REFERENCE: http://davidgohel.github.io/ReporteRs/lists.html ReporteRs is an R package for creating Microsoft (Word docx and Powerpoint pptx) and html documents. It does not require any Microsoft component to be used. It runs on Windows, Linux, Unix and Mac OS systems. This is the ideal tool to automate reporting generation from R.

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Prepared by Volkan OBAN
REFERENCE: http://davidgohel.github.io/ReporteRs/lists.html
Produce nice outputs for
graphical, tabular and textual
reporting in R
ReporteRs is an R package for creating Microsoft (Word docxand Powerpoint pptx) and
html documents. It does not require any Microsoft component to be used. It runs on Windows, Linux,
Unix and Mac OS systems. This is the ideal tool to automate reporting generation from R.
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
5.1 3.5 1.4 0.2 setosa
4.9 3.0 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5.0 3.6 1.4 0.2 setosa
5.4 3.9 1.7 0.4 setosa
4.6 3.4 1.4 0.3 setosa
5.0 3.4 1.5 0.2 setosa
4.4 2.9 1.4 0.2 setosa
4.9 3.1 1.5 0.1 setosa

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HelloWorld!
1 TABLEOF CONTENTS

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2 LISTOF GRAPHICS

4. 4
3 IRIS OUTPUTS
3.1 PLOT

5. 5
4 MTCARS OUTPUTS
4.1 PLOT

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Codes:
> library( ReporteRs )
> library( ggplot2 )
Stackoverflow is a great place to get help:
http://stackoverflow.com/tags/ggplot2.
> library(magrittr)
>
> myplot1 <- qplot(Sepal.Length, Petal.Length,
+ data = iris, color = Species,
+ size = Petal.Width, alpha = I(0.7))
> myplot2 <- qplot(mpg, wt, data = mtcars, colour = cyl )
>
> # Create a new document
> mydoc <- docx( template = "files/word/template_toc.docx" )
>
> # display available styles
> styles( mydoc )
Normal heading 1 heading 2
"Normal" "Titre1" "Titre2"
heading 3 heading 4 heading 5
"Titre3" "Titre4" "Titre5"
heading 6 heading 7 heading 8
"Titre6" "Titre7" "Titre8"
heading 9 Title Subtitle
"Titre9" "Titre" "Sous-titre"
Quote Intense Quote caption
"Citation" "Citationintense" "Lgende"
TOC Heading No Spacing List Paragraph
"En-ttedetabledesmatires" "Sansinterligne" "Paragraphedeliste"
rPlotLegend header footer
"rPlotLegend" "En-tte" "Pieddepage"
Titre1 BulletList Titre2
"Titre10" "BulletList" "Titre20"
TitleDoc rRawOutput rTableLegend
"TitleDoc" "rRawOutput" "rTableLegend"
DocDefaults

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"DocDefaults"
> mydoc <- mydoc %>% addTitle( "Table of contents", level = 1 ) %>%
+ addTOC( ) %>% # add a table of content
+ addPageBreak() # add a page break
>
> mydoc <- mydoc %>% addTitle( "List of graphics", level = 1 ) %>%
+ addTOC( stylename = "figurereference" ) %>%
+ addTitle( "List of tables", level = 1 ) %>%
+ addTOC( stylename = "tablereference" )
> mydoc <- addPageBreak( mydoc )
>
> mydoc <- mydoc %>% addTitle( "iris outputs", level = 1 ) %>%
+ addTitle( "Plot", level = 2 ) %>%
+ addPlot(function( ) print( myplot1 ) ) %>%
+ addParagraph( value = "iris plot",
+ stylename = "figurereference") %>% # Add a legend below
the plot
+ addTitle( "Table", level = 2 ) %>%
+ addFlexTable( vanilla.table( head( iris ) ) ) %>%
+ addParagraph( value = "iris table",
+ stylename = "tablereference") # Add a legend below the ta
ble
>
> mydoc <- mydoc %>% addTitle( "mtcars outputs", level = 1 ) %>%
+ addTitle( "Plot", level = 2 ) %>%
+ addPlot(function( ) print( myplot2 ) ) %>%
+ addParagraph( value = "mtcars plot",
+ stylename = "figurereference") %>% # Add a legend below
the plot
+ addTitle( "Table", level = 2 ) %>%
+ addFlexTable( vanilla.table( head( mtcars ) ) ) %>%
+ addParagraph( value = "mtcars table",
+ stylename = "tablereference") # Add a legend below the ta
ble
>
> writeDoc( mydoc, file = "files/vv/volkan_demo.docx")
EXAMPLE: FLEXTABLE EXAMPLE
> library( boot )
> library( dplyr )
> data(mtcars)
> mtcars$cyl = as.factor(mtcars$cyl)
> data = summary(lm(mpg ~ wt + cyl, data=mtcars))$coefficients
>
> data = as.data.frame( data )
>
>
> # get signif codes
> signif.codes = cut( data[,4]

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+ , breaks = c( -Inf, 0.001, 0.01, 0.05, Inf)
+ , labels= c("***", "**", "*", "" ) )
>
> # format the data values
> data[, 1] = formatC( data[, 1], digits=3, format = "f")
> data[, 2] = formatC( data[, 2], digits=3, format = "f")
> data[, 3] = formatC( data[, 3], digits=3, format = "f")
> data[, 4] = ifelse( data[, 4] < 0.001, "&lt; 0.001", formatC( data[, 4], di
gits=5, format = "f"))
> # add signif codes to data
> data$Signif = signif.codes
>
> # create an empty FlexTable
> coef_ft = FlexTable( data = data, add.rownames=TRUE
+ , body.par.props = parRight(), header.text.props = tex
tBold()
+ , header.columns = T
+ )
> # center the first column and set text as bold italic
> coef_ft[,1] = parCenter()
> coef_ft[,1] = textBoldItalic()
>
> # define borders
> coef_ft = setFlexTableBorders( coef_ft
+ , inner.vertical = borderNone(), inner.horiz
ontal = borderDotted()
+ , outer.vertical = borderNone(), outer.horiz
ontal = borderSolid()
+ )
> coef_ft

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CONDITIONAL COLORED TABLE
# a summary of mtcars
dataset = aggregate( mtcars[, c("disp", "mpg", "wt")]
, by = mtcars[, c("cyl", "gear", "carb")]
, FUN = mean )
dataset = dataset[ order(dataset$cyl, dataset$gear, dataset$carb), ]
# set cell padding defaut to 2
baseCellProp = cellProperties( padding = 2 )
# Create a FlexTable with data.frame dataset
my_ft = FlexTable( data = dataset
, body.cell.props = baseCellProp
, header.cell.props = baseCellProp
, header.par.props = parProperties(text.align = "center" )
)
# set columns widths (in inches)
my_ft = setFlexTableWidths( my_ft, widths = c(0.5, 0.5, 0.5, 0.7, 0.7, 0.7) )
# span successive identical cells within column 1
my_ft = spanFlexTableRows( my_ft, j = 1, runs = as.character( dataset$cyl ) )
# overwrites some text formatting properties
my_ft[dataset$wt < 3, 6] = textProperties( color="#FF3333", font.weight = "bo
ld" )
my_ft[dataset$mpg < 20, 5] = chprop( baseCellProp, background.color = "#00AAA
A")
# overwrites some paragraph formatting properties
my_ft[, 4:6] = parProperties(text.align = "right")
# applies a border grid on table
my_ft = setFlexTableBorders( my_ft

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, outer.vertical = borderProperties( width = 2 )
, outer.horizontal = borderProperties( width = 2 )
)
my_ft

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Example:
Codes:
1 VOLKANOBAN
Data science isan interdisciplinaryfieldaboutprocessesandsystemstoextractknowledgeorinsights
fromdata in variousforms,eitherstructuredorunstructured,which isacontinuationof some of the
data analysisfieldssuchasstatistics,datamining,andpredictiveanalytics,similartoKnowledge
DiscoveryinDatabases(KDD).
Data science employstechniquesandtheoriesdrawnfrommanyfieldswithinthe broadareas of
mathematics,statistics,operationsresearch,[4] informationscience,andcomputerscience,including
signal processing,probabilitymodels,machine learning,statistical learning,datamining,database,data
engineering,patternrecognitionandlearning,visualization,predictive analytics,uncertaintymodeling,
data warehousing,datacompression,computerprogramming,artificial intelligence,andhigh
performance computing. DATA SCİENCE.1
1
This is another reference

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EXAMPLE:
LISTS
ReporteRs lets you create ordered or unordered lists.
1 ORDERED LIST
1. Data Science-Analytics.
1. Analyticsisthe discovery,interpretation,andcommunicationof meaningfulpatternsin
data. Especiallyvaluableinareasrichwithrecordedinformation,analyticsreliesonthe
simultaneousapplicationof statistics,computerprogrammingandoperationsresearchto
quantifyperformance.Analyticsoftenfavorsdatavisualizationtocommunicate insight.
2. Analyticsisthe discovery,interpretation,andcommunicationof meaningfulpatternsin
data. Especiallyvaluableinareasrichwithrecordedinformation,analyticsreliesonthe
simultaneousapplicationof statistics,computerprogrammingandoperationsresearchto
quantifyperformance.Analyticsoftenfavorsdatavisualizationtocommunicate insight.
2. Data Science-Analytics.
3. Analyticsisthe discovery,interpretation,andcommunicationof meaningfulpatternsindata.
Especiallyvaluable inareasrichwithrecordedinformation,analyticsreliesonthe simultaneous
applicationof statistics,computerprogrammingandoperationsresearchtoquantify
performance.Analyticsoftenfavorsdatavisualizationtocommunicate insight.
4. Organizationsmayapplyanalyticstobusinessdatatodescribe,predict,andimprove business
performance.Specifically,areaswithinanalyticsinclude predictive analytics,prescriptive
analytics,enterprise decisionmanagement,retail analytics,store assortmentandstock-keeping
unitoptimization,marketingoptimizationandmarketingmix modeling,webanalytics,salesforce
sizingandoptimization,price andpromotionmodeling,predictive science,creditriskanalysis,and
fraudanalytics.Since analyticscanrequire extensivecomputation(see bigdata),the algorithms
and software usedforanalyticsharnessthe mostcurrentmethodsincomputerscience,statistics,
and mathematics
5. Data Science-Analytics.
6. Analyticsisthe discovery,interpretation,andcommunicationof meaningfulpatternsindata.
Especiallyvaluable inareasrichwithrecordedinformation,analyticsreliesonthe simultaneous
applicationof statistics,computerprogrammingandoperationsresearchtoquantify
performance.Analyticsoftenfavorsdatavisualizationtocommunicate insight.
7. Organizationsmayapplyanalyticstobusinessdatatodescribe,predict,andimprove business
performance.Specifically,areaswithinanalyticsinclude predictive analytics,prescriptive
analytics,enterprise decisionmanagement,retail analytics,store assortmentandstock-keeping
unitoptimization,marketingoptimizationandmarketingmix modeling,webanalytics,salesforce
sizingandoptimization,price andpromotionmodeling,predictive science,creditriskanalysis,and
fraudanalytics.Since analyticscanrequire extensivecomputation(see bigdata),the algorithms

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and software usedforanalyticsharnessthe mostcurrentmethodsincomputerscience,statistics,
and mathematics
2 UNORDEREDLIST
● Data Science-Analytics.
○ Analyticsisthe discovery,interpretation,andcommunicationof meaningfulpatternsin
data. Especiallyvaluableinareasrichwithrecordedinformation,analyticsreliesonthe
simultaneousapplicationof statistics,computerprogrammingandoperationsresearchto
quantifyperformance.Analyticsoftenfavorsdatavisualizationtocommunicate insight.
Codes:
Reference:
http://davidgohel.github.io/ReporteRs/index.html
http://www.sthda.com/english/wiki/create-and-format-powerpoint-documents-from-r-
software