Professional Output & Reporting

Communicating your analysis like a professional

← Lab 8: Tidyr Lab 9 of 10 Lab 10: Capstone →

⏳ Loading R... (first load takes ~15 seconds)

📖 Concept Recap

R gives you powerful tools for formatting output that looks like a real professional report:

cat() — print text with \n newlines and \t tabs; concatenates multiple values
sprintf() — C-style string formatting: %s (string), %d (integer), %.2f (float 2 decimal), %-20s (left-aligned 20 wide)
paste() / paste0() — combine strings; paste0() with no separator
format() — format numbers with big.mark=",", nsmall=2
strrep() — repeat a string: strrep("=", 50) makes a 50-char divider

The key is building report generator functions that take data and produce formatted, readable output automatically.

👀 Worked Example

A professional report generator using aligned columns and dividers:

generate_report <- function(data, title = "Analysis Report") { cat(strrep("=", 50), "\n", title, "\n", strrep("=", 50), "\n\n", sep="") cat("SUMMARY STATISTICS\n", strrep("-", 30), "\n", sep="") cat(sprintf("%-20s %10s\n", "Metric", "Value")) cat(strrep("-", 30), "\n", sep="") cat(sprintf("%-20s %10d\n", "Observations:", nrow(data))) cat(sprintf("%-20s %10.2f\n", "Mean Revenue:", mean(data$revenue))) cat(sprintf("%-20s %10.2f\n", "Median Revenue:", median(data$revenue))) cat(sprintf("%-20s %10.2f\n", "Std Deviation:", sd(data$revenue))) cat(strrep("-", 30), "\n", sep="") } sales <- data.frame(rep=c("Alice","Bob","Carol","David"), revenue=c(38300,29200,47500,28800)) generate_report(sales, "Q1 Sales Report")

✏️ Guided

Exercise 1 — Sales Leaderboard with Medals

Run this leaderboard printer and study the sprintf formatting. Then try adding a 4th and 5th place row without medals.

sales <- data.frame(
  rep     = c("Carol","Alice","David","Bob","Eve"),
  revenue = c(47500, 38300, 31200, 29200, 24800),
  deals   = c(12, 9, 8, 7, 6)
)

print_leaderboard <- function(df) {
  medals <- c("\U0001F947", "\U0001F948", "\U0001F949")  # gold, silver, bronze
  cat(strrep("=", 48), "\n", sep="")
  cat("       SALES LEADERBOARD\n")
  cat(strrep("=", 48), "\n", sep="")
  cat(sprintf("%-4s %-12s %12s %8s\n", "Rank", "Rep", "Revenue", "Deals"))
  cat(strrep("-", 48), "\n", sep="")
  for (i in 1:nrow(df)) {
    medal <- if (i <= 3) medals[i] else "   "
    cat(sprintf("%s #%-2d %-12s $%10s %8d\n",
                medal, i, df$rep[i],
                format(df$revenue[i], big.mark=","), df$deals[i]))
  }
  cat(strrep("-", 48), "\n", sep="")
  cat(sprintf("%-16s $%10s\n", "TEAM TOTAL:",
              format(sum(df$revenue), big.mark=",")))
}

print_leaderboard(sales)

Output will appear here...

💡 Hint: %-12s means left-aligned, 12 characters wide — the - flag left-justifies text. %10s right-aligns to 10 chars. format(n, big.mark=",") adds thousands separators like “47,500”.

💪 Independent

Exercise 2 — Monthly Report Function

Write a monthly_report(sales_df) function that prints a complete professional report with: a header, summary stats table, top 3 performers section, monthly totals section, and a footer with a simulated timestamp.

sales_df <- data.frame(
  rep     = c("Alice","Bob","Carol","Alice","David","Bob","Carol","David"),
  month   = c("Jan","Jan","Jan","Feb","Jan","Feb","Feb","Feb"),
  revenue = c(18500,12300,22100,19800,15600,16900,25400,13200),
  stringsAsFactors = FALSE
)

monthly_report <- function(df) {
  # --- Header ---
  cat(strrep("*", 50), "\n")
  cat("  MONTHLY SALES PERFORMANCE REPORT\n")
  cat(strrep("*", 50), "\n\n")

  # --- Summary Stats ---
  cat("OVERALL SUMMARY\n", strrep("-", 30), "\n", sep="")
  cat(sprintf("  Total Revenue:  $%s\n", format(sum(df$revenue), big.mark=",")))
  cat(sprintf("  Average Deal:   $%s\n", format(round(mean(df$revenue)), big.mark=",")))
  cat(sprintf("  Total Records:  %d\n\n", nrow(df)))

  # --- Top 3 Performers ---
  cat("TOP 3 PERFORMERS\n", strrep("-", 30), "\n", sep="")
  totals <- sort(tapply(df$revenue, df$rep, sum), decreasing=TRUE)
  for (i in 1:min(3, length(totals))) {
    cat(sprintf("  %d. %-10s $%s\n", i, names(totals)[i],
                format(totals[i], big.mark=",")))
  }

  # --- Monthly Totals ---
  cat("\nMONTHLY TOTALS\n", strrep("-", 30), "\n", sep="")
  monthly <- sort(tapply(df$revenue, df$month, sum), decreasing=TRUE)
  for (m in names(monthly)) {
    cat(sprintf("  %-6s $%s\n", m, format(monthly[m], big.mark=",")))
  }

  # --- Footer ---
  cat("\n", strrep("-", 50), "\n", sep="")
  cat("Report generated: 2026-04-02  |  R Practice Labs\n")
  cat(strrep("-", 50), "\n", sep="")
}

monthly_report(sales_df)

Output will appear here...

💡 Hint: tapply(revenue, rep, sum) gives total revenue per rep. Wrap in sort(..., decreasing=TRUE) for the leaderboard order. Use names() to get the rep names from the sorted result.

🔥 Challenge

Exercise 3 — Side-by-Side Comparison Report

Write comparison_report(group1, group2, label1, label2) that prints: side-by-side stats, marks which group is higher with ▲, a plain-English t-test interpretation, and a text bar chart of the two means.

set.seed(42)
group1 <- rnorm(25, mean=78, sd=10)
group2 <- rnorm(25, mean=72, sd=12)

comparison_report <- function(g1, g2, label1="Group A", label2="Group B") {
  m1 <- round(mean(g1), 2); m2 <- round(mean(g2), 2)
  s1 <- round(sd(g1), 2);   s2 <- round(sd(g2), 2)
  ttest <- t.test(g1, g2)

  cat(strrep("=", 50), "\n")
  cat(sprintf("  COMPARISON: %s vs %s\n", label1, label2))
  cat(strrep("=", 50), "\n\n")

  # Side-by-side stats
  cat(sprintf("%-12s %12s %12s\n", "Statistic", label1, label2))
  cat(strrep("-", 36), "\n")
  hi1 <- if(m1 > m2) " \u25B2" else ""
  hi2 <- if(m2 > m1) " \u25B2" else ""
  cat(sprintf("%-12s %11.2f%s %11.2f%s\n", "Mean:",   m1, hi1, m2, hi2))
  cat(sprintf("%-12s %12.2f %12.2f\n",     "SD:",     s1, s2))
  cat(sprintf("%-12s %12d %12d\n",          "N:",     length(g1), length(g2)))

  # T-test interpretation
  p <- round(ttest$p.value, 4)
  cat("\nT-TEST RESULT\n", strrep("-", 36), "\n", sep="")
  cat(sprintf("  p-value = %.4f\n", p))
  if (p < 0.05) {
    cat(sprintf("  The difference is statistically significant (p<0.05).\n"))
    cat(sprintf("  %s has a significantly higher mean than %s.\n",
                if(m1 > m2) label1 else label2,
                if(m1 > m2) label2 else label1))
  } else {
    cat("  No statistically significant difference detected (p>=0.05).\n")
  }

  # Text bar chart
  cat("\nMEAN COMPARISON (text chart)\n", strrep("-", 36), "\n", sep="")
  max_m <- max(m1, m2)
  bar1  <- strrep("|", round(m1 / max_m * 20))
  bar2  <- strrep("|", round(m2 / max_m * 20))
  cat(sprintf("  %-10s %s %.1f\n", label1, bar1, m1))
  cat(sprintf("  %-10s %s %.1f\n", label2, bar2, m2))
}

comparison_report(group1, group2, "Treatment", "Control")

Output will appear here...

💡 Hint: \u25B2 is the Unicode up-arrow ▲. strrep("|", n) creates a text bar of length n. Scale bars to the maximum mean so the wider bar always fills to 20 characters.

🏆 Mini Project — Executive Summary Generator

Auto-Generated Executive Report

Build a complete exec_summary(sales) function that automatically generates a multi-section executive report from any sales data frame, including auto-written insight sentences.

sales <- data.frame(
  rep     = c("Alice","Bob","Carol","Alice","David","Bob","Carol","David","Eve","Eve"),
  region  = c("West","East","West","West","South","East","West","South","North","North"),
  month   = c("Jan","Jan","Jan","Feb","Jan","Feb","Feb","Feb","Jan","Feb"),
  revenue = c(18500,12300,22100,19800,15600,16900,25400,13200,11800,14200),
  stringsAsFactors = FALSE
)

exec_summary <- function(df) {
  # Computed values
  rep_totals  <- sort(tapply(df$revenue, df$rep,    sum),    decreasing=TRUE)
  reg_totals  <- sort(tapply(df$revenue, df$region, sum),    decreasing=TRUE)
  mo_totals   <- tapply(df$revenue, df$month,  sum)
  team_avg    <- mean(df$revenue)
  top_rep     <- names(rep_totals)[1]
  top_rev     <- rep_totals[1]
  pct_above   <- round((top_rev / sum(rep_totals) * 100), 1)
  best_region <- names(reg_totals)[1]

  # --- Header ---
  cat(strrep("#", 52), "\n")
  cat("#   EXECUTIVE SALES SUMMARY REPORT                 #\n")
  cat(strrep("#", 52), "\n\n")

  # --- Overview ---
  cat("1. OVERVIEW\n", strrep("-", 40), "\n", sep="")
  cat(sprintf("   Team size:        %d reps\n",       length(unique(df$rep))))
  cat(sprintf("   Total revenue:    $%s\n",
              format(sum(df$revenue), big.mark=",")))
  cat(sprintf("   Avg per deal:     $%s\n",
              format(round(team_avg), big.mark=",")))
  cat(sprintf("   Months covered:   %s\n\n",
              paste(sort(unique(df$month)), collapse=", ")))

  # --- Team Rankings ---
  cat("2. TEAM RANKINGS\n", strrep("-", 40), "\n", sep="")
  for (i in seq_along(rep_totals)) {
    pct <- round(rep_totals[i] / sum(rep_totals) * 100, 1)
    cat(sprintf("   %d. %-8s $%s (%s%%)\n", i, names(rep_totals)[i],
                format(rep_totals[i], big.mark=","), pct))
  }

  # --- Regional Breakdown ---
  cat("\n3. REGIONAL BREAKDOWN\n", strrep("-", 40), "\n", sep="")
  for (r in names(reg_totals)) {
    cat(sprintf("   %-8s $%s\n", r, format(reg_totals[r], big.mark=",")))
  }

  # --- Monthly Trend ---
  cat("\n4. MONTHLY TREND\n", strrep("-", 40), "\n", sep="")
  for (m in sort(names(mo_totals))) {
    bar <- strrep("|", round(mo_totals[m] / max(mo_totals) * 15))
    cat(sprintf("   %-4s %s $%s\n", m, bar,
                format(mo_totals[m], big.mark=",")))
  }

  # --- Auto-Generated Insights ---
  cat("\n5. KEY INSIGHTS\n", strrep("-", 40), "\n", sep="")
  cat(sprintf("   - %s had the highest revenue at $%s, accounting for %s%% of team total.\n",
              top_rep, format(top_rev, big.mark=","), pct_above))
  cat(sprintf("   - The %s region led all regions in total revenue.\n", best_region))
  quota_met <- sum(df$revenue > 15000)
  cat(sprintf("   - %d of %d deals exceeded the $15,000 quota threshold.\n",
              quota_met, nrow(df)))

  cat("\n", strrep("#", 52), "\n", sep="")
}

exec_summary(sales)

Output will appear here...

💡 Hint: Store computed values in named variables at the top of the function so the insight sentences can reference them naturally: cat(sprintf("... %s ...", top_rep)). This makes the report auto-update when the data changes.

✅ Lab 9 Complete!

You can now build professional, auto-generated reports in R using sprintf, cat, and structured formatting. This skill transforms raw analysis into polished deliverables that communicate clearly to any audience.

Continue to Lab 10: Capstone Analysis →

← Lab 8: Tidyr Lab 9 of 10 Lab 10: Capstone →