Story4: How much do Data Practitioner get paid?
Souleymane Doumbia
2024-11-10
Introduction
In today’s data-driven world, professionals known as — including Data Scientists, Data Engineers, Data Analysts, Business Intelligence Analysts, and Database Architects — play a crucial role in shaping business strategies and decision-making processes. This analysis aims to provide insight into how much Data Practitioners are paid, exploring salary variation by role and state. We use 2023 Occupational Employment and Wage data from the U.S. Bureau of Labor Statistics to uncover trends in average salary and employment concentration across the United States, allowing us to highlight regional differences and compensation ranges within each role.
Loading Data
#-----------------------------------------------------------------------
# Loading 2023 BLS Wage Data for Data Practioner - Loading the CSV file
filtered_data_cleaned <- read.csv("/Users/souleymanedoumbia/Library/Mobile Documents/com~apple~CloudDocs/CUNY SPS CLASSES/MSDS CLASSES/DATA 608 FALL 2024/Week8/Story4/2023_BLS_DataPractioner_Wage_Cleaned.csv")
# List of columns to convert
percentile_columns <- c("TOT_EMP","A_MEAN","A_PCT10", "A_PCT25", "A_MEDIAN", "A_PCT75", "A_PCT90")
# Remove commas and convert to numeric
filtered_data_cleaned[percentile_columns] <- lapply(filtered_data_cleaned[percentile_columns], function(x) {
as.numeric(gsub(",", "", x))
})
#-----------------------------------------------------------------------
#-----------------------------------------------------------------------
# Add a Classification column to the original data for data practioner role
filtered_data_cleaned <- filtered_data_cleaned %>%
mutate(Classification = case_when(
OCC_CODE == "15-1242" ~ "Data Engineer",
OCC_CODE %in% c("15-1243", "11-3021") ~ "Data Architect",
OCC_CODE == "15-2051" ~ "Data Scientist",
OCC_CODE %in% c("15-2031", "15-2041") ~ "Data Analyst",
OCC_CODE %in% c("13-2051", "13-2031", "13-2041") ~ "Business Intelligence",
TRUE ~ NA_character_ # Mark any unmatched OCC_CODE as NA for clarity
))
# Define the function to classify based on OWN_CODE
classify_ownership <- function(own_code) {
case_when(
own_code == 1 ~ "Federal Government",
own_code == 2 ~ "State Government",
own_code == 3 ~ "Local Government",
own_code == 123 ~ "Government (Federal, State, Local)",
own_code == 235 ~ "Mixed Public-Private",
own_code == 35 ~ "Private and Local Government",
own_code == 5 ~ "Private Sector",
own_code == 57 ~ "Gambling & Casino",
own_code == 58 ~ "Health & Government",
own_code == 59 ~ "Private & Postal Service",
own_code == 1235 ~ "All Sectors (Gov & Private)",
TRUE ~ NA_character_ # Assign NA for any unmatched code
)
}
# Apply the classification to create a new column in the dataset
filtered_data_cleaned <- filtered_data_cleaned %>%
mutate(Broad_Industry = sapply(OWN_CODE, classify_ownership))
#-----------------------------------------------------------------------National Average Salary and Total Employment by Data Practitioner Role
#-----------------------------------------------------------------------
## Data overview: summarizing total employment and mean salary by role
role_overview <- filtered_data_cleaned %>%
filter(AREA_TYPE == '1', OWN_CODE == '1235') %>%
select(Classification, TOT_EMP, A_MEAN) %>%
group_by(Classification) %>%
summarise(Average_Salary = mean(A_MEAN, na.rm = TRUE), Total_emp = sum(TOT_EMP, na.rm = TRUE))
#-----------------------------------------------------------------------
# Sort roles by Average Salary in descending order
sorted_roles <- role_overview %>%
arrange(desc(Average_Salary)) %>%
pull(Classification)
# Reshape data for faceting and set "Classification" with sorted levels
role_overview_long <- role_overview %>%
pivot_longer(cols = c(Average_Salary, Total_emp), names_to = "Metric", values_to = "Value") %>%
mutate(Classification = factor(Classification, levels = sorted_roles),
Color = ifelse(Classification == "Data Architect", "highlight", "gray"))
# Plot with faceting and enhanced formatting
ggplot(role_overview_long, aes(x = Classification, y = Value, fill = Color)) +
geom_bar(stat = "identity") +
coord_flip() +
facet_wrap(~ Metric, scales = "free_x", labeller = as_labeller(c(Average_Salary = "Average Salary (USD)", Total_emp = "Total Employment"))) +
scale_fill_manual(values = c("highlight" = "steelblue", "gray" = "lightgray"), guide = "none") +
labs(title = "National Average Salary and Total Employment by Data Practitioner Role",
x = "Data Practitioner Role",
y = "") +
theme_minimal() +
theme(
panel.grid = element_blank(), # Remove background grid
strip.text = element_text(face = "bold"), # Bold facet labels
axis.text.x = element_text(size = 10), # Adjust size for clarity
plot.title = element_text(hjust = 0.5) # Center title
) +
scale_y_continuous(labels = scales::comma) # Format large numbers with commas
Data Practioner Average Salary by Industry (National Level)
# Reshaping the data and applying the required transformations
industries_salary <- filtered_data_cleaned %>%
filter(AREA_TYPE == '1', OWN_CODE != '1235', !Broad_Industry %in% c("Government (Federal, State, Local)", "Private & Postal Service")) %>%
select(Broad_Industry, TOT_EMP, A_MEAN) %>%
group_by(Broad_Industry) %>%
summarise(Average_Salary = mean(A_MEAN, na.rm = TRUE), Total_emp = sum(TOT_EMP, na.rm = TRUE))
# Assign color for Federal Government and Private Sector, gray for others
industries_salary$Color <- ifelse(industries_salary$Broad_Industry %in% c("Federal Government", "Private Sector"),
ifelse(industries_salary$Broad_Industry == "Federal Government", "steelblue", "lightblue"),
"gray")
# Bar chart of average salary by role
ggplot(industries_salary, aes(x = reorder(Broad_Industry, Average_Salary), y = Average_Salary, fill = Color)) +
geom_bar(stat = "identity") +
geom_hline(yintercept = 113268.18, color = "lightblue", linetype = "dashed", linewidth = 0.7) + # Add red dashed line at $110,000
annotate("text", x = "Mixed Public-Private", y = 113268.18, label = "$113,268", vjust = -.5, hjust = 0.5, color = "lightblue", size = 3.5, fontface = "bold", angle = 90) +
geom_hline(yintercept = 115394, color = "steelblue", linetype = "dashed", linewidth = 0.7) +
annotate("text", x = "Mixed Public-Private", y = 115394, label = "$115,394", vjust = 1.3, hjust = 0.5, color = "steelblue", size = 3.5, fontface = "bold", angle = 90) + # Add label along
scale_fill_identity() + # Use the assigned colors directly
scale_y_continuous(labels = comma) +
coord_flip() + # Flip the axes to have horizontal bars
labs(title = "Data Practitioner Average Salary by Industry",
x = "Data Practitioner",
y = "Average Salary (USD)") +
theme_minimal() +
theme(
axis.text.x = element_text(size = 6, face = "bold"),
axis.text.y = element_text(size = 7, face = "bold"),
legend.position = "none", # Hide the legend
plot.title = element_text(hjust = 0.5, size = 15, face = "bold"), # Center title
axis.title.x = element_text(size = 11, face = "bold"),
axis.title.y = element_text(size = 11, face = "bold"),
panel.grid = element_blank(), # Remove minor grid lines
plot.margin = margin(t = 15, r = 15, b = 15, l = 15), # Adjust plot margins
)
Top 10 High-Paying Private Sectors for Data Practitioners
#-----------------------------------------------------------------------
## Data overview: summarizing total employment and mean salary by role
industries_private <- filtered_data_cleaned %>%
filter(AREA_TYPE == '1', OWN_CODE != '1235', Broad_Industry == "Private Sector" ) %>%
select(Broad_Industry, NAICS_TITLE, TOT_EMP, A_MEAN) %>%
group_by(NAICS_TITLE) %>%
summarise(Average_Salary = mean(A_MEAN, na.rm = TRUE), Total_emp = sum(TOT_EMP, na.rm = TRUE)) %>%
arrange(desc(Average_Salary)) %>%
head(10)
# Update the NAICS_TITLE values to shorter names
industries_private <- industries_private %>%
mutate(NAICS_TITLE = case_when(
NAICS_TITLE == "Taxi and Limousine Service" ~ "Taxi Services",
NAICS_TITLE == "Jewelry, Luggage, and Leather Goods Retailers" ~ "Luxury Retail",
NAICS_TITLE == "Local Messengers and Local Delivery" ~ "Delivery Services",
NAICS_TITLE == "Support Activities for Rail Transportation" ~ "Rail Support",
NAICS_TITLE == "Hydroelectric Power Generation" ~ "Hydropower",
NAICS_TITLE == "Farm and Garden Machinery and Equipment Merchant Wholesalers" ~ "Farm Wholesalers",
NAICS_TITLE == "Support Activities for Crop Production" ~ "Crop Support",
NAICS_TITLE == "Textile and Fabric Finishing and Fabric Coating Mills" ~ "Textile Mills",
NAICS_TITLE == "Other Investment Pools and Funds" ~ "Investment Funds",
TRUE ~ NAICS_TITLE # Leave other titles as they are
))
#-----------------------------------------------------------------------
# Assign color for Federal Government and Private Sector, gray for others
industries_private$Color <- ifelse(industries_private$NAICS_TITLE %in% c("Photographic Services", "Taxi Services"),
ifelse(industries_private$NAICS_TITLE == "Photographic Services", "steelblue", "steelblue"),
"lightblue")
#-----------------------------------------------------------------------
# Generate the bar chart with updated titles
ggplot(industries_private, aes(x = reorder(NAICS_TITLE, Average_Salary), y = Average_Salary, fill = Color)) +
geom_bar(stat = "identity") +
geom_hline(yintercept = 209000, color = "steelblue", linetype = "dashed", linewidth = 0.9) + # Add red dashed line at $200,000
annotate("text", x = "Hydropower", y = 209000, label = "$210,000", vjust = -.5, hjust = 0.5, color = "steelblue", size = 4.5, fontface = "bold", angle = 90) +
geom_hline(yintercept = 167467.5, color = "green", linetype = "dashed", linewidth = 0.9) + # Add red dashed line at $200,000
annotate("text", x = "Investment Funds", y = 167000, label = "$167,000", vjust = 1.45, hjust = 0.5, color = "steelblue", size = 3.8, fontface = "bold", angle = 90) +
coord_flip() +
scale_fill_identity() + # Use the assigned colors directly
scale_y_continuous(labels = comma) +
labs(title = "Top 10 High-Paying Private Sectors for Data Practitioners",
x = "Private Industry",
y = "Average Salary (USD)") +
theme_minimal() +
theme(axis.text.x = element_text(size = 6, face = "bold"),
axis.text.y = element_text(size = 7, face = "bold"),
plot.title = element_text(hjust = 0.5, size = 15, face = "bold"), # Center title
axis.title.x = element_text(size = 11, face = "bold"),
axis.title.y = element_text(size = 11, face = "bold"),
legend.position = "none", # Hide the legend
panel.grid = element_blank(), # Remove minor grid lines
plot.margin = margin(t = 15, r = 15, b = 15, l = 15), # Adjust plot margins)
)
#-----------------------------------------------------------------------Average Salary by State and Data Practioner Role
#-----------------------------------------------------------------------
## Data overview: summarizing total employment and mean salary by role
suppressMessages(
{role_state_overview <- filtered_data_cleaned %>%
filter(AREA_TYPE == '2') %>% # Only OWN_CODE == '1235' is that AREA_TYPE
select(AREA_TITLE, Classification, TOT_EMP, A_MEAN) %>%
group_by(AREA_TITLE, Classification) %>%
summarise(Average_Salary = mean(A_MEAN, na.rm = TRUE), Total_emp = sum(TOT_EMP, na.rm = TRUE))
})
#-----------------------------------------------------------------------
#-----------------------------------------------------------------------
#- Heatmap of average salary by state and role
ggplot(role_state_overview, aes(x = AREA_TITLE, y = Classification, fill = Average_Salary)) +
geom_tile(color = "white") +
scale_fill_gradient(low = "lightblue", high = "darkblue", na.value = "grey") +
labs(title = "Average Salary by State and Data Practioner Role",
x = "State",
y = "Data Practioner Role",
fill = "Average Salary (USD)") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 6),
axis.text.y = element_text(size = 8),
plot.title = element_text(face = "bold", size = 16, hjust = 0.5),
axis.title.x = element_text(face = "bold", size = 11),
axis.title.y = element_text(face = "bold", size = 11),
legend.position = "right",
legend.title = element_text(size = 12),
legend.text = element_text(size = 10),
panel.grid.major = element_blank(), # Remove background grid
panel.grid.minor = element_blank(), # Remove minor grid lines
plot.margin = unit(c(0.15, 0.15, 0.15, 0.15), "cm") # Add spacing around the plot
)
#-----------------------------------------------------------------------Conclusion
Data Architect as Top Earner: Commands the highest average salary among data roles, exceeding $150,000, showing strong demand for high-level expertise.
Federal vs. Private Sector: Federal Government leads in salaries for data practitioners at over $115,000, with the Private Sector closely following, highlighting competitiveness in attracting talent.
Top-Paying Private Industries: Photographic and Taxi Services offer average salaries above $200,000, with all top ten private industries paying over $160,000, reflecting robust investment in data roles.
High-Paying States: California, New York, and Washington consistently offer the highest salaries for data roles, driven by strong tech and finance sectors.
Source
- Bureau of Labor Statistics (BLS) - Occupational Employment and Wage Statistics (OEWS) - May 2023 All Data: https://www.bls.gov