#html_text

[gloriousfestgentlemen02]

Sure, here is a 500-word article on "SEO automation with R" as per your request:

SEO Automation with R TG@yuantou2048

Search Engine Optimization (SEO) is a critical aspect of digital marketing that helps websites rank higher in search engine results pages (SERPs). Traditionally, SEO tasks have been manual and time-consuming, but with the advent of advanced programming languages like R, many of these tasks can now be automated using R.

Why Use R for SEO Automation?

R is a powerful statistical programming language that offers a wide range of packages specifically designed for data manipulation, analysis, and visualization. Here are some reasons why R is an excellent choice for automating SEO tasks:

1. Data Handling: R excels at handling large datasets, which is crucial for SEO where you often need to analyze vast amounts of data from various sources such as Google Analytics, SEMrush, Ahrefs, etc.

2. Automation: With R, you can automate repetitive tasks such as keyword research, backlink analysis, and content optimization. This not only saves time but also reduces the risk of human error.

3. Customization: R allows for high customization, enabling you to tailor solutions to specific needs. You can create custom scripts to scrape data from different sources, perform complex calculations, and generate reports automatically.

4. Integration: R integrates well with other tools and platforms. You can easily connect to APIs from tools like Google Search Console, Moz, and others, making it easier to gather and process data efficiently.

5. Visualization: R has robust visualization capabilities, allowing you to create insightful visual representations of your SEO data, helping you make informed decisions based on data-driven insights.

6. Community Support: The R community is vast and active, providing extensive support through packages like `httr` for web scraping, `dplyr` for data manipulation, and `ggplot2` for creating detailed visualizations that help in understanding trends and patterns in your SEO metrics.

Steps to Automate SEO Tasks

Step 1: Data Collection

Use packages like `httr` and `rvest` to scrape data from websites and APIs. For example, you can use `httr` to fetch data from APIs and `rvest` to extract data from HTML documents. This makes it easy to collect and clean data from multiple sources.

Example: Keyword Research

```r

library(httr)

library(rvest)

Fetching data from a website

url <- "https://example.com"

page <- GET(url)

content <- read_html(page)

keywords <- html_nodes(content, "h1") %>% html_text()

print(keywords)

```

This snippet demonstrates how to scrape keywords from a webpage. By leveraging these packages, you can automate the collection of data from SEO tools and websites.

Step-by-Step Guide

1. Install Required Packages

```r

install.packages("httr")

install.packages("rvest")

```

2. Scrape Data

```r

url <- "https://example.com"

page <- read_html(url)

titles <- html_nodes(page, "h1") %>% html_text()

print(titles)

```

3. Data Analysis

```r

library(dplyr)

library(ggplot2)

Example: Extracting H1 tags from a webpage

url <- "https://example.com"

page <- read_html(url)

h1_tags <- html_nodes(page, "h1") %>% html_text()

print(h1_tags)

```

4. Data Manipulation

```r

library(dplyr)

library(stringr)

library(tidyr)

Scrape data

url <- "https://example.com"

page <- read_html(url)

titles <- html_nodes(page, "h1") %>% html_text()

```

5. Data Cleaning and Analysis

```r

df <- data.frame(titles = titles)

df <- df %>% mutate(word_count = str_length(titles))

```

6. Analysis

```r

df <- df %>% mutate(word_count = str_length(titles))

```

7. Visualization

```r

library(ggplot2)

ggplot(df, aes(x = titles)) +

geom_bar() +

labs(title = "Keyword Frequency", x = "Keywords", y = "Frequency")

Analyze and visualize data

df <- df %>% group_by(titles) %>%

summarize(count = n())

```

8. Visualization

```r

ggplot(df, aes(x = titles, y = count)) +

geom_bar(stat = "identity")

```

9. Reporting

```r

ggplot(df, aes(x = titles, y = count)) +

geom_bar(stat = "identity")

```

10. Conclusion

By automating these tasks, you can streamline your SEO workflow, saving hours of manual labor.

11. Automated Reports

```r

ggplot(df, aes(x = titles, y = count)) +

theme_minimal()

ggplot(df, aes(x = titles, y = count)) +

geom_bar(stat = "identity")

```

12. Automate Reporting

```r

ggsave("report.png")

```

13. Conclusion

By automating SEO tasks with R, you can focus more on strategic decisions rather than spending time on mundane tasks. This approach ensures consistency and accuracy in your SEO efforts, leading to better insights and faster decision-making.

14. Summary

In conclusion, automating SEO tasks with R can significantly enhance your SEO strategy by providing actionable insights quickly and efficiently. Whether you're a beginner or an experienced SEO professional, integrating R into your workflow can transform your SEO strategy, making it more efficient and effective. Start exploring R today to elevate your SEO efforts and stay ahead in the competitive digital landscape.

15. Final Thoughts

Automating SEO processes with R empowers marketers to focus on strategic planning and execution, ultimately driving better rankings and improving overall performance.

16. Next Steps

Explore more packages like `shiny` for interactive dashboards or `shiny` for interactive dashboards and reports.

17. TG@yuantou2048

```

By leveraging R, you can save time and gain deeper insights into your website's performance and make data-driven decisions.

18. TG@yuantou2048

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```

Feel free to reach out if you need further assistance or have any questions!

加飞机@yuantou2048

EPS Machine

EPP Machine

[sekhondandc]

Input Text With Border Bottom | HTML CSS | Sekhon Design & Code

[scotianostra]

On 12th of June the year 1300 The Scots, under the guardian, John [Red] Comyn, beat an English army at Dillecarew field near Lindores in Fife.

This a follow up to the post about Wallace and Black Earnside, I’ve tried to cobble a post together before about it but this time spent longer trying.........

This is confusing for me, I am merely laying it down here to show you what an amateur sleuth/historian is up against while posting about the First War of Scottish Independence......... 

According to sources Sir William Wallace was again said to be present at this battle, Comyn and he  would pair up again three years later at Roslin, another battle that get’s overlooked a wee bit lesser so than this one though.

It’s very difficult reconciling the facts of what happened with so little being written,  Sir John Fraser “the Patriot”, another present at Roslin, is said to have  4000 men to the rear of the English army, but as I understand it, Fraser was still on the side of Edward I at the time, Wallace, the only one of the three who never fought for the English.

 According to the source, Fraser had Sir William Wallace at his side and the Scots made short work of the English army, killing the English General  Sir John Siward. I can’t find any mention of Siward being killed, or even being an English general, other Siwards are from earlier in our history, most notably fighting alongside King Malcolm II. 

The numbers killed are said to be about 3,000 English as opposed to 300 Scot’s. I found a brief mention of the battle in another source that states that the Scots casualties included a Christopher Seton, but the only Seton of that era I found is said to have been executed in 1306 at Dumfries, another source says he, and another two notable Scots, “ Sir Thomas Lochore, and Sir John Balfour, Sheriff of Fife, were wounded and hurt. “ 

So I’m scratching around trying to make sense of all this and I come across this 

“extensive forest of Blackironside , and , after an obstinate conflict , the invaders were defeated with the loss of 1580 men . This engagement , which is sometimes called the Battle of Dillecarew ,

And then there was this.

Sir Duncan Balfour, sheriff of Fife, one of the patriotic few who adhered to the fortunes of the renowned Sir William Wal- lace. He was slain 12th June, 1298, at the battle of Blackironside, where the Scottish hero defeated, with great slaughter, tlie English under Aymer de Valence, Earl of Pembroke. Sir Duncan's son, Sir John Balfour, who succeeded to his father's estates and office, that of sheriff of Fife. Sir John participated in the victory obtained at Dillecarew, in 1300, by Sir John Fraser and Sir W^illiam Wallace, but re- ceived a severe wound in the conflict. His son and successor.

So was there two battles in roughly the same area, or was it the same battle, it seems the historians can’t agree so what chance do I have? 

So it’s here I have decided to leave it, I will no doubt return to it this time next year, but my head hurts now.

Sources include

https://randomscottishhistory.com/2018/06/01/six-protectors-or-governors-after-king-alexander-iii-s-death-updated-pp-77-88/

https://digital.nls.uk/histories-of-scottish-families/archive/96737156?mode=transcription

https://books.google.co.uk/books?id=vCxwDwAAQBAJ&pg=PA16&dq=Dillecarew&hl=en&output=html_text&newbks=1&newbks_redir=1&sa=X&ved=2ahUKEwjz47OF96f4AhUElFwKHQ5qAvAQ6AF6BAgDEAI

https://www.ebooksread.com/authors-eng/john-burke/a-genealogical-and-heraldic-history-of-the-commoners-of-great-britain-and-irelan-kru-758/page-24-a-genealogical-and-heraldic-history-of-the-commoners-of-great-britain-and-irelan-kru-758.shtml

[juliesandothings]

From a story on the fur trade along the Great Lakes entitled Relics from the Rapids by Sigurd F. Olson, with photographs by David S. Boyer (first published in National Geographic magazine in 1963).

Top: depictions of a cow moose and calf on a rock beside Darky Lake, Ontario

Bottom: a fur buyer, appraises a beaver hide at Port Arthur using a flexible rule and touch to determine its grade

https://books.google.ca/books/about/Relics_from_the_Rapids.html?id=Uvh-nQEACAAJ&hl=en&output=html_text&redir_esc=y

[engdashboard]

READ MY FACE - DRAWING PORTRAITS WITH TEXT

Source: http://ift.tt/2fJxe5r

DRAWING PORTRAITS WITH TEXT

PUBLISHED SUN, JUL 9, 2017 BY GIORA SIMCHONI

Recently I’ve seen some interesting posts showing how to make ASCII art in R (see here and here). Why limit ourselves to ASCII, I thought. Lincoln’s portrait could be drawn with the Gettysberg Address instead of commas and semicolons. And Trump’s portrait really deserves his tweets1.

Lincoln/Gettysberg

Let’s load Lincoln’s image using the imager package. I’ll resize it because it’s huge, and convert it to grayscale:

library(tidyverse) library(stringr) library(imager) library(abind) img <- load.image("~/lincoln.jpg") %>% resize(700, 500) %>% grayscale() plot(img)

Now let’s get the Gettysberg Address from here using the rvest package:

library(rvest) text <- read_html("http://ift.tt/RykWYq") %>% html_nodes("p") %>% html_text() text

## [1] "\"Fourscore and seven years ago our fathers brought forth on this continent a new nation, conceived in liberty and dedicated to the proposition that all men are created equal. Now we are engaged in a great civil war, testing whether that nation or any nation so conceived and so dedicated can long endure. We are met on a great battlefield of that war. We have come to dedicate a portion of that field as a final resting-place for those who here gave their lives that that nation might live. It is altogether fitting and proper that we should do this. But in a larger sense, we cannot dedicate, we cannot consecrate, we cannot hallow this ground. The brave men, living and dead who struggled here have consecrated it far above our poor power to add or detract. The world will little note nor long remember what we say here, but it can never forget what they did here. It is for us the living rather to be dedicated here to the unfinished work which they who fought here have thus far so nobly advanced. It is rather for us to be here dedicated to the great task remaining before us--that from these honored dead we take increased devotion to that cause for which they gave the last full measure of devotion--that we here highly resolve that these dead shall not have died in vain, that this nation under God shall have a new birth of freedom, and that government of the people, by the people, for the people shall not perish from the earth.\" "

Now let’s convert Lincoln’s image into a 500 x 700 (transposed) matrix. It is now in grayscale mode, so there is only a single color channel, with values randing from 0 to 1:

imgGSMat <- img %>% as.matrix() %>% t() dim(imgGSMat)

## [1] 500 700

summary(c(imgGSMat))

## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.03137 0.09804 0.60000 0.46649 0.80784 0.99608

Let’s suppose the region we’ll print the text in, is where the pixels value is lower (= darker) than a certain threshold, say 0.5, and let’s plot this region to see it makes sense:

plot(as.cimg(imgGSMat > 0.5))

Don’t worry about the image being rotated, this will work out eventually. For now I just wanted to make sure the “dark” region with our chosen threshold looks OK, and it appears so.

To fill the dark region with the Gettysberg Address let’s split the text into characters. Then, we’ll loop over the entire matrix and text, and when we’re in the “dark” region, we’ll plot the current text character. We’ll use the grid package for plotting.

library(grid) text <- str_split(text, "")[[1]] grid.newpage() counter <- 0 for (i in seq(1, nrow(imgGSMat), 13)) { for (j in seq(1, ncol(imgGSMat), 5)) { if (imgGSMat[i, j] < 0.5) { counter <- ifelse(counter < length(text), counter + 1, 1) grid.text(text[counter], x = j / ncol(imgGSMat), y = 1 - i / nrow(imgGSMat), gp = gpar(fontsize = 10), just = "left") } } }

This is a good start, but a failure nonetheless. A few things to notice:

We’re not really looping through the entire matrix of pixels. Since the font size is currently 10, we’re looping through rows (image height) with the i variable in steps of 13 pixels.

We’re looping through columns (image width) with the j variable in fixed steps of 5 - which is clearly a problem, since there’s a variablity in the characters width.

Once the text is over we start from the beginning.

The grid.text function does not want our i and j as pixels, but as fractions from the image’s height and width respectively.

So the main issue here is the assumption of fixed width for all letters. Let’s change that with distinguishing between “fat” and “skinny” letters (the rest will be “regular”):

fatChars <- c(LETTERS[-which(LETTERS == "I")], "m", "w", "@") skinnyChars <- c("l", "I", "i", "t", "'", "f") grid.newpage() counter <- 0 for (i in seq(1, nrow(imgGSMat), 13)) { for (j in seq(1, ncol(imgGSMat), 10)) { if (imgGSMat[i, j] < 0.5) { counter <- ifelse(counter < length(text), counter + 1, 1) beforeLastChar <- ifelse(counter > 2, lastChar, " ") lastChar <- ifelse(counter > 1, char, " ") char <- text[counter] grid.text(char, x = j/ncol(imgGSMat) + 0.004 * (lastChar %in% fatChars) - 0.003 * (lastChar %in% skinnyChars) + 0.003 * (beforeLastChar %in% fatChars) - 0.002 * (beforeLastChar %in% skinnyChars), y = 1 - i / nrow(imgGSMat), gp = gpar(fontsize = 10), just = "left") } } }

Looks much better although the “algorithm” is somewhat dirty.

Let’s put it in a function anyway, making the threshold, font size and some other parameters configurable:

drawImageWithText <- function(img, text, thresh, fontSize = 10, fileName = "myfile.png", resize = TRUE, saveToDisk = FALSE) { text <- paste(text, collapse = " ") text <- str_replace_all(text, "\n+", " ") text <- str_replace_all(text, " +", " ") text <- str_split(text, "")[[1]] if (resize) img <- resize(img, 700, 500) imgGSMat <- img %>% grayscale %>% as.matrix %>% t() fatChars <- c(LETTERS[-which(LETTERS == "I")], "m", "w", "@") skinnyChars <- c("l", "I", "i", "t", "'", "f") if (saveToDisk) png(fileName, width(img), height(img)) grid.newpage() counter <- 0 for (i in seq(1, nrow(imgGSMat) - fontSize, fontSize + floor(fontSize / 3))) { for (j in seq(1, ncol(imgGSMat) - fontSize, fontSize)) { if (imgGSMat[i, j] < thresh) { counter <- ifelse(counter < length(text), counter + 1, 1) beforeLastChar <- ifelse(counter > 2, lastChar, " ") lastChar <- ifelse(counter > 1, char, " ") char <- text[counter] grid.text(char, x = 0.01 + j/ncol(imgGSMat) + 0.004 * (lastChar %in% fatChars) - 0.003 * (lastChar %in% skinnyChars) + 0.003 * (beforeLastChar %in% fatChars) - 0.002 * (beforeLastChar %in% skinnyChars), y = 1 - i / nrow(imgGSMat) - 0.01, gp = gpar(fontsize = fontSize), just = "left") } } } if (saveToDisk) dev.off() }

Martin Luther King Jr./I Have A Dream

Let’s test our function on Martin Luther King Jr. and his iconic speech taken from here:

img <- load.image("~/mlkj.jpg") text <- read_lines("~/ihaveadream.txt") drawImageWithText(img, text, thresh = 0.3, fontSize = 5)

Free, at last.

Marylin Monroe/Her Wikipedia Article

img <- load.image("~/marylin.jpg") text <- read_html("http://ift.tt/OBYUG0") %>% html_nodes("p") %>% html_text() %>% str_replace_all(., "\\[[0-9]+\\]", "") drawImageWithText(img, text, thresh = 0.5, fontSize = 8)

Adele/Rolling In The Deep

img <- load.image("~/adele.jpg") text <- read_lines("~/rollinginthedeep.txt") drawImageWithText(img, text, thresh = 0.5, fontSize = 10)

Hadley Wickham/The dplyr code

img <- load.image("~/hadley.jpg") text <- read_html("http://ift.tt/2hqEe7q") %>% html_nodes("div") %>% .[[47]] %>% html_text() drawImageWithText(img, text, thresh = 0.75, fontSize = 5)

This example with Hadley is a bit different, because here the original image isn’t black and white. It has colors, and we could use some color, so let’s change the function a bit:

drawImageWithText <- function(img, text, thresh, color = FALSE, fontSize = 10, fileName = "myfile.png", resize = TRUE, saveToDisk = FALSE) { if (color) { if (spectrum(img) == 1) { warning("Image is in grayscale mode, setting color to FALSE.") color = FALSE } } text <- paste(text, collapse = " ") text <- str_replace_all(text, "\n+", " ") text <- str_replace_all(text, " +", " ") text <- str_split(text, "")[[1]] if (resize) img <- resize(img, 700, 500) imgMat <- img %>% as.array() %>% adrop(3) %>% aperm(c(2, 1, 3)) imgGSMat <- img %>% grayscale %>% as.matrix %>% t() fatChars <- c(LETTERS[-which(LETTERS == "I")], "m", "w", "@") skinnyChars <- c("l", "I", "i", "t", "'", "f") if (saveToDisk) png(fileName, width(img), height(img)) grid.newpage() counter <- 0 for (i in seq(1, nrow(imgGSMat) - fontSize, fontSize + 1)) { for (j in seq(1, ncol(imgGSMat) - fontSize, fontSize)) { if (imgGSMat[i, j] < thresh) { counter <- ifelse(counter < length(text), counter + 1, 1) beforeLastChar <- ifelse(counter > 2, lastChar, " ") lastChar <- ifelse(counter > 1, char, " ") char <- text[counter] grid.text(char, x = 0.01 + j/ncol(imgGSMat) + 0.004 * (lastChar %in% fatChars) - 0.003 * (lastChar %in% skinnyChars) + 0.003 * (beforeLastChar %in% fatChars) - 0.002 * (beforeLastChar %in% skinnyChars), y = 1 - i / nrow(imgGSMat) - 0.01, gp = gpar(fontsize = fontSize, col = ifelse(!color, "black", rgb(imgMat[i, j, 1], imgMat[i, j, 2], imgMat[i, j, 3]))), just = "left") } } } if (saveToDisk) suppressMessages(dev.off()) } drawImageWithText(img, text, thresh = 0.9, color = TRUE, fontSize = 5)

Nice! Sorry, Hadley.

Trump/His Tweets

Let’s write a wrapper around our drawImageWithText function that will automatically download a Twitter user’s image and tweets - and use those as img and text. We’ll use the wonderful rtweet package for this:

library(rtweet) drawImageWithTextFromTwitter <- function(username, thresh, ...) { text <- get_timeline(username, n = 200) %>% select(text) %>% unlist() %>% discard(str_detect(., "^RT")) %>% str_replace(., "(http|https)[^([:blank:]|\"|<|&|#\n\r)]+", "") %>% str_extract_all(., "[a-zA-Z0-9[:punct:]]+") %>% unlist %>% paste(., collapse = " ") img <- load.image(lookup_users(username)$profile_image_url) drawImageWithText(img, text, thresh, ...) }

Let’s do Trump:

drawImageWithTextFromTwitter("realDonaldTrump", 0.55, fontSize = 8)

Tyra Banks/Her Tweets

drawImageWithTextFromTwitter("tyrabanks", 0.9, color = TRUE, fontSize = 5)

Congratulations. You’re still in the running towards becoming America’s Next Top Model.

That’s It

Next step is, obviously, making T-shirts. Enjoy!

via Blogger http://ift.tt/2hntD0o

[gloriousfestgentlemen02]

```markdown

SEO Automation with R

In today's digital landscape, Search Engine Optimization (SEO) is more critical than ever for businesses looking to increase their online visibility and drive traffic to their websites. However, the process of optimizing a website for search engines can be time-consuming and complex. This is where automation comes into play. In this article, we will explore how you can use R, a powerful programming language for statistical computing and graphics, to automate your SEO tasks.

Why Use R for SEO?

R offers a wide range of packages that can help with various aspects of SEO, from data collection and analysis to visualization. Here are some key reasons why R is an excellent choice for automating SEO tasks:

1. Data Collection: With packages like `rvest` and `httr`, you can easily scrape web pages and collect data such as keywords, backlinks, and other relevant metrics.

2. Data Analysis: R has robust capabilities for data analysis. Packages like `dplyr` and `tidyr` allow you to manipulate and clean your data efficiently.

3. Visualization: Visualizing data is crucial in SEO to understand trends and patterns. R’s `ggplot2` package provides a flexible system for creating high-quality graphs and charts.

4. Automation: By writing scripts in R, you can automate repetitive tasks, saving you time and effort.

Steps to Automate SEO with R

Step 1: Data Collection

The first step is to gather the necessary data. You can use the `rvest` package to scrape websites and extract information. For example, you can scrape Google search results to get keyword rankings or use `httr` to fetch data from APIs.

```r

library(rvest)

url <- "https://www.example.com"

page <- read_html(url)

keywords <- page %>% html_nodes("h1") %>% html_text()

```

Step 2: Data Analysis

Once you have collected the data, you need to analyze it. The `dplyr` package is incredibly useful for data manipulation.

```r

library(dplyr)

data <- data %>%

mutate(new_column = old_column * 2) %>%

filter(column > 10)

```

Step 3: Visualization

After analyzing the data, you can create visualizations to better understand the insights. The `ggplot2` package is perfect for this task.

```r

library(ggplot2)

ggplot(data, aes(x = column1, y = column2)) +

geom_point() +

labs(title = "SEO Metrics", x = "X-axis Label", y = "Y-axis Label")

```

Step 4: Automation

Finally, you can automate these steps by writing a script that runs periodically. This can be done using a cron job on a server or a scheduled task on a local machine.

Conclusion

Automating SEO with R can significantly improve your efficiency and effectiveness in managing your website’s search engine performance. By leveraging R’s powerful tools, you can streamline your SEO processes, gain deeper insights into your data, and make informed decisions to boost your online presence.

What are some specific SEO tasks you would like to automate? Share your thoughts and experiences in the comments below!

```

加飞机@yuantou2048

SEO优化

蜘蛛池出租

[sekhondandc]

Input Text With Border | HTML CSS | Sekhon Design & Code

[sekhondandc]

Input Text With Icon Slidein | HTML CSS | Sekhon Design & Code

[sekhondandc]

Input Text With Icon | HTML CSS | Sekhon Design & Code

[sekhondandc]

Input Text With Label Slideout | HTML CSS | Sekhon Design & Code

[sekhondandc]

Input Text With Label Move Down | HTML CSS | Sekhon Design & Code

[sekhondandc]

Input Text With Fixed Label | HTML CSS | Sekhon Design & Code