Merge pull request #1 from dxy99999/xinyu

Create a primary shiny app with basic function

inst/shinys/fixed_effects_animation/app.R

@@ -0,0 +1,76 @@
+library(shiny)
+library(tidyverse)
+library(gganimate)
+library(ggthemes)
+
+
+# p <- ggplot(dffull,aes(y=Y,x=X,color=as.factor(Person)))+geom_point()+
+#     geom_vline(aes(xintercept=mean_X,color=as.factor(Person)))+
+#     geom_hline(aes(yintercept=mean_Y,color=as.factor(Person)))+
+#     guides(color=guide_legend(title="Individual"))+
+#     scale_color_colorblind()+
+#     labs(title = 'The Relationship between Y and X, with Individual Fixed Effects \n{next_state}')+
+#     transition_states(time,transition_length=c(12,32,12,32,12,12),state_length=c(160,100,75,100,75,160),wrap=FALSE)+
+#     ease_aes('sine-in-out')+
+#     exit_fade()+enter_fade()
+
+
+ui <- fluidPage(
+
+    # Application title
+    titlePanel("Fixed effects animation"),
+
+    # Math Model
+    h5("The model generating our data:"),
+    withMathJax("$$X=0.5+0.5(Person-2.5)+\\varepsilon$$"),
+    withMathJax("$$Y=\\beta X + (Person-2.5)+\\varepsilon$$"),
+    withMathJax("$$\\varepsilon \\sim N(0,1)$$"),
+
+    sidebarLayout(
+        sidebarPanel(
+            sliderInput("cor",
+                        "the coefficient of X in Y:",
+                        min = -1,
+                        max = 1,
+                        value = -0.5,step = 0.1)
+        ),
+
+        mainPanel(
+           plotOutput("scatterPlot")
+        )
+    )
+)
+
+
+server <- function(input, output) {
+
+    output$scatterPlot <- renderPlot({
+
+        df <- data.frame(Person = rep(1:4,50)) %>%
+            mutate(X = .5+.5*(Person-2.5) + rnorm(200)) %>%
+            mutate(Y = (input$cor)*X + (Person-2.5) + 1 + rnorm(200),time="1") %>%
+            group_by(Person) %>%
+            mutate(mean_X=mean(X),mean_Y=mean(Y)) %>%
+            ungroup()
+
+        dffull <- rbind(
+            #Step 1: Raw data only
+            df %>% mutate(mean_X=NA,mean_Y=NA,time=before_cor),
+            #Step 2: Add x-lines
+            df %>% mutate(mean_Y=NA,time='2. Figure out any between-Individual differences in X'),
+            #Step 3: X de-meaned 
+            df %>% mutate(X = X - mean_X,mean_X=0,mean_Y=NA,time="3. Remove all between-Individual differences in X"),
+            #Step 4: Remove X lines, add Y
+            df %>% mutate(X = X - mean_X,mean_X=NA,time="4. Figure out any between-Individual differences in Y"),
+            #Step 5: Y de-meaned
+            df %>% mutate(X = X - mean_X,Y = Y - mean_Y,mean_X=NA,mean_Y=0,time="5. Remove all between-Individual differences in Y"),
+            #Step 6: Raw demeaned data only
+            df %>% mutate(X = X - mean_X,Y = Y - mean_Y,mean_X=NA,mean_Y=NA,time=after_cor))
+
+        ggplot(dffull,aes(y=Y,x=X,color=as.factor(Person)))+geom_point()
+
+    })
+}
+
+# Run the application 
+shinyApp(ui = ui, server = server)