r - 将单选按钮链接到闪亮的反应数据
问题描述
我正在尝试为我的论文制作一个闪亮的应用程序。在我的论文中，我有许多变量 (16)，我正在分析 5 个条件。
在我的应用程序中，我希望有一个带有变量和条件列表的侧面板作为单选按钮。
在我的主面板中，我想要以下输出：
- Plotmeans 图与条件的平均值（我有这部分）
- 所选变量和所选条件的摘要
- 所选变量和所选条件的密度图
- 从 shapiro.test 输出选定变量和选定条件
- shapiro.test 的解释（即正常/不正常）
我可以通过条件轻松地获得绘图，但是在显示其余输出时遇到问题。我为条件引用单选按钮的方式一定有问题，因为在我单击“分析”后，我不断收到错误消息，告诉我找不到我选择的变量。
请查看我的代码，因为我将不胜感激：
#Shiny app to display means, summary, and normality interpretation for each
variable and condition in study 3
library(shiny)
#############################################################################
# Define UI
ui <- fluidPage(
# Application title
titlePanel(
h1("Variable Means by Condition (Study 3)", align = "center", style =
"color:black")),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
radioButtons(inputId = "var", label = "Select a Variable:",
c("Time from Catch to Lowest COM" = "T0_1",
"Time from Lowest COM to Release" = "T1_2",
"Release Time" = "T0_2",
"Knee Extension at Catch" = "T0_Knee_Ext",
"Hip Extension at Catch" = "T0_Hip_Ext",
"Minimum Ball Height" = "Min_Ball_Ht",
"Ball Height at Lowest COM" = "T1_Ball_Ht",
"Knee Extension at Lowest COM" = "T1_Knee_Ext",
"Hip Extension at Lowest COM" = "T1_Hip_Ext",
"Shoulder Flexion at Release" = "T2_Sh_Flex",
"Elbow Extension at Release" = "T2_Elb_Ext",
"Release Height" = "T2_Rel_Ht",
"Jump Height" = "T2_Jump_Ht",
"Wrist Extension at Follow-Through" = "T2_Wr_Ext",
"Accuracy" = "ACCURACY",
"Overall Performance" = "Acc.Spd")),
#Add radio buttons to choose a condition
radioButtons(inputId = "cond", label = "Select a Condition:",
c("Condition 1" = 1,
"Condition 2" = 2,
"Condition 3" = 3,
"Condition 4" = 4,
"Condition 5" = 5)),
#Add action button
actionButton("goButton","Analyze")),
# Show a plot of the mean of the selected variable
mainPanel(
#create a plot for selected variable
plotOutput("mean_plot"),
#Get summary for selected variable and selected condition
verbatimTextOutput("summ"),
#Get density plot for selected variable and selected condition
plotOutput("dens_plot"),
#Calculate shapiro wilk test for selected variable and selected
condition
verbatimTextOutput("shap"),
#Return if the selected variable and selected condition is normal or
not
verbatimTextOutput("norm"))
)
)
####################################################################
# Define server logic required to draw plotmeans
server <- function(input, output) {
#import data
library(readr)
dt <- read_csv("dt.csv")
dt$CONDITIONf <- factor(dt$CONDITION, levels = c(1,2,3,4,5), labels =
c("Normal","None","Wrist","Elb. Ht.","Rim"))
#subset data on various inputs from ui
subsetData <- reactive({
new_data <- dt[,CONDITION == input$cond]
return(new_data)
})
#After clicking goButton....
observeEvent(input$goButton, {
#Create plot
output$mean_plot <- renderPlot({
#using gplots plotmeans
library(gplots)
p <- plotmeans(get(input$var) ~ CONDITIONf, data = dt, connect = FALSE,
n.label = FALSE,
mean.labels = TRUE, digits = 2, xlab = "Condition", ylab =
"Mean", main =
"Variable Means by Condition", pch = " ")})
#Get summary for selected variable and condition
#Create density plot
output$dens_plot <- renderPlot({
hist(subsetData[,get(input$var)])
})
#Run shapiro wilk test
output$shap <- renderPrint({
shapiro.test(subsetData[,get(input$var)])
})
#Print interpretation of shapiro.test (ifelse(p-value from shapiro.test <
0.05, "Not Normal", "Normal")
output$norm <- renderPrint({
ifelse(output$shap < 0.05, return("Not Normal", return("Normal")))
})
})
}
#############################################################################
# Run the application
shinyApp(ui = ui, server = server)
如果您需要数据集，请与我联系，我将发送给您。提前致谢！
运行后：
dplot(head(dt, 20))
在我的输出中，我得到：
structure(list(X1 = 1:20, PRIM_KEY = 1:20, NAME = c("Andrew Grajeda",
"Andrew Grajeda", "Andrew Grajeda", "Andrew Grajeda", "Andrew Grajeda",
"Andrew Grajeda", "Andrew Grajeda", "Andrew Grajeda", "Andrew Grajeda",
"Andrew Grajeda", "Andrew Grajeda", "Andrew Grajeda", "Andrew Grajeda",
"Andrew Grajeda", "Andrew Grajeda", "Andrew Grajeda", "Andrew Grajeda",
"Andrew Grajeda", "Andrew Grajeda", "Andrew Grajeda"), SUBJECT = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), BIRTHDAY = structure(c(11860, 11860, 11860, 11860,
11860, 11860, 11860, 11860, 11860, 11860, 11860, 11860, 11860,
11860, 11860, 11860, 11860, 11860, 11860, 11860), class = "Date"),
TODAY_DATE = structure(c(17616, 17616, 17616, 17616, 17616,
17616, 17616, 17616, 17616, 17616, 17616, 17616, 17616, 17616,
17616, 17616, 17616, 17616, 17616, 17616), class = "Date"),
AGE = c(15.7698630136986, 15.7698630136986, 15.7698630136986,
15.7698630136986, 15.7698630136986, 15.7698630136986,
15.7698630136986,
15.7698630136986, 15.7698630136986, 15.7698630136986,
15.7698630136986,
15.7698630136986, 15.7698630136986, 15.7698630136986,
15.7698630136986,
15.7698630136986, 15.7698630136986, 15.7698630136986,
15.7698630136986,
15.7698630136986), YOE = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), DAILY_SHOTS = c(50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L), CLIP = c("00_1", "00_1",
"00_1", "00_1", "00_1", "00_1", "00_1", "00_1", "00_1", "00_1",
"00_1", "00_1", "00_1", "00_1", "00_1", "00_1", "00_1", "00_1",
"00_1", "00_1"), HEIGHT = c(1.73, 1.73, 1.73, 1.73, 1.73,
1.73, 1.73, 1.73, 1.73, 1.73, 1.73, 1.73, 1.73, 1.73, 1.73,
1.73, 1.73, 1.73, 1.73, 1.73), Group = c(1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L), CONDITION = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), SHOT = c(1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L), ACCURACY = c(4.5, 4.5, 4, 4.5, 4, 4.5,
4.5, 4, 3.5, 4.5, 3, 2, 2, 2, 3, 4.5, 4.5, 2, 3, 3), Make = c(1L,
1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 0L), T0 = structure(c(-2209075175, -2209075170,
-2209075164, -2209075158, -2209075153, -2209075149, -2209075143,
-2209075136, -2209075130, -2209075126, -2209075040, -2209075035,
-2209075030, -2209075025, -2209075020, -2209075015, -2209075010,
-2209075006, -2209075001, -2209074998), class = c("POSIXct",
"POSIXt"), tzone = "UTC"), T0_Knee_Ext = c(169.7, 165.7,
169.3, 173, 171.3, 168.7, 164.1, 165.7, 166.8, 165.7, 164,
157.4, 157.4, 157.4, 147.2, 147.2, 150, 149.9, 152, 149),
T0_Hip_Ext = c(172.6, 172.6, 172.6, 176.7, 171.7, 171.7,
161.1, 161.1, 168.9, 171.7, 163.7, 160.9, 160.9, 160.9, 154.5,
156.2, 156.2, 156.2, 156.5, 156.2), Min_Ball_Ht = c(0.93,
0.94, 0.96, 0.92, 0.95, 0.94, 0.94, 0.93, 0.94, 0.93, 0.8,
0.81, 0.8, 0.8, 0.81, 0.81, 0.8, 0.8, 0.81, 0.8), T1 =
structure(c(-2209075175,
-2209075169, -2209075163, -2209075157, -2209075152, -2209075148,
-2209075143, -2209075135, -2209075129, -2209075125, -2209075039,
-2209075034, -2209075029, -2209075025, -2209075020, -2209075015,
-2209075010, -2209075005, -2209075001, -2209074997), class =
c("POSIXct",
"POSIXt"), tzone = "UTC"), T0_1 = c(0.601, 0.534, 0.601,
0.567, 0.601, 0.601, 0.584, 0.6, 0.567, 0.6, 0.422, 0.372,
0.339, 0.355, 0.288, 0.272, 0.339, 0.289, 0.222, 0.289),
T1_Ball_Ht = c(1.04, 1.03, 1.02, 1.03, 1.04, 1.05, 1.04,
1.03, 1.03, 1.04, 0.97, 0.94, 0.95, 0.96, 0.97, 0.96, 0.95,
0.94, 0.95, 0.96), T1_Knee_Ext = c(116.3, 119.6, 122.9, 119.2,
127.4, 126.9, 134.4, 129, 134.5, 134.4, 112.3, 116.4, 122.3,
119.7, 121.6, 121.6, 117.7, 117.7, 117.7, 117.7), T1_Hip_Ext = c(142,
138.4, 138.4, 138.4, 142.9, 147.9, 147.9, 147.9, 147.9, 147.9,
133.5, 133.5, 141.5, 148.2, 145.4, 145.4, 145.4, 145.4, 145.4,
145.4), T2 = structure(c(-2209075174, -2209075169, -2209075163,
-2209075157, -2209075152, -2209075148, -2209075142, -2209075135,
-2209075129, -2209075125, -2209075039, -2209075034, -2209075029,
-2209075025, -2209075020, -2209075014, -2209075010, -2209075005,
-2209075001, -2209074997), class = c("POSIXct", "POSIXt"), tzone =
"UTC"),
T1_2 = c(0.267, 0.3, 0.3, 0.267, 0.266, 0.283, 0.267, 0.267,
0.3, 0.3, 0.267, 0.267, 0.283, 0.217, 0.267, 0.333, 0.284,
0.267, 0.334, 0.267), T0_2 = c(0.868, 0.834, 0.901, 0.834,
0.867, 0.884, 0.851, 0.867, 0.867, 0.9, 0.689, 0.639, 0.622,
0.572, 0.555, 0.605, 0.623, 0.556, 0.556, 0.556), T2_Sh_Flex =
c(137.3,
140.8, 134.2, 138.6, 138, 138.6, 138.6, 134.2, 134.2, 140.8,
138, 138, 136, 136, 136, 137, 137, 136, 136, 136), T2_Elb_Ext =
c(179.8,
179.8, 179, 179, 178.5, 179.4, 179.2, 179, 178.9, 179.8,
174.9, 174.9, 174.9, 174.9, 174.9, 175, 174.8, 174.9, 175,
174.8), T2_Rel_Ht = c(2.17, 2.18, 2.17, 2.18, 2.17, 2.17,
2.18, 2.17, 2.18, 2.17, 2.17, 2.17, 2.18, 2.17, 2.17, 2.18,
2.17, 2.18, 2.18, 2.17), T2_Jump_Ht = c(0.05, 0.06, 0.05,
0.06, 0.05, 0.05, 0.06, 0.05, 0.06, 0.05, 0.05, 0.05, 0.06,
0.05, 0.05, 0.06, 0.05, 0.06, 0.06, 0.05), T2_Wr_Ext = c(109.3,
106.8, 106.8, 106.8, 107.9, 109.1, 106.8, 107.8, 107, 107.5,
120, 113.5, 107.9, 100.5, 100.5, 100.5, 100.5, 100.5, 100.5,
100.5), CONDITIONf = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label =
c("Normal",
"None", "Wrist", "Elb. Ht.", "Rim"), class = "factor"), Makef =
c("Make",
"Make", "Miss", "Make", "Miss", "Make", "Make", "Miss", "Make",
"Make", "Miss", "Miss", "Miss", "Miss", "Miss", "Make", "Make",
"Miss", "Miss", "Miss"), ACCURACYf = c("Inside Rim - Make",
"Inside Rim - Make", "Inside Rim - Miss", "Inside Rim - Make",
"Inside Rim - Miss", "Inside Rim - Make", "Inside Rim - Make",
"Inside Rim - Miss", "Top Rim - Make", "Inside Rim - Make",
"Top Rim - Miss", "Outside Rim", "Outside Rim", "Outside Rim",
"Top Rim - Miss", "Inside Rim - Make", "Inside Rim - Make",
"Outside Rim", "Top Rim - Miss", "Top Rim - Miss"), ACCURACYnorm =
c(0.875,
0.875, 0.75, 0.875, 0.75, 0.875, 0.875, 0.75, 0.625, 0.875,
0.5, 0.25, 0.25, 0.25, 0.5, 0.875, 0.875, 0.25, 0.5, 0.5),
T0_2norm = c(0.317038102084831, 0.292595255212078, 0.340762041696621,
0.292595255212078, 0.316319194823868, 0.328540618260244,
0.304816678648454, 0.316319194823868, 0.316319194823868,
0.340043134435658, 0.188353702372394, 0.152408339324227,
0.14018691588785, 0.104241552839684, 0.092020129403307,
0.127965492451474,
0.140905823148814, 0.0927390366642703, 0.0927390366642703,
0.0927390366642703), T0_2norm.inv = c(0.682961897915169,
0.707404744787922, 0.659237958303379, 0.707404744787922,
0.683680805176132, 0.671459381739756, 0.695183321351546,
0.683680805176132, 0.683680805176132, 0.659956865564342,
0.811646297627606, 0.847591660675773, 0.85981308411215,
0.895758447160316,
0.907979870596693, 0.872034507548526, 0.859094176851186,
0.90726096333573, 0.90726096333573, 0.90726096333573), Acc.Spd =
c(1.55796189791517,
1.58240474478792, 1.40923795830338, 1.58240474478792,
1.43368080517613,
1.54645938173976, 1.57018332135155, 1.43368080517613,
1.30868080517613,
1.53495686556434, 1.31164629762761, 1.09759166067577,
1.10981308411215,
1.14575844716032, 1.40797987059669, 1.74703450754853,
1.73409417685119,
1.15726096333573, 1.40726096333573, 1.40726096333573)), .Names =
c("X1",
"PRIM_KEY", "NAME", "SUBJECT", "BIRTHDAY", "TODAY_DATE", "AGE",
"YOE", "DAILY_SHOTS", "CLIP", "HEIGHT", "Group", "CONDITION",
"SHOT", "ACCURACY", "Make", "T0", "T0_Knee_Ext", "T0_Hip_Ext",
"Min_Ball_Ht", "T1", "T0_1", "T1_Ball_Ht", "T1_Knee_Ext", "T1_Hip_Ext",
"T2", "T1_2", "T0_2", "T2_Sh_Flex", "T2_Elb_Ext", "T2_Rel_Ht",
"T2_Jump_Ht", "T2_Wr_Ext", "CONDITIONf", "Makef", "ACCURACYf",
"ACCURACYnorm", "T0_2norm", "T0_2norm.inv", "Acc.Spd"), row.names = c(NA,
-20L), class = c("tbl_df", "tbl", "data.frame"))
解决方案
您的应用程序表明您没有牢牢掌握 Shiny 的基础知识，并且应该阅读教程（再次，如果您已经掌握）。
- 不要在 s 中定义
reactives 或render*sobserver，使用eventReactives 等待事件（如点击按钮），req等待变量和条件 - 当你定义 a
reactive它是一个函数，而不是一个数据对象，所以你必须用括号调用它 - 在服务器函数内部
global.R或外部加载一次性数据集app.R
这是一个工作应用程序：
library("shiny")
library("readr")
library("gplots")
# Data Input --------------------------------------------------------------
dt <-
structure(
list(
X1 = 1:20,
PRIM_KEY = 1:20,
NAME = c(
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda",
"Andrew Grajeda"
),
SUBJECT = c(
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L
),
BIRTHDAY = structure(
c(
11860,
11860,
11860,
11860,
11860,
11860,
11860,
11860,
11860,
11860,
11860,
11860,
11860,
11860,
11860,
11860,
11860,
11860,
11860,
11860
),
class = "Date"
),
TODAY_DATE = structure(
c(
17616,
17616,
17616,
17616,
17616,
17616,
17616,
17616,
17616,
17616,
17616,
17616,
17616,
17616,
17616,
17616,
17616,
17616,
17616,
17616
),
class = "Date"
),
AGE = c(
15.7698630136986,
15.7698630136986,
15.7698630136986,
15.7698630136986,
15.7698630136986,
15.7698630136986,
15.7698630136986,
15.7698630136986,
15.7698630136986,
15.7698630136986,
15.7698630136986,
15.7698630136986,
15.7698630136986,
15.7698630136986,
15.7698630136986,
15.7698630136986,
15.7698630136986,
15.7698630136986,
15.7698630136986,
15.7698630136986
),
YOE = c(
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L
),
DAILY_SHOTS = c(
50L,
50L,
50L,
50L,
50L,
50L,
50L,
50L,
50L,
50L,
50L,
50L,
50L,
50L,
50L,
50L,
50L,
50L,
50L,
50L
),
CLIP = c(
"00_1",
"00_1",
"00_1",
"00_1",
"00_1",
"00_1",
"00_1",
"00_1",
"00_1",
"00_1",
"00_1",
"00_1",
"00_1",
"00_1",
"00_1",
"00_1",
"00_1",
"00_1",
"00_1",
"00_1"
),
HEIGHT = c(
1.73,
1.73,
1.73,
1.73,
1.73,
1.73,
1.73,
1.73,
1.73,
1.73,
1.73,
1.73,
1.73,
1.73,
1.73,
1.73,
1.73,
1.73,
1.73,
1.73
),
Group = c(
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L
),
CONDITION = c(
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L
),
SHOT = c(
1L,
2L,
3L,
4L,
5L,
6L,
7L,
8L,
9L,
10L,
1L,
2L,
3L,
4L,
5L,
6L,
7L,
8L,
9L,
10L
),
ACCURACY = c(4.5, 4.5, 4, 4.5, 4, 4.5,
4.5, 4, 3.5, 4.5, 3, 2, 2, 2, 3, 4.5, 4.5, 2, 3, 3),
Make = c(
1L,
1L,
0L,
1L,
0L,
1L,
1L,
0L,
1L,
1L,
0L,
0L,
0L,
0L,
0L,
1L,
1L,
0L,
0L,
0L
),
T0 = structure(
c(
-2209075175,
-2209075170,-2209075164,
-2209075158,
-2209075153,
-2209075149,
-2209075143,-2209075136,
-2209075130,
-2209075126,
-2209075040,
-2209075035,-2209075030,
-2209075025,
-2209075020,
-2209075015,
-2209075010,-2209075006,
-2209075001,
-2209074998
),
class = c("POSIXct",
"POSIXt"),
tzone = "UTC"
),
T0_Knee_Ext = c(
169.7,
165.7,
169.3,
173,
171.3,
168.7,
164.1,
165.7,
166.8,
165.7,
164,
157.4,
157.4,
157.4,
147.2,
147.2,
150,
149.9,
152,
149
),
T0_Hip_Ext = c(
172.6,
172.6,
172.6,
176.7,
171.7,
171.7,
161.1,
161.1,
168.9,
171.7,
163.7,
160.9,
160.9,
160.9,
154.5,
156.2,
156.2,
156.2,
156.5,
156.2
),
Min_Ball_Ht = c(
0.93,
0.94,
0.96,
0.92,
0.95,
0.94,
0.94,
0.93,
0.94,
0.93,
0.8,
0.81,
0.8,
0.8,
0.81,
0.81,
0.8,
0.8,
0.81,
0.8
),
T1 =
structure(
c(
-2209075175,-2209075169,
-2209075163,
-2209075157,
-2209075152,
-2209075148,-2209075143,
-2209075135,
-2209075129,
-2209075125,
-2209075039,-2209075034,
-2209075029,
-2209075025,
-2209075020,
-2209075015,-2209075010,
-2209075005,
-2209075001,
-2209074997
),
class =
c("POSIXct",
"POSIXt"),
tzone = "UTC"
),
T0_1 = c(
0.601,
0.534,
0.601,
0.567,
0.601,
0.601,
0.584,
0.6,
0.567,
0.6,
0.422,
0.372,
0.339,
0.355,
0.288,
0.272,
0.339,
0.289,
0.222,
0.289
),
T1_Ball_Ht = c(
1.04,
1.03,
1.02,
1.03,
1.04,
1.05,
1.04,
1.03,
1.03,
1.04,
0.97,
0.94,
0.95,
0.96,
0.97,
0.96,
0.95,
0.94,
0.95,
0.96
),
T1_Knee_Ext = c(
116.3,
119.6,
122.9,
119.2,
127.4,
126.9,
134.4,
129,
134.5,
134.4,
112.3,
116.4,
122.3,
119.7,
121.6,
121.6,
117.7,
117.7,
117.7,
117.7
),
T1_Hip_Ext = c(
142,
138.4,
138.4,
138.4,
142.9,
147.9,
147.9,
147.9,
147.9,
147.9,
133.5,
133.5,
141.5,
148.2,
145.4,
145.4,
145.4,
145.4,
145.4,
145.4
),
T2 = structure(
c(
-2209075174,
-2209075169,
-2209075163,-2209075157,
-2209075152,
-2209075148,
-2209075142,
-2209075135,-2209075129,
-2209075125,
-2209075039,
-2209075034,
-2209075029,-2209075025,
-2209075020,
-2209075014,
-2209075010,
-2209075005,-2209075001,
-2209074997
),
class = c("POSIXct", "POSIXt"),
tzone =
"UTC"
),
T1_2 = c(
0.267,
0.3,
0.3,
0.267,
0.266,
0.283,
0.267,
0.267,
0.3,
0.3,
0.267,
0.267,
0.283,
0.217,
0.267,
0.333,
0.284,
0.267,
0.334,
0.267
),
T0_2 = c(
0.868,
0.834,
0.901,
0.834,
0.867,
0.884,
0.851,
0.867,
0.867,
0.9,
0.689,
0.639,
0.622,
0.572,
0.555,
0.605,
0.623,
0.556,
0.556,
0.556
),
T2_Sh_Flex =
c(
137.3,
140.8,
134.2,
138.6,
138,
138.6,
138.6,
134.2,
134.2,
140.8,
138,
138,
136,
136,
136,
137,
137,
136,
136,
136
),
T2_Elb_Ext =
c(
179.8,
179.8,
179,
179,
178.5,
179.4,
179.2,
179,
178.9,
179.8,
174.9,
174.9,
174.9,
174.9,
174.9,
175,
174.8,
174.9,
175,
174.8
),
T2_Rel_Ht = c(
2.17,
2.18,
2.17,
2.18,
2.17,
2.17,
2.18,
2.17,
2.18,
2.17,
2.17,
2.17,
2.18,
2.17,
2.17,
2.18,
2.17,
2.18,
2.18,
2.17
),
T2_Jump_Ht = c(
0.05,
0.06,
0.05,
0.06,
0.05,
0.05,
0.06,
0.05,
0.06,
0.05,
0.05,
0.05,
0.06,
0.05,
0.05,
0.06,
0.05,
0.06,
0.06,
0.05
),
T2_Wr_Ext = c(
109.3,
106.8,
106.8,
106.8,
107.9,
109.1,
106.8,
107.8,
107,
107.5,
120,
113.5,
107.9,
100.5,
100.5,
100.5,
100.5,
100.5,
100.5,
100.5
),
CONDITIONf = structure(
c(
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
1L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L,
2L
),
.Label =
c("Normal",
"None", "Wrist", "Elb. Ht.", "Rim"),
class = "factor"
),
Makef =
c(
"Make",
"Make",
"Miss",
"Make",
"Miss",
"Make",
"Make",
"Miss",
"Make",
"Make",
"Miss",
"Miss",
"Miss",
"Miss",
"Miss",
"Make",
"Make",
"Miss",
"Miss",
"Miss"
),
ACCURACYf = c(
"Inside Rim - Make",
"Inside Rim - Make",
"Inside Rim - Miss",
"Inside Rim - Make",
"Inside Rim - Miss",
"Inside Rim - Make",
"Inside Rim - Make",
"Inside Rim - Miss",
"Top Rim - Make",
"Inside Rim - Make",
"Top Rim - Miss",
"Outside Rim",
"Outside Rim",
"Outside Rim",
"Top Rim - Miss",
"Inside Rim - Make",
"Inside Rim - Make",
"Outside Rim",
"Top Rim - Miss",
"Top Rim - Miss"
),
ACCURACYnorm =
c(
0.875,
0.875,
0.75,
0.875,
0.75,
0.875,
0.875,
0.75,
0.625,
0.875,
0.5,
0.25,
0.25,
0.25,
0.5,
0.875,
0.875,
0.25,
0.5,
0.5
),
T0_2norm = c(
0.317038102084831,
0.292595255212078,
0.340762041696621,
0.292595255212078,
0.316319194823868,
0.328540618260244,
0.304816678648454,
0.316319194823868,
0.316319194823868,
0.340043134435658,
0.188353702372394,
0.152408339324227,
0.14018691588785,
0.104241552839684,
0.092020129403307,
0.127965492451474,
0.140905823148814,
0.0927390366642703,
0.0927390366642703,
0.0927390366642703
),
T0_2norm.inv = c(
0.682961897915169,
0.707404744787922,
0.659237958303379,
0.707404744787922,
0.683680805176132,
0.671459381739756,
0.695183321351546,
0.683680805176132,
0.683680805176132,
0.659956865564342,
0.811646297627606,
0.847591660675773,
0.85981308411215,
0.895758447160316,
0.907979870596693,
0.872034507548526,
0.859094176851186,
0.90726096333573,
0.90726096333573,
0.90726096333573
),
Acc.Spd =
c(
1.55796189791517,
1.58240474478792,
1.40923795830338,
1.58240474478792,
1.43368080517613,
1.54645938173976,
1.57018332135155,
1.43368080517613,
1.30868080517613,
1.53495686556434,
1.31164629762761,
1.09759166067577,
1.10981308411215,
1.14575844716032,
1.40797987059669,
1.74703450754853,
1.73409417685119,
1.15726096333573,
1.40726096333573,
1.40726096333573
)
),
.Names =
c(
"X1",
"PRIM_KEY",
"NAME",
"SUBJECT",
"BIRTHDAY",
"TODAY_DATE",
"AGE",
"YOE",
"DAILY_SHOTS",
"CLIP",
"HEIGHT",
"Group",
"CONDITION",
"SHOT",
"ACCURACY",
"Make",
"T0",
"T0_Knee_Ext",
"T0_Hip_Ext",
"Min_Ball_Ht",
"T1",
"T0_1",
"T1_Ball_Ht",
"T1_Knee_Ext",
"T1_Hip_Ext",
"T2",
"T1_2",
"T0_2",
"T2_Sh_Flex",
"T2_Elb_Ext",
"T2_Rel_Ht",
"T2_Jump_Ht",
"T2_Wr_Ext",
"CONDITIONf",
"Makef",
"ACCURACYf",
"ACCURACYnorm",
"T0_2norm",
"T0_2norm.inv",
"Acc.Spd"
),
row.names = c(NA,-20L),
class = c("tbl_df", "tbl", "data.frame")
)
#import data
# dt <- read_csv("dt.csv")
dt$CONDITIONf <- factor(dt$CONDITION, levels = c(1,2,3,4,5), labels =
c("Normal","None","Wrist","Elb. Ht.","Rim"))
# UI --------------------------------------------------------------
# Define UI
ui <- fluidPage(
# Application title
titlePanel(
h1("Variable Means by Condition (Study 3)", align = "center", style =
"color:black")),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
radioButtons(inputId = "var", label = "Select a Variable:",
c("Time from Catch to Lowest COM" = "T0_1",
"Time from Lowest COM to Release" = "T1_2",
"Release Time" = "T0_2",
"Knee Extension at Catch" = "T0_Knee_Ext",
"Hip Extension at Catch" = "T0_Hip_Ext",
"Minimum Ball Height" = "Min_Ball_Ht",
"Ball Height at Lowest COM" = "T1_Ball_Ht",
"Knee Extension at Lowest COM" = "T1_Knee_Ext",
"Hip Extension at Lowest COM" = "T1_Hip_Ext",
"Shoulder Flexion at Release" = "T2_Sh_Flex",
"Elbow Extension at Release" = "T2_Elb_Ext",
"Release Height" = "T2_Rel_Ht",
"Jump Height" = "T2_Jump_Ht",
"Wrist Extension at Follow-Through" = "T2_Wr_Ext",
"Accuracy" = "ACCURACY",
"Overall Performance" = "Acc.Spd")),
#Add radio buttons to choose a condition
radioButtons(inputId = "cond", label = "Select a Condition:",
c("Condition 1" = 1,
"Condition 2" = 2,
"Condition 3" = 3,
"Condition 4" = 4,
"Condition 5" = 5))),
# Show a plot of the mean of the selected variable
mainPanel(
#create a plot for selected variable
plotOutput("mean_plot"),
#Get summary for selected variable and selected condition
verbatimTextOutput("summ"),
#Get density plot for selected variable and selected condition
plotOutput("dens_plot"),
#Calculate shapiro wilk test for selected variable and selected condition
verbatimTextOutput("shap"),
#Return if the selected variable and selected condition is normal or not
verbatimTextOutput("norm"))
)
)
# Server --------------------------------------------------------------
# Define server logic required to draw plotmeans
server <- function(input, output) {
#subset data on various inputs from ui
subsetData <- reactive({
# subset the data with the selected condition
dt[dt$CONDITION == input$cond, ]
})
variableData <- reactive({
var_dat <- subsetData()[[input$var]]
# make sure there is actually something to plot
shiny::validate(
need(length(var_dat) >= 3, "Not enough data (need at least 3 points) found for that condition and variable combination!")
)
var_dat
})
variableShapiro <- reactive({
# return the object as a reactive
shapiro.test(variableData())
})
#Create plot
output$mean_plot <- renderPlot({
input$goButton
#using gplots plotmeans
# use 'isolate' here to prevent the plot from changing when the input 'var'
# changes, only want to change when button is clicked
plot_formula <- as.formula(paste(isolate(input$var), "CONDITIONf", sep = "~"))
plotmeans(
formula = plot_formula
, data = dt
, connect = FALSE
, n.label = FALSE
, mean.labels = TRUE
, digits = 2
, xlab = "Condition"
, ylab = "Mean"
, main = "Variable Means by Condition"
, pch = " "
)
})
output$dens_plot <- renderPlot({
#Create density plot
hist(variableData())
})
#Run shapiro wilk test
output$shap <- renderPrint({
variableShapiro()
})
output$norm <- renderPrint({
ifelse(variableShapiro()$p.value < 0.05
, "Reject the Null Hypothesis: Evidence found that the distribution is not Normal"
, "Failed to Reject the Null Hypothesis: No evidence found that the distribution is not Normal")
})
}
# Run--------------------------------------------------------------
# Run the application
shinyApp(ui = ui, server = server)
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