我正在创建一个shinyapp，我想根据用户的选择显示一个boxplot和anova表。 目前我的selectInput正在工作，我正在绘制箱图，但我的anova收到错误。我添加了一些打印消息来帮助调试，也许它与反应变量有关。
第一次打印我有一个空的otu变量，因此我的数据表dfM.sub是空的。
感谢任何帮助。
这是一些示例数据：
> dput(f_genus[,1:10])
structure(list(Acaulospora = c(0, 0, 0, 0, 0, 0, 2.26747086299941e-05,
0, 0, 0, 0, 0, 0.000120048019207683, 0, 0, 0.000766283524904215,
0.000207569362762056, 0, 0, 6.24375624375624e-05, 0, 0, 0.000163478829491581,
0, 8.6884747382597e-05, 0.000185431282257317), Acaulosporaceae_unclassified = c(1.833415837046e-05,
0, 0, 0, 0, 2.23338916806253e-05, 2.26747086299941e-05, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Acremonium = c(0.00060502722622518,
0.000363538956069187, 0.000367167850782985, 0.000292958736761927,
0.000361125862984599, 0.000781686208821887, 0.000453494172599882,
0.00042863266180883, 0.000702540856096215, 0.000124340992738486,
0.000492340320778655, 0.00226802090523617, 0.00260104041616647,
0.00295825967747754, 0.00592563133823822, 0.00322645694696511,
0.00294056597246246, 0.000659568453783096, 0.00360192642357036,
0.00118631368631369, 0.00338657501010918, 0.00288747723335258,
0.00245218244237371, 0.00536866754240676, 0.00364915939006907,
0.00361591000401768), Acrocalymma = c(0, 0, 0, 0, 4.24853956452469e-05,
0, 4.53494172599882e-05, 0, 0, 0, 3.78723323675889e-05, 0, 0.00040016006402561,
0.000252534362711498, 0, 0, 0, 0, 0, 0.000187312687312687, 0,
0.000888454533339256, 0.000122609122118686, 0.000456907875949512,
0, 0.000494483419352845), Agaricales_unclassified = c(0, 0, 1.83583925391493e-05,
0.000190423178895253, 8.49707912904939e-05, 2.23338916806253e-05,
0.000408144755339894, 0.000782720512868298, 0.000351270428048107,
0.000522232169501641, 0.000473404154594861, 0.0141997830588699,
0.0251300520208083, 0.0215375735055377, 0.0124080334062438, 0.00786448880822747,
0.0132498443229779, 0.0143220578535758, 0.00882269618357683,
0.00705544455544456, 0.0143044884755358, 0.00266536360001777,
0.00470001634788295, 0.0275858130104518, 0.0296711412311569,
0.0323886639676113), Agaricomycetes_unclassified = c(5.500247511138e-05,
1.91336292667993e-05, 3.67167850782985e-05, 5.85917473523854e-05,
0.000106213489113117, 0.000111669458403127, 0.000113373543149971,
0.000969082539741702, 0, 4.97363970953944e-05, 1.89361661837944e-05,
0.0132136870131151, 0.0112444977991196, 0.00999314549586926,
0.00914694770332074, 0.0140350877192982, 0.0126963260222791,
0.012060680297748, 0.00392569509085758, 0.00842907092907093,
0.0253740396279822, 0.00946204078006308, 0.0042504495667811,
0.0116511508367125, 0.00886224423302489, 0.00750996693142133),
Agrocybe = c(0, 0, 0.000128508747774045, 0, 0.00424853956452469,
0.0017643774427694, 0, 0.000223634432248085, 5.01814897211582e-05,
0.00131801452302795, 0, 0, 0, 3.60763375302139e-05, 0, 0.0014519056261343,
0, 0, 0.0226233356266947, 0.000187312687312687, 0.00161746866154468,
0.00186575452001244, 0.00122609122118686, 0, 0, 0), Alatospora = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.000285567422468445, 0.000173769494765194, 0.000154526068547764
), Alternaria = c(0.00022000990044552, 0.000401806214602786,
0.0012667290852013, 0.000893524147123878, 0.000191184280403611,
0.000402010050251256, 0.000294771212189923, 0.000242270634935426,
0.000150544469163475, 0.000223813786929275, 0.00160957412562253,
0.00157775367320777, 0.000640256102440976, 0.00194812222663155,
0.00039769337840525, 0.00221818915103852, 0.00214488341520792,
0.00179025723169698, 0.00165931441984702, 0.00224775224775225,
0.00409421754953498, 0.00151037270667674, 0.00114435180644107,
0.000628248329430579, 0.000738520352752074, 0.00135982940322032
), Amphisphaeriaceae_unclassified = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0.000240096038415366, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.000912289847517268, 0.000649009487900609)), .Names = c("Acaulospora",
"Acaulosporaceae_unclassified", "Acremonium", "Acrocalymma",
"Agaricales_unclassified", "Agaricomycetes_unclassified", "Agrocybe",
"Alatospora", "Alternaria", "Amphisphaeriaceae_unclassified"), class = "data.frame", row.names = c("R-B1",
"R-B2", "R-B3", "R-BF-1", "R-BF-2", "R-BF-3", "R-BFi-1", "R-BFi-2",
"R-Bi-1", "R-Bi-2", "R-Bi-3", "S-B1", "S-B2", "S-B3", "S-Bi-1",
"S-Bi-2", "S-Bi-3", "S-BF-1", "S-BF-2", "S-BF-3", "S-BFi-1",
"S-BFi-2", "S-BFi-3", "S1", "S2", "S3"))
和元数据：
> dput(sample_metadata)
structure(list(Location = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L), .Label = c("Root", "Soil"), class = "factor"),
Bean = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L), .Label = c("Bean", "No bean"), class = "factor"),
Fungi = structure(c(2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L), .Label = c("Fungi", "NF"), class = "factor"), Insect = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L), .Label = c("Insect",
"NI"), class = "factor")), .Names = c("Location", "Bean",
"Fungi", "Insect"), class = "data.frame", row.names = c("R-B1",
"R-B2", "R-B3", "R-BF-1", "R-BF-2", "R-BF-3", "R-BFi-1", "R-BFi-2",
"R-BFi-3", "R-Bi-1", "R-Bi-2", "R-Bi-3", "S-B1", "S-B2", "S-B3",
"S-BF-1", "S-BF-2", "S-BF-3", "S-BFi-1", "S-BFi-2", "S-BFi-3",
"S-Bi-1", "S-Bi-2", "S-Bi-3", "S1", "S2", "S3"))
继承错误：
Listening on http://127.0.0.1:7179
Warning in is.na(e2) :
is.na() applied to non-(list or vector) of type 'NULL'
Warning: Error in : Faceting variables must have at least one value
Stack trace (innermost first):
111: combine_vars
110: plyr::unrowname
109: f
108: self$compute_layout
107: f
106: self$facet$train
105: f
104: layout$setup
103: ggplot2::ggplot_build
102: print.ggplot
101: print
90: <reactive:plotObj>
79: plotObj
78: origRenderFunc
77: output$plot
1: runApp
[1] "otu selected: "
Warning in is.na(e2) :
is.na() applied to non-(list or vector) of type 'NULL'
[1] "dim dfM.sub: 0" "dim dfM.sub: 6"
Warning: Error in contrasts<-: contrasts can be applied only to factors with 2 or more levels
Stack trace (innermost first):
87: contrasts<-
86: model.matrix.default
85: model.matrix
84: stats::lm
83: eval
82: eval
81: aov
80: renderTable [/..../shiny/app.R#137]
79: func
78: origRenderFunc
77: output$table
1: runApp
[1] "otu selected: Metarhizium"
[1] "dim dfM.sub: 23" "dim dfM.sub: 6"
正如您所见，dfM.sub对所有因素都有2个级别＆gt;
> str(dfM.sub)
'data.frame': 26 obs. of 6 variables:
$ Location: Factor w/ 2 levels "Root","Soil": 1 1 1 1 1 1 1 1 1 1 ...
$ Bean : Factor w/ 2 levels "Bean","No bean": 1 1 1 1 1 1 1 1 1 1 ...
$ Fungi : Factor w/ 2 levels "Metarhizium",..: 2 2 2 1 1 1 1 1 2 2 ...
$ Insect : Factor w/ 2 levels "Insect","NI": 2 2 2 2 2 2 1 1 1 1 ...
$ variable: Factor w/ 384 levels "Acaulospora",..: 169 169 169 169 169 169 169 169 169 169 ...
$ abund : num 0.00548 0.00377 0.00415 0.00333 0.0044 ...
此处是我的代码：
# ========================= LOAD REQUIRED PACKAGES, DATA AND FUNCTIONS ####
# load necessary packages
library('shiny')
library('ggplot2')
library('reshape2')
library("data.table")
library("dplyr")
library("vegan")
library("gdata")
#
library(shinyjs)
library(logging)
#
# library("stringr")
# load reference data
# none right now
# Load experimental data (abundance tables)
# first row is the header, and first column is rownames (ie. doesn't need corresponding column name)
f_genus<-read.table("b_Genus.csv",header=T,sep=",",row.names=1)
f_family<-read.table("b_Family.csv",header=T,sep=",",row.names=1)
f_phylum<-read.table("b_Phylum.csv",header=T,sep=",",row.names=1)
# read in bacterial data
bact_genus<-read.table("ss_Genus.csv",header=T,sep=",",row.names=1)
bact_family<-read.table("ss_Family.csv",header=T,sep=",",row.names=1)
bact_phylum<-read.table("ss_Phylum.csv",header=T,sep=",",row.names=1)
# sample metadata
sample_metadata<-read.csv("sample_metadata.csv",row.names=1,header=T)
##
abundance_tables<-list(fungi_genus=f_genus,fungi_family=f_family,fungi_phylum=f_phylum,
bact_genus=bact_genus,bact_family=bact_family,bact_phylum=bact_phylum)
## add diversity and species count columns to each abundance table
abundance_tables<-lapply(abundance_tables,function(tab) {tab$diversity<-diversity(tab) ; tab})
abundance_tables<-lapply(abundance_tables,function(tab){
tab$species_count<-apply(tab,1, function(x) {length(x[x>0])});tab})
##
FactorsOfInt<-c("Metarhizium","Insect","Sample_Type","Metarhizium*Insect","Metarhizium*Sample_Type")
# ========================= UI ####
ui <- fluidPage(
# Make a title to display in the app
titlePanel(" Exploring the Effect of Metarhizium on the Soil and Root Microbiome "),
# Make the Sidebar layout
sidebarLayout(
# Put in the sidebar all the input functions
sidebarPanel(
tabsetPanel(id="tabs",
tabPanel("otu", selectInput('dataset', 'dataset', names(abundance_tables),selected=names(abundance_tables)[1]),
uiOutput("otu"), br(),
# Add comment
p("For details on OTU identification please refer to the original publications")),
tabPanel("anova", sliderInput('pval','p-value for significance',
value=0.1,min=0,max=0.5,step=0.00001),
selectInput('dataset', 'dataset', names(abundance_tables)),
selectInput('fact_ofInt','factor of interest',FactorsOfInt,selected="Metarhizium"))
)
),
# Put in the main panel of the layout the output functions
mainPanel(
conditionalPanel(condition="input.tabs == 'otu'",
plotOutput('plot'),
dataTableOutput("table")
),
conditionalPanel(condition="input.tabs == 'anova'",
#plotOutput('plot2')
verbatimTextOutput("anovaText")
# dataTableOutput("anova_tab2")
)
)
)
)
# ========================= SERVER ####
server <- function(input, output){
# Return the requested dataset ----
datasetInput <- reactive({
abundance_tables[[input$dataset]]
})
pvalInput<-reactive({
input$pval
})
comparisonInput<-reactive({
input$FactorsOfInt
})
#
# output otus to choose basaed on dataset selection
output$otu <- renderUI({
selectInput(inputId = "otu", label = "otu",
choices = colnames(datasetInput()),selected="Metarhizium")
})
otuInput<-reactive({
input$otu
})
output$plot <- renderPlot({
df<-datasetInput()
otu<-otuInput()
## melt and add sample metadata
df_annot<-merge(df,sample_metadata,by="row.names",all.x=T)
rownames(df_annot)<-df_annot[,1]
df_annot<-df_annot[,-1]
#
df_annot<-subset(df_annot,df_annot$Bean =="Bean")
#
dfM<-melt(df_annot,id.vars = c("Location","Bean","Fungi","Insect"),value.name="abund")
# renaming Fungi level to metarhizium
levels(dfM$Fungi)<-c("Metarhizium","No Meta")
# subset based on otu of interest
dfM.sub<-subset(dfM,dfM$variable==otu)
#
ggplot(dfM.sub,aes(x=Insect,y=abund,fill=Fungi))+geom_boxplot()+
facet_wrap(~Location,scales="free_y" )+
guides(fill=guide_legend("Metarhizium")) +
ggtitle(otu)
})
## now make anova table
output$table <- renderTable({
df<-datasetInput()
otu<-otuInput()
print(paste("otu selected:",otu))
## melt and add sample metadata
df_annot<-merge(df,sample_metadata,by="row.names",all.x=T)
rownames(df_annot)<-df_annot[,1]
df_annot<-df_annot[,-1]
#
df_annot<-subset(df_annot,df_annot$Bean =="Bean")
df_annot<-drop.levels(df_annot)
#
dfM<-melt(df_annot,id.vars = c("Location","Bean","Fungi","Insect"),value.name="abund")
# renaming Fungi level to metarhizium
# levels(dfM$Fungi)<-c("Metarhizium","No Meta")
# subset based on otu of interest
dfM.sub<-subset(dfM,dfM$variable==otu)
print(paste("dim dfM.sub: ",dim(dfM.sub)))
aov.ex <- aov(dfM.sub$abund~dfM.sub$Fungi)
anova_table<-as.data.frame(summary(aov.ex)[[1]])
})
#### if anova tab selected
output$anovaText<-renderText({
"anova Table"
})
})
### end of server
}
##
shinyApp(ui=ui,server=server)
答案 0 :(得分：3)
此处的主要问题是UI中的dataTableOutput()与服务器中的renderTable()不匹配。这些总是需要匹配;即，tableOutput()与renderTable()一致，dataTableOutput()与renderDataTable()一致。
替换
conditionalPanel(condition="input.tabs == 'otu'",
plotOutput('plot'),
dataTableOutput("table")
),
带
conditionalPanel(condition="input.tabs == 'otu'",
plotOutput('plot'),
tableOutput("table")
),
你的anova表将显示在你的情节之下。 或者，您可以替换
output$table <- renderTable({
...
})
带
output$table <- renderDataTable({
...
})
要么让你的桌子显示出来。
至于您注意到的错误，您是对的，因为它们是对应用加载时input$otu内没有任何内容的回应。为了避免在应用程序赶上之前抛出错误，您可以要求在尝试绘制数据之前填充input$otu，或者在renderPlot()和renderTable()来电中的第一行放入表格：
output$plot <- renderPlot({
req(is.null(input$otu)==F)
...
})
您会在应用加载时注意到相同的延迟，但没有错误。