Introduction

Kaggle is one of the most well-known communities of data analysts/scientists with over 10 million active users (Heads or Tails, 2020). Besides that, Kaggle offers an abundance of functionalities (Notebooks), information (through Discussions between users) and Competitions. It is worth noting that there are other similar communities but they cannot compare to the full functionality of Kaggle. For example, DrivenData could be considered an alternative for participating in ML competitions, but it neither provides the possibility to create notebooks nor has a large number of users.

Kaggle Machine Learning & Data Science Survey is an annual survey conducted by Kaggle. The platform asks its users to analyze users’ data in the context of a competition. In this notebook, I conduct an analysis based on 2021’s survey in order to compare Greek data analysts with the rest of the world.

Prerequisites

Import libraries

This notebook will definitely make some charts, so the ggplot2 package is necessary. Also, having variables with too many values (e.g. country of each Kaggle user) is an indication of using tables, and for this the reactablefmtr package will help to get a nice result.

# General purpose R libraries
library(readr)
library(dplyr)
library(tidyr)
library(forcats)
library(gridExtra)
library(countrycode)

# Tables
library(kableExtra)
library(reactablefmtr)

# Graphs
library(ggplot2)
library(ggtext) # Add support for HTML/CSS on ggplot
library(showtext)
library(sysfonts) # System / Google fonts
library(glue)
library(ggflags)

# Other R packages
library(fontawesome)
#library(htmltools) # for building div/links

# Other settings
options(digits=4) # print only 4 decimals
options(warn = -1)

## Load fonts

# font_families_google() ## see list with available Google fonts

font_add_google(name = "Lilita One", family = "title", db_cache = F)
font_add_google(name = "Ysabeau Office", family = "subtitle", db_cache = F)
font_add_google(name = "Spline Sans", family = "text", db_cache = F)

showtext_auto()
showtext::showtext_opts(dpi = 300)

Import data

Using read.csv() from readr package, I import my dataset and I name it as kaggle_2021. The dataset includes in the first line the question which is not required for my data analysis, so I exclude it from my dataset.

kaggle_2021 = read_csv("data/kaggle_survey_2021.csv")

# Delete second line
kaggle_2021 = kaggle_2021[-c(1),]

Prepare Data

Since my analysis is based on Greek users, I split the dataset into two parts. One part includes exclusively Greek users and all the rest another. Thus, we can observe any differences or similarities with broader Kaggle’s userbase.

# Recoding Q2 variable

kaggle_2021$Q2 = kaggle_2021$Q2 %>%
  fct_recode(
    "Other" = "Nonbinary",
    "Other" = "Prefer not to say",
    "Other" = "Prefer to self-describe"
  )

## Recoding kaggle_2021$Q3

kaggle_2021$Q3 <- kaggle_2021$Q3 %>%
  fct_recode(
    "Hong Kong" = "Hong Kong (S.A.R.)",
    "Other" = "I do not wish to disclose my location",
    "Iran" = "Iran, Islamic Republic of...",
    "UAE" = "United Arab Emirates",
    "UK" = "United Kingdom of Great Britain and Northern Ireland",
    "USA" = "United States of America",
    "Vietnam" = "Viet Nam"
  )

## Recoding kaggle_2021$Q6
kaggle_2021$Q6 <- kaggle_2021$Q6 %>%
  fct_recode(
    "0 years" = "I have never written code"
  )

## Recoding kaggle_2021$Q4
kaggle_2021$Q4 <- kaggle_2021$Q4 %>%
  fct_recode(
    "Bachelor" = "Bachelor’s degree",
    "PhD" = "Doctoral degree",
    "Other" = "I prefer not to answer",
    "Master" = "Master’s degree",
    "No" = "No formal education past high school",
    "ProfDoc" = "Professional doctorate",
    "UniNoDegree" = "Some college/university study without earning a bachelor’s degree"
  )

kaggle_2021$Q4 <- kaggle_2021$Q4 %>%
  fct_relevel(
    "No", "UniNoDegree", "Bachelor", "Master", "PhD", "ProfDoc",
    "Other"
  )


kaggle_2021_compare = kaggle_2021 %>%
  mutate(Q3 = if_else(Q3 != "Greece", "Other", Q3))

Kaggle’s community

One of the first thing I observed when I signed up on Kaggle was the vast majority of nationalities and the multicultural origin of the platform. Many people from many countries all in one platform gathered sharing the same passion for Data Science and Data Analytics. Something like Facebook but for Statistics :)

Nevermind, I decided to make a reactable to see from which nationalities the platform is comprised from. One out of four users are from India which makes them the most populous nation in platform. By the way, Greek users are way less making a 0.39% of Kaggle’s userbase.

Assumptions Note

We should note that the results are from Kaggle’s Survey. That is to say from people that participated. An assumption has to be done that the distribution of the users that participated is the same with the ones who didn’t.

a = kaggle_2021 %>%
  group_by(Q3) %>%
  summarise(n = n()) %>%
  mutate(pct = round(n/nrow(kaggle_2021) * 100, digits = 2)) %>%
  arrange(desc(pct)) %>%
            reactable(.,
              defaultPageSize = 6,
              theme = espn(),
              columns = list(
                Q3 = colDef(name = "Country"),
                n = colDef(name = "Population", 
                           defaultSortOrder = "desc"),
                pct = colDef(name = "Percentage (%)")),
              defaultColDef = colDef(
                cell = data_bars(data = ., 
                                 text_position = "outside-base",
                                 fill_opacity = 0.5, round_edges = T
                )
              )
            ) %>%
 add_title(
    title = reactablefmtr::html("Kaggle's Community by Country <img src='https://www.svgrepo.com/show/387896/laptop.svg' alt='' width='40' height='40'>"),
    margin = reactablefmtr::margin(t=0,r=0,b=3,l=0)
    )  %>% 
            add_subtitle("India is the most represented country, followed by the United States.", font_size = 14, font_weight = "normal") %>% 
            add_source("stesiam | Data : Kaggle Survey 2021", font_size = 12, align = "right")

a

Kaggle's Community by Country

India is the most represented country, followed by the United States.

stesiam | Data : Kaggle Survey 2021

Women participation in DS

Generally, women are under-represented in labor market. According to the World Bank (2023), only one in two women participates in the labor market, in contrast to men whose corresponding participation is 7 in 10. Does DS community follow the same pattern? As it seems, it varies.

data = kaggle_2021 %>%
  group_by(Q3) %>%
  summarise(n = n(),
            Women = sum(factor(Q2) == "Woman"),
            pct_women = Women/n *100) %>%
  dplyr::filter(Q3 != "Other")

data$iso2c <- countrycode(data$Q3, "country.name", "iso2c")
data$iso2c = tolower(data$iso2c)

theme_set(theme_light())
data %>%
  ggplot() +
  geom_col(aes(x = pct_women, y = reorder(Q3, pct_women), fill = pct_women)) +
  scale_fill_gradient2(low="purple", high="purple4")+
  geom_label(data = subset(data, pct_women == max(pct_women) | Q3 == "Greece" | pct_women == min(pct_women)), aes(x = pct_women - 1.5, y = reorder(Q3, pct_women), label = paste0(round(pct_women, digits = 1), "%")), family = "text", size = 2.3) +
 ggflags::geom_flag(x = 0.3, aes(y = Q3,
                         country = iso2c), 
                         size = 4) + 
  geom_vline(xintercept = mean(data$pct_women), linetype = "dashed", color = "pink1") +
  geom_text(aes(x= mean(data$pct_women)+1, label=paste0("Average: ", round(mean(data$pct_women), digits = 2), " %"), y = "Colombia"), angle=270,
family = "text") +
  scale_x_continuous(limits = c(0,50)) +
  labs(
    title = "<b>Women participation in DS community per country</b>",
    subtitle = glue("Based on Kaggle's 2021 Survey women are underrepresented in DS. The country with the <br> highest women participation is Tunisia and Peru is one with the lowest. Last but not least,<br> **<span style= 'color: #001489;'>Greece</span>** has a relatively disappointing rate of women participation holding 15th place with <br> 15.7\\%, given the fact that the average is {round(mean(data$pct_women),digits = 2)} %"),
    caption = "**Data:** Kaggle Survey 2021<br><b>stesiam</b>, 2023",
    x = "Percentage (%) of Women",
    y = "Countries"
  ) +
  coord_cartesian(expand = FALSE)

Age Distribution

Greece’s Kaggle Community it is comprised from more elerly people comapred to the rest of Kaggle’s community. More specifically, Greece’s most prevalent age group is 25-29 and a sufficient proportion of users on their 40s. On the contrary, Kaggle’s community is quite youthy with most prevalent the three youngest age groups. Aggrevetating, those groups consist six out of ten userbase of Kaggle’s community.

theme_set(theme_light())

kaggle_2021_compare %>%
  select(Q3, Q1) %>%
  group_by(Q3,Q1) %>%
  summarise(n = n()) %>%
  group_by(Q3) %>%
  mutate(total = sum(n),
         pct = round(n/total *100, digits = 1)) %>%
  select(Q3, Q1, pct) %>%
  ggplot(aes(x = Q1, y = pct+1, fill = Q3)) +
  geom_bar(stat = "identity", alpha = 0.5, position = "identity") +
  scale_fill_manual(values = c("Greece" = "#1f77b4", "Other" = "#ff7f0e"))+ 
  scale_y_continuous(expand = c(0, 0)) +
  labs(
    title = "<b>Age Distribution of Kaggle Community</b>",
    subtitle = "Greek Kagglers have a proportionally lower presence at younger ages (<24 years) compared to the rest <br> of the world and almost zero presence at older ages (>60 years). For the most part, Greek analysts on <br> the  platform belong to the 25-29, 30-34 and 45-49 age groups. On the other hand, the rest of Kaggle's <br> users have a tremendous superiority to the three smaller age groups (18-21, 22-24, 25-29).",
    caption = "stesiam, 2023",
    x = "Age Group",
    y = "Percentage",
    fill = ""
  ) +
  theme_set(theme_light()) +
    theme(
    plot.title = element_markdown(family = "title"),
    plot.subtitle = element_markdown(family = "subtitle", lineheight = 0.4),
    plot.caption = element_markdown(family = "subtitle"),
    text = element_text(family = "subtitle"),
    legend.position = c(0.85, 0.85)
  )

`summarise()` has grouped output by 'Q3'. You can override using the `.groups`
argument.

Educational Background

kaggle_2021_compare %>%
  select(Q3, Q4) %>%
  group_by(Q3, Q4) %>%
  summarise(n = n()) %>%
  group_by(Q3) %>%
  mutate(pct = round(n/sum(n)*100, digits = 1)) %>%
  ungroup() %>%
  ggplot() +
  geom_col(stat = "identity", alpha = 0.5, position = "identity", aes(x = Q4, y = pct, fill = Q3)) +
  scale_fill_manual(values = c("Greece" = "#1f77b4", "Other" = "#ff7f0e"))+ 
  labs(
    title = "Educational Background of Kagglers",
    subtitle = "Γρεεκ,",
    caption = "stesiam, 2023",
    x = "Education",
    y = "Percentage"
  ) +
  scale_y_continuous(expand = c(0, 0)) +
  theme_classic() +
    theme(
    plot.title = element_markdown(family = "title"),
    plot.subtitle = element_markdown(family = "subtitle", lineheight = 0.4),
    plot.caption = element_markdown(family = "subtitle"),
    text = element_text(family = "subtitle"),
    legend.position = c(0.85, 0.85)
  )

`summarise()` has grouped output by 'Q3'. You can override using the `.groups`
argument.

Programming Language

d1 = kaggle_2021_compare %>%
  select(Q3, Q7_Part_1, Q7_Part_2) %>%
  group_by(Q3) %>%
  count(Q7_Part_1 == "Python") %>%
  mutate(pct  = round(n/sum(n)*100, digits = 1)) %>%
  na.omit()

d2 = kaggle_2021_compare %>%
  select(Q3, Q7_Part_2) %>%
  group_by(Q3) %>%
  count(Q7_Part_2 == "R") %>%
  mutate(pct  = round(n/sum(n)*100, digits = 1)) %>%
  na.omit()

d3 = kaggle_2021_compare %>%
  select(Q3, Q7_Part_8) %>%
  group_by(Q3) %>%
  count(Q7_Part_8 == "Julia") %>%
  mutate(pct  = round(n/sum(n)*100, digits = 1)) %>%
  na.omit()

Common Stacks

p1 = kaggle_2021_compare %>%
  select(Q3, starts_with("Q7")) %>%
  unite(Combined, starts_with("Q7"), sep = ",", na.rm = T) %>%
  count(Q3, Combined) %>%
  filter(Q3 == "Greece") %>%
  mutate(pct = round(n/sum(n)*100, digits = 1)) %>%
  arrange(-pct) %>%
  filter(pct >=3) %>%
  ggplot() +
  geom_col(aes(x = reorder(Combined, -pct), y = pct)) +
  geom_text(aes(x = Combined, y = pct+ 1, label = paste0(pct, "%", "", " (", n, ")"))) +
  labs(
    title = "Popular Stacks in Greece",
    subtitle = "Python seems to be THE tool for a big portion of Greek Analysts.",
    caption = "stesiam, 2023",
    x = "Stacks",
    y = "Percentage"
  ) + 
  theme_classic() +
  theme(
    plot.title = element_markdown(family = "title"),
    plot.subtitle = element_markdown(family = "subtitle", lineheight = 0.4),
    plot.caption = element_markdown(family = "subtitle"),
    text = element_text(family = "subtitle")
  )

kaggle_2021_compare %>%
  select(Q3, starts_with("Q7")) %>%
  unite(Combined, starts_with("Q7"), sep = ",", na.rm = T) %>%
  count(Q3, Combined) %>%
  filter(Q3 == "Other") %>%
  mutate(pct = round(n/sum(n)*100, digits = 1)) %>%
  arrange(-pct) %>%
  filter(pct >=3) %>%
  ggplot() +
  geom_col(aes(x = reorder(Combined, -pct), y = pct)) +
  geom_text(aes(x = Combined, y = pct+ 1, label = paste0(pct, "%", "", " (", n, ")"))) +
  labs(
    title = "Popular Stacks in Greece",
    subtitle = "Python seems to be THE tool for a big portion of Greek Analysts.",
    caption = "stesiam, 2023",
    x = "Stacks",
    y = "Percentage"
  ) + 
  theme_classic() +
  theme(
    plot.title = element_markdown(family = "title"),
    plot.subtitle = element_markdown(family = "subtitle", lineheight = 0.4),
    plot.caption = element_markdown(family = "subtitle"),
    text = element_text(family = "subtitle")
  )

p2 = kaggle_2021_compare %>%
  select(Q3, starts_with("Q7")) %>%
  unite(Combined, starts_with("Q7"), sep = ",", na.rm = T) %>%
  count(Q3, Combined) %>%
  filter(Q3 == "Other") %>%
  mutate(pct = round(n/sum(n)*100, digits = 1)) %>%
  arrange(-pct) %>%
  filter(pct >=3) %>%
  ggplot() +
  geom_col(aes(x = reorder(Combined, -pct), y = pct)) +
  geom_text(aes(x = Combined, y = pct+ 1, label = paste0(pct, "%", "", " (", n, ")"))) +
  labs(
    title = "Popular Stacks in Greece",
    subtitle = "Python seems to be THE tool for a big portion of Greek Analysts.",
    caption = "stesiam, 2023",
    x = "Stacks",
    y = "Percentage"
  ) + 
  theme_classic() +
  theme(
    plot.title = element_markdown(family = "title"),
    plot.subtitle = element_markdown(family = "subtitle", lineheight = 0.4),
    plot.caption = element_markdown(family = "subtitle"),
    text = element_text(family = "subtitle")
  )

IDEs

Python users

R users

Jobs

jobs1 = kaggle_2021_compare %>%
  select(Q3, Q5) %>%
  filter(Q3 == "Greece") %>%
  count(Q5) %>%
  arrange(-n) %>%
  head(8) %>%
  mutate(pct = round(n/sum(n)*100, 1))

jobs2 = kaggle_2021_compare %>%
  select(Q3, Q5) %>%
  filter(Q3 == "Other") %>%
  count(Q5) %>%
  arrange(-n) %>%
  head()  %>%
  mutate(pct = round(n/sum(n)*100, 1))

Conclusions

Acknowledgements

Dataset based on 2021 Kaggle Machine Learning & Data Science Survey

Image by Christina Smith from Pixabay

References

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Citation

BibTeX citation:

@online{2023,
  author = {, stesiam},
  title = {Kaggle’s {Greek} {Community}},
  date = {2023-05-06},
  url = {https://stesiam.com/posts/2023-05-06-Kaggle-Greek-Community/},
  langid = {en}
}

For attribution, please cite this work as:

stesiam. (2023, May 6). Kaggle’s Greek Community. Retrieved from https://stesiam.com/posts/2023-05-06-Kaggle-Greek-Community/