Human Resources Analytics: Exploring Employee Data in R

Applied Data Science in Human Resources, M.D. Rodrigo Gómez.

Hello again dear Shantal,

In this exercise I would like you to be able to complete the following proposed exercises in order to apply what you have learned with Tidyverse in exploring data from this HR-ready dataset. The files to be able to carry out the exercise can be downloaded from this link.

Project

• Then you will dive into a case study where you’ll analyze and visualize recruiting data to determine which source of new candidates ultimately produces the best new hires.

• The dataset you’ll use in this and the other exercises is synthetic, to maintain the privacy of actual employees.

Looking at the recruiting data

Real HR datasets are tough to find because of privacy and ethical concerns about sharing sensitive employee data.

In this chapter, you’ll be focusing on the sales department and the recruiting channels they were hired from.

Exercise 1:

# Load the readr and tidyverse package  
---
---

# Import the recruitment data
--- <- --- # Use "complete_base" to name the new dataset.

# Look at the first few rows of the dataset
--- # Look at the first rows of recruitment with head()
  
# Explore the variables contained in the Data Set.
--- # Use glimpse() to explore de variables, type and observations.

Identifying groups in data

You would like to help the talent acquisition team understand which recruiting channel will produce the best sales hires. You can apply the HR analytics process to help them. Start by examining the recruiting channels in the data.

Exercise 2:

# Get an overview of the recruitment data usin summary()
---
  
# See which recruiting sources the company has been using
---  # Don´t forget using the PIPE %>%
---  # Use count() on the recruiting column to get more information.
  

Sales numbers by recruiting source

Which recruiting channel produces the best salespeople? One quality of hire metric you can use is sales quota attainment, or how much a salesperson sold last year relative to their quota. An employee whose sales_quota_pct equals .75 sold 75% of their quota, for example. This metric can be helpful because raw sales numbers are not always comparable between employees.

Calculate the average sales quota attainment achieved by hires from each recruiting source.

Excersise 3:

# Find the average sales quota attainment. 
recruitment %>%
  ___ # use summarize() to calculate the avg_sales and mean() to know the result.

# Find the average sales quota attainment for each recruiting source. Store it in a new column called "avg_sales".

avg_sales <- ___ # With this code "<-" you save a new vector with the results with the name "avg_sales". 
--- # Hint: use group_by()
--- # Hint: use summarize()
 
# Display the result and arrange the results to been easier to know which channel had the highest performing.
---(avg_sales, ---) # Hint: use arrange() 

Attrition rates by recruiting source

Another quality of hire metric you can consider is the attrition rate, or how often hires leave the company. Determine which recruiting channels have the highest and lowest attrition rates.

Excersise 4:

# Find the average attrition for the sales team, by recruiting source, sorted from lowest attrition rate to highest
avg_attrition <- recruitment %>%
  --- %>% 
  --- %>% 
  ---

# Display the result
avg_attrition

You can practice and most learning in DataCamp sources.