app_ui.R

# UI for project
library("shiny")
library("ggplot2")
library("plotly")

project_overview <- tabPanel(
  "Overview of our Project",

    p("Since the pandemic hit the world, it has drastically turned the lives 
    of humans all of the world upside down. One of many concerns that the 
    COVID-19 pandemic as highlighted is that of mental health. Being under 
    lockdown and limiting social interactions has led way for increased cases 
    of anxiety and depression in people.The data we are analyzing was collected 
    from a national household pulse survey
    by the U.S. Census Bureau meant to produce data on the social and
    economic impacts of Covid-19 on households in the US. This portion 
    of the survey is on the indicators of anxiety or depression changing 
    over time, during the pandemic, on different ages, races, education 
    levels and states. We chose this dataset because it is very relevant for 
    observing how many people are experiencing mental health issues during 
    the pandemic. The questions we want to answer are: QUESTION ONE, 
    Do Changes in Percentage of Mental Health Symptoms Correlate With 
    Amounts of COVID-19 Cases Over Time?, Do Minority Groups Experience 
    Symptoms of Depression and Anxiety at a Higher Rate?"),
  a("https://data.cdc.gov/NCHS/Indicators-of-Anxiety-or-Depression-Based-on-Repor/8pt5-q6wp"),
  
  img(src = "Nicole_Hwang_Coronavirus_DRS-01.png", height = "50%", width = "50%")
  
)
# Here is page two
page_one_widget <- sidebarPanel(
  radioButtons(
    inputId = "symptom_select",
    label = ("Symptom Selection:"),
    choices = c("Anxiety" = "Symptoms of Anxiety Disorder",
                "Depression" = "Symptoms of Depressive Disorder",
                "Anxiety or Depression" = "Symptoms of Anxiety Disorder or Depressive Disorder"
    ),
    selected = "Symptoms of Anxiety Disorder"
  )  
)

page_one_main <- mainPanel(
  
  plotlyOutput("case_type_plot"),
  
  p("This first plot above was to compare the different reported symptoms of 
  either, Anxiety Disorder, Depressive Disorder or both. Here you can choose
    which reported symptom to fill in for each time period. This gives us 
    an additonal insight as to what time periods had the highest reported 
    symptoms."),

  plotlyOutput("normalized_plot"),
  
  p("This plot takes each time period and averages the reported percentage 
    of people with symptoms of all and/or both disorders. Then it normalizes 
    each time period with a z-score to show which time periods had above average
    reported symptoms.")
)

page_one <- tabPanel(
  "Phases of the study by reported cases",
  
  titlePanel("Which phases of the study were above the 
    average reported percent of cases?"),
page_one_main,
page_one_widget
  )




#Here is Page Two
page_two_widget <- sidebarPanel(
  radioButtons(
    inputId = "symp_select",
    label = ("Symptom Selection:"),
    choices = c("Anxiety" = "Symptoms of Anxiety Disorder",
    "Depression" = "Symptoms of Depressive Disorder",
    "Anxiety or Depression" = "Symptoms of Anxiety Disorder or Depressive Disorder"
    ),
    selected = "Symptoms of Anxiety Disorder"
  )  
)

page_two_main <- mainPanel(

  plotlyOutput("national_plot"),
  p("This first plot above was created to answer how the percent of 
    people feeling depressed and/or anxious differs over time. 
    The time period of this chart goes from the beginning to the 
    end of the survey, from Apr 23 - May 5 to Oct 14 - Oct 26 of 2020. 
    There are three groups that can be selected in this chart: people 
    suffering from anxiety, people suffering from depression and 
    people suffering from either."),
  
  plotlyOutput("case_plot"),
  p("This second plot above was created to show the number of new cases on the
    first days of the Time Periods that the survey uses. 
    (Apr 23 to Oct 14 of 2020.)"),
)

page_two <- tabPanel(
  "Mental Health Changes Relating to COVID-19 Cases",
  
  titlePanel("Do Changes in Percentage of Mental Health Symptoms Correlate With Amounts of COVID-19 Cases Over Time?"),
  
  p("The two plots below show trends for how the percent of people experiencing
  mental health symptoms and COVID-19 cases changes over time. When looking at
  the two graphs together they resemble a very similar graph with peaks and 
  valleys at almost identical time points. The highest symptom percentages for 
  all groups was in the time period from July 16 - July 21 2020, and the highest 
  daily COVID-19 cases out of all the time periods was the same period. The 
  trends following a similar trajectory, suggesting that anxiety and
  depression could be triggered by the severity of daily cases reported in 
  the same time periods in the United States."),
  
  sidebarLayout(
    page_two_main,
    page_two_widget
  )
)


page_three_widget <- sidebarPanel(
  radioButtons(
    inputId = "race_select",
    label = ("Race Selection:"),
    choices = c("White" = "Non-Hispanic white, single race",
                "Black" = "Non-Hispanic black, single race",
                "Asian" = "Non-Hispanic Asian, single race",
                "Other/Mixed Races" = "Non-Hispanic, other races and multiple races",
                "Hispanic or Latino" = "Hispanic or Latino"
    ),
    selected = "Hispanic or Latino"
  )  
)

page_three_main <- mainPanel(
  plotlyOutput("race_plot"),
  p("This plot first plot shows the percentage of individuals reporting symptoms
  in different race/ethnicity groups. The purpose of this was to see if
  minority groups such as the Asian, Hispanic, Black, and mixed race populations
  were affected on a higher rate than the majority. The chart shows the
  reported cases through time as a way to guage the average. There are five
  groups about whom information can be displayed - people identifying as White,
  Black, Asian, Hispanic/Latino, or Mixed/Other Races."),
  
  plotlyOutput("gender_plot"),
  p("The second plot compares the percentage of individuals who reported having
    anxiety or depression symptoms over time between individuals who identify
    as male vs. female. This was created in order to see if the female
    population, who, in minority populations, are often the smallest minority,
    are more likely to report symptoms."),
)

page_three <- tabPanel(
  "Race and Gender vs. the Number of Reported Cases",
  
  titlePanel("Do Minority Groups Experience Symptoms of Depression and Anxiety at a Higher Rate?"),
  
  p("From the two plots, it is clear that under-represented minorities
    reported symptoms on a higher percentage than did others. From the first
    plot, it is clear that the smallest group - Mixed/Other Races - reported
    the highest percentage on average. Similarly, women - who are
    under-represented in the economy and other spheres also reported symptoms
    more on average."),
  sidebarLayout(
    page_three_main,
    page_three_widget
  )
)


summary_page <- tabPanel(
  "Summary of our Project",

#First Takeaway

h4("First Takeaway: Dates of most reported mental health cases"),
p("According to the first graph on page one,  we can see that most cases occured
  evenly throughout all time periods with no one period standing out as a 
  significant outlier. This is mirrored in the second graph on page one 
  where we find both earlier time periods, May and April falling below the 
  average z score of percent reported, as well as July and June months falling
  above the average. Since the time period of the study went until October
  there is not one time period that stands as being above or below the mean.
  This could point towards people feeling anxious and/or depressed despite of 
  the time of year of the survey, but rather due to lockdowns/restrictions 
  that have occured throughout all the time-periods of the covid pandemic."),
img(src = "summary_1_graph.png", height = "80%", width = "80%"),
#Second Takeaway
h4("Second Takeaway: Mental Health Relations With COVID-19 Cases"),
p("In our second interactive page of the report there we wanted to know if 
    changes in mental health symptoms, depression or anxiety, had a 
    relationship with the daily number of new COVID-19 cases for the same time
    periods. In comparing the two graphs we could observe that they both share
    a very similar pattern. The graph that matched most closely was Symptoms 
    of  Depression which as pictured below follows the same peaks and
    valleys as the COVID-19 cases increases and decreases. From this similar
    pattern of increasing new daily cases while symptoms of depression
    increase it could be that the COVID-19 case levels are directly affecting
    the percent of Americans affected with Depression. This would mean that
    COVID-19 not only is affecting the physical health of people in the 
    United States, but also their mental health and wellbeing from 
    concerns due to the growing number of people sick with COVID-19."),
img(src = "summary2.png"),
#Third Takeaway
h4("Third Takeaway: Minorities and Mental Health"),
p("According to the first plot, it is clear that out of all of the groups,
  minorities generally have higher percentages of reports. Asiasn remained 
  as the only outlier to this. Not considering this group, individuals
  indentifying as black, hispanic/latino, mixed/other races all had higher
  percentages of reports. The highest of that group was individuals of mixed/
  other races, who are among the smallest minorities in the United States. This
  suppored the claim that minority race/ethnicities were affected more by
  symptoms of anxiety and/or depression. We also know that women are the
  under-represented minority in the workforce. I wanted to see if this
  corrolated with the report patterns. I found that individuals indentifying as
  female were more likely to report such sympotoms. The key takeaway from this
  ended up telling me that minorities have been affected the pandemic more than 
  we might understand, and in more ways that the obvious."),
img(src = "gender_vs_reports.png")
  
)

ui <- fluidPage(
  includeCSS("styles.css"),
  navbarPage(
  "Final Deliverable: Mental Health During the COVID-19 Pandemic",
  project_overview,
  page_one,
  page_two,
  page_three,
  summary_page
  )
)