Week 6 - Review
Emorie D Beck
Outline
- Questions on Homework
dplyrtidyr- Functions
purrr
- Note: my office hour is canceled because I will be traveling. If you need to meet this week, please make an appointment.
tidyverse: chaining (|> or |>)
dplyr: select() and filter()
1. select()
Add or remove using select() helper functions.
-
starts_with()
ends_with()contains()matches()num_range()one_of()all_of()
C1 C2 C3 C4 C5
61617 2 3 3 4 4
61618 5 4 4 3 4
61620 4 5 4 2 5
61621 4 4 3 5 5
61622 4 4 5 3 2
61623 6 6 6 1 3
61624 5 4 4 2 3
61629 3 2 4 2 4
61630 6 6 3 4 5
61633 6 5 6 2 1
61634 4 3 5 3 2
61636 5 4 5 4 5
61637 5 4 3 2 2
61639 4 4 4 2 1
61640 5 5 5 2 2
61643 5 5 5 3 5
61650 4 4 4 4 4
61651 5 5 5 4 3
61653 5 4 5 4 6
61654 1 1 1 5 6
61656 4 6 5 5 4
61659 5 4 4 2 3
61661 4 3 2 4 5
61664 3 5 6 3 6
61667 5 5 4 1 1
61668 5 2 5 1 1
61669 6 5 6 1 1
61670 4 5 4 3 4
61672 5 4 5 2 5
61673 5 5 3 5 4
61678 5 5 5 4 3
61679 5 5 5 2 4
61682 1 5 6 4 6
61683 4 6 4 2 4
61684 4 3 3 3 4
61685 2 2 4 3 4
61686 5 6 3 1 5
61687 6 5 6 3 4
61688 6 6 6 1 1
61691 6 5 5 2 2
61692 4 4 4 3 4
61693 5 2 5 2 4
61696 4 5 4 3 3
61698 5 3 3 4 5
61700 5 4 5 3 4
61701 5 2 1 2 1
61702 4 4 3 4 5
61703 3 2 3 4 6
61713 3 6 3 1 3
61715 5 5 4 1 2
61716 4 3 1 4 2
61723 6 6 6 6 2
61724 5 5 3 2 5
61725 5 6 2 5 2
61728 6 5 5 2 2
61730 5 5 4 4 5
61731 5 5 5 5 1
61732 5 4 5 2 4
61740 6 6 6 1 4
61742 6 4 4 2 2
61748 5 3 2 4 6
61749 5 5 4 3 4
61754 NA 6 6 2 3
61756 6 5 5 2 2
61757 4 6 6 1 4
61759 5 4 5 2 1
61761 1 2 2 2 6
61762 4 2 2 4 5
61763 5 5 4 2 2
61764 6 5 6 1 4
61771 4 6 6 1 2
61772 2 4 4 1 1
61773 5 4 6 2 4
61775 5 5 5 1 2
61776 5 5 5 1 1
61777 5 4 6 3 2
61778 5 4 5 1 2
61780 5 5 6 3 4
61782 2 4 2 5 1
61783 4 2 3 5 4
61784 6 5 4 4 3
61788 6 6 5 1 6
61789 3 3 4 4 5
61793 5 6 5 1 1
61794 4 5 5 2 3
61797 2 5 4 4 3
61798 4 4 5 2 2
61801 4 5 4 4 5
61808 4 4 5 2 4
61812 3 3 NA 3 3
61813 5 2 4 1 2
61816 5 4 5 3 4
61818 2 2 3 4 4
61819 6 6 5 1 1
61821 4 5 6 3 1
61822 5 6 4 1 4
61825 2 1 2 6 6
61826 6 4 4 1 1
61829 4 5 6 2 6
61831 4 2 5 3 5
61834 4 3 4 3 5
61835 4 3 4 4 2
61838 6 6 4 1 5
61839 2 1 2 4 6
61840 6 4 5 2 1
61841 5 3 4 6 5
61847 6 NA 6 1 1
61848 3 6 3 3 4
61851 5 4 4 4 6
61852 3 5 2 3 4
61854 4 4 5 3 4
61856 5 5 5 1 5
61857 5 3 5 1 2
61861 6 6 5 1 6
61862 5 4 5 2 2
61865 4 1 2 6 6
61868 5 6 4 1 4
61873 2 2 2 3 5
61874 5 4 5 2 3
61880 6 5 4 2 6
61886 4 6 5 5 2
61888 4 5 5 5 5
61889 5 5 6 1 1
61890 5 5 5 3 6
61891 5 5 4 6 6
61895 5 5 5 2 6
61896 5 4 5 3 2
61900 5 4 5 3 5
61901 2 3 4 5 6
61907 5 3 4 2 5
61908 4 4 4 4 5
61909 4 4 5 3 5
61911 6 5 5 2 3
61913 4 4 4 3 6
61915 6 6 3 6 5
61918 6 1 3 4 5
61921 1 1 3 1 1
61922 6 6 4 1 1
61923 5 5 4 1 4
61925 2 3 2 5 5
61926 3 3 3 2 6
61928 4 4 6 4 2
61932 6 5 4 2 2
61935 3 2 4 2 3
61936 3 5 3 3 6
61939 6 4 4 2 4
61944 6 6 6 1 5
61945 4 4 5 3 5
61949 4 4 6 4 5
61952 5 5 5 2 2
61953 5 2 2 5 6
61954 6 5 3 1 1
61957 4 4 2 5 4
61958 5 4 4 1 1
61965 5 5 6 2 1
61967 4 5 4 2 4
61968 5 3 4 4 4
61969 3 1 4 4 5
61971 3 5 4 4 5
61972 5 6 5 2 1
61973 5 4 5 1 1
61974 5 3 2 3 4
61975 2 3 2 4 5
61976 2 5 6 5 2
61978 5 5 5 2 2
61979 1 1 3 4 5
61983 4 6 4 1 2
61986 4 2 4 5 5
61987 5 6 4 2 2
61989 6 6 5 1 4
61990 3 3 5 3 6
61992 5 6 6 4 2
61993 4 4 4 4 5
61994 5 4 5 3 4
61999 5 5 5 2 1
62001 6 4 6 5 6
62003 4 5 3 3 4
62004 6 6 6 1 3
62005 4 3 5 4 4
62007 3 2 2 4 1
62009 5 2 4 4 5
62011 5 4 5 2 4
62013 3 3 4 4 4
62014 4 3 3 4 5
62015 5 5 4 3 2
62022 3 5 1 1 6
62023 4 4 4 4 4
62024 6 6 6 1 1
62025 5 5 5 1 2
62026 5 5 5 1 2
62029 3 1 1 3 2
62031 4 4 5 2 5
62032 4 5 4 2 5
62033 4 4 4 4 5
62034 5 5 5 2 4
62038 1 3 2 2 5
62039 5 3 3 3 4
62041 2 4 4 3 2
62042 2 4 3 4 6
62043 3 2 5 2 1
62044 5 6 5 1 2
62047 2 2 6 2 3
62048 4 4 4 3 4
62051 5 6 4 1 2
62052 5 4 4 3 4
62054 4 5 5 1 1
62055 2 4 2 2 4
62056 6 4 4 1 1
62059 3 3 3 4 4
62060 1 2 2 1 5
62063 6 2 5 2 2
62064 4 5 4 5 6
62067 5 4 NA 4 4
62070 3 3 3 4 6
62073 5 5 5 1 3
62075 5 4 4 2 2
62077 6 5 4 1 2
62079 5 1 3 2 1
62082 6 6 6 1 6
62084 6 2 2 4 5
62090 5 5 5 4 3
62092 6 6 1 1 1
62094 5 5 5 3 3
62099 5 3 4 3 5
62101 2 5 5 2 2
62102 1 5 6 3 2
62103 4 3 4 3 3
62105 6 5 6 2 4
62106 5 3 4 1 5
62107 5 5 4 2 1
62111 1 4 6 2 2
62115 5 3 3 2 4
62118 5 6 4 1 1
62119 5 4 6 3 3
62120 6 5 5 1 1
62121 3 3 4 5 4
62124 5 5 2 3 3
62128 4 3 4 5 6
62130 5 5 5 2 4
62132 5 6 5 2 1
62133 5 5 6 1 1
62136 4 5 4 2 4
62137 5 4 5 2 4
62142 5 4 5 1 2
62144 5 5 5 2 2
62147 4 3 3 3 5
62151 2 1 2 5 6
62156 5 2 5 2 3
62160 5 4 4 1 5
62161 4 2 4 4 4
62162 4 4 6 3 3
62163 6 5 5 2 2
62164 5 6 5 1 1
62165 6 6 6 1 1
62166 5 4 5 2 2
62168 4 4 4 2 2
62170 5 4 6 1 2
62171 3 3 5 4 4
62173 6 4 4 1 5
62176 4 5 3 5 4
62179 6 5 2 6 6
62180 6 5 5 1 3
62181 5 5 6 6 1
62182 6 5 5 1 1
62183 6 4 4 2 6
62189 5 6 5 2 1
62192 4 4 4 2 3
62197 5 4 5 1 1
62198 5 6 5 2 5
62199 4 4 5 4 5
62201 5 5 3 4 6
62202 4 5 5 3 3
62203 6 5 6 1 3
62204 5 6 6 5 1
62205 4 5 4 2 4
62206 4 5 6 3 1
62208 6 5 5 1 2
62209 5 4 3 2 4
62212 4 4 5 2 5
62213 3 3 5 3 5
62214 4 4 3 4 5
62215 5 4 5 1 1
62216 3 2 4 4 6
62219 4 5 4 3 4
62220 4 4 5 5 4
62224 5 4 5 2 1
62225 3 5 5 2 1
62226 5 3 5 2 3
62227 6 4 4 3 4
62228 5 4 6 2 2
62231 2 4 5 5 5
62233 5 5 5 2 1
62237 4 4 5 4 4
62239 4 4 5 4 4
62240 5 3 3 4 5
62242 4 5 4 3 5
62244 5 6 6 5 6
62245 3 2 2 4 6
62246 3 5 6 2 1
62252 4 4 4 4 3
62259 5 6 2 1 5
62260 4 4 4 2 2
62261 5 2 6 5 5
62263 4 3 5 3 4
62264 5 5 4 1 1
62265 5 5 4 2 4
62266 5 4 3 2 3
62267 5 6 6 2 1
62272 6 6 5 1 2
62276 4 5 5 2 3
62278 3 5 3 2 2
62279 6 5 5 1 2
62280 4 4 4 1 2
62281 5 4 6 1 2
62282 6 5 5 4 6
62287 5 5 5 2 1
62288 2 1 5 6 6
62289 4 4 4 2 2
62290 5 3 4 4 4
62293 4 5 5 1 2
62295 6 6 6 1 2
62296 4 5 4 2 2
62298 5 5 6 2 5
62299 5 5 5 5 5
62300 4 4 3 4 4
62301 5 5 5 2 4
62303 5 5 6 1 2
62305 4 4 3 3 4
62307 6 6 2 6 6
62312 5 5 4 3 5
62313 5 5 3 4 5
62316 4 6 5 1 2
62317 4 4 5 2 3
62325 5 6 4 1 1
62327 4 6 6 2 5
62328 3 1 5 2 5
62330 3 5 5 2 1
62333 6 6 6 1 1
62335 3 3 4 4 2
62336 6 5 2 1 5
62339 3 2 5 4 5
62342 4 1 2 6 3
62343 5 4 4 6 5
62344 5 6 4 2 4
62345 4 4 4 3 4
62346 5 4 5 3 3
62347 4 3 3 5 5
62348 4 5 6 4 3
62349 6 6 5 1 3
62350 4 4 6 4 2
62351 6 4 6 1 4
62352 5 5 5 1 1
62353 2 4 4 4 4
62354 4 4 5 3 2
62358 3 3 5 3 4
62359 5 5 5 2 4
62360 4 4 3 4 6
62362 5 4 5 4 5
62363 4 5 2 4 4
62366 5 6 5 1 3
62367 6 4 5 1 2
62368 4 1 2 5 6
62369 2 5 5 1 2
62370 4 5 2 3 6
62371 3 2 4 3 6
62375 5 6 6 1 2
62376 4 3 3 3 4
62377 5 5 5 5 4
62380 4 1 5 6 6
62382 3 6 2 5 5
62384 5 5 5 3 4
62387 5 5 4 2 4
62390 6 5 4 1 1
62391 5 5 5 1 4
62394 5 4 3 1 4
62397 6 6 6 1 1
62401 5 5 4 1 1
62408 5 4 4 2 5
62412 3 5 3 1 1
62416 6 5 5 3 2
62419 4 5 6 NA 1
62421 5 5 4 2 3
62423 5 6 5 2 3
62426 5 4 5 4 4
62433 5 6 2 2 4
62434 6 5 5 1 1
62435 5 5 5 2 2
62438 4 4 4 NA 3
62440 3 4 5 5 6
62443 5 5 4 1 2
62444 2 3 3 4 3
62447 5 5 5 1 5
62448 5 5 5 4 2
62450 6 5 3 3 4
62453 4 5 4 2 3
62454 3 4 3 4 4
62457 5 3 4 3 3
62462 6 4 4 1 1
62463 5 2 5 3 2
62464 5 4 4 4 4
62467 3 2 2 3 2
62468 4 2 2 5 4
62469 5 5 6 1 1
62470 5 4 2 2 4
62474 2 6 6 1 1
62476 4 2 4 4 5
62479 5 6 5 1 1
62480 5 5 4 2 4
62481 5 5 6 1 2
62486 5 3 4 1 1
62489 4 4 5 2 5
62491 5 2 5 4 3
62493 2 3 2 3 4
62494 6 5 5 1 1
62496 5 2 4 1 5
62497 5 3 2 3 5
62498 1 5 6 4 1
62499 5 4 5 2 3
62500 5 6 5 1 3
62502 1 5 6 1 1
62505 5 6 4 3 4
62508 5 6 6 1 2
62509 6 5 6 2 2
62512 6 6 5 NA 2
62514 6 6 5 1 2
62518 6 6 5 1 1
62520 4 5 4 1 1
62522 6 5 4 2 3
62526 5 4 1 3 1
62527 2 3 2 3 5
62528 5 6 5 1 3
62529 5 6 5 1 3
62530 5 6 5 1 3
62531 5 6 5 1 3
62532 5 6 4 2 4
62533 5 6 4 2 4
62535 5 6 4 2 4
62537 2 2 2 4 5
62538 6 6 6 1 1
62541 6 5 6 4 4
62542 6 5 6 1 3
62543 4 5 5 1 NA
62545 2 5 2 4 3
62546 4 4 4 2 4
62547 6 5 6 5 4
62548 4 3 5 1 2
62550 4 4 4 2 4
62551 4 4 3 6 6
62552 6 6 6 1 1
62553 6 6 6 1 1
62555 3 4 4 5 5
62556 5 5 4 3 3
62557 5 5 5 1 2
62559 5 5 5 4 3
62561 6 4 5 1 1
62562 3 5 5 1 2
62565 1 6 6 1 1
62567 6 6 4 1 5
62570 3 4 2 5 5
62573 6 6 6 1 6
62574 4 3 3 2 4
62577 2 1 2 5 4
62578 5 5 6 1 3
62582 5 3 3 2 2
62589 5 4 3 2 2
62590 5 4 4 1 4
62594 6 5 6 2 3
62597 6 2 4 2 5
62599 6 1 5 3 1
62604 6 5 6 1 2
62605 5 5 3 4 1
62606 5 5 3 1 1
62610 5 6 5 1 1
62611 4 3 4 3 4
62612 4 2 4 4 2
62613 3 1 2 4 2
62615 3 1 4 5 2
62617 6 6 6 1 1
62618 4 3 4 5 4
62622 6 6 5 1 4
62623 5 4 2 1 2
62625 6 5 5 1 2
62627 5 5 5 2 4
62635 4 4 NA 4 5
62638 6 3 5 1 2
62640 6 5 5 5 1
62642 6 5 5 3 3
62643 3 4 2 2 2
62644 5 6 5 1 2
62645 5 5 5 1 3
62646 3 4 6 5 2
62647 5 5 5 2 2
62648 1 4 6 4 NA
62650 6 6 5 1 1
62652 4 3 3 1 1
62653 4 6 5 2 3
62654 5 6 6 1 2
62657 6 3 5 2 1
62662 5 4 4 5 5
62664 6 5 5 1 2
62665 4 5 3 1 1
62667 5 6 5 1 1
62668 1 6 5 3 4
62669 5 4 6 2 4
62670 5 5 5 2 2
62673 5 5 5 NA 2
62675 6 6 4 1 2
62677 1 4 4 4 1
62679 5 4 4 1 2
62681 5 4 4 1 2
62682 3 6 3 3 1
62683 6 5 6 1 1
62684 5 5 5 1 2
62685 6 2 5 1 2
62686 6 5 3 2 2
62687 2 6 5 3 2
62688 6 6 5 1 2
62690 4 6 5 4 1
62692 6 6 5 1 1
62694 6 6 5 1 1
62698 4 3 4 4 3
62700 4 4 6 3 3
62703 4 3 5 2 3
62706 4 5 6 1 1
62707 3 5 4 2 2
62708 5 6 6 1 1
62710 5 4 4 3 4
62712 6 5 5 1 3
62715 5 5 5 2 4
62716 3 1 1 3 6
62717 5 4 5 4 2
62718 5 4 3 3 4
62719 5 2 6 1 1
62720 1 5 2 1 2
62722 5 4 5 2 5
62726 2 5 4 2 1
62728 5 5 4 2 5
62729 3 2 3 4 4
62731 5 2 2 4 2
62740 5 4 3 2 4
62741 5 4 5 2 1
62744 5 5 5 3 2
62745 2 5 6 4 6
62749 2 5 6 1 2
62750 5 2 5 1 2
62751 4 4 5 4 5
62757 5 5 5 2 2
62758 2 2 4 2 5
62761 6 5 6 1 2
62764 4 5 5 1 2
62765 1 4 6 1 1
62766 5 3 3 3 5
62767 1 1 2 4 5
62768 6 6 5 1 2
62770 2 5 5 2 2
62772 4 5 4 4 4
62776 2 2 4 4 4
62778 NA 5 5 1 1
62779 3 5 4 2 4
62780 6 6 6 1 2
62781 6 5 4 2 1
62783 5 5 5 5 5
62785 5 6 5 1 4
62786 6 6 5 1 4
62787 5 3 1 2 5
62788 6 6 5 2 3
62789 4 4 3 5 2
62790 6 5 6 1 1
62792 4 3 4 4 3
62795 5 4 1 5 5
62796 2 4 4 6 5
62797 3 4 1 1 4
62800 3 5 3 1 1
62801 5 6 5 1 1
62803 4 4 4 4 2
62804 6 3 3 2 4
62805 4 5 5 2 4
62809 NA 3 2 2 3
62810 6 6 6 1 1
62816 6 5 6 1 6
62817 3 5 6 5 3
62819 3 4 2 2 5
62821 6 5 6 1 2
62822 4 4 4 3 2
62825 4 2 4 4 5
62826 4 5 4 3 4
62827 4 6 6 4 4
62828 6 6 5 4 2
62831 6 5 3 5 3
62832 5 5 5 3 3
62834 4 4 6 2 2
62835 4 4 3 NA 4
62837 1 5 6 1 4
62839 5 5 5 2 1
62840 4 4 4 1 2
62844 5 5 5 2 2
62846 4 5 4 2 4
62847 6 6 5 1 1
62849 4 6 5 1 2
62851 6 4 6 4 2
62853 5 6 6 2 4
62856 4 5 5 1 2
62857 4 4 3 4 4
62858 5 5 5 2 2
62861 4 4 2 4 4
62863 3 2 5 2 NA
62864 5 1 4 2 1
62867 4 3 4 4 4
62869 4 4 4 2 5
62870 2 NA 6 1 NA
62872 2 5 6 1 NA
62874 5 6 6 1 1
62876 4 2 3 NA 3
62877 5 4 5 1 4
62878 2 6 4 1 3
62879 6 6 5 2 1
62881 6 5 6 1 1
62883 4 4 4 2 4
62887 6 6 5 1 1
62889 4 2 5 4 3
62890 5 4 5 4 5
62891 6 6 6 1 2
62897 4 5 5 1 1
62898 6 6 6 1 1
62899 5 6 5 1 1
62901 4 3 5 4 3
62903 5 5 2 4 2
62908 4 3 3 5 5
62910 5 5 5 2 2
62911 5 4 4 1 2
62916 2 1 4 4 1
62918 6 4 4 2 2
62920 5 5 5 2 3
62922 5 5 5 2 4
62926 5 5 2 4 5
62931 5 5 6 1 2
62933 1 6 4 1 1
62934 4 4 4 3 4
62936 3 1 2 2 2
62938 6 5 4 2 2
62939 4 4 4 3 3
62941 4 4 4 3 3
62942 5 6 4 2 2
62948 4 3 1 3 2
62949 6 4 4 2 3
62950 6 5 4 1 1
62951 4 2 4 2 4
62953 6 5 5 1 2
62954 5 5 1 1 6
62957 6 5 4 1 2
62962 4 2 4 1 1
62965 5 5 4 4 6
62968 2 4 4 3 4
62969 6 5 5 1 5
62971 5 5 5 2 2
62974 5 5 4 1 1
62976 6 5 5 3 1
62983 5 3 4 2 3
62984 4 2 4 5 3
62989 3 5 5 1 1
62990 6 6 6 1 1
62991 5 5 5 3 1
62994 5 5 3 3 5
62995 2 4 2 4 4
62996 4 5 5 2 1
62997 2 1 5 2 6
63004 4 5 5 2 2
63007 4 6 6 3 5
63013 3 4 3 4 5
63017 6 4 4 2 4
63018 5 5 5 2 4
63021 5 4 5 3 4
63023 6 6 5 2 2
63026 6 6 6 1 1
63027 1 2 3 3 1
63030 6 NA NA NA 2
63034 4 6 5 3 3
63035 5 5 5 1 2
63036 4 3 4 1 1
63037 6 6 4 4 4
63038 5 5 4 2 4
63039 6 5 4 2 5
63040 4 4 3 NA 4
63042 4 4 3 4 3
63047 4 4 2 3 5
63048 5 5 2 4 5
63049 6 6 6 1 1
63050 5 4 3 NA 4
63051 NA 5 2 1 2
63054 4 5 5 2 5
63055 5 6 6 2 1
63059 6 6 6 1 1
63062 5 3 4 3 5
63063 3 4 3 4 4
63069 6 6 5 3 3
63070 6 4 5 2 4
63071 6 5 6 1 1
63073 6 4 4 2 5
63075 6 5 5 4 2
63077 5 5 5 2 1
63081 4 6 4 1 1
63083 3 4 4 3 3
63084 5 4 5 2 1
63089 5 6 3 1 6
63090 5 5 4 2 3
63092 3 5 5 4 2
63094 4 3 3 3 5
63096 4 5 5 1 2
63097 5 6 6 2 1
63099 3 5 5 2 2
63100 5 6 3 2 4
63102 5 5 5 1 4
63103 5 6 6 1 2
63104 3 3 3 3 4
63108 3 2 5 4 6
63109 5 5 5 1 1
63112 3 2 3 4 4
63115 5 5 5 5 4
63116 4 5 5 2 1
63120 5 4 6 1 4
63121 6 6 6 1 5
63122 3 4 3 3 4
63123 5 6 1 2 1
63125 5 5 5 2 2
63127 5 5 5 1 2
63128 5 5 6 2 2
63130 5 4 5 2 6
63131 5 6 5 NA 1
63135 4 4 5 3 4
63136 4 6 5 2 4
63139 5 5 6 2 2
63141 1 6 6 1 1
63142 3 4 5 2 4
63146 4 2 2 2 4
63147 6 5 6 1 1
63152 5 6 4 4 4
63154 5 5 6 2 3
63155 5 5 6 2 4
63158 6 5 2 1 5
63161 NA 3 3 1 1
63163 6 6 6 1 2
63165 3 4 3 2 2
63171 4 4 4 3 5
63172 6 5 5 1 1
63173 4 4 6 2 4
63176 4 5 5 2 2
63181 3 5 4 2 3
63182 4 5 4 2 2
63183 3 3 2 3 4
63187 3 5 5 1 6
63192 4 3 4 3 4
63193 5 1 2 2 4
63194 4 4 4 4 4
63197 5 5 4 3 4
63200 3 4 6 2 1
63201 5 4 4 2 2
63203 4 2 2 4 3
63209 6 6 2 1 1
63215 5 5 2 3 5
63222 4 5 3 3 5
63223 3 2 2 5 6
63225 6 6 6 1 4
63227 4 4 4 2 4
63228 3 4 3 4 4
63233 5 5 3 2 3
63237 5 5 5 3 4
63238 5 3 2 2 4
63239 6 5 6 2 1
63242 4 4 5 2 2
63245 5 6 6 4 3
63246 5 3 5 1 1
63250 5 4 4 2 5
63252 5 5 5 2 2
63253 2 2 4 3 4
63258 4 5 5 2 3
63261 4 6 5 2 2
63264 2 2 4 5 6
63266 5 5 4 2 2
63271 3 3 3 3 4
63277 4 4 3 3 5
63278 3 3 3 2 3
63280 4 4 6 3 5
63281 5 5 5 3 3
63286 4 4 4 4 5
63287 5 5 6 1 1
63289 1 5 5 6 6
63292 5 4 5 3 3
63293 4 4 4 2 2
63294 4 3 2 4 4
63301 3 5 5 2 2
63302 5 5 3 2 4
63307 5 4 5 4 4
63309 3 5 5 4 1
63313 5 4 4 4 5
63317 4 4 4 2 3
63320 5 6 5 1 1
63321 4 4 3 3 3
63322 6 4 4 2 5
63324 1 1 1 6 6
63326 3 3 4 2 3
63328 6 5 6 1 1
63330 4 4 4 5 5
63331 4 2 3 2 2
63332 6 6 4 1 1
63334 4 5 5 6 4
63335 5 4 4 3 3
63337 6 6 6 1 1
63338 4 5 4 4 4
63339 6 5 5 1 2
63340 6 6 5 1 3
63342 5 5 4 3 2
63346 5 5 6 1 4
63351 5 5 4 2 2
63352 4 2 4 3 5
63358 4 5 2 4 5
63361 3 5 4 2 2
63364 3 3 4 3 4
63369 4 5 5 5 6
63370 4 4 5 1 2
63371 3 4 4 3 5
63372 4 1 2 5 5
63375 3 3 4 4 4
63376 4 6 5 2 2
63378 5 5 5 2 NA
63379 6 5 5 2 4
63382 5 5 5 2 2
63383 5 4 4 2 3
63386 4 4 5 1 4
63389 5 6 6 1 2
63395 5 NA 5 1 1
63399 5 2 5 2 4
63400 4 5 6 NA 1
63402 4 4 4 5 6
63404 5 6 5 2 4
63406 6 3 4 3 5
63408 4 4 6 3 3
63412 5 5 6 2 1
63413 6 6 5 1 1
63414 5 5 4 5 6
63420 5 3 5 2 5
63421 5 2 5 2 2
63427 5 4 5 2 2
63428 6 6 6 1 2
63430 6 6 6 1 1
63431 4 3 4 4 4
63432 5 5 5 2 4
63435 2 2 2 4 6
63437 2 1 1 4 6
63438 4 4 5 3 3
63439 4 4 4 4 4
63441 3 3 5 5 5
63442 4 4 5 3 NA
63444 4 4 5 3 5
63445 4 6 4 4 5
63446 2 3 2 4 5
63448 6 4 6 1 2
63450 5 5 4 3 4
63452 6 5 4 1 2
63454 4 4 4 2 4
63455 5 6 6 2 4
63458 2 2 4 3 5
63459 6 6 2 2 4
63463 3 1 2 4 6
63464 6 5 5 1 2
63465 4 1 5 4 6
63466 6 5 5 1 1
63467 6 5 1 1 1
63468 5 5 4 2 5
63470 6 6 6 4 1
63472 5 5 5 2 4
63473 3 2 5 4 5
63476 5 5 6 2 3
63479 3 4 5 1 1
63480 5 6 5 2 4
63481 4 4 2 2 4
63485 2 3 5 4 5
63486 4 6 4 2 3
63488 5 5 2 2 2
63491 6 6 6 1 1
63493 3 3 2 3 6
63494 5 5 4 3 5
63496 3 5 5 2 1
63498 4 3 3 2 3
63499 4 2 4 4 5
63501 2 5 6 1 1
63502 3 4 4 4 1
63504 6 2 5 2 1
63505 6 5 5 1 6
63509 5 5 5 3 5
63510 6 6 6 1 1
63512 5 5 4 4 6
63513 3 2 3 4 5
63516 6 6 6 2 5
63518 5 6 1 6 6
63521 3 6 3 2 2
63525 5 4 4 4 2
63528 1 2 1 2 4
63530 5 6 5 1 2
63531 5 2 4 1 3
63533 5 6 5 1 6
63534 4 4 4 3 5
63536 6 4 6 1 3
63538 5 5 5 1 4
63547 5 5 2 1 1
63549 5 6 4 NA 2
63551 4 3 4 4 6
63554 4 5 5 3 6
63555 5 4 5 3 5
63556 3 5 4 2 5
63557 5 5 6 3 4
63558 4 3 6 4 3
63559 5 5 6 1 1
63560 6 6 6 1 4
63562 3 4 4 5 6
63567 2 3 3 3 5
63575 4 4 3 4 5
63578 5 4 5 2 4
63579 6 6 5 1 3
63580 3 4 3 5 4
63581 5 5 5 1 3
63582 2 5 5 2 6
63583 6 6 3 1 1
63584 NA 4 4 3 5
63586 5 5 5 2 4
63587 5 5 4 4 2
63588 5 4 5 2 2
63589 3 3 4 6 4
63590 5 5 5 2 2
63591 2 2 2 2 6
63595 5 5 6 2 2
63596 6 5 5 2 2
63597 4 4 4 4 4
63602 6 6 6 1 1
63608 1 5 5 1 2
63609 3 2 1 5 6
63613 5 6 5 1 1
63616 4 3 3 3 2
63617 5 5 4 1 4
63618 6 5 6 2 2
63619 5 3 2 6 6
63621 4 5 5 4 5
63623 4 5 6 1 1
63625 6 4 5 1 1
63631 5 5 4 3 4
63636 6 5 6 3 6
63637 5 5 5 2 5
63638 4 4 3 4 6
63642 5 5 4 3 5
63644 3 5 5 5 5
63648 5 4 4 3 1
63649 4 5 4 4 3
63655 5 5 5 2 1
63657 6 5 5 5 6
63658 3 4 2 2 2
63659 4 3 4 3 4
63661 4 4 4 4 4
63668 6 6 5 1 1
63669 4 3 5 5 4
63670 5 5 3 3 4
63672 5 4 4 2 2
63675 5 4 5 2 4
63676 4 5 3 2 6
63677 6 4 2 1 6
63681 5 5 5 1 5
63682 3 2 3 2 4
63683 5 3 4 2 5
63687 3 4 3 5 6
63690 4 3 4 3 5
63692 5 2 4 2 3
63693 4 4 4 5 5
63696 3 2 2 4 4
63697 6 6 4 1 1
63699 5 4 3 3 1
63700 5 5 5 2 4
63701 6 6 6 1 3
63704 5 NA 5 1 2
63705 6 6 6 1 4
63706 5 4 4 NA 1
63707 3 4 2 3 4
63709 4 4 4 3 4
63711 5 3 2 4 4
63712 6 6 5 2 4
63713 6 6 5 1 1
63721 6 6 6 4 1
63723 2 5 5 1 2
63724 2 3 4 4 5
63726 5 5 5 2 5
63727 6 5 6 1 2
63728 3 2 5 4 4
63729 4 3 4 2 4
63732 2 3 4 2 2
63734 3 4 2 4 4
63735 5 2 4 2 3
63738 5 5 5 4 4
63739 6 6 6 1 NA
63742 6 6 1 1 6
63743 5 3 3 2 3
63744 3 5 4 2 5
63745 5 4 3 3 4
63746 5 2 5 3 5
63748 3 1 5 1 4
63750 6 4 4 2 4
63752 6 5 5 1 5
63760 4 3 3 2 3
63761 2 2 2 4 5
63762 1 1 1 6 6
63763 1 6 5 4 2
63766 5 4 5 1 1
63767 6 6 6 1 1
63768 5 4 5 2 3
63770 5 5 3 2 4
63773 6 6 5 1 4
63775 5 4 4 1 2
63776 5 6 NA 2 6
63778 5 3 6 1 5
63788 5 5 1 5 6
63789 4 5 5 1 2
63791 5 5 5 1 1
63792 4 3 2 2 3
63793 4 3 3 2 3
63794 6 6 4 1 1
63796 4 3 4 3 6
63798 6 6 5 1 4
63799 4 5 5 1 1
63801 5 5 4 2 3
63803 6 4 5 2 4
63807 3 3 3 2 6
63810 5 4 4 4 6
63811 6 6 6 1 3
63812 3 5 5 1 2
63815 4 5 6 2 1
63816 6 5 4 1 2
63817 6 6 6 1 1
63818 3 5 4 4 4
63820 5 6 5 2 4
63822 2 5 2 1 5
63823 4 4 2 3 4
63824 1 6 5 2 5
63825 4 2 5 3 5
63826 4 5 5 5 NA
63827 6 4 5 1 5
63828 5 5 3 3 6
63829 3 5 4 2 3
63834 5 5 4 2 5
63835 4 5 5 3 3
63837 5 4 3 1 1
63838 5 5 5 1 1
63839 4 6 6 6 4
63846 6 3 5 1 1
63847 5 5 5 2 3
63849 6 6 6 1 1
63851 5 5 6 2 4
63852 5 5 6 2 4
63853 5 5 5 2 5
63854 6 6 1 2 5
63855 6 5 6 1 2
63856 4 5 1 2 6
63862 2 5 2 3 5
63863 5 6 5 2 2
63866 4 2 4 3 4
63868 4 3 3 2 4
63871 6 6 6 1 1
63873 3 3 4 4 4
63875 6 5 6 1 3
63877 6 3 2 5 5
63879 4 3 5 2 1
63880 5 5 6 1 5
63881 5 4 5 5 5
63882 5 3 4 1 6
63883 6 2 4 1 2
63884 4 3 NA 2 4
63885 5 5 4 2 4
63887 5 5 4 4 4
63888 3 4 3 2 3
63890 4 2 2 2 2
63897 4 2 5 5 6
63898 2 5 2 5 2
63899 5 6 5 4 4
63900 5 4 6 2 2
63902 5 5 5 2 2
63907 6 5 5 2 5
63909 3 3 4 2 4
63911 5 5 5 1 5
63912 4 3 1 4 2
63913 6 5 5 1 1
63918 6 5 5 1 2
63924 4 3 4 4 3
63925 4 4 4 2 3
63930 4 5 5 2 2
63931 5 5 4 2 5
63932 6 5 5 2 2
63934 5 5 6 1 1
63937 2 5 5 4 6
63939 5 4 4 4 4
63942 4 2 2 6 6
63946 2 4 2 4 5
63947 5 5 5 1 1
63948 4 3 1 5 5
63950 4 4 3 4 5
63952 4 1 5 3 6
63955 1 4 2 3 6
63956 6 6 5 3 2
63957 5 6 5 1 6
63958 5 4 4 2 5
63959 5 5 5 5 5
63961 5 6 4 2 6
63962 5 3 6 1 4
63963 5 4 5 5 6
63966 4 5 5 4 5
63967 5 5 6 1 1
63971 5 5 6 2 4
63977 6 5 4 2 4
63978 6 6 6 5 4
63979 6 6 2 5 5
63980 4 5 2 3 6
63982 6 5 5 3 3
63983 5 3 5 5 5
63984 6 5 5 1 4
63985 4 4 4 4 4
63987 5 5 4 4 3
63990 4 4 3 2 6
63991 3 NA NA NA 3
63993 5 2 3 3 4
63997 4 4 4 3 4
63999 4 2 4 4 4
64000 2 2 4 5 6
64001 6 6 5 1 5
64003 4 5 5 1 2
64005 4 2 6 2 5
64006 5 5 5 1 1
64010 4 5 4 4 4
64012 4 4 5 3 5
64014 5 3 5 2 2
64016 6 6 6 1 1
64017 3 6 6 1 1
64018 6 5 6 2 2
64020 6 2 6 1 2
64021 2 5 4 1 6
64024 5 6 5 1 1
64025 6 4 5 1 2
64028 3 2 4 NA 4
64029 5 6 5 5 NA
64030 4 1 3 3 4
64031 6 6 5 3 1
64032 4 3 3 4 5
64033 2 3 3 1 6
64036 4 5 5 2 2
64044 3 3 5 5 4
64046 4 4 4 3 3
64047 3 4 3 3 4
64049 2 1 6 2 6
64050 5 1 4 3 4
64051 5 5 6 NA 2
64053 6 5 5 1 1
64056 2 2 3 5 6
64057 4 4 4 5 3
64065 4 5 6 2 2
64066 4 1 5 3 2
64069 2 3 6 5 5
64072 6 6 6 1 2
64074 5 5 4 1 2
64075 6 5 6 1 4
64076 2 5 5 3 5
64078 4 4 2 2 5
64079 6 6 6 1 2
64086 4 2 2 5 5
64087 5 3 5 3 5
64089 5 1 2 5 5
64091 2 1 1 4 4
64092 5 5 5 3 3
64094 5 5 3 4 5
64097 3 5 4 2 2
64099 5 5 4 5 6
64100 4 6 4 2 4
64101 5 5 4 2 4
64102 4 4 4 2 2
64103 5 4 4 3 2
64108 5 5 4 1 4
64111 5 5 5 3 3
64113 1 4 5 1 4
64116 4 4 3 3 4
64119 2 2 3 5 6
64120 3 1 3 5 6
64122 6 6 6 1 1
64125 2 2 1 2 6
64128 4 3 3 4 4
64129 5 4 4 2 4
64132 3 4 5 4 5
64134 5 5 4 2 4
64136 5 4 5 2 4
64137 5 4 2 4 4
64138 3 4 2 4 4
64141 5 5 5 4 4
64142 4 4 4 3 4
64143 4 5 5 2 1
64144 2 2 4 3 4
64147 4 5 4 4 4
64148 6 6 4 2 4
64150 3 3 3 3 5
64151 5 6 5 1 3
64152 2 1 4 4 6
64154 6 6 6 1 4
64155 4 4 5 2 5
64156 4 4 5 2 2
64158 4 2 2 4 6
64163 6 5 6 1 4
64168 5 2 3 4 4
64169 6 6 6 2 1
64178 4 5 5 2 2
64179 6 4 2 2 4
64180 3 4 5 1 1
64185 5 4 4 3 5
64192 1 1 1 6 6
64193 4 3 3 3 5
64195 5 5 5 1 2
64200 4 4 5 2 3
64202 1 1 2 6 6
64204 2 4 5 5 2
64206 5 5 6 1 4
64212 2 3 5 5 5
64216 4 4 4 4 6
64217 5 3 2 4 4
64222 5 4 5 2 2
64226 4 2 2 4 6
64228 4 4 4 2 5
64231 1 5 5 1 1
64235 3 2 4 5 4
64236 4 2 5 5 5
64240 NA 5 5 1 1
64241 3 4 5 3 3
64242 5 4 5 1 2
64243 2 4 2 5 5
64244 6 5 5 1 3
64248 2 3 2 5 4
64250 6 6 6 1 6
64251 3 2 4 4 5
64254 4 5 4 2 1
64256 3 6 6 1 1
64261 4 NA 2 4 4
64263 5 3 5 4 4
64264 3 5 6 5 1
64266 6 1 6 1 2
64267 5 5 2 2 4
64274 5 5 5 1 4
64275 6 6 5 2 6
64279 5 5 4 2 2
64280 4 6 NA 1 1
64284 5 5 6 2 3
64286 5 4 5 2 2
64289 5 6 5 1 1
64290 NA 6 5 1 1
64295 5 5 4 2 6
64296 4 5 4 3 6
64297 4 2 3 2 3
64298 4 3 4 3 1
64299 5 5 6 2 2
64300 4 6 6 5 4
64303 5 3 4 3 4
64305 4 5 5 5 2
64308 6 5 4 1 5
64310 5 5 5 2 3
64311 3 4 4 3 4
64312 5 6 4 2 3
64315 6 5 3 3 1
64318 5 5 5 3 4
64320 5 4 5 2 3
64321 6 6 6 1 1
64322 6 2 4 1 1
64323 1 6 6 1 1
64324 4 5 4 5 5
64326 4 6 5 2 4
64327 3 4 5 4 4
64329 3 4 5 4 5
64332 5 3 5 2 3
64333 6 5 5 1 1
64334 4 3 4 3 3
64335 6 3 6 1 2
64338 5 4 5 3 4
64339 4 4 3 2 5
64340 6 5 6 1 3
64341 1 1 6 6 6
64342 4 4 5 4 3
64344 4 5 4 5 5
64345 5 5 5 1 2
64347 4 4 4 3 5
64349 5 5 4 1 2
64352 4 4 4 4 4
64356 4 6 5 2 1
64359 6 5 5 2 2
64363 5 4 2 2 4
64365 5 5 4 3 6
64367 4 5 3 2 2
64368 5 5 5 5 4
64370 5 3 3 3 3
64371 5 5 5 2 2
64372 3 3 3 5 5
64374 5 3 4 3 3
64375 3 2 4 4 6
64377 4 2 4 4 6
64378 5 5 5 2 5
64379 1 1 1 6 6
64382 5 3 4 4 6
64385 5 4 4 2 2
64389 3 6 6 1 1
64390 5 2 5 2 1
64392 6 5 5 1 2
64393 NA 5 2 3 6
64396 3 4 2 2 2
64399 2 2 4 3 5
64400 4 3 4 2 4
64401 6 4 4 2 3
64403 6 6 5 1 2
64413 6 6 6 1 1
64414 5 4 4 2 4
64417 5 6 5 1 1
64418 6 4 5 1 3
64421 5 4 4 2 4
64422 5 1 5 2 4
64424 5 4 3 4 3
64425 3 3 2 2 1
64426 5 5 5 2 4
64427 6 5 2 1 4
64429 5 5 5 1 2
64430 5 5 5 2 3
64431 3 1 3 4 5
64432 4 2 4 2 3
64435 5 5 5 1 2
64436 5 5 4 2 4
64437 5 6 5 2 2
64438 4 1 5 5 5
64439 6 6 4 1 3
64440 3 3 2 5 5
64446 2 4 4 5 6
64450 3 4 3 3 2
64453 5 6 4 4 2
64456 4 2 4 4 4
64458 2 6 1 5 6
64459 4 4 5 4 4
64460 2 5 4 3 4
64461 6 5 6 1 1
64462 5 NA NA 4 5
64463 4 5 6 4 1
64464 5 6 4 2 4
64466 4 4 5 2 2
64467 3 4 4 4 4
64468 5 5 4 2 2
64469 5 6 2 5 6
64471 6 5 6 2 5
64479 3 2 3 4 6
64481 6 4 6 1 1
64483 6 5 5 1 1
64484 5 2 4 1 4
64487 4 4 6 1 3
64488 5 2 6 2 1
64490 5 4 5 4 4
64491 5 5 5 2 2
64493 6 4 2 5 5
64495 5 5 6 1 1
64496 6 6 3 3 2
64499 6 6 3 2 2
64501 4 3 4 4 6
64507 4 4 5 3 5
64508 5 5 4 4 5
64510 5 5 6 1 1
64511 6 6 6 1 1
64512 6 6 6 2 4
64513 5 6 4 2 4
64514 4 6 4 2 2
64516 4 2 4 3 4
64518 6 5 5 1 2
64519 1 5 5 1 2
64520 6 6 6 1 1
64522 5 5 4 4 5
64525 6 2 2 5 6
64528 4 5 5 1 3
64530 5 4 4 2 2
64532 4 2 3 3 2
64537 6 5 4 1 1
64540 6 5 6 1 2
64541 2 2 2 4 4
64543 3 4 2 3 5
64544 4 2 4 2 4
64545 6 6 6 1 1
64546 5 5 5 3 4
64547 5 5 2 1 5
64548 4 5 4 3 6
64549 5 5 6 2 1
64552 2 2 3 3 5
64555 6 5 6 2 5
64556 6 5 5 2 4
64557 3 4 3 5 5
64562 2 2 4 4 5
64563 4 4 6 4 2
64564 6 5 3 2 3
64565 5 4 6 1 1
64567 5 6 5 4 5
64568 5 5 4 2 4
64571 5 3 4 2 5
64572 4 4 6 3 4
64573 4 3 5 4 5
64577 6 5 4 3 1
64579 3 4 1 4 5
64581 6 5 5 1 1
64583 2 3 3 1 4
64585 5 5 5 2 2
64586 4 4 5 3 4
64588 6 5 3 5 3
64591 6 6 5 2 5
64593 4 5 5 2 3
64595 3 4 4 3 4
64600 1 5 1 2 5
64603 6 4 4 2 5
64605 6 5 4 1 6
64606 3 4 2 4 5
64607 6 5 4 2 3
64612 4 5 4 3 4
64614 2 3 3 2 NA
64618 3 4 4 3 3
64620 2 5 5 2 5
64621 6 6 5 1 1
64622 2 6 6 4 4
64623 4 4 4 3 6
64626 5 5 4 3 4
64627 5 5 6 1 3
64630 4 6 6 1 1
64634 4 1 2 4 4
64636 5 5 6 1 1
64638 3 2 6 2 5
64639 4 4 6 2 5
64642 1 1 1 1 1
64648 6 6 5 1 2
64652 5 5 5 1 4
64656 6 6 4 3 4
64658 5 3 5 3 5
64660 5 6 6 1 1
64661 5 5 4 1 2
64664 5 5 6 1 4
64665 4 4 6 2 4
64668 3 5 4 3 5
64669 6 5 6 2 5
64670 5 5 4 2 4
64671 3 3 5 3 4
64673 6 6 4 2 6
64675 3 2 2 4 5
64679 5 4 5 2 3
64680 5 5 5 3 6
64682 5 4 4 2 1
64683 5 2 6 1 1
64685 6 6 6 1 1
64686 4 5 1 5 6
64688 5 3 5 2 4
64689 1 1 4 6 5
64693 4 2 5 5 5
64696 5 5 5 2 1
64697 5 5 6 2 1
64698 5 5 4 1 4
64702 6 6 4 1 2
64706 4 4 4 2 2
64707 5 4 2 3 5
64709 5 5 6 3 1
64712 3 5 3 5 6
64714 3 2 2 4 5
64715 4 4 5 5 5
64716 4 3 4 4 5
64718 2 5 2 2 6
64720 5 3 5 1 1
64724 1 6 6 1 1
64725 5 3 4 6 4
64727 4 5 5 5 5
64729 5 4 4 2 6
64730 5 5 5 1 4
64733 3 2 2 4 6
64735 5 3 4 3 4
64737 5 4 4 4 6
64738 5 2 4 5 5
64739 4 4 6 3 5
64742 5 4 5 5 5
64744 5 5 5 2 3
64747 4 3 4 2 2
64753 3 2 1 5 6
64754 3 4 2 2 2
64758 4 5 4 2 4
64759 5 6 5 2 2
64763 5 5 4 2 4
64768 6 6 5 4 1
64769 6 5 5 5 2
64770 5 6 5 1 3
64771 5 3 5 1 4
64772 4 3 3 3 5
64773 4 4 4 3 4
64774 5 3 5 4 5
64780 4 1 6 5 3
64804 4 5 5 1 2
64807 6 6 5 1 1
64810 3 2 1 2 1
64814 2 2 4 2 5
64815 5 3 5 3 4
64817 6 6 3 2 2
64819 4 4 4 2 4
64822 5 5 5 1 3
64824 5 5 2 2 2
64825 5 5 6 1 1
64826 6 5 4 3 4
64830 6 6 5 6 3
64831 6 6 5 6 3
64835 1 1 1 6 6
64838 5 5 5 2 4
64839 2 3 4 3 1
64842 5 6 1 3 6
64843 5 5 4 2 2
64844 6 2 1 4 6
64845 5 5 4 3 4
64846 5 4 3 2 4
64847 6 6 6 2 2
64849 3 3 4 4 4
64850 5 5 4 5 5
64852 5 5 6 2 1
64857 5 4 2 4 5
64861 5 4 2 2 6
64865 5 5 3 3 3
64866 6 4 5 1 2
64874 4 4 6 2 1
64876 6 5 3 1 5
64877 6 6 5 1 2
64880 1 2 1 5 6
64886 6 5 6 1 1
64887 5 5 4 2 5
64888 5 5 1 4 6
64890 4 3 5 3 2
64891 5 2 4 3 6
64894 3 5 2 2 5
64898 4 5 5 2 6
64902 2 1 4 4 3
64907 5 4 5 3 2
64912 4 3 2 4 5
64914 5 2 3 6 5
64915 5 5 3 2 4
64917 NA 5 5 3 4
64918 4 5 2 3 4
64919 6 6 6 1 6
64920 6 6 5 1 2
64921 2 3 3 4 4
64926 5 2 2 3 6
64931 5 6 4 2 4
64936 6 5 5 2 5
64937 5 6 4 3 2
64938 5 5 5 1 4
64939 3 2 2 3 6
64940 4 3 5 2 2
64944 4 6 6 5 4
64946 6 4 5 6 3
64949 6 6 5 2 1
64950 4 3 3 4 5
64952 4 5 4 2 5
64953 5 5 5 5 5
64954 6 5 5 2 5
64958 5 6 5 NA 6
64960 5 5 3 5 5
64961 5 4 5 2 4
64962 5 6 6 4 6
64963 6 5 4 1 1
64967 5 6 2 5 2
64968 5 6 6 5 6
64969 5 6 6 5 1
64970 5 6 3 5 1
64971 5 6 6 1 1
64975 5 4 6 1 1
64978 5 4 6 1 1
64979 5 4 6 1 1
64980 5 4 6 1 1
64981 5 4 6 1 1
64982 5 4 6 1 1
64984 5 4 5 4 3
64987 5 5 5 3 5
64990 5 6 5 4 4
64991 5 6 6 4 2
64992 4 4 5 3 3
64993 4 3 3 2 4
64996 6 6 4 1 1
64997 6 5 5 1 2
65017 5 5 4 2 3
65018 3 2 5 4 2
65020 5 5 5 2 5
65022 4 4 4 3 2
65025 5 5 5 1 1
65026 4 2 2 3 2
65027 4 4 4 2 5
65028 6 5 2 2 3
65033 4 3 2 2 5
65041 4 4 4 2 2
65042 5 5 5 5 1
65044 5 6 1 4 6
65047 5 5 5 3 2
65049 5 2 5 3 5
65050 6 6 5 2 1
65052 5 4 2 2 NA
65054 5 3 4 3 3
65056 5 5 5 5 4
65058 4 3 4 3 4
65059 4 4 5 2 3
65060 5 5 5 5 1
65061 5 5 5 2 5
65070 4 4 6 2 4
65071 1 6 6 4 5
65075 6 6 6 1 1
65078 6 5 6 1 6
65079 6 6 6 1 1
65080 6 6 5 1 1
65081 4 4 4 3 6
65082 5 5 4 2 3
65083 6 5 6 2 4
65084 5 4 4 4 4
65086 3 3 5 5 5
65087 6 6 3 1 1
65088 5 5 5 1 2
65091 5 4 3 3 4
65092 5 4 4 2 5
65096 4 4 4 2 2
65097 5 4 4 3 4
65099 4 4 2 5 5
65100 4 3 4 2 5
65101 4 3 3 3 3
65105 6 4 4 5 6
65108 5 5 6 4 2
65109 4 4 2 4 2
65111 6 5 5 2 4
65114 5 4 1 4 6
65117 5 4 4 4 4
65121 NA 6 5 5 4
65122 6 3 4 5 5
65124 6 6 6 2 1
65126 6 6 5 2 1
65129 5 5 6 6 5
65130 4 3 NA 6 6
65140 5 5 5 1 2
65142 3 2 2 5 6
65144 6 5 5 2 6
65147 6 5 5 1 3
65150 6 6 6 1 1
65151 3 2 2 4 6
65152 5 3 5 2 2
65156 5 3 5 2 2
65157 2 2 3 5 4
65162 5 5 5 2 3
65163 5 5 5 2 5
65164 4 2 4 5 2
65166 5 4 3 3 4
65168 1 NA NA NA 1
65169 4 5 4 2 2
65170 1 6 6 2 1
65172 4 3 4 3 3
65173 4 3 2 5 5
65174 5 4 6 2 4
65175 3 4 2 2 2
65179 3 2 3 3 3
65180 6 6 3 1 1
65185 5 5 5 1 1
65186 4 4 3 4 1
65188 5 4 5 4 5
65190 6 4 5 5 5
65191 5 6 6 1 1
65193 4 4 4 5 4
65195 4 5 4 3 4
65196 5 3 2 4 4
65197 4 5 4 4 5
65199 5 5 4 2 2
65201 4 5 5 4 2
65202 5 5 2 3 2
65204 5 5 5 3 4
65206 5 5 2 3 3
65207 5 3 5 4 4
65212 6 5 5 1 4
65213 4 4 5 2 6
65215 5 5 4 5 4
65220 4 2 5 4 5
65223 5 5 3 4 5
65224 6 6 6 2 2
65229 4 5 5 1 2
65230 5 5 5 2 1
65232 4 3 3 5 5
65235 4 3 4 3 4
65236 4 3 3 2 2
65237 4 4 5 2 3
65238 6 6 6 1 1
65240 5 5 2 2 6
65241 5 5 5 1 2
65245 1 6 1 1 5
65247 5 2 3 4 5
65249 5 5 3 2 5
65252 5 4 4 2 4
65254 5 5 4 1 4
65255 6 5 4 1 1
65256 5 5 4 2 5
65257 4 4 6 1 5
65258 6 6 6 1 1
65260 5 4 4 2 2
65262 5 5 3 3 4
65266 5 6 6 2 1
65267 5 5 6 2 2
65268 5 6 6 3 6
65271 3 2 4 2 5
65273 5 2 4 5 2
65274 5 6 3 1 3
65275 6 5 4 4 6
65278 5 4 5 3 4
65279 5 2 2 3 2
65281 2 3 5 1 2
65282 6 6 5 1 4
65285 5 4 4 2 5
65286 6 5 5 1 2
65288 5 2 3 5 4
65289 2 4 4 1 2
65291 6 2 3 3 4
65295 4 3 4 4 3
65296 5 5 5 4 4
65297 4 2 2 1 5
65299 4 2 4 3 5
65300 6 6 6 1 1
65301 4 2 2 4 4
65307 5 5 6 4 4
65309 2 4 3 4 5
65311 6 1 6 1 1
65313 6 6 5 1 5
65314 5 6 5 1 4
65316 5 4 5 3 5
65319 2 2 4 4 5
65320 2 5 2 4 5
65322 6 6 5 2 3
65326 3 2 4 3 4
65327 6 5 4 3 4
65332 3 4 5 5 5
65335 6 5 5 1 3
65342 3 2 5 3 4
65343 3 3 5 3 4
65344 3 3 4 2 4
65346 3 3 4 2 4
65347 5 4 2 2 5
65348 5 3 4 3 2
65349 5 6 3 2 5
65352 6 6 5 1 1
65353 5 2 2 2 3
65356 5 2 5 1 4
65359 5 5 3 3 5
65361 5 2 4 3 5
65362 4 4 5 NA 1
65363 5 4 4 5 6
65370 6 5 4 5 5
65372 6 6 5 1 5
65374 5 4 5 1 1
65377 6 6 6 2 1
65378 5 6 6 1 2
65381 4 6 6 1 5
65382 6 6 6 1 3
65385 2 3 3 4 6
65386 2 1 2 4 5
65387 3 6 6 1 1
65388 6 6 6 1 1
65391 5 5 5 1 1
65392 3 5 2 5 5
65395 4 5 5 4 4
65401 3 2 5 3 5
65402 4 4 1 5 5
65403 6 6 4 1 3
65404 4 3 1 4 6
65405 2 5 5 6 5
65407 1 6 5 1 1
65408 5 5 4 3 2
65411 4 3 4 4 5
65412 5 4 5 5 6
65413 5 6 5 1 3
65415 3 6 5 2 3
65417 5 4 4 2 5
65419 4 5 5 1 3
65420 1 6 5 1 1
65421 4 5 5 2 5
65424 4 NA 5 4 5
65426 5 5 6 2 2
65428 4 5 5 2 2
65430 4 2 5 4 6
65432 6 4 3 6 4
65433 3 5 6 3 2
65434 5 5 5 2 2
65435 4 5 6 3 3
65437 6 5 6 1 2
65439 6 4 2 6 5
65440 5 4 4 4 6
65442 5 5 5 1 1
65445 1 5 6 1 1
65446 4 4 4 4 6
65448 5 3 1 6 2
65450 5 5 4 3 4
65452 6 4 4 4 5
65453 6 6 3 2 2
65455 5 6 3 1 2
65457 4 5 2 3 5
65458 5 5 3 2 3
65459 6 5 5 2 1
65460 4 4 5 2 4
65462 5 6 4 2 2
65466 4 6 5 4 5
65467 5 5 4 3 2
65470 5 4 4 1 2
65472 5 5 4 2 2
65473 4 4 4 2 2
65474 5 3 3 2 3
65475 5 6 2 2 4
65476 5 2 5 3 5
65477 6 6 4 1 4
65479 6 5 5 2 3
65484 5 5 3 2 6
65487 6 5 5 1 4
65488 4 4 6 5 1
65490 5 5 4 4 2
65491 3 2 4 3 6
65494 4 5 5 1 2
65496 3 4 4 3 4
65499 5 4 5 1 1
65501 5 5 4 2 3
65502 6 5 4 1 4
65503 5 5 5 1 1
65504 3 3 4 5 3
65506 5 6 5 2 2
65510 6 4 4 5 6
65511 1 4 1 4 6
65512 5 5 3 2 4
65515 5 6 4 1 5
65519 2 2 4 6 4
65522 5 5 5 1 4
65523 4 5 5 4 6
65524 6 5 6 1 1
65526 4 4 4 2 3
65527 6 6 6 1 2
65528 3 4 3 2 3
65529 3 4 5 2 2
65532 5 4 5 2 1
65536 5 5 3 2 4
65538 6 3 4 1 3
65545 4 4 2 2 5
65547 6 5 5 2 4
65548 6 6 NA 1 1
65549 2 4 4 6 6
65551 5 4 4 1 3
65552 5 6 6 1 1
65556 4 4 4 2 2
65557 5 5 5 2 3
65563 6 2 5 1 1
65564 6 5 1 4 1
65565 6 6 6 1 1
65566 6 6 6 1 1
65568 6 5 5 5 5
65569 4 5 6 3 2
65571 5 5 4 3 5
65575 5 2 2 6 5
65577 1 5 2 4 6
65578 5 5 6 1 1
65580 5 2 1 5 5
65583 6 6 6 2 1
65584 4 3 5 3 2
65586 5 6 5 1 1
65589 6 6 3 1 1
65591 4 3 4 5 1
65592 4 6 3 3 2
65593 6 5 6 1 2
65595 4 5 6 2 3
65598 5 5 2 3 5
65599 5 6 6 5 6
65600 4 3 4 5 5
65602 5 4 4 4 6
65605 4 4 4 1 1
65606 3 5 3 4 4
65612 5 3 3 4 5
65613 3 3 1 5 6
65618 NA 3 4 3 1
65620 5 5 5 1 2
65628 4 4 3 2 2
65629 3 2 NA 2 3
65630 2 2 5 5 6
65631 4 2 5 2 2
65632 4 5 5 5 2
65633 6 6 3 1 1
65634 6 6 4 1 2
65635 4 4 2 2 2
65636 6 6 5 2 3
65641 5 5 4 3 3
65643 4 2 3 4 5
65646 5 4 5 2 2
65648 6 5 5 2 2
65649 4 4 5 2 1
65651 5 4 6 2 2
65652 5 5 4 1 2
65653 4 4 3 3 4
65654 6 5 6 1 5
65656 4 5 5 4 3
65659 6 3 4 2 2
65664 4 5 5 4 2
65668 5 6 6 3 1
65671 2 2 1 3 5
65673 5 5 4 2 2
65674 4 3 5 2 3
65675 5 6 6 1 2
65678 6 6 4 2 5
65679 4 5 3 4 4
65680 5 4 5 2 2
65682 4 6 6 1 2
65684 5 6 6 1 1
65687 3 6 6 1 3
65694 6 2 3 4 6
65695 5 5 2 1 5
65696 5 5 2 1 5
65698 5 5 6 NA 3
65699 5 5 6 3 3
65700 4 3 3 1 2
65702 4 3 5 1 2
65703 6 NA 6 1 4
65704 6 5 5 1 2
65705 5 5 4 3 5
65706 6 6 6 1 1
65708 2 1 4 4 1
65709 3 5 6 3 6
65710 2 3 2 6 6
65711 6 5 5 3 3
65712 5 4 6 2 4
65713 6 5 5 1 2
65714 4 5 4 4 4
65715 6 6 5 1 3
65720 6 6 6 1 1
65724 4 5 3 2 5
65726 2 6 5 1 2
65727 6 5 6 6 6
65728 4 5 5 4 5
65730 4 3 3 2 3
65732 1 5 1 4 1
65733 6 5 4 2 5
65734 6 6 6 1 2
65738 5 5 5 2 1
65740 5 4 5 4 4
65742 2 3 2 4 4
65743 6 5 5 1 2
65744 5 6 2 2 2
65745 5 4 5 2 2
65746 5 5 4 1 3
65747 4 2 4 3 4
65748 6 6 5 3 4
65752 4 4 3 3 2
65758 5 1 5 1 1
65762 4 6 5 4 2
65763 6 6 6 1 1
65764 4 5 4 2 2
65765 3 4 4 2 2
65767 6 5 4 4 6
65777 4 5 3 2 2
65778 6 6 6 1 2
65779 5 4 4 5 6
65793 5 5 6 3 2
65794 3 6 4 1 1
65798 4 2 2 4 2
65801 6 6 6 1 1
65802 3 5 3 2 2
65803 6 5 5 5 2
65804 5 6 6 4 5
65809 4 4 5 2 2
65811 2 2 5 2 4
65813 4 4 4 3 4
65816 4 6 3 6 5
65818 4 6 6 1 1
65820 5 4 3 1 1
65822 4 5 5 1 2
65825 5 5 5 1 1
65826 5 6 4 1 2
65828 4 3 5 3 3
65831 5 5 6 1 3
65834 5 4 4 2 3
65836 3 6 1 1 3
65838 6 5 4 6 4
65839 5 5 3 5 4
65840 4 5 4 1 1
65841 6 6 5 1 1
65842 4 4 4 4 5
65844 5 4 4 3 2
65847 6 6 6 2 1
65848 4 5 4 3 2
65849 5 NA 4 2 4
65851 4 4 4 2 5
65852 6 5 5 1 3
65856 4 5 5 2 4
65857 NA 5 2 5 6
65862 4 3 4 3 4
65863 5 5 5 4 2
65864 6 5 6 1 1
65865 4 4 4 4 5
65870 5 4 5 2 4
65874 4 4 5 3 5
65876 2 5 6 1 1
65879 6 2 4 1 2
65880 5 4 5 1 2
65883 4 5 5 4 5
65886 5 5 5 1 1
65888 5 1 5 1 4
65890 5 4 5 2 1
65891 4 4 3 4 2
65892 4 6 4 3 6
65893 6 5 6 1 1
65894 5 4 5 1 1
65895 3 5 3 1 4
65896 5 6 4 1 4
65897 4 4 6 1 3
65899 6 4 3 5 1
65900 3 4 3 4 4
65901 5 5 5 5 2
65902 4 5 1 3 4
65903 6 6 6 2 4
65905 6 6 5 1 1
65909 3 5 4 3 4
65913 5 4 5 3 4
65917 4 5 5 1 3
65918 2 6 1 6 1
65921 5 5 5 4 6
65924 NA 6 5 2 2
65925 6 6 6 2 3
65926 6 5 3 1 1
65929 4 5 3 4 NA
65930 4 5 3 4 3
65932 6 3 4 2 5
65933 NA 4 NA 3 3
65936 4 4 4 3 3
65937 6 5 6 1 1
65938 5 5 4 3 4
65940 4 6 3 1 2
65941 5 4 5 3 1
65942 5 2 4 4 4
65946 3 4 1 4 5
65948 5 6 5 1 1
65950 3 2 2 3 6
65951 6 6 5 1 1
65959 6 6 2 1 1
65961 4 4 3 1 1
65962 NA 4 2 1 1
65965 4 3 4 5 5
65969 5 5 5 4 1
65971 3 4 4 3 3
65972 5 4 4 2 NA
65973 5 5 4 1 2
65974 1 1 1 1 1
65976 4 4 4 1 2
65977 6 5 5 1 1
65981 5 5 5 1 4
65986 5 5 5 4 2
65987 5 6 6 1 2
65988 4 4 4 2 5
65989 4 5 4 3 4
65990 4 2 4 4 2
65992 5 4 5 1 2
65995 5 2 3 4 5
65998 6 6 6 1 1
65999 5 4 4 4 5
66000 5 5 4 1 2
66001 6 6 5 5 3
66002 6 5 5 1 1
66003 6 5 6 1 3
66004 6 6 5 1 1
66006 6 6 5 1 2
66007 6 6 6 1 4
66013 6 4 5 3 1
66014 5 6 5 3 1
66015 5 5 6 2 2
66016 5 4 6 3 4
66017 4 1 1 4 4
66022 5 6 4 1 4
66024 4 3 5 4 5
66032 5 5 5 3 1
66034 5 6 6 1 4
66037 4 4 4 2 1
66039 5 6 5 3 4
66042 4 5 6 1 1
66045 4 2 2 4 3
66047 5 5 4 2 1
66049 4 5 2 4 3
66050 6 6 5 1 1
66053 5 6 5 2 4
66057 4 3 4 4 5
66058 6 5 4 2 1
66060 6 5 4 5 4
66061 6 6 6 4 2
66062 3 5 5 3 2
66063 5 6 6 1 6
66064 6 5 5 3 6
66065 6 4 4 6 1
66067 3 6 6 1 2
66068 5 NA 6 5 3
66070 6 6 5 1 1
66071 5 6 1 4 6
66072 5 6 1 5 5
66077 5 5 4 4 4
66080 6 5 6 2 3
66081 5 6 5 2 1
66082 6 5 6 1 1
66083 6 2 5 2 1
66086 1 1 3 4 5
66088 5 NA 6 4 4
66092 6 6 6 1 1
66093 6 5 4 1 1
66094 5 4 4 2 5
66095 5 2 5 5 6
66096 5 2 4 2 5
66097 4 4 3 4 6
66098 5 4 5 1 2
66102 1 3 2 4 6
66106 6 6 6 1 1
66107 5 5 5 1 1
66108 4 1 5 2 5
66109 4 5 4 2 3
66114 2 4 2 3 6
66115 6 5 5 1 1
66116 4 4 4 1 2
66117 4 5 5 3 4
66119 6 6 6 4 1
66120 4 2 5 5 6
66121 4 5 6 2 4
66122 5 5 6 2 2
66125 6 5 5 2 2
66127 5 5 6 2 1
66135 6 2 5 4 1
66136 6 2 5 4 1
66138 4 4 6 3 5
66139 3 5 6 5 4
66141 6 6 6 1 1
66144 4 4 4 2 3
66150 5 4 5 2 2
66151 4 2 5 4 5
66153 5 4 2 3 2
66156 4 4 5 2 2
66157 6 5 5 1 4
66158 5 5 4 1 1
66161 5 3 6 2 4
66163 6 6 5 2 2
66164 4 NA 5 1 1
66169 6 5 5 2 4
66170 4 4 4 1 1
66171 3 1 2 5 5
66172 5 4 4 2 2
66173 1 6 6 1 5
66176 5 4 4 4 6
66181 6 5 5 1 1
66187 2 3 3 3 5
66188 5 5 4 1 2
66190 4 5 4 1 3
66191 4 4 5 1 2
66195 5 5 2 2 4
66198 5 2 5 1 2
66199 5 5 4 2 2
66201 6 6 6 1 1
66204 4 3 6 1 2
66205 4 5 4 1 1
66210 2 5 6 1 2
66212 5 3 4 4 4
66215 4 4 4 2 4
66216 4 5 3 1 2
66218 5 2 4 2 6
66219 5 5 3 2 5
66224 5 4 4 3 4
66225 5 5 5 2 3
66227 6 6 4 1 6
66231 5 5 6 1 4
66233 5 4 4 2 3
66234 3 4 3 2 4
66237 5 4 5 2 2
66244 4 4 5 2 3
66245 5 5 4 4 4
66246 3 2 4 3 2
66247 2 3 3 4 5
66251 4 4 4 4 5
66253 4 6 5 3 2
66255 4 5 6 2 3
66256 6 4 4 3 5
66257 6 6 5 1 2
66259 5 6 5 1 NA
66264 4 5 5 1 2
66265 6 6 6 2 4
66266 6 4 5 1 2
66271 5 6 5 1 2
66272 4 6 5 2 3
66273 6 5 6 2 4
66274 3 3 2 3 3
66277 5 5 5 2 2
66278 3 2 2 4 6
66279 4 4 3 3 3
66281 4 4 4 3 1
66283 6 1 6 3 2
66286 6 6 6 1 2
66288 4 6 5 2 3
66289 5 5 4 5 5
66291 5 5 5 1 3
66294 2 6 3 1 5
66295 3 3 3 3 3
66297 4 4 3 4 5
66299 4 3 4 3 5
66304 4 3 5 2 4
66306 4 4 4 2 4
66307 5 5 4 3 3
66309 6 6 1 4 6
66311 5 6 6 2 2
66312 5 4 3 3 4
66314 6 5 6 2 1
66317 5 5 5 3 3
66318 5 5 5 2 4
66323 4 5 5 4 3
66324 3 2 4 2 4
66325 5 5 4 2 5
66326 5 5 5 3 2
66329 4 4 4 2 3
66331 5 4 6 1 1
66337 2 1 2 5 5
66341 4 4 4 2 2
66344 4 2 2 4 4
66346 5 5 5 2 2
66348 3 6 4 1 3
66349 5 6 6 1 1
66357 5 3 4 4 4
66360 3 6 3 3 4
66364 1 1 5 2 6
66368 4 NA 2 3 4
66370 5 4 2 3 4
66371 5 4 2 3 3
66372 5 5 2 2 3
66373 5 6 2 3 3
66376 2 5 4 3 2
66381 4 2 2 4 3
66382 6 5 4 2 2
66389 4 5 2 3 2
66391 6 6 5 1 4
66392 4 5 4 1 1
66396 4 4 4 3 4
66399 4 3 5 2 3
66402 5 4 4 1 3
66403 4 3 4 4 6
66404 5 5 5 2 4
66405 3 2 3 4 4
66406 6 6 6 3 1
66408 6 6 5 1 1
66409 6 5 4 1 1
66410 3 5 4 4 6
66413 4 2 3 4 5
66415 5 6 4 2 3
66419 3 3 3 4 4
66421 4 4 5 2 1
66425 6 3 6 NA 3
66429 5 5 5 2 2
66431 5 5 5 1 1
66434 5 5 5 2 2
66437 5 6 6 2 3
66438 3 4 4 2 2
66439 5 2 5 2 3
66440 1 2 5 5 6
66441 4 4 4 4 4
66444 5 5 2 1 2
66445 3 5 5 2 3
66447 4 6 5 2 2
66450 4 5 5 2 2
66451 2 4 4 1 2
66459 4 3 5 3 3
66463 2 5 4 3 6
66464 4 5 5 5 5
66469 4 4 3 2 4
66472 6 4 4 4 6
66474 5 5 6 4 2
66475 6 6 4 2 6
66476 6 5 6 1 4
66483 5 5 4 1 2
66484 1 5 6 3 4
66485 4 6 5 1 1
66486 5 NA 5 1 2
66487 NA 5 4 1 6
66488 6 4 5 4 5
66491 4 4 5 4 4
66492 2 2 4 3 2
66493 3 5 5 3 4
66495 6 6 4 1 4
66496 6 4 4 2 4
66497 4 5 5 3 3
66498 3 5 2 2 4
66499 5 5 5 2 3
66500 6 6 6 1 1
66501 5 5 5 1 2
66503 6 4 6 4 2
66504 6 6 4 2 5
66506 5 4 5 2 3
66507 6 6 6 1 2
66509 6 1 5 1 1
66511 6 5 5 2 4
66512 6 6 6 1 1
66513 5 3 5 1 1
66514 4 3 3 5 4
66517 6 6 5 2 4
66520 3 1 3 1 2
66522 5 5 4 2 3
66525 5 5 5 3 5
66526 5 5 4 1 3
66528 4 2 5 2 2
66529 6 5 6 1 1
66531 3 4 6 3 3
66532 5 5 5 3 3
66533 5 6 4 5 4
66534 5 5 5 4 6
66538 3 3 5 3 2
66540 6 4 6 1 2
66545 5 5 5 3 5
66546 6 NA NA NA 4
66549 5 5 5 4 4
66550 5 5 4 2 4
66551 5 5 4 3 3
66552 6 6 3 2 1
66553 4 5 6 1 1
66554 6 5 6 1 1
66560 4 4 5 3 4
66562 6 5 5 1 4
66563 5 4 4 2 4
66564 5 4 4 4 6
66566 2 2 1 4 5
66568 4 5 6 2 1
66569 5 5 4 1 2
66570 4 5 4 4 5
66572 3 5 5 3 1
66574 3 3 4 5 5
66575 6 4 1 2 1
66576 3 4 6 4 5
66582 4 4 4 1 4
66583 5 3 6 2 6
66585 4 3 5 3 4
66588 4 6 6 1 3
66589 5 5 5 4 3
66591 5 5 3 3 4
66596 5 5 5 2 1
66598 5 6 6 1 2
66601 3 4 5 3 NA
66604 5 5 4 1 2
66605 5 5 2 4 6
66606 6 5 4 2 2
66607 4 6 5 3 3
66609 5 5 5 4 4
66610 4 5 3 5 6
66612 6 6 5 2 6
66615 5 5 2 3 3
66617 6 6 4 2 4
66619 6 5 5 1 1
66620 5 5 4 3 6
66621 6 6 6 1 1
66622 4 2 2 5 5
66623 4 5 3 3 5
66630 4 3 5 3 2
66635 2 5 2 5 6
66637 3 4 5 3 2
66638 5 4 3 2 2
66642 5 6 5 4 3
66645 6 4 4 3 3
66646 6 5 5 2 5
66647 5 5 5 4 4
66648 5 5 2 2 2
66649 5 4 4 1 4
66651 4 5 2 3 3
66653 5 4 5 2 6
66657 5 6 6 3 1
66660 4 4 5 3 3
66662 6 5 6 2 1
66663 4 5 4 4 4
66664 5 6 2 2 2
66665 4 3 4 2 3
66666 5 6 5 2 1
66668 4 6 4 1 1
66669 4 5 5 2 4
66671 4 NA NA 2 2
66674 6 6 6 1 1
66676 2 3 4 3 2
66679 5 3 4 4 4
66680 5 4 4 2 4
66681 4 6 4 2 2
66682 5 6 4 1 2
66683 4 3 4 3 3
66684 5 5 3 2 3
66685 5 5 4 2 2
66689 4 3 5 4 4
66690 3 2 4 3 4
66692 6 6 NA 5 1
66694 5 4 5 2 3
66700 6 6 5 1 2
66701 5 6 6 3 4
66703 5 4 6 4 4
66704 3 3 3 5 5
66708 6 5 6 2 3
66711 3 3 5 3 5
66712 5 5 5 4 3
66713 5 5 4 1 6
66716 5 6 5 2 6
66717 3 4 2 5 6
66718 2 4 5 3 4
66722 6 6 3 1 2
66723 2 6 4 3 6
66724 5 5 6 3 3
66727 6 6 6 1 1
66728 4 5 5 2 3
66729 6 5 5 1 5
66730 2 NA 3 2 6
66731 6 5 5 3 3
66733 4 4 4 2 4
66734 5 3 4 2 4
66737 4 4 5 1 1
66739 4 4 5 4 5
66741 5 4 6 3 4
66743 5 5 3 2 2
66744 5 5 4 2 5
66745 5 6 5 1 4
66748 6 6 6 1 1
66749 6 6 5 1 1
66751 5 5 5 1 1
66755 4 5 4 3 3
66756 NA 6 5 5 1
66757 4 4 4 4 4
66760 4 4 4 2 2
66764 1 5 5 NA 2
66766 3 2 2 5 5
66768 6 5 6 1 1
66769 5 5 4 4 2
66770 6 5 5 4 4
66771 2 2 2 5 5
66772 6 5 5 4 2
66775 5 5 5 1 2
66778 6 5 5 1 1
66779 5 5 5 1 3
66785 3 3 6 1 1
66788 5 6 6 2 6
66790 5 6 5 1 5
66792 2 6 5 2 1
66793 4 5 5 1 2
66797 5 5 2 2 5
66800 5 5 5 1 2
66801 4 5 5 1 3
66804 6 5 4 1 1
66806 4 5 5 3 3
66807 2 4 4 4 3
66810 5 4 5 2 5
66812 5 5 5 2 6
66813 5 4 2 4 4
66814 5 6 4 2 4
66817 3 2 4 5 4
66819 4 3 5 3 6
66820 6 5 4 2 4
66824 4 6 5 3 4
66826 2 2 4 3 4
66830 3 2 3 4 4
66831 5 4 5 3 2
66832 3 1 3 5 5
66833 1 6 6 1 3
66838 6 6 5 2 2
66839 4 2 2 2 3
66840 6 4 5 2 2
66842 5 4 3 1 1
66843 5 3 5 5 5
66845 5 5 5 1 3
66847 4 3 2 2 4
66848 6 6 6 1 2
66850 4 5 4 2 2
66853 5 4 5 2 2
66855 5 4 4 2 5
66856 4 5 4 2 4
66857 3 5 NA 2 5
66858 5 3 5 2 6
66860 5 4 4 3 4
66863 4 5 4 3 6
66864 5 6 5 3 6
66867 3 5 4 NA 2
66868 2 1 1 4 3
66869 5 5 2 2 2
66872 5 6 4 2 3
66873 5 4 6 1 1
66879 NA 4 5 2 2
66886 6 6 5 1 1
66887 5 5 4 1 2
66890 1 1 3 5 6
66892 6 6 6 1 2
66896 5 4 2 3 2
66897 5 5 6 1 2
66898 4 4 4 3 5
66899 4 2 3 5 6
66902 3 6 6 1 4
66903 3 4 2 4 5
66905 4 4 5 4 2
66911 5 4 5 2 5
66916 5 5 4 3 2
66917 4 2 4 4 4
66918 5 2 1 1 6
66919 5 5 6 2 4
66920 5 5 6 1 5
66921 4 3 3 4 4
66924 4 5 4 1 3
66925 6 5 5 2 1
66926 5 5 5 1 5
66927 5 5 5 2 4
66929 6 5 5 3 4
66930 4 5 5 2 2
66932 6 5 6 2 4
66934 5 2 4 2 3
66935 5 4 3 2 5
66937 6 5 6 2 2
66944 5 5 3 4 3
66945 5 6 5 1 2
66946 2 1 4 5 4
66947 5 5 5 2 4
66949 5 6 3 3 5
66953 2 1 3 6 5
66960 3 4 5 4 4
66962 5 5 5 3 5
66965 5 4 4 3 4
66968 5 4 5 5 6
66970 6 6 1 3 1
66982 5 6 5 1 1
66983 6 5 6 5 1
66985 5 5 5 2 2
66986 3 3 2 3 5
66987 2 5 2 1 4
66988 6 4 5 2 5
66994 6 5 2 4 6
66995 4 6 5 4 5
66997 4 1 6 2 4
67000 1 1 1 1 4
67003 3 1 5 4 6
67007 5 6 2 2 4
67008 5 6 5 4 3
67013 4 2 4 4 4
67014 5 4 5 5 2
67017 6 5 4 2 6
67018 6 6 5 1 3
67019 6 6 4 1 2
67022 3 5 2 4 2
67023 5 5 4 1 3
67024 6 4 6 2 3
67025 6 6 5 1 2
67026 2 3 5 3 5
67027 5 3 5 2 4
67029 5 6 4 2 5
67031 5 5 5 2 3
67033 5 5 6 3 2
67034 4 4 4 3 2
67036 4 5 5 1 2
67039 4 4 4 4 6
67040 3 3 2 3 4
67041 6 5 6 1 1
67042 4 4 4 3 2
67043 5 5 5 2 2
67045 6 6 4 1 2
67046 6 5 6 1 4
67049 5 6 5 1 1
67055 6 5 6 1 2
67056 6 5 6 2 5
67057 6 5 5 1 2
67059 4 6 3 1 3
67064 NA 6 5 4 4
67065 5 2 4 2 2
67066 5 5 2 2 6
67070 NA 6 4 2 2
67072 4 5 5 2 1
67073 3 6 6 1 1
67075 5 3 4 2 3
67078 5 4 5 2 5
67079 5 5 5 1 2
67080 1 1 2 6 6
67082 5 6 5 5 5
67085 6 5 5 2 1
67087 5 4 4 2 2
67091 3 5 3 3 4
67093 3 4 2 4 4
67095 6 6 4 3 6
67096 3 2 4 3 6
67098 6 6 6 1 1
67101 3 4 4 2 2
67104 3 4 4 4 4
67107 6 5 4 2 4
67109 5 5 5 1 2
67115 5 5 4 3 4
67116 4 3 3 2 6
67118 2 4 1 6 6
67119 1 6 2 1 4
67121 3 3 5 2 4
67123 5 4 4 1 1
67124 1 3 1 4 5
67126 6 6 3 1 4
67132 5 4 3 2 6
67134 4 2 1 4 6
67135 4 4 5 2 4
67136 4 4 5 6 2
67140 5 5 5 3 4
67143 4 5 5 1 4
67144 5 6 5 3 2
67145 2 3 4 5 6
67146 2 2 4 3 1
67148 3 3 3 2 4
67149 5 2 5 6 4
67152 5 6 5 1 1
67154 2 5 5 5 2
67155 5 6 1 3 1
67156 5 6 5 4 3
67157 4 5 4 3 3
67158 6 5 5 3 3
67161 5 6 6 2 1
67162 2 4 4 4 2
67168 6 6 4 4 2
67174 5 4 3 2 2
67179 6 6 5 1 1
67180 5 2 5 4 5
67183 4 5 3 1 4
67184 4 2 3 3 4
67185 5 4 5 2 4
67186 6 4 1 3 2
67188 1 1 4 5 5
67189 6 NA 3 1 1
67190 5 4 3 4 5
67191 3 3 3 5 6
67197 5 4 5 4 2
67202 3 2 5 6 6
67203 6 6 5 3 3
67206 6 6 NA 3 4
67213 4 3 4 4 3
67215 4 2 5 4 4
67220 2 3 4 4 4
67222 5 4 6 2 4
67223 6 6 6 1 1
67224 3 5 2 3 5
67225 6 5 3 1 4
67226 1 1 4 5 6
67229 1 1 2 5 6
67230 4 4 6 3 3
67232 5 5 4 1 1
67234 6 5 5 2 6
67237 5 5 5 2 2
67238 6 5 2 5 5
67239 6 6 5 2 4
67240 4 5 2 2 6
67242 4 3 3 3 3
67246 4 4 4 2 2
67247 4 4 3 4 4
67251 5 5 6 1 1
67254 2 6 3 4 5
67255 6 6 6 1 1
67257 4 5 4 4 5
67258 6 5 4 2 5
67259 4 NA 6 NA 1
67260 4 2 1 2 2
67262 4 5 6 1 2
67263 4 3 4 3 4
67269 4 3 6 4 1
67270 5 5 5 2 4
67271 3 4 4 3 4
67272 2 3 4 5 2
67274 3 4 5 6 6
67275 6 5 5 1 1
67277 4 4 5 2 2
67278 4 5 2 4 6
67279 5 3 5 2 2
67283 6 5 5 1 4
67284 3 2 4 2 4
67286 5 5 3 1 1
67287 6 6 4 1 2
67288 6 6 1 1 1
67289 5 4 5 2 4
67294 4 4 4 4 5
67295 6 6 5 1 1
67298 5 5 6 3 1
67299 6 4 4 3 3
67303 3 4 5 3 3
67307 2 1 1 6 6
67309 3 5 5 3 3
67310 4 4 4 3 4
67312 3 4 4 4 5
67313 6 6 2 1 4
67315 5 5 5 1 1
67316 4 4 4 2 3
67322 2 3 4 2 5
67324 5 6 5 4 4
67325 6 6 6 1 1
67328 2 2 4 5 3
67331 5 5 5 4 5
67332 5 4 5 3 2
67334 5 6 6 1 3
67335 4 5 4 2 2
67336 5 5 5 2 5
67338 4 5 5 2 4
67339 6 6 5 1 2
67341 5 4 5 2 1
67342 4 4 6 1 1
67344 3 1 1 6 5
67345 5 5 1 5 5
67346 3 6 3 2 6
67347 2 4 NA 4 4
67352 5 6 6 1 4
67355 6 5 6 1 2
67356 6 5 6 1 2
67357 5 2 4 2 5
67360 6 4 6 2 5
67363 3 2 4 4 1
67364 6 6 2 4 4
67365 6 6 6 1 1
67366 5 6 4 2 2
67367 6 6 6 1 2
67368 2 NA 6 NA 3
67370 5 5 6 5 4
67371 5 5 6 5 4
67374 5 4 3 2 6
67376 6 6 5 3 3
67377 4 4 4 3 4
67378 4 4 4 3 5
67379 5 3 5 3 2
67380 2 1 4 6 4
67382 5 4 4 1 3
67388 5 4 5 2 2
67390 4 5 6 4 5
67392 6 5 4 3 4
67393 3 5 5 2 5
67395 5 5 4 5 5
67396 4 4 4 4 5
67397 4 5 5 3 1
67399 3 3 4 4 4
67401 6 5 5 1 1
67403 2 2 4 5 5
67406 5 4 2 5 6
67408 6 6 6 1 3
67413 1 1 1 1 6
67415 6 6 3 1 1
67417 1 3 2 5 6
67418 5 5 5 2 5
67420 4 4 5 3 5
67421 6 6 6 2 1
67426 5 5 5 2 5
67427 5 1 2 6 4
67429 5 3 5 3 5
67431 4 5 5 2 4
67436 5 4 5 3 4
67438 5 4 4 3 2
67439 5 4 4 3 2
67440 4 6 3 4 5
67441 4 3 4 2 3
67442 6 6 2 3 3
67445 5 4 5 2 2
67446 3 3 5 2 2
67447 5 5 6 3 4
67449 6 4 2 5 6
67451 2 4 4 1 2
67453 6 5 5 1 4
67454 5 5 3 2 4
67455 6 6 6 2 1
67458 3 3 1 3 4
67460 3 3 6 3 6
67462 5 4 4 3 5
67465 6 6 6 6 6
67471 4 4 4 2 5
67473 4 5 5 2 2
67479 4 3 3 4 2
67480 3 6 1 5 6
67483 5 3 4 2 3
67485 6 5 5 1 5
67487 6 5 5 1 1
67488 5 3 5 3 3
67489 4 4 3 3 4
67490 6 5 5 5 3
67495 4 4 2 2 2
67497 5 6 5 1 2
67502 4 4 4 4 3
67503 2 4 4 3 3
67504 5 3 3 5 5
67505 5 4 5 1 2
67506 6 6 6 1 1
67507 4 6 4 1 2
67508 5 4 4 2 2
67509 6 6 5 2 6
67510 2 4 4 5 1
67512 6 5 2 4 1
67516 3 5 4 4 4
67518 5 5 5 3 3
67521 5 6 5 2 2
67522 6 6 5 1 1
67523 5 6 6 1 1
67524 6 NA 5 2 2
67525 6 6 5 1 3
67526 5 5 5 2 5
67528 5 5 4 4 5
67529 5 5 5 2 3
67530 5 4 5 2 3
67531 6 5 5 2 4
67533 5 6 5 1 2
67534 5 2 2 4 3
67535 5 5 5 4 1
67537 4 4 1 4 1
67539 6 6 5 2 1
67540 5 4 3 4 4
67541 3 5 3 6 4
67544 6 6 2 4 5
67547 5 4 5 3 4
67549 5 4 2 3 5
67551 6 6 6 1 1
67552 2 3 4 4 3
67556 5 5 5 1 1
67559 5 5 5 2 6
67560 5 5 3 3 32. filter()
- Often times, when conducting research (experiments or otherwise), there are observations (people, specific trials, etc.) that you don’t want to include.
- We can use
filter()with logical statements to include only rows that match certain conditions - We can refer to both bare quoted columns and objects in the global environment
-
==or!= -
<or<= -
>or>= %in%
all_of()one_of()!-
|and&
tidyr: pivot_longer() and pivot_wider()
1. pivot_longer()
- (Formerly
gather()) Makes wide data long, based on a key - Core arguments:
-
data: the data, blank if piped -
cols: columns to be made long, selected viaselect()calls -
names_to: name(s) of key column(s) in new long data frame (string or string vector) -
values_to: name of values in new long data frame (string) -
names_sep: separator in column headers, if multiple keys -
values_drop_na: drop missing cells (similar tona.rm = T)
-
1. pivot_longer()
Why would I make my data longer?
- Main reason: Columns names sometimes contain data.
- Example: Billboard data has time information in column names
spc_tbl_ [317 × 79] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ artist : chr [1:317] "2 Pac" "2Ge+her" "3 Doors Down" "3 Doors Down" ...
$ track : chr [1:317] "Baby Don't Cry (Keep..." "The Hardest Part Of ..." "Kryptonite" "Loser" ...
$ date.entered: Date[1:317], format: "2000-02-26" "2000-09-02" ...
$ wk1 : num [1:317] 87 91 81 76 57 51 97 84 59 76 ...
$ wk2 : num [1:317] 82 87 70 76 34 39 97 62 53 76 ...
$ wk3 : num [1:317] 72 92 68 72 25 34 96 51 38 74 ...
$ wk4 : num [1:317] 77 NA 67 69 17 26 95 41 28 69 ...
$ wk5 : num [1:317] 87 NA 66 67 17 26 100 38 21 68 ...
$ wk6 : num [1:317] 94 NA 57 65 31 19 NA 35 18 67 ...
$ wk7 : num [1:317] 99 NA 54 55 36 2 NA 35 16 61 ...
$ wk8 : num [1:317] NA NA 53 59 49 2 NA 38 14 58 ...
$ wk9 : num [1:317] NA NA 51 62 53 3 NA 38 12 57 ...
$ wk10 : num [1:317] NA NA 51 61 57 6 NA 36 10 59 ...
$ wk11 : num [1:317] NA NA 51 61 64 7 NA 37 9 66 ...
$ wk12 : num [1:317] NA NA 51 59 70 22 NA 37 8 68 ...
$ wk13 : num [1:317] NA NA 47 61 75 29 NA 38 6 61 ...
$ wk14 : num [1:317] NA NA 44 66 76 36 NA 49 1 67 ...
$ wk15 : num [1:317] NA NA 38 72 78 47 NA 61 2 59 ...
$ wk16 : num [1:317] NA NA 28 76 85 67 NA 63 2 63 ...
$ wk17 : num [1:317] NA NA 22 75 92 66 NA 62 2 67 ...
$ wk18 : num [1:317] NA NA 18 67 96 84 NA 67 2 71 ...
$ wk19 : num [1:317] NA NA 18 73 NA 93 NA 83 3 79 ...
$ wk20 : num [1:317] NA NA 14 70 NA 94 NA 86 4 89 ...
$ wk21 : num [1:317] NA NA 12 NA NA NA NA NA 5 NA ...
$ wk22 : num [1:317] NA NA 7 NA NA NA NA NA 5 NA ...
$ wk23 : num [1:317] NA NA 6 NA NA NA NA NA 6 NA ...
$ wk24 : num [1:317] NA NA 6 NA NA NA NA NA 9 NA ...
$ wk25 : num [1:317] NA NA 6 NA NA NA NA NA 13 NA ...
$ wk26 : num [1:317] NA NA 5 NA NA NA NA NA 14 NA ...
$ wk27 : num [1:317] NA NA 5 NA NA NA NA NA 16 NA ...
$ wk28 : num [1:317] NA NA 4 NA NA NA NA NA 23 NA ...
$ wk29 : num [1:317] NA NA 4 NA NA NA NA NA 22 NA ...
$ wk30 : num [1:317] NA NA 4 NA NA NA NA NA 33 NA ...
$ wk31 : num [1:317] NA NA 4 NA NA NA NA NA 36 NA ...
$ wk32 : num [1:317] NA NA 3 NA NA NA NA NA 43 NA ...
$ wk33 : num [1:317] NA NA 3 NA NA NA NA NA NA NA ...
$ wk34 : num [1:317] NA NA 3 NA NA NA NA NA NA NA ...
$ wk35 : num [1:317] NA NA 4 NA NA NA NA NA NA NA ...
$ wk36 : num [1:317] NA NA 5 NA NA NA NA NA NA NA ...
$ wk37 : num [1:317] NA NA 5 NA NA NA NA NA NA NA ...
$ wk38 : num [1:317] NA NA 9 NA NA NA NA NA NA NA ...
$ wk39 : num [1:317] NA NA 9 NA NA NA NA NA NA NA ...
$ wk40 : num [1:317] NA NA 15 NA NA NA NA NA NA NA ...
$ wk41 : num [1:317] NA NA 14 NA NA NA NA NA NA NA ...
$ wk42 : num [1:317] NA NA 13 NA NA NA NA NA NA NA ...
$ wk43 : num [1:317] NA NA 14 NA NA NA NA NA NA NA ...
$ wk44 : num [1:317] NA NA 16 NA NA NA NA NA NA NA ...
$ wk45 : num [1:317] NA NA 17 NA NA NA NA NA NA NA ...
$ wk46 : num [1:317] NA NA 21 NA NA NA NA NA NA NA ...
$ wk47 : num [1:317] NA NA 22 NA NA NA NA NA NA NA ...
$ wk48 : num [1:317] NA NA 24 NA NA NA NA NA NA NA ...
$ wk49 : num [1:317] NA NA 28 NA NA NA NA NA NA NA ...
$ wk50 : num [1:317] NA NA 33 NA NA NA NA NA NA NA ...
$ wk51 : num [1:317] NA NA 42 NA NA NA NA NA NA NA ...
$ wk52 : num [1:317] NA NA 42 NA NA NA NA NA NA NA ...
$ wk53 : num [1:317] NA NA 49 NA NA NA NA NA NA NA ...
$ wk54 : num [1:317] NA NA NA NA NA NA NA NA NA NA ...
$ wk55 : num [1:317] NA NA NA NA NA NA NA NA NA NA ...
$ wk56 : num [1:317] NA NA NA NA NA NA NA NA NA NA ...
$ wk57 : num [1:317] NA NA NA NA NA NA NA NA NA NA ...
$ wk58 : num [1:317] NA NA NA NA NA NA NA NA NA NA ...
$ wk59 : num [1:317] NA NA NA NA NA NA NA NA NA NA ...
$ wk60 : num [1:317] NA NA NA NA NA NA NA NA NA NA ...
$ wk61 : num [1:317] NA NA NA NA NA NA NA NA NA NA ...
$ wk62 : num [1:317] NA NA NA NA NA NA NA NA NA NA ...
$ wk63 : num [1:317] NA NA NA NA NA NA NA NA NA NA ...
$ wk64 : num [1:317] NA NA NA NA NA NA NA NA NA NA ...
$ wk65 : num [1:317] NA NA NA NA NA NA NA NA NA NA ...
$ wk66 : logi [1:317] NA NA NA NA NA NA ...
$ wk67 : logi [1:317] NA NA NA NA NA NA ...
$ wk68 : logi [1:317] NA NA NA NA NA NA ...
$ wk69 : logi [1:317] NA NA NA NA NA NA ...
$ wk70 : logi [1:317] NA NA NA NA NA NA ...
$ wk71 : logi [1:317] NA NA NA NA NA NA ...
$ wk72 : logi [1:317] NA NA NA NA NA NA ...
$ wk73 : logi [1:317] NA NA NA NA NA NA ...
$ wk74 : logi [1:317] NA NA NA NA NA NA ...
$ wk75 : logi [1:317] NA NA NA NA NA NA ...
$ wk76 : logi [1:317] NA NA NA NA NA NA ...
- attr(*, "spec")=
.. cols(
.. year = col_skip(),
.. artist = col_character(),
.. track = col_character(),
.. time = col_skip(),
.. date.entered = col_date(format = ""),
.. wk1 = col_double(),
.. wk2 = col_double(),
.. wk3 = col_double(),
.. wk4 = col_double(),
.. wk5 = col_double(),
.. wk6 = col_double(),
.. wk7 = col_double(),
.. wk8 = col_double(),
.. wk9 = col_double(),
.. wk10 = col_double(),
.. wk11 = col_double(),
.. wk12 = col_double(),
.. wk13 = col_double(),
.. wk14 = col_double(),
.. wk15 = col_double(),
.. wk16 = col_double(),
.. wk17 = col_double(),
.. wk18 = col_double(),
.. wk19 = col_double(),
.. wk20 = col_double(),
.. wk21 = col_double(),
.. wk22 = col_double(),
.. wk23 = col_double(),
.. wk24 = col_double(),
.. wk25 = col_double(),
.. wk26 = col_double(),
.. wk27 = col_double(),
.. wk28 = col_double(),
.. wk29 = col_double(),
.. wk30 = col_double(),
.. wk31 = col_double(),
.. wk32 = col_double(),
.. wk33 = col_double(),
.. wk34 = col_double(),
.. wk35 = col_double(),
.. wk36 = col_double(),
.. wk37 = col_double(),
.. wk38 = col_double(),
.. wk39 = col_double(),
.. wk40 = col_double(),
.. wk41 = col_double(),
.. wk42 = col_double(),
.. wk43 = col_double(),
.. wk44 = col_double(),
.. wk45 = col_double(),
.. wk46 = col_double(),
.. wk47 = col_double(),
.. wk48 = col_double(),
.. wk49 = col_double(),
.. wk50 = col_double(),
.. wk51 = col_double(),
.. wk52 = col_double(),
.. wk53 = col_double(),
.. wk54 = col_double(),
.. wk55 = col_double(),
.. wk56 = col_double(),
.. wk57 = col_double(),
.. wk58 = col_double(),
.. wk59 = col_double(),
.. wk60 = col_double(),
.. wk61 = col_double(),
.. wk62 = col_double(),
.. wk63 = col_double(),
.. wk64 = col_double(),
.. wk65 = col_double(),
.. wk66 = col_logical(),
.. wk67 = col_logical(),
.. wk68 = col_logical(),
.. wk69 = col_logical(),
.. wk70 = col_logical(),
.. wk71 = col_logical(),
.. wk72 = col_logical(),
.. wk73 = col_logical(),
.. wk74 = col_logical(),
.. wk75 = col_logical(),
.. wk76 = col_logical()
.. )1. pivot_longer()
Why would I make my data longer?
billboard |>
pivot_longer(
cols = starts_with("wk"),
names_to = "week",
names_prefix = "wk",
names_transform = as.numeric,
values_to = "rank"
)# A tibble: 24,092 × 5
artist track date.entered week rank
<chr> <chr> <date> <dbl> <dbl>
1 2 Pac Baby Don't Cry (Keep... 2000-02-26 1 87
2 2 Pac Baby Don't Cry (Keep... 2000-02-26 2 82
3 2 Pac Baby Don't Cry (Keep... 2000-02-26 3 72
4 2 Pac Baby Don't Cry (Keep... 2000-02-26 4 77
5 2 Pac Baby Don't Cry (Keep... 2000-02-26 5 87
6 2 Pac Baby Don't Cry (Keep... 2000-02-26 6 94
7 2 Pac Baby Don't Cry (Keep... 2000-02-26 7 99
8 2 Pac Baby Don't Cry (Keep... 2000-02-26 8 NA
9 2 Pac Baby Don't Cry (Keep... 2000-02-26 9 NA
10 2 Pac Baby Don't Cry (Keep... 2000-02-26 10 NA
# ℹ 24,082 more rows1. pivot_longer()
Why would I make my data longer?
- This doesn’t just apply to longitudinal data. This is also important when thinking about iteration.
- For example, if you variables can be grouped into different categories (covariates, IVs/predictors, DVs/outcomes, moderators, etc.), then your column names contain implicit data
- The data below contain both time, variable, and category information
# load the codebook
(codebook <- read_csv("week6-codebook.csv") |>
mutate(old_name = str_to_lower(old_name)))# A tibble: 153 × 14
dataset old_name item_text scale category label item_name year new_name
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <chr>
1 <NA> persnr Never Changin… <NA> Procedu… <NA> SID 0 Proc_SID
2 <NA> hhnr household ID <NA> Procedu… <NA> household 0 Proc_HH…
3 ppfad gebjahr Year of Birth "num… Demogra… <NA> DOB 0 Demo_dob
4 ppfad sex Sex "\n1… Demogra… <NA> Sex 0 Demo__s…
5 vp vp12501 Thorough Work… <NA> big5 C thorough 2005 big5_C_…
6 zp zp12001 Thorough Work… <NA> big5 C thorough 2006 big5_C_…
7 bdp bdp15101 Thorough Work… <NA> big5 C thorough 2013 big5_C_…
8 vp vp12502 Am communicat… <NA> big5 E communic 2005 big5_E_…
9 zp zp12002 Am communicat… <NA> big5 E communic 2009 big5_E_…
10 bdp bdp15102 Am communicat… <NA> big5 E communic 2013 big5_E_…
# ℹ 143 more rows
# ℹ 5 more variables: reverse <dbl>, mini <dbl>, maxi <dbl>, recode <chr>,
# lavaan_name <chr>old.names <- codebook$old_name # get old column names
new.names <- codebook$new_name # get new column names
soep <- read_csv("week6-data.csv") |>
select(all_of(old.names))
soep# A tibble: 28,290 × 153
persnr hhnr gebjahr sex vp12501 zp12001 bdp15101 vp12502 zp12002 bdp15102
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 901 94 1951 2 6 -1 7 4 4 5
2 1202 124 1913 2 7 NA NA 6 NA NA
3 2301 230 1946 1 7 6 6 6 4 5
4 2302 230 1946 2 6 7 7 5 5 5
5 2304 230 1978 1 5 NA NA 7 NA NA
6 2305 230 1946 2 NA NA NA NA NA NA
7 4601 469 1933 2 6 NA NA 6 NA NA
8 4701 477 1919 2 6 6 NA 6 5 NA
9 4901 493 1925 2 5 6 6 7 6 5
10 5201 523 1955 1 7 7 7 5 6 4
# ℹ 28,280 more rows
# ℹ 143 more variables: vp12503 <dbl>, zp12003 <dbl>, bdp15103 <dbl>,
# vp12504 <dbl>, zp12004 <dbl>, bdp15104 <dbl>, vp12505 <dbl>, zp12005 <dbl>,
# bdp15105 <dbl>, vp12506 <dbl>, zp12006 <dbl>, bdp15106 <dbl>,
# vp12507 <dbl>, zp12007 <dbl>, bdp15107 <dbl>, vp12508 <dbl>, zp12008 <dbl>,
# bdp15108 <dbl>, vp12509 <dbl>, zp12009 <dbl>, bdp15109 <dbl>,
# vp12510 <dbl>, zp12010 <dbl>, bdp15110 <dbl>, vp12511 <dbl>, …1. pivot_longer()
Why would I make my data longer?
- By pivoting our data longer, we can more easily extract information from the column names
- Pivot the data below longer. Some hints:
- don’t make the procedural or demographic variables long
- split the column names you make long into four chunks:
- “category”, “label”, “item_name”, “year”
- drop na values
# A tibble: 28,290 × 153
Proc_SID Proc_HHID Demo_dob Demo__sex big5_C_thorough_2005
<dbl> <dbl> <dbl> <dbl> <dbl>
1 901 94 1951 2 6
2 1202 124 1913 2 7
3 2301 230 1946 1 7
4 2302 230 1946 2 6
5 2304 230 1978 1 5
6 2305 230 1946 2 NA
7 4601 469 1933 2 6
8 4701 477 1919 2 6
9 4901 493 1925 2 5
10 5201 523 1955 1 7
# ℹ 28,280 more rows
# ℹ 148 more variables: big5_C_thorough_2006 <dbl>, big5_C_thorough_2013 <dbl>,
# big5_E_communic_2005 <dbl>, big5_E_communic_2009 <dbl>,
# big5_E_communic_2013 <dbl>, big5_A_coarse_2005 <dbl>,
# big5_A_coarse_2009 <dbl>, big5_A_coarse_2013 <dbl>,
# big5_O_original_2005 <dbl>, big5_O_original_2009 <dbl>,
# big5_O_original_2013 <dbl>, big5_N_worry_2005 <dbl>, …1. pivot_longer()
Why would I make my data longer?
By pivoting our data longer, we can more easily extract information from the column names
soep_long <- soep |>
setNames(new.names) |>
pivot_longer(
cols = c(-starts_with("Proc"), -starts_with("Dem"))
, names_to = c("category", "label", "item_name", "year")
, names_pattern = "(.*)_(.*)_(.*)_(.*)"
, values_to = "value"
, values_drop_na = T
) |> mutate(year = as.numeric(year))
soep_long# A tibble: 1,845,743 × 9
Proc_SID Proc_HHID Demo_dob Demo__sex category label item_name year value
<dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <dbl> <dbl>
1 901 94 1951 2 big5 C thorough 2005 6
2 901 94 1951 2 big5 C thorough 2006 -1
3 901 94 1951 2 big5 C thorough 2013 7
4 901 94 1951 2 big5 E communic 2005 4
5 901 94 1951 2 big5 E communic 2009 4
6 901 94 1951 2 big5 E communic 2013 5
7 901 94 1951 2 big5 A coarse 2005 4
8 901 94 1951 2 big5 A coarse 2009 3
9 901 94 1951 2 big5 A coarse 2013 4
10 901 94 1951 2 big5 O original 2005 5
# ℹ 1,845,733 more rows1. pivot_longer()
Why would I make my data longer?
- Long format data are easier to clean (we’ll come back to this but we’ll create the cleaned data frame to use for merging practice)
soep_big5 <- soep_long |>
filter(category == "big5") |>
mutate(value = mapvalues(value, seq(-8,0), rep(NA, 9))) |>
drop_na(value) |>
group_by(Proc_SID, label, year) |>
summarize(value = mean(value)) |>
ungroup()
soep_le <- soep_long |>
filter(category == "le") |>
mutate(value = mapvalues(value, seq(-8,1), c(rep(NA, 6), 0, NA, NA, 1))) |>
drop_na(value) |>
group_by(Proc_SID, label) |>
summarize(value = sum(value)) |>
ungroup()
soep_clean <- soep_big5 |>
rename(trait = label, p_value = value) |>
inner_join(
soep_le |>
rename(le = label, le_value = value)
)2. pivot_wider()
- (Formerly
spread()) Makes wide data long, based on a key - Core arguments:
-
data: the data, blank if piped -
names_from: name(s) of key column(s) in new long data frame (string or string vector) -
names_sep: separator in column headers, if multiple keys -
names_glue: specify multiple or custom separators of multiple keys -
values_from: name of values in new long data frame (string) -
values_fn: function applied to data with duplicate labels
-
2. pivot_wider()
Why would I pivot wider?
- Some analyses require wide format data
- For example, SEM in
lavaanin R requires that both indicators and time are wide format. - The code below uses the codebook to rename items according to a numbered format that isn’t specific to any trait
big5 <- codebook |> filter(category == "big5")
soep_lavaan <- soep_long |>
filter(category == "big5") |>
mutate(item_name = mapvalues(item_name, big5$item_name, big5$lavaan_name, warn_missing = F))
soep_lavaan# A tibble: 544,830 × 9
Proc_SID Proc_HHID Demo_dob Demo__sex category label item_name year value
<dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <dbl> <dbl>
1 901 94 1951 2 big5 C I1 2005 6
2 901 94 1951 2 big5 C I1 2006 -1
3 901 94 1951 2 big5 C I1 2013 7
4 901 94 1951 2 big5 E I1 2005 4
5 901 94 1951 2 big5 E I1 2009 4
6 901 94 1951 2 big5 E I1 2013 5
7 901 94 1951 2 big5 A I1 2005 4
8 901 94 1951 2 big5 A I1 2009 3
9 901 94 1951 2 big5 A I1 2013 4
10 901 94 1951 2 big5 O I1 2005 5
# ℹ 544,820 more rows2. pivot_wider()
Why would I pivot wider?
- Change the
soep_lavaandata frame to be in wide format usingpivot_wider():- pull the
namesfrom two sources:item_name()andyear
- pull the
# A tibble: 113,635 × 16
Proc_SID Proc_HHID Demo_dob Demo__sex category label I1_2005 I1_2006 I1_2013
<dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
1 901 94 1951 2 big5 C 6 -1 7
2 901 94 1951 2 big5 E 4 NA 5
3 901 94 1951 2 big5 A 4 NA 4
4 901 94 1951 2 big5 O 5 NA 3
5 901 94 1951 2 big5 N 6 NA 6
6 1202 124 1913 2 big5 C 7 NA NA
7 1202 124 1913 2 big5 E 6 NA NA
8 1202 124 1913 2 big5 A 1 NA NA
9 1202 124 1913 2 big5 O 6 NA NA
10 1202 124 1913 2 big5 N 6 NA NA
# ℹ 113,625 more rows
# ℹ 7 more variables: I1_2009 <dbl>, I2_2005 <dbl>, I2_2009 <dbl>,
# I2_2013 <dbl>, I3_2005 <dbl>, I3_2009 <dbl>, I3_2013 <dbl>
dplyr: `_join()’
The _join() Functions
- Often we may need to pull different data from different sources
- There are lots of reasons to need to do this
- We don’t have time to get into all the use cases here, so we’ll talk about them in high level terms
- We’ll focus on:
full_join()inner_join()left_join()right_join()
3. full_join()
- Most simply, we can put those back together keeping all observations.
- Pro: sometimes we want to maintain missing data (i.e. some people are randomly missing variables and we don’t want to drop them completely)
- Con: can leave you with lots of
NAs
3. full_join()
soep_long |>
filter(!category == "big5") |>
full_join(codebook |> select(category, label, item_name, year, item_text)) # A tibble: 1,300,962 × 10
Proc_SID Proc_HHID Demo_dob Demo__sex category label item_name year value
<dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <dbl> <dbl>
1 901 94 1951 2 le ChldBrth ChldBrth 2005 -2
2 901 94 1951 2 le ChldBrth ChldBrth 2006 -2
3 901 94 1951 2 le ChldBrth ChldBrth 2007 -2
4 901 94 1951 2 le ChldBrth ChldBrth 2008 -2
5 901 94 1951 2 le ChldBrth ChldBrth 2009 -2
6 901 94 1951 2 le ChldBrth ChldBrth 2010 -2
7 901 94 1951 2 le ChldBrth ChldBrth 2011 -2
8 901 94 1951 2 le ChldBrth ChldBrth 2012 -2
9 901 94 1951 2 le ChldBrth ChldBrth 2013 -2
10 901 94 1951 2 le ChldBrth ChldBrth 2014 -2
# ℹ 1,300,952 more rows
# ℹ 1 more variable: item_text <chr>- note we have lots of missing data because the Big Five portions of the codebook were joined even though we removed that data
4. inner_join()
- We can also keep all rows present in both data frames
- Pro: Won’t add rows with missing values in key variables
- Con: will drop observations that you want want for counts, correlations, etc.
4. inner_join()
- Note that filtering, renaming/selecting, and joining is a common workflow
soep_long |>
filter(category == "big5") |>
select(Proc_SID, trait = label, item_name, year, p_value = value) |>
inner_join(
soep_long |>
filter(category == "le") |>
select(Proc_SID, le = label, year, le_value = value)
)# A tibble: 5,118,954 × 7
Proc_SID trait item_name year p_value le le_value
<dbl> <chr> <chr> <dbl> <dbl> <chr> <dbl>
1 901 C thorough 2005 6 ChldBrth -2
2 901 C thorough 2005 6 ChldMvOut -2
3 901 C thorough 2005 6 Divorce -2
4 901 C thorough 2005 6 DadDied -2
5 901 C thorough 2005 6 Married -2
6 901 C thorough 2005 6 MomDied -2
7 901 C thorough 2005 6 MoveIn -2
8 901 C thorough 2005 6 PartDied -2
9 901 C thorough 2005 6 SepPart -2
10 901 C thorough 2006 -1 ChldBrth -2
# ℹ 5,118,944 more rows5. left_join()
- Or all rows present in the left (first) data frame, perhaps if it’s a subset of people with complete data
soep_long |>
filter(category == "big5") |>
select(Proc_SID, trait = label, item_name, year, p_value = value) |>
left_join(
soep_long |>
filter(category == "le") |>
select(Proc_SID, le = label, year, le_value = value)
)# A tibble: 5,120,918 × 7
Proc_SID trait item_name year p_value le le_value
<dbl> <chr> <chr> <dbl> <dbl> <chr> <dbl>
1 901 C thorough 2005 6 ChldBrth -2
2 901 C thorough 2005 6 ChldMvOut -2
3 901 C thorough 2005 6 Divorce -2
4 901 C thorough 2005 6 DadDied -2
5 901 C thorough 2005 6 Married -2
6 901 C thorough 2005 6 MomDied -2
7 901 C thorough 2005 6 MoveIn -2
8 901 C thorough 2005 6 PartDied -2
9 901 C thorough 2005 6 SepPart -2
10 901 C thorough 2006 -1 ChldBrth -2
# ℹ 5,120,908 more rows6. right_join()
- Or all rows present in the right (second) data frame, such as I do when I join a codebook with raw data
soep_long |>
filter(category == "big5") |>
select(Proc_SID, trait = label, item_name, year, p_value = value) |>
right_join(
soep_long |>
filter(category == "le") |>
select(Proc_SID, le = label, year, le_value = value)
)# A tibble: 6,002,158 × 7
Proc_SID trait item_name year p_value le le_value
<dbl> <chr> <chr> <dbl> <dbl> <chr> <dbl>
1 901 C thorough 2005 6 ChldBrth -2
2 901 C thorough 2005 6 ChldMvOut -2
3 901 C thorough 2005 6 Divorce -2
4 901 C thorough 2005 6 DadDied -2
5 901 C thorough 2005 6 Married -2
6 901 C thorough 2005 6 MomDied -2
7 901 C thorough 2005 6 MoveIn -2
8 901 C thorough 2005 6 PartDied -2
9 901 C thorough 2005 6 SepPart -2
10 901 C thorough 2006 -1 ChldBrth -2
# ℹ 6,002,148 more rowsYour Turn
In small groups, discuss what’s happening when you use full_join(), left_join(), right_join(), inner_join(), and anti_join() with the code below. Which is correct in this use case?
dplyr: split-apply-combine
Bringing it all together: Split-Apply-Combine
Much of the power of
dplyrfunctions lay in the split-apply-combine method-
A given set of of data are:
- split into smaller chunks
- then a function or series of functions are applied to each chunk
- and then the chunks are combined back together
3. group_by()
- The
group_by()function is the “split” of the method - It basically implicitly breaks the data set into chunks by whatever bare quoted column(s)/variable(s) are supplied as arguments.
4. mutate()
-
mutate()is one of your “apply” functions - When you use
mutate(), the resulting data frame will have the same number of rows you started with - You are directly mutating the existing data frame, either modifying existing columns or creating new ones
5. summarize() / summarise()
-
summarize()is one of your “apply” functions - The resulting data frame will have the same number of rows as your grouping variable
- You number of groups is 1 for ungrouped data frames
Exercise 1
- Remember when I said that long format data are easier to clean. Let’s do that now.
Question 1:
- Let’s start with the Big Five data:
-
filter()out only Big Five rows -
mutate()each observation so that values less than one are changed toNA - Remove any missing values using
filter()ordrop_na() - Group (split) the data so that you have a “group” for each person x trait x year combination
-
summarize()the values to get a composite score for each Big Five trait for each person in each year:
Question 1: Solution
Remember when I said that long format data are easier to clean. Let’s do that now.
soep_big5 <- soep_long |>
filter(category == "big5") |>
mutate(value = mapvalues(value, seq(-8,0), rep(NA, 9))) |>
drop_na(value) |>
group_by(Proc_SID, label, year) |>
summarize(value = mean(value)) |>
ungroup()
soep_big5# A tibble: 161,432 × 4
Proc_SID label year value
<dbl> <chr> <dbl> <dbl>
1 901 A 2005 4.67
2 901 A 2009 4.33
3 901 A 2013 4.67
4 901 C 2005 5
5 901 C 2009 5
6 901 C 2013 5
7 901 E 2005 5
8 901 E 2009 5
9 901 E 2013 5
10 901 N 2005 4
# ℹ 161,422 more rowsQuestion 2:
Now let’s take care of the life event data:
1. filter() out only life event rows
2. mutate() each observation so that
- -2 = 0
- 1 = 1
- everything else is
NA
- Remove any missing values using
filter()ordrop_na() - Group (split) the data so that you have a “group” for each person x event combination
-
summarize()the values to get asumscore for each event for each person across all years:
Question 2: Solution
soep_le <- soep_long |>
filter(category == "le") |>
mutate(value = mapvalues(value, seq(-8,1), c(rep(NA, 6), 0, NA, NA, 1))) |>
drop_na(value) |>
group_by(Proc_SID, label) |>
summarize(value = sum(value)) |>
ungroup()
soep_le# A tibble: 253,363 × 3
Proc_SID label value
<dbl> <chr> <dbl>
1 901 ChldBrth 0
2 901 ChldMvOut 0
3 901 DadDied 0
4 901 Divorce 0
5 901 Married 0
6 901 MomDied 2
7 901 MoveIn 0
8 901 NewPart 0
9 901 PartDied 0
10 901 SepPart 0
# ℹ 253,353 more rowsQuestion 3:
Just for practice, now make your Big Five data frame wide, leaving the time variable (year) long
Question 3: Solution
# A tibble: 40,504 × 7
Proc_SID year A C E N O
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 901 2005 4.67 5 5 4 4
2 901 2009 4.33 5 5 4.67 4
3 901 2013 4.67 5 5 4.33 3.33
4 1202 2005 4 4 6.67 5 5.67
5 2301 2005 5.67 5.67 5 4.33 4.33
6 2301 2009 5.67 4.5 4 5.33 5
7 2301 2013 4.67 5.33 5.33 3.67 5
8 2301 2006 NA 6 NA NA NA
9 2302 2005 4.67 4.67 4.67 3.67 5
10 2302 2009 4 5.5 5.33 3 4.33
# ℹ 40,494 more rowsQuestion 4:
- Now, let’s join the data frames back together.
- Which join function do you think is most appropriate?
- Hint: You will need to rename the
labelandvaluecolumns to reflect the category of the data
Question 4: Solution
soep_clean <- soep_big5 |>
rename(trait = label, p_value = value) |>
inner_join(
soep_le |>
rename(le = label, le_value = value)
)
soep_clean# A tibble: 1,577,725 × 6
Proc_SID trait year p_value le le_value
<dbl> <chr> <dbl> <dbl> <chr> <dbl>
1 901 A 2005 4.67 ChldBrth 0
2 901 A 2005 4.67 ChldMvOut 0
3 901 A 2005 4.67 DadDied 0
4 901 A 2005 4.67 Divorce 0
5 901 A 2005 4.67 Married 0
6 901 A 2005 4.67 MomDied 2
7 901 A 2005 4.67 MoveIn 0
8 901 A 2005 4.67 NewPart 0
9 901 A 2005 4.67 PartDied 0
10 901 A 2005 4.67 SepPart 0
# ℹ 1,577,715 more rowsFunctions
Functions
How to approach writing functions? (broad recipe)
- Experiment with performing the task outside of a function
- Experiment with performing task with different sets of inputs
- Often, you must revise this code, when an approach that worked outside a function does not work within a function
- Write the function
- Test the function
- Try to “break” it
- Continual improvement. As you use the function, make continual improvements going back-and-forth between steps 1-3
Basics of writing functions
Three components of a function:
-
Function name
- Define a function using
function()and give it a name using the assignment operator<-
- Define a function using
-
Function arguments (sometimes called “inputs”)
- Inputs that the function takes; they go inside the parentheses of
function()- Can be vectors, data frames, logical statements, strings, etc.
- In the above hypothetical code, the function took three inputs
arg1,arg2,arg3, but we could have written:-
function(x, y, z)orfunction(Larry, Curly, Moe)
-
- In the “function call,” you specify values to assign to these function arguments
- Inputs that the function takes; they go inside the parentheses of
-
Function body
- What the function does to the inputs
- Function body goes inside the pair of curly brackets (
{}) that followsfunction() - Above hypothetical function doesn’t do anything, but your function can return a value (covered in later section)
Exercise 2
Some common tasks when working with survey data:
- Identify number of observations with
NAvalues for a specific variable - Identify number of observations with negative values for a specific variable
- Replace negative values with
NAfor a specific variable
Exercise 2: num_negative() function
Task: Write function called num_negative()
- Write a function that counts the number of observations with negative values for a specific variable
- Apply this function to variables from dataframe
df(created below) - Adapted from Ben Skinner’s Programming 1 R Workshop HERE
Steps:
Recommended steps:
- Perform task outside of function
- HINT:
sum(data_frame_name$var_name<0)
- HINT:
- Write function
- Apply/test function on variables
Step 1: Perform task outside of function
[1] "id" "age" "sibage" "parage" [1] 17 15 -97 13 -97 12 -99 -97 16 16 -98 20 -99 20 11 20 12 17
[19] 19 17 -97 -99 12 13 11 15 20 14 -99 11 20 -98 11 -98 12 16
[37] 12 18 12 19 12 -97 20 17 11 19 19 12 -98 11 15 18 15 -98
[55] 15 19 -97 13 -98 16 13 12 16 19 -99 19 -98 13 -97 20 15 19
[73] 15 12 18 -99 18 -98 -98 -98 -97 12 14 19 -97 11 20 18 14 -99
[91] 15 20 -97 14 14 19 18 17 20 15[1] 27Step 2: Write function
##Step 3: Apply function
Exercise 3: num_missing() function
In survey data, negative values often refer to reason for missing values:
- E.g.,
-8refers to “didn’t take survey” - E.g.,
-7refers to “took survey, but didn’t answer this question”
Exercise 3: num_missing() function
Task: Write function called num_negative()
- Write a function that counts number of missing observations for a variable and allows you to specify which values are associated with missing for that variable. This function will take two arguments:
-
x: The variable (e.g.,df$sibage) -
miss_vals: Vector of values you want to associate with “missing” variable- Values to associate with missing for
df$age:-97,-98,-99 - Values to associate with missing for
df$sibage:-97,-98,-99 - Values to associate with missing for
df$parage:-4,-7,-8
- Values to associate with missing for
-
Steps
Recommended steps:
- Perform task outside of function
- HINT:
sum(data_frame_name$var_name %in% c(-4,-5))
- HINT:
- Write function
- Apply/test function on variables
Step 1: Perform task outside of function
##Step 2: Write function
Step 3: Apply function
purrr
purrr::map()
-
map()functions are the tidyverse alternative to for loops and chaotic lists with deep nesting structures -
map()functions, unlike_apply()functions can take any number of inputs, which mimics nestedforloops -
map()functions can return any output type, including heterogeneous outputs (at least if you return it as a list)
purrr::map() inputs
- You control how many inputs using the following:
-
map(): one input, arguments aremap(.x, .f) -
map2(): two inputs, arguments aremap2(.x, y., .f) -
pmap(): any number of inputs, arguments arepmap(.l, .f)- Note the
.lbecuase this means we have to wrap inputs in alist()
- Note the
-
purrr::map() ouputs
- You can also control the output of
purrr::map():-
map(): outputs a list -
map_chr(): outputs a character vector
-
map_dbl(): outputs a numeric vector
-
map_lgl(): outputs a logical vector
-
map_int(): outputs a integer vector
-
map_vec(): outputs essentially any type of vector
-
- Note that if one input combination fails, all will fail and nothing will be outputted
purrr error handling
- Having everything fail because one thing went wrong is really frustrating
- There are a number of functions in
purrrto help with that:-
possibly(.f, otherwise): returns whatever you ask it return withotherwisewhen a .f call fails -
safely(.f): returns a list with the output, if successful, and errors, if unsuccessful - Others: see documentation.
-
purrr list columns
- One of the easiest ways to work with
purrris using list columns in nested data frames
- You can create a nested data frame using
tidyr::nest()ortibble()(where one column is a list itself)
# A tibble: 160 × 4
trait year le data
<chr> <dbl> <chr> <list>
1 A 2005 ChldBrth <tibble [10,419 × 3]>
2 A 2005 ChldMvOut <tibble [10,419 × 3]>
3 A 2005 DadDied <tibble [10,419 × 3]>
4 A 2005 Divorce <tibble [10,419 × 3]>
5 A 2005 Married <tibble [10,419 × 3]>
6 A 2005 MomDied <tibble [10,419 × 3]>
7 A 2005 MoveIn <tibble [10,419 × 3]>
8 A 2005 NewPart <tibble [6,364 × 3]>
9 A 2005 PartDied <tibble [10,419 × 3]>
10 A 2005 SepPart <tibble [10,419 × 3]>
# ℹ 150 more rowsExercise 4
Question 1:
- Create a data frame called
soep_nestedthat creates a list column of the data split by trait and life event.
Question 2:
- Using
mutate(), create a new list column calledmodelthat runs the following function
Question 2: Solution
- Using
mutate(), create a new list column calledmodelthat runs the following function
lmer_fun <- function(d){
d <- d |>
mutate(wave = year - 2005) |>
group_by(Proc_SID) |>
filter(n() > 1)
m <- lmer(p_value ~ wave + le_value + le_value:wave + (1 + wave | Proc_SID), data = d)
return(m)
}
soep_nested <- soep_nested |>
mutate(model = map(data, lmer_fun))
soep_nested# A tibble: 50 × 4
trait le data model
<chr> <chr> <list> <list>
1 A ChldBrth <tibble [30,248 × 4]> <lmerMod>
2 A ChldMvOut <tibble [30,248 × 4]> <lmerMod>
3 A DadDied <tibble [30,248 × 4]> <lmerMod>
4 A Divorce <tibble [30,248 × 4]> <lmerMod>
5 A Married <tibble [30,248 × 4]> <lmerMod>
6 A MomDied <tibble [30,248 × 4]> <lmerMod>
7 A MoveIn <tibble [30,248 × 4]> <lmerMod>
8 A NewPart <tibble [23,467 × 4]> <lmerMod>
9 A PartDied <tibble [30,248 × 4]> <lmerMod>
10 A SepPart <tibble [30,248 × 4]> <lmerMod>
# ℹ 40 more rowsQuestion 3:
- Use the following function to extract the number of people we estimated slopes for in this model. Output the result as an integer to a new column called
npeople
# A tibble: 50 × 5
trait le data model npeople
<chr> <chr> <list> <list> <int>
1 A ChldBrth <tibble [30,248 × 4]> <lmerMod> 8563
2 A ChldMvOut <tibble [30,248 × 4]> <lmerMod> 8563
3 A DadDied <tibble [30,248 × 4]> <lmerMod> 8563
4 A Divorce <tibble [30,248 × 4]> <lmerMod> 8563
5 A Married <tibble [30,248 × 4]> <lmerMod> 8563
6 A MomDied <tibble [30,248 × 4]> <lmerMod> 8563
7 A MoveIn <tibble [30,248 × 4]> <lmerMod> 8563
8 A NewPart <tibble [23,467 × 4]> <lmerMod> 7359
9 A PartDied <tibble [30,248 × 4]> <lmerMod> 8563
10 A SepPart <tibble [30,248 × 4]> <lmerMod> 8563
# ℹ 40 more rowsQuestion 4:
- Use the
tidy()function from thebroom.mixedpackage to extract the coefficients from the model and their confidence intervals. Save it to the column “tidy” - Hints:
- Use the argument
conf.int = Tto get the confidence intervals - Additional arguments to the
.ffunction called inmap()can be just included as addition arguments (e.g.,map(.x, .f, conf.int = T))
- Use the argument
Question 4: Solution
Question 5:
- Let’s practice making a super simple table. Do the following:
- remove the data and model columns from the data frame
-
unnest()thetidycolumn - Keep only fixed effects (
effect == "fixed") - We only care about the interaction, so only keep the interaction
term - round the estimate, conf.low, and conf.high columns to 2 decimal places
- Keep the
trait,le,estimate,conf.low, andconf.highcolumns only -
pivot_wider()by trait forestimate,conf.low, andconf.high
Question 5: Solution
soep_tab <- soep_nested |>
select(-data, -model) |>
unnest(tidy) |>
filter(effect == "fixed" & grepl(":", term)) |>
mutate(across(c(estimate, conf.low, conf.high), \(x) round(x, 2))) |>
select(trait, le, estimate, conf.low, conf.high) |>
pivot_wider(
names_from = "trait"
, names_glue = "{trait}_{.value}"
, values_from = c(estimate, conf.low, conf.high)
)
soep_tab# A tibble: 10 × 16
le A_estimate C_estimate E_estimate N_estimate O_estimate A_conf.low
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 ChldBrth 0 0.01 0 0 0 0
2 ChldMvOut 0 0.01 0 0 0 -0.01
3 DadDied 0 0.02 0 0 -0.01 -0.01
4 Divorce 0.01 0.01 0.01 0 0.01 0
5 Married 0 0.02 0 0 0 0
6 MomDied 0 0.01 0 0 0.01 -0.01
7 MoveIn 0 0.01 0 0 0 0
8 NewPart 0.01 0.03 0 0 -0.01 0
9 PartDied 0 0.01 0.01 0 0.01 -0.01
10 SepPart 0 0.02 0.01 0 0 0
# ℹ 9 more variables: C_conf.low <dbl>, E_conf.low <dbl>, N_conf.low <dbl>,
# O_conf.low <dbl>, A_conf.high <dbl>, C_conf.high <dbl>, E_conf.high <dbl>,
# N_conf.high <dbl>, O_conf.high <dbl>Question 6:
Use the function below to get model predictions
# A tibble: 50 × 7
trait le data model npeople tidy pred
<chr> <chr> <list> <list> <int> <list> <list>
1 A ChldBrth <tibble [30,248 × 4]> <lmerMod> 8563 <tibble> <df>
2 A ChldMvOut <tibble [30,248 × 4]> <lmerMod> 8563 <tibble> <df>
3 A DadDied <tibble [30,248 × 4]> <lmerMod> 8563 <tibble> <df>
4 A Divorce <tibble [30,248 × 4]> <lmerMod> 8563 <tibble> <df>
5 A Married <tibble [30,248 × 4]> <lmerMod> 8563 <tibble> <df>
6 A MomDied <tibble [30,248 × 4]> <lmerMod> 8563 <tibble> <df>
7 A MoveIn <tibble [30,248 × 4]> <lmerMod> 8563 <tibble> <df>
8 A NewPart <tibble [23,467 × 4]> <lmerMod> 7359 <tibble> <df>
9 A PartDied <tibble [30,248 × 4]> <lmerMod> 8563 <tibble> <df>
10 A SepPart <tibble [30,248 × 4]> <lmerMod> 8563 <tibble> <df>
# ℹ 40 more rowsQuestion 7:
- Let’s practice making a super simple table. Do the following:
- remove the data, model, and tidy columns from the data frame
-
unnest()thetidycolumn -
group_by()life event andnest()+ungroup() - save this as
soep_pred
Question 7: Solutions
soep_pred <- soep_nested |>
select(-data, -model, -tidy) |>
unnest(pred) |>
group_by(trait) |>
nest() |>
ungroup()
soep_pred# A tibble: 5 × 2
trait data
<chr> <list>
1 A <tibble [218,522 × 6]>
2 C <tibble [334,426 × 6]>
3 E <tibble [218,454 × 6]>
4 N <tibble [218,464 × 6]>
5 O <tibble [218,354 × 6]>Question 8:
- Use the following function to create a new column
pthat contains spaghetti plots - Note that the function takes two inputs!
spag_plot_fun <- function(d, trait){
set.seed(6)
d |>
group_by(le) |>
nest() |>
mutate(data = map(data, ~filter(., Proc_SID %in% sample(unique(.$Proc_SID), 100)))) |>
unnest(data) |>
ungroup() |>
mutate(le_value = ifelse(le_value > 1, 1, le_value)) |>
ggplot(aes(x = wave, y = pred)) +
geom_line(aes(group = Proc_SID, color = factor(le_value)), alpha = .3) +
geom_smooth(method = "lm", se = F, color = "darkblue") +
scale_color_manual(values = c("grey", "blue"), labels = c("No Event", "Event")) +
labs(x = "Wave", y = "Predicted Trait Levels", color = "Life Event", title = trait) +
facet_wrap(~le) +
theme_classic() +
theme(legend.position = c(.7, .1))
}Question 8: Solution
Wrap-Up
Wrap-Up
- Today’s goal was to review the coding concepts we’ve used so far and ask you to apply them using a series of guided examples
- The biggest takeaway I wanted you to have is chaining, or how you can use
tidyversefunctions in chains to accomplish a bunch of goals simultaneously - We cleaned, composited, and ran 50 models across thousands of people, including predictions and tables in less than 100 lines of code. Just doing the models,
tidy(), andpredict()parts of that alone would have been 150 lines of code and introduced huge opportunities for errors!
PSC 290 - Data Management and Cleaning