Beatriz Jimenez Franco
I hope you enjoy the presentation!
In this app we study the relationship between two variables, carat and price of the dataset diamonds, that yoy can find loading ggplot2. With a subset of the datframe we can also observe the predicted values of price. Notice that ib the difeerent tabs you can find interactive plots and tables. Link to the shinyApp :
https://spoke.shinyapps.io/Diamonds/
Link to my presentation in shinyapps.io: <https://spoke.shinyapps.io/shiny-presentation/#1
Link to my Github repository:
https://github.com/singulritarian7/Shiny_assingment
Link to my RPubs presentation: https://rpubs.com/spoke/706042
library(shiny)
library(ggplot2)
head(diamonds)
# A tibble: 6 x 10
carat cut color clarity depth table price x y z
<dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63
5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48
library(shiny);library(ggplot2);data("diamonds")
shinyUI(
navbarPage("Shiny Application",
tabPanel("Model",
fluidPage(
titlePanel("Diamonds - Influences on the Carat/Price Relationship"),
sidebarLayout(
sidebarPanel(
h4("Filter Variables"),
selectInput("cut",
"Cut",
(sort(
unique(diamonds$cut), decreasing = T
))),
selectInput("color",
"Color",
(sort(
unique(diamonds$color)
))),
selectInput("clarity",
"Clarity",
(sort(
unique(diamonds$clarity), decreasing = T
))),
actionButton("showall",
"Show All"),
actionButton("appfil",
"Filter Mode"),
h4("Price Summary"),
verbatimTextOutput("summary"),
sliderInput(
"lm",
"Carat",
min = min(diamonds$carat),
max = max(diamonds$carat),
value = max(diamonds$carat) / 2,
step = 0.1
),
h4("Predicted Price"),
verbatimTextOutput("predict"),
width = 4
),
mainPanel(tabsetPanel(
tabPanel("Plot",
plotOutput("distPlot",click = "plot_click" ),
verbatimTextOutput("info")
),
tabPanel("Table", dataTableOutput("table")),
tabPanel(
"Documentation",
br(),
helpText(
"This app enables you to display various subsets of the diamond data set (as included in the ggplot2 R-package) and check the influence on the
carat/price relationship in the data. A basic linear model relationship is displayed based on the selected subset of the data and price prediction
is possible via selecting the respective filter variables and a carat value."
),
br(),
helpText(
"Furthermore it is possible to remove the filter and display the whole dataset by pressing the button SHOW ALL.
To go back the the filtered view, press FILTER MODE and all your filters are reapplied. Per default, filter
mode is active."
),
br(),
helpText("You can also see the x, y coordenates by clicking the interactive plot."
),
br(),
helpText(
"Finally a data summary is displayed and a price can be predicted by selecting a subset of the data and choosing a carat value.
Simply choose cut, color, clarity and carat as you see fit and a value is predicted based on your selection."
)),
tabPanel(
"Data Description",
br(),
helpText("See data description here:"),
br(),
tags$a(
"http://ggplot2.tidyverse.org/reference/diamonds.html",
href = "http://ggplot2.tidyverse.org/reference/diamonds.html"
)
)
))
)
)
)))
<!–html_preserve–>
library(shiny);library(ggplot2);library(tidyverse);
library(curl)
shinyServer(function(input, output) {
data(diamonds)
output$table <- renderDataTable({
head(diamonds)
})
output$distPlot <- renderPlot({
diamonds_sub <-
subset(
diamonds,
cut == input$cut &
color == input$color &
clarity == input$clarity
)
output$info <- renderPrint({
req(input$plot_click)
x <- round(input$plot_click$x, 2)
y <- round(input$plot_click$y, 2)
cat("[", x, ", ", y, "]", sep = "")
})
p <-
ggplot(data = diamonds_sub, aes(x = carat, y = price)) + geom_point()
p <-
p + geom_smooth(method = "lm") + xlab("Carat") + ylab("Price")
p <- p + xlim(0, 6) + ylim (0, 20000)
p
}, height = 700)
output$summary <- renderPrint({
diamonds_sub <-
subset(
diamonds,
cut == input$cut &
color == input$color &
clarity == input$clarity
)
summary(diamonds_sub$price)
})
output$predict <- renderPrint({
diamonds_sub <-
subset(
diamonds,
cut == input$cut &
color == input$color &
clarity == input$clarity
)
fit <- lm(price~carat,data=diamonds_sub)
unname(predict(fit, data.frame(carat = input$lm)))
})
observeEvent(input$showall, {
distPlot <<- NULL
output$distPlot <- renderPlot({
p <-
ggplot(data = diamonds, aes(x = carat, y = price)) + geom_point()
p <-
p + geom_smooth(method = "lm") + xlab("Carat") + ylab("Price")
p <- p + xlim(0, 6) + ylim (0, 20000)
p
}, height = 700)
output$summary <- renderPrint(summary(diamonds$price))
output$predict <- renderPrint({
fit <- lm(price~carat,data=diamonds)
unname(predict(fit, data.frame(carat = input$lm)))
})
})
observeEvent(input$appfil, {
distPlot <<- NULL
output$distPlot <- renderPlot({
diamonds_sub <-
subset(
diamonds,
cut == input$cut &
color == input$color &
clarity == input$clarity
)
p <-
ggplot(data = diamonds_sub, aes(x = carat, y = price)) + geom_point()
p <-
p + geom_smooth(method = "lm") + xlab("Carat") + ylab("Price")
p <- p + xlim(0, 6) + ylim (0, 20000)
p
}, height = 700)
output$summary <- renderPrint({
diamonds_sub <-
subset(
diamonds,
cut == input$cut &
color == input$color &
clarity == input$clarity
)
summary(diamonds_sub$price)
})
output$predict <- renderPrint({
diamonds_sub <-
subset(
diamonds,
cut == input$cut &
color == input$color &
clarity == input$clarity
)
fit <- lm(price~carat,data=diamonds_sub)
unname(predict(fit, data.frame(carat = input$lm)))
})
})
})