Baby Boy Names 2020

Reading the HTML code

webpage <- read_html(html_form_page)

summary(webpage)

##      Length Class  Mode       
## node 1      -none- externalptr
## doc  1      -none- externalptr

head(webpage)

## $node
## <pointer: 0x0000000017a89170>
## 
## $doc
## <pointer: 0x0000000017fa9fd0>

names <- webpage %>%
  html_nodes("li")%>%
  html_text()

Creating a visualization

head(names)

## [1] "  Log In / Join "                                                                                                                                        
## [2] " Getting Pregnant    Fertility  Ovulation  Preparing for Pregnancy "                                                                                     
## [3] " Fertility "                                                                                                                                             
## [4] " Ovulation "                                                                                                                                             
## [5] " Preparing for Pregnancy "                                                                                                                               
## [6] " Pregnancy    Week By Week  Symptoms  Baby Names  Baby Shower  Complications  Due Date Calculator  Labor & Delivery  Screenings & Tests  Signs of Labor "

tail(names)

## [1] "What to Expect Bookstore"            "Advertising Policy"                 
## [3] "Do Not Sell My Personal Information" "Help"                               
## [5] " AdChoices "                         "Feedback"

class(names)

## [1] "character"

This dataset is not a dataframe; we need to turn it into one.

names <- data.frame(names)

head(names)

##                                                                                                                                                      names
## 1                                                                                                                                           Log In / Join 
## 2                                                                                       Getting Pregnant    Fertility  Ovulation  Preparing for Pregnancy 
## 3                                                                                                                                               Fertility 
## 4                                                                                                                                               Ovulation 
## 5                                                                                                                                 Preparing for Pregnancy 
## 6  Pregnancy    Week By Week  Symptoms  Baby Names  Baby Shower  Complications  Due Date Calculator  Labor & Delivery  Screenings & Tests  Signs of Labor

class(names)

## [1] "data.frame"

Need to get rid of rows 1-65, and potentially more

names <- names[-1:-65,]

head(names)

## [1] Liam     Noah     William  James    Oliver   Benjamin
## 1083 Levels:    Log In / Join   AdChoices  ... Zyaire

class(names)###factor?

## [1] "factor"

names <- data.frame(names)

names <- names[-1002:-1021,] ###I just wanted to make sure I didn't delete any names

2 extra rows as predicted

names <- data.frame(names)

names <- names[-1001:-1002,]

Baby_Boy_Names_2020 <- names

Ranking <- c(1:1000)

names_ranked <- cbind.data.frame(Ranking,Baby_Boy_Names_2020)

head(names_ranked)

##   Ranking Baby_Boy_Names_2020
## 1       1                Liam
## 2       2                Noah
## 3       3             William
## 4       4               James
## 5       5              Oliver
## 6       6            Benjamin

##————————— Now you want to remove any names that do not meet your conditions.—————————————

1. How many boys were born in said year? 2019 is the only year I gathered data for number of births using this number for the 2020 baby names, because it seems the baby names were from the 2019 data and used as 2020 names.

Around 37,308,668 boys and 35,730,482 girls according to: https://datacenter.kidscount.org/data/tables/102-child-population-by-gender#detailed/1/any/false/1729,37,871,870,573,869,36,868,867,133/14,15,65/421,422

Percentage/number of boys with top 2 names: Liam and Noah in 2019?

.010741 * 37308668

## [1] 400732.4

.009979 * 37308668

## [1] 372303.2

400,732 We won’t count the .4 of a person

372,303 We won’t count the .2 of a person

These top two names I will exclude from the data set.

Percentage/number of girls with top name: Olivia 2020?

.0010122 * 35730482

## [1] 36166.39

36,166 Once again, we won’t count the .39 of a person. Since I don’t have a dataset of girl names I will not finish this analysis…

however, I would like to look at it in the future! Note:This post took more time than I initially anticipated, please request more if you like it.

For boys names, I am going to strip out the top two: Liam and Noah

names_ranked <- names_ranked[-1:-2,]

head(names_ranked)###William is the new top name for 2020

##   Ranking Baby_Boy_Names_2020
## 3       3             William
## 4       4               James
## 5       5              Oliver
## 6       6            Benjamin
## 7       7              Elijah
## 8       8               Lucas

2. Cannot be the same as my name, my parents, or etc, etc

Taylor, cristiano, Madona, Shaquille, Jojo, Messi, Michael

Do a text string search for those names or names like it…in sQL…

family_names <-c("Taylor", "cristiano", "Madona", "Shaquille", "Jojo", "Messi", "Michael",
                ###adding on abbreviated names
                "Tay","Chris","Shaqy","Jo","Mike")

sqldf("SELECT *
      FROM names_ranked
      where Baby_Boy_Names_2020 = 'William'")

##   Ranking Baby_Boy_Names_2020
## 1       3             William

new_boy_names <- sqldf("SELECT *
      FROM names_ranked
      WHERE Baby_Boy_Names_2020 NOT IN(
       'Taylor', 
        'cristiano', 
        'Madona', 
        'Shaquille', 
        'Jojo', 
        'Messi', 
        'Michael',
        'Tay',
        'Chris',
        'Shaqy',
        'Jo',
        'Mike',
        'Nate')
        AND Baby_Boy_Names_2020 NOT IN(SELECT Baby_Boy_Names_2020
              FROM names_ranked 
              WHERE (Baby_Boy_Names_2020 LIKE '%r%'
              OR Baby_Boy_Names_2020 LIKE '%y%'
              OR Baby_Boy_Names_2020 LIKE '%i%'))
        AND (Baby_Boy_Names_2020 LIKE '%a%'
              OR Baby_Boy_Names_2020 LIKE '%e%')
      ")

class(new_boy_names)

## [1] "data.frame"

head(new_boy_names)

##   Ranking Baby_Boy_Names_2020
## 1       4               James
## 2       8               Lucas
## 3       9               Mason
## 4      10               Logan
## 5      12               Ethan
## 6      13               Jacob

summary(new_boy_names)

##     Ranking      Baby_Boy_Names_2020
##  Min.   :  4.0   Abdullah:  1       
##  1st Qu.:216.2   Abel    :  1       
##  Median :445.5   Ace     :  1       
##  Mean   :461.6   Adam    :  1       
##  3rd Qu.:691.2   Adan    :  1       
##  Max.   :999.0   Aden    :  1       
##                  (Other) :264

knitr::kable(new_boy_names, caption = "Dataset After Meeting Specified Conditions")

Dataset After Meeting Specified Conditions
Ranking	Baby_Boy_Names_2020
4	James
8	Lucas
9	Mason
10	Logan
12	Ethan
13	Jacob
17	Jackson
20	Matthew
21	Samuel
23	Joseph
25	Owen
28	Jack
29	Luke
37	Mateo
39	Jaxon
41	Joshua
45	Caleb
48	Nathan
49	Thomas
50	Leo
61	Landon
63	Jonathan
64	Nolan
66	Easton
72	Angel
76	Jaxson
78	Adam
86	Evan
89	Jose
90	Jace
91	Jameson
94	Axel
100	Jason
101	Declan
102	Weston
106	Luca
112	Chase
114	Emmett
118	Cole
120	Bennett
128	Ashton
132	Gael
135	Maxwell
136	Max
139	Juan
140	Maddox
145	Jonah
146	Abel
148	Jesus
151	Beau
152	Camden
153	Alex
157	Jude
158	Blake
159	Emmanuel
170	August
172	Alan
173	Dean
185	Jesse
187	Joel
194	Dawson
196	Matteo
198	Steven
200	Zane
202	Judah
207	Kaleb
214	Jax
216	Holden
217	Legend
220	Kaden
221	Paxton
225	Josue
226	Kenneth
227	Beckett
228	Enzo
233	Lukas
234	Paul
237	Caden
238	Leon
243	Theo
246	Jaden
255	Ace
256	Nash
262	Jake
269	Sean
270	Chance
276	Cash
284	Stephen
287	Dallas
289	Manuel
290	Lane
291	Atlas
293	Jensen
295	Beckham
296	Daxton
304	Jett
305	Cohen
316	Dante
319	Kane
320	Luka
321	Kash
323	Desmond
324	Donovan
330	Angelo
345	Muhammad
346	Jaxton
349	Dakota
351	Keegan
355	Kade
357	Leonel
361	Wade
370	Jase
371	Lennox
372	Shane
376	Seth
379	Lawson
381	Gage
385	Cade
386	Johnathan
393	Shawn
394	Malcolm
397	Dalton
403	Kason
405	Noel
419	Leland
420	Pablo
421	Allen
427	Damon
428	Emanuel
431	Bowen
434	Kasen
437	Jonas
438	Sage
440	Esteban
442	Kashton
449	Adan
453	Dax
454	Mohamed
456	Kamden
457	Hank
460	Augustus
465	Benson
472	Alonzo
473	Landen
486	Deacon
488	Eden
495	Tate
499	Moses
506	Case
508	Asa
511	Aden
517	Apollo
526	Donald
528	Saul
531	Duke
533	Tatum
534	Ahmed
535	Moshe
538	Cannon
539	Alec
541	Keaton
547	Samson
550	Cason
551	Ahmad
552	Jalen
557	Callum
570	Callen
574	Kobe
577	Mathew
579	Johan
582	Stetson
588	Callan
589	Cullen
593	Kannon
595	Axton
603	Sam
605	Mohammad
607	Gustavo
612	Hamza
617	Kellan
619	Kase
625	Kohen
627	Mohammed
630	Lucca
632	Mack
638	Alden
642	Zeke
650	Lance
655	Amos
660	Casen
661	Colten
667	Devon
669	Boone
671	Nelson
672	Douglas
675	Lennon
679	Noe
682	Lochlan
685	Langston
686	Lachlan
688	Abdullah
689	Lee
692	Ben
695	Joe
699	Kellen
701	Jakob
708	Tomas
710	Thaddeus
711	Watson
714	Koda
716	Nathanael
732	Santana
735	Wells
741	Axl
745	Musa
747	Enoch
750	Talon
756	Dane
765	Hassan
766	Jamal
772	Kole
775	Alonso
777	Madden
778	Allan
780	Jaxen
782	Magnus
784	Dash
798	Jaxxon
809	Keanu
816	Koa
818	Coen
827	Van
829	Canaan
836	Maxton
837	Tadeo
839	Aldo
853	Blaze
855	Kace
862	Eugene
866	Nova
873	Kenzo
878	Stefan
879	Wallace
881	Kendall
885	Anson
886	Gannon
890	Dangelo
893	Bentlee
897	Chad
899	Mustafa
912	Wesson
913	Alfonso
916	Juelz
917	Duncan
918	Keagan
919	Deshawn
920	Bode
926	Keenan
928	Jaxx
936	Heath
939	Elon
943	Maddux
948	Vance
949	Boden
969	Jad
975	Zev
983	Deangelo
986	Kalel
998	Benton
999	Coleman