Summary of the Data
Mridula
January 20, 2017
This is a simple app that lets you understand how different diets affect the weights in chicks.
You can select different variables and view the affect of predictor variables and response variables.
The data set gives Weight versus Age of chicks on different diets
It has 578 records and 4 variables
Time (age) is measured in number of days, and Weight is measured in grams
weight Time Chick Diet
1 42 0 1 1
2 51 2 1 1
3 59 4 1 1
4 64 6 1 1
5 76 8 1 1
6 93 10 1 1
library(shiny)
shinyUI(fluidPage(
titlePanel("Chick Weights With Different Diets"),
sidebarLayout(
sidebarPanel(
helpText("Select Variables to See the Relationship"),
selectInput("xcol", "X Variable", names(ChickWeight[,3:4])),
selectInput("ycol", "Y Variable", names(ChickWeight[,1:2])),
numericInput("clusters", "Number of Clusters", 3, min = 1, max = 21)
),
# Show a plot of the generated distribution
mainPanel(
tabsetPanel(
tabPanel("Plot", plotOutput("Plot")),
tabPanel("User Guide", verbatimTextOutput("User Guide"))
)
)
)
)) library(shiny)
shinyServer(function(input, output, session) {
inputData <- reactive({ChickWeight[,c(input$xcol, input$ycol)]})
clusters <- reactive({kmeans(inputData(), input$clusters)})
output$Plot <- renderPlot({
palette(c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3",
"#FF7F00", "#FFFF33", "#A65628", "#F781BF", "#999999"))
plot(inputData(),
col = clusters()$cluster,
pch = 20,
cex = 3)
points(clusters()$centers,
pch = 25,
cex = 2,
col = "orange",
bg = "purple")
})
output$`User Guide` <- renderText("This graph depicts the relationships between different variables.
Select one variable each for X-axis and Y-axis, and see how
the response varables (y-axis) change based on changing predictor values (x-axis).")
})Link to the App: https://vmridula.shinyapps.io/ChickWeight/