pdf viewer library c# : Copy image from pdf to powerpoint software SDK dll winforms wpf asp.net web forms TextMiningO3-part1802

Data Science with R
Hands-On
Text Mining
13.3 Adding Some Colour
larg
evalu
import
random
like
section
understand
includ
subset
method
individu
perform
combin
step
event
work
attribut
select
form
averag
year
effect
univers
class
link
repres
call
reaction
support
function
base
user
explor
variabl
measur
rank
inform
tabl
statist
studi
analysi
area
distanc
tool
level
outlier
servic
experi
time
data
search
cost
william
subspac
patient
show
implement
make
similar
databas
consist
record
lead
unexpect
exist
propos
utar
angioedema
tempor
pattern
stage
intellig
figur
distribut
condit
result
discuss
accuraci
drug
australia
identifi
domain
appli
hospit
general
mani
number
period
paper
case
featur
build
process
describ
hot
technolog
sequenc
ratio
insur
day
well
given
kdd
use
rnn
order
within
node
target
applic
two
chang
discov
error
total
transact
small
graham
acsi
fig
need
expect
often
learn
multipl
structur
interest
current
claim
new
detect
journal
observ
report
allow
size
one
adr
provid
approach
high
can
find
classif
window
weight
model
compar
neural
differ
mine
requir
three
australian
particular
vector
sourc
health
singl
indic
entiti
system
valu
common
advers
pmml
occur
cluster
exampl
state
increas
howev
rattl
task
hybrid
usual
network
defin
mean
regress
unit
dataset
rule
visual
relat
algorithm
follow
page
intern
http
type
point
expert
proceed
effici
machin
open
avail
collect
object
csiro
discoveri
train
generat
scienc
map
consid
confer
may
tree
packag
refer
sampl
will
group
forest
popul
also
polici
comput
interesting
first
oper
episod
associ
problem
medic
decis
set
interv
signific
predict
classifi
research
contain
knowledg
risk
present
develop
care
nugget
techniqu
estim
test
age
spot
We can also add some colour to the display. Here we make use of brewer.pal() from RColor-
Brewer (Neuwirth,2014) to generate a palette of colours to use.
set.seed(142)
wordcloud(names(freq), freq, min.freq=100colors=brewer.pal(6"Dark2"))
Copyright
2013-2015 Graham@togaware.com
Module: TextMiningO
Page: 30 of46
Draft Only
Generated 2016-01-10 10:00:58+11:00
Copy image from pdf to powerpoint - copy, paste, cut PDF images in C#.net, ASP.NET, MVC, Ajax, WinForms, WPF
Detailed tutorial for copying, pasting, and cutting image in PDF page using C# class code
how to copy pdf image; how to copy pdf image to powerpoint
Copy image from pdf to powerpoint - VB.NET PDF copy, paste image library: copy, paste, cut PDF images in vb.net, ASP.NET, MVC, Ajax, WinForms, WPF
VB.NET Tutorial for How to Cut or Copy an Image from One Page and Paste to Another
how to copy text from pdf image; how to copy and paste a pdf image
Data Science with R
Hands-On
Text Mining
13.4 Varying the Scaling
larg
evalu
import
random
like
section
understand
includ
subset
method
individu
perform
combin
step
event
work
attribut
select
form
averag
year
effect
univers
class
link
repres
call
reaction
support
function
base
user
explor
variabl
measur
rank
inform
tabl
statist
studi
analysi
area
distanc
tool
level
outlier
servic
experi
time
data
search
cost
william
subspac
patient
show
implement
make
similar
databas
consist
record
lead
unexpect
exist
propos
utar
angioedema
tempor
pattern
stage
intellig
figur
distribut
condit
result
discuss
accuraci
drug
australia
identifi
domain
appli
hospit
general
mani
number
period
paper
case
featur
build
process
describ
hot
technolog
sequenc
ratio
insur
day
well
given
kdd
use
rnn
order
within
node
target
applic
two
chang
discov
error
total
transact
small
graham
acsi
fig
need
expect
often
learn
multipl
structur
interest
current
claim
new
detect
journal
observ
report
allow
size
one
adr
provid
approach
high
can
find
classif
window
weight
model
compar
neural
differ
mine
requir
three
australian
particular
vector
sourc
health
singl
indic
entiti
system
valu
common
advers
pmml
occur
cluster
exampl
state
increas
howev
rattl
task
hybrid
usual
network
defin
mean
regress
unit
dataset
rule
visual
relat
algorithm
follow
page
intern
http
type
point
expert
proceed
effici
machin
open
avail
collect
object
csiro
discoveri
train
generat
scienc
map
consid
confer
may
tree
packag
refer
sampl
will
group
forest
popul
also
polici
comput
interesting
first
oper
episod
associ
problem
medic
decis
set
interv
signific
predict
classifi
research
contain
knowledg
risk
present
develop
care
nugget
techniqu
estim
test
age
spot
We can change the range of font sizes used in the plot using the scale= option. By default the
most frequent words have a scale of 4 and the least have a scale of 0.5. Here we illustrate the
eect of increasing the scale range.
set.seed(142)
wordcloud(names(freq), freq, min.freq=100scale=c(5.1), colors=brewer.pal(6"Dark2"))
Copyright
2013-2015 Graham@togaware.com
Module: TextMiningO
Page: 31 of46
Draft Only
Generated 2016-01-10 10:00:58+11:00
VB.NET PDF Image Extract Library: Select, copy, paste PDF images
VB.NET PDF - Extract Image from PDF Document in VB.NET. Support PDF VB.NET : Select An Image from PDF Page by Position. Sample for
copy picture from pdf to powerpoint; how to copy images from pdf file
C# PDF Image Extract Library: Select, copy, paste PDF images in C#
How to C#: Extract Image from PDF Document. List<PDFImage> allImages = PDFImageHandler. ExtractImages(page); C#: Select An Image from PDF Page by Position.
how to copy pictures from a pdf; paste image into pdf reader
Data Science with R
Hands-On
Text Mining
13.5 Rotating Words
larg
evalu
import
random
like
section
understand
includ
subset
method
individu
perform
combin
step
event
work
attribut
select
form
averag
year
effect
univers
class
link
repres
call
reaction
support
function
base
user
explor
variabl
measur
rank
inform
tabl
statist
studi
analysi
area
distanc
tool
level
outlier
servic
experi
time
data
search
cost
william
subspac
patient
show
implement
make
similar
databas
consist
record
lead
unexpect
exist
propos
utar
angioedema
tempor
pattern
stage
intellig
figur
distribut
condit
result
discuss
accuraci
drug
australia
identifi
domain
appli
hospit
general
mani
number
period
paper
case
featur
build
process
describ
hot
technolog
sequenc
ratio
insur
day
well
given
kdd
use
rnn
order
within
node
target
applic
two
chang
discov
error
total
transact
small
graham
acsi
fig
need
expect
often
learn
multipl
structur
interest
current
claim
new
detect
journal
observ
report
allow
size
one
adr
provid
approach
high
can
find
classif
window
weight
model
compar
neural
differ
mine
requir
three
australian
particular
vector
sourc
health
singl
indic
entiti
system
valu
common
advers
pmml
occur
cluster
exampl
state
increas
howev
rattl
task
hybrid
usual
network
defin
mean
regress
unit
dataset
rule
visual
relat
algorithm
follow
page
intern
http
type
point
expert
proceed
effici
machin
open
avail
collect
object
csiro
discoveri
train
generat
scienc
map
consid
confer
may
tree
packag
refer
sampl
will
group
forest
popul
also
polici
comput
interesting
first
oper
episod
associ
problem
medic
decis
set
interv
signific
predict
classifi
research
contain
knowledg
risk
present
develop
care
nugget
techniqu
estim
test
age
spot
We can change the proportion of words that are rotated by 90 degrees from the default 10% to,
say, 20% using rot.per=0.2.
set.seed(142)
dark2 <- brewer.pal(6"Dark2")
wordcloud(names(freq), freq, min.freq=100rot.per=0.2colors=dark2)
Copyright
2013-2015 Graham@togaware.com
Module: TextMiningO
Page: 32 of46
Draft Only
Generated 2016-01-10 10:00:58+11:00
C# PDF Page Extract Library: copy, paste, cut PDF pages in C#.net
C#.NET PDF Library - Copy and Paste PDF Pages in C#.NET. Easy Ability to copy selected PDF pages and paste into another PDF file. The
how to copy picture from pdf; paste picture to pdf
VB.NET PDF Page Extract Library: copy, paste, cut PDF pages in vb.
Dim page As PDFPage = doc.GetPage(3) ' Select image by the point VB.NET: Clone a PDF Page. Dim doc As PDFDocument = New PDFDocument(filepath) ' Copy the first
copying images from pdf files; paste picture into pdf preview
Data Science with R
Hands-On
Text Mining
14 Quantitative Analysis of Text
The qdap (Rinker,2015) package provides an extensive suite of functions to support the quanti-
tative analysis of text.
We can obtain simple summaries of a list of words, and to do so we will illustrate with the
terms from our Term Document Matrix tdm. We rst extract the shorter terms from each of our
documents into one long word list. To do so we convert tdm into a matrix, extract the column
names (the terms) and retain those shorter than 20 characters.
words <- dtm
%>%
as.matrix
%>%
colnames
%>%
(function(x) x[nchar(x) 20])
We can then summarise the word list. Notice, in particular, the use of dist
tab() from qdap to
generate frequencies and percentages.
length(words)
## [1] 6456
head(words, 15)
## [1] "aaai"
"aab"
"aad"
"aadrbhtm"
"aadrbltn"
## [6] "aadrhtmliv" "aai"
"aam"
"aba"
"abbrev"
## [11] "abbrevi"
"abc"
"abcd"
"abdul"
"abel"
summary(nchar(words))
##
Min. 1st Qu. Median
Mean 3rd Qu.
Max.
##
3.000
5.000
6.000
6.644
8.000 19.000
table(nchar(words))
##
##
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
## 579 867 1044 1114 935 651 397 268 200 138
79
63
34
28
22
##
18
19
##
21
16
dist_tab(nchar(words))
##
interval freq cum.freq percent cum.percent
## 1
3 579
579
8.97
8.97
## 2
4 867
1446
13.43
22.40
## 3
5 1044
2490
16.17
38.57
## 4
6 1114
3604
17.26
55.82
## 5
7 935
4539
14.48
70.31
## 6
8 651
5190
10.08
80.39
## 7
9 397
5587
6.15
86.54
## 8
10 268
5855
4.15
90.69
## 9
11 200
6055
3.10
93.79
## 10
12 138
6193
2.14
95.93
....
Copyright
2013-2015 Graham@togaware.com
Module: TextMiningO
Page: 33 of46
Draft Only
Generated 2016-01-10 10:00:58+11:00
C# Create PDF from PowerPoint Library to convert pptx, ppt to PDF
Create PDF from PowerPoint. |. Home ›› XDoc.PDF ›› C# PDF: Create PDF from PowerPoint. C#.NET PDF SDK- Create PDF from PowerPoint in C#.
cut image from pdf online; paste jpeg into pdf
VB.NET PDF insert image library: insert images into PDF in vb.net
VB.NET PDF - Add Image to PDF Page in VB.NET. Insert Image to PDF Page Using VB. Add necessary references: RasterEdge.Imaging.Basic.dll.
copy pdf picture; paste picture into pdf
Data Science with R
Hands-On
Text Mining
14.1 Word Length Counts
0
300
600
900
5
10
15
20
Number of Letters
Number of Words
Asimple plot is then eective in showing the distribution of the word lengths. Here we create a
single column data frame that is passed on to ggplot() to generate a histogram, with a vertical
line to show the mean length of words.
data.frame(nletters=nchar(words))
%>%
ggplot(aes(x=nletters))
+
geom_histogram(binwidth=1)
+
geom_vline(xintercept=mean(nchar(words)),
colour="green", size=1, alpha=.5)
+
labs(x="Number of Letters"y="Number of Words")
Copyright
2013-2015 Graham@togaware.com
Module: TextMiningO
Page: 34 of46
Draft Only
Generated 2016-01-10 10:00:58+11:00
VB.NET Create PDF from PowerPoint Library to convert pptx, ppt to
from Word. Create PDF from Excel. Create PDF from PowerPoint. Create PDF Image: Insert Image to PDF. Image: Remove Image from PDF Page. Image: Copy, Paste, Cut
how to copy image from pdf to word; paste image into pdf acrobat
C# Create PDF from images Library to convert Jpeg, png images to
Best and professional C# image to PDF converter SDK for Visual Studio .NET. C#.NET Example: Convert One Image to PDF in Visual C# .NET Class.
how to copy a picture from a pdf file; cut and paste pdf image
Data Science with R
Hands-On
Text Mining
14.2 Letter Frequency
Z
J
Q
X
W
Y
K
V
F
B
G
H
P
D
M
C
U
L
S
N
O
T
R
A
I
E
0%
2%
4%
6%
8%
10%
12%
Proportion
Letter
Next we want to review the frequency of letters across all of the words in the discourse. Some
data preparation will transform the vector of words into a list of letters, which we then construct
afrequency count for, and pass this on to be plotted.
We again use a pipeline to string together the operations on the data. Starting from the vec-
tor of words stored in word we split the words into characters using str
split() from stringr
(Wickham,2015), removing the rst string (an empty string) from each of the results (using
sapply()). Reducing the result into a simple vector, using unlist(), we then generate a data
frame recording the letter frequencies, using dist
tab() from qdap. We can then plot the letter
proportions.
library(dplyr)
library(stringr)
words
%>%
str_split("")
%>%
sapply(function(x) x[-1])
%>%
unlist
%>%
dist_tab
%>%
mutate(Letter=factor(toupper(interval),
levels=toupper(interval[order(freq)])))
%>%
ggplot(aes(Letter, weight=percent))
+
geom_bar()
+
coord_flip()
+
labs(y="Proportion")
+
scale_y_continuous(breaks=seq(0122),
label=function(xpaste0(x, "%"),
Copyright
2013-2015 Graham@togaware.com
Module: TextMiningO
Page: 35 of46
Draft Only
Generated 2016-01-10 10:00:58+11:00
Data Science with R
Hands-On
Text Mining
expand=c(0,0), limits=c(0,12))
Copyright
2013-2015 Graham@togaware.com
Module: TextMiningO
Page: 36 of46
Draft Only
Generated 2016-01-10 10:00:58+11:00
Data Science with R
Hands-On
Text Mining
14.3 Letter and Position Heatmap
.010
.019
.013
.010
.010
.007
.005
.003
.002
.002
.001
.001
.001
.000
.000
.000
.000
.000
.000
.006
.001
.004
.002
.002
.002
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.013
.003
.007
.006
.004
.004
.003
.002
.001
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.008
.002
.005
.005
.004
.003
.002
.001
.001
.001
.001
.000
.000
.000
.000
.000
.000
.000
.000
.006
.021
.010
.016
.014
.008
.005
.003
.002
.001
.001
.001
.000
.000
.000
.000
.000
.000
.000
.005
.001
.003
.002
.001
.001
.001
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.004
.001
.004
.004
.002
.002
.002
.001
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.005
.005
.002
.004
.003
.002
.001
.001
.001
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.007
.015
.009
.011
.012
.009
.007
.005
.003
.002
.001
.001
.001
.000
.000
.000
.000
.000
.000
.002
.000
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.002
.000
.001
.003
.001
.000
.001
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.005
.005
.008
.008
.006
.004
.004
.002
.001
.001
.001
.001
.000
.000
.000
.000
.000
.000
.000
.009
.003
.007
.005
.003
.003
.002
.002
.001
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.005
.010
.012
.008
.007
.009
.005
.004
.003
.002
.001
.001
.000
.000
.000
.000
.000
.000
.000
.005
.021
.009
.008
.009
.005
.005
.003
.002
.002
.001
.001
.000
.000
.000
.000
.000
.000
.000
.011
.003
.006
.005
.002
.002
.001
.001
.001
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.001
.001
.001
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.009
.012
.013
.009
.010
.009
.006
.004
.002
.002
.001
.001
.000
.000
.000
.000
.000
.000
.000
.015
.004
.011
.008
.007
.006
.005
.003
.002
.001
.001
.001
.000
.000
.000
.000
.000
.000
.000
.008
.005
.012
.013
.009
.008
.007
.005
.003
.002
.001
.001
.001
.000
.000
.000
.000
.000
.000
.004
.010
.005
.005
.004
.003
.002
.002
.001
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.003
.001
.003
.002
.001
.001
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.005
.002
.002
.001
.001
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.001
.002
.001
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.001
.001
.002
.001
.001
.001
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.001
.000
.000
.001
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
Z
Y
X
W
V
U
T
S
R
Q
P
O
N
M
L
K
J
I
H
G
F
E
D
C
B
A
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Position
Letter
Proportion
0.000
0.005
0.010
0.015
0.020
The qheat() function from qdap provides an eective visualisation of tabular data. Here we
transform the list of words into a position count of each letter, and constructing a table of the
proportions that is passed on to qheat() to do the plotting.
words
%>%
lapply(function(x) sapply(letters, gregexpr, x, fixed=TRUE))
%>%
unlist
%>%
(function(x) x[x!=-1])
%>%
(function(xsetNames(x, gsub("nnd"""names(x))))
%>%
(function(xapply(table(data.frame(letter=toupper(names(x)),
position=unname(x))),
1function(y) y/length(x)))
%>%
qheat(high="green"low="yellow"by.column=NULL,
values=TRUE, digits=3, plot=FALSE)
+
labs(y="Letter"x="Position"+
theme(axis.text.x=element_text(angle=0))
+
guides(fill=guide_legend(title="Proportion"))
Copyright
2013-2015 Graham@togaware.com
Module: TextMiningO
Page: 37 of46
Draft Only
Generated 2016-01-10 10:00:58+11:00
Data Science with R
Hands-On
Text Mining
14.4 Miscellaneous Functions
We can generate gender from a name list, using the genderdata (?) package
devtools::install_github("lmullen/gender-data-pkg")
name2sex(qcv(graham, frank, leslie, james, jacqui, jack, kerry, kerrie))
## The genderdata package needs to be installed.
## Error in install
genderdata
package(): Failed to install the genderdata package.
## Please try installing the package for yourself using the following command:
##
install.packages("genderdata", repos = "http://packages.ropensci.org", type
= "source")
Copyright
2013-2015 Graham@togaware.com
Module: TextMiningO
Page: 38 of46
Draft Only
Generated 2016-01-10 10:00:58+11:00
Data Science with R
Hands-On
Text Mining
15 Word Distances
Continuous bag of words (CBOW). Word2Vec associates each wordin a vocabulary with aunique
vector of real numbers of length d. Words that have a similar syntactic context appear closer
together within the vector space. The syntactic context is based on a set of words within a
specic window size.
install.packages("tmcn.word2vec"repos="http://R-Forge.R-project.org")
## Installing package into ’/home/gjw/R/x86
64-pc-linux-gnu-library/3.2’
## (as ’lib’ is unspecified)
##
## The downloaded source packages are in
##  /tmp/Rtmpt1u3GR/downloaded_packages
library(tmcn.word2vec)
model <- word2vec(system.file("examples""rfaq.txt"package = "tmcn.word2vec"))
## The model was generated in  /home/gjw/R/x86_64-pc-linux-gnu-library/3.2/tm...
distance(model$model_file, "the")
##
Word
CosDist
## 1
a 0.8694174
## 2
is 0.8063422
## 3
and 0.7908007
## 4
an 0.7738196
## 5
please 0.7595193
....
Copyright
2013-2015 Graham@togaware.com
Module: TextMiningO
Page: 39 of46
Draft Only
Generated 2016-01-10 10:00:58+11:00
Documents you may be interested
Documents you may be interested