Jacob Stoughton and Jakub Kepa

Tasks: Perform four (4) correlations using any appropriate variables (continuous).

#Loading data

library(readr)

hr <- read_csv('https://raw.githubusercontent.com/aiplanethub/Datasets/refs/heads/master/HR_comma_sep.csv')
## Rows: 14999 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Department, salary
## dbl (8): satisfaction_level, last_evaluation, number_project, average_montly...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ purrr     1.1.0
## ✔ forcats   1.0.0     ✔ stringr   1.5.2
## ✔ ggplot2   4.0.0     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(plotly)
## 
## Attaching package: 'plotly'
## 
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following object is masked from 'package:graphics':
## 
##     layout
# Correlation 1
cor.test(hr$average_montly_hours, hr$satisfaction_level)
## 
##  Pearson's product-moment correlation
## 
## data:  hr$average_montly_hours and hr$satisfaction_level
## t = -2.4556, df = 14997, p-value = 0.01408
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.036040356 -0.004045605
## sample estimates:
##         cor 
## -0.02004811
library(ggplot2)

ggplot(hr, aes(x = average_montly_hours, y = satisfaction_level)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(title = "Average Monthly Hours Do Not Affect Satisfaction",
       x = "Average Monthly Hours",
       y = "Satisfaction Level")
## `geom_smooth()` using formula = 'y ~ x'

p-value interpretation: The p-value is small, therefore there is technically a significant correlation of the two variables. However, the correlation is so weak that the variables are practically unrelated.

correlation estimate interpretation: The correlation is negative and very small.

non-technical interpretation: There is not any significant relation between an employees average monthly hours and their satisfaction level at this company.

# Correlation 2
cor.test(hr$time_spend_company, hr$last_evaluation)
## 
##  Pearson's product-moment correlation
## 
## data:  hr$time_spend_company and hr$last_evaluation
## t = 16.256, df = 14997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1158309 0.1472844
## sample estimates:
##       cor 
## 0.1315907
ggplot(hr, aes(x = time_spend_company, y = last_evaluation)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(title = "Longer Tenured Employees Have Higher Evaluation Scores",
       x = "Time at Company (years)",
       y = "Last Evaluation Score")
## `geom_smooth()` using formula = 'y ~ x'

p-value interpretation: The p-value is very small, therefore there is a significant correlation between the time spent at the company and the last evaluation.

correlation estimate interpretation: The correlation is positive and small.

non-technical interpretation: At this company longer tenure tends to lead to a higher evaluation scores.

# Correlation 3
cor.test(hr$time_spend_company, hr$number_project)
## 
##  Pearson's product-moment correlation
## 
## data:  hr$time_spend_company and hr$number_project
## t = 24.579, df = 14997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1813532 0.2121217
## sample estimates:
##       cor 
## 0.1967859
ggplot(hr, aes(x = time_spend_company, y = number_project)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(title = "Longer Tenured Employees Have Completed More Projects",
       x = "Time at Company",
       y = "Number of Projects")
## `geom_smooth()` using formula = 'y ~ x'

p-value interpretation: The p-value is very small, therefore there is a significant correlation between time at company and number of projects.

correlation estimate interpretation: The correlation is positive and small.

non-technical interpretation: At this company longer tenure often leads to more work on various projects

# Correlation 4
cor.test(hr$last_evaluation, hr$satisfaction_level)
## 
##  Pearson's product-moment correlation
## 
## data:  hr$last_evaluation and hr$satisfaction_level
## t = 12.933, df = 14997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.08916727 0.12082195
## sample estimates:
##       cor 
## 0.1050212
ggplot(hr, aes(x = last_evaluation, y = satisfaction_level)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE, color = "red") +
  labs(title = "Employees with Higher Satisfaction have Higher Evaluations",
       x = "Last Evaluation Score",
       y = "Satisfaction Level")
## `geom_smooth()` using formula = 'y ~ x'

p-value interpretation: The p-value is very small, therefore there is a significant correlation between last evaluation and satisfaction level.

correlation estimate interpretation: The correlation is positive and small.

non-technical interpretation: At this company greater satisfaction level results in greater evaluation scores