Uncategorized – DateA Set

Why learn a language? This is one avenue of life that I am constantly working on. If I had the ability to speak fluently in another language, life would be easier (emphasis on the would be). Whatever your reason for studying a language, you can keep track of your progress over time with Duolingo.

I want to show you how to export your own data from Duolingo and hopefully inspire you to do better than myself in your language learning practice. You will see what I mean… 😉

How to download your data from Duolingo?

First, you want to log into your account, then go to the “Settings.”

Next, you will want to find “Export my data,” and then click that.

There was a message that popped up stating that it could take up to 30 days for them to send my data, but the reality is, I got an email within an hour stating that the data was ready to be downloaded.

We did it!!! Now, let’s analyze the results! They should come in a csv file, so if you want to use Excel for analysis, that is an option, Tableau, R, Python, and Power BI, to name a few, are other viable options. I am going to use R. Here is a complete version of what I did: http://rpubs.com/natester/duolingoanalysis

Here is the summarized version of what I did….Looking at totals for 2019 and 2020, you will see I did better in 2019.

leaderboard_barchart <- ggplot(data=leaderboard_groupedby_year, aes(x=year, y=TOTAL_SCORE))

leaderboard_barchart + 
  geom_col(color= c('orangered','blue'),fill=c('orangered','blue'))+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank())+
  theme_void()+
  labs(title= "Total Score for 2019 (Orange) and 2020 (Blue)")+
  geom_text(aes(label = TOTAL_SCORE), 
            position = position_dodge(0.9),
            vjust = -0.5,
            size =5,
            color=c('orangered','blue'))

Visualizing the Data by Month

head(leaderboard)
##   leaderboard       date timestamp tier score year
## 1     leagues 2019-05-18  20:00:35    0    20 2019
## 2     leagues 2019-05-20  11:14:16    0    50 2019
## 3     leagues 2019-05-27  12:41:58    0    60 2019
## 4     leagues 2019-06-03  11:58:17    0    40 2019
## 5     leagues 2019-06-10  22:35:45    0    50 2019
## 6     leagues 2019-06-17  11:36:58    0    40 2019

leaderboard_linechart <- ggplot(data=leaderboard, aes(x=date, y=score))

leaderboard_linechart + 
  geom_line(color= c('cornflowerblue'),size =1)+
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank())+
  labs(title= "Score Over Time for 2019 through 2020")

It looks like I reached my highest peak in my score at the beginning of 2020. What was that score?

sqldf("SELECT date, MAX(score) AS HIGHEST_SCORE
       FROM leaderboard 
       ")
##         date HIGHEST_SCORE
## 1 2020-01-27           109

109 was my highest score on ‘2020-01-27’, however, as we discovered. I showed a greater total score for 2019.

The take away from my charts are, overtime consistency matters more than one single learning sprint; especially, when it comes to learning languages.

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First Reactions to the title: You might be asking me in your mind, why Baby Names? And my response to you is, why not? The next question you probably have is, why DaDaset…that’s not how you say “data”? And my response to you is, some of our fellow Americans would beg to differ.

When Picking a baby name, the first thing you need to make sure of is that you have conditions for what your baby name must have, and, if you want to go crazy, must not have. I went crazy. See the fictional conditions below.

Baby Names Conditions:

Unique
Cannot be the same as my name, my parents, or etc, etc
Needs to have the letter “A” or “E”
No “R”s and no “Y”s

When I am looking at a name, usually there is some inexpressible reason why I do or don’t like a name. You may experience something similar when I ask: Would you name your child Mkjsodnfgonsdfououosdfonsf? This was a sub-random string of characters I typed into my keyboard meant as a humorous illustration, but still, I believe the point is made clear; you either don’t like the name or do don’t like the name, so the first step is figuring out what names you do like. Here are a few categories of names you may want to consider:

Historical names: These could be the names of biblical, religious, celebrated, and famous individuals.

Names that carry meaning: These are any names that have a special meaning or significance attached to them. Like Emmanuel, for which means “God with us.”

Current or past names that have been used: The government has been keeping track of this information and posting it to this site: http://www.ssa.gov/OACT/babynames/index.html. This site is incredibly useful if you are picking out a name! Take a look at the different years and female and male names that were picked by parents in different years.

Names that are adopted from another language: Keeping one’s native tongue alive is a great idea. There are other reasons why you might choose to use a different language, such as the sound of the name in another language, or many other reasons.

Once you have gathered your dataset, you are ready to meet with your significant other to go through the list using the conditions that you set for the name. Your conditions can ABSOLUTELY be different from the fictional conditions I listed above.

Why uniqueness? What if we move to another country where everyone else’s name is different? This is a possibility, and, I want to emphasize that there is a list of possible location outcomes for your child. Having a kid or kids with the same name does mean it is the end of the world! Thank goodness for nicknames, middle names, and initials! If you have a common first name, you can always pick a slightly more unique middle name to increase the chances that your middle name won’t be the name of another person. Therefore, you can truly tell your child you tried to avoid this scenario…

[Illustration of students in a classroom, the teacher is going through the list, Nate?….the two student’s with the name “Nate” look at each other confused. Questions running through their heads may be: Who is she asking for? Why didn’t we put our nicknames down on the class roster? Why can’t I be NT? Why is Nate 3 absent today?]

Check out Rpub to get a better presentation of the code and…the results:

http://rpubs.com/natester/boybabynames

Baby Boy Names 2020

library(dplyr)
library(rvest)
library(prettifyAddins)
library(ggplot2)
library(stringr)
library(sqldf)
html_form_page <- 'http://www.whattoexpect.com/baby-names/list/top-baby-names-for-boys/###top-names' 
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

Please send me an email with your thought or comment! Thank you for reading!

Category Archives: Uncategorized

Balancing the Management of your Time and your Energy

Learning a Language? Export your Data from Duolingo.

Creating a DaDaset for Baby Names