Reasons for divorce over time

setting <- list(show = T,type= "scroll",orient= "horizontal", pageButtonPosition= 'start',
                right= 5,top = 30, icon = 'circle', align= 'right', height='85%')

s_ %>% filter(Year>2003) %>% mutate(count = as.numeric(count)) %>%
  mutate(count = ifelse(is.na(count),0,count)) %>% 
  group_by(Year)|>
  ec.init(
    xAxis = list(name = 'Durations',nameLocation = 'end',
                 nameTextStyle = list(fontWeight ='bolder',padding = 2,width = 1,
                                      overflow = 'breakAll'),
                 axisLabel = list(rotate = 346,width = 65,
                                  overflow = 'truncate')),
    yAxis = list(name = "Count",nameLocation = 'end',
                 nameTextStyle = list(fontWeight ='bolder')),
    tl.series = list(playInterval = 200,type  ='bar',stack = "grp",
                     encode = list(x = 'Duration',y = 'count'), groupBy= 'Reason',
                     emphasis= list(focus= 'series',
                                    itemStyle=list(shadowBlur=10,
                                                   shadowColor='rgba(0,0,0,0.5)'),
                                    label= list(position= 'right',
                                                rotate = 350,
                                                show=TRUE)),
                     title = list(text = "Outbreak incidents by year", 
                                  left = "10%", top = 10, textStyle = list(fontWeight = "normal", fontSize = 20),
                                  text = "WAHIS Dataset", 
                                  left = "10%", top = 17, textStyle = list(fontWeight = "normal", fontSize = 14))),
    tooltip = list(show = T))|>ec.theme("something",roma)

In dissecting the anatomy of annulled marriages, the stacked barplot unfurls a tapestry of fractured unions, each band representing the reasons that wove into the dissolution. The x-axis traces the passage of time, dividing the durations of these severed ties into delineated segments.

However, a crucial nuance blankets this visual revelation—the absence of marriage numbers renders the totality of divorces depicted less resonant. Within each segment of time, the proportions of the reasons for divorce stand as islands amidst an ocean of undisclosed marriages, their significance diluted without the context of the whole. The barplot, though rich in the distribution of reasons, remains an incomplete mural, capturing the hues of divergence but unable to paint the broader canvas of the marriage landscape it reflects.

Reasons for divorce between genders over time

genders %>% filter(Year>2003) %>% mutate(count = as.numeric(count)) %>%
  mutate(count = ifelse(is.na(count),0,count)) %>% 
  group_by(Year)|>
  ec.init(
    xAxis = list(name = 'Durations',nameLocation = 'end',
                 nameTextStyle = list(fontWeight ='bolder',padding = 2,width = 1,
                                      overflow = 'breakAll'),
                 axisLabel = list(rotate = 346,width = 65,
                                  overflow = 'truncate')),
    yAxis = list(name = "Count",nameLocation = 'end',
                 nameTextStyle = list(fontWeight ='bolder')),
    tl.series = list(playInterval = 200,type  ='bar',stack = "grp",
                     encode = list(x = 'Duration',y = 'count'), groupBy= 'Gender',
                     emphasis= list(focus= 'series',
                                    itemStyle=list(shadowBlur=10,
                                                   shadowColor='rgba(0,0,0,0.5)'),
                                    label= list(position= 'right',
                                                rotate = 350,
                                                show=TRUE)),
                     title = list(text = "Outbreak incidents by year", 
                                  left = "10%", top = 10, textStyle = list(fontWeight = "normal", fontSize = 20),
                                  text = "WAHIS Dataset", 
                                  left = "10%", top = 17, textStyle = list(fontWeight = "normal", fontSize = 14))),
    tooltip = list(show = T,trigger = "axis"))|>ec.theme("something",roma)

Beneath the stark lines and plotted points tracing the ebb and flow of divorces, a nuanced narrative emerges—a tale of reasons threading through the unraveling unions, intricately weaving between the genders. The distribution of these reasons unfolds over time, revealing the divergent paths that lead to marital dissolution.

Yet, amid this mosaic lies a crucial disparity, often overlooked in sensationalized narratives. The commonly spotlighted reasons, such as the fiery specter of adultery, occupy a mere fragment of the officially recorded reasons for divorce. Beyond the glare of sensational headlines, a spectrum of complexities paints the true landscape—a majority of causes that find little mention in the attention-grabbing anecdotes.

This plot, a herald of gender-divided divorce dynamics, unveils a story painted by myriad reasons. Within this narrative, the attention-grabbing anecdotes stand as vivid outliers against the backdrop of more prevalent and intricate relational challenges faced by both men and women.

Notice that between adultery and desertion, adultery is a more common reason compared to desertion for divorcing amongst women (they make up about 1-5% or so of the total logged divorces themselves) while the contrast is true for men. Also note that divorces prior to 2004 did not have any reasons for divorce recorded.

 

A work by Mr Misc

override334@gmail.com