Going Pro in Data Science by Jerry Overton

Author:Jerry Overton
Language: eng
Format: mobi, epub
Publisher: O'Reilly Media, Inc.
Published: 2016-03-10T05:00:00+00:00

#Read in the employee ratings ratings <- read.csv(file = "dummy_company_reviews.csv") #This function takes raw employee ratings, processes them, #builds a topic model then displays the topics topic_model <- function(ratings){ topic_model_stub <- c("topic1","topic2","topic3") } #Perform a topic analysis on the reviewers with factors that #match happy employees. The resulting topics will give us more #information about why the employees are happy ratings.happy <- subset(ratings, Compensation.Benefits > 2 & Management > 3 & Job.Security.Advancement > 2 & Helpful.Yes > 0 ) ratings.happy.desc <- ratings.happy[,"Review.Description.Translated"] topic_model(ratings.happy.desc)

Notice that I wrote the code to perform the high-level steps of the analysis, but I stubbed out the functionality for providing data and doing the topic modeling. For the data, I just wrote a dummy file with text I made up. For the topic model algorithm, I wrote just enough code to produce an output that could be used by the control logic. After I was confident that I understood how the individual pieces fit, and that I had control logic that worked, I started working on gathering real input data. I wrote the comments of happy employees to a file, then read that data in for analysis. After updating the control with the following code, I ran the entire script to make sure that things were still executing as expected:

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