Wrong list. Shiny is software from an external provider, POSIT (formerly RStudio) and not part of R, itself. They have a community support site at:
https://forum.posit.co/?_gl=1*bh848i*_ga*MTY2NjcwNzQ4LjE3MTYyMzkzODQ.*_ga_2C0WZ1JHG0*MTcxNzY4NTExNi4yLjAuMTcxNzY4NTExNi4wLjAuMA .. Cheers, Bert On Thu, Jun 6, 2024 at 12:09 AM I B <ibarnhart...@gmail.com> wrote: > Hello everybody, > > I have experience coding with R, but am brand new to R Shiny. I am trying > to produce an application that will allow users to upload their own > dataset, select columns they want an ANOVA analysis run on, and generate > graphs that will allow users to view their results. However, I am getting > the following error: *"Argument is of length zero."* > > Being new to Shiny, I am having trouble passing the user input column to an > emmeans argument in order to do a post hoc analysis, and using that > information to produce a graph. Can somebody help me with this? *The code > for my dataset and application are provided below; copying and pasting > directly into R should generate the reproducible example.* > > In my application, the following columns should be selected after uploading > the dataset: > > - Select response variable: "ndvi" > - Select first independent variable: "genotype" > - Select second independent variable: "rate" > - Select random variable: "rep" > > For this example, the final two drop-down selections should be: > > - "Which variable would you like to graph? "genotype" > - "Which ANOVA model do you want to graph? "Main effects model" > > Any help would be great. Thank you so much! > > Sincerely, > Isaac Barnhart, PhD > > > *Here is my dataset:* > > data <- data.frame(rep = > c(1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3), > genotype = > > c('a','a','a','a','b','b','b','b','c','c','c','c','a','a','a','a','b','b','b','b','c','c','c','c','a','a','a','a','b','b','b','b','c','c','c','c'), > rate = > > c('1x','2x','4x','8x','1x','2x','4x','8x','1x','2x','4x','8x','1x','2x','4x','8x','1x','2x','4x','8x','1x','2x','4x','8x','1x','2x','4x','8x','1x','2x','4x','8x','1x','2x','4x','8x'), > ndvi = > > c(0.584947811,0.642112121,0.654116902,0.785068313,0.79665163,0.674549249,0.958911611,0.547077528,0.613315552,0.768646411,0.97107949,0.680942649,0.520576242,0.723920266,0.868779972,0.834257732,0.554506685,0.520458208,0.617282262,0.80128067,0.875192693,0.572153151,0.850305042,0.500760522,0.796305833,0.643719779,0.590512435,0.522884966,0.905197544,0.663792758,0.690415735,0.975449466,0.621379163,0.734904647,0.812023395,0.928144532)) > > > *Here is my code:* > > library(shiny) > library(ggplot2) > library(tidyverse) > library(emmeans) > library(DHARMa) > library(lme4) > > ui <- fluidPage( > fileInput("file1", "Choose .csv or .xlsx file", > accept = c("text/csv", > "text/comma-separated-values", > ".csv", > ".xlsx")), > textOutput("data_info"), > verbatimTextOutput("data_head"), > uiOutput("column_selector_1"), > uiOutput("column_selector_2"), > uiOutput("column_selector_3"), > uiOutput("column_selector_4"), > textOutput("dist_info"), > plotOutput("dist"), > textOutput("str_info"), > verbatimTextOutput("selected_columns"), > textOutput("two_way_anova"), > verbatimTextOutput("model_summary"), > textOutput("main_effects"), > verbatimTextOutput("model_summary_ME"), > textOutput("mod_diagnostic"), > plotOutput("diagnostic_plot"), > uiOutput("graph"), > uiOutput("model_selection"), > plotOutput("graph_plot") > ) > > server <- function(input, output, session) { > # Open file > req(data <- reactive({ > infile <- input$file1 > if (is.null(infile)) { > return(NULL) > } > read.csv(infile$datapath, header = TRUE) > })) > > # Preview data > output$data_info <- renderText({ > req(data()) > "Preview of the data uploaded:" > }) > > output$data_head <- renderPrint({ > req(data()) > head(data(), 20) > }) > > # Select response variable > output$column_selector_1 <- renderUI({ > req(data()) > selectInput("column1","Select response variable", choices = > names(data())) > }) > > # Select first independent variable > output$column_selector_2 <- renderUI({ > req(data()) > selectInput("column2", "Select first independent variable", choices = > names(data())) > }) > > # Select second independent variable > output$column_selector_3 <- renderUI({ > req(data()) > selectInput("column3", "Select second independent variable", choices = > names(data())) > }) > > # Select random variable > output$column_selector_4 <- renderUI({ > req(data()) > selectInput("column4", "Select random variable", choices = > names(data())) > }) > > # Assigning user inputs to correct variables > selected_columns <- reactive({ > req(data(), input$column1, input$column2, input$column3, input$column4) > list( > dependent = data()[[input$column1]], > independent1 = as.factor(data()[[input$column2]]), > independent2 = as.factor(data()[[input$column3]]), > random = as.factor(data()[[input$column4]]), > > # Define column names for later use > column1 <- input$column1, > column2 <- input$column2, > column3 <- input$column3, > column4 <- input$column4 > ) > }) > > # Instructional text > output$dist_info <- renderText({ > req(data()) > "The graph below shows the shape of response variable distribution" > }) > > > # Distribution plot > output$dist <- renderPlot({ > req(selected_columns()) > cols <- selected_columns() > plot(density(cols$dependent), main = "Distribution Plot") > }) > > # Instructional text > output$str_info <- renderText({ > req(data()) > "The code below verifies that our data are in the correct structure for > ANOVA analyses. All independent variables should be changed to 'Factor'. " > }) > > # Verifies that columns are in the correct structure > output$selected_columns <- renderPrint({ > cols <- selected_columns() > str(cols) > }) > > # Instructional text > output$two_way_anova <- renderText({ > req(data()) > "Two-way ANOVA with interaction" > }) > > # Build ANOVA model with two-way interaction > interaction_model <- reactive({ > cols <- selected_columns() > req(cols) > data <- data() > data$dep <- cols$dependent > data$indep1 <- cols$independent1 > data$indep2 <- cols$independent2 > data$rand <- cols$random > lmer(dep ~ indep1 * indep2 + (1|rand), data = data) > }) > > # Run two-way ANOVA model with interaction > output$model_summary <- renderPrint({ > req(interaction_model()) > Anova(interaction_model(), type = c("III")) > }) > > # Build main effects ANOVA model > main_effects_model <- reactive({ > cols <- selected_columns() > req(cols) > data <- data() > data$dep <- cols$dependent > data$indep1 <- cols$independent1 > data$indep2 <- cols$independent2 > data$rand <- cols$random > lmer(dep ~ indep1 + indep2 + (1|rand), data = data) > }) > > # Instructional text > output$main_effects <- renderText({ > req(data()) > "Two-way ANOVA, main effects" > }) > > # Run two-way main effects model > output$model_summary_ME <- renderPrint({ > req(main_effects_model()) > Anova(main_effects_model(), type = c("III")) > }) > > > output$mod_diagnostic <- renderText({ > req(main_effects_model()) > "Below, the `DHARMa` package is used to check model assumptions. If > assumptions are not met, then data transformations may be needed before > proceeding." > }) > > output$diagnostic_plot <- renderPlot({ > req(interaction_model()) > plotQQunif(interaction_model()) > }) > > output$graph <- renderUI({ > req(data(), input$column2, input$column3) > selectInput("graph", "Which variable would you like to graph?", choices > = c(input$column2, input$column3, "interaction")) > }) > > output$model_selection <- renderUI({ > req(main_effects_model(), interaction_model()) > selectInput("model_selection", "Which ANOVA model do you want to > graph?", choices = c("Main effects model", "Interaction model")) > }) > > # Create a reactive expression to store the selected graph variable > selected_graph <- reactive({ > input$graph > }) > > # Create a reactive expression to store the selected model > selected_model <- reactive({ > input$model_selection > }) > > # Render the plot based on the selected variables > output$graph_plot <- renderPlot({ > req(data(), selected_graph(), selected_model()) > cols <- selected_columns() > > if (selected_graph() == cols$column2 | selected_model() == "Main > effects model") { > > # Mean separation: First column > mod_means_cotr1 <- emmeans(main_effects_model(), pairwise ~ > cols$column2, > adjust = 'tukey', > type = 'response') > > mod_means1 <- multcomp::cld(object = mod_means_cotr1$emmeans, > LETTERS = "letters") > > # Graph: First column > ggplot(mod_means1, aes_string(x = cols$column2, y = cols_column1))+ > geom_bar(stat="identity", width = 0.6, position = "dodge", col = > "black", fill = "purple3")+ > geom_errorbar(aes(ymin = emmean, ymax = emmean + SE), width = 0.3, > position = position_dodge(0.6))+ > xlab(cols$column2)+ > ylab(cols$column1)+ > theme(plot.title=element_text(hjust=0.5, size = 20), > plot.subtitle = element_text(hjust = 0.5, size = 15), > axis.text = element_text(size = 17), > axis.title = element_text(size = 20))+ > geom_text(aes(label=.group, y=emmean + SE), vjust = -0.9, size = 8) > + > #ylim(0, 105)+ > scale_x_discrete(labels = function(x) str_wrap(x, width = 7)) > > > } > }) > > } > shinyApp(ui, server) > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
