gender: Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1...
Amount.Of.Children: int 19414 19246 16237 16070 14722 14366 13030 11699 10926 10733... C. Your client tells you that there is a problem with the raw file. One name was entered twice and misspelled. The client cannot remember which name it is; there are thousands he saw! But he did mention he accidentally put three y’s at the end of the name. Write an R command to figure out which name it is and display it.
grep("yyy", PopularChildrensNames2016$First.Name)
[1] 212PopularChildrensNames2016$First.Name[212]
[1] FionayyyD. Upon finding the misspelled name, please remove this particular observation, as the client says it’s redundant. Save the remaining dataset as an object: y2016
Remove mispelled name “Fionayy”
y2016<-PopularChildrensNames2016[-c(212), ]A. Like 1a, please import the .txt file into R. Look at the file before you do. You might have to change some options to import it properly. Again, please give the dataframe human-readable column names. Assign the dataframe to y2015.
yob2015 <- read.csv("~/SMUDataScience/msds6306/Lesson5/Assignment/yob2015.txt", header=FALSE)
y2015 <- yob2015names(y2015) = c("First Name", "Gender", "Amount Of Children")B. Display the last ten rows in the dataframe. Describe something you find interesting about these 10 rows. .
The information below showcases that the last 10 rows in the dataframe include the first names start with a Z, gender for all z names are males and the total amount of children is 5.
Last10y2015 <- y2015[33053:33063, ] First Name Gender Amount Of Children
33053 Ziyi M 5
33054 Ziyu M 5
33055 Zoel M 5
33056 Zohar M 5
33057 Zolton M 5
33058 Zyah M 5
33059 Zykell M 5
33060 Zyking M 5
33061 Zykir M 5
33062 Zyrus M 5
33063 Zyus M 5C. Merge y2016 and y2015 by your Name column; assign it to final. The client only cares about names that have data for both 2016 and 2015; there should be no NA values in either of your amount of children rows after mergin .
Merge data
MergeData2 <- merge(y2015, y2016, union("First.Name"), all=TRUE)a. Create a new column called “Total” in final that adds the amount of children in 2015 and 2016 together. In those two years combined, how many people were given popular names?
cbind(y2015, y2016)Totals
summary(Final)
First.Name Gender Amount Of Children
Aalijah: 4 F:37811 Min. : 5
Aamari : 4 M:28120 1st Qu.: 7
Aarian : 4 Median : 11
Aaron : 4 Mean : 111
Aarya : 4 3rd Qu.: 30
Aaryn : 4 Max. :20415
(Other):65907 b. Sort the data by Total. What are the top 10 most popular names?
Final[rev(order(Final$`Amount Of Children`)),] First.Name Gender Amount Of Children
1 Emma F 20415
2 Olivia F 19638
19055 Noah M 19594
110000 Emma F 19414
210000 Olivia F 19246
187591 Noah M 19015
19056 Liam M 18330
187601 Liam M 18138
3 Sophia F 17381
19057 Mason M 16591c. The client is expecting a girl! Omit boys and give the top 10 most popular girl’s names.
FinalFemalesTop10Names <- Final[c("1", "2", "110000", "210000", "3", "4", "33064", "41000", "5", "6"),]d. Write these top 10 girl names and their Totals to a CSV file. Leave out the other columns entirely.
First.Name Gender Amount Of Children
1 Emma F 20415
2 Olivia F 19638
110000 Emma F 19414
210000 Olivia F 19246
3 Sophia F 17381
4 Ava F 16340
33064 Ava F 16237
41000 Sophia F 16070
5 Isabella F 15574
6 Mia F 14871Push at minimum your RMarkdown for this homework assignment and a Codebook to one of your GitHub repositories (you might place this in a Homework repo like last week). The Codebook should contain a short definition of each object you create, and if creating multiple files, which file it is contained in. You are welcome and encouraged to add other files—just make sure you have a description and directions that are helpful for the grader.