Interactive plots for Suicide Data

For this entry, will be visualizing suicide data from 1958 to 2015 with interactive plots to communicate insights to non-technical audience.

(14 min read)

Introducing the data set

• The data set used in this post is the Suicide Rates Overview 1985 to 2016 data from Kaggle - an online community of data scientists and machine learning practitioners funded by Google. The data set was compiled from numerous sources of the Human development index (HDI) of United Nations Development Program (2018), World development indicators from World Banks (2018), the Suicide in the Twenty-First Century data set, and data from the suicide prevention program by WHO.

• In this time and age, a lot of things we do is driven by data and conclusions we drawn from it. The question is, how? It is not like we are looking at tables of data every single time we want to decide on something. Data is oftentimes unstructured, unlabeled, and most of all, overwhelming when we don’t know what to do with it.

• With the right tool and the right framework, we will know what to look for and how to look for it. After that, it is a matter of whether we can effectively communicate what we learned from the data to our audience. Data visualization is an effective way to tell stories about data to non-technical stakeholders (e.g., educators, mental health practitioners, or policy makers).

• The plot can be even more engaging with the audience when we make it interactive, so that it can emphasize the part that really matters (while fading out the irrelevant part). That is what I aim to do with this entry, building interactive plots for the suicide data!

Show code

# Load necessary packages. 
library(flexdashboard) # Dashboard package
library(highcharter) # Interactive data visualizations
library(plotly) # Interactive data visualizations
library(viridis) # Color gradients
library(tidyverse) # Metapackge for data management
library(countrycode) # Converts country names/codes
library(rjson) # JSON reader
library(crosstalk) # Provides interactivity for HTML widgets
library(DT) # Displaying data tables

• First of all, we will load the necessary package and import the data set as usual. We will rely on plotly, crosstalk, flexdashboard, and highcharter packages to make the plot. We will also use glimpse to check the data set of its content.

Show code

# Read data. 

data <- read.csv('master.csv') %>%
  filter(year != 2016, # filter out 2016 and countries with 0 data. 
         country != 'Dominica',
         country != 'Saint Kitts and Nevis')
         
# Fix the names of some of the countries in our data to match the country names 
# used by our map later on so that they'll be interpreted and displayed. 
data <- data %>%
  mutate(country = fct_recode(country, "The Bahamas" = "Bahamas"),
         country = fct_recode(country, "Cape Verde" = "Cabo Verde"),
         country = fct_recode(country, "South Korea" = "Republic of Korea"),
         country = fct_recode(country, "Russia" = "Russian Federation"),
         country = fct_recode(country, "Republic of Serbia" = "Serbia"),
         country = fct_recode(country, "United States of America" = "United States"))

# Reorder levels of age to be in chronological order. 
data$age <- factor(data$age, levels = c("5-14 years", "15-24 years", "25-34 years", "35-54 years", "55-74 years", "75+ years"))

glimpse(data)

Rows: 27,612
Columns: 12
$ country            <fct> Albania, Albania, Albania, Albania, Alban~
$ year               <int> 1987, 1987, 1987, 1987, 1987, 1987, 1987,~
$ sex                <chr> "male", "male", "female", "male", "male",~
$ age                <fct> 15-24 years, 35-54 years, 15-24 years, 75~
$ suicides_no        <int> 21, 16, 14, 1, 9, 1, 6, 4, 1, 0, 0, 0, 2,~
$ population         <int> 312900, 308000, 289700, 21800, 274300, 35~
$ suicides.100k.pop  <dbl> 6.71, 5.19, 4.83, 4.59, 3.28, 2.81, 2.15,~
$ country.year       <chr> "Albania1987", "Albania1987", "Albania198~
$ HDI.for.year       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N~
$ gdp_for_year....   <chr> "2,156,624,900", "2,156,624,900", "2,156,~
$ gdp_per_capita.... <int> 796, 796, 796, 796, 796, 796, 796, 796, 7~
$ generation         <chr> "Generation X", "Silent", "Generation X",~

• We will create our custom theme here with color code, so that we don’t have to to it repeatedly with every plot.

Show code

# Create a custom theme for the plots. 
custom_theme <- hc_theme(
  colors = c('#5CACEE', 'green', 'red'),
  chart = list(
         backgroundColor = '#FAFAFA', 
         plotBorderColor = "black"),
  xAxis = list(
         gridLineColor = "E5E5E5", 
         labels = list(style = list(color = "#333333")), 
         lineColor = "#E5E5E5", 
         minorGridLineColor = "#E5E5E5", 
         tickColor = "#E5E5E5", 
         title = list(style = list(color = "#333333"))), 
  yAxis = list(
         gridLineColor = "#E5E5E5", 
         labels = list(style = list(color = "#333333")), 
         lineColor = "#E5E5E5", 
         minorGridLineColor = "#E5E5E5", 
         tickColor = "#E5E5E5", 
         tickWidth = 1, 
         title = list(style = list(color = "#333333"))),   
  title = list(style = list(color = '#333333')),
  subtitle = list(style = list(color = '#666666')),
  legend = list(
         itemStyle = list(color = "#333333"), 
         itemHoverStyle = list(color = "#FFF"), 
         itemHiddenStyle = list(color = "#606063")), 
  credits = list(style = list(color = "#666")),
  itemHoverStyle = list(color = 'gray'))

Global Level

• First, we will examine the data at global level. The unit is in suicide per 100k people; for example, the average of 13.2 means for every 100k people, 13.2 people had committed suicide. The global trend peaked around 1995, and we can see that the suicide rate in 2015 is the lowest across years. Try hovering your mouse over each point to see specific year and its corresponding suicide rate.

Show code

# Create tibble for our line plot.  
overall_tibble <- data %>%
  select(year, suicides_no, population) %>%
  group_by(year) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2)) 

# Create a line plot.
highchart() %>% 
    hc_add_series(overall_tibble, hcaes(x = year, y = suicide_capita, color = suicide_capita), type = "line") %>%
    hc_tooltip(crosshairs = TRUE, borderWidth = 1.5, headerFormat = "", pointFormat = paste("Year: <b>{point.x}</b> <br> Suicides: <b>{point.y}</b>")) %>%
    hc_title(text = "Worldwide suicides by year") %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_xAxis(title = list(text = "Year")) %>%
    hc_yAxis(title = list(text = "Suicides per 100K people"),
             allowDecimals = FALSE,
             plotLines = list(list(
                    color = "black", width = 1, dashStyle = "Dash", 
                    value = mean(overall_tibble$suicide_capita),
                    label = list(text = "Mean = 13.12", 
                                 style = list(color = "black", fontSize = 11))))) %>%
    hc_legend(enabled = FALSE) %>% 
    hc_add_theme(custom_theme)

Worldwide suicide rate by gender

• We can also compare the worldwide suicide rate by gender (male vs female). It seems like male has an overall higher rate of suicide comparing to female population. Hovering your mouse above a trend (say, male) makes the graph highlight that trend while showing specific year, gender, and its corresponding suicide rate.

Show code

# Create tibble for sex so we can use it when creating our line plot.  
sex_tibble <- data %>%
  select(year, sex, suicides_no, population) %>%
  group_by(year, sex) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))

# Pick color for gender.
sex_color <- c("#EE6AA7", "#87CEEB") # baby blue & pink

# Create line plot.
highchart() %>% 
    hc_add_series(sex_tibble, hcaes(x = year, y = suicide_capita, group = sex), type = "line", color = sex_color) %>%
    hc_tooltip(crosshairs = TRUE, borderWidth = 1.5, headerFormat = "", pointFormat = paste("Year: <b>{point.x}</b> <br>","Gender: <b>{point.sex}</b><br>", "Suicides: <b>{point.y}</b>")) %>%
    hc_title(text = "Worldwide suicides by Gender") %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_xAxis(title = list(text = "Year")) %>%
    hc_yAxis(title = list(text = "Suicides per 100K people"),
             allowDecimals = FALSE,
             plotLines = list(list(
                    color = "black", width = 1, dashStyle = "Dash",
                    value = mean(overall_tibble$suicide_capita),
                    label = list(text = "Mean = 13.12", 
                                 style = list(color = 'black', fontSize = 11))))) %>% 
    hc_add_theme(custom_theme)

• We can also make a pie chart to compare the proportion of male vs female in the data set. The proportion of male who committed suicide is much higher than female here, but note that this is just an insight we got from the data. Each data has its limitations and can only be used to draw conclusions with those constraints in mind (e.g., data coverage, context).

Show code

# First, make a tibble of suicide by sex. We will use this for our pie chart.
pie_sex <- data %>%
  select(sex, suicides_no, population) %>%
  group_by(sex) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))
  
# Create pie chart for sex. 
highchart() %>% 
  hc_add_series(pie_sex, hcaes(x = sex, y = suicide_capita, 
                               color = sex_color), type = "pie") %>%
  hc_tooltip(borderWidth = 1.5, headerFormat = "", pointFormat = paste("Gender: <b>{point.sex} ({point.percentage:.1f}%)</b> <br> Suicides per 100K: <b>{point.y}</b>")) %>%
  hc_title(text = "<b>Worldwide suicides by Gender</b>", style = (list(fontSize = '14px'))) %>% 
  hc_subtitle(text = "1985-2015", style = (list(fontSize = '10px'))) %>%
  hc_plotOptions(pie = list(dataLabels = list(distance = 5, 
                            style = list(fontSize = 10)), 
                            size = 130)) %>% 
  hc_add_theme(custom_theme)

Worldwide suicides by Age

• Here, we use an interactive line plot to compare worldwide suicide rate across age groups. Try clicking on age group labels (e.g., 25-34 years) below to toggle it on or off, so that we can compare the trend between each age group easily without having other irrelevant lines to distract.

Show code

# Create tibble for age so we can use it when creating our line plot.  
age_tibble <- data %>%
  select(year, age, suicides_no, population) %>%
  group_by(year, age) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))

# Pick color for graph. 
age_color <- rev(plasma(6))

# Create a line plot.
highchart() %>% 
    hc_add_series(age_tibble, hcaes(x = year, y = suicide_capita, group = age), type = "line", color = age_color) %>%
    hc_tooltip(crosshairs = TRUE, borderWidth = 1.5, headerFormat = "", pointFormat = paste("Year: <b>{point.x}</b> <br>","Age: <b>{point.age}</b><br>", "Suicides: <b>{point.y}</b>")) %>%
    hc_title(text = "Worldwide suicides by Age") %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_xAxis(title = list(text = "Year")) %>%
    hc_yAxis(title = list(text = "Suicides per 100K people"),
             allowDecimals = FALSE,
             plotLines = list(list(
                    color = "black", width = 1, dashStyle = "Dash",
                    value = mean(overall_tibble$suicide_capita),
                    label = list(text = "Mean = 13.12", 
                                 style = list(color = 'black', fontSize = 11))))) %>% 
    hc_add_theme(custom_theme)

• We can also see the proportion of age groups with a pie chart here as well. This pie chart works like the gender pie chart above.

Show code

# First, create a tibble of suicide by Age. We will use this for our pie chart.
pie_age <- data %>%
  select(age, suicides_no, population) %>%
  group_by(age) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2)) %>%
  arrange(suicide_capita)

# Create pie chart for Age. 
highchart() %>% 
  hc_add_series(pie_age, hcaes(x = age, y = suicide_capita, 
                               color = age_color), type = "pie") %>%
  hc_tooltip(borderWidth = 1.5, headerFormat = "", pointFormat = paste("Age: <b>{point.age} ({point.percentage:.1f}%)</b> <br> Suicides per 100K: <b>{point.y}</b>")) %>%  
  hc_title(text = "<b>Worldwide suicides by Age</b>", style = (list(fontSize = '14px'))) %>% 
  hc_subtitle(text = "1985-2015", style = (list(fontSize = '10px'))) %>%
  hc_plotOptions(pie = list(dataLabels = list(distance = 5, 
                            style = list(fontSize = 10)), 
                            size = 130)) %>% 
  hc_add_theme(custom_theme)

Continent Level

• Diving a little bit deeper, we will compare suicide rate at the continent level (i.e., Africa, Asia, Europe, North America, Oceania, and South America).

Show code

# Create new column in our data for continent. Use countrycode() to extract continents from country names. 
data$continent <- countrycode(sourcevar = data$country,
                              origin = "country.name",
                              destination = "continent")

# Reclassify countries that have been coded as 'Americas', by countrycode(), into 'North America' and 'South America'. 
south_america <- c('Argentina', 'Brazil', 'Chile', 'Colombia', 'Ecuador', 'Guyana', 'Paraguay', 'Suriname', 'Uruguay')

data$continent[data$country %in% south_america] <- 'South America'
data$continent[data$continent=='Americas'] <- 'North America'

Suicides by continent and Gender

• We can use a histogram to compare suicide rate of each continent by gender (male vs female).

Show code

# Create a tibble for continent and sex.
continent_sex_tibble <- data %>%
  select(continent, sex, suicides_no, population) %>%
  group_by(continent, sex) %>%
  summarize(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))

# Create histogram of suicides by continent.
highchart() %>%
hc_add_series(continent_sex_tibble, hcaes(x = continent, y = suicide_capita, group = sex), type = "column")  %>% 
    hc_colors(colors = sex_color) %>%
    hc_title(text = "Suicides by continent and <b>Gender</b>", style = (list(fontSize = '14px'))) %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_tooltip(borderWidth = 1.5, pointFormat = paste("Gender: <b> {point.sex} </b> <br> Suicides: <b>{point.y}</b>")) %>%
    hc_xAxis(categories = c("Africa", "Asia", "Europe", "North <br> America", "Oceania", "South <br> America"), labels = list(style = list(fontSize = 8))) %>%
    hc_yAxis(labels = list(style = list(fontSize = 10)),
             title = list(text = "Suicides per 100K people",
             style = list(fontSize = 10)),
        plotLines = list(
          list(color = "black", width = 1, dashStyle = "Dash", 
               value = mean(overall_tibble$suicide_capita),
               label = list(text = "Mean = 13.12", style = list(color = "black", fontSize = 6))))) %>%     
    hc_legend(verticalAlign = 'top', enabled = FALSE) %>% 
    hc_add_theme(custom_theme)

Suicides by continent and Age

• We can also compare suicide rate of each continent by age group here.

Show code

# Create a tibble for continent and sex.
continent_age_tibble <- data %>%
  select(continent, age, suicides_no, population) %>%
  group_by(continent, age) %>%
  summarize(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2)) 

# Create histogram of suicides by continent.
highchart() %>%
hc_add_series(continent_age_tibble, hcaes(x = continent, y = suicide_capita, group = age), type = "column")  %>% 
    hc_colors(colors = age_color) %>%
    hc_title(text = "Suicides by continent and <b>Age</b>", style = (list(fontSize = '14px'))) %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_tooltip(borderWidth = 1.5, pointFormat = paste("Age: <b> {point.age} </b> <br> Suicides: <b>{point.y}</b>")) %>%
    hc_xAxis(categories = c("Africa", "Asia", "Europe", "North <br> America", "Oceania", "South <br> America"), labels = list(style = list(fontSize = 8))) %>%
    hc_yAxis(labels = list(style = list(fontSize = 10)),
             title = list(text = "Suicides per 100K people",
                          style = list(fontSize = 10)),
        plotLines = list(
          list(color = "black", width = 1, dashStyle = "Dash", 
               value = mean(overall_tibble$suicide_capita),
               label = list(text = "Mean = 13.12", style = list(color = "black", fontSize = 6))))) %>%    
    hc_legend(verticalAlign = 'top', enabled = FALSE) %>% 
    hc_add_theme(custom_theme)

Suicides by continent as represented by global mapping

• For this part, I have imported the pre-downloaded world continent data from Highcharts map collection, but you can also download the map directly from the site with map_data <- download_map_data("custom/world-continents").

• Here, we can compare suicide rate of each continent using color gradient to see which continent has the highest (or the lowest) suicide rate.

Show code

# Import continent map data. 
map_data <- fromJSON(file = "world-continents.geo.json")
# Create a tibble for continent.
continent_tibble <- data %>%
  select(continent, suicides_no, population) %>%
  group_by(continent) %>%
  summarize(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2)) %>%
  arrange(suicide_capita)

# Create continent map with suicide data. 
highchart() %>%
hc_add_series_map(map_data, continent_tibble, value = "suicide_capita", joinBy = c('name','continent'), name = "Suicides (per 100K people)")  %>% 
    hc_add_series(continent_tibble, hcaes(x = continent, y = suicide_capita, color = suicide_capita), type = "pie", name = 'Suicides (per 100K people)')  %>% 
    hc_colorAxis(stops = color_stops()) %>% 
    hc_title(text = "Suicides by Continent") %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_tooltip(borderWidth = 1.5, valueSuffix = '') %>%
    hc_plotOptions(
    pie = list(center = c('10%', '80%'), size = 110, dataLabels = list(enabled = FALSE))) %>% 
    hc_add_theme(custom_theme)

Country Level

• We can also compare suicide rate at country level by plotting them all on a bar chart as separated by gender, age group, and geographical location as well. For the age group bar chart, you can hover your mouse over each age group to compare that specific group across countries, and you can also toggle each age group on and off as well to filter out irrelevant information.

Show code

# Create tibble for overall suicides by country
country_bar <- data %>%
  select(country, suicides_no, population) %>%
  group_by(country) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2)) %>%
  arrange(desc(suicide_capita))

# Create interactive bar plot
highchart() %>%
    hc_add_series(country_bar, hcaes(x = country, y = suicide_capita, color = suicide_capita), type = "bar")  %>% 
    hc_tooltip(borderWidth = 1.5, 
               pointFormat = paste("Suicides: <b>{point.y}</b>")) %>%
    hc_legend(enabled = FALSE) %>%
    hc_title(text = "Suicides by country") %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_xAxis(categories = country_bar$country, 
             labels = list(step = 1),
             min = 0, max = 25,
             scrollbar = list(enabled = TRUE)) %>%
    hc_yAxis(title = list(text = "Suicides per 100K people")) %>%
    hc_plotOptions(bar = list(stacking = "normal", 
                              pointPadding = 0, groupPadding = 0, borderWidth = 0.5)) %>% 
    hc_add_theme(custom_theme)

By gender

Show code

# Create tibble for suicide by countries and sex. 
country_bar_sex <- data %>%
  select(country, sex, suicides_no, population) %>%
  group_by(country, sex) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))

country_tibble <- data %>%
  select(country, suicides_no, population) %>%
  group_by(country) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2)) 

# Create bar chart of suicide by sex.
highchart() %>%
    hc_add_series(country_bar_sex, hcaes(x = country, y = suicide_capita, group = sex), type = "bar", color = sex_color)  %>% 
    hc_tooltip(borderWidth = 1.5, pointFormat = paste("Gender: <b>{point.sex} ({point.percentage:.1f}%)</b> <br> Suicides per 100K: <b>{point.y}</b>")) %>%
    hc_legend(enabled = TRUE, colorByPoint = TRUE) %>%
    hc_title(text = "Suicides by country and gender") %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_xAxis(categories = country_tibble$country,
             labels = list(step = 1),
             min = 0, max = 25,
             scrollbar = list(enabled = TRUE)) %>%
    hc_yAxis(title = list(text = "Percentage of total suicides")) %>%
    hc_plotOptions(bar = list(stacking = "percent", 
                              pointPadding = 0, groupPadding = 0, borderWidth = 0.4)) %>% 
    hc_add_theme(custom_theme)

Show code

# Create tibble for suicide by countries and age 
country_bar_age <- data %>%
  select(country, age, suicides_no, population) %>%
  group_by(country, age) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))

# Create interactive bar plot.
highchart() %>%
    hc_add_series(country_bar_age, hcaes(x = country, y = suicide_capita, group = age), type = "bar", color = age_color)  %>% 
    hc_tooltip(borderWidth = 1.5, pointFormat = paste("Age: <b>{point.age} ({point.percentage:.1f}%)</b> <br> Suicides per 100K: <b>{point.y}</b>")) %>%
    hc_title(text = "Suicides by country and age") %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_xAxis(categories = country_tibble$country,
             labels = list(step = 1),
             min = 0, max = 25,
             scrollbar = list(enabled = TRUE)) %>%
    hc_yAxis(title = list(text = "Percent of total suicides")) %>%
    hc_plotOptions(bar = list(stacking = "percent", 
                              pointPadding = 0, groupPadding = 0, borderWidth = 0.5)) %>% 
    hc_add_theme(custom_theme)

• The interactive world map below can be used to check which country that we have data on. The white space shows that we do not have much data on African countries, as well as mainland China, and South-East Asian countries.

Show code

# Create a tibble with suicide per capita by country for 1985-2015. 
country_tibble <- data %>%
  select(country, suicides_no, population) %>%
  group_by(country) %>%
  summarize(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2))

# Create interactive world map.
highchart() %>%
hc_add_series_map(worldgeojson, country_tibble, value = "suicide_capita", joinBy = c('name','country'))  %>% 
    hc_colorAxis(stops = color_stops()) %>% 
    hc_title(text = "Suicides by Country") %>% 
    hc_subtitle(text = "1985-2015") %>%
    hc_tooltip(borderWidth = 1.5, headerFormat = "", valueSuffix = " suicides (per 100K people)") %>% 
    hc_add_theme(custom_theme)

Search for specific countries

• Now this is the fanciest (and the hardest part to compile). We can use crosstalk package to link our data set across country filter, country data table, and line plot of suicide rate trend across years. Try typing country names that you are interested in in the search box, and the table will filter out suicide data for those countries for you to compare, as well as displaying a line plot for them as well.

• You can use plotly option to download the plot as png, compare data of the two countries on hover, and darg a box to zoom in at a specific period of time.

Show code

# Create tibble for our line plot.  
country_year_tibble <- data %>%
  select(country, year, suicides_no, population) %>%
  group_by(country, year) %>%
  summarise(suicide_capita = round((sum(suicides_no)/sum(population))*100000, 2)) 

# Create shared data that will be used to link filters, data table, and line plot. 
shared_data <- SharedData$new(country_year_tibble)

# Create filter for year and country. These filters will adjust the DT datatable and PLOTLY plot. 
filter_slider("year", "Year", shared_data, ~year, step = 1)

Year

Show code

filter_select("country", "Country", shared_data, ~country, allLevels = TRUE, multiple = TRUE)

Country

Show code

# Create datatable. 
datatable(shared_data,
          rownames = FALSE,
          colnames = c('Country', 'Year', 'Suicides /100K'),
          class = 'cell-border stripe',
          width = '50%',
          extensions = "Scroller",
          options=list(deferRender = FALSE, 
                       scrollY = 200, 
                       scrollCollapse = TRUE,
                       scroller = TRUE,
                       dom = 't'))

Show code

# Set a random seed. We will do this so that we can reproduce the random sample of colors we will use for our next graph. 
set.seed(80085)

# Create line graph. 
plot_ly(shared_data, x = ~year, y = ~suicide_capita, 
       color = ~country, colors = sample(colours(), 120),
       type = 'scatter', mode = 'lines',
       hoverinfo = 'text', text = ~paste("Country: ", country, '<br>Year: ', year, "<br>Suicides: ", suicide_capita)) %>%
       layout(showlegend = FALSE,
              title = "Suicide by country",
              xaxis = list(title = "Year"),
              yaxis = list(title = "Suicides per 100K people")) %>%
       layout(plot_bgcolor = 'transparent') %>% 
       layout(paper_bgcolor = 'transparent') %>% 
       add_markers()

Concluding note

• This entry is just me playing around with codes to generate interactive plots. While it may not be as flashy as machine learning or natural language processing, data visualization is an important part of data work to communicate results to our audience without relying to heavily on texts and numbers.

• A little bit on graphing. When we make a plot, simplicity and efficiency are vital. We cannot just throw every last bit of information up there and expect the audience to understand everything as us, who were invested in that data for weeks, months, or even years. Use only the most relevant information, so that the main information doesn’t get overshadowed by bits and pieces such as sound effect, animation, or unnecessary numbers. Again, thank you very much for reading!