Overview:

We conducted an analysis of the text data from a selected document. Our focus was on two key aspects: the number of lines sampled from the document and the word count within these lines.

Key Findings:

Line Count: Our sample consisted of a total of [X] lines from the document. This sample size provides a good representation of the overall content.

Mean Calculation Example

Word Count Analysis:

{r var} file_path <- "E:/r/capstone/final/en_US/en_US.blogs.txt" con <- readLines(file_path, n = 200) con

Has the data scientist done basic summaries of the three files?

Word counts, line counts and basic data tables?

Word counts

```{r word_counts} sampled_lines <- iconv(con, “latin1”, “ASCII”, sub = ““) word_counts <- sapply(sampled_lines, function(line) { words <- strsplit(line,”\s+“)[[1]] length(words) })

total_word_count <- sum(word_counts) total_word_count

## line counts
```{r line_counts}
line_count <- length(con)
line_count

basic data tables

```{r viz} data_table <- data.frame( LineNumber = 1:length(sampled_lines), LineContent = con, WordCount = word_counts, stringsAsFactors = FALSE )

print(data_table)

library(ggplot2) # Create a data frame from word counts word_count_df <- data.frame(WordCount = word_counts)

Create a histogram using ggplot2

ggplot(word_count_df, aes(x = WordCount)) + geom_histogram(binwidth = 1, fill = “blue”, color = “black”) + theme_minimal() + labs(title = “Histogram of Word Counts”, x = “Word Count”, y = “Frequency”) ```

The total number of words across all sampled lines is [Y]. On average, each line contains approximately [Z] words. This gives an indication of the density of information per line. The distribution of word counts was visualized using a histogram, which helps in understanding the variability of content across different lines. Visual Representation:

A histogram was created to illustrate the distribution of word counts per line. This graphical representation is particularly useful for quickly grasping the spread and concentration of word usage across the sampled text. Implications:

The analysis provides insights into the text’s complexity and density. Understanding the average word count per line helps in estimating the time and effort required for tasks like editing or detailed review. The word count distribution can also be indicative of consistency in writing style or content density across the document.

Conclusion:

This brief analysis offers a snapshot of the document’s structure in terms of line and word counts. Such insights are valuable for planning further content management strategies, editorial processes, or even for automated text processing tasks.