Yeah I thought of putting the entire code but I feel it is like asking the solution itself. However I have added the repex below

I have a dataframe df that has 4 columns(ColA, ColB, ColC and ColD).

ColA and ColB are factors and rest are numeric

I am putting 1 filter called "Variables" as shown below in the dashboard. Now I am planning to make some reactive inputs. Like if I select ColA from the 1st Filter, now 2nd filter should pop up called "Level" with all the values of ColA. Similarly for ColB. I tried with the below where I have incorporated for loop (Below code is only a part of the entire code set). What wrong I am doing here

```
---
title: "Untitled"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: scroll
runtime: shiny
source_code: embed
theme: cosmo
---
```{r setup, include=FALSE}
library(flexdashboard)
library(magrittr)
library(ggplot2)
library(cowplot)
library(tidyverse)
library(dplyr)
library(tidyr)
```
```{r}
df <- structure(list(ColA = c("gf", "dfg", "er", "gfs", "fdg", "sdf",
"er", "dgh", "dfg", "sfdg", "jyfj", "asgfg", "jgh", "ghjhg",
"ghj", "gjgj", "dgrert", "tyew", "ewt", "tyu", "hgj", "hjghj",
"dsgdg", "yt", "ryuy", "tyutyu", "uiuy", "yoiy", "ret", "e",
"dsgdfg", "hgdhg", "gfdg", "dghgd", "hdsger", "gdfgd", "gt",
"fdgd", "sgdf", "gdfsh", "sdfh", "dfh", "dgsh", "fg", "dds",
"gh", "rth"), ColB = c("A", "A", "A", "A", "A", "A", "A", "A",
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B",
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B"
), ColC = 1:47, ColD = 2:48), class = "data.frame", row.names = c(NA,
-47L))
df <- as.data.frame(df)
df <- as.data.frame(unclass(df))
# percentage of count for categorical univariate ------------------------
p1 <- list()
p <- list()
bs <- names(Filter(is.factor, df))
for(i in bs)
{
p1[[i]] <- as.data.frame(round(prop.table(table(df[,i]))*100,1))
do.call(rbind,p1) %>% as.data.frame()
}
# mean for numeric variable univariate ------------------------------------
bs1 <- c("mean","median","standard_deviation")
p2 <- df %>% summarise_if(is.numeric, list(mean=mean), na.rm = TRUE)
p2 <- round(p2,1)
p2 <- stack(p2)
p2$ind <- as.character(p2$ind)
p2 <- p2 %>% extract(ind, into = c("Measurement", "stat"), "(.*)_([^_]+)$")
# median for numeric variable univariate ------------------------------------
p3 <- df %>% summarise_if(is.numeric, list(median=median), na.rm = TRUE)
p3 <- round(p3,1)
p3 <- stack(p3)
p3$ind <- as.character(p3$ind)
p3 <- p3 %>% extract(ind, into = c("Measurement", "stat"), "(.*)_([^_]+)$")
# sd for numeric variable univariate ------------------------------------
p4 <- df %>% summarise_if(is.numeric, list(sd=sd), na.rm = TRUE)
p4 <- round(p4,1)
p4 <- stack(p4)
p4$ind <- as.character(p4$ind)
p4 <- p4 %>% extract(ind, into = c("Measurement", "stat"), "(.*)_([^_]+)$")
# Data Distribution -------------------------------------------------------
df <- as.data.frame(df)
p10 <- list()
fg <- list()
zs <- names(Filter(is.numeric, df))
dis <- function(data,variable)
{
fg <- qplot(variable, data = data, geom = "density")
fg
}
# Box plot -------------------------------------------------------
fg1 <- list()
box_plot <- function(data,variable)
{
fg1 <- ggplot(data = data, aes(x = "", y = variable)) + geom_boxplot()
fg1
}
p11 <- list()
ps <- names(Filter(is.numeric, df))
# Bi Variate --------------------------------------------------------------
d1 <- stack(lapply(df, class))[2:1] #df should be dataframe
s1 <- as.data.frame(combn(names(df),2,paste,collapse="&"))
colnames(s1) <- "d"
s1$d1 <- sub("\\&.*", "", s1$d)
s1$d2 <- sapply(strsplit(s1$d,"\\&"),'[',2)
s1$d3 <- d1$values[match(s1$d1,d1$ind)]
s1$d4 <- d1$values[match(s1$d2,d1$ind)]
s1$d3 <- replace(s1$d3,s1$d3=="integer","numeric")
s1$d4 <- replace(s1$d4,s1$d4=="integer","numeric")
s1$d5 <- paste(s1$d3,s1$d4,sep = "&")
# created a class called class1
class1 <- function(data,var) {
data[which(data$d==var),"d5"]}
# if the variable is equal to "numeric&numeric" and so on ----------------
fv <- list()
for(i in s1$d)
{
while (class1(s1,i)=="numeric&numeric") {
fv[i] <- paste(s1[which(s1$d==i),"d1"],s1[which(s1$d==i),"d2"],sep = "&")
break
}}
fv <- stack(fv)
fv$ind <- as.character(fv$ind)
fv$v1 <- sapply(strsplit(fv$ind,"\\&"),'[',1)
fv$v2 <- sapply(strsplit(fv$ind,"\\&"),'[',2)
p96 <- list()
# if the variable is equal to "factor&factor" and so on ----------------
fv1 <- list()
for(i in s1$d)
{
while (class1(s1,i)=="factor&factor") {
fv1[i] <- paste(s1[which(s1$d==i),"d1"],s1[which(s1$d==i),"d2"],sep = "&")
break
}}
fv1 <- stack(fv1)
fv1$ind <- as.character(fv1$ind)
fv1$v1 <- sapply(strsplit(fv1$ind,"\\&"),'[',1)
fv1$v2 <- sapply(strsplit(fv1$ind,"\\&"),'[',2)
fv1$v3 <- c("percentage")
io <- list()
asd <- list()
for(i in 1:nrow(fv1))
{ io[[i]] <- tapply(df[,fv1$v1[i]], df[,fv1$v2[i]], FUN = function(x) length(unique(x)))
}
for(i in 1:nrow(fv1))
{ asd[[i]] <- stack(unlist(io[i]))
colnames(asd[[i]]) <- c(fv1$v1[i],fv1$v2[i])
asd[[i]] <- data.frame(asd[[i]]) }
for(i in 1:nrow(fv1))
{asd[[i]]$percentage <- round((asd[[i]][1]/sum(asd[[i]][1]))*100,1)}
p97 <- list()
# Filtering code ----------------------------------------------------------
theNames <- names(Filter(is.factor,df))
MyList <- vector(mode = "list")
for(i in theNames){
MyList[[i]] <- levels(df[,i])
}
```
Summary
=================
Inputs {.sidebar} {data-width=140}
-----------------------------------------------------------------------
```{r}
h6(selectInput("se1","Variables",choices = c("",names(filter(df))),width = 150))
w <- list()
output$filter2 <- renderUI({
sa <- names(Filter(is.factor,df))
for (i in sa)
{
if (input$se1 == i) {
label = "Levels"
h6(selectInput("b",label,choices = c("",levels(factor(df[,i]))),width = 150))
}}
})
uiOutput("filter2")
```
Column {data-width=650}
-----------------------------------------------------------------------
### Chart A
```{r}
```
Column {data-width=350}
-----------------------------------------------------------------------
### Chart B
```{r}
```
### Chart C
```{r}
```
```