Listing All Variables (Column Names) in R Shiny's checkboxGroupInput - r

Question

I'm writing an R shiny application. I'm facing much trouble, particularly the checkboxGroupInput function. I'm hoping that I will be able to create a dynamic list that will automatically list down all columns except the first column, source_file$Date of a dataset named source_file, and I'm not entirely sure on it. Would greatly appreciate any help you can provide!
Sample dataset of source_file would look something like this:
Date
Index 1
Index 2
Index 3
Index 4
Index 5
2016-01-01
+5%
-2%
+5%
+10%
+12%
2016-01-08
+3%
+13%
-8%
-3%
+10%
2016-01-15
+2%
+11%
-3%
+4%
-15%
The end goal is that I hope the checkboxGroupInput function will be able to automatically read all columns starting from the second column (ignore Date). In this case, the check box would load up 5 options, Index 1 to Index 5. It should be replicable such that it can load any number of indexes depending on the data specified. I tried hard-coding each individual index in but it's definitely counter-intuitive and so frustrating to do.
tabPanel("Target Volatility Portfolio",
sidebarPanel(
tags$h3("Find an optimised portfolio to achieve maximum return for a given level of risk/volatility"),
tags$h4("Input:"),
checkboxGroupInput("portfolio_selection",
"Select Number of Indexes for Portfolio",
choices = list(#####please send help here#####)
Edits: Would appreciate if you could help me fix this.
I want to reference the output that comes from the checkbox into my global.R in this format. Basically, I want to use the selected variables to plot a graph. A selection of 2 variables will result in a graph plotting a graph related to the 2 variables, whereas a selection of 10 variables will create a plot involving all 10 variables. (I'm basically plotting the efficient market frontier of x number of stocks where x is the number of variables selected. Its a little hard to explain but I hope attaching the code can provide you some insight) The hashed line is what I need help fixing. Thank you!
plot_emf = function(n_points, target_vol, portfolio_selection)
{
first <- portfolio_selection[1]
last <- portfolio_selection[length(portfolio_selection)]
#######asset_returns = source_file[first:last]########
# Extract necessary parameters
n_assets = ncol(asset_returns)
n_obs = nrow(asset_returns)
n_years = n_obs / 52
# Initialize containers for holding return and vol simulations
return_vector = c()
vol_vector = c()
sharpe_vector = c()
for (i in 1:n_points)
{
# Generate random weights for n assets from uniform(0,1)
asset_weights = runif(n_assets, min = 0, max = 1)
normalization_ratio = sum(asset_weights)
# Asset weights need to add up to 100%
asset_weights = asset_weights / normalization_ratio
# print(asset_weights)
# print(asset_returns)
# Generate the portfolio return vector using these weights
random_portfolio_returns = emf_portfolio_returns(
asset_weights,
asset_returns)
# print(random_portfolio_returns)
# plot_returns_histogram(random_portfolio_returns$portfolio_returns)
cumulative_return = calculate_cumulative_return(random_portfolio_returns$portfolio_returns)
annualized_return = 100*((1 + cumulative_return/100)^(1/n_years) - 1)
annualized_vol = sd(random_portfolio_returns$portfolio_returns)*(52^0.5)
sharpe = annualized_return / annualized_vol
return_vector = append(return_vector, annualized_return)
vol_vector = append(vol_vector, annualized_vol)
sharpe_vector = append(sharpe_vector, sharpe)
#print(paste("Asset weights:",asset_weights))
#print(paste("Anualized return:",annualized_return))
#print(paste("Annualized vol:",annualized_vol))
}
g = ggplot(data = data.frame(vol_vector, return_vector, sharpe_vector),
aes(x = vol_vector, y = return_vector, color = sharpe_vector)) +
scale_color_gradient(low = "red", high = "blue", name = "Sharpe Ratio\n(Return/Risk)") +
ggtitle("Efficient Market Frontier") +
xlab("Annualized Vol (%)") +
ylab("Annualized Return (%)") +
theme(plot.title = element_text(hjust=0.5)) + geom_vline(xintercept=target_vol) +
geom_point()
print(g)
}

Answer 1

You can try something like the following which uses colnames() to extract the new choices, and then updates the checkboxGroupInput with updateCheckboxGroupInput():
server <- function(input, output, session) {
# Read the data once per session - this step might be better to
# put in a `global.R` file
source_file <- read.csv("source_file.csv")
# Column names we want to show - all except `Date`
opts <- setdiff(colnames(source_file), "Date")
# Update your checkboxGroupInput:
updateCheckboxGroupInput(
session, "portfolio_selection", choices = opts
)
# Rest of app after this point --------------------------------------
}