# Part of code within listings package is cut off below the page (Float to large) when compiling PDF

I have two problems:

1) I have a bit of longer code that I want to display in my document using the listings package, wrapped within a figure. It worked well so far, however after compiling the entire document into PDF, the code is cut off at some point and the remaining code is not displayed on any page within the document (see picture below). This was not a problem in the beginning. I also get the warning:

LaTeX Warning: Float too large for page by 970.93338pt on input line 181.

Maybe a solution would be to adjust the textheight somehow? Not sure how to do that in this example. I will provide a MWE below (with a shortened code).

2) I want to frame the code within a box (for which I put the frame = single command in the preamble) but it does not show. Why is that?

\documentclass[12pt, a4paper]{article}
\usepackage[paper=a4paper,left=3cm,right=2.5cm,top=2.5cm,bottom=2.5cm]{geometry}
\usepackage[format=hang,font={small,it},labelfont={bf,it},labelsep=space]{caption}
\captionsetup[figure]{labelsep=space,justification=raggedright,singlelinecheck=off}
\usepackage{listings}
\lstset{
numbers=left,
numberstyle=\small,
numbersep=7pt,
basicstyle=\footnotesize\ttfamily,
breaklines=true,
postbreak=\mbox{\textcolor{red}{$\hookrightarrow$}\space},
frame = single}
\lstset{framextopmargin=50pt,frame=bottomline}
\usepackage{courier}

\begin{document}

\subsubsection*{Long-term Analysis: Paris Agreement - Code Example}
\noindent The following code example was used to conduct the \textbf{long-term analysis} of the Paris Agreement for the EURO STOXX 50 index. The analysis was executed using the software R . For the short-term analysis, the R-packages were used.
\bigskip
\\ \noindent Pre: Load necessary packages. Please ensure that R and all packages are up to date, as complications with old versions can sometimes ensue.
\begin{figure}[!htb]
\caption{R-code example: Long-term analysis \label{fig:Codelong}}
\begin{lstlisting}
library(tidyverse) # General data science package
library(readxl) # To import data from excel sheets
library(broom) # To create specific tidy regression output data frames
\end{lstlisting}

\noindent Import firm and market index data sets from an Excel Sheet called "Eurostoxx\_PA\_long.xlsx" which contains the time-series data for each stock and the index from December 2018. (Data in the Excel Sheet has been obtained from Thomson Reuters Eikon.)
\begin{lstlisting}
# Import all firm data into separate data frames (df) named firmname.data using readxl-package
# Example for ABInBev:
abi.data <- subset((read_excel("Eurostoxx_PA_long.xlsx")), select = c("Date", "ABInBev"), col_types = c("date", "numeric"))

# Import market index data into a df called mkt.data
mkt.data <- subset((read_excel("Eurostoxx_PA_long.xlsx")), select = c("Date", "EUROSTOXX50"), col_types = c("date", "numeric"))

# Create lists containing respective low- and high-carbon stocks
low_data <- list(MKT = mkt.data,
...)

high_data <- list(MKT = mkt.data,
ABInBev = abi.data,
...)

# Create a function to check NAs for all data frames at once
na <- function(x) {
any(is.na(x))
}
lapply(low_data, na)
lapply(high_data, na)

# Rename columns
new_col_name <- c("Date", "Close")
low_data_clean <- lapply(low_data, setNames, nm = new_col_name)
high_data_clean <- lapply(high_data, setNames, nm = new_col_name)

# Create a function that will calculate the daily returns for all df and adds the outcome to a new column called Return
return <- function(x) { x %>%
mutate(Return = (Close - lag(Close)) / lag(Close))
}
low_data.ret <- lapply(low_data_clean, return)
high_data.ret <- lapply(high_data_clean, return)

# Transfer cleaned/new market data into mkt.data_clean data frame
low_mkt.data_clean <- low_data.ret$MKT head(low_mkt.data_clean) # Transfer cleaned/new firm data into new list called firm.data_clean low_firm.data_clean <- low_data.ret[-1] # Add mkt.data to each firm data frame in the list as a new column low_data.mkt <- lapply(low_firm.data_clean, function(x) mutate(x, MktReturn = (low_mkt.data_clean$Return)))
high_mkt.data_clean <- high_data.ret$MKT high_firm.data_clean <- high_data.ret[-1] high_data.mkt <- lapply(high_firm.data_clean, function(x) mutate(x, MktReturn = (high_mkt.data_clean$Return))

# Delete the first row in all data frames since it does not contain any returns.
low_data.mkt_clean <- lapply(low_data.mkt, function(x) x[-1, ])
high_data.mkt_clean <- lapply(high_data.mkt, function(x) x[-1, ])

# First create a column for the 0/1 dummy variable
low_data.mkt_dum <- lapply(low_data.mkt_clean, function(x)
mutate(x, Dummy = (if_else(x$Date > "2015-12-14", 1, 0)))) # Then create another column where the MktReturn and the Dummy are multiplied low_data_final <- lapply(low_data.mkt_dum, function(x) mutate(x, MktRetDum = (x$MktReturn * x$Dummy))) high_data.mkt_dum <- lapply(high_data.mkt_clean, function(x) mutate(x, Dummy = (if_else(x$Date > "2015-12-14", 1, 0))))
high_data_final <- lapply(high_data.mkt_dum, function(x)
mutate(x, MktRetDum = (x$MktReturn * x$Dummy)))

# Run regression
low_regression <- lapply(low_data_final, function (x)
lm(x$Return ~ x$MktReturn + x$MktRetDum, data = low_data_final)) # Create tables used later on to display the output low_df <- lapply(low_regression, function(x) tidy(x)) high_regression <- lapply(high_data_final, function (x) lm(x$Return ~ x$MktReturn + x$MktRetDum, data = high_data_final))
# Create tables used later on to display the output
high_df <- lapply(high_regression, function(x)
tidy(x))

# Display regression output in a table that reports the intercept, beta and gamma values for each firm
low_table <- map_df(low_df, ~
.x %>%
mutate(std.error = str_c("(", round(std.error, 4), ")",
c("***", "**", "*", "")[findInterval((p.value/2),
c(0.01, 0.05, 0.1))+1]),
estimate = round(estimate, 4)) %>%
select(estimate, std.error) %>%
t %>%
as.data.frame %>%
rename_all(~ c("Intercept",  "Beta",    "Gamma")), .id = "Firm")
low_table
high_table <- map_df(high_df, ~
.x %>%
mutate(std.error = str_c("(", round(std.error, 4), ")",
c("***", "**", "*", "")[findInterval((p.value/2),
c(0.01, 0.05, 0.1))+1]),
estimate = round(estimate, 4)) %>%
select(estimate, std.error) %>%
t %>%
as.data.frame %>%
rename_all(~ c("Intercept",  "Beta",    "Gamma")), .id = "Firm")
high_table

\end{lstlisting}
\end{figure}

\end{document}

• Your code is not compileable. – AndréC Jul 27 at 7:05
• @AndréC sorry about that, I updated the code. Now it should compile (at least it does for me). – User2010S Jul 27 at 7:16

You cannot have page breaks in a figure environment, so don't place the listing in a figure. The lstlisting environment has a caption parameter that you can use to add the caption.

Regarding the frame, you first have frame=single, then you have a second \lstset with frame=bottomrule, meaning you only get a line at end of the listing. Remove the second \lstset.

Unrelated: If you find yourself always doing \noindent\bigskip\\, then you're doing things wrong. Load the parskip package and use an empty line to indicate a paragraph break.

In the code below I added the text you had between the two listings as a comment, but you can of course change that, and add a caption for the second part as well.

\documentclass[12pt, a4paper]{article}
\usepackage{xcolor} % this was missing
\usepackage{parskip}

\usepackage[paper=a4paper,left=3cm,right=2.5cm,top=2.5cm,bottom=2.5cm]{geometry}
\usepackage[format=hang,font={small,it},labelfont={bf,it},labelsep=space]{caption}
\captionsetup[figure]{labelsep=space,justification=raggedright,singlelinecheck=off}
\usepackage{listings}
\lstset{
numbers=left,
numberstyle=\small,
numbersep=7pt,
basicstyle=\footnotesize\ttfamily,
breaklines=true,
postbreak=\mbox{\textcolor{red}{$\hookrightarrow$}\space},
frame = tlrb,
}
%\lstset{framextopmargin=50pt,frame=bottomline}
\usepackage{courier}

\begin{document}

\subsubsection*{Long-term Analysis: Paris Agreement - Code Example}
The following code example was used to conduct the \textbf{long-term analysis} of the Paris Agreement for the EURO STOXX 50 index. The analysis was executed using the software R . For the short-term analysis, the R-packages were used.

Pre: Load necessary packages. Please ensure that R and all packages are up to date, as complications with old versions can sometimes ensue.

\begin{lstlisting}[caption={R-code example: Long-term analysis}, label=fig:Codelong]
library(tidyverse) # General data science package
library(readxl) # To import data from excel sheets
library(broom) # To create specific tidy regression output data frames

#Import firm and market index data sets from an Excel Sheet called
#"Eurostoxx_PA_long.xlsx" which contains the time-series data for each
#stock and the index from December 2018. (Data in the Excel Sheet has
#been obtained from Thomson Reuters Eikon.)

# Import all firm data into separate data frames (df) named firmname.data using readxl-package
# Example for ABInBev:
abi.data <- subset((read_excel("Eurostoxx_PA_long.xlsx")), select = c("Date", "ABInBev"), col_types = c("date", "numeric"))

# Import market index data into a df called mkt.data
mkt.data <- subset((read_excel("Eurostoxx_PA_long.xlsx")), select = c("Date", "EUROSTOXX50"), col_types = c("date", "numeric"))

# Create lists containing respective low- and high-carbon stocks
low_data <- list(MKT = mkt.data,
...)

high_data <- list(MKT = mkt.data,
ABInBev = abi.data,
...)

# Create a function to check NAs for all data frames at once
na <- function(x) {
any(is.na(x))
}
lapply(low_data, na)
lapply(high_data, na)

# Rename columns
new_col_name <- c("Date", "Close")
low_data_clean <- lapply(low_data, setNames, nm = new_col_name)
high_data_clean <- lapply(high_data, setNames, nm = new_col_name)

# Create a function that will calculate the daily returns for all df and adds the outcome to a new column called Return
return <- function(x) { x %>%
mutate(Return = (Close - lag(Close)) / lag(Close))
}
low_data.ret <- lapply(low_data_clean, return)
high_data.ret <- lapply(high_data_clean, return)

# Transfer cleaned/new market data into mkt.data_clean data frame
low_mkt.data_clean <- low_data.ret$MKT head(low_mkt.data_clean) # Transfer cleaned/new firm data into new list called firm.data_clean low_firm.data_clean <- low_data.ret[-1] # Add mkt.data to each firm data frame in the list as a new column low_data.mkt <- lapply(low_firm.data_clean, function(x) mutate(x, MktReturn = (low_mkt.data_clean$Return)))
high_mkt.data_clean <- high_data.ret$MKT high_firm.data_clean <- high_data.ret[-1] high_data.mkt <- lapply(high_firm.data_clean, function(x) mutate(x, MktReturn = (high_mkt.data_clean$Return))

# Delete the first row in all data frames since it does not contain any returns.
low_data.mkt_clean <- lapply(low_data.mkt, function(x) x[-1, ])
high_data.mkt_clean <- lapply(high_data.mkt, function(x) x[-1, ])

# First create a column for the 0/1 dummy variable
low_data.mkt_dum <- lapply(low_data.mkt_clean, function(x)
mutate(x, Dummy = (if_else(x$Date > "2015-12-14", 1, 0)))) # Then create another column where the MktReturn and the Dummy are multiplied low_data_final <- lapply(low_data.mkt_dum, function(x) mutate(x, MktRetDum = (x$MktReturn * x$Dummy))) high_data.mkt_dum <- lapply(high_data.mkt_clean, function(x) mutate(x, Dummy = (if_else(x$Date > "2015-12-14", 1, 0))))
high_data_final <- lapply(high_data.mkt_dum, function(x)
mutate(x, MktRetDum = (x$MktReturn * x$Dummy)))

# Run regression
low_regression <- lapply(low_data_final, function (x)
lm(x$Return ~ x$MktReturn + x$MktRetDum, data = low_data_final)) # Create tables used later on to display the output low_df <- lapply(low_regression, function(x) tidy(x)) high_regression <- lapply(high_data_final, function (x) lm(x$Return ~ x$MktReturn + x$MktRetDum, data = high_data_final))
# Create tables used later on to display the output
high_df <- lapply(high_regression, function(x)
tidy(x))

# Display regression output in a table that reports the intercept, beta and gamma values for each firm
low_table <- map_df(low_df, ~
.x %>%
mutate(std.error = str_c("(", round(std.error, 4), ")",
c("***", "**", "*", "")[findInterval((p.value/2),
c(0.01, 0.05, 0.1))+1]),
estimate = round(estimate, 4)) %>%
select(estimate, std.error) %>%
t %>%
as.data.frame %>%
rename_all(~ c("Intercept",  "Beta",    "Gamma")), .id = "Firm")
low_table
high_table <- map_df(high_df, ~
.x %>%
mutate(std.error = str_c("(", round(std.error, 4), ")",
c("***", "**", "*", "")[findInterval((p.value/2),
c(0.01, 0.05, 0.1))+1]),
estimate = round(estimate, 4)) %>%
select(estimate, std.error) %>%
t %>%
as.data.frame %>%
rename_all(~ c("Intercept",  "Beta",    "Gamma")), .id = "Firm")
high_table

\end{lstlisting}

\end{document}

• Awesome, thank you very much! – User2010S Jul 27 at 7:47
• any idea how to align the caption of the listing with the text to the left? – User2010S Jul 27 at 8:14
• @User2010S Copy the \captionsetup line you already have there, and change figure to lstlisting. – Torbjørn T. Jul 27 at 8:24
• thank you, that worked. – User2010S Jul 27 at 15:00