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index.tex
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% Options for packages loaded elsewhere
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%
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% Correct order of tables after \paragraph or \subparagraph
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% Allow footnotes in longtable head/foot
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\def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth\else\Gin@nat@width\fi}
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% Scale images if necessary, so that they will not overflow the page
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% using explicit options in \includegraphics[width, height, ...]{}
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% Set default figure placement to htbp
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\@ifpackageloaded{caption}{}{\usepackage{caption}}
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\@ifpackageloaded{float}{}{\usepackage{float}}
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\@ifpackageloaded{caption}{}{\usepackage{caption}}
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\ifLuaTeX
\usepackage{selnolig} % disable illegal ligatures
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\IfFileExists{bookmark.sty}{\usepackage{bookmark}}{\usepackage{hyperref}}
\IfFileExists{xurl.sty}{\usepackage{xurl}}{} % add URL line breaks if available
\urlstyle{same} % disable monospaced font for URLs
\hypersetup{
pdftitle={RwithSLING},
pdfauthor={Bo Burla},
colorlinks=true,
linkcolor={blue},
filecolor={Maroon},
citecolor={Blue},
urlcolor={Blue},
pdfcreator={LaTeX via pandoc}}
\title{RwithSLING}
\author{Bo Burla}
\date{5/13/2022}
\begin{document}
\maketitle
\ifdefined\Shaded\renewenvironment{Shaded}{\begin{tcolorbox}[frame hidden, sharp corners, borderline west={3pt}{0pt}{shadecolor}, breakable, boxrule=0pt, interior hidden, enhanced]}{\end{tcolorbox}}\fi
\renewcommand*\contentsname{Table of contents}
{
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}
\bookmarksetup{startatroot}
\hypertarget{welcome}{%
\chapter*{Welcome}\label{welcome}}
\addcontentsline{toc}{chapter}{Welcome}
This online book aims to provide an applied quick-start introduction
into the processing, management, and analysis of mass spectrometry
(MS)-based lipidomics datasets and associated study metadata using R.
The books is structured into two parts: (i) \textbf{Tutorials} introduce
a few basic but applied concepts of using R , and (ii) \textbf{Recipes}
provide workflows and sample code for data processing, and data analysis
and visualization tasks frequently used in the lab.
This book was and is mainly written during the RwithSLING workshop
sessions at SLING that started in 2022.
Feedback and contributions are very welcome!
\hypertarget{acknowledgements}{%
\section*{Acknowledgements}\label{acknowledgements}}
\addcontentsline{toc}{section}{Acknowledgements}
\bookmarksetup{startatroot}
\hypertarget{sec-intro}{%
\chapter{Introduction}\label{sec-intro}}
\hypertarget{prerequisites}{%
\section{Prerequisites}\label{prerequisites}}
Following software need to be installed on your computer to work with
the examples shown in this online book:
\begin{itemize}
\item
\texttt{R} Version 4.1 (or higher) \url{https://cloud.r-project.org/}.
Check your R version by running following command in your console:
\begin{Shaded}
\begin{Highlighting}[]
\FunctionTok{R.Version}\NormalTok{()}\SpecialCharTok{$}\NormalTok{version.string}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
#> [1] "R version 4.2.0 (2022-04-22 ucrt)"
\end{verbatim}
\item
\texttt{RStudio} Version 2022.02 or higher
\url{https://www.rstudio.com/products/rstudio/download/\#download}.
Check your \texttt{RStudio} version by either looking clicking
\emph{About RStudio} under the menu \emph{Help}, or by running
following command in your console
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{rstudioapi}\SpecialCharTok{::}\FunctionTok{versionInfo}\NormalTok{()}\SpecialCharTok{$}\NormalTok{version}
\end{Highlighting}
\end{Shaded}
\end{itemize}
Following software are only needed for specific chapters/examples:
\begin{itemize}
\tightlist
\item
\texttt{Git} \url{https://git-scm.com/downloads}
\item
For Windows: \texttt{Rtools}
\textless https://cran.r-project.org/bin/windows/Rtools/
\end{itemize}
\hypertarget{frequently-used-r-packages}{%
\section{Frequently used R packages}\label{frequently-used-r-packages}}
See also @ref(Installing R packages)
Following R packages will be often used in the given examples and it is
thus recommended to install them before starting with this book
\begin{itemize}
\tightlist
\item
\texttt{here}
\item
\texttt{tidyverse}(installs
\texttt{ggplot2,\ dplyr,\ tidyr,\ tibble,\ readr,\ forcats,\ stringr,\ purrr})
\item
\texttt{readxl}
\item
\texttt{remotes}
\end{itemize}
Run this in your R command line to install these packages:
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{pkg\_list }\OtherTok{\textless{}{-}} \FunctionTok{c}\NormalTok{(}\StringTok{"here"}\NormalTok{, }\StringTok{"tidyverse"}\NormalTok{, }\StringTok{"readxl"}\NormalTok{, }\StringTok{"remotes"}\NormalTok{)}
\FunctionTok{install.packages}\NormalTok{(pkg\_list)}
\end{Highlighting}
\end{Shaded}
\part{Tutorials}
\hypertarget{import-data-into-r}{%
\chapter{Import data into R}\label{import-data-into-r}}
\hypertarget{introduction}{%
\section{Introduction}\label{introduction}}
\begin{tcolorbox}[enhanced jigsaw, colframe=quarto-callout-note-color-frame, breakable, bottomrule=.15mm, left=2mm, arc=.35mm, leftrule=.75mm, rightrule=.15mm, toprule=.15mm, opacityback=0, colback=white]
\begin{minipage}[t]{5.5mm}
\textcolor{quarto-callout-note-color}{\faInfo}
\end{minipage}%
\begin{minipage}[t]{\textwidth - 5.5mm}
Verify the integrity of your data files and imported data - do not just
assume all is fine\end{minipage}%
\end{tcolorbox}
Data analysis projects often start by importing data from files. It is
easier to start with clearly structured, clean and consistent data
files:
\begin{itemize}
\tightlist
\item
Tidy data: \emph{observations} (e.g.~samples) as \emph{rows},
\emph{variables} (e.g.~compounds) as \emph{columns}
\item
First row contains column names
\item
No nested columns and rows
\item
No text before and after the table
\end{itemize}
We will be using \texttt{readr} (from tidyverse) and \texttt{readxl}
packages in this chapter:
\begin{Shaded}
\begin{Highlighting}[]
\FunctionTok{library}\NormalTok{(here)}
\FunctionTok{library}\NormalTok{(tidyverse)}
\FunctionTok{library}\NormalTok{(readxl)}
\end{Highlighting}
\end{Shaded}
\hypertarget{read-csv-files}{%
\section{Read CSV files}\label{read-csv-files}}
\textbf{Hint}: Inspect the summery provided by \texttt{read\_csv()} to
verify correct data types were assigned
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{d\_wide }\OtherTok{\textless{}{-}} \FunctionTok{read\_csv}\NormalTok{(}\AttributeTok{file =} \FunctionTok{here}\NormalTok{(}\StringTok{"data/Testdata\_Lipidomics\_flat\_wide\_V2.csv"}\NormalTok{), }
\AttributeTok{col\_names =} \ConstantTok{TRUE}\NormalTok{, }
\AttributeTok{trim\_ws =} \ConstantTok{TRUE}\NormalTok{)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
#> Rows: 215 Columns: 430
#> -- Column specification --------------------------------------------------------
#> Delimiter: ","
#> chr (3): DataFileme, SPLType, VialPosition
#> dbl (426): CE 14:0, CE 15:0, CE 16:0, CE 16:1, CE 16:2, CE 17:0, CE 17:1, C...
#> dttm (1): AcqTimeStamp
#>
#> i Use `spec()` to retrieve the full column specification for this data.
#> i Specify the column types or set `show_col_types = FALSE` to quiet this message.
\end{verbatim}
To remove white-spaces before and after text, set
\texttt{trim\_ws\ =\ TRUE}.
\hypertarget{read-excel-tables}{%
\section{Read Excel tables}\label{read-excel-tables}}
Importing Excel tables can be done via the package s \texttt{readxl}.
Note: you will need to define which sheet to import (e.g.~below
\texttt{Sheet1}).
\begin{Shaded}
\begin{Highlighting}[]
\FunctionTok{library}\NormalTok{(readxl)}
\NormalTok{d\_wide }\OtherTok{\textless{}{-}} \FunctionTok{read\_xlsx}\NormalTok{(}\AttributeTok{path =}\NormalTok{ here}\SpecialCharTok{::}\FunctionTok{here}\NormalTok{(}\StringTok{"data/Testdata\_Lipidomics\_flat\_wide\_V2.xlsx"}\NormalTok{),}
\AttributeTok{sheet =} \StringTok{"Sheet1"}\NormalTok{,}
\AttributeTok{trim\_ws =} \ConstantTok{TRUE}\NormalTok{, }
\AttributeTok{na =} \FunctionTok{c}\NormalTok{(}\StringTok{"ND"}\NormalTok{))}
\FunctionTok{print}\NormalTok{(d\_wide)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
#> # A tibble: 215 x 432
#> DataFileName AcqTimeStamp SampleType VialPosition `CE 14:0` `CE 15:0`
#> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 001_EQC_TQC prerun 0~ 2018-04-12T~ EQC Vial 2 1532 515
#> 2 002_EQC_TQC prerun 0~ 2018-04-12T~ EQC Vial 2 1029 911
#> 3 003_EQC_TQC prerun 0~ 2018-04-12T~ EQC Vial 2 685 649
#> 4 004_EQC_TQC prerun 0~ 2018-04-12T~ EQC Vial 2 1283 576
#> 5 005_EQC_TQC prerun 0~ 2018-04-12T~ EQC Vial 2 946 732
#> 6 006_EBLK_Extracted B~ 2018-04-12T~ PBLK P1-A1 132 NA
#> # ... with 209 more rows, and 426 more variables: `CE 16:0` <dbl>,
#> # `CE 16:1` <dbl>, `CE 16:2` <dbl>, `CE 17:0` <chr>, `CE 17:1` <dbl>,
#> # `CE 18:0` <dbl>, `CE 18:1` <dbl>, `CE 18:1 d7 (ISTD)` <dbl>, ...
\end{verbatim}
\begin{tcolorbox}[enhanced jigsaw, coltitle=black, breakable, bottomrule=.15mm, left=2mm, arc=.35mm, colbacktitle=quarto-callout-note-color!10!white, leftrule=.75mm, colframe=quarto-callout-note-color-frame, colback=white, title=\textcolor{quarto-callout-note-color}{\faInfo}\hspace{0.5em}{Verify integrity of imported data}, titlerule=0mm, opacitybacktitle=0.6, rightrule=.15mm, bottomtitle=1mm, toptitle=1mm, opacityback=0, toprule=.15mm]
\begin{itemize}
\tightlist
\item
Do not assume imported data fine without having reason to do so
\item
Ensure data types were correctly assigned
\item
Ensure missing values are correctly handled
\end{itemize}
\end{tcolorbox}
\hypertarget{data-types}{%
\section{Data Types}\label{data-types}}
\begin{itemize}
\tightlist
\item
\textbf{Note:} \texttt{read\_csv()} or \texttt{read\_xlsx()} will by
default \textbf{\emph{guess}} the data type of each column.
\item
Typical data types are numbers (\texttt{dbl}), text (\texttt{chr}),
logical (\texttt{lgl}) and factor (\texttt{fct})
\item
You can define the column types already at import (see
\texttt{?read\_csv}).
\end{itemize}
\hypertarget{missing-values}{%
\section{Missing Values}\label{missing-values}}
\begin{itemize}
\tightlist
\item
Data files often contain missing values, i.e.~as empty cells.
\item
\texttt{read\_csv()} or \texttt{read\_xlsx()} assign empty/missing
values as \texttt{NA} (\emph{Not Available}).
\item
Missing values will in general not affect to data type guessed by
\texttt{read\_csv()}
\item
Exception: Column contains only massing values, in this case it will
be of type \texttt{logical}
\item
However, if data files contain specific text values for missing values
(e.g.,``\emph{ND''},''\emph{LOD''}, \emph{``na'')}, then
\texttt{read\_csv()} or \texttt{read\_xlsx()} assign the column by
default as character (text)
\end{itemize}
Results from e.g.~clinical chemistry, frequently indicate not available
data with a short text such as \emph{``clotted''}, \emph{``hemolytic''},
\emph{``no received''} to explain why a value was not reported. What
happens when we import such a file using \texttt{read\_csv} with default
parameters:
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{data\_file\_path }\OtherTok{\textless{}{-}}\NormalTok{ here}\SpecialCharTok{::}\FunctionTok{here}\NormalTok{(}\StringTok{"data/Testdata\_Lipidomics\_flat\_wide\_with\_differentNA\_V2.csv"}\NormalTok{)}
\NormalTok{d\_wide }\OtherTok{\textless{}{-}}\NormalTok{ readr}\SpecialCharTok{::}\FunctionTok{read\_csv}\NormalTok{(data\_file\_path, }\AttributeTok{trim\_ws =} \ConstantTok{TRUE}\NormalTok{)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
#> Rows: 215 Columns: 432
#> -- Column specification --------------------------------------------------------
#> Delimiter: ","
#> chr (109): DataFileNDme, SampleType, VialPosition, CE 15:0, CE 16:0, CE 17:...
#> dbl (320): CE 14:0, CE 16:1, CE 17:1, CE 18:0, CE 18:1, CE 18:1 d7 (ISTD), ...
#> lgl (2): CE 16:2, LPC 16:0
#> dttm (1): AcqTimeStamp
#>
#> i Use `spec()` to retrieve the full column specification for this data.
#> i Specify the column types or set `show_col_types = FALSE` to quiet this message.
\end{verbatim}
\begin{itemize}
\tightlist
\item
\texttt{read\_csv()} was mislead by the presence of unknown text
values and guessed the data types wrongly as
\texttt{\textless{}char\textgreater{}} for some columns.
\item
We also have two columns of the type
\texttt{\textless{}lgl\textgreater{}}, which stand for logical
(\texttt{TRUE}/\texttt{FALSE}). In this case all peak areas of CE 16:2
were missing, and \texttt{read\_csv} has no way to guess the intended
data type. In which case it chooses
\texttt{\textless{}lgl\textgreater{}} will be chosen as default.
\item
We will not be able to perform any calculations for columns that are
text or logical.
\item
We need to explicitly tell R which values to interpret as missing
values (\texttt{NA}).
\item
We can do by providing the \texttt{na} parameter a list of texts that
should be interpreted as \texttt{NA} :
\end{itemize}
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{data\_file\_path }\OtherTok{\textless{}{-}}\NormalTok{ here}\SpecialCharTok{::}\FunctionTok{here}\NormalTok{(}\StringTok{"data/Testdata\_Lipidomics\_flat\_wide\_with\_differentNA\_V2.csv"}\NormalTok{)}
\NormalTok{d\_wide }\OtherTok{\textless{}{-}}\NormalTok{ readr}\SpecialCharTok{::}\FunctionTok{read\_csv}\NormalTok{(data\_file\_path, }
\AttributeTok{trim\_ws =} \ConstantTok{TRUE}\NormalTok{, }
\AttributeTok{na =} \FunctionTok{c}\NormalTok{(}\StringTok{"ND"}\NormalTok{, }\StringTok{"LOD"}\NormalTok{))}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
#> Warning: One or more parsing issues, see `problems()` for details
\end{verbatim}
\begin{verbatim}
#> Rows: 215 Columns: 432
#> -- Column specification --------------------------------------------------------
#> Delimiter: ","
#> chr (3): DataFileNDme, SampleType, VialPosition
#> dbl (425): CE 14:0, CE 15:0, CE 16:0, CE 16:1, CE 17:0, CE 17:1, CE 18:0, C...
#> lgl (3): CE 16:2, Cer d18:2/24:0, LPC 16:0
#> dttm (1): AcqTimeStamp
#>
#> i Use `spec()` to retrieve the full column specification for this data.
#> i Specify the column types or set `show_col_types = FALSE` to quiet this message.
\end{verbatim}
\begin{Shaded}
\begin{Highlighting}[]
\FunctionTok{print}\NormalTok{(d\_wide)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
#> # A tibble: 215 x 432
#> DataFileNDme AcqTimeStamp SampleType VialPosition `CE 14:0` `CE 15:0`
#> <chr> <dttm> <chr> <chr> <dbl> <dbl>
#> 1 001_EQC_TQC p~ 2018-04-12 18:28:00 EQC Vial 2 1532 515
#> 2 002_EQC_TQC p~ 2018-04-12 18:39:00 EQC Vial 2 1029 NA
#> 3 003_EQC_TQC p~ 2018-04-12 18:51:00 EQC Vial 2 685 649
#> 4 004_EQC_TQC p~ 2018-04-12 19:02:00 EQC Vial 2 1283 576
#> 5 005_EQC_TQC p~ 2018-04-12 19:13:00 EQC Vial 2 946 732
#> 6 006_EBLK_Extr~ 2018-04-12 19:25:00 PBLK P1-A1 132 NA
#> # ... with 209 more rows, and 426 more variables: `CE 16:0` <dbl>,
#> # `CE 16:1` <dbl>, `CE 16:2` <lgl>, `CE 17:0` <dbl>, `CE 17:1` <dbl>,
#> # `CE 18:0` <dbl>, `CE 18:1` <dbl>, `CE 18:1 d7 (ISTD)` <dbl>, ...
\end{verbatim}
\hypertarget{other-considerations}{%
\section{Other Considerations}\label{other-considerations}}
\begin{itemize}
\tightlist
\item
White spaces or double spaces, White spaces before or after text
fields can (and often will) cause issues downstream, therefore I
suggest to trim whitespaces, e.g.~by setting
\texttt{trim\_ws\ =\ TRUE}
\end{itemize}
\hypertarget{tables-in-r}{%
\chapter{Tables in R}\label{tables-in-r}}
\hypertarget{introduction-1}{%
\section{Introduction}\label{introduction-1}}
In this chapter we will look at how data is used and managed within R.
\hypertarget{prerequisites-1}{%
\subsection{Prerequisites}\label{prerequisites-1}}
\begin{Shaded}
\begin{Highlighting}[]
\FunctionTok{library}\NormalTok{(here)}
\FunctionTok{library}\NormalTok{(tidyverse)}
\end{Highlighting}
\end{Shaded}
\hypertarget{base}{%
\subsubsection{Base}\label{base}}
\begin{Shaded}
\begin{Highlighting}[]
\FunctionTok{plot}\NormalTok{(cars)}
\end{Highlighting}
\end{Shaded}
\begin{figure}[H]
{\centering \includegraphics[width=18.75in,height=\textheight]{./tables_files/figure-pdf/unnamed-chunk-3-1.pdf}
}
\end{figure}
\hypertarget{ggplot2}{%
\subsubsection{ggplot2}\label{ggplot2}}
\begin{Shaded}
\begin{Highlighting}[]
\FunctionTok{ggplot}\NormalTok{(}\AttributeTok{data =}\NormalTok{ cars, }\FunctionTok{aes}\NormalTok{(}\AttributeTok{x =}\NormalTok{ speed, }\AttributeTok{y =}\NormalTok{ dist)) }\SpecialCharTok{+} \FunctionTok{geom\_point}\NormalTok{()}
\end{Highlighting}
\end{Shaded}
\begin{figure}[H]
{\centering \includegraphics[width=18.75in,height=\textheight]{./tables_files/figure-pdf/unnamed-chunk-4-1.pdf}
}
\end{figure}
\hypertarget{important-r-data-formats}{%
\section{Important R data formats}\label{important-r-data-formats}}
\begin{longtable}[]{@{}
>{\raggedright\arraybackslash}p{(\columnwidth - 4\tabcolsep) * \real{0.0686}}
>{\raggedright\arraybackslash}p{(\columnwidth - 4\tabcolsep) * \real{0.3824}}
>{\raggedright\arraybackslash}p{(\columnwidth - 4\tabcolsep) * \real{0.5490}}@{}}
\toprule()
\begin{minipage}[b]{\linewidth}\raggedright
Type
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
Description
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
Example(s)
\end{minipage} \\
\midrule()
\endhead
Vector & Vector (series) of values. All values are of the same data type
(see below) &
\texttt{c(1,2,3,4)c("TQC",\ "BQC",\ "SPL")c(TRUE,\ FALSE)} \\
Matrix & 2-dimensional set of values. All values are of the same data
type &
\texttt{matrix(data\ =\ c(1:2),nrow\ =\ 2,ncol\ =\ 3)matrix(data\ =\ c("BQC",\ "SPL"),nrow\ =\ 2,ncol\ =\ 3)} \\
\begin{minipage}[t]{\linewidth}\raggedright
Data Frame\\
Tibble\strut
\end{minipage} & Table with columns that can have different data types &
\texttt{tibble(\ \ \ No\ =\ c(1,2,3),\ \ \ \ Sample\ =\ c("SPL\_1",\ "SPL\_2",\ "SPL\_3"))} \\
List & Series of objects &
\texttt{list(\ \ \ Studysite\ =\ c("NUH",\ "SGH"),\ \ \ Cohort\ =\ \ \ \ \ \ tibble(No\ =\ c(1,2),\ \ \ \ \ \ \ \ \ \ \ \ \ Size\ =\ c(110,332)\ \ \ ))} \\
\bottomrule()
\end{longtable}
\hypertarget{important-r-data-types}{%
\section{Important R data types}\label{important-r-data-types}}
\begin{longtable}[]{@{}
>{\raggedright\arraybackslash}p{(\columnwidth - 6\tabcolsep) * \real{0.2400}}
>{\raggedright\arraybackslash}p{(\columnwidth - 6\tabcolsep) * \real{0.1333}}
>{\raggedright\arraybackslash}p{(\columnwidth - 6\tabcolsep) * \real{0.3333}}
>{\raggedright\arraybackslash}p{(\columnwidth - 6\tabcolsep) * \real{0.2933}}@{}}
\toprule()
\begin{minipage}[b]{\linewidth}\raggedright
Type
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
Short
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
Description
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
Example(s)
\end{minipage} \\
\midrule()
\endhead
Numeric (double) & \texttt{\textless{}dbl\textgreater{}} & Floating
point number & \texttt{3.14159} \\
Character & \texttt{\textless{}char\textgreater{}} & Character (text)
string & \texttt{"S1P\ d18:1",\ "BQC"} \\
Logical & \texttt{\textless{}lgl\textgreater{}} & True or False, 1 or 0
& \texttt{TRUE;\ FALSE} \\
Integer & \texttt{\textless{}int\textgreater{}} & Number without digits
& \texttt{3L;\ 7011L;\ -13L} \\
Factor & \texttt{\textless{}fct\textgreater{}} & Categorical data &
\texttt{BQC;\ TQC;\ SPL} \\
\bottomrule()
\end{longtable}
Moreover there are data and time types, i.e.~date
\texttt{\textless{}dt\textgreater{}}, time
\texttt{\textless{}tm\textgreater{}} and datetime
\texttt{\textless{}dttm\textgreater{}}
You can print tables in the console or in R notebook (.Rmd), which will
also show the column types:
\hypertarget{select-and-plot}{%
\chapter{Select and plot}\label{select-and-plot}}
\hypertarget{introduction-2}{%
\section{Introduction}\label{introduction-2}}
In this chapter we will look at how read data from files into R \#\#\#
Prerequisites
\begin{Shaded}
\begin{Highlighting}[]
\FunctionTok{library}\NormalTok{(here)}
\FunctionTok{library}\NormalTok{(tidyverse)}
\FunctionTok{library}\NormalTok{(SLINGtools)}
\end{Highlighting}
\end{Shaded}
\hypertarget{import-an-agilent-masshunter-csv-file}{%
\section{Import an Agilent MassHunter CSV
file}\label{import-an-agilent-masshunter-csv-file}}
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{data\_file\_path }\OtherTok{\textless{}{-}} \FunctionTok{here}\NormalTok{(}\StringTok{"data/Testdata\_Lipidomics\_MHQuant\_Detailed\_V2.csv"}\NormalTok{)}
\NormalTok{d\_orig }\OtherTok{\textless{}{-}} \FunctionTok{read\_MassHunterCSV}\NormalTok{(data\_file\_path)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
#> Reading 'Testdata_Lipidomics_MHQuant_Detailed_V2.csv' ...
#>
indexing Testdata_Lipidomics_MHQuant_Detailed_V2.csv [======] 2.15GB/s, eta: 0s
Imported 215 samples with 428 transitions
\end{verbatim}
\begin{Shaded}
\begin{Highlighting}[]
\FunctionTok{print}\NormalTok{(d\_orig)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
#> # A tibble: 92,020 x 14
#> DataFileName DataName SampleType AcqTimeStamp VialPosition Feature
#> <chr> <chr> <chr> <dttm> <chr> <chr>
#> 1 001_EQC_TQC prer~ 001_EQC~ EQC 2018-04-12 18:28:00 Vial 2 CE 14:0
#> 2 001_EQC_TQC prer~ 001_EQC~ EQC 2018-04-12 18:28:00 Vial 2 CE 15:0
#> 3 001_EQC_TQC prer~ 001_EQC~ EQC 2018-04-12 18:28:00 Vial 2 CE 16:0
#> 4 001_EQC_TQC prer~ 001_EQC~ EQC 2018-04-12 18:28:00 Vial 2 CE 16:1
#> 5 001_EQC_TQC prer~ 001_EQC~ EQC 2018-04-12 18:28:00 Vial 2 CE 16:2
#> 6 001_EQC_TQC prer~ 001_EQC~ EQC 2018-04-12 18:28:00 Vial 2 CE 17:0
#> # ... with 92,014 more rows, and 8 more variables: IonPolarity <fct>,
#> # PrecursorMZ <dbl>, ProductMZ <dbl>, CollisionEnergy <dbl>, RT <dbl>,
#> # Area <dbl>, FWHM <dbl>, MI <lgl>
\end{verbatim}
\hypertarget{select-remove-re-order-and-rename-columns}{%
\section{Select, remove, re-order, and rename
columns}\label{select-remove-re-order-and-rename-columns}}
Use the function \texttt{select()}
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{d }\OtherTok{\textless{}{-}}\NormalTok{ d\_orig }\SpecialCharTok{|\textgreater{}}
\NormalTok{ dplyr}\SpecialCharTok{::}\FunctionTok{select}\NormalTok{(}\AttributeTok{AnalysisID =} \StringTok{"DataFileName"}\NormalTok{, }
\AttributeTok{QCtype =}\NormalTok{ SampleType,}
\AttributeTok{Compound =}\NormalTok{ Feature, }
\AttributeTok{Intensity =}\NormalTok{ Area,}
\NormalTok{ RT,}
\NormalTok{ PrecursorMZ)}
\FunctionTok{print}\NormalTok{(d)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
#> # A tibble: 92,020 x 6
#> AnalysisID QCtype Compound Intensity RT PrecursorMZ
#> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 001_EQC_TQC prerun 01.d EQC CE 14:0 1532 6.98 615.
#> 2 001_EQC_TQC prerun 01.d EQC CE 15:0 515 7.13 629.
#> 3 001_EQC_TQC prerun 01.d EQC CE 16:0 127953 7.16 643.
#> 4 001_EQC_TQC prerun 01.d EQC CE 16:1 40374 7.00 641.
#> 5 001_EQC_TQC prerun 01.d EQC CE 16:2 1340 6.82 639.
#> 6 001_EQC_TQC prerun 01.d EQC CE 17:0 7227 7.22 657.
#> # ... with 92,014 more rows
\end{verbatim}
\hypertarget{filter-rows}{%
\section{Filter rows}\label{filter-rows}}
Use the function \texttt{filter()}.
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{d }\SpecialCharTok{|\textgreater{}} \FunctionTok{filter}\NormalTok{(AnalysisID }\SpecialCharTok{==} \StringTok{"149\_BQC\_PQC17.d "}\NormalTok{)}
\NormalTok{d }\SpecialCharTok{|\textgreater{}} \FunctionTok{filter}\NormalTok{(AnalysisID }\SpecialCharTok{==} \StringTok{"149{-}bQC\_PQC17.d"}\NormalTok{, Compound }\SpecialCharTok{==} \StringTok{"TG 48:1 [{-}18:1]"}\NormalTok{)}
\NormalTok{d }\SpecialCharTok{|\textgreater{}} \FunctionTok{filter}\NormalTok{(QCtype }\SpecialCharTok{==} \StringTok{"BQC"}\NormalTok{)}
\NormalTok{d }\SpecialCharTok{|\textgreater{}} \FunctionTok{filter}\NormalTok{(QCtype }\SpecialCharTok{==} \StringTok{"BQC"} \SpecialCharTok{|}\NormalTok{ QCtype }\SpecialCharTok{==} \StringTok{"TQC"}\NormalTok{ )}
\NormalTok{d }\SpecialCharTok{|\textgreater{}} \FunctionTok{filter}\NormalTok{(QCtype }\SpecialCharTok{==} \StringTok{"BQC"} \SpecialCharTok{\&}\NormalTok{ QCtype }\SpecialCharTok{==} \StringTok{"TQC"}\NormalTok{ )}
\NormalTok{d }\SpecialCharTok{|\textgreater{}} \FunctionTok{filter}\NormalTok{(QCtype }\SpecialCharTok{\%in\%} \FunctionTok{c}\NormalTok{(}\StringTok{"BQC"}\NormalTok{, }\StringTok{"TQC"}\NormalTok{))}
\end{Highlighting}
\end{Shaded}
\hypertarget{lets-finally-plot}{%
\section{Let's finally plot}\label{lets-finally-plot}}
\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# For this we take one single sample, let\textquotesingle{}s say a BQC}
\NormalTok{d\_plot }\OtherTok{\textless{}{-}}\NormalTok{ d }\SpecialCharTok{|\textgreater{}}
\FunctionTok{filter}\NormalTok{(AnalysisID }\SpecialCharTok{==} \StringTok{"066\_BQC\_PQC07.d"}\NormalTok{)}
\CommentTok{\# Base R}
\FunctionTok{plot}\NormalTok{(}\AttributeTok{x =}\NormalTok{ d\_plot}\SpecialCharTok{$}\NormalTok{PrecursorMZ, }\AttributeTok{y =}\NormalTok{ d\_plot}\SpecialCharTok{$}\NormalTok{RT)}
\CommentTok{\# ggplot}
\FunctionTok{ggplot}\NormalTok{(d\_plot, }\FunctionTok{aes}\NormalTok{(}\AttributeTok{x =}\NormalTok{ PrecursorMZ, }\AttributeTok{y =}\NormalTok{ RT)) }\SpecialCharTok{+}
\FunctionTok{geom\_point}\NormalTok{(}\AttributeTok{size =} \DecValTok{2}\NormalTok{, }\AttributeTok{color =} \StringTok{"blue"}\NormalTok{)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
#> Warning: Removed 2 rows containing missing values (geom_point).
\end{verbatim}
\begin{figure}[H]
{\centering \includegraphics[width=18.75in,height=\textheight]{./datawrangling_files/figure-pdf/unnamed-chunk-4-1.pdf}
}
\end{figure}
\begin{figure}[H]
{\centering \includegraphics[width=18.75in,height=\textheight]{./datawrangling_files/figure-pdf/unnamed-chunk-4-2.pdf}
}
\end{figure}
\hypertarget{split-column-to-get-lipid-class}{%
\section{Split column to get lipid
class}\label{split-column-to-get-lipid-class}}
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{d\_plot\_wclass }\OtherTok{\textless{}{-}}\NormalTok{ d\_plot }\SpecialCharTok{|\textgreater{}}
\FunctionTok{separate}\NormalTok{(}
\AttributeTok{col =}\NormalTok{ Compound,}
\AttributeTok{into =} \FunctionTok{c}\NormalTok{(}\StringTok{"lipidclass"}\NormalTok{, }\StringTok{"chain"}\NormalTok{), }
\AttributeTok{sep =} \StringTok{" "}\NormalTok{,}
\AttributeTok{extra =} \StringTok{"merge"}\NormalTok{, }
\AttributeTok{remove =} \ConstantTok{FALSE}\NormalTok{) }
\NormalTok{d\_plot\_wclass}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
#> # A tibble: 428 x 8
#> AnalysisID QCtype Compound lipidclass chain Intensity RT PrecursorMZ
#> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 066_BQC_PQC07.d BQC CE 14:0 CE 14:0 1152 6.98 615.
#> 2 066_BQC_PQC07.d BQC CE 15:0 CE 15:0 984 7.06 629.
#> 3 066_BQC_PQC07.d BQC CE 16:0 CE 16:0 93268 7.16 643.
#> 4 066_BQC_PQC07.d BQC CE 16:1 CE 16:1 51054 7.01 641.
#> 5 066_BQC_PQC07.d BQC CE 16:2 CE 16:2 1870 6.83 639.
#> 6 066_BQC_PQC07.d BQC CE 17:0 CE 17:0 9050 7.22 657.
#> # ... with 422 more rows
\end{verbatim}
\hypertarget{now-lets-plot-again}{%
\section{Now let's plot again}\label{now-lets-plot-again}}
\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# ggplot}
\FunctionTok{ggplot}\NormalTok{(d\_plot\_wclass, }\FunctionTok{aes}\NormalTok{(}\AttributeTok{x =}\NormalTok{ PrecursorMZ, }\AttributeTok{y =}\NormalTok{ RT, }\AttributeTok{color =}\NormalTok{ lipidclass)) }\SpecialCharTok{+}
\FunctionTok{geom\_point}\NormalTok{(}\AttributeTok{size =} \DecValTok{2}\NormalTok{)}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
#> Warning: Removed 2 rows containing missing values (geom_point).
\end{verbatim}
\begin{figure}[H]
{\centering \includegraphics[width=18.75in,height=\textheight]{./datawrangling_files/figure-pdf/unnamed-chunk-5-1.pdf}
}
\end{figure}
\hypertarget{another-time}{%
\section{\ldots{} another time}\label{another-time}}
\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# ggplot}
\FunctionTok{ggplot}\NormalTok{(d\_plot\_wclass, }\FunctionTok{aes}\NormalTok{(}\AttributeTok{x =}\NormalTok{ PrecursorMZ, }\AttributeTok{y =}\NormalTok{ RT, }\AttributeTok{color =}\NormalTok{ lipidclass, }\AttributeTok{size =}\NormalTok{ Intensity}\SpecialCharTok{/}\DecValTok{10}\NormalTok{)) }\SpecialCharTok{+}
\FunctionTok{geom\_point}\NormalTok{()}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
#> Warning: Removed 2 rows containing missing values (geom_point).
\end{verbatim}
\begin{figure}[H]
{\centering \includegraphics[width=18.75in,height=\textheight]{./datawrangling_files/figure-pdf/unnamed-chunk-6-1.pdf}
}
\end{figure}
\hypertarget{comparisons-in-r}{%
\section{Comparisons in R}\label{comparisons-in-r}}
Run following lines and try understand the result
\begin{Shaded}
\begin{Highlighting}[]
\StringTok{"CE 18:1"} \SpecialCharTok{==} \StringTok{"CE 18:1"}
\StringTok{"CE 18:1"} \SpecialCharTok{==} \StringTok{"CE 18:1 "}
\StringTok{"CE 18:1"} \SpecialCharTok{==} \StringTok{"CE 18:1"}
\StringTok{"Ce 18:1"} \SpecialCharTok{==} \StringTok{"CE 18:1"}
\NormalTok{stringr}\SpecialCharTok{::}\FunctionTok{str\_trim}\NormalTok{(}\StringTok{"CE 18:1 "}\NormalTok{)}
\NormalTok{stringr}\SpecialCharTok{::}\FunctionTok{str\_trim}\NormalTok{(}\StringTok{" CE 18:1 "}\NormalTok{)}
\NormalTok{stringr}\SpecialCharTok{::}\FunctionTok{str\_squish}\NormalTok{(}\StringTok{" CE 18:1 "}\NormalTok{)}
\NormalTok{stringr}\SpecialCharTok{::}\FunctionTok{str\_detect}\NormalTok{(}\StringTok{"LPC 18:1 (IS)"}\NormalTok{, }\AttributeTok{pattern =} \StringTok{"IS"}\NormalTok{)}
\NormalTok{stringr}\SpecialCharTok{::}\FunctionTok{str\_detect}\NormalTok{(}\StringTok{"LPC 18:1 (ISTD)"}\NormalTok{, }\AttributeTok{pattern =} \StringTok{"IS"}\NormalTok{)}
\NormalTok{stringr}\SpecialCharTok{::}\FunctionTok{str\_detect}\NormalTok{(}\StringTok{"LPC 18:1 (IS)"}\NormalTok{, }\AttributeTok{pattern =} \StringTok{"ISTD"}\NormalTok{)}
\NormalTok{stringr}\SpecialCharTok{::}\FunctionTok{str\_detect}\NormalTok{(}\StringTok{"LPC 18:1 (IS)"}\NormalTok{, }\AttributeTok{pattern =} \StringTok{"LPC"}\NormalTok{)}
\NormalTok{stringr}\SpecialCharTok{::}\FunctionTok{str\_replace}\NormalTok{(}\StringTok{"Acylcarnitine 18:1"}\NormalTok{, }
\AttributeTok{pattern =} \StringTok{"Acylcarnitine"}\NormalTok{,}
\AttributeTok{replacement =} \StringTok{"CAR"}\NormalTok{)}
\NormalTok{stringr}\SpecialCharTok{::}\FunctionTok{str\_replace}\NormalTok{(}\StringTok{"TG 48:2 [SIM] Results"}\NormalTok{, }
\AttributeTok{pattern =} \StringTok{" Results"}\NormalTok{,}
\AttributeTok{replacement =} \StringTok{""}\NormalTok{)}
\NormalTok{stringr}\SpecialCharTok{::}\FunctionTok{str\_replace}\NormalTok{(}\StringTok{"112\_BQC\_A9334.d"}\NormalTok{, }
\AttributeTok{pattern =} \StringTok{".d"}\NormalTok{,}
\AttributeTok{replacement =} \StringTok{""}\NormalTok{)}
\NormalTok{stringr}\SpecialCharTok{::}\FunctionTok{str\_to\_lower}\NormalTok{(}\StringTok{"CE 18:1"}\NormalTok{)}
\NormalTok{Sample\_ID }\OtherTok{\textless{}{-}} \DecValTok{1}
\FunctionTok{try}\NormalTok{(Sample}\SpecialCharTok{{-}}\NormalTok{ID }\OtherTok{\textless{}{-}} \DecValTok{1}\NormalTok{)}
\CommentTok{\# d |\textgreater{} filter(AnalysisID == "149\_BQC\_PQC17.d", }
\CommentTok{\# str\_detect(Compound, "IS|LPI") )}
\CommentTok{\# }