Nothing
ANOVA Interactive"
In ANOVAIREVA: Interactive Document for Working with Analysis of Variance
knitr::opts_chunk$set(echo = TRUE)
library(dplyr)
library(car)# for Anova
library(ggplot2)# for plots
library(plotly)# plots
library(datasets)
ANOVA (short of Analysis of Variance) is a statistical tool for determining differences/Variances between two or more groups of independent variables.
One way ANOVA checks for one independent variable whereas Two way ANOVA checks for two independent variables.
They can also be checked for interaction effect i.e. The presence of one causes changes in the other.
Further t test is performed to find which level of independent variable shows variation.
Independent variable and Dependent variable are Categorical and Metric respectively.
cat(sprintf("\n "))
#inputpanel
sidebarPanel(
inputPanel(
checkboxInput("Choice","Check to choose your own file",value=FALSE),
fileInput("file","Upload a file in *.csv format with headers",placeholder = "No file selected"),
uiOutput("Var1"),
uiOutput("Var2"),
uiOutput("Var3"),
selectInput("way", " select the anova technique to be used", choices= c("one way","two way")),
uiOutput("int"),
downloadButton("dataset"," Download dataset"),
downloadButton("plot","Download Plot")
)
)
#selection ofvariable 1
output$Var1<- renderUI({
if (input$Choice==FALSE) {
data("iris")
data= iris
data=select_if(data,is.numeric)
selectInput("var1"," Select the dependent variable",choices = colnames(data))
}
else {
file1=input$file
if (is.null(file1)){return()}
data = read.table(file = file1$datapath,sep = ",",header = TRUE)
if (is.null(data())){return()}
data=select_if(data,is.numeric)
selectInput("var1"," Select the dependent variable",choices = colnames(data))
}
})
#selection of variable 2
output$Var2<- renderUI({
if (input$Choice==FALSE) {
data("iris")
data= iris
data=select_if(data,is.factor)
selectInput("var2","Select the independent variable 1",choices = colnames(data))
}
else{
file1=input$file
if (is.null(file1)){return()}
data = read.table(file = file1$datapath,sep = ",",header = TRUE)
if (is.null(data())){return()}
data=select_if(data,is.factor)
selectInput("var2","Select the independent variable 1",choices = colnames(data))
}
})
#selection of third variable for two way
output$Var3<- renderUI({
if (input$way=="two way") {
if (input$Choice==FALSE) {
data("iris")
data= iris
data=select_if(data,is.factor)
data=select(data,-c(input$var2))
selectInput("var3","Select the independent variable 2",choices = colnames(data))
}
else{
file1=input$file
if (is.null(file1)){return()}
data = read.table(file = file1$datapath,sep = ",",header = TRUE)
if (is.null(data())){return()}
data=select_if(data,is.factor)
data=select(data,-c(input$var2))
selectInput("var3","Select the independent variable 2",choices = colnames(data))
}
}
})
#selection of whether or not to check for interaction effect
output$int<-renderUI({
if (input$way=="two way") {
selectInput("inter","Please select whether to check for interaction or not",choices = c("no","yes"))
}
})
# tabs
mainPanel(
tabsetPanel(
tabPanel("Summary",verbatimTextOutput("summ")),
tabPanel("Anova",verbatimTextOutput("ANOVA")),
tabPanel("Post Hoc(for single variables) ",verbatimTextOutput("PH")),
tabPanel("Visualization",plotlyOutput("Plot"))
),
h6("Designed and Developed by : Ms. Revathi Kumar", tags$img(src ="R.JPG", height= 200, width=200)),
h6("Mentored by :", tags$img(src ="K.JPG", height= 300, width=300))
)
#for the summary of the data
output$summ<- renderPrint({
if (input$Choice==FALSE) {
data("iris")
data= iris
str(data)
summary(data)
cat(sprintf("\n The summary of the dataset is as follows:\n"))
print(summary(data))
}
else{
file1=input$file
if (is.null(file1)){return()}
data = read.table(file = file1$datapath,sep = ",",header = TRUE)
if (is.null(data())){return()}
str(data)
summary(data)
cat(sprintf("\n The summary of the dataset is as follows:\n"))
print(summary(data))
}
})
#for ANOVA test
output$ANOVA<- renderPrint({
if (input$Choice==FALSE) {
data("iris")
data= iris
if (input$way=="one way") {
cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var2))
cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var2))
mod= paste(input$var1,input$var2,sep = "~")
model=lm(formula = as.formula(mod),data = data)
a<- aov(model)
summary(a)
}
else {
if (input$inter=="no") {
cat(sprintf("\n Hypothesis 1\n"))
cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var2))
cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var2))
cat(sprintf("\n Hypothesis 2\n"))
cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var3))
cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var3))
mo=paste(input$var2,input$var3,sep = "+")
mod1= paste(input$var1,mo,sep = "~")
model=lm(formula = as.formula(mod1),data = data)
a<- Anova(model)
}
else{
cat(sprintf("\n Hypothesis 1\n"))
cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var2))
cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var2))
cat(sprintf("\n Hypothesis 2\n"))
cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var3))
cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var3))
cat(sprintf("\n Hypothesis 3\n"))
cat(sprintf("\n Null Hypothesis : there is no interaction between %s and %s",input$var2,input$var3))
cat(sprintf("\n Alternate Hypothesis : there is interaction between %s and %s\n",input$var2,input$var3))
m=paste(input$var2,input$var3,sep = "*")
mo=paste(input$var2,input$var3,m,sep = "+")
mod1= paste(input$var1,mo,sep = "~")
model=lm(formula = as.formula(mod1),data = data)
a<- Anova(model)
}
print(a)
}
}
else {
file1=input$file
if (is.null(file1)){return()}
data = read.table(file = file1$datapath,sep = ",",header = TRUE)
if (is.null(data())){return()}
if (input$way=="one way") {
cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var2))
cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var2))
mod= paste(input$var1,input$var2,sep = "~")
model=lm(formula = as.formula(mod),data = data)
a<- aov(model)
summary(a)
}
else {
if (input$inter=="no") {
cat(sprintf("\n Hypothesis 1\n"))
cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var2))
cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var2))
cat(sprintf("\n Hypothesis 2\n"))
cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var3))
cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var3))
mo=paste(input$var2,input$var3,sep = "+")
mod1= paste(input$var1,mo,sep = "~")
model=lm(formula = as.formula(mod1),data = data)
a<- Anova(model)
}
else{
cat(sprintf("\n Hypothesis 1\n"))
cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var2))
cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var2))
cat(sprintf("\n Hypothesis 2\n"))
cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var3))
cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var3))
cat(sprintf("\n Hypothesis 3\n"))
cat(sprintf("\n Null Hypothesis : there is no interaction between %s and %s",input$var2,input$var3))
cat(sprintf("\n Alternate Hypothesis : there is interaction between %s and %s\n",input$var2,input$var3))
m=paste(input$var2,input$var3,sep = "*")
mo=paste(input$var2,input$var3,m,sep = "+")
mod1= paste(input$var1,mo,sep = "~")
model=lm(formula = as.formula(mod1),data = data)
a<- Anova(model)
}
print(a)
}
}
})
#for Post Hoc test
output$PH<- renderPrint({
if(input$Choice==FALSE){
data("iris")
data= iris
mod= paste(input$var1,input$var2,sep = "~")
model=lm(formula = as.formula(mod),data = data)
a<- aov(model)
TukeyHSD(a)
}
else{
file1=input$file
if (is.null(file1)){return()}
data = read.table(file = file1$datapath,sep = ",",header = TRUE)
if (is.null(data())){return()}
mod= paste(input$var1,input$var2,sep = "~")
model=lm(formula = as.formula(mod),data = data)
a<- aov(model)
TukeyHSD(a)
}
})
#plot
output$Plot <- renderPlotly({
if(input$Choice==FALSE){
data("iris")
data= iris
if(input$way=="one way"){
b<-ggplot(data = data, aes_string(x= input$var2,y= input$var1, fill = input$var2 ))+labs(y=input$var1,x=input$var2)+ geom_boxplot()
b<- ggplotly(b)
print(b)}
else
{
w= aggregate(data[,input$var1],by=list(k= data[,input$var2],l= data[,input$var3]),mean)
b<-ggplot(data= w, aes_string(x= w$k, y= w$x,fill= w$l ))+labs(y=input$var1,x=input$var2,fill= input$var3 )+theme(legend.position = "top")+geom_line(aes_string(group= w$l))+ geom_point()
b<- ggplotly(b)
print(b)
}
}
else{
file1=input$file
if (is.null(file1)){return()}
data = read.table(file = file1$datapath,sep = ",",header = TRUE)
if (is.null(data())){return()}
if(input$way=="one way"){
b<-ggplot(data = data, aes_string(x= input$var2,y= input$var1, fill = input$var2 ))+labs(y=input$var1,x=input$var2)+ geom_boxplot()
b<- ggplotly(b)
print(b)}
else
{
w= aggregate(data[,input$var1],by=list(k= data[,input$var2],l= data[,input$var3]),mean)
b<-ggplot(data= w, aes_string(x= w$k, y= w$x,fill= w$l ))+labs(y=input$var1,x=input$var2,fill= input$var3 )+theme(legend.position = "top")+geom_line(aes_string(group= w$l))+ geom_point()
b<- ggplotly(b)
print(b)
}
}
})
#for downloading the dataset
datasetInput1<- reactive({
if(input$Choice==FALSE){
data("iris")
data= iris
data1<- data.frame(data)
}
else
{
file1=input$file
if (is.null(file1)){return()}
data = read.table(file = file1$datapath,sep = ",",header = TRUE)
if (is.null(data())){return()}
data1<-data.frame(data)
}
})
output$dataset<-downloadHandler(
filename = function(){
paste("dataset",".csv",sep = "")
},
content = function(file){write.csv(datasetInput1(),file,row.names = FALSE)}
)
plotInput1<-reactive({
if (input$Choice==FALSE) {
data("iris")
data= iris
}
else{
file1=input$file
if (is.null(file1)){return()}
data = read.table(file = file1$datapath,sep = ",",header = TRUE)
if (is.null(data())){return()}
}
if(input$way=="one way"){
b<-ggplot(data = data, aes_string(x= input$var2,y= input$var1, fill = input$var2 ))+labs(y=input$var1,x=input$var2)+ geom_boxplot()
print(b)
}
else
{
w= aggregate(data[,input$var1],by=list(k= data[,input$var2],l= data[,input$var3]),mean)
b<-ggplot(data= w, aes_string(x= w$k, y= w$x,fill= w$l ))+labs(y=input$var1,x=input$var2,fill= input$var3 )+theme(legend.position = "top")+geom_line(aes_string(group= w$l))+ geom_point()
print(b)
}
})
output$plot<-downloadHandler(
filename = function() { paste("PLOT", '.png', sep='') },
content = function(file) {
ggsave(file,plotInput1())
})
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ANOVAIREVA documentation built on Oct. 30, 2019, 9:43 a.m.
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knitr::opts_chunk$set(echo = TRUE) library(dplyr) library(car)# for Anova library(ggplot2)# for plots library(plotly)# plots library(datasets)
ANOVA (short of Analysis of Variance) is a statistical tool for determining differences/Variances between two or more groups of independent variables. One way ANOVA checks for one independent variable whereas Two way ANOVA checks for two independent variables. They can also be checked for interaction effect i.e. The presence of one causes changes in the other. Further t test is performed to find which level of independent variable shows variation. Independent variable and Dependent variable are Categorical and Metric respectively.
cat(sprintf("\n ")) #inputpanel sidebarPanel( inputPanel( checkboxInput("Choice","Check to choose your own file",value=FALSE), fileInput("file","Upload a file in *.csv format with headers",placeholder = "No file selected"), uiOutput("Var1"), uiOutput("Var2"), uiOutput("Var3"), selectInput("way", " select the anova technique to be used", choices= c("one way","two way")), uiOutput("int"), downloadButton("dataset"," Download dataset"), downloadButton("plot","Download Plot") ) ) #selection ofvariable 1 output$Var1<- renderUI({ if (input$Choice==FALSE) { data("iris") data= iris data=select_if(data,is.numeric) selectInput("var1"," Select the dependent variable",choices = colnames(data)) } else { file1=input$file if (is.null(file1)){return()} data = read.table(file = file1$datapath,sep = ",",header = TRUE) if (is.null(data())){return()} data=select_if(data,is.numeric) selectInput("var1"," Select the dependent variable",choices = colnames(data)) } }) #selection of variable 2 output$Var2<- renderUI({ if (input$Choice==FALSE) { data("iris") data= iris data=select_if(data,is.factor) selectInput("var2","Select the independent variable 1",choices = colnames(data)) } else{ file1=input$file if (is.null(file1)){return()} data = read.table(file = file1$datapath,sep = ",",header = TRUE) if (is.null(data())){return()} data=select_if(data,is.factor) selectInput("var2","Select the independent variable 1",choices = colnames(data)) } }) #selection of third variable for two way output$Var3<- renderUI({ if (input$way=="two way") { if (input$Choice==FALSE) { data("iris") data= iris data=select_if(data,is.factor) data=select(data,-c(input$var2)) selectInput("var3","Select the independent variable 2",choices = colnames(data)) } else{ file1=input$file if (is.null(file1)){return()} data = read.table(file = file1$datapath,sep = ",",header = TRUE) if (is.null(data())){return()} data=select_if(data,is.factor) data=select(data,-c(input$var2)) selectInput("var3","Select the independent variable 2",choices = colnames(data)) } } }) #selection of whether or not to check for interaction effect output$int<-renderUI({ if (input$way=="two way") { selectInput("inter","Please select whether to check for interaction or not",choices = c("no","yes")) } }) # tabs mainPanel( tabsetPanel( tabPanel("Summary",verbatimTextOutput("summ")), tabPanel("Anova",verbatimTextOutput("ANOVA")), tabPanel("Post Hoc(for single variables) ",verbatimTextOutput("PH")), tabPanel("Visualization",plotlyOutput("Plot")) ), h6("Designed and Developed by : Ms. Revathi Kumar", tags$img(src ="R.JPG", height= 200, width=200)), h6("Mentored by :", tags$img(src ="K.JPG", height= 300, width=300)) ) #for the summary of the data output$summ<- renderPrint({ if (input$Choice==FALSE) { data("iris") data= iris str(data) summary(data) cat(sprintf("\n The summary of the dataset is as follows:\n")) print(summary(data)) } else{ file1=input$file if (is.null(file1)){return()} data = read.table(file = file1$datapath,sep = ",",header = TRUE) if (is.null(data())){return()} str(data) summary(data) cat(sprintf("\n The summary of the dataset is as follows:\n")) print(summary(data)) } }) #for ANOVA test output$ANOVA<- renderPrint({ if (input$Choice==FALSE) { data("iris") data= iris if (input$way=="one way") { cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var2)) cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var2)) mod= paste(input$var1,input$var2,sep = "~") model=lm(formula = as.formula(mod),data = data) a<- aov(model) summary(a) } else { if (input$inter=="no") { cat(sprintf("\n Hypothesis 1\n")) cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var2)) cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var2)) cat(sprintf("\n Hypothesis 2\n")) cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var3)) cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var3)) mo=paste(input$var2,input$var3,sep = "+") mod1= paste(input$var1,mo,sep = "~") model=lm(formula = as.formula(mod1),data = data) a<- Anova(model) } else{ cat(sprintf("\n Hypothesis 1\n")) cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var2)) cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var2)) cat(sprintf("\n Hypothesis 2\n")) cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var3)) cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var3)) cat(sprintf("\n Hypothesis 3\n")) cat(sprintf("\n Null Hypothesis : there is no interaction between %s and %s",input$var2,input$var3)) cat(sprintf("\n Alternate Hypothesis : there is interaction between %s and %s\n",input$var2,input$var3)) m=paste(input$var2,input$var3,sep = "*") mo=paste(input$var2,input$var3,m,sep = "+") mod1= paste(input$var1,mo,sep = "~") model=lm(formula = as.formula(mod1),data = data) a<- Anova(model) } print(a) } } else { file1=input$file if (is.null(file1)){return()} data = read.table(file = file1$datapath,sep = ",",header = TRUE) if (is.null(data())){return()} if (input$way=="one way") { cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var2)) cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var2)) mod= paste(input$var1,input$var2,sep = "~") model=lm(formula = as.formula(mod),data = data) a<- aov(model) summary(a) } else { if (input$inter=="no") { cat(sprintf("\n Hypothesis 1\n")) cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var2)) cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var2)) cat(sprintf("\n Hypothesis 2\n")) cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var3)) cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var3)) mo=paste(input$var2,input$var3,sep = "+") mod1= paste(input$var1,mo,sep = "~") model=lm(formula = as.formula(mod1),data = data) a<- Anova(model) } else{ cat(sprintf("\n Hypothesis 1\n")) cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var2)) cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var2)) cat(sprintf("\n Hypothesis 2\n")) cat(sprintf("\n Null Hypothesis : means of %s is uniform across all groups of %s\n",input$var1,input$var3)) cat(sprintf("\n Alternate Hypothesis : means of %s is not uniform across all groups of %s\n",input$var1,input$var3)) cat(sprintf("\n Hypothesis 3\n")) cat(sprintf("\n Null Hypothesis : there is no interaction between %s and %s",input$var2,input$var3)) cat(sprintf("\n Alternate Hypothesis : there is interaction between %s and %s\n",input$var2,input$var3)) m=paste(input$var2,input$var3,sep = "*") mo=paste(input$var2,input$var3,m,sep = "+") mod1= paste(input$var1,mo,sep = "~") model=lm(formula = as.formula(mod1),data = data) a<- Anova(model) } print(a) } } }) #for Post Hoc test output$PH<- renderPrint({ if(input$Choice==FALSE){ data("iris") data= iris mod= paste(input$var1,input$var2,sep = "~") model=lm(formula = as.formula(mod),data = data) a<- aov(model) TukeyHSD(a) } else{ file1=input$file if (is.null(file1)){return()} data = read.table(file = file1$datapath,sep = ",",header = TRUE) if (is.null(data())){return()} mod= paste(input$var1,input$var2,sep = "~") model=lm(formula = as.formula(mod),data = data) a<- aov(model) TukeyHSD(a) } }) #plot output$Plot <- renderPlotly({ if(input$Choice==FALSE){ data("iris") data= iris if(input$way=="one way"){ b<-ggplot(data = data, aes_string(x= input$var2,y= input$var1, fill = input$var2 ))+labs(y=input$var1,x=input$var2)+ geom_boxplot() b<- ggplotly(b) print(b)} else { w= aggregate(data[,input$var1],by=list(k= data[,input$var2],l= data[,input$var3]),mean) b<-ggplot(data= w, aes_string(x= w$k, y= w$x,fill= w$l ))+labs(y=input$var1,x=input$var2,fill= input$var3 )+theme(legend.position = "top")+geom_line(aes_string(group= w$l))+ geom_point() b<- ggplotly(b) print(b) } } else{ file1=input$file if (is.null(file1)){return()} data = read.table(file = file1$datapath,sep = ",",header = TRUE) if (is.null(data())){return()} if(input$way=="one way"){ b<-ggplot(data = data, aes_string(x= input$var2,y= input$var1, fill = input$var2 ))+labs(y=input$var1,x=input$var2)+ geom_boxplot() b<- ggplotly(b) print(b)} else { w= aggregate(data[,input$var1],by=list(k= data[,input$var2],l= data[,input$var3]),mean) b<-ggplot(data= w, aes_string(x= w$k, y= w$x,fill= w$l ))+labs(y=input$var1,x=input$var2,fill= input$var3 )+theme(legend.position = "top")+geom_line(aes_string(group= w$l))+ geom_point() b<- ggplotly(b) print(b) } } }) #for downloading the dataset datasetInput1<- reactive({ if(input$Choice==FALSE){ data("iris") data= iris data1<- data.frame(data) } else { file1=input$file if (is.null(file1)){return()} data = read.table(file = file1$datapath,sep = ",",header = TRUE) if (is.null(data())){return()} data1<-data.frame(data) } }) output$dataset<-downloadHandler( filename = function(){ paste("dataset",".csv",sep = "") }, content = function(file){write.csv(datasetInput1(),file,row.names = FALSE)} ) plotInput1<-reactive({ if (input$Choice==FALSE) { data("iris") data= iris } else{ file1=input$file if (is.null(file1)){return()} data = read.table(file = file1$datapath,sep = ",",header = TRUE) if (is.null(data())){return()} } if(input$way=="one way"){ b<-ggplot(data = data, aes_string(x= input$var2,y= input$var1, fill = input$var2 ))+labs(y=input$var1,x=input$var2)+ geom_boxplot() print(b) } else { w= aggregate(data[,input$var1],by=list(k= data[,input$var2],l= data[,input$var3]),mean) b<-ggplot(data= w, aes_string(x= w$k, y= w$x,fill= w$l ))+labs(y=input$var1,x=input$var2,fill= input$var3 )+theme(legend.position = "top")+geom_line(aes_string(group= w$l))+ geom_point() print(b) } }) output$plot<-downloadHandler( filename = function() { paste("PLOT", '.png', sep='') }, content = function(file) { ggsave(file,plotInput1()) })
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