library(pivotea)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(ggplot2)You can use pivot to make a pivot table easily.
starwars %>%
pivot(row = "homeworld", col = "species", value = "name", split = "sex")
#> $female
#> # A tibble: 15 × 9
#> sex homeworld Clawdite Human Kaminoan Mirialan Tholothian Togruta `Twi'lek`
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 fema… Alderaan <NA> Leia… <NA> <NA> <NA> <NA> <NA>
#> 2 fema… Chandrila <NA> Mon … <NA> <NA> <NA> <NA> <NA>
#> 3 fema… Coruscant <NA> Joca… <NA> <NA> Adi Gallia <NA> <NA>
#> 4 fema… Kamino <NA> <NA> Taun We <NA> <NA> <NA> <NA>
#> 5 fema… Mirial <NA> <NA> <NA> Luminar… <NA> <NA> <NA>
#> 6 fema… Mirial <NA> <NA> <NA> Barriss… <NA> <NA> <NA>
#> 7 fema… Naboo <NA> Cordé <NA> <NA> <NA> <NA> <NA>
#> 8 fema… Naboo <NA> Dormé <NA> <NA> <NA> <NA> <NA>
#> 9 fema… Naboo <NA> Padm… <NA> <NA> <NA> <NA> <NA>
#> 10 fema… Ryloth <NA> <NA> <NA> <NA> <NA> <NA> Ayla Sec…
#> 11 fema… Shili <NA> <NA> <NA> <NA> <NA> Shaak … <NA>
#> 12 fema… Tatooine <NA> Beru… <NA> <NA> <NA> <NA> <NA>
#> 13 fema… Tatooine <NA> Shmi… <NA> <NA> <NA> <NA> <NA>
#> 14 fema… Zolan Zam Wes… <NA> <NA> <NA> <NA> <NA> <NA>
#> 15 fema… <NA> <NA> Rey <NA> <NA> <NA> <NA> <NA>
#>
#> $hermaphroditic
#> # A tibble: 1 × 3
#> sex homeworld Hutt
#> <chr> <chr> <chr>
#> 1 hermaphroditic Nal Hutta Jabba Desilijic Tiure
#>
#> $male
#> # A tibble: 56 × 33
#> sex homeworld Aleena Besalisk Cerean Chagrian Dug Ewok Geonosian Gungan
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 male Alderaan <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 2 male Alderaan <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 3 male Aleen Min… Ratts… <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 4 male Bespin <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 5 male Bestine IV <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 6 male Cato Neim… <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 7 male Cerea <NA> <NA> Ki-Ad… <NA> <NA> <NA> <NA> <NA>
#> 8 male Champala <NA> <NA> <NA> Mas Ame… <NA> <NA> <NA> <NA>
#> 9 male Concord D… <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> 10 male Corellia <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
#> # ℹ 46 more rows
#> # ℹ 23 more variables: Human <chr>, Iktotchi <chr>, Kaleesh <chr>,
#> # Kaminoan <chr>, `Kel Dor` <chr>, `Mon Calamari` <chr>, Muun <chr>,
#> # Nautolan <chr>, Neimodian <chr>, `Pau'an` <chr>, Quermian <chr>,
#> # Rodian <chr>, Skakoan <chr>, Sullustan <chr>, Toong <chr>, Toydarian <chr>,
#> # Trandoshan <chr>, `Twi'lek` <chr>, Vulptereen <chr>, Wookiee <chr>,
#> # Xexto <chr>, `Yoda's species` <chr>, Zabrak <chr>
#>
#> $none
#> # A tibble: 6 × 3
#> sex homeworld Droid
#> <chr> <chr> <chr>
#> 1 none Naboo R2-D2
#> 2 none Tatooine C-3PO
#> 3 none Tatooine R5-D4
#> 4 none <NA> IG-88
#> 5 none <NA> R4-P17
#> 6 none <NA> BB8
msleep %>%
pivot(row = "vore", col = "conservation", value = "name") %>%
na2empty() %>%
print(n = nrow(.))
#> # A tibble: 33 × 8
#> vore cd domesticated en lc nt vu `NA`
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 "carni" "Pilot whale" "Dog" "Tiger" "Che… "Jag… "Nor… "Slo…
#> 2 "carni" "" "Domestic cat" "" "Lon… "" "Lio… "Bot…
#> 3 "carni" "" "" "" "Gra… "" "Cas… "Gen…
#> 4 "carni" "" "" "" "Thi… "" "Com… "Arc…
#> 5 "carni" "" "" "" "Nor… "" "" "Red…
#> 6 "herbi" "Giraffe" "Cow" "Asian elepha… "Roe… "Mou… "Mon… "Thr…
#> 7 "herbi" "" "Guinea pig" "Golden hamst… "Goa… "Hou… "Afr… "Wes…
#> 8 "herbi" "" "Chinchilla" "" "Tre… "Rou… "Bra… "Vol…
#> 9 "herbi" "" "Horse" "" "Gra… "" "" "Pot…
#> 10 "herbi" "" "Donkey" "" "Mon… "" "" "Cot…
#> 11 "herbi" "" "Rabbit" "" "Deg… "" "" "Eas…
#> 12 "herbi" "" "Sheep" "" "Lab… "" "" ""
#> 13 "herbi" "" "" "" "Arc… "" "" ""
#> 14 "herbi" "" "" "" "Thi… "" "" ""
#> 15 "herbi" "" "" "" "Gol… "" "" ""
#> 16 "insecti" "" "" "Giant armadi… "Big… "" "" "Lit…
#> 17 "insecti" "" "" "" "Eas… "" "" "Sho…
#> 18 "omni" "" "Pig" "" "Gre… "" "" "Owl…
#> 19 "omni" "" "" "" "Gri… "" "" "Afr…
#> 20 "omni" "" "" "" "Sta… "" "" "Gal…
#> 21 "omni" "" "" "" "Les… "" "" "Hum…
#> 22 "omni" "" "" "" "Nor… "" "" "Mac…
#> 23 "omni" "" "" "" "Eur… "" "" "Chi…
#> 24 "omni" "" "" "" "Pat… "" "" "Bab…
#> 25 "omni" "" "" "" "Pot… "" "" "Afr…
#> 26 "omni" "" "" "" "" "" "" "Squ…
#> 27 "omni" "" "" "" "" "" "" "Ten…
#> 28 "omni" "" "" "" "" "" "" "Tre…
#> 29 "" "" "" "" "Des… "" "" "Ves…
#> 30 "" "" "" "" "Roc… "" "" "Dee…
#> 31 "" "" "" "" "" "" "" "Pha…
#> 32 "" "" "" "" "" "" "" "Mol…
#> 33 "" "" "" "" "" "" "" "Mus…
as_tibble(Titanic) %>%
pivot(row = "Age", col = c("Sex", "Survived"), value = "n", split = "Class")
#> $`1st`
#> # A tibble: 2 × 6
#> Class Age Female_No Female_Yes Male_No Male_Yes
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1st Adult 4 140 118 57
#> 2 1st Child 0 1 0 5
#>
#> $`2nd`
#> # A tibble: 2 × 6
#> Class Age Female_No Female_Yes Male_No Male_Yes
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 2nd Adult 13 80 154 14
#> 2 2nd Child 0 13 0 11
#>
#> $`3rd`
#> # A tibble: 2 × 6
#> Class Age Female_No Female_Yes Male_No Male_Yes
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 3rd Adult 89 76 387 75
#> 2 3rd Child 17 14 35 13
#>
#> $Crew
#> # A tibble: 2 × 6
#> Class Age Female_No Female_Yes Male_No Male_Yes
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Crew Adult 3 20 670 192
#> 2 Crew Child 0 0 0 0
diamonds %>%
pivot(row = "cut", col = "color", value = "price", split = "clarity")
#> $I1
#> # A tibble: 176 × 9
#> clarity cut D E F G H I J
#> <ord> <ord> <int> <int> <int> <int> <int> <int> <int>
#> 1 I1 Fair 5460 2788 2809 2808 2892 2935 3011
#> 2 I1 Fair 5617 3692 3734 4167 2964 3011 4284
#> 3 I1 Fair 15964 1232 4320 4277 2968 3745 5083
#> 4 I1 Fair 2491 1262 584 4849 2999 4368 5240
#> 5 I1 Fair NA 1273 4698 6315 3175 4704 5405
#> 6 I1 Fair NA 1865 5554 6346 3504 5040 5430
#> 7 I1 Fair NA 2036 6532 6377 3765 5074 5607
#> 8 I1 Fair NA 2138 6753 6503 4140 5667 5733
#> 9 I1 Fair NA 2571 7019 6597 4145 5696 5859
#> 10 I1 Fair NA NA 613 6817 4648 5750 6289
#> # ℹ 166 more rows
#>
#> $SI2
#> # A tibble: 1,494 × 9
#> clarity cut D E F G H I J
#> <ord> <ord> <int> <int> <int> <int> <int> <int> <int>
#> 1 SI2 Fair 2848 2757 2759 2834 2763 2811 2789
#> 2 SI2 Fair 2885 2797 2815 2856 2777 2884 2992
#> 3 SI2 Fair 2974 2815 2867 2858 2818 2948 3119
#> 4 SI2 Fair 3003 2855 2958 2942 2823 3054 3149
#> 5 SI2 Fair 3079 2862 2964 3027 2825 3084 3199
#> 6 SI2 Fair 3205 2930 2996 3104 2846 3290 3246
#> 7 SI2 Fair 3304 2948 3062 3142 2856 3348 3302
#> 8 SI2 Fair 3384 2982 3146 3172 2875 3530 3403
#> 9 SI2 Fair 3473 3011 3170 3173 2883 3669 3984
#> 10 SI2 Fair 3634 3084 3210 3332 2912 3802 4056
#> # ℹ 1,484 more rows
#>
#> $SI1
#> # A tibble: 1,828 × 9
#> clarity cut D E F G H I J
#> <ord> <ord> <int> <int> <int> <int> <int> <int> <int>
#> 1 SI1 Fair 3382 2885 2838 2782 2854 2804 2812
#> 2 SI1 Fair 3689 3332 2882 2841 2873 2856 2949
#> 3 SI1 Fair 3730 3407 2971 2964 3024 2873 3027
#> 4 SI1 Fair 3751 3508 3117 2993 3057 2875 3154
#> 5 SI1 Fair 3812 3696 3160 3052 3145 2912 3175
#> 6 SI1 Fair 3855 3743 3197 3145 3180 3280 3427
#> 7 SI1 Fair 3945 3891 3323 3250 3278 3337 3780
#> 8 SI1 Fair 4118 579 3326 3299 3282 3584 4123
#> 9 SI1 Fair 4252 4106 3387 3304 3285 3892 4147
#> 10 SI1 Fair 4276 4140 3437 3358 3387 3920 4838
#> # ℹ 1,818 more rows
#>
#> $VS2
#> # A tibble: 1,526 × 9
#> clarity cut D E F G H I J
#> <ord> <ord> <int> <int> <int> <int> <int> <int> <int>
#> 1 VS2 Fair 2858 337 2762 3418 3226 2921 2811
#> 2 VS2 Fair 3077 2902 2805 4032 3375 3033 2815
#> 3 VS2 Fair 3931 2940 3172 4072 3538 3166 2854
#> 4 VS2 Fair 4107 3875 3750 4309 3593 3215 3190
#> 5 VS2 Fair 4277 4057 3837 4435 3658 3246 3422
#> 6 VS2 Fair 4439 4125 3931 4480 3697 3258 3443
#> 7 VS2 Fair 4765 581 3992 4838 3725 3265 3886
#> 8 VS2 Fair 592 4480 4229 4860 3733 3597 3980
#> 9 VS2 Fair 5078 4525 4312 4887 3819 4553 416
#> 10 VS2 Fair 5797 4550 4474 5161 4166 4695 5846
#> # ℹ 1,516 more rows
#>
#> $VS1
#> # A tibble: 1,355 × 9
#> clarity cut D E F G H I J
#> <ord> <ord> <int> <int> <int> <int> <int> <int> <int>
#> 1 VS1 Fair 3047 2964 2859 2800 3500 3387 2875
#> 2 VS1 Fair 563 3276 2879 2998 3676 3763 2964
#> 3 VS1 Fair 7083 4770 3002 3756 3924 3801 3080
#> 4 VS1 Fair 1166 7392 3031 3806 4044 3951 7139
#> 5 VS1 Fair 2747 640 3039 4455 4368 4263 7186
#> 6 VS1 Fair NA 15584 3750 4630 4455 4281 7819
#> 7 VS1 Fair NA 361 4183 4864 4732 4328 9314
#> 8 VS1 Fair NA 371 593 4892 5062 6727 13399
#> 9 VS1 Fair NA 799 4984 5487 5242 11322 14402
#> 10 VS1 Fair NA 1778 5833 5633 5460 12244 14750
#> # ℹ 1,345 more rows
#>
#> $VVS2
#> # A tibble: 885 × 9
#> clarity cut D E F G H I J
#> <ord> <ord> <int> <int> <int> <int> <int> <int> <int>
#> 1 VVS2 Fair 3296 3813 3920 2959 4312 3074 2998
#> 2 VVS2 Fair 4040 586 4368 3323 4355 3288 NA
#> 3 VVS2 Fair 8716 5430 6863 4534 5171 7303 NA
#> 4 VVS2 Fair 10562 5551 16364 4928 7476 1401 NA
#> 5 VVS2 Fair 772 7553 693 6736 922 2117 NA
#> 6 VVS2 Fair 997 7918 743 629 1227 2131 NA
#> 7 VVS2 Fair 1011 738 757 13553 2500 2292 NA
#> 8 VVS2 Fair 1089 945 1902 369 2608 2351 NA
#> 9 VVS2 Fair 1980 1069 2088 819 2699 NA NA
#> 10 VVS2 Fair NA 1705 2484 833 2717 NA NA
#> # ℹ 875 more rows
#>
#> $VVS1
#> # A tibble: 578 × 9
#> clarity cut D E F G H I J
#> <ord> <ord> <int> <int> <int> <int> <int> <int> <int>
#> 1 VVS1 Fair 10752 2805 7960 2797 4115 4194 1691
#> 2 VVS1 Fair 875 8529 12648 3062 NA NA NA
#> 3 VVS1 Fair 1792 1012 740 790 NA NA NA
#> 4 VVS1 Fair NA NA 1011 NA NA NA NA
#> 5 VVS1 Fair NA NA 1040 NA NA NA NA
#> 6 VVS1 Good 4198 554 3018 2899 2877 571 4633
#> 7 VVS1 Good 4274 2990 3206 3072 3096 3741 NA
#> 8 VVS1 Good 4407 575 564 3188 4381 3972 NA
#> 9 VVS1 Good 5029 586 3310 3225 4763 4114 NA
#> 10 VVS1 Good 8239 597 3536 3380 4847 4187 NA
#> # ℹ 568 more rows
#>
#> $IF
#> # A tibble: 362 × 9
#> clarity cut D E F G H I J
#> <ord> <ord> <int> <int> <int> <int> <int> <int> <int>
#> 1 IF Fair 1208 NA 2861 1127 NA NA NA
#> 2 IF Fair 1440 NA 3205 1849 NA NA NA
#> 3 IF Fair 2211 NA 1166 NA NA NA NA
#> 4 IF Fair NA NA 2144 NA NA NA NA
#> 5 IF Good 3378 597 2980 3304 6703 3669 5361
#> 6 IF Good 3509 6804 3393 3583 13248 3734 6299
#> 7 IF Good 15081 612 9451 5771 1440 631 827
#> 8 IF Good 15928 813 9660 5855 2404 886 1061
#> 9 IF Good 16406 912 9867 8973 NA 945 2053
#> 10 IF Good 17499 1052 633 13998 NA NA NA
#> # ℹ 352 more rows