Data cleaning

Here, we use regex (regular expression) string matching to filter on the Character column. The str_detect() function from the {stringr} package detects whether a pattern or expression exists in a string, and returns a logical value of either TRUE or FALSE (which is what dplyr::filter() takes in the second argument). I also used the stringr::regex() function and set the ignore_case argument to TRUE, which makes the filter case-insensitive, such that cases of ‘Kame’ and ‘kAMe’ are also picked up if they do exist.

dball_data %>%
  filter(str_detect(Character, regex("kameha", ignore_case = TRUE))) -> dball_data_1

dball_data_1 %>% head()
## # A tibble: 6 x 4
##   Character                     Power_Level Saga_or_Movie      Dragon_Ball_Seri~
##   <chr>                         <chr>       <chr>              <chr>            
## 1 Master Roshi's Max Power Kam~ 180         Emperor Pilaf Saga Dragon Ball      
## 2 Goku's Kamehameha             12          Emperor Pilaf Saga Dragon Ball      
## 3 Jackie Chun's Max power Kame~ 330         Tournament Saga    Dragon Ball      
## 4 Goku's Kamehameha             90          Red Ribbon Army S~ Dragon Ball      
## 5 Goku's Kamehameha             90          Red Ribbon Army S~ Dragon Ball      
## 6 Goku's Super Kamehameha       740         Piccolo Jr. Saga   Dragon Ball

If this filter feels convoluted, it’s for a good reason. There is a variation of cases and spellings used in this dataset, which a ‘straightforward’ filter wouldn’t have picked up. So there are two of these:

dball_data %>%
  filter(str_detect(Character, "Kamehameha")) -> dball_data_1b

## Show the rows which do not appears on BOTH datasets
dball_data_1 %>%
  dplyr::anti_join(dball_data_1b, by = "Character")
## # A tibble: 2 x 4
##   Character                        Power_Level Saga_or_Movie   Dragon_Ball_Seri~
##   <chr>                            <chr>       <chr>           <chr>            
## 1 Jackie Chun's Max power Kameham~ 330         Tournament Saga Dragon Ball      
## 2 Android 19 (Goku's kamehameha a~ 230,000,000 Android Saga    Dragon Ball Z

Before we go any further with any analysis, we’ll also need to do something about Power_Level, as it is currently in the form of character / text, which means we can’t do any meaningful analysis until we convert it to numeric. To do this, we can start with removing the comma separators with stringr::str_remove_all(), and then run as.numeric().

In ‘real life’, you often get data saved with k and m suffixes for thousands and millions, which will require a bit more cleaning to do – so here, I’m just thankful that all I have to do is to remove some comma separators.

dball_data_1 %>%
  mutate_at("Power_Level", ~str_remove_all(., ",")) %>%
  mutate_at("Power_Level", ~as.numeric(.)) -> dball_data_2

dball_data_2 %>% tail()
## # A tibble: 6 x 4
##   Character           Power_Level Saga_or_Movie                Dragon_Ball_Seri~
##   <chr>                     <dbl> <chr>                        <chr>            
## 1 Goku's Super Kame~  25300000000 OVA: Plan to Eradicate the ~ Dragon Ball Z    
## 2 Family Kamehameha  300000000000 Movie 10: Broly- The Second~ Dragon Ball Z    
## 3 Krillin's Kameham~      8000000 Movie 11: Bio-Broly          Dragon Ball Z    
## 4 Goten's Kamehameha    950000000 Movie 11: Bio-Broly          Dragon Ball Z    
## 5 Trunk's Kamehameha    980000000 Movie 11: Bio-Broly          Dragon Ball Z    
## 6 Goten's Super Kam~   3000000000 Movie 11: Bio-Broly          Dragon Ball Z

Now that we’ve fixed the Power_Level column, the next step is to isolate the information about the characters from the Character column. The reason why we have to do this is because, inconveniently, the column provides information for both the character and the occasion of when the kamehameha is used, which means we won’t be able to easily filter or group the dataset by the characters only.

One way to overcome this problem is to use the apostrophe (or single quote) as a delimiter to extract the characters from the column. Before I do this, I will take another manual step to remove the rows corresponding to absorbed kamehamehas, e.g. Android 19 (Goku’s kamehameha absorbed), as it refers to the character’s power level after absorbing the attack, rather than the attack itself. (Yes, some characters are able to absorb kamehameha attacks and make themselves stronger..!)

After applying the filter, I use mutate() to create a new column called Character_Single, and then str_remove_all() to remove all the characters that appear after the apostrophe:

dball_data_2 %>%
  filter(!str_detect(Character, "absorbed")) %>% # Remove 2 rows unrelated to kamehameha attacks
  mutate(Character_Single = str_remove_all(Character, "\\'.+")) %>% # Remove everything after apostrophe
  select(Character_Single, everything()) -> dball_data_3
## # A tibble: 10 x 5
##    Character_Single Character       Power_Level Saga_or_Movie   Dragon_Ball_Ser~
##    <chr>            <chr>                 <dbl> <chr>           <chr>           
##  1 Master Roshi     Master Roshi's~         180 Emperor Pilaf ~ Dragon Ball     
##  2 Goku             Goku's Kameham~          12 Emperor Pilaf ~ Dragon Ball     
##  3 Jackie Chun      Jackie Chun's ~         330 Tournament Saga Dragon Ball     
##  4 Goku             Goku's Kameham~          90 Red Ribbon Arm~ Dragon Ball     
##  5 Goku             Goku's Kameham~          90 Red Ribbon Arm~ Dragon Ball     
##  6 Goku             Goku's Super K~         740 Piccolo Jr. Sa~ Dragon Ball     
##  7 Goku             Goku's Kameham~         950 Saiyan Saga     Dragon Ball Z   
##  8 Goku             Goku's Kameham~       36000 Saiyan Saga     Dragon Ball Z   
##  9 Goku             Goku's Kameham~       44000 Saiyan Saga     Dragon Ball Z   
## 10 Goku             Goku's Angry K~   180000000 Frieza Saga     Dragon Ball Z

Note that the apostrophe is a special character, and therefore it needs to be escaped by adding two forward slashes before it. The dot (.) matches all characters, and + tells R to match the preceding dot to match one or more times. Regex is a very useful thing to learn, and I would highly recommend just reading through the linked references below if you’ve never used regular expressions before.¹

Analysis

Now that we’ve got a clean dataset, what can we find out about the Kamehamehas?

The Kamehameha – image from Giphy

My approach is start with calculating the average power levels of Kamehamehas in R, grouped by Character_Single. The resulting table tells us that on average, Goku’s Kamehameha is the most powerful, followed by Gohan:

dball_data_3 %>%
  group_by(Character_Single) %>%
  summarise_at(vars(Power_Level), ~mean(.)) %>%
  arrange(desc(Power_Level)) -> kame_data_grouped # Sort by descending

kame_data_grouped
## # A tibble: 11 x 2
##    Character_Single           Power_Level
##    <chr>                            <dbl>
##  1 Goku                           3.46e14
##  2 Gohan                          1.82e12
##  3 Family Kamehameha              3.00e11
##  4 Super Perfect Cell             8.00e10
##  5 Perfect Cell                   3.02e10
##  6 Goten                          1.98e 9
##  7 Trunk                          9.80e 8
##  8 Krillin                        8.00e 6
##  9 Student-Teacher Kamehameha     1.70e 4
## 10 Jackie Chun                    3.30e 2
## 11 Master Roshi                   1.80e 2

However, it’s not helpful to directly visualise this on a bar chart, as the Power Level of the strongest Kamehameha is 175,433 times greater than the median!

kame_data_grouped %>%
  pull(Power_Level) %>%
  summary()
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 1.800e+02 4.008e+06 1.975e+09 3.170e+13 1.900e+11 3.465e+14

A way around this is to log transform the Power_Level variable prior to visualising it, which I’ve saved the data into a new column called Power_Index. Then, we can pipe the data directly into a ggplot chain, and set a dark mode using theme():

kame_data_grouped %>%
  mutate(Power_Index = log(Power_Level)) %>% # Log transform Power Levels
  ggplot(aes(x = reorder(Character_Single, Power_Level),
             y = Power_Index,
             fill = Character_Single)) +
  geom_col() +
  coord_flip() +
  scale_fill_brewer(palette = "Spectral") +
  theme_minimal() +
  geom_text(aes(y = Power_Index,
                label = round(Power_Index, 1),
                hjust = -.2),
            colour = "#FFFFFF") +
  ggtitle("Power Levels of Kamehamehas", subtitle = "By Dragon Ball characters") +
  theme(plot.background = element_rect(fill = "grey20"),
        text = element_text(colour = "#FFFFFF"),
        panel.grid = element_blank(),
        plot.title = element_text(colour="#FFFFFF", face="bold", size=20),
        axis.line = element_line(colour = "#FFFFFF"),
        legend.position = "none",
        axis.title = element_text(colour = "#FFFFFF", size = 12),
        axis.text = element_text(colour = "#FFFFFF", size = 12)) +
  ylab("Power Levels (log transformed)") +
  xlab(" ")

So as it turns out, the results aren’t too surprising. Goku’s Kamehameha is the strongest of all the characters on average, although it has been referenced several times in the series that his son Gohan’s latent powers are beyond Goku’s.

Also, it is perhaps unsurprising that Master Roshi’s Kamehameha is the least powerful, given a highly powered comparison set of characters. Interestingly, Roshi’s Kamehameha is stronger as ‘Jackie Chun’ than as himself.

We can also see the extent to which Goku’s Kamehameha has grown more powerful across the series. This is available in the column Saga_or_Movie. In the same approach as above, we can do this by grouping the data by Saga_or_Movie, and pipe this into a ggplot bar chart:

dball_data_3 %>%
  filter(Character_Single == "Goku") %>%
  mutate(Power_Index = log(Power_Level)) %>% # Log transform Power Levels
  group_by(Saga_or_Movie) %>%
  summarise(Power_Index = mean(Power_Index)) %>%
  ggplot(aes(x = reorder(Saga_or_Movie, Power_Index),
             y = Power_Index)) +
  geom_col(fill = "#F85B1A") +
  theme_minimal() +
  geom_text(aes(y = Power_Index,
                label = round(Power_Index, 1),
                vjust = -.5),
                colour = "#FFFFFF") +
  ggtitle("Power Levels of Goku's Kamehamehas", subtitle = "By Saga/Movie") +
  scale_y_continuous(limits = c(0, 40)) +
  theme(plot.background = element_rect(fill = "grey20"),
        text = element_text(colour = "#FFFFFF"),
        panel.grid = element_blank(),
        plot.title = element_text(colour="#FFFFFF", face="bold", size=20),
        plot.subtitle = element_text(colour="#FFFFFF", face="bold", size=12),
        axis.line = element_line(colour = "#FFFFFF"),
        legend.position = "none",
        axis.title = element_text(colour = "#FFFFFF", size = 10),
        axis.text.y = element_text(colour = "#FFFFFF", size = 8),
        axis.text.x = element_text(colour = "#FFFFFF", size = 8, angle = 45, hjust = 1)) +
  ylab("Power Levels (log transformed)") +
  xlab(" ")

I don’t have full knowledge of the chronology of the franchise, but I do know that Emperor Pilaf Saga, Red Ribbon Army Saga, and Piccolo Jr. Saga are the earliest story arcs where Goku’s martial arts abilities are still developing. It also appears that if I’d like to witness Goku’s most powerful Kamehameha attack, I should find this in the Baby Saga!