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ui.R
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ui.R
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require(shiny)
#library(shinyapps)
require(shinyIncubator)
#function to create the interface for filtering the sample sets
filterOptions<-function(id,data_select,filterTemp,filterAcc){
tabsetPanel(
# position="left", # the "position" function is deprecated"
tabPanel(id=paste0("bldg",id),title="building",
wellPanel(
checkboxGroupInput(paste0("dataset",id), "Database:",
list("Database 1" = "Database 1",
"Database 2" = "Database 2"),
selected=data_select)),
if(filterTemp){
if(filterAcc){sliderInput
wellPanel(
selectInput(paste0("metric",id), "Satisfaction metric:",
list("Acceptability" = "tsa_ash_level",
# "Sensation +-1.5" = "ash15_level",
"Comfort" = "comf_ash_level"),
# "Preference" = "mci_level"),
selectize=F, selected="tsa_ash_level")
)
}
},
wellPanel(
uiOutput(paste0("type",id))),
wellPanel(
uiOutput(paste0("BLTYPE",id))
)
),
tabPanel(id=paste0("geog",id),title="geography",
wellPanel(
checkboxInput(paste0("season_check",id),"Filter by seasons"),
conditionalPanel(condition = paste0("input.season_check",id),
uiOutput(paste0("seasons",id))),
checkboxInput(paste0("climate_check",id),"Filter by climates"),
conditionalPanel(condition = paste0("input.climate_check",id),
uiOutput(paste0("climates",id))),
checkboxInput(paste0("country_check",id),"Filter by countries"),
conditionalPanel(condition = paste0("input.country_check",id),
uiOutput(paste0("countries",id))),
checkboxInput(paste0("city_check",id),"Filter by cities"),
conditionalPanel(condition = paste0("input.city_check",id),
uiOutput(paste0("cities",id)))
)
),
tabPanel(id=paste0("conditions",id),title="conditions",
#only include the temperature filters for the probits since they're controlled as axis limits for the 2d Adaptive model
if(filterTemp){
wellPanel(
checkboxInput(paste0("prev",id),"Filter by Monthly mean outdoor temperature"),
conditionalPanel(condition=paste0("input.prev",id, "& input.units", substring(id,0,nchar(id)-1),"=='Metric'"),
sliderInput(paste0("outRange",id), label="",
min = -25, max = 50, value = c(-25,50), step= 1, post="°C")),
conditionalPanel(condition=paste0("input.prev",id,"& input.units", substring(id,0,nchar(id)-1),"=='Imperial'"),
sliderInput(paste0("outRange_F",id), label="",
min = -13, max = 104, value = c(-13,104), step= 1, post="°C")),
checkboxInput(paste0("taav",id),"Filter by indoor temperature"),
conditionalPanel(condition=paste0("input.taav",id, "& input.units", substring(id,0,nchar(id)-1),"=='Metric'"),
sliderInput(paste0("inRange",id), label="",
min = -25, max = 50, value = c(-25,50), step= 1, post="°C")),
conditionalPanel(condition=paste0("input.taav",id,"& input.units", substring(id,0,nchar(id)-1),"=='Imperial'"),
sliderInput(paste0("inRange_F",id), label="",
min = -13, max = 104, value = c(-13,104), step= 1, post="°F")),
checkboxInput(paste0("trav",id),"Filter by radiant temperature"),
conditionalPanel(condition=paste0("input.trav",id, "& input.units", substring(id,0,nchar(id)-1),"=='Metric'"),
sliderInput(paste0("radRange",id), label="",
min = -25, max = 50, value = c(-25,50), step= 1, post="°C")),
conditionalPanel(condition=paste0("input.trav",id,"& input.units", substring(id,0,nchar(id)-1),"=='Imperial'"),
sliderInput(paste0("radRange_F",id), label="",
min = -13, max = 104, value = c(-13,104), step= 1, post="°F")),
checkboxInput(paste0("top",id),"Filter by operative temperature"),
conditionalPanel(condition=paste0("input.top",id, "& input.units", substring(id,0,nchar(id)-1),"=='Metric'"),
sliderInput(paste0("optRange",id), label="",
min = 0, max = 50, value = c(0,50), step= 1, post="°C")),
conditionalPanel(condition=paste0("input.top",id,"& input.units", substring(id,0,nchar(id)-1),"=='Imperial'"),
sliderInput(paste0("optRange_F",id), label="",
min = 32, max = 104, value = c(32,104), step= 1, post="°F"))
)
},
wellPanel(
checkboxInput(paste0("rh",id),"Filter by relative humidity"),
conditionalPanel(condition=paste0("input.rh",id),
sliderInput(paste0("RH",id), "",
min = 0, max = 100, value = c(0,100), step= 1, round = TRUE, post="%")),
checkboxInput(paste0("velfilter",id),"Filter by air speed"),
conditionalPanel(condition=paste0("input.velfilter",id, "& input.units", substring(id,0,nchar(id)-1),"=='Metric'"),
sliderInput(paste0("vel",id), "",
min = 0, max = 6, value = c(0,2), step= 0.1,post="m/s")),
conditionalPanel(condition=paste0("input.velfilter",id, "& input.units", substring(id,0,nchar(id)-1),"=='Imperial'"),
sliderInput(paste0("vel_fpm",id), "",
min = 0, max = 1180, value = c(0,400), step= 10,post="fpm")))
),
tabPanel(id=paste0("other",id),title="human factors",
wellPanel(
checkboxInput(paste0("curfilter",id),"Filter by blind (curtain) control"),
conditionalPanel(condition=paste0("input.curfilter",id),
checkboxGroupInput(paste0("cur",id),label="",choices=list("Open" = "0","Close" = "1"),selected=c("0","1"))),
checkboxInput(paste0("fanfilter",id),"Filter by fan control"),
conditionalPanel(condition=paste0("input.fanfilter",id),
checkboxGroupInput(paste0("fan",id),label="",choices=list("Off" = "0","On" = "1"),selected=c("0","1"))),
checkboxInput(paste0("winfilter",id),"Filter by window control"),
conditionalPanel(condition=paste0("input.winfilter",id),
checkboxGroupInput(paste0("win",id),label="",choices=list("Open" = "0","Close" = "1"),selected=c("0","1"))),
checkboxInput(paste0("doorfilter",id),"Filter by door control"),
conditionalPanel(condition=paste0("input.doorfilter",id),
checkboxGroupInput(paste0("door",id),label="",choices=list("Open" = "0","Close" = "1"),selected=c("0","1"))),
checkboxInput(paste0("heatfilter",id),"Filter by heater control"),
conditionalPanel(condition=paste0("input.heatfilter",id),
checkboxGroupInput(paste0("heat",id),label="",choices=list("Off" = "0","On" = "1"),selected=c("0","1")))
),
wellPanel(
checkboxInput(paste0("sexfilter",id),"Filter by sex"),
conditionalPanel(condition=paste0("input.sexfilter",id),
#checkboxGroupInput(paste0("sex",id),label="",choices=list("Female" = "1","Male" = "0"),selected=c("1","0"))),
checkboxGroupInput(paste0("sex",id),label="",choices=list("Female" = "Female","Male" = "Male"),selected=c("Female","Male"))),
checkboxInput(paste0("agefilter",id),"Filter by age"),
conditionalPanel(condition=paste0("input.agefilter",id),
sliderInput(paste0("age",id), "",
min = 10, max = 100, value = c(20,90), step= 1)),
checkboxInput(paste0("clofilter",id),"Filter by clothing insulation"),
conditionalPanel(condition=paste0("input.clofilter",id),
sliderInput(paste0("clo",id), "",
min = 0.2, max = 2.3, value = c(0.2,2.3), step= 0.1)),
checkboxInput(paste0("metfilter",id),"Filter by metabolic rate"),
conditionalPanel(condition=paste0("input.metfilter",id),
sliderInput(paste0("met",id), "",
min = 0.8, max = 4.5, value = c(0.8,4.5), step= 0.1))
)
)
)
}
#probitSample<-function(id,color,data_select){
probitSample<-function(id,color,data_select,active=T){
column(12,
withTags(p(checkboxInput(paste0("compare",id),paste("Sample set",id),value=active),style=paste0("background-color:",color))),
conditionalPanel(condition=paste0("input.compare",id),
filterOptions(id,data_select,TRUE,TRUE)
)
)
}
shinyUI(
navbarPage("ASHRAE Global Thermal Comfort Database II Visualization",
tabPanel("Satisfaction", fluidPage(
fluidRow(
column(2,
selectInput("xaxis", "Model selection (X-axis):",
list("Thermal sensation" = "ASH",
"PMV" = "PMV"),
selectize=F)
),
column(2,
checkboxInput("showCI","Show 95% confidence intervals",TRUE),
checkboxInput("descriptive","Show descriptive plots",value=TRUE)
),
column(1,
radioButtons("units","",choices=c("Metric","Imperial"))),
column(3,
selectInput("xstat","Select the parameter for bar chart",
list("Country" = "country",
"Ventilation type" = "cooling.strategy",
"Building type" = "BLTYPE",
"City"="city",
"Climate"="climate",
"Season"="season",
"SEX"="SEX"),
selectize=F)
),
# column(2,
# div("Press 4 times to initiate the program", style = "color:red"),
# actionButton("go","Run")
# ),
column(4,
downloadButton('downloadPlot', 'Download plot')
)),
hr(),
fluidRow(
column(4,
plotOutput("pmvPlot")),
withTags(column(4,
conditionalPanel(condition="input.descriptive",
plotOutput("cityPlotted")
)
#style="overflow-y:scroll;height=400")),
)),
withTags(column(3,
conditionalPanel(condition="input.descriptive",
plotOutput("boxplot1",height=200),
plotOutput("boxplot2",height=200)
))),
withTags(column(1,
conditionalPanel(condition="input.descriptive",
br(),
br(),
br(),
br(),
br(),
#br(),
selectInput("boxplot1","boxplot1 x-axis",
list("Op. Temp"="TOP",
"Air Temp"="AirTemp",
"Outdoor Temp"="OutTemp",
"Relative humidity"="RH",
"Air Speed"="AirSpeed",
"Metabolic rate"="MET",
"Clothing insulation"="INSUL",
"AGE"="AGE",
"TSV"="TSV",
"PMV"="PMV"), selectize=F, selected="TOP"),
br(),
br(),
br(),
br(),
br(),
br(),
selectInput("boxplot2","boxplot2 x-axis",
list("Op. Temp"="TOP",
"Air Temp"="AirTemp",
"Outdoor Temp"="OutTemp",
"Relative humidity"="RH",
"Air Speed"="AirSpeed",
"Metabolic rate"="MET",
"Clothing insulation"="INSUL",
"AGE"="AGE",
"TSV"="TSV",
"PMV"="PMV"), selectize=F, selected="AirTemp"))))
),
fluidRow(
HTML('<hr style="border-color: black;">')
),
# HTML('<hr style="border-color: black;">'),
fluidRow(
column(4,
probitSample("1","rgba(124,160,255,.5)","Database 2")
),
column(4,
probitSample("2","rgba(178,125,113,.5)","Database 1")
),
column(4,
probitSample("3","rgba(138,178,93,.5)","Database 2",F)
)
)
)
),
tabPanel("Adaptive model",fluidPage(
fluidRow(
column(3,
selectInput(inputId="satmet", label="Satisfaction metric:",
choices=list(#"All data" = "ASH",
"Acceptability (all range)" = "TSA",
"Acceptability (TSV+-1.5)" = "acc15",
"Acceptability (TSV+-2)" = "acc2",
"General thermal comfort" = "acc_comf"),selectize=F),
numericInput("binwidth", "Width of temperature bins:",1),
checkboxInput("sizeN","Vary size based on N",value=T),
radioButtons("units_bins","",choices=c("Metric","Imperial"))
),
column(3,
selectInput("xaxis_bins", "Outdoor temperature:",
list("Monthly mean" = "prev_ta"
# "Mean daily" = "dayav_ta"),selectize=F),
),selectize=F),
conditionalPanel(condition="input.units_bins=='Metric'",
sliderInput("outRange_bins", "Range of outdoor temperature:",
min = -25, max = 50, value = c(-25,40), step= 1, post="°C")),
conditionalPanel(condition="input.units_bins=='Imperial'",
sliderInput("outRange_bins_F", "Range of outdoor temperature:",
min = -13, max = 122, value = c(-13,104), step= 1, post="°F")),
checkboxInput("ashrae","Show ASHRAE 55 comfort zone",value=F),
checkboxInput("en","Show EN 15251 comfort zone", value=F)
),
column(3,
selectInput("yaxis_bins", "Indoor temperature:",
list("Operative" = "TOP",
"Air" = "TAAV",
"Radiant" = "TRAV"),selectize=F),
conditionalPanel(condition="input.units_bins=='Metric'",
sliderInput("inRange_bins", "Range of indoor temperature:",
min = 0, max = 50, value = c(6,46), step= 1, post="°C")
),
conditionalPanel(condition="input.units_bins=='Imperial'",
sliderInput("inRange_bins_F", "Range of indoor temperature:",
min = 32, max = 122, value = c(42,115), step= 1, post="°F")
)),
column(3,
downloadButton('downloadPlot_bins', 'Download plot(s)')
)
),
hr(),
fluidRow(
column(6,
plotOutput("heatPlot")
),
column(6,
conditionalPanel(condition="input.compare_bins",
plotOutput("heatPlot2")
)
)
),
HTML('<hr style="border-color: black;">'),
fluidRow(
column(4,
br(),
filterOptions("_bins1","Database 2",F,F)
),
column(2,
br()
),
column(4,
checkboxInput("compare_bins","Compare",value=F),
conditionalPanel(condition="input.compare_bins",
filterOptions("_bins2","Database 2",F,F))
),
column(2)
)
)
),
#-------------------------------------------------Scatter graph ----------------------------------------------------------------
tabPanel("Scatter",fluidPage(
fluidRow(
column(2,
selectInput(inputId="satmet_S", label="Satisfaction metric:",
choices=list("All data" = "ASH",
"Acceptability" = "TSA",
"Acceptability (TSV+-1.5)" = "acc15",
# "Preference" = "mci_level",
"Comfort" = "comf_ash_level",
"PMV"="PMV"),selectize=F),
radioButtons("units_S","",choices=c("Metric","Imperial"))
),
column(3,
selectInput("xaxis_elev", "Selected x-axis temperature:",
list("Operative" = "TOP",
"Air" = "TAAV",
"Monthly mean outdoor" = "prev_ta",
"Radiant" = "TRAV"),selectize=F),
conditionalPanel(condition="input.units_S=='Metric'",
sliderInput("tempRange_S", "Range of temperature in x-axis:",
min = -25, max = 50, value = c(10,40), step= 1, post="°C")),
conditionalPanel(condition="input.units_S=='Imperial'",
sliderInput("tempRange_S_F", "Range of temperature in x-axis:",
min = -13, max = 122, value = c(45,110), step= 1, post="°F")),
conditionalPanel(condition="input.xaxis_elev == 'TOP'",
checkboxInput("elev05","Show elevated velocity comfort zone (0.5clo)", value=F),
checkboxInput("elev10","Show elevated velocity comfort zone (1.0clo)", value=F))
),
column(3,
selectInput("yaxis_sca_1", "Selected y-axis for plot-1:",
list("Operative Temp."="TOP",
"Air Temp."="AirTemp",
"Outdoor Temp."="OutTemp",
"Relative humidity"="RH",
"Air Speed"="AirSpeed",
"Metabolic rate"="MET",
"Clothing insulation"="INSUL",
"TSV"="TSV",
"PMV"="PMV"), selectize=F, selected="PMV"),
selectInput("xaxis_sca_1", "Selected x-axis for plot-1:",
list("Operative Temp."="TOP",
"Air Temp."="AirTemp",
"Outdoor Temp."="OutTemp",
"Relative humidity"="RH",
"Air Speed"="AirSpeed",
"Metabolic rate"="MET",
"Clothing insulation"="INSUL",
"TSV"="TSV",
"PMV"="PMV"), selectize=F, selected="AirTemp"),
checkboxInput("sca_1_linear","Show regression line in plot-1", value=F),
p("(It may take a while in loading regression line "),
p("for large dataset)")
),
column(2,
selectInput("yaxis_sca_2", "Selected y-axis for plot-2:",
list("Operative Temp"="TOP",
"Air Temp"="AirTemp",
"Outdoor Temp"="OutTemp",
"Relative humidity"="RH",
"Air Speed"="AirSpeed",
"Metabolic rate"="MET",
"Clothing insulation"="INSUL",
"TSV"="TSV",
"PMV"="PMV"), selectize=F, selected="TSV"),
selectInput("xaxis_sca_2", "Selected x-axis for plot-2:",
list("Operative Temp."="TOP",
"Air Temp."="AirTemp",
"Outdoor Temp."="OutTemp",
"Relative humidity"="RH",
"Air Speed"="AirSpeed",
"Metabolic rate"="MET",
"Clothing insulation"="INSUL",
"TSV"="TSV",
"PMV"="PMV"), selectize=F, selected="AirTemp"),
checkboxInput("sca_2_linear","Show regression line in plot-2", value=F),
p("(It may take a while in loading regression line"),
p("for large dataset)")
),
column(2,
downloadButton('downloadPlot_S', 'Download plot(s)')
)
),
hr(),
fluidRow(
column(4,
plotOutput("elevPlot")
),
column(4,
plotOutput("scatterPlot_1")
),
column(4,
plotOutput("scatterPlot_2")
)
),
HTML('<hr style="border-color: black;">'),
fluidRow(
column(4,
checkboxInput("compare_S1","Sample set 1",value=TRUE),
conditionalPanel(condition="input.compare_S1",
filterOptions("_S1","Database 1",T,F))
),
column(4,
checkboxInput("compare_S2","Sample set 2",value=F),
conditionalPanel(condition="input.compare_S2",
filterOptions("_S2","Database 2",T,F))
),
column(4,
checkboxInput("compare_S3","Sample set 3",value=F),
conditionalPanel(condition="input.compare_S3",
filterOptions("_S3","Database 2",T,F))
)
)
)
),
# ------------------------------------------------------------------------------------------------------------------
tabPanel("About",fluidPage(
fluidRow(
h4(""),
h4("Data description"),
p("The data shown in this tool is a compilation of many individual thermal comfort field studies in which building occupants were surveyed about their thermal comfort while simultaneous physical measurements were taken.
The data is contributed by multiple independent studies, not all information are available for each observation.
The database used in this tool is named 'ASHRAE Global Thermal Comfort Database II', which is a combination of ASHRAE Comfort Database I and Database II, and it is a public-domain resource."),
p("The ASHRAE Comfort Database I was built up from raw data files generated by researchers in the 1990s.
This approach allowed a variety of quality controls to be applied and enhanced the consistency and integrity of the entire database.
The ASHRAE Comfort Database I comprised over 21,000 rows of paired subjective comfort votes and objective instrumental measurements of concurrent indoor climatic parameters (de Dear et al, 1998)."),
p("Recognizing the value of open-source research databases, in 2014, project of the ASHRAE Comfort Database II was launched that took on the challenge of systematically collecting and harmonizing the raw data collected in comfort field studies since 1997 (when the ASHRAE database I was built) from around the world into an expanded, quality-assured database.
More than thirty-five thermal comfort research groups from around the world generously made their raw data freely available to the project and in total contributed with more than 81,000 rows of paired subjective comfort votes and objective instrumental measurements of thermal comfort parameters."),
br(),
h4("Classification of the data"),
div(HTML("<ol>
<li>Database data</li>
<ul>
<li>Database 1</li>
<li>Database 2</li>
</ul>
<li>Building level data</li>
<ul>
<li>Building locations: Country (N>23), City (N>65)</li>
<li>Climate: Season (Spring, Summer, Autumn, Winter), Climatic zone </li>
<li>Conditioning types: Air-conditioned, Naturally ventilated, Mixed mode, Mechanical ventilated</li>
<li>Building types: Multifamily housing, Office, Classroom, Senior center, Others </li>
</ul>
<li>Survey information</li>
<ul>
<li>Thermal sensation: How do you feel right now? (-3: cold to +3: hot)</li>
<li>Thermal acceptability: Right now, is the temperature around you acceptable? (yes or no)</li>
<li>Thermal comfort: Right now, do you feel thermally comfortable? (yes or no)</li>
<li>Thermal preference: Right now, would you prefer to be cooler, the same, or warmer?</li>
<li>Environment control status to blind/curtain, fan, window, door and heater</li>
<li>Clothing insulation</li>
<li>Metabolic rate (Activity status)</li>
<li>Demographic (Sex and Age)</li>
</ul>
<li>Physical measurements</li>
<ul>
<li>Indoor temperature: Air, Radiant, Operative</li>
<li>Outdoor temperature: Monthly mean</li>
<li>Indoor relative humidity</li>
<li>Indoor air speed</li>
</ul>
</ol>")),
br(),
h4("Detail tool descriptions"),
div(HTML("<ol>
<li>Introduction</li>
<p> Large sets of thermal comfort field data have been analysed in detail to inform generalized thermal comfort standards, but there is specific information that might be relevant to particular projects that is not easily accessed by practitioners. This interactive tool allows people to explore the data and look at the subsets due to individual interest regarding the difference between database I and II, space conditioning type, building type, geographical locations, human characteristics, etc. The tool is divided into three pages, including:</p>
<ol type=i>
<li>“Satisfaction” tab:</li>
<p> To display the subset statistic data and to show the probit curves of dissatisfaction percentage against thermal sensation and PMV scale. </p>
<li>“Adaptive model” tab:</li>
<p> To perform the satisfaction percentages with combinations of indoor and outdoor temperature for comparison with adaptive comfort standards. </p>
<li>“Scatter” tab:</li>
<p> To evaluate the subset data by scatter plots for any possible correlations and to display the air speed against different kinds of temperature for comparison with the elevated velocity comfort zone in ASHRAE Standard 55. </p>
</ol>
<li>Subset parameters</li>
<p>The choices of subset a database can be classified into 4 categories: building, geography, conditions and human factors. The “building” tab controls the major classification of the dataset including the choice of database, satisfaction metric, conditioning types and building types. The “geography“ tab describes the locations and climatic characteristics for the selected data including seasons, climates, countries, and cities. The “conditions“ tab helps to bound the physical parameter of selected data in desire ranges, such as monthly mean outdoor, indoor, radiant and operative temperature, indoor relative humidity and indoor air speed. Lastly, the “human factors“ tab clarifies the characteristics of subjects during the survey, including sex, age, clothing insulation and metabolic rate. Besides, the possibility of environmental controls, if provided in corresponding study, are considered, such as blind (curtain), fan, window, door and heater.</p>
<li>Satisfaction tab</li>
<p>ASHRAE 55 defines thermal comfort as the “condition of mind that expresses satisfaction with the thermal environment and is assessed by subjective evaluation“. Since most field studies don't ask directly about satisfaction with the thermal environment, researchers look to questions about thermal sensation, acceptability and comfort to assess satisfaction. The “Satisfaction“ tab explores the relation between sensation and the other satisfaction metrics using multinomial probits. The probit plot displays curves of dissatisfaction percentages against the subject's thermal sensation vote and PMV based on the satisfaction metrics of acceptability and comfort. In addition, the statistical data of selected subset for observed factors and recorded parameters is respectively summarized in a bar chart and two boxplots.</p>
<li>Adaptive model tab</li>
<p>Adaptive comfort standards in ASHRAE 55 and EN 15251 relate comfortable indoor temperatures to outdoor temperature. The “Adaptive model“ tab looks at the data in this framework by binning thermal comfort votes according to the indoor and outdoor temperature conditions under which they were given. Then the percentage of satisfied votes is calculated within each two-dimensional bin. An accumulation of bins with at least 80% satisfaction delineates the comfort zone. </p>
<p>For example, the image below shows that the bin with an outdoor and indoor temperature of 20°C has 100 acceptability votes of which 80 are “acceptable”, so the (20°, 20°) bin is colored green to mark it as having 80% satisfaction. Similarly, there are 50 votes in the bin of 20°C outdoor and 30°C indoor temperature, and 10 of them are “acceptable,” so the (20°, 30°) bin is colored red to mark it as having 20% satisfaction.</p>
<img src='binningexplanation.png', width=500.8, height=352, margin-bottom:100px, alt=Explanation of binning method,style='margin-left: 100px; margin-top: 100px; margin-bottom:100px'>
<li>Scatter tab</li>
<p>Elevated air speed comfort zone in ASHRAE 55 is adopted when the average air speed exceeding 0.2m/s (40 fpm), subject's metabolic rates between 1 and 2 met and clothing insulation between 0 and 1.5 clo. It is permissible to determine the operative temperature range by linear interpolation between the limits found in corresponding comfort zones. The “Scatter“ tab considers the data in this aspect and creates scatter plot of raw data regarding two comfort zones criteria (for clothing insulation = 0.5 and 1 clo) at 1.1 met. Furthermore, two extra scatter plots, with changeable x-axis and y-axis for different variables, are provided to observe any possible relationship between parameters. </p>
</ol>")),
br(),
h4("References"),
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br(),
h4("Acknowledgement"),
p("This tool is originally established by", a("Margaret Pigman",href="https://www.linkedin.com/in/margaret-pigman-044a0024/"), "in 2014 for Database I, thereafter modified by ", a("Toby Cheung",href="https://www.researchgate.net/profile/Chin_To_Cheung"), "in 2016 implementing the survey of Database II and new features.
Development of Database II is mainly contributed by the Centre for Built Environment (CBE) team, including ", a("Edward Arens",href="http://www.cbe.berkeley.edu/aboutus/staff-edward.htm"), ", ", a("Hui Zhang",href="http://www.cbe.berkeley.edu/aboutus/staff-hui.htm"), ", ", a("Gail Brager",href="https://www.cbe.berkeley.edu/aboutus/staff-gail.htm"), ",
", a("Stefano Schiavon",href="https://www.cbe.berkeley.edu/aboutus/staff-stefano.htm"), ",", a("Veronika Foldvary",href="https://scholar.google.com/citations?user=xdHLdncAAAAJ&hl=en"), ",", a("Maohui Luo",href="https://www.researchgate.net/profile/Maohui_Luo"),",", a("Ariel Li",href="https://www.linkedin.com/in/ariel-peixian-li-4585ba77/")," and ", a("Soazig Kaam",href="https://scholar.google.com/citations?user=yvuCjNUAAAAJ&hl=en"), ",
assisted by the research team in University of Sydney represented by ", a("Richard de Dear",href="http://sydney.edu.au/architecture/about/people/profiles/richard.dedear.php"), " and ", a("Thomas Parkinson",href="https://www.researchgate.net/profile/Thomas_Parkinson2"), ", and by research team in Yonsei University Korea, presented by ", a("Chungyoon Chun",href="http://web.yonsei.ac.kr/hbelabenglish/Professor.htm"), ".
The ASHRAE Comfort Database Project II referred in this description is supported by an American Society of Heating, Refrigerating and Air Conditioning Engineers Grant-In-Aid,
British Council and UK Government under the Global Innovation Initiative project scheme, Korea National Science Foundation and the Center for the Built Environment, University of California at Berkeley,
Additional support is provided by the Republic of Singapore's National Research Foundation through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Sinapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program.
The project is preformed within the framework of the International Energy Agency Energy in Buildings and Communities programme (IEA-EBC) Annex 69 'Strategy and PRactice of Adaptive Thermal Comfort in Low Energy Buildings.'
The authors also thank ", a("Michael Humphreys",href="https://www.researchgate.net/profile/Michael_Humphreys2"), " for his continuous scientific support; students who help to organize and format the database including Tina Lee (UC Berkeley), Youngjoo Son (Yonsei University), Sijie Liu and Xiuyuan Du (The University of Sydney);
and ", a("Tyler Hoyt",href="https://cbe.berkeley.edu/aboutus/staff-tyler.htm"), " (UC Berkeley) for the initial suggestion to build the Comfort Database.
"),
br(),
p("We are also thankful to the data contributors, the list is presented here: "),
p("Akande Oluwafemi, Alison Kwok, Andreas Wagner, Anoop Honnekeri, Antonio Augusto Xavier, Bin Cao, Chandra Sekhar, Chiheb Bouden, Christina Candido, Chungyoon Chun, Cornelia Moosmann,
David Cheong, Dennis Loveday, Despoina Teli, Dusan Petras, Federico Tartarini, Fergus Nicol, Francesco Babich, Gail Brager, Gwelen Paliaga,
Hana Pustayova, Harimi Djamila, Holger Wallbaum, Hyojin Kim, Jared Langevin, Joon Ho Choi, Jorn Toftum, Jungsoo Kim, Kathryn McCartney, Kazuyo Tsuzuki, Kwokwai Tham, Kyle Konis,
Linda Toledo, Liu Yang, Lorenzo Pagliano, Lynda Webb, Madhavi Indraganti, Malcolm Cook, Manoj Singh, Maohui Luo, Marcel Schweiker, Maren Hawighorst, Max Deuble, Michael Adebamowo, Michael Humphreys, Mary Myla Andamon,
Nigel Oseland, Paolo Zangheri, Paul Cropper, Quan Jin, Rajan Rawal, Ramona Romero, Renata De Vecchi, Roberto Lamberts, Ryozo Ooka, Peixian Li,
Salvatore Carlucci, Sanyogita Manu, Shahin Heidari, Shin-ichi Tanabe, Stefano Schiavon, Stine Pedersens, Soazig Kaam, Veronika Foldvary, Xiang Zhou,
Yasuto Nakamura, Yingxin Zhu, Yongchao Zhai, Yufeng Zhang, Zhaojun Wang.
"),
br(),
p("Contact Hui Zhang ([email protected]) if you have data to contribute."),
br(),
withTags(p("This tool was built with the", a("statistical language R", href="http://www.R-project.org/",target="_blank"), "using the", a("ggplot2 package",href="http://ggplot2.org/",target="_blank"), "for visualization, the",
a("oridinal package",href="http://cran.r-project.org/web/packages/ordinal/index.html",target="_blank"), "for probit analysis, and the", a("shiny package", href="http://shiny.rstudio.com/",target="_blank"), "as the interface between R and html.",style="font-size:12px"))
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