Web Scraping and Visual Analytics

Notebook Instructions

For your assignment you may be using different dataset than what is included here.
Always read carefully the instructions on Sakai.
Tasks/questions to be completed/answered are highlighted in larger bolded fonts and numbered according to their section.

About

Web scraping, web harvesting, or web data extraction is data scraping used for extracting data from websites.
This technique mostly focuses on the transformation of unstructured data (HTML format) on the web into structured data (database or spreadsheet)
A fictitious London-based training company, WeTrainYou, wants to start a local training facility in California. It is looking for a city where ample Salesforce* development jobs are available.
Its goal is to train engineers and fulfill full-time and part-time jobs. WeTrainYou has hired you to determine where they should set up the business.
Case Study: https://software.intel.com/en-us/articles/using-visualization-to-tell-a-compelling-data-story

Load Packages in R/RStudio

We are going to use tidyverse a collection of R packages designed for data science.

Info: https://www.tidyverse.org/

## Loading required package: tidyverse

## ── Attaching packages ──────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 2.2.1     ✔ purrr   0.2.4
## ✔ tibble  1.4.2     ✔ dplyr   0.7.4
## ✔ tidyr   0.7.2     ✔ stringr 1.2.0
## ✔ readr   1.1.1     ✔ forcats 0.2.0

## ── Conflicts ─────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

## Loading required package: rvest

## Loading required package: xml2

## 
## Attaching package: 'rvest'

## The following object is masked from 'package:purrr':
## 
##     pluck

## The following object is masked from 'package:readr':
## 
##     guess_encoding

Web Scraping Functions

# This function to creates a URL for the www.dice.com website and extract the data 
create_url <- function(website, title, location, radius, page){
  url <- paste0(website, "?q=", title, "&l=", location, "&radius=", radius)
  url <- paste0(url, "&startPage=", page, "&jobs")
  return(url)
}

# This function use the unstructure data from the html file to create a dataframe
# with only the data that is needed for analysis
create_tibble <- function(html){
  
  search_title <- html %>% 
    html_nodes(".complete-serp-result-div") %>%
    html_nodes(xpath = "//a/@title") %>%
    html_text()
  
  search_region <- html %>% 
    html_nodes(".complete-serp-result-div") %>% 
    html_nodes('[itemprop="address"]') %>%
    html_nodes("[itemprop=addressRegion]") %>%
    html_text()
  
  search_zipcode <- html %>% 
    html_nodes(".complete-serp-result-div") %>% 
    html_nodes('[itemprop="address"]') %>%
    html_nodes("[itemprop=postalCode]") %>%
    html_text()
  
  search_address <- html %>% 
    html_nodes(".complete-serp-result-div") %>% 
    html_nodes('[itemprop="address"]') %>%
    html_nodes("[itemprop=streetAddress]") %>%
    html_text() %>% 
    str_replace(pattern = paste0(", ",search_region), "")
  
  search_company <- html %>% 
    html_nodes(".complete-serp-result-div") %>% 
    html_nodes('[itemprop="hiringOrganization"]') %>%
    html_nodes("[itemprop=name]") %>%
    html_text()
  
    df <- tibble(title = search_title,
                 company = search_company,
                 city = search_address, 
                 state = search_region,
                 zipcode = search_zipcode)

    return(df)
}

Task 1: Data Collection - Web Scraping

1A) Use the create_url() function and the given website to scope the data that you will collection. In the function for “title” use one of the “Trending Searches” from the website or a job title that you are interested. For “location” use a US State (CA, IL, …), for “radius” use 30 miles, finally we are only interested in the first page. Take an screenshot of only the data that you need

Trending Searches: www.dice.com
Job Website: “https://www.dice.com/jobs”
Commands: create_url(website = JOBSITE , title = JOBTITLE, location = STATE, radius = NUM_MILES, page = PAG_NUM)

Create the url then go to the url using the browser and post an screenshot of only the data that you need

#Paste the link that is provided into safari and take a screenshot of the data that you need.
site = "https://www.dice.com/jobs"
job = "Data+Analyst"
region = "NY"
miles = 30
pag = 1

url <- create_url(website = site, title = job, location = region, radius = 30, page = 1)
url

## [1] "https://www.dice.com/jobs?q=Data+Analyst&l=NY&radius=30&startPage=1&jobs"

1B) From the target website determine the number of pages for the given search. Create a variable “num_pages” equal to the max number of pages for the job search. Create a variable “url” using the create_url() function with the same parameters than the previous search, for the page number use “i” as we are looping over all the pages.

Commands: create_url(website = JOBSITE , title = JOBTITLE, location = STATE, radius = NUM_MILES, page = PAG_NUM)

#site = JOB_WEBSITE
#job = "Data+Analyst"
#region = STATE_TWO_LETTERS
#miles = MILES_NUM
#pag = PAG_NUM
num_pages = 92

# COMMENT: Loop over the max number of pages for the job search
for (i in 1:num_pages) {
  
  # TODO: Create a url for the job search
  url <- create_url(website = site, title = job, location = region, radius = 30, page = i)
  
  # COMMENT: read the created URL and collects the html code
  web_html <- read_html(url)

  # COMMENT: If statement to create the first dataframe
  if(i == 1) {
    
    # COMMENT: Creates a tibble dataframe extracting information from the html code
    job_data <- create_tibble(html = web_html)
    
  }else{
  
      # COMMENT: We add new observation to the first dataframe
    df <- create_tibble(html = web_html)
    job_data <- bind_rows(job_data, df)
  }
  
  # COMMENT: We have to wait a couple of seconds before moving to the next page
  Sys.sleep(1.0)
}

1C) Make sure that the data was collected correctly. By using the functions to inspect and summarize the data. Describe the summary statistics and note any significant observations.

Dataframe: job_data
Commands: head() summary()

head(job_data)

## # A tibble: 6 x 5
##   title                      company              city       state zipcode
##   <chr>                      <chr>                <chr>      <chr> <chr>  
## 1 Data Analyst               Talent Minds Networ… New York … NY    10023  
## 2 Data Analyst - Data Mining Harris Corporation   Rochester  NY    14602  
## 3 Business/Data Analyst      Morgan Stanley UK L… New York   NY    10001  
## 4 Data Analyst - MDM         Publishers Clearing… Jericho    NY    11753  
## 5 Data Analyst Intern        DST Systems, Inc     New York   NY    10001  
## 6 Data Analyst               Matlen Silver        New York   NY    10036

There are 2,735 jobs for the Data Analyst in NYC. All the data is classified as chaacter. All job postings have a city, title, company, state, and zip code.

summary(job_data)

##     title             company              city          
##  Length:2714        Length:2714        Length:2714       
##  Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character  
##     state             zipcode         
##  Length:2714        Length:2714       
##  Class :character   Class :character  
##  Mode  :character   Mode  :character

1D) After making sure that the data was collected correctly, save the data as csv file.

Commands: write_csv(x = DATAFRAME, path = “data/DATAFRAME” )

write_csv(x = job_data, path = "data/dice.ny.csv")

Task 2: Visualization Analysis - Tableau

2A) Upload your data in csv format to Tableau, make any changes to the data types (GEOLOCATION, TEXT, NUMERIC). Take an screenshot of Tableu’s data inspection.

There is a variety of jobs.

2B) Using tableau geolocation features map cities using bubbles where the size of the bubble is cumulative number of jobs listing in that city. Note any interesting patterns, add an screenshot of your visualization.

2C) Create a tree map, to compare the different cities and the cumulative number of job posting in each city. Note any interesting patterns, add an screenshot of your visualization.

NYC has more data analytics jobs than any other city.

Therefore, removed NYC to see other job areas.

2D) Create a Bar plot by State and City. To display the cumulative number of jobs in each city. Note any interesting patterns, add an screenshot of your visualization.

2E) Create a dashboard to display the three plots above. Use half of the dashboard to display the map with the location of the cities. On the bottom of the dashboard place the other two charts, add titles to each chart. Note any interesting patterns, add an screenshot of your visualization.

The dashboard has the combination of the three different visualization.

Task 3: Watson Analysis

To complete the last task, follow the directions found below. Make sure to screenshot and attach any pictures of the results obtained or any questions asked.

3A) Upload you data to watson, explore the different insights. Take 3 screenshots of your insights and describe your findings.

The reliability of the data was described as 39%.

Findings 1

The most jobs are in NYC. Second is Brooklyn and Rochester.

Findings 2

Data Analyst, Data Engineer, Data Architect, and Data Scientist are the most common jobs.

Findings 3

Most of the jobs are in NYC downtown. Similar visualization as the previous.