titanic_4 <- titanic %>%
  select(Survived, Pclass, Age, Sex) %>%
  filter(!is.na(Age)) %>%
  mutate(agecat = cut(Age, breaks = c(0, 14.99, 50, 150),
                      include.lowest = TRUE,
                      labels = c("Under 15", "15 to 50",
                                 "Over 50"))) %>%
  group_by(Pclass,agecat,Sex) %>%
  summarize(N=n(), survivors = sum(Survived))%>%
  mutate(perc_survived = (signif((100*survivors/N), digits=8)))

print(titanic_4)

# A tibble: 18 x 6
# Groups:   Pclass, agecat [9]
   Pclass   agecat    Sex     N survivors perc_survived
    <int>   <fctr>  <chr> <int>     <int>         <dbl>
 1      1 Under 15 female     2         1     50.000000
 2      1 Under 15   male     3         3    100.000000
 3      1 15 to 50 female    70        68     97.142857
 4      1 15 to 50   male    72        32     44.444444
 5      1  Over 50 female    13        13    100.000000
 6      1  Over 50   male    26         5     19.230769
 7      2 Under 15 female    10        10    100.000000
 8      2 Under 15   male     9         9    100.000000
 9      2 15 to 50 female    61        56     91.803279
10      2 15 to 50   male    78         5      6.410256
11      2  Over 50 female     3         2     66.666667
12      2  Over 50   male    12         1      8.333333
13      3 Under 15 female    27        13     48.148148
14      3 Under 15   male    27         9     33.333333
15      3 15 to 50 female    74        33     44.594595
16      3 15 to 50   male   217        29     13.364055
17      3  Over 50 female     1         1    100.000000
18      3  Over 50   male     9         0      0.000000

# A tibble: 6 x 6
# Groups:   Pclass, agecat [3]
  Pclass   agecat    Sex     N survivors perc_survived
   <int>   <fctr>  <chr> <int>     <int>         <dbl>
1      1 Under 15 female     2         1      50.00000
2      1 Under 15   male     3         3     100.00000
3      1 15 to 50 female    70        68      97.14286
4      1 15 to 50   male    72        32      44.44444
5      1  Over 50 female    13        13     100.00000
6      1  Over 50   male    26         5      19.23077

## Pclass   agecat    Sex      N     survivors   perc_survived
## <int>   <fctr>    <chr>   <int>     <int>         <dbl>
##   1    Under 15  female     2         1        50.000000
##   1    Under 15    male     3         3       100.000000
##   1    15 to 50  female    70        68        97.142857
##   1    15 to 50    male    72        32        44.444444
##   1    Over 50   female    13        13       100.000000
##   1    Over 50     male    26         5        19.230769