Final Project
Molly
11/28/2021
Introduction
Korean pop music, also better known as Kpop, has recently had a boom in interest in the west. Kpop houses many different type of artists, however what I am most interested in has to do with Kpop groups, often known in Korea as idol groups and idol music. My research question is how has kpop changed over the years with more global popularity?
To answer this research question I want to focus on these topics:
Debuted Groups - Due to kpop becoming more lucrative due to more fans worldwide, I believe that there are more groups debuting each year than in the early 2000s. I also believe that there will not be a significant difference in how many boy groups verses girl groups each year. These are the research questions I focused on:
- How many kpop groups debut by year?
- How many girl groups vs boy groups debut each year?
Foreign Idols - Kpop idols have always focused on domestic success first. However as kpop expanded to other Asian countries such as China and Japan for more lucrative deals, more foreign kpop idols have appeared to cater to this audience. I believe that there are more foreign idols debuting over time. These are the research questions I focused on:
- How many foreign idols debut in a group by year?
- How many groups debut in a year with at least one foreign idol?
Kpop Age Statistics - A long standing question in the kpop community, especially of fans outside of Korea, is has the debut age gone down for idols over the years? I believe that it has not. I also will look into how many kpop idols are still active as they grow older. I believe the older an idol gets the less likely they will still be active in the industry even though there are many global fans in their mid to late 20s. These are the research questions I focused on:
- What is minimum, maximum, and average ages of kpop idols by year?
- What is the age of debuting idols by year?
- What is the age of idols if they’re currently active in kpop?
Data
The data source I will be using is dbkpop.com.1 Database of Kpop-Idols (dbkpop) is a dedicated database of most kpop idols. Due to the overwhelming number of groups that debut each year, dbkpop will not have every group. Many of the smaller groups not listed are never heard of due to them disbanding quickly, only promoting at in person festivals in Korea, or their company not having enough funding to promote them. As of September 27th, 2021 dbkpop has 363 boy and girl groups2 (my kpop group data table) and 1,535 idols3 (my kpop idols table) on their website. Additionally the website gives important information for my questions,such as how many members are in a group, what age are the members when they debut, how many members are in a group,and how many active groups there are currently. There are also other values that may be interesting such as what country idols were born and how many idols were in a former group.
Reading in the data
The first task is reading in the two data sets kpop groups and kpop idols. One is a list of all kpop groups and the other is a list of all kpop idols that are currently on dbkpop. You will find the two tables of the current data below.
# I named this kpopg for kpop groups
kpopg <- read_csv(here("_data", "All Kpop Groups 9.27.21.csv"))## Rows: 384 Columns: 10## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): Girl Group or Boy Group?, Name, Short, Korean Name, Debut, Company,...
## dbl (2): Members, Orig. Memb.##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# I named this kpopi for kpop idols
kpopi <- read_csv(here("_data", "All Kpop Idols 9.27.21.csv"))## Rows: 1522 Columns: 18## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (15): Stage Name, Full Name, Korean Name, K. Stage Name, Group, Current...
## dbl (2): Height, Weight
## date (1): Date of Birth##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Below are the kpop group table and the kpop idol table. You can see all of the data if you page through it.
datatable(kpopg, class = 'table-bordered',
caption = 'Kpop Groups Information Unedited',
width = '100%', options = list(scrollX = TRUE, pageLength = 5))datatable(kpopi, class = 'table-bordered',
caption = 'Kpop Idol Information Unedited',
width = '100%', options = list(scrollX = TRUE, pageLength = 5))Cleaning the data
Although the data is currently fairly clean I did make some changes. For example I removed the columns Korean name, Korean stage name, second country, height, weight, birthplace, position, instagram, and twitter from the kpop idols table. This information is not useful for my research. For the kpop group table I removed the columns short (a different way of written out the name usually in English), and Fanclub name.
For both of these tables I took out the spaces and put underscores between them for easier reading. I used the functions rename(), select(), and colnames().
Rename() will allow me to rename columns using the formula (“new_category” = “old category). Another way you could rename is using col_names = c() to rename all columns. However this way requires me to rewrite each column name out and since I have so few I decided rename() was the quicker way.
Select() is used with the ! means the opposite value of the command. While select usually only selects the column requested, ! means to show all the columns but that one. I also used start_with(““) to be able to call the column by name.
Colnames() is used in this case with tolower() to rename the rest of the columns to lower case that I didn’t rename.
# Cleaning and renaming columns for kpop groups
kpopg <- rename(kpopg, "group.type" = "Girl Group or Boy Group?",
"korean.name" = "Korean Name",
"original.members" = "Orig. Memb.",
"group" = "Name"
) %>%
select(!starts_with("short")) %>%
select(!starts_with("fanclub"))
colnames(kpopg)<-tolower(colnames(kpopg))
# Cleaning and renaming columns for kpop idols
kpopi <- rename(kpopi, "stage.name" = "Stage Name",
"full.name" = "Full Name",
"dob" = "Date of Birth",
"other.group" = "Other Group",
"former.group" = "Former Group",
"currently.in.group" = "Currently in Group"
) %>%
select(! starts_with( "Korean Name")) %>%
select(! starts_with( "K. Stage Name")) %>%
select(! starts_with( "Second Country")) %>%
select(! starts_with( "Height")) %>%
select(! starts_with( "Weight")) %>%
select(! starts_with( "Birthplace")) %>%
select(! starts_with( "Position")) %>%
select(! starts_with( "Instagram")) %>%
select(! starts_with( "Twitter"))
colnames(kpopi)<-tolower(colnames(kpopi)) Below are tidied versions of the kpop group table and kpop idol table. You can see all of the data if you page through it.
datatable(kpopg, class = 'cell-border stripe',
caption = 'Tidy Kpop Groups Information',
width = '100%', options = list(scrollX = TRUE, pageLength = 5))datatable(kpopi, class = 'cell-border stripe',
caption = 'Tidy Kpop Idol Information',
width = '100%', options = list(scrollX = TRUE, pageLength = 5))Merging both sheets together
Now that I have cleaned the data I merged the two worksheets together and wrote a new csv file. I did this by usnig the merge() function in order to do an outer join to have all columns from each sheet in one. This is because some of the groups do not have their idols listed and would disappear if I used inner join.
I also changed the groups name to lower case using tolower() so that groups will merge correctly.
I merged by using the group column on both sheets.
#Changing the case of groups
kpopi <- kpopi%>%
mutate(group = tolower(group)) %>%
mutate(former.group = tolower(former.group)) %>%
mutate(other.group = tolower(other.group))
kpopg <- kpopg %>% mutate(group = tolower(group))
# Creating a new csv file
kpopgi <- merge(kpopi,kpopg, by=c('group','group'),all=T, ignore_case =T)
write.csv(kpopgi, here("_data", "kpop_group_and_idols_9.27.21.csv") , all(T) )## Warning in utils::write.table(kpopgi, here("_data",
## "kpop_group_and_idols_9.27.21.csv"), : appending column names to fileFiltering and Moving columns
I removed the idols that are soloist from the list if they were not previously in a group. I did this by using filter() and across(). Next I moved the columns so groups columns are at the beginning. I also changed the debut column to a date format.
kpopgi <- kpopgi %>%
filter(!across(c(group, other.group, former.group), ~ is.na(.))) %>%
relocate(korean.name, group.type, debut,
company, members, original.members, active, .after = group) %>%
mutate(debut = ymd(debut))Below is a combined kpop group and kpop idol table. You can see all of the data if you page through it.
datatable(kpopgi, class = 'table-bordered table-condensed',
caption = 'Kpop Groups and Idols list',
width = '100%', options = list(scrollX = TRUE, pageLength = 5))Visualization
General Usage of Graphs
For my graphs types I decided to focus on bar graphs and line graphs.
Answering most of the research questions required a count as a y-axis and year on the x-axis. This led me to use bar graphs.
However for my research question focusing on statistics of age over time in kpop I used a line graph. This is because that required information over time.
Debuted Groups
How many kpop groups debut by year
For this question, and How many girl groups vs boy groups debut by year? I decided to use my kpopg table. This is because I did not need any information on idols.
group.dy <- kpopg %>%
mutate(year = year(debut)) %>%
mutate(debut = ymd(debut)) %>%
mutate(group = group == group)
p1 <- ggplot(group.dy, aes(x=year, fill=group)) +
geom_bar(position=position_dodge()) +
scale_x_continuous(breaks = c(1995:2021)) +
theme_minimal() +
theme(axis.text.x = element_text(angle =45, hjust = 1), legend.position = "none") +
labs(title= "Kpop Groups Debut by Year", x= "year", y = "count") +
scale_fill_brewer(palette = "Pastel1")
p1
This graph shows:
x-axis: kpop group debut year
y-axis: count of kpop groups debuting
This is a graph of how many kpop groups are debuting by year
This graph shows an obvious growth of kpop groups from 1995-2012. However from 2014 onward it seems that groups tended to increase slightly, with the highest amount of groups coming from 2017.
There is a noticeable gap in 2013 for reasons unknown. This may due to a variety of reasons:
Missing data, kpop database websites in English generally didn’t exist at this time and thus smaller groups may have disbanded before someone created this database
Because kpop was still largely dependent on a domestic fanbase and with so many kpop groups from 2012, there wasn’t enough room for a new group to become popular and bring in money
Bigger companies may have debuted new groups in 2012 and wouldn’t want to debut a new group or have the money to spend on creating a new group
In 2017 Kpop had started to become popular in western countries due to the popularity of groups such as BTS and Blackpink. Thus smaller kpop companies, companies that are formed and only debut one group, may have decided to debut groups in hopes of striking it rich in the west.
Additionally during 2016 and 2017 a show called Produce 101 that had 101 trainees (what idols are called before they debut) performing against each other by singing, rapping, and dancing. The public would vote for their favorite trainee over multiple weeks and the top 11 trainees with the most votes would form a temporary group. Due to the overwhelming success of this show, the first two seasons had many smaller companies debuting their idols immediately after they were eliminated from the show to try to capitalize on the hype their idol may have received on the show. This show did have another two season in 2018 and 2019, however ratings were not as high and by 2018 many small companies saw the “produce effect” which means a trainee could be popular on Produce but not popular outside of the show.
For 2021 we can assume that since the year hasn’t ended there will still be groups debuting. We also know that the pandemic has caused creations of groups to slow down due to how expensive groups are to create. 2020 may have had groups that debuted with money previously saved from 2019.
How many girl groups vs boy groups debut each year
p2 <- ggplot(group.dy, aes(x=year, fill=group.type)) +
geom_bar(position=position_dodge()) +
scale_x_continuous(breaks = c(1995:2021)) +
theme_minimal() +
theme(axis.text.x = element_text(angle =45, hjust = 1)) +
labs(title= "Kpop Boy/Girl Groups Debut by Year", x= "year", y = "count") +
theme(legend.key.size = unit(3, 'mm'),legend.key.width = unit(9,"mm"),
legend.position="bottom", axis.text.x = element_text(angle =45, hjust = 1)) +
scale_fill_brewer(palette = "Pastel2")
p2
This graph shows:
x-axis: kpop group debut year
y-axis: count of boy groups versus girl groups debuting
fill colors: if the group is a boy group or a girl group
This is a graph of how many boy groups versus girl groups debut in a year
There are complaints online among kpop fans that more boy groups debut than girl groups. If we look at the bar chart we can see that over time boy groups versus girl groups tend to stay relatively close in numbers throughout the years. The largest gap is 14 more girl groups debuted during 2018, which is opposite of the complaints. However overall boy groups tend to narrowly debut more than girl groups.
This may happen because boy group fans tend to stick around through the group’s entire contract and buy all merchandise. On the other hand, girl group fans are generally less loyal and buy less merchandise.
However looking into fanbases is unfortunate past the limits of this data I’ve collected, so I am unable to confirm or deny if that is the reason boy groups generally debut slightly more than girl groups.
Foreign Idols
How many foreign idols debut in a group by year
For this graph I struggled to decide between using a bar graph and line graph. However I ended up choosing a bar graph due to the amount of one persons a year for each country for continuous years that made a line graph look odd.
group.foreign <- kpopgi %>%
filter(! country == "South Korea") %>%
mutate(year = year(debut)) %>%
mutate(debut = ymd(debut))
p3 <- ggplot(group.foreign, aes(x=year, fill=country)) +
geom_bar(position=position_dodge()) +
scale_x_continuous(breaks = c(2005:2021)) +
labs(title= "Foregin Idol Debut by Year and Country", x= "year", y = "count") +
theme_minimal() +
theme(legend.key.size = unit(3, 'mm'),legend.key.width = unit(9,"mm"),
legend.position="bottom") +
scale_fill_brewer(palette = "Set3")
p3
This graph shows:
x-axis: foreign idol debut year
y-axis: count of foreign idols debuting by country
fill colors: what country is each foreign idol from
This is a graph of how many foreign idols from different countries debut each year
Looking at this graph we can see a couple of main points immediately.
- China and Japan have by far the largest amount of foreign members adding to kpop groups.
- The US tends to always have at least one member debuting each year since 2008.
- From 2016 on there seems to be a significant increase of foreign members overall.
I also noticed while foreign idols came from 12 different countries, there were 5 main countries consistently picked: China, Japan, US, Hong Kong, and Thailand. Out of these five countries the most foreign idols came from Japan and China.
However Japan has risen over years while China has fallen and this could be for a few reasons:
Kpop has always imported their kpop groups music to Japan for extra money (Japan is the second biggest music market in the world, with US being the first). Thus there are more often “Japanese versions”, a song that was translated from Korean to Japanese, of a groups tracks or songs that are originally songs fully in Japanese from a kpop group. So then logically if you are going to promote in Japan, having a Japanese speaking member would be extremely helpful.
With China being the largest population on Earth a lot of companies went between China and Japan as their secondary source of income. However in 2016 the US sent over at Korea’s request some Terminal High Altitude Area Defense (THAAD), an anti-ballistic missile defense system, which made China unhappy due to the closeness of THAAD to China. This created a soft ban on Korean items.4 This could cause a lot of kpop companies not to see a reason necessary to add a Chinese speaking member.
With a growth of interest in kpop in western countries starting in 2017 many countries may want to take an approach to debuting an idol that speaks English. Since English is a more universal language and many Asian countries learn English basics in schools, having a member from a western country may appeal more now that companies have observed a stable fanbase growth in the west.
Over time I believe that English speaking idols, coming from a place that speaks English fluently, will become more popular. This is because kpop starting to establish its own niche in western countries in the past few years.
How many groups debut in a year with at least one foreign idol
p5 <- ggplot(group.foreign, aes(x=group, fill=group.type)) +
geom_bar(position=position_dodge()) +
labs(title= "Foreign Idol Debut by Group", x= "group", y = "count") +
theme_minimal() +
theme(legend.key.size = unit(3, 'mm'),legend.key.width = unit(9,"mm"),
legend.position="bottom", axis.text.x = element_text(angle =80, hjust = 1)) +
scale_fill_brewer(palette = "Pastel2")
p5
This graph shows:
x-axis: name of group that contains a foreign idol
y-axis: count of foreign idols in a boy groups versus girl groups
fill colors: if the group that contains a foreign idol is a boy group or a girl group
This is a graph of how many foreign idols are in a boy group or girl group
Breaking down foreign idols in groups differently, I decided to focus on how many groups debut at least one foreign idol. We can see a clear outlier in our data, the group Nct debuted 10 foreign idols in a group! If you look into Nct you will notice that their concept is “limitless members”, of which they already have 23 members currently, which is unusual in kpop.
If we ignore that data point we can can see that over all debuting less than 4 foreign members seem to be the trend in kpop groups over time. We can also notice that only having one foreign member happens the most, which could be for various reasons:
Finding foreign idols that want to be kpop idols may be difficult even now. Although there are more people that do and will sign up, many are often unprepared for how difficult it is to learn to sing, dance, and rap properly along with learning and speaking a foreign culture. In this video 5 we can see how difficult it is to be a foreign idol.
A company may not have the money to scout or train multiple foreign idols. Besides the normal lessons a trainee would have to learn, foreign idols would need extra lessons on how to adapt to Korean culture and speak Korean. These can be costly for companies that don’t have foreign idols that may have trained in Korea already.
The group members may have a difficult time getting along due to different cultural values. What may be considered normal in someone’s country may be frowned upon in Korea. If members do not get along then it may show during a performance or during other interactions and turn fans off from the group. This could limit a companies pool of foreign candidates if the company has a set number of Korean trainees that will debut.
A foreign member could still be Korean. For example someone from the US may have Korean heritage but be a US citizen. A company may scout for a candidate like this because a candidate would already speak Korean and know the Korean culture. The candidate would also have the benefit of speaking English and appealing to a western crowd by knowing western cultures.
Overall this graph does not show anything surprising. If a kpop group is debuting in Korea then it’s likely to be mostly made of Korean members.
Kpop Age Stastics
Kpop Descpritve Stastics
Kpop by Minimum, Maximum, and Average Ages by Year
What is minimum, maximum, and average deubt ages of kpop by year
age.ranges <- kpopgi %>%
#Calculate age by current year
mutate(cageinterval = dob %--% Sys.Date()) %>%
mutate(current.age= cageinterval %/% years(1)) %>%
#Calculate age by debut year
mutate(dageinterval = dob %--% debut) %>%
mutate(debut.age= dageinterval %/% years(1)) %>%
#Remove interval columns and filter out nas for columns that only had groups
select(! starts_with("cageinterval")) %>%
select(! starts_with("dageinterval")) %>%
filter(! across(c(current.age, debut.age), ~ is.na(.))) %>%
mutate(year = year(debut)) %>%
#To use for fill in graph
mutate(debut.age = as.character(debut.age)) %>%
mutate(current.age = as.character(current.age)) %>%
group_by(debut.age)%>%
mutate(count=n()) %>%
mutate(group = group == group)
debut.age.stastics <- age.ranges %>%
group_by(year) %>%
mutate(debut.age = as.numeric(debut.age)) %>%
summarise(average.age = mean(debut.age),
min.age = min(debut.age),
max.age = max(debut.age),
sd.age = sd(debut.age)
) %>%
pivot_longer(cols = -c("sd.age", "year"), names_to = "age.stats", values_to = "ages")
p6 <- ggplot(debut.age.stastics, aes(x=year, y=ages, group=age.stats, color=age.stats)) +
geom_line() +
geom_point() +
labs(title= "Kpop Idols Age at Debut Statstics", x= "year", y = "ages") +
theme_minimal() +
theme(legend.key.size = unit(3, 'mm'),legend.key.width = unit(9,"mm"),
legend.position="bottom") +
scale_colour_brewer(palette = "Pastel1")
p6
This graph shows:
x-axis: kpop idol debut year
y-axis: ages that kpop idols debut
fill colors: These are the minimum, maximum and average ages of kpop idols that debuted
This is a graph exploring kpop debut ages over time through minimum, maximum, and average.
For maximum age by year there seems to be quite an increase over time. Although we see the oldest age starts around 19 it seems that overall the oldest age tends to change significantly each year with the oldest in 2020 being 33. This could be because an idol may have debuted already and then re-debuted in another group. In my database this would count as a “fresh” debut instead of a second debut. Another reason might be a smaller company is more willing to debut an older member that has trained for a long time, due to them not having the resources to train or find younger members.
For minimum age the age has started to drop around 2006, with the youngest being 11 in 2008. After looking at my database I understood that the idol that was 11 joined the group much later and did not debut in 2008. My database unfortunately does not account for idols that are “added” to the group later on. However after studying my data once more I can confirm that age 12 is the youngest an idol debuted in 2017.
The average column gives the more accurate information. Here we are able to see that on average kpop idols ages have stayed in a similar range of 16-20 over about 25 years. This could be because kpop is aimed at a younger audience in Korea, thus idols should be around a similar age so fans can “relate” to them or for younger fans idols they can look up to.
What is the age of debuting idols by year
p7 <- ggplot(age.ranges, aes(x=debut.age, fill=group)) +
geom_bar(position=position_dodge()) +
labs(title= "Kpop Debut Age Idols", x= "age", y = "count") +
theme_minimal() +
theme(legend.key.size = unit(3, 'mm'),legend.key.width = unit(9,"mm"),
legend.position="none") +
scale_fill_brewer(palette = "Pastel1")
p7
This graph shows:
x-axis: kpop idol debut age
y-axis: count of how many kpop idols debut at a certain age
This is a graph how many kpop idols debut at each age
Looking into the debuting age of kpop idols more closely we can see that over 200 kpop idols debut between the ages of 18-20. This also shows us how kpop idols generally follow a bell curve. Most kpop idols debut from 16-20 with a steep drop off when you go below 16 or over 20. However interestingly there seems to be more of a chance to debut at an older age, or perhaps “re-debut” if you already had debuted in a group and the group had disbanded, than there are for kpop idols under the age of 16 to debut early.
What is the age of idols if they’re currently active in kpop
p8 <- ggplot(age.ranges, aes(x=current.age, fill=active)) +
geom_bar(position=position_dodge()) +
labs(title= "Kpop Current Age Idols by if They're in an Active Group",
x= "age", y = "count") +
theme_minimal() +
theme(legend.key.size = unit(3, 'mm'),legend.key.width = unit(9,"mm"),
legend.position="bottom") +
scale_fill_brewer(palette = "Pastel1")
p8
This graph shows:
x-axis: what age a kpop idol is currently
y-axis: count of how many kpop idols are currently at a certain age
fill colors: is the idol currently in a group that is active (yes), disbanded (no), or on hiatus
This is a graph of how many current idols there are by age and if they are in an active group or not.
In this graph we’re able to see that kpop idols in this database are all relatively in active groups when they are from age 14 to age 29. However even as we approach age 27 the amount of groups that are in disbanded groups (called “no”) start to rapidly increase compared to active idols. At age 22 we see that about 16% are idols that are in a disbanded group but by age 27 about 34% are in a disbanded group. This may be due to a few factors:
Kpop group contracts are now mandated to only be up to 7 years. This is due to a previous company forcing their idols to be in a contract for 10-13 years without any negotation of terms until the contract is over. In my graph on Kpop Debut Age Idol Statistics we see on average group members debut in their mid teens to early twenties. This would put a 7 year contract ending age at anywhere from 22-28 which is when we see the large increase of disbanded groups.
Many kpop idols tend to want to try new things after seven years. Even if a group does extremely well each person in the group has their own dreams and goals. While some many want to continue on, others may want to branch out into different entertainment careers or completely leave the spotlight. If a group can’t come to an agreement often a group will disband, or go on a hiatus to be able to keep using the group name for promotions in other areas.
Some groups tend to disband quickly. Due to extenuating circumstances such as the pandemic or not being able to gain enough fans. This is shown by the start of disbanded groups increasing at 20.
Overall this graph confirms what we have seen in previous graphs. The shelf life of a kpop idol is short and decreases extremely fast once you hit 27.
Reflection
What decisions in your pipeline are you most concerned about potentially influencing your findings?
After working with the data my biggest concern is that this is a sample size of kpop groups and idols. While I was able to capture a decent chunk of data, this is a very small percentage of kpop groups and idols.
For kpop groups there were very few groups recorded in the late 90s till mid 2000s which could skew the data for those years. Additionally with so many smaller groups that debut but disband quickly there is probably more groups that debut each year and disband which would better show how tough an industry this is to make it in.
For kpop idols although I had about 1,500 I believe there’s a lot more idols that debut but never gain recognition and don’t end up in the database I used. Although I doubt the age range statistics would have changed drastically, I do believe that it could’ve effected how many idols are currently active. There could also be more foreign idols that debuted as well.
I also believe that the disbandment rate may be too low. As mentioned previously this does not account for all smaller kpop groups that may have disbanded too soon to make it into this database. This also does not account for why groups may disband. Besides contract expiration,there could be issues between the members, money issues with the company, or a variety of other reasons.
What were the most challenging and time-consuming aspects of the project? What do you wish you had been able to do?
The most challenging and time-consuming aspect of this project was adjusting the graphs accordingly and choosing the best design for them. This required a lot of trial and error for how I would like the graphs to look and additional time if extra columns were needed. Additionally I wanted to create multiple data tables so the information wouldn’t be mixed up for each question.
What do you wish you had been able to do?
I wish I had been able to look into if girl groups disband quicker than boy groups. Although I had if a group disbanded or not I did not have the year they disbanded. This is a question that a lot of girl group fans fans argue about. Girl group fans feel like kpop companies spend less money on their girl groups because the company sees them as less profitable.
If you were to continue the project, what would your next steps be?
If I was to continue the project I would look more into album sales for groups. If a group has a strong fanbase the group should sell more albums and more extra merchandise. While there is no public information on how much a group sells of additional merchandise, album sales are recorded by a company akin to Billboard.
Conclusion
Overall I found that my data was able to answer how kpop has changed as it has become more global. Here are a few key take aways:
- More kpop groups seem to debut than in the early 2000s. As time has gone on, kpop groups debuting have seemed to stabilize each year.
- Around the same amount of girl groups and boy groups debut each year. Group sizes have maintained the same average over the whole database, with the maximum amount of members radically changing each year.
- Over time the country where the most foreign idols have come from has changed from China to Japan. The amount of foreign idols debuting has increased over time with a similar amount of foreign boy idols and girl idols debuting in the past couple of years.
- The average age of debuting kpop idols has stayed in a similar mid to late teens range.
- Once an idol turns over 27 there is a high chance the idol will not be active in a kpop group.
I had fun exploring this database and understanding kpop data at a much deeper level. I also believe there was a lot of information that I wasn’t expecting to find, such as kpop boy groups and girl groups debuting around the same amount each year. I think if I had more time I would try to find a way to look at groups album sales data over time to see if there’s a trend to more albums being purchased each year than the previous.
Kpop Database. Retrieved from https://dbkpop.com/k-pop-database-tables↩︎
Kpop Database. Retrieved from https://dbkpop.com/db/k-pop-girlgroups & https://dbkpop.com/db/k-pop-boybands↩︎
Kpop Database. Retrieved from https://dbkpop.com/db/all-k-pop-idols↩︎
Lee Y. & Bodeen C.,(2017, March 20) THAAD’s connection to candy, makeup and K-pop Retrieved from https://www.defensenews.com/pentagon/2017/03/20/thaad-s-connection-to-candy-makeup-and-k-pop/↩︎
CNA Insider. (2019, June 12). How To Become A K-Pop Idol: Life As A Foreign Trainee. YouTube. https://www.youtube.com/watch?v=G66uRJ6pAfI↩︎