In this data set we wil primary deal with missing values. If the amount of missing data is not big relative to the size of the dataset then leaving the missing values might be the best strategy, however leaging the available data doesn’t produce the best information and we need to look for fixes before leaving out the potential useful data points.
This data set is from kaggle and is described in detail on webpage. Following is the description of data set.
VARIABLE DESCRIPTIONS: survival Survival (0 = No; 1 = Yes) pclass Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd) name Name sex Sex age Age sibsp Number of Siblings/Spouses Aboard parch Number of Parents/Children Aboard ticket Ticket Number fare Passenger Fare cabin Cabin embarked Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)
Attaching libraries
library(data.table)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:data.table':
##
## between, last## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)
library(readr)0: Load the data in RStudio
Save the data set as a CSV file called titanic_original.csv and load it in RStudio into a data frame
Read the data
titanic_original <- read.csv("C:/Users/6430/Desktop/Project/titanic_original.csv", header = TRUE)
df<-read.csv("C:/Users/6430/Desktop/Project/titanic_original.csv",na.strings=c("","na"))# we take care of the missing values right here.
str(df)## 'data.frame': 1309 obs. of 14 variables:
## $ pclass : int 1 1 1 1 1 1 1 1 1 1 ...
## $ survived : int 1 1 0 0 0 1 1 0 1 0 ...
## $ name : Factor w/ 1307 levels "Abbing, Mr. Anthony",..: 22 24 25 26 27 31 46 47 51 55 ...
## $ sex : Factor w/ 2 levels "female","male": 1 2 1 2 1 2 1 2 1 2 ...
## $ age : num 29 0.917 2 30 25 ...
## $ sibsp : int 0 1 1 1 1 0 1 0 2 0 ...
## $ parch : int 0 2 2 2 2 0 0 0 0 0 ...
## $ ticket : Factor w/ 929 levels "110152","110413",..: 188 50 50 50 50 125 93 16 77 826 ...
## $ fare : num 211 152 152 152 152 ...
## $ cabin : Factor w/ 186 levels "A10","A11","A14",..: 44 80 80 80 80 150 146 16 62 NA ...
## $ embarked : Factor w/ 3 levels "C","Q","S": 3 3 3 3 3 3 3 3 3 1 ...
## $ boat : Factor w/ 27 levels "1","10","11",..: 12 3 NA NA NA 13 2 NA 27 NA ...
## $ body : int NA NA NA 135 NA NA NA NA NA 22 ...
## $ home.dest: Factor w/ 369 levels "?Havana, Cuba",..: 309 231 231 231 231 237 162 24 22 229 ...class(df)## [1] "data.frame"1: Port of embarkation
The embarked column has some missing values, which are known to correspond to passengers who actually embarked at Southampton. Find the missing values and replace them with S. (Caution: Sometimes a missing value might be read into R as a blank or empty string.)
df[is.na(df$embarked),] #only the rows 169 and 285## pclass survived name sex age
## 169 1 1 Icard, Miss. Amelie female 38
## 285 1 1 Stone, Mrs. George Nelson (Martha Evelyn) female 62
## sibsp parch ticket fare cabin embarked boat body home.dest
## 169 0 0 113572 80 B28 <NA> 6 NA <NA>
## 285 0 0 113572 80 B28 <NA> 6 NA Cincinatti, OHdf1<-df
df1$embarked <-lapply(df1$embarked, as.character)# Since embarked column is factor, it has to be converted into "character" first to input string.
df1$embarked[which(is.na(df1$embarked))] <-"s"
df2<-df1
df2[is.na(df2$embarked),] ## return null i.e "na" values have been replaced by "s" as required.## [1] pclass survived name sex age sibsp parch
## [8] ticket fare cabin embarked boat body home.dest
## <0 rows> (or 0-length row.names)2: Age
You’ll notice that a lot of the values in the Age column are missing. While there are many ways to fill these missing values, using the mean or median of the rest of the values is quite common in such cases.
Calculate the mean of the Age column and use that value to populate the missing values
Think about other ways you could have populated the missing values in the age column. Why would you pick any of those over the mean (or not)?
df2[is.na(df2$age),]# quite many rows as suggested above## pclass survived
## 16 1 0
## 38 1 1
## 41 1 0
## 47 1 0
## 60 1 1
## 70 1 1
## 71 1 0
## 75 1 0
## 81 1 0
## 107 1 0
## 108 1 1
## 109 1 1
## 119 1 0
## 122 1 1
## 126 1 0
## 135 1 1
## 148 1 0
## 153 1 1
## 158 1 0
## 167 1 0
## 177 1 1
## 180 1 0
## 185 1 0
## 197 1 1
## 205 1 1
## 220 1 1
## 224 1 0
## 236 1 1
## 238 1 0
## 242 1 0
## 255 1 1
## 257 1 1
## 270 1 0
## 278 1 1
## 284 1 0
## 294 1 1
## 298 1 1
## 319 1 0
## 321 1 1
## 364 2 0
## 383 2 0
## 385 2 0
## 411 2 0
## 470 2 1
## 474 2 0
## 478 2 0
## 484 2 1
## 492 2 0
## 496 2 0
## 525 2 1
## 529 2 0
## 532 2 0
## 582 2 0
## 596 2 0
## 598 2 1
## 673 3 0
## 681 3 0
## 682 3 0
## 683 3 0
## 706 3 0
## 707 3 0
## 757 3 0
## 758 3 1
## 768 3 0
## 769 3 0
## 776 3 0
## 790 3 0
## 796 3 0
## 799 3 1
## 801 3 0
## 802 3 0
## 803 3 0
## 805 3 0
## 806 3 1
## 809 3 0
## 813 3 0
## 814 3 0
## 816 3 0
## 817 3 0
## 820 3 1
## 836 3 0
## 843 3 0
## 844 3 0
## 853 3 0
## 855 3 0
## 857 3 1
## 859 3 1
## 866 3 0
## 872 3 0
## 873 3 1
## 875 3 1
## 877 3 0
## 880 3 0
## 883 3 0
## 887 3 1
## 888 3 1
## 901 3 0
## 902 3 0
## 903 3 0
## 904 3 0
## 919 3 0
## 921 3 0
## 922 3 0
## 923 3 1
## 924 3 1
## 927 3 1
## 928 3 0
## 929 3 0
## 930 3 0
## 931 3 0
## 932 3 0
## 941 3 0
## 943 3 0
## 945 3 0
## 946 3 1
## 947 3 0
## 949 3 0
## 955 3 0
## 956 3 0
## 957 3 0
## 958 3 0
## 959 3 0
## 962 3 0
## 963 3 0
## 972 3 0
## 974 3 0
## 977 3 0
## 983 3 0
## 984 3 0
## 985 3 1
## 988 3 0
## 989 3 0
## 990 3 0
## 992 3 1
## 994 3 1
## 995 3 0
## 998 3 1
## 999 3 0
## 1000 3 1
## 1001 3 1
## 1002 3 1
## 1003 3 1
## 1004 3 1
## 1005 3 1
## 1006 3 0
## 1007 3 1
## 1010 3 0
## 1013 3 0
## 1014 3 0
## 1015 3 0
## 1017 3 0
## 1019 3 0
## 1023 3 0
## 1024 3 1
## 1028 3 0
## 1029 3 1
## 1030 3 0
## 1031 3 0
## 1033 3 0
## 1034 3 1
## 1035 3 1
## 1036 3 1
## 1037 3 1
## 1038 3 1
## 1039 3 0
## 1040 3 1
## 1042 3 0
## 1043 3 1
## 1044 3 1
## 1045 3 1
## 1053 3 0
## 1054 3 0
## 1055 3 0
## 1056 3 0
## 1070 3 0
## 1071 3 0
## 1072 3 1
## 1073 3 0
## 1074 3 0
## 1075 3 0
## 1077 3 0
## 1078 3 1
## 1079 3 1
## 1081 3 1
## 1082 3 1
## 1086 3 0
## 1096 3 0
## 1110 3 0
## 1115 3 0
## 1116 3 0
## 1117 3 0
## 1122 3 1
## 1123 3 1
## 1124 3 1
## 1125 3 0
## 1129 3 0
## 1133 3 0
## 1136 3 0
## 1137 3 0
## 1138 3 0
## 1139 3 0
## 1150 3 1
## 1151 3 0
## 1152 3 0
## 1155 3 0
## 1156 3 0
## 1160 3 1
## 1163 3 1
## 1164 3 0
## 1165 3 0
## 1167 3 0
## 1168 3 0
## 1169 3 0
## 1171 3 0
## 1173 3 0
## 1174 3 0
## 1175 3 0
## 1176 3 0
## 1177 3 0
## 1178 3 0
## 1179 3 0
## 1180 3 0
## 1181 3 0
## 1185 3 0
## 1186 3 0
## 1187 3 0
## 1194 3 0
## 1195 3 0
## 1196 3 0
## 1198 3 0
## 1199 3 1
## 1200 3 0
## 1201 3 0
## 1203 3 0
## 1213 3 0
## 1214 3 0
## 1215 3 0
## 1216 3 0
## 1217 3 1
## 1220 3 0
## 1222 3 0
## 1242 3 0
## 1243 3 0
## 1244 3 0
## 1246 3 0
## 1247 3 0
## 1248 3 1
## 1250 3 0
## 1251 3 0
## 1254 3 0
## 1256 3 0
## 1263 3 0
## 1269 3 0
## 1283 3 0
## 1284 3 0
## 1285 3 0
## 1292 3 0
## 1293 3 0
## 1294 3 0
## 1298 3 0
## 1303 3 0
## 1304 3 0
## 1306 3 0
## name sex
## 16 Baumann, Mr. John D male
## 38 Bradley, Mr. George ("George Arthur Brayton") male
## 41 Brewe, Dr. Arthur Jackson male
## 47 Cairns, Mr. Alexander male
## 60 Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genevieve Fosdick) female
## 70 Chibnall, Mrs. (Edith Martha Bowerman) female
## 71 Chisholm, Mr. Roderick Robert Crispin male
## 75 Clifford, Mr. George Quincy male
## 81 Crafton, Mr. John Bertram male
## 107 Farthing, Mr. John male
## 108 Flegenheim, Mrs. Alfred (Antoinette) female
## 109 Fleming, Miss. Margaret female
## 119 Franklin, Mr. Thomas Parham male
## 122 Frauenthal, Mrs. Henry William (Clara Heinsheimer) female
## 126 Fry, Mr. Richard male
## 135 Goldenberg, Mrs. Samuel L (Edwiga Grabowska) female
## 148 Harrington, Mr. Charles H male
## 153 Hawksford, Mr. Walter James male
## 158 Hilliard, Mr. Herbert Henry male
## 167 Hoyt, Mr. William Fisher male
## 177 Kenyon, Mrs. Frederick R (Marion) female
## 180 Klaber, Mr. Herman male
## 185 Lewy, Mr. Ervin G male
## 197 Marechal, Mr. Pierre male
## 205 Meyer, Mrs. Edgar Joseph (Leila Saks) female
## 220 Omont, Mr. Alfred Fernand male
## 224 Parr, Mr. William Henry Marsh male
## 236 Rheims, Mr. George Alexander Lucien male
## 238 Robbins, Mr. Victor male
## 242 Rood, Mr. Hugh Roscoe male
## 255 Saalfeld, Mr. Adolphe male
## 257 Salomon, Mr. Abraham L male
## 270 Smith, Mr. Richard William male
## 278 Spencer, Mrs. William Augustus (Marie Eugenie) female
## 284 Stewart, Mr. Albert A male
## 294 Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright) female
## 298 Thorne, Mrs. Gertrude Maybelle female
## 319 Williams-Lambert, Mr. Fletcher Fellows male
## 321 Woolner, Mr. Hugh male
## 364 Campbell, Mr. William male
## 383 Corey, Mrs. Percy C (Mary Phyllis Elizabeth Miller) female
## 385 Cunningham, Mr. Alfred Fleming male
## 411 Frost, Mr. Anthony Wood "Archie" male
## 470 Keane, Miss. Nora A female
## 474 Knight, Mr. Robert J male
## 478 Lamb, Mr. John Joseph male
## 484 Leitch, Miss. Jessie Wills female
## 492 Malachard, Mr. Noel male
## 496 Mangiavacchi, Mr. Serafino Emilio male
## 525 Padro y Manent, Mr. Julian male
## 529 Parkes, Mr. Francis "Frank" male
## 532 Pernot, Mr. Rene male
## 582 Watson, Mr. Ennis Hastings male
## 596 Wheeler, Mr. Edwin "Frederick" male
## 598 Williams, Mr. Charles Eugene male
## 673 Betros, Master. Seman male
## 681 Boulos, Mr. Hanna male
## 682 Boulos, Mrs. Joseph (Sultana) female
## 683 Bourke, Miss. Mary female
## 706 Caram, Mr. Joseph male
## 707 Caram, Mrs. Joseph (Maria Elias) female
## 757 Davison, Mr. Thomas Henry male
## 758 Davison, Mrs. Thomas Henry (Mary E Finck) female
## 768 Demetri, Mr. Marinko male
## 769 Denkoff, Mr. Mitto male
## 776 Doharr, Mr. Tannous male
## 790 Elias, Mr. Dibo male
## 796 Emir, Mr. Farred Chehab male
## 799 Finoli, Mr. Luigi male
## 801 Fleming, Miss. Honora female
## 802 Flynn, Mr. James male
## 803 Flynn, Mr. John male
## 805 Foley, Mr. William male
## 806 Foo, Mr. Choong male
## 809 Ford, Mr. Arthur male
## 813 Fox, Mr. Patrick male
## 814 Franklin, Mr. Charles (Charles Fardon) male
## 816 Garfirth, Mr. John male
## 817 Gheorgheff, Mr. Stanio male
## 820 Glynn, Miss. Mary Agatha female
## 836 Guest, Mr. Robert male
## 843 Hagland, Mr. Ingvald Olai Olsen male
## 844 Hagland, Mr. Konrad Mathias Reiersen male
## 853 Harknett, Miss. Alice Phoebe female
## 855 Hart, Mr. Henry male
## 857 Healy, Miss. Hanora "Nora" female
## 859 Hee, Mr. Ling male
## 866 Henry, Miss. Delia female
## 872 Horgan, Mr. John male
## 873 Howard, Miss. May Elizabeth female
## 875 Hyman, Mr. Abraham male
## 877 Ilieff, Mr. Ylio male
## 880 Ivanoff, Mr. Kanio male
## 883 Jardin, Mr. Jose Neto male
## 887 Jermyn, Miss. Annie female
## 888 Johannesen-Bratthammer, Mr. Bernt male
## 901 Johnston, Master. William Arthur "Willie" male
## 902 Johnston, Miss. Catherine Helen "Carrie" female
## 903 Johnston, Mr. Andrew G male
## 904 Johnston, Mrs. Andrew G (Elizabeth "Lily" Watson) female
## 919 Kassem, Mr. Fared male
## 921 Keane, Mr. Andrew "Andy" male
## 922 Keefe, Mr. Arthur male
## 923 Kelly, Miss. Anna Katherine "Annie Kate" female
## 924 Kelly, Miss. Mary female
## 927 Kennedy, Mr. John male
## 928 Khalil, Mr. Betros male
## 929 Khalil, Mrs. Betros (Zahie "Maria" Elias) female
## 930 Kiernan, Mr. John male
## 931 Kiernan, Mr. Philip male
## 932 Kilgannon, Mr. Thomas J male
## 941 Kraeff, Mr. Theodor male
## 943 Lahoud, Mr. Sarkis male
## 945 Laleff, Mr. Kristo male
## 946 Lam, Mr. Ali male
## 947 Lam, Mr. Len male
## 949 Lane, Mr. Patrick male
## 955 Lefebre, Master. Henry Forbes male
## 956 Lefebre, Miss. Ida female
## 957 Lefebre, Miss. Jeannie female
## 958 Lefebre, Miss. Mathilde female
## 959 Lefebre, Mrs. Frank (Frances) female
## 962 Lennon, Miss. Mary female
## 963 Lennon, Mr. Denis male
## 972 Linehan, Mr. Michael male
## 974 Lithman, Mr. Simon male
## 977 Lockyer, Mr. Edward male
## 983 Lyntakoff, Mr. Stanko male
## 984 MacKay, Mr. George William male
## 985 Madigan, Miss. Margaret "Maggie" female
## 988 Mahon, Miss. Bridget Delia female
## 989 Mahon, Mr. John male
## 990 Maisner, Mr. Simon male
## 992 Mamee, Mr. Hanna male
## 994 Mannion, Miss. Margareth female
## 995 Mardirosian, Mr. Sarkis male
## 998 Masselmani, Mrs. Fatima female
## 999 Matinoff, Mr. Nicola male
## 1000 McCarthy, Miss. Catherine "Katie" female
## 1001 McCormack, Mr. Thomas Joseph male
## 1002 McCoy, Miss. Agnes female
## 1003 McCoy, Miss. Alicia female
## 1004 McCoy, Mr. Bernard male
## 1005 McDermott, Miss. Brigdet Delia female
## 1006 McEvoy, Mr. Michael male
## 1007 McGovern, Miss. Mary female
## 1010 McMahon, Mr. Martin male
## 1013 McNeill, Miss. Bridget female
## 1014 Meanwell, Miss. (Marion Ogden) female
## 1015 Meek, Mrs. Thomas (Annie Louise Rowley) female
## 1017 Mernagh, Mr. Robert male
## 1019 Miles, Mr. Frank male
## 1023 Mitkoff, Mr. Mito male
## 1024 Mockler, Miss. Helen Mary "Ellie" female
## 1028 Moore, Mr. Leonard Charles male
## 1029 Moran, Miss. Bertha female
## 1030 Moran, Mr. Daniel J male
## 1031 Moran, Mr. James male
## 1033 Morrow, Mr. Thomas Rowan male
## 1034 Moss, Mr. Albert Johan male
## 1035 Moubarek, Master. Gerios male
## 1036 Moubarek, Master. Halim Gonios ("William George") male
## 1037 Moubarek, Mrs. George (Omine "Amenia" Alexander) female
## 1038 Moussa, Mrs. (Mantoura Boulos) female
## 1039 Moutal, Mr. Rahamin Haim male
## 1040 Mullens, Miss. Katherine "Katie" female
## 1042 Murdlin, Mr. Joseph male
## 1043 Murphy, Miss. Katherine "Kate" female
## 1044 Murphy, Miss. Margaret Jane female
## 1045 Murphy, Miss. Nora female
## 1053 Nankoff, Mr. Minko male
## 1054 Nasr, Mr. Mustafa male
## 1055 Naughton, Miss. Hannah female
## 1056 Nenkoff, Mr. Christo male
## 1070 O'Brien, Mr. Thomas male
## 1071 O'Brien, Mr. Timothy male
## 1072 O'Brien, Mrs. Thomas (Johanna "Hannah" Godfrey) female
## 1073 O'Connell, Mr. Patrick D male
## 1074 O'Connor, Mr. Maurice male
## 1075 O'Connor, Mr. Patrick male
## 1077 O'Donoghue, Ms. Bridget female
## 1078 O'Driscoll, Miss. Bridget female
## 1079 O'Dwyer, Miss. Ellen "Nellie" female
## 1081 O'Keefe, Mr. Patrick male
## 1082 O'Leary, Miss. Hanora "Norah" female
## 1086 Olsen, Mr. Ole Martin male
## 1096 O'Sullivan, Miss. Bridget Mary female
## 1110 Paulner, Mr. Uscher male
## 1115 Pearce, Mr. Ernest male
## 1116 Pedersen, Mr. Olaf male
## 1117 Peduzzi, Mr. Joseph male
## 1122 Peter, Master. Michael J male
## 1123 Peter, Miss. Anna female
## 1124 Peter, Mrs. Catherine (Catherine Rizk) female
## 1125 Peters, Miss. Katie female
## 1129 Petroff, Mr. Pastcho ("Pentcho") male
## 1133 Plotcharsky, Mr. Vasil male
## 1136 Radeff, Mr. Alexander male
## 1137 Rasmussen, Mrs. (Lena Jacobsen Solvang) female
## 1138 Razi, Mr. Raihed male
## 1139 Reed, Mr. James George male
## 1150 Riordan, Miss. Johanna "Hannah" female
## 1151 Risien, Mr. Samuel Beard male
## 1152 Risien, Mrs. Samuel (Emma) female
## 1155 Rogers, Mr. William John male
## 1156 Rommetvedt, Mr. Knud Paust male
## 1160 Roth, Miss. Sarah A female
## 1163 Ryan, Mr. Edward male
## 1164 Ryan, Mr. Patrick male
## 1165 Saad, Mr. Amin male
## 1167 Saade, Mr. Jean Nassr male
## 1168 Sadlier, Mr. Matthew male
## 1169 Sadowitz, Mr. Harry male
## 1171 Sage, Master. Thomas Henry male
## 1173 Sage, Miss. Ada female
## 1174 Sage, Miss. Constance Gladys female
## 1175 Sage, Miss. Dorothy Edith "Dolly" female
## 1176 Sage, Miss. Stella Anna female
## 1177 Sage, Mr. Douglas Bullen male
## 1178 Sage, Mr. Frederick male
## 1179 Sage, Mr. George John Jr male
## 1180 Sage, Mr. John George male
## 1181 Sage, Mrs. John (Annie Bullen) female
## 1185 Samaan, Mr. Elias male
## 1186 Samaan, Mr. Hanna male
## 1187 Samaan, Mr. Youssef male
## 1194 Scanlan, Mr. James male
## 1195 Sdycoff, Mr. Todor male
## 1196 Shaughnessy, Mr. Patrick male
## 1198 Shellard, Mr. Frederick William male
## 1199 Shine, Miss. Ellen Natalia female
## 1200 Shorney, Mr. Charles Joseph male
## 1201 Simmons, Mr. John male
## 1203 Sirota, Mr. Maurice male
## 1213 Slabenoff, Mr. Petco male
## 1214 Slocovski, Mr. Selman Francis male
## 1215 Smiljanic, Mr. Mile male
## 1216 Smith, Mr. Thomas male
## 1217 Smyth, Miss. Julia female
## 1220 Spector, Mr. Woolf male
## 1222 Staneff, Mr. Ivan male
## 1242 Thomas, Mr. Charles P male
## 1243 Thomas, Mr. John male
## 1244 Thomas, Mr. Tannous male
## 1246 Thomson, Mr. Alexander Morrison male
## 1247 Thorneycroft, Mr. Percival male
## 1248 Thorneycroft, Mrs. Percival (Florence Kate White) female
## 1250 Tobin, Mr. Roger male
## 1251 Todoroff, Mr. Lalio male
## 1254 Torfa, Mr. Assad male
## 1256 Toufik, Mr. Nakli male
## 1263 van Billiard, Master. James William male
## 1269 van Melkebeke, Mr. Philemon male
## 1283 Ware, Mr. Frederick male
## 1284 Warren, Mr. Charles William male
## 1285 Webber, Mr. James male
## 1292 Willer, Mr. Aaron ("Abi Weller") male
## 1293 Willey, Mr. Edward male
## 1294 Williams, Mr. Howard Hugh "Harry" male
## 1298 Wiseman, Mr. Phillippe male
## 1303 Yousif, Mr. Wazli male
## 1304 Yousseff, Mr. Gerious male
## 1306 Zabour, Miss. Thamine female
## age sibsp parch ticket fare cabin embarked boat body
## 16 NA 0 0 PC 17318 25.9250 <NA> S <NA> NA
## 38 NA 0 0 111427 26.5500 <NA> S 9 NA
## 41 NA 0 0 112379 39.6000 <NA> C <NA> NA
## 47 NA 0 0 113798 31.0000 <NA> S <NA> NA
## 60 NA 0 0 17770 27.7208 <NA> C 5 NA
## 70 NA 0 1 113505 55.0000 E33 S 6 NA
## 71 NA 0 0 112051 0.0000 <NA> S <NA> NA
## 75 NA 0 0 110465 52.0000 A14 S <NA> NA
## 81 NA 0 0 113791 26.5500 <NA> S <NA> NA
## 107 NA 0 0 PC 17483 221.7792 C95 S <NA> NA
## 108 NA 0 0 PC 17598 31.6833 <NA> S 7 NA
## 109 NA 0 0 17421 110.8833 <NA> C 4 NA
## 119 NA 0 0 113778 26.5500 D34 S <NA> NA
## 122 NA 1 0 PC 17611 133.6500 <NA> S 5 NA
## 126 NA 0 0 112058 0.0000 B102 S <NA> NA
## 135 NA 1 0 17453 89.1042 C92 C 5 NA
## 148 NA 0 0 113796 42.4000 <NA> S <NA> NA
## 153 NA 0 0 16988 30.0000 D45 S 3 NA
## 158 NA 0 0 17463 51.8625 E46 S <NA> NA
## 167 NA 0 0 PC 17600 30.6958 <NA> C 14 NA
## 177 NA 1 0 17464 51.8625 D21 S 8 NA
## 180 NA 0 0 113028 26.5500 C124 S <NA> NA
## 185 NA 0 0 PC 17612 27.7208 <NA> C <NA> NA
## 197 NA 0 0 11774 29.7000 C47 C 7 NA
## 205 NA 1 0 PC 17604 82.1708 <NA> C 6 NA
## 220 NA 0 0 F.C. 12998 25.7417 <NA> C 7 NA
## 224 NA 0 0 112052 0.0000 <NA> S <NA> NA
## 236 NA 0 0 PC 17607 39.6000 <NA> S A NA
## 238 NA 0 0 PC 17757 227.5250 <NA> C <NA> NA
## 242 NA 0 0 113767 50.0000 A32 S <NA> NA
## 255 NA 0 0 19988 30.5000 C106 S 3 NA
## 257 NA 0 0 111163 26.0000 <NA> S 1 NA
## 270 NA 0 0 113056 26.0000 A19 S <NA> NA
## 278 NA 1 0 PC 17569 146.5208 B78 C 6 NA
## 284 NA 0 0 PC 17605 27.7208 <NA> C <NA> NA
## 294 NA 1 0 19996 52.0000 C126 S 5 7 NA
## 298 NA 0 0 PC 17585 79.2000 <NA> C D NA
## 319 NA 0 0 113510 35.0000 C128 S <NA> NA
## 321 NA 0 0 19947 35.5000 C52 S D NA
## 364 NA 0 0 239853 0.0000 <NA> S <NA> NA
## 383 NA 0 0 F.C.C. 13534 21.0000 <NA> S <NA> NA
## 385 NA 0 0 239853 0.0000 <NA> S <NA> NA
## 411 NA 0 0 239854 0.0000 <NA> S <NA> NA
## 470 NA 0 0 226593 12.3500 E101 Q 10 NA
## 474 NA 0 0 239855 0.0000 <NA> S <NA> NA
## 478 NA 0 0 240261 10.7083 <NA> Q <NA> NA
## 484 NA 0 0 248727 33.0000 <NA> S 11 NA
## 492 NA 0 0 237735 15.0458 D C <NA> NA
## 496 NA 0 0 SC/A.3 2861 15.5792 <NA> C <NA> NA
## 525 NA 0 0 SC/PARIS 2146 13.8625 <NA> C 9 NA
## 529 NA 0 0 239853 0.0000 <NA> S <NA> NA
## 532 NA 0 0 SC/PARIS 2131 15.0500 <NA> C <NA> NA
## 582 NA 0 0 239856 0.0000 <NA> S <NA> NA
## 596 NA 0 0 SC/PARIS 2159 12.8750 <NA> S <NA> NA
## 598 NA 0 0 244373 13.0000 <NA> S 14 NA
## 673 NA 0 0 2622 7.2292 <NA> C <NA> NA
## 681 NA 0 0 2664 7.2250 <NA> C <NA> NA
## 682 NA 0 2 2678 15.2458 <NA> C <NA> NA
## 683 NA 0 2 364848 7.7500 <NA> Q <NA> NA
## 706 NA 1 0 2689 14.4583 <NA> C <NA> NA
## 707 NA 1 0 2689 14.4583 <NA> C <NA> NA
## 757 NA 1 0 386525 16.1000 <NA> S <NA> NA
## 758 NA 1 0 386525 16.1000 <NA> S 16 NA
## 768 NA 0 0 349238 7.8958 <NA> S <NA> NA
## 769 NA 0 0 349225 7.8958 <NA> S <NA> NA
## 776 NA 0 0 2686 7.2292 <NA> C <NA> NA
## 790 NA 0 0 2674 7.2250 <NA> C <NA> NA
## 796 NA 0 0 2631 7.2250 <NA> C <NA> NA
## 799 NA 0 0 SOTON/O.Q. 3101308 7.0500 <NA> S 15 NA
## 801 NA 0 0 364859 7.7500 <NA> Q <NA> NA
## 802 NA 0 0 364851 7.7500 <NA> Q <NA> NA
## 803 NA 0 0 368323 6.9500 <NA> Q <NA> NA
## 805 NA 0 0 365235 7.7500 <NA> Q <NA> NA
## 806 NA 0 0 1601 56.4958 <NA> S 13 NA
## 809 NA 0 0 A/5 1478 8.0500 <NA> S <NA> NA
## 813 NA 0 0 368573 7.7500 <NA> Q <NA> NA
## 814 NA 0 0 SOTON/O.Q. 3101314 7.2500 <NA> S <NA> NA
## 816 NA 0 0 358585 14.5000 <NA> S <NA> NA
## 817 NA 0 0 349254 7.8958 <NA> C <NA> NA
## 820 NA 0 0 335677 7.7500 <NA> Q 13 NA
## 836 NA 0 0 376563 8.0500 <NA> S <NA> NA
## 843 NA 1 0 65303 19.9667 <NA> S <NA> NA
## 844 NA 1 0 65304 19.9667 <NA> S <NA> NA
## 853 NA 0 0 W./C. 6609 7.5500 <NA> S <NA> NA
## 855 NA 0 0 394140 6.8583 <NA> Q <NA> NA
## 857 NA 0 0 370375 7.7500 <NA> Q 16 NA
## 859 NA 0 0 1601 56.4958 <NA> S C NA
## 866 NA 0 0 382649 7.7500 <NA> Q <NA> NA
## 872 NA 0 0 370377 7.7500 <NA> Q <NA> NA
## 873 NA 0 0 A. 2. 39186 8.0500 <NA> S C NA
## 875 NA 0 0 3470 7.8875 <NA> S C NA
## 877 NA 0 0 349220 7.8958 <NA> S <NA> NA
## 880 NA 0 0 349201 7.8958 <NA> S <NA> NA
## 883 NA 0 0 SOTON/O.Q. 3101305 7.0500 <NA> S <NA> NA
## 887 NA 0 0 14313 7.7500 <NA> Q D NA
## 888 NA 0 0 65306 8.1125 <NA> S 13 NA
## 901 NA 1 2 W./C. 6607 23.4500 <NA> S <NA> NA
## 902 NA 1 2 W./C. 6607 23.4500 <NA> S <NA> NA
## 903 NA 1 2 W./C. 6607 23.4500 <NA> S <NA> NA
## 904 NA 1 2 W./C. 6607 23.4500 <NA> S <NA> NA
## 919 NA 0 0 2700 7.2292 <NA> C <NA> NA
## 921 NA 0 0 12460 7.7500 <NA> Q <NA> NA
## 922 NA 0 0 323592 7.2500 <NA> S A NA
## 923 NA 0 0 9234 7.7500 <NA> Q 16 NA
## 924 NA 0 0 14312 7.7500 <NA> Q D NA
## 927 NA 0 0 368783 7.7500 <NA> Q <NA> NA
## 928 NA 1 0 2660 14.4542 <NA> C <NA> NA
## 929 NA 1 0 2660 14.4542 <NA> C <NA> NA
## 930 NA 1 0 367227 7.7500 <NA> Q <NA> NA
## 931 NA 1 0 367229 7.7500 <NA> Q <NA> NA
## 932 NA 0 0 36865 7.7375 <NA> Q <NA> NA
## 941 NA 0 0 349253 7.8958 <NA> C <NA> NA
## 943 NA 0 0 2624 7.2250 <NA> C <NA> NA
## 945 NA 0 0 349217 7.8958 <NA> S <NA> NA
## 946 NA 0 0 1601 56.4958 <NA> S C NA
## 947 NA 0 0 1601 56.4958 <NA> S <NA> NA
## 949 NA 0 0 7935 7.7500 <NA> Q <NA> NA
## 955 NA 3 1 4133 25.4667 <NA> S <NA> NA
## 956 NA 3 1 4133 25.4667 <NA> S <NA> NA
## 957 NA 3 1 4133 25.4667 <NA> S <NA> NA
## 958 NA 3 1 4133 25.4667 <NA> S <NA> NA
## 959 NA 0 4 4133 25.4667 <NA> S <NA> NA
## 962 NA 1 0 370371 15.5000 <NA> Q <NA> NA
## 963 NA 1 0 370371 15.5000 <NA> Q <NA> NA
## 972 NA 0 0 330971 7.8792 <NA> Q <NA> NA
## 974 NA 0 0 S.O./P.P. 251 7.5500 <NA> S <NA> NA
## 977 NA 0 0 1222 7.8792 <NA> S <NA> 153
## 983 NA 0 0 349235 7.8958 <NA> S <NA> NA
## 984 NA 0 0 C.A. 42795 7.5500 <NA> S <NA> NA
## 985 NA 0 0 370370 7.7500 <NA> Q 15 NA
## 988 NA 0 0 330924 7.8792 <NA> Q <NA> NA
## 989 NA 0 0 AQ/4 3130 7.7500 <NA> Q <NA> NA
## 990 NA 0 0 A/S 2816 8.0500 <NA> S <NA> NA
## 992 NA 0 0 2677 7.2292 <NA> C 15 NA
## 994 NA 0 0 36866 7.7375 <NA> Q 16 NA
## 995 NA 0 0 2655 7.2292 F E46 C <NA> NA
## 998 NA 0 0 2649 7.2250 <NA> C C NA
## 999 NA 0 0 349255 7.8958 <NA> C <NA> NA
## 1000 NA 0 0 383123 7.7500 <NA> Q 15 16 NA
## 1001 NA 0 0 367228 7.7500 <NA> Q <NA> NA
## 1002 NA 2 0 367226 23.2500 <NA> Q 16 NA
## 1003 NA 2 0 367226 23.2500 <NA> Q 16 NA
## 1004 NA 2 0 367226 23.2500 <NA> Q 16 NA
## 1005 NA 0 0 330932 7.7875 <NA> Q 13 NA
## 1006 NA 0 0 36568 15.5000 <NA> Q <NA> NA
## 1007 NA 0 0 330931 7.8792 <NA> Q 13 NA
## 1010 NA 0 0 370372 7.7500 <NA> Q <NA> NA
## 1013 NA 0 0 370368 7.7500 <NA> Q <NA> NA
## 1014 NA 0 0 SOTON/O.Q. 392087 8.0500 <NA> S <NA> NA
## 1015 NA 0 0 343095 8.0500 <NA> S <NA> NA
## 1017 NA 0 0 368703 7.7500 <NA> Q <NA> NA
## 1019 NA 0 0 359306 8.0500 <NA> S <NA> NA
## 1023 NA 0 0 349221 7.8958 <NA> S <NA> NA
## 1024 NA 0 0 330980 7.8792 <NA> Q 16 NA
## 1028 NA 0 0 A4. 54510 8.0500 <NA> S <NA> NA
## 1029 NA 1 0 371110 24.1500 <NA> Q 16 NA
## 1030 NA 1 0 371110 24.1500 <NA> Q <NA> NA
## 1031 NA 0 0 330877 8.4583 <NA> Q <NA> NA
## 1033 NA 0 0 372622 7.7500 <NA> Q <NA> NA
## 1034 NA 0 0 312991 7.7750 <NA> S B NA
## 1035 NA 1 1 2661 15.2458 <NA> C C NA
## 1036 NA 1 1 2661 15.2458 <NA> C C NA
## 1037 NA 0 2 2661 15.2458 <NA> C C NA
## 1038 NA 0 0 2626 7.2292 <NA> C <NA> NA
## 1039 NA 0 0 374746 8.0500 <NA> S <NA> NA
## 1040 NA 0 0 35852 7.7333 <NA> Q 16 NA
## 1042 NA 0 0 A./5. 3235 8.0500 <NA> S <NA> NA
## 1043 NA 1 0 367230 15.5000 <NA> Q 16 NA
## 1044 NA 1 0 367230 15.5000 <NA> Q 16 NA
## 1045 NA 0 0 36568 15.5000 <NA> Q 16 NA
## 1053 NA 0 0 349218 7.8958 <NA> S <NA> NA
## 1054 NA 0 0 2652 7.2292 <NA> C <NA> NA
## 1055 NA 0 0 365237 7.7500 <NA> Q <NA> NA
## 1056 NA 0 0 349234 7.8958 <NA> S <NA> NA
## 1070 NA 1 0 370365 15.5000 <NA> Q <NA> NA
## 1071 NA 0 0 330979 7.8292 <NA> Q <NA> NA
## 1072 NA 1 0 370365 15.5000 <NA> Q <NA> NA
## 1073 NA 0 0 334912 7.7333 <NA> Q <NA> NA
## 1074 NA 0 0 371060 7.7500 <NA> Q <NA> NA
## 1075 NA 0 0 366713 7.7500 <NA> Q <NA> NA
## 1077 NA 0 0 364856 7.7500 <NA> Q <NA> NA
## 1078 NA 0 0 14311 7.7500 <NA> Q D NA
## 1079 NA 0 0 330959 7.8792 <NA> Q <NA> NA
## 1081 NA 0 0 368402 7.7500 <NA> Q B NA
## 1082 NA 0 0 330919 7.8292 <NA> Q 13 NA
## 1086 NA 0 0 Fa 265302 7.3125 <NA> S <NA> NA
## 1096 NA 0 0 330909 7.6292 <NA> Q <NA> NA
## 1110 NA 0 0 3411 8.7125 <NA> C <NA> NA
## 1115 NA 0 0 343271 7.0000 <NA> S <NA> NA
## 1116 NA 0 0 345498 7.7750 <NA> S <NA> NA
## 1117 NA 0 0 A/5 2817 8.0500 <NA> S <NA> NA
## 1122 NA 1 1 2668 22.3583 <NA> C C NA
## 1123 NA 1 1 2668 22.3583 F E69 C D NA
## 1124 NA 0 2 2668 22.3583 <NA> C D NA
## 1125 NA 0 0 330935 8.1375 <NA> Q <NA> NA
## 1129 NA 0 0 349215 7.8958 <NA> S <NA> NA
## 1133 NA 0 0 349227 7.8958 <NA> S <NA> NA
## 1136 NA 0 0 349223 7.8958 <NA> S <NA> NA
## 1137 NA 0 0 65305 8.1125 <NA> S <NA> NA
## 1138 NA 0 0 2629 7.2292 <NA> C <NA> NA
## 1139 NA 0 0 362316 7.2500 <NA> S <NA> NA
## 1150 NA 0 0 334915 7.7208 <NA> Q 13 NA
## 1151 NA 0 0 364498 14.5000 <NA> S <NA> NA
## 1152 NA 0 0 364498 14.5000 <NA> S <NA> NA
## 1155 NA 0 0 S.C./A.4. 23567 8.0500 <NA> S <NA> NA
## 1156 NA 0 0 312993 7.7750 <NA> S <NA> NA
## 1160 NA 0 0 342712 8.0500 <NA> S C NA
## 1163 NA 0 0 383162 7.7500 <NA> Q 14 NA
## 1164 NA 0 0 371110 24.1500 <NA> Q <NA> NA
## 1165 NA 0 0 2671 7.2292 <NA> C <NA> NA
## 1167 NA 0 0 2676 7.2250 <NA> C <NA> NA
## 1168 NA 0 0 367655 7.7292 <NA> Q <NA> NA
## 1169 NA 0 0 LP 1588 7.5750 <NA> S <NA> NA
## 1171 NA 8 2 CA. 2343 69.5500 <NA> S <NA> NA
## 1173 NA 8 2 CA. 2343 69.5500 <NA> S <NA> NA
## 1174 NA 8 2 CA. 2343 69.5500 <NA> S <NA> NA
## 1175 NA 8 2 CA. 2343 69.5500 <NA> S <NA> NA
## 1176 NA 8 2 CA. 2343 69.5500 <NA> S <NA> NA
## 1177 NA 8 2 CA. 2343 69.5500 <NA> S <NA> NA
## 1178 NA 8 2 CA. 2343 69.5500 <NA> S <NA> NA
## 1179 NA 8 2 CA. 2343 69.5500 <NA> S <NA> NA
## 1180 NA 1 9 CA. 2343 69.5500 <NA> S <NA> NA
## 1181 NA 1 9 CA. 2343 69.5500 <NA> S <NA> NA
## 1185 NA 2 0 2662 21.6792 <NA> C <NA> NA
## 1186 NA 2 0 2662 21.6792 <NA> C <NA> NA
## 1187 NA 2 0 2662 21.6792 <NA> C <NA> NA
## 1194 NA 0 0 36209 7.7250 <NA> Q <NA> NA
## 1195 NA 0 0 349222 7.8958 <NA> S <NA> NA
## 1196 NA 0 0 370374 7.7500 <NA> Q <NA> NA
## 1198 NA 0 0 C.A. 6212 15.1000 <NA> S <NA> NA
## 1199 NA 0 0 330968 7.7792 <NA> Q <NA> NA
## 1200 NA 0 0 374910 8.0500 <NA> S <NA> NA
## 1201 NA 0 0 SOTON/OQ 392082 8.0500 <NA> S <NA> NA
## 1203 NA 0 0 392092 8.0500 <NA> S <NA> NA
## 1213 NA 0 0 349214 7.8958 <NA> S <NA> NA
## 1214 NA 0 0 SOTON/OQ 392086 8.0500 <NA> S <NA> NA
## 1215 NA 0 0 315037 8.6625 <NA> S <NA> NA
## 1216 NA 0 0 384461 7.7500 <NA> Q <NA> NA
## 1217 NA 0 0 335432 7.7333 <NA> Q 13 NA
## 1220 NA 0 0 A.5. 3236 8.0500 <NA> S <NA> NA
## 1222 NA 0 0 349208 7.8958 <NA> S <NA> NA
## 1242 NA 1 0 2621 6.4375 <NA> C <NA> NA
## 1243 NA 0 0 2681 6.4375 <NA> C <NA> NA
## 1244 NA 0 0 2684 7.2250 <NA> C <NA> NA
## 1246 NA 0 0 32302 8.0500 <NA> S <NA> NA
## 1247 NA 1 0 376564 16.1000 <NA> S <NA> NA
## 1248 NA 1 0 376564 16.1000 <NA> S 10 NA
## 1250 NA 0 0 383121 7.7500 F38 Q <NA> NA
## 1251 NA 0 0 349216 7.8958 <NA> S <NA> NA
## 1254 NA 0 0 2673 7.2292 <NA> C <NA> NA
## 1256 NA 0 0 2641 7.2292 <NA> C <NA> NA
## 1263 NA 1 1 A/5. 851 14.5000 <NA> S <NA> NA
## 1269 NA 0 0 345777 9.5000 <NA> S <NA> NA
## 1283 NA 0 0 359309 8.0500 <NA> S <NA> NA
## 1284 NA 0 0 C.A. 49867 7.5500 <NA> S <NA> NA
## 1285 NA 0 0 SOTON/OQ 3101316 8.0500 <NA> S <NA> NA
## 1292 NA 0 0 3410 8.7125 <NA> S <NA> NA
## 1293 NA 0 0 S.O./P.P. 751 7.5500 <NA> S <NA> NA
## 1294 NA 0 0 A/5 2466 8.0500 <NA> S <NA> NA
## 1298 NA 0 0 A/4. 34244 7.2500 <NA> S <NA> NA
## 1303 NA 0 0 2647 7.2250 <NA> C <NA> NA
## 1304 NA 0 0 2627 14.4583 <NA> C <NA> NA
## 1306 NA 1 0 2665 14.4542 <NA> C <NA> NA
## home.dest
## 16 New York, NY
## 38 Los Angeles, CA
## 41 Philadelphia, PA
## 47 <NA>
## 60 New York, NY
## 70 St Leonards-on-Sea, England Ohio
## 71 Liverpool, England / Belfast
## 75 Stoughton, MA
## 81 Roachdale, IN
## 107 <NA>
## 108 New York, NY
## 109 <NA>
## 119 Westcliff-on-Sea, Essex
## 122 New York, NY
## 126 <NA>
## 135 Paris, France / New York, NY
## 148 <NA>
## 153 Kingston, Surrey
## 158 Brighton, MA
## 167 New York, NY
## 177 Southington / Noank, CT
## 180 Portland, OR
## 185 Chicago, IL
## 197 Paris, France
## 205 New York, NY
## 220 Paris, France
## 224 Belfast
## 236 Paris / New York, NY
## 238 <NA>
## 242 Seattle, WA
## 255 Manchester, England
## 257 New York, NY
## 270 Streatham, Surrey
## 278 Paris, France
## 284 Gallipolis, Ohio / ? Paris / New York
## 294 London / East Orange, NJ
## 298 New York, NY
## 319 London, England
## 321 London, England
## 364 Belfast
## 383 Upper Burma, India Pittsburgh, PA
## 385 Belfast
## 411 Belfast
## 470 Harrisburg, PA
## 474 Belfast
## 478 <NA>
## 484 London / Chicago, IL
## 492 Paris
## 496 New York, NY
## 525 Spain / Havana, Cuba
## 529 Belfast
## 532 <NA>
## 582 Belfast
## 596 <NA>
## 598 Harrow, England
## 673 <NA>
## 681 Syria
## 682 Syria Kent, ON
## 683 Ireland Chicago, IL
## 706 Ottawa, ON
## 707 Ottawa, ON
## 757 Liverpool, England Bedford, OH
## 758 Liverpool, England Bedford, OH
## 768 <NA>
## 769 Bulgaria Coon Rapids, IA
## 776 <NA>
## 790 <NA>
## 796 <NA>
## 799 Italy Philadelphia, PA
## 801 <NA>
## 802 <NA>
## 803 <NA>
## 805 Ireland
## 806 Hong Kong New York, NY
## 809 Bridgwater, Somerset, England
## 813 Ireland New York, NY
## 814 <NA>
## 816 <NA>
## 817 <NA>
## 820 Co Clare, Ireland Washington, DC
## 836 <NA>
## 843 <NA>
## 844 <NA>
## 853 <NA>
## 855 <NA>
## 857 <NA>
## 859 <NA>
## 866 <NA>
## 872 <NA>
## 873 <NA>
## 875 <NA>
## 877 <NA>
## 880 <NA>
## 883 <NA>
## 887 <NA>
## 888 <NA>
## 901 <NA>
## 902 <NA>
## 903 <NA>
## 904 <NA>
## 919 <NA>
## 921 <NA>
## 922 <NA>
## 923 <NA>
## 924 <NA>
## 927 <NA>
## 928 <NA>
## 929 <NA>
## 930 <NA>
## 931 <NA>
## 932 <NA>
## 941 <NA>
## 943 <NA>
## 945 <NA>
## 946 <NA>
## 947 <NA>
## 949 <NA>
## 955 <NA>
## 956 <NA>
## 957 <NA>
## 958 <NA>
## 959 <NA>
## 962 <NA>
## 963 <NA>
## 972 <NA>
## 974 <NA>
## 977 <NA>
## 983 <NA>
## 984 <NA>
## 985 <NA>
## 988 <NA>
## 989 <NA>
## 990 <NA>
## 992 <NA>
## 994 <NA>
## 995 <NA>
## 998 <NA>
## 999 <NA>
## 1000 <NA>
## 1001 <NA>
## 1002 <NA>
## 1003 <NA>
## 1004 <NA>
## 1005 <NA>
## 1006 <NA>
## 1007 <NA>
## 1010 <NA>
## 1013 <NA>
## 1014 <NA>
## 1015 <NA>
## 1017 <NA>
## 1019 <NA>
## 1023 <NA>
## 1024 <NA>
## 1028 <NA>
## 1029 <NA>
## 1030 <NA>
## 1031 <NA>
## 1033 <NA>
## 1034 <NA>
## 1035 <NA>
## 1036 <NA>
## 1037 <NA>
## 1038 <NA>
## 1039 <NA>
## 1040 <NA>
## 1042 <NA>
## 1043 <NA>
## 1044 <NA>
## 1045 <NA>
## 1053 <NA>
## 1054 <NA>
## 1055 <NA>
## 1056 <NA>
## 1070 <NA>
## 1071 <NA>
## 1072 <NA>
## 1073 <NA>
## 1074 <NA>
## 1075 <NA>
## 1077 <NA>
## 1078 <NA>
## 1079 <NA>
## 1081 <NA>
## 1082 <NA>
## 1086 <NA>
## 1096 <NA>
## 1110 <NA>
## 1115 <NA>
## 1116 <NA>
## 1117 <NA>
## 1122 <NA>
## 1123 <NA>
## 1124 <NA>
## 1125 <NA>
## 1129 <NA>
## 1133 <NA>
## 1136 <NA>
## 1137 <NA>
## 1138 <NA>
## 1139 <NA>
## 1150 <NA>
## 1151 <NA>
## 1152 <NA>
## 1155 <NA>
## 1156 <NA>
## 1160 <NA>
## 1163 <NA>
## 1164 <NA>
## 1165 <NA>
## 1167 <NA>
## 1168 <NA>
## 1169 <NA>
## 1171 <NA>
## 1173 <NA>
## 1174 <NA>
## 1175 <NA>
## 1176 <NA>
## 1177 <NA>
## 1178 <NA>
## 1179 <NA>
## 1180 <NA>
## 1181 <NA>
## 1185 <NA>
## 1186 <NA>
## 1187 <NA>
## 1194 <NA>
## 1195 <NA>
## 1196 <NA>
## 1198 <NA>
## 1199 <NA>
## 1200 <NA>
## 1201 <NA>
## 1203 <NA>
## 1213 <NA>
## 1214 <NA>
## 1215 <NA>
## 1216 <NA>
## 1217 <NA>
## 1220 <NA>
## 1222 <NA>
## 1242 <NA>
## 1243 <NA>
## 1244 <NA>
## 1246 <NA>
## 1247 <NA>
## 1248 <NA>
## 1250 <NA>
## 1251 <NA>
## 1254 <NA>
## 1256 <NA>
## 1263 <NA>
## 1269 <NA>
## 1283 <NA>
## 1284 <NA>
## 1285 <NA>
## 1292 <NA>
## 1293 <NA>
## 1294 <NA>
## 1298 <NA>
## 1303 <NA>
## 1304 <NA>
## 1306 <NA>df2$age[which(is.na(df2$age))] <- mean(df2$age,na.rm = TRUE)
df2[is.na(df2$age),]# "na" values have been replaces by mean of rest of the values## [1] pclass survived name sex age sibsp parch
## [8] ticket fare cabin embarked boat body home.dest
## <0 rows> (or 0-length row.names)## I don't think mean age is the right way to go. Men and women have different life span and inputting the same mean value for each missing one can be quite misleading.
df3<-df23: Lifeboat
You’re interested in looking at the distribution of passengers in different lifeboats, but as we know, many passengers did not make it to a boat :-( This means that there are a lot of missing values in the boat column. Fill these empty slots with a dummy value e.g. the string ‘None’ or ‘NA’
This question was answered when we first loaded the data and “na” values were input
4: Cabin
You notice that many passengers don’t have a cabin number associated with them.
Does it make sense to fill missing cabin numbers with a value?
What does a missing value here mean?You have a hunch that the fact that the cabin number is missing might be a useful indicator of survival. Create a new column has_cabin_number which has 1 if there is a cabin number, and 0 otherwise.
df3$cabin <-lapply(df3$cabin, as.character)
df3$has_cabin_number <- ifelse(df3$cabin == "NA", 0, 1)
df4<-df3
df4$has_cabin_number[which(is.na(df4$has_cabin_number))] <- 0
str(df4)## 'data.frame': 1309 obs. of 15 variables:
## $ pclass : int 1 1 1 1 1 1 1 1 1 1 ...
## $ survived : int 1 1 0 0 0 1 1 0 1 0 ...
## $ name : Factor w/ 1307 levels "Abbing, Mr. Anthony",..: 22 24 25 26 27 31 46 47 51 55 ...
## $ sex : Factor w/ 2 levels "female","male": 1 2 1 2 1 2 1 2 1 2 ...
## $ age : num 29 0.917 2 30 25 ...
## $ sibsp : int 0 1 1 1 1 0 1 0 2 0 ...
## $ parch : int 0 2 2 2 2 0 0 0 0 0 ...
## $ ticket : Factor w/ 929 levels "110152","110413",..: 188 50 50 50 50 125 93 16 77 826 ...
## $ fare : num 211 152 152 152 152 ...
## $ cabin :List of 1309
## ..$ : chr "B5"
## ..$ : chr "C22 C26"
## ..$ : chr "C22 C26"
## ..$ : chr "C22 C26"
## ..$ : chr "C22 C26"
## ..$ : chr "E12"
## ..$ : chr "D7"
## ..$ : chr "A36"
## ..$ : chr "C101"
## ..$ : chr NA
## ..$ : chr "C62 C64"
## ..$ : chr "C62 C64"
## ..$ : chr "B35"
## ..$ : chr NA
## ..$ : chr "A23"
## ..$ : chr NA
## ..$ : chr "B58 B60"
## ..$ : chr "B58 B60"
## ..$ : chr "D15"
## ..$ : chr "C6"
## ..$ : chr "D35"
## ..$ : chr "D35"
## ..$ : chr "C148"
## ..$ : chr NA
## ..$ : chr "C97"
## ..$ : chr NA
## ..$ : chr "B49"
## ..$ : chr "B49"
## ..$ : chr "C99"
## ..$ : chr "C52"
## ..$ : chr "T"
## ..$ : chr "A31"
## ..$ : chr "C7"
## ..$ : chr "C103"
## ..$ : chr "D22"
## ..$ : chr NA
## ..$ : chr "E33"
## ..$ : chr NA
## ..$ : chr "A21"
## ..$ : chr "B10"
## ..$ : chr NA
## ..$ : chr "B4"
## ..$ : chr "C101"
## ..$ : chr "D15"
## ..$ : chr "E40"
## ..$ : chr "B38"
## ..$ : chr NA
## ..$ : chr "E24"
## ..$ : chr NA
## ..$ : chr "B51 B53 B55"
## ..$ : chr "B51 B53 B55"
## ..$ : chr "B51 B53 B55"
## ..$ : chr NA
## ..$ : chr NA
## ..$ : chr "B96 B98"
## ..$ : chr "B96 B98"
## ..$ : chr "B96 B98"
## ..$ : chr "B96 B98"
## ..$ : chr NA
## ..$ : chr NA
## ..$ : chr "C46"
## ..$ : chr "C46"
## ..$ : chr "E31"
## ..$ : chr "E31"
## ..$ : chr "E8"
## ..$ : chr "E8"
## ..$ : chr "B61"
## ..$ : chr "B77"
## ..$ : chr "A9"
## ..$ : chr "E33"
## ..$ : chr NA
## ..$ : chr "C89"
## ..$ : chr "C89"
## ..$ : chr NA
## ..$ : chr "A14"
## ..$ : chr "E58"
## ..$ : chr "E49"
## ..$ : chr "E52"
## ..$ : chr "E45"
## ..$ : chr "C101"
## ..$ : chr NA
## ..$ : chr "B22"
## ..$ : chr "B22"
## ..$ : chr "B26"
## ..$ : chr "C85"
## ..$ : chr "C85"
## ..$ : chr "E17"
## ..$ : chr NA
## ..$ : chr NA
## ..$ : chr "B71"
## ..$ : chr "B71"
## ..$ : chr "B20"
## ..$ : chr "B20"
## ..$ : chr "A34"
## ..$ : chr "A34"
## ..$ : chr "A34"
## ..$ : chr "C86"
## ..$ : chr "B58 B60"
## ..$ : chr "C86"
## .. [list output truncated]
## $ embarked :List of 1309
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "S"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "C"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "C"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "S"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "C"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "C"
## ..$ : chr "S"
## ..$ : chr "C"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "S"
## ..$ : chr "C"
## ..$ : chr "C"
## ..$ : chr "C"
## .. [list output truncated]
## $ boat : Factor w/ 27 levels "1","10","11",..: 12 3 NA NA NA 13 2 NA 27 NA ...
## $ body : int NA NA NA 135 NA NA NA NA NA 22 ...
## $ home.dest : Factor w/ 369 levels "?Havana, Cuba",..: 309 231 231 231 231 237 162 24 22 229 ...
## $ has_cabin_number: num 1 1 1 1 1 1 1 1 1 0 ...