将单选按钮链接到闪亮的反应数据
Linking radio buttons to reactive data in shiny
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，我在 5 个条件下分析了许多变量 (16)。
在我的应用程序中，我希望有一个侧面板，其中包含作为单选按钮的变量和条件列表。
在我的主面板中，我想要以下输出：
- 按条件绘制均值图（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
- 来自 shapiro.test 的针对所选变量和所选条件的输出
- 解释shapiro.test（即normal/not正常）
我可以很容易地通过条件获取绘图，但是我在显示其余输出时遇到了问题。我为条件引用单选按钮的方式一定有问题，因为在我单击 "Analyze" 之后，我一直收到错误消息，告诉我找不到我选择的变量。
请查看我的代码，如有任何帮助，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，请联系我，我会发送给您。提前致谢！
运行之后：
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"))
您的应用表明您没有牢牢掌握 Shiny 的基础知识，应该通过 tutorials（再次强调，如果您已经掌握）。
- 不要在
observer 中定义 reactive 或 render*，使用 eventReactive 等待事件（如单击按钮），req 等待变量和条件
- 当你定义一个
reactive 时，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
- 在服务器函数外
global.R或app.R加载一次性数据集
这是一个可用的应用程序：
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)
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，我在 5 个条件下分析了许多变量 (16)。
在我的应用程序中，我希望有一个侧面板，其中包含作为单选按钮的变量和条件列表。
在我的主面板中，我想要以下输出：
- 按条件绘制均值图（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
- 来自 shapiro.test 的针对所选变量和所选条件的输出
- 解释shapiro.test（即normal/not正常）
我可以很容易地通过条件获取绘图，但是我在显示其余输出时遇到了问题。我为条件引用单选按钮的方式一定有问题，因为在我单击 "Analyze" 之后，我一直收到错误消息，告诉我找不到我选择的变量。
请查看我的代码，如有任何帮助，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，请联系我，我会发送给您。提前致谢！
运行之后：
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"))
您的应用表明您没有牢牢掌握 Shiny 的基础知识，应该通过 tutorials（再次强调，如果您已经掌握）。
- 不要在
observer中定义reactive或render*，使用eventReactive等待事件（如单击按钮），req等待变量和条件 - 当你定义一个
reactive时，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 - 在服务器函数外
global.R或app.R加载一次性数据集
这是一个可用的应用程序：
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)