Titanic Passengers and Crew

Two ages are NA; other fields have no missing values. Fractional infant ages are retained. Records are ordered by class, sex, numeric age, outcome and embarkation, with missing ages last within class/sex groups. The build does not impute ages, deduplicate people or reconstruct identifiers.

Listing 1 shows ten lines without truncating the files used for calculations. Set rows to change only this preview.

head -n "$rows" data/titanic.csv

class,sex,age,survived,embarked
1st,female,2,0,S
1st,female,13,1,S
1st,female,16,1,C
1st,female,16,1,S
1st,female,17,1,C
1st,female,17,1,S
1st,female,18,1,C
1st,female,18,1,C
1st,female,18,1,C

Listing 2 rejects malformed rows, unexpected categories, ages outside the documented range and changed cohort totals. Tangling preserves every record and its order. The five embedded fields contain neither quoted text nor commas, so FS = "," is sufficient here; it is not a general parser for arbitrary quoted CSV files.

BEGIN { FS = "," }
 NR == 1 {
  if ($0 != "class,sex,age,survived,embarked") bad = 1
  next
}
{
  if (NF != 5 || $1 !~ /^(1st|2nd|3rd|Crew)$/ ||
      $2 !~ /^(female|male)$/ || $4 !~ /^[01]$/ ||
      $5 !~ /^[BCQS]$/) bad = 1
  if ($3 == "NA") missing++
  else if ($3 !~ /^[0-9]+([.][0-9]+)?$/ || $3 < 0 || $3 >= 75) bad = 1
  n++; survived += $4; crew += ($1 == "Crew")
}
END {
  if (bad || n != 2207 || survived != 711 || crew != 890 || missing != 2) {
    print "Invalid Titanic snapshot" > "/dev/stderr"; exit 1
  }
}

Listing 3 counts everyone, including the two unknown ages. For each group, deaths are aboard minus survivors. The explicit loop fixes the display order; associative-array iteration would not guarantee it.

 BEGIN { FS = OFS = ","; print "Class", "Aboard", "Survived", "Died" }
NR > 1 { n[$1]++; s[$1] += $4 }
END {
  split("1st 2nd 3rd Crew", label, " ")
  for (c = 1; c <= 4; c++) {
    k = label[c]; print k, n[k]+0, s[k]+0, n[k]-s[k]
  }
}

data/titanic_by_class.csv

Listing 4 displays the generated class-and-crew counts.

cat data/titanic_by_class.csv

Class,Aboard,Survived,Died
1st,324,201,123
2nd,284,118,166
3rd,709,181,528
Crew,890,211,679

Listing 5 uses a two-part key. Women's counts occupy columns 2–4, men's columns 5–7. Adding the two sexes recovers the class totals.

 BEGIN {
  FS = OFS = ","
  print "Class", "Women aboard", "Women survived", "Women died", \
        "Men aboard", "Men survived", "Men died"
}
NR > 1 { n[$1, $2]++; s[$1, $2] += $4 }
END {
  split("1st 2nd 3rd Crew", label, " ")
  for (c = 1; c <= 4; c++) {
    k = label[c]
    print k, n[k,"female"]+0, s[k,"female"]+0, n[k,"female"]-s[k,"female"], \
             n[k,"male"]+0, s[k,"male"]+0, n[k,"male"]-s[k,"male"]
  }
}

data/titanic_by_sex.csv

Listing 6 displays the generated table.

cat data/titanic_by_sex.csv

Class,Women aboard,Women survived,Women died,Men aboard,Men survived,Men died
1st,144,139,5,180,62,118
2nd,106,94,12,178,24,154
3rd,216,106,110,493,75,418
Crew,23,20,3,867,191,676

Listing 7 groups unrounded ages into [0,3), [3,6), through [72,75), pooling sexes. The labels 1, 4, …, 73 are the middle completed-year ages, not exact continuous interval midpoints. Class codes 1–4 identify first, second, third and crew. Unknown ages are excluded.

All 25 × 4 combinations are emitted, ordered by age and then class. Empty cells have n=0, survivors=0 and NA, not 0%. Percentages have six decimal places. Each age band is followed by its four class/crew categories in order.

 BEGIN {
  FS = OFS = ","
  code["1st"]=1; code["2nd"]=2; code["3rd"]=3; code["Crew"]=4
  print "age_midpoint", "class_code", "survival_pct", "n", "survivors"
}
NR > 1 && $3 != "NA" {
  band = int($3 / 3); c = code[$1]
  n[band,c]++; s[band,c] += $4
}
END {
  for (band = 0; band < 25; band++)
    for (c = 1; c <= 4; c++)
      print 3*band+1, c, \
            n[band,c] ? sprintf("%.6f", 100*s[band,c]/n[band,c]) : "NA", \
            n[band,c]+0, s[band,c]+0
}

data/titanic_by_age_and_class.csv

cat data/titanic_by_age_and_class.csv

age_midpoint,class_code,survival_pct,n,survivors
1,1,50.000000,2,1
1,2,100.000000,10,10
1,3,45.454545,22,10
1,4,NA,0,0
4,1,100.000000,1,1
4,2,100.000000,6,6
4,3,57.142857,21,12
4,4,NA,0,0
7,1,100.000000,1,1
7,2,100.000000,6,6
7,3,20.000000,15,3
7,4,NA,0,0
10,1,100.000000,1,1
10,2,NA,0,0
10,3,23.809524,21,5
10,4,NA,0,0
13,1,100.000000,2,2
13,2,80.000000,5,4
13,3,27.272727,11,3
13,4,NA,0,0
16,1,83.333333,6,5
16,2,57.142857,7,4
16,3,26.666667,45,12
16,4,5.555556,18,1
19,1,83.333333,12,10
19,2,33.333333,24,8
19,3,21.649485,97,21
19,4,19.696970,66,13
22,1,80.952381,21,17
22,2,31.034483,29,9
22,3,23.893805,113,27
22,4,29.292929,99,29
25,1,70.588235,17,12
25,2,35.294118,34,12
25,3,39.240506,79,31
25,4,20.192308,104,21
28,1,66.666667,21,14
28,2,46.428571,28,13
28,3,21.794872,78,17
28,4,30.275229,109,33
31,1,65.217391,23,15
31,2,36.363636,33,12
31,3,29.629630,54,16
31,4,29.032258,124,36
34,1,88.888889,18,16
34,2,41.176471,17,7
34,3,24.324324,37,9
34,4,19.047619,84,16
37,1,51.851852,27,14
37,2,31.578947,19,6
37,3,23.076923,26,6
37,4,15.730337,89,14
40,1,57.142857,28,16
40,2,46.153846,13,6
40,3,10.000000,30,3
40,4,28.571429,77,22
43,1,57.894737,19,11
43,2,20.000000,10,2
43,3,5.263158,19,1
43,4,17.948718,39,7
46,1,51.515152,33,17
46,2,10.000000,10,1
46,3,30.769231,13,4
46,4,30.303030,33,10
49,1,66.666667,27,18
49,2,60.000000,10,6
49,3,0.000000,9,0
49,4,14.285714,21,3
52,1,75.000000,12,9
52,2,20.000000,5,1
52,3,0.000000,1,0
52,4,36.363636,11,4
55,1,53.333333,15,8
55,2,40.000000,5,2
55,3,0.000000,4,0
55,4,0.000000,7,0
58,1,38.461538,13,5
58,2,40.000000,5,2
58,3,0.000000,2,0
58,4,16.666667,6,1
61,1,46.153846,13,6
61,2,25.000000,4,1
61,3,0.000000,2,0
61,4,50.000000,2,1
64,1,25.000000,8,2
64,2,0.000000,3,0
64,3,25.000000,4,1
64,4,0.000000,1,0
67,1,0.000000,1,0
67,2,NA,0,0
67,3,0.000000,2,0
67,4,NA,0,0
70,1,0.000000,3,0
70,2,NA,0,0
70,3,0.000000,1,0
70,4,NA,0,0
73,1,NA,0,0
73,2,0.000000,1,0
73,3,0.000000,1,0
73,4,NA,0,0

Listing 9 uses the same 25 three-year intervals as the age/class summary: [0,3), [3,6), through [72,75). Labels 0-2, 3-5, …, 72-74 describe completed years; fractional ages are grouped without rounding. There is no combined 60+ group. All classes, crew and both sexes are pooled, and the same two unknown ages are excluded. Percentages have one decimal place; empty groups use NaN to distinguish an undefined percentage from zero.

 BEGIN { FS = OFS = ","; print "Age", "People", "Survived %" }
NR > 1 && $3 != "NA" {
  g = int($3 / 3)
  n[g]++; s[g] += $4
}
END {
  for (g = 0; g < 25; g++)
    print sprintf("%d-%d", 3*g, 3*g+2), n[g]+0, \
          n[g] ? sprintf("%.1f", 100*s[g]/n[g]) : "NaN"
}

data/titanic_by_age.csv

Listing 10 displays all 25 bins.

cat data/titanic_by_age.csv

Age,People,Survived %
0-2,34,61.8
3-5,28,67.9
6-8,22,45.5
9-11,22,27.3
12-14,18,50.0
15-17,76,28.9
18-20,199,26.1
21-23,262,31.3
24-26,234,32.5
27-29,236,32.6
30-32,234,33.8
33-35,156,30.8
36-38,161,24.8
39-41,148,31.8
42-44,87,24.1
45-47,89,36.0
48-50,67,40.3
51-53,29,48.3
54-56,31,32.3
57-59,26,30.8
60-62,21,38.1
63-65,16,18.8
66-68,3,0.0
69-71,4,0.0
72-74,2,0.0

Listing 11 creates a manifest covering all five CSV files. Hashes detect file changes; they do not prove historical accuracy.

set -o pipefail
(
  cd data
  if command -v sha256sum >/dev/null 2>&1; then
    sha256sum titanic.csv titanic_by_class.csv titanic_by_sex.csv \
      titanic_by_age_and_class.csv titanic_by_age.csv
  else
    shasum -a 256 titanic.csv titanic_by_class.csv titanic_by_sex.csv \
      titanic_by_age_and_class.csv titanic_by_age.csv
  fi
) | tee data/SHA256SUMS

Listing 12 checks the exported totals against the source snapshot. The following checks cover row counts, empty age/class cells and valid percentages.

BEGIN { FS = "," }
NR > 1 { n += $2; s += $3; if ($2 != $3+$4) bad = 1 }
END { exit (NR != 5 || n != 2207 || s != 711 || bad) }

Listing 13 checks the separate female and male counts.

BEGIN { FS = "," }
NR > 1 {
  n += $2+$5; s += $3+$6
  if ($2 != $3+$4 || $5 != $6+$7) bad = 1
}
END { exit (NR != 5 || n != 2207 || s != 711 || bad) }

Listing 14 checks all 100 age/class cells.

BEGIN { FS = "," }
NR > 1 {
  n += $4; s += $5; empty += ($4 == 0)
  if (($4 == 0 && $3 != "NA") ||
      ($4 > 0 && ($3 == "NA" || $3 < 0 || $3 > 100))) bad = 1
}
END { exit (NR != 101 || n != 2205 || s != 711 || empty != 12 || bad) }

Listing 15 checks all 25 age-only groups.

BEGIN { FS = "," }
NR > 1 {
  n += $2
  if ($2 > 0 && ($3 == "NaN" || $3 < 0 || $3 > 100)) bad = 1
}
END { exit (NR != 26 || n != 2205 || bad) }