Overview

Analysis Overview

Universal Data Explorer

Analysis overview and configuration

Configuration

Analysis TypeUniversal Data Explorer
CompanyIBM HR Analytics
ObjectiveExplore employee dataset — demographics, compensation, and satisfaction patterns
Analysis Date2026-03-12
Processing Idtest_1773379850
Total Observations500

Module Parameters

ParameterValue_row
cardsc("dept_breakdown", "income_by_dept", "income_by_age", "income_satisfaction_tenure", "age_vs_income", "income_by_education", "age_histogram", "attrition_by_travel", "distance_income", "satisfaction_by_role_years", "income_wlb_by_level", "experience_vs_income", "income_by_marital", "income_histogram", "edu_satisfaction_heatmap", "job_role_stats", "dept_summary"), c("bar", "bar", "line", "line", "scatter", "box", "histogram", "bar", "line", "line", "line", "scatter", "box", "histogram", "heatmap", "table", "table"), c("Employees by Department", "Average Monthly Income by Department", "Average Income by Age", "Income & Satisfaction by Years at Company", "Age vs Monthly Income", "Income Distribution by Education Level", "Age Distribution", "Attrition Count by Business Travel", "Average Income by Distance from Home", "Job Satisfaction by Years in Current Role", "Income & Work-Life Balance by Job Level", "Total Working Years vs Monthly Income", "Income Distribution by Marital Status", "Monthly Income Distribution", "Education Level vs Job Satisfaction", "Job Role Summary Statistics", "Department Summary"), c("Department", "Department", "Age", "Years at Company", "Age", "Education", "Age", "Business Travel", "Distance (miles)", "Years in Current Role", "Job Level", "Total Working Years", "Marital Status", "Monthly Income", "Education Level", NA, NA), c("Count", "Avg Monthly Income", "Avg Monthly Income", "Avg Monthly Income", "Monthly Income", "Monthly Income", "Count", "Attrition Count", "Avg Monthly Income", "Avg Satisfaction", "Avg Monthly Income", "Monthly Income", "Monthly Income", "Count", "Job Satisfaction", NA, NA), c("\n freq <- as.data.frame(table(df$Department), stringsAsFactors = FALSE)\n colnames(freq) <- c('category', 'value')\n freq[order(-freq$value), ]\n ", "\n agg <- aggregate(MonthlyIncome ~ Department, data = df, FUN = mean)\n data.frame(category = agg$Department, value = round(agg$MonthlyIncome, 0))\n ", "\n agg <- aggregate(MonthlyIncome ~ Age, data = df, FUN = mean)\n agg <- agg[order(agg$Age), ]\n data.frame(x = agg$Age, y = round(agg$MonthlyIncome, 0))\n ", "\n agg1 <- aggregate(MonthlyIncome ~ YearsAtCompany, data = df, FUN = mean)\n agg2 <- aggregate(JobSatisfaction ~ YearsAtCompany, data = df, FUN = mean)\n merged <- merge(agg1, agg2, by = 'YearsAtCompany')\n merged <- merged[order(merged$YearsAtCompany), ]\n data.frame(x = merged$YearsAtCompany, y = round(merged$MonthlyIncome, 0), y2 = round(merged$JobSatisfaction, 2))\n ", "\n data.frame(x = df$Age, y = df$MonthlyIncome)\n ", "\n edu_labels <- c('1' = 'Below College', '2' = 'College', '3' = 'Bachelor', '4' = 'Master', '5' = 'Doctor')\n data.frame(group = edu_labels[as.character(df$Education)], value = df$MonthlyIncome)\n ", "\n data.frame(value = df$Age)\n ", "\n att <- df[df$Attrition == 'Yes', ]\n freq <- as.data.frame(table(att$BusinessTravel), stringsAsFactors = FALSE)\n colnames(freq) <- c('category', 'value')\n freq[order(-freq$value), ]\n ", "\n agg <- aggregate(MonthlyIncome ~ DistanceFromHome, data = df, FUN = mean)\n agg <- agg[order(agg$DistanceFromHome), ]\n data.frame(x = agg$DistanceFromHome, y = round(agg$MonthlyIncome, 0))\n ", "\n agg <- aggregate(JobSatisfaction ~ YearsInCurrentRole, data = df, FUN = mean)\n agg <- agg[order(agg$YearsInCurrentRole), ]\n data.frame(x = agg$YearsInCurrentRole, y = round(agg$JobSatisfaction, 2))\n ", "\n agg1 <- aggregate(MonthlyIncome ~ JobLevel, data = df, FUN = mean)\n agg2 <- aggregate(WorkLifeBalance ~ JobLevel, data = df, FUN = mean)\n merged <- merge(agg1, agg2, by = 'JobLevel')\n merged <- merged[order(merged$JobLevel), ]\n data.frame(x = merged$JobLevel, y = round(merged$MonthlyIncome, 0), y2 = round(merged$WorkLifeBalance, 2))\n ", "\n data.frame(x = df$TotalWorkingYears, y = df$MonthlyIncome)\n ", "\n data.frame(group = df$MaritalStatus, value = df$MonthlyIncome)\n ", "\n data.frame(value = df$MonthlyIncome)\n ", "\n ct <- as.data.frame(table(Education = df$Education, Satisfaction = df$JobSatisfaction), stringsAsFactors = FALSE)\n data.frame(x = ct$Education, y = ct$Satisfaction, z = ct$Freq)\n ", "\n roles <- unique(df$JobRole)\n do.call(rbind, lapply(roles, function(r) {\n subset_df <- df[df$JobRole == r, ]\n data.frame(\n JobRole = r,\n Count = nrow(subset_df),\n AvgIncome = round(mean(subset_df$MonthlyIncome), 0),\n AvgAge = round(mean(subset_df$Age), 1),\n AvgSatisfaction = round(mean(subset_df$JobSatisfaction), 2),\n stringsAsFactors = FALSE\n )\n }))\n ", "\n depts <- unique(df$Department)\n do.call(rbind, lapply(depts, function(d) {\n subset_df <- df[df$Department == d, ]\n data.frame(\n Department = d,\n Employees = nrow(subset_df),\n AvgIncome = round(mean(subset_df$MonthlyIncome), 0),\n AttritionRate = round(sum(subset_df$Attrition == 'Yes') / nrow(subset_df) * 100, 1),\n AvgTenure = round(mean(subset_df$YearsAtCompany), 1),\n stringsAsFactors = FALSE\n )\n }))\n " ), c(NA, NA, NA, "Avg Job Satisfaction", NA, NA, NA, NA, NA, NA, "Avg Work-Life Balance", NA, NA, NA, NA, NA, NA)cards
Universal Data Explorer analysis for IBM HR Analytics
Data Preparation

Data Preprocessing

Data Quality & Completeness

Data preprocessing and column mapping

Data Quality

Initial Rows500
Final Rows500
Rows Removed0
Retention Rate100

Data Quality

MetricValue
Initial Rows500
Final Rows500
Rows Removed0
Retention Rate100%
Processed 500 observations, retained 500 (100.0%) after cleaning
Executive Summary

Executive Summary

Key Findings from Data Exploration

Key Metrics

total_rows
500
total_columns
35
cards_generated
17
cards_failed
0

Summary

Explored dataset with 500 rows and 35 columns. 17 of 17 requested visualizations generated successfully. Visualization cards generated: - Employees by Department (bar) - Average Monthly Income by Department (bar) - Average Income by Age (line) - Income & Satisfaction by Years at Company (line) - Age vs Monthly Income (scatter) - Income Distribution by Education Level (box) - Age Distribution (histogram) - Attrition Count by Business Travel (bar) - Average Income by Distance from Home (line) - Job Satisfaction by Years in Current Role (line) - Income & Work-Life Balance by Job Level (line) - Total Working Years vs Monthly Income (scatter) - Income Distribution by Marital Status (box) - Monthly Income Distribution (histogram) - Education Level vs Job Satisfaction (heatmap) - Job Role Summary Statistics (table) - Department Summary (table)
Figure 4

Bar Chart

Categorical Comparison

Employees by Department

Figure 5

Bar Chart

Categorical Comparison

Average Monthly Income by Department

Figure 6

Bar Chart

Categorical Comparison

Attrition Count by Business Travel

Figure 7

Line Chart

Trend Analysis

Average Income by Age

Figure 8

Line Chart

Trend Analysis

Average Income by Distance from Home

Figure 9

Line Chart

Trend Analysis

Job Satisfaction by Years in Current Role

Figure 10

Dual Line Chart

Trend Comparison

Income & Satisfaction by Years at Company

Figure 11

Dual Line Chart

Trend Comparison

Income & Work-Life Balance by Job Level

Figure 12

Scatter Plot

Relationship Analysis

Age vs Monthly Income

Figure 13

Scatter Plot

Relationship Analysis

Total Working Years vs Monthly Income

Figure 14

Box Plot

Distribution Comparison

Income Distribution by Education Level

Figure 15

Box Plot

Distribution Comparison

Income Distribution by Marital Status

Figure 16

Histogram

Distribution Analysis

Age Distribution

Figure 17

Histogram

Distribution Analysis

Monthly Income Distribution

Figure 18

Heatmap

Matrix Visualization

Education Level vs Job Satisfaction

Table 19

Data Table

Tabular Results

Job Role Summary Statistics

JobRoleCountAvgIncomeAvgAgeAvgSatisfaction
Sales Executive108691636.12.94
Research Scientist99334534.32.75
Laboratory Technician93312634.32.85
Manufacturing Director48657637.72.75
Healthcare Representative40815041.52.85
Manager421671446.12.81
Sales Representative30255730.22.53
Research Director291590143.42.76
Human Resources11445435.82.55
Table 20

Data Table

Tabular Results

Department Summary

DepartmentEmployeesAvgIncomeAttritionRateAvgTenure
Sales1536966197.5
Research & Development333639713.56.9
Human Resources14738328.66.1
Want to run this analysis on your own data? Upload CSV — Free Analysis See Pricing