Data Visualization

Task 1

First, we load the data

data("CreditCard")
options(scipen=999)
CreditCard$expenditure %>% na.omit()

##    [1]  124.9833000    9.8541670   15.0000000  137.8692000  546.5033000
##    [6]   91.9966700   40.8333300  150.7900000  777.8217000   52.5800000
##   [11]  256.6642000    0.0000000    0.0000000   78.8741700   42.6150000
##   [16]  335.4350000  248.7192000    0.0000000  548.0350000    0.0000000
##   [21]   43.3391700    0.0000000  218.5200000  170.6408000   37.5833300
##   [26]  502.2017000    0.0000000   73.1766700    0.0000000 1532.7730000
##   [31]   42.6908300  417.8350000    0.0000000  552.7242000  222.5392000
##   [36]  541.2958000    0.0000000  568.7742000  344.4708000  405.3517000
##   [41]  310.9375000   53.6450000   63.9166700  165.8550000    9.5833330
##   [46]    0.0000000  319.4900000    0.0000000   83.0833400  644.8283000
##   [51]    0.0000000   93.1975000  105.0450000   34.1250000   41.1891700
##   [56]  169.8883000 1898.0330000  810.3917000    0.0000000   32.7750000
##   [61]   95.7991600   27.7800000  215.0692000   79.5108300    0.0000000
##   [66]    0.0000000  306.0317000  104.5358000    0.0000000  642.4742000
##   [71]  308.0492000  186.3533000   56.1491700  129.3733000   93.1066700
##   [76]    0.0000000  292.6633000   98.4641600  258.5492000  101.6833000
##   [81]    0.0000000   65.2466700  108.6075000   49.5558300    0.0000000
##   [86]  235.5717000    0.0000000    0.0000000    0.0000000    0.0000000
##   [91]    0.0000000    0.0000000   68.3841700    0.0000000    0.0000000
##   [96]    0.0000000  474.1500000  234.0483000  451.2050000  251.5208000
##  [101]  917.5400000    0.0000000    0.0000000  147.7233000  166.7267000
##  [106]  741.5042000  106.1692000  271.4675000  635.0508000  182.8250000
##  [111]   89.3708300 1011.3330000  227.3683000  115.4167000    0.0000000
##  [116]  297.9717000  101.2625000   56.6941700   77.3566700  122.5150000
##  [121]    0.0000000   13.4025000  127.5342000    8.3333330   78.4283400
##  [126]   11.1666700  131.8783000  277.9892000  126.0225000  238.8558000
##  [131]    0.0000000    0.0000000   37.0833300    0.0000000  156.5833000
##  [136]    0.0000000    9.7533330  108.3967000  271.5533000   53.3016700
##  [141]  282.5633000  263.3275000    6.5175000    0.0000000   43.0391700
##  [146]   73.7975000   15.4166700  313.3525000   59.1941700  175.9975000
##  [151]  129.3933000   30.3691700    0.0000000    0.0000000  461.2600000
##  [156]  199.7333000   38.7141600   92.2633400  145.3792000  400.9117000
##  [161]  138.7133000  110.3033000   54.6466700    4.5833330    0.0000000
##  [166]  320.5167000  197.9233000   68.8708300  148.2600000   68.5333300
##  [171]    0.0000000    0.0000000    0.0000000  121.5558000   86.2266700
##  [176]  105.0950000   58.9908300    0.0000000    0.0000000  123.8917000
##  [181]   78.5416600    0.0000000   38.0625000   46.1416700    0.0000000
##  [186]  265.0142000   74.7266700  107.7642000    6.5000000  209.0558000
##  [191]  193.0675000  144.3842000  358.9725000   59.1475000    0.0000000
##  [196]    0.0000000  465.1950000  142.2425000  501.8025000   88.5925000
##  [201]  282.8758000  361.1617000  150.1767000   76.2441600   58.2458300
##  [206]   88.2333300    7.0833330    0.0000000  289.0442000  102.9767000
##  [211]   13.9166700  641.4216000    0.0000000    0.0000000    0.0000000
##  [216]   84.2750000  132.8508000  159.5950000  135.0450000  250.5208000
##  [221]  188.6208000   23.2500000  214.0650000    0.0000000    0.0000000
##  [226]   42.6308300    0.0000000    0.0000000  474.5000000  304.3817000
##  [231]   78.7508300  238.3275000  311.1425000   77.0466700  120.4033000
##  [236]  156.5725000   10.5833300  114.8158000    0.0000000  179.7658000
##  [241]  625.3675000   84.9025000    0.0000000   67.6666600  111.6925000
##  [246]  257.3017000  675.7708000    0.0000000  245.3308000   13.3333300
##  [251]  149.5450000  253.3658000   20.4791700   41.8166700  118.7133000
##  [256]  225.4592000  101.2008000    0.0000000   10.4200000    0.0000000
##  [261]    7.1666670   12.0833300    0.0000000  146.8625000  324.6183000
##  [266]  268.7242000  249.1658000   50.0833300   87.6366700  203.3667000
##  [271]  105.1250000   50.1783300  327.1100000    0.0000000  175.1367000
##  [276]    0.0000000  408.0825000   33.5708300  404.4900000    0.0000000
##  [281]   80.4508400    8.3333330  240.2775000    0.0000000   88.9466700
##  [286]    0.0000000   11.0041700  281.8067000    0.0000000   17.1716700
##  [291]    0.0000000    0.0000000  119.6250000   86.5466700   94.1183300
##  [296]  832.2433000    0.0000000  762.3784000   26.0000000  227.6017000
##  [301]  379.8658000  620.0550000    0.0000000   48.6150000  195.4508000
##  [306]    0.0000000  122.1917000  384.8992000  129.3333000    0.0000000
##  [311]    4.5833330  210.6475000  198.5483000    3.1666670    0.0000000
##  [316]    0.0000000  153.7267000   34.1558300    0.0000000    0.0000000
##  [321]    0.0000000    0.0000000  344.1808000  497.7058000    0.0000000
##  [326]    0.0000000   87.1050000    0.0000000    0.0000000   80.8683300
##  [331]  143.3467000  105.2833000  583.7783000    0.0000000  950.8641000
##  [336]  105.9617000    0.0000000    0.0000000  137.0717000    0.0000000
##  [341]    0.3125000  100.1433000  320.6758000  683.4283000    0.0000000
##  [346]  145.6958000   93.9191700    0.0000000  167.8267000  703.4067000
##  [351]    0.0000000 1292.0030000    0.0000000    0.0000000  248.9058000
##  [356]   71.5300000  347.9483000    4.5833330  831.7317000  101.7633000
##  [361]  424.1767000    0.0000000  232.2758000  335.5625000   20.1808300
##  [366]    0.0000000  119.0000000   69.0325000  131.6742000    0.0000000
##  [371]  523.1250000    0.0000000    0.0000000  391.4175000   99.1033300
##  [376]    0.0000000  379.2983000    0.0000000  398.8508000   34.0916700
##  [381]  423.1267000    0.0000000  241.4233000  145.7192000    0.9166667
##  [386]  512.0125000  318.5100000  189.6842000  194.4667000  182.6092000
##  [391]  297.0317000   37.9741700  395.5850000  204.5367000  148.3725000
##  [396]  170.4667000  122.9292000    4.5833330  430.6925000   20.0833300
##  [401]   17.7850000  376.6342000    0.0000000  474.0650000    0.0000000
##  [406]  293.0658000  631.9725000   69.7933300    0.0000000    0.0000000
##  [411]    0.0000000    8.7500000  127.4725000  182.0958000    6.1458330
##  [416]  412.1867000   35.6750000  205.9358000   43.9741700  188.1050000
##  [421]    7.0833330  484.4392000    0.0000000    8.7783340  174.9250000
##  [426]   21.3333300  360.9950000  114.6017000  113.2958000   33.5075000
##  [431]   93.6250000    0.0000000  415.8058000  875.8884000  173.0233000
##  [436]    7.4166670  165.9300000   15.5833300  178.9158000   44.9425000
##  [441]  149.5508000    3.7500000    0.0000000   82.9208400    0.0000000
##  [446]    8.3333330 3099.5050000  300.4608000  112.9167000   94.1450000
##  [451]  215.4392000   35.1683300   22.4625000  294.5800000    0.0000000
##  [456]   10.2908300  632.0808000  326.5667000  747.6700000  170.1808000
##  [461] 1510.5340000    0.0000000  191.7017000    0.0000000  188.1858000
##  [466]    0.0000000 1096.6570000  276.9842000    0.0000000    0.0000000
##  [471]   38.3650000 1482.3680000   45.8758300   64.0741700    0.0000000
##  [476]   35.1666700  129.4167000  663.7692000    0.0000000    0.0000000
##  [481]   75.6341700    3.7500000  301.2525000  224.5817000   60.5841700
##  [486]    0.0000000  498.9267000    8.3333330  105.5075000   56.3333300
##  [491]  212.4783000   14.3316700    0.0000000    0.0000000   58.9308300
##  [496]    0.0000000   66.2641700    0.0000000   91.9641700  215.8817000
##  [501]   22.9866700    0.0000000  240.3800000   89.9550000  126.6508000
##  [506]  140.3467000    0.0000000  294.5592000   68.3591700  101.4942000
##  [511] 1088.7390000   47.7875000   50.0283300  344.0067000  188.2950000
##  [516]  245.3517000  285.2825000   17.5833300 1569.6770000   43.7441700
##  [521]   55.7608300   97.5125000   58.0541600  213.1292000   95.9975000
##  [526]   61.1991700    0.0000000    0.0000000  640.6533000   61.7358300
##  [531]   34.5316700  213.2925000   14.0000000  164.6433000  158.6067000
##  [536]  133.9692000    0.0000000    0.0000000    0.0000000    0.0000000
##  [541]   66.3008300   58.5058300    0.0000000  100.4167000    0.0000000
##  [546]   25.5075000  420.5808000  309.9100000   78.2558400  123.2225000
##  [551]   79.1758300  431.1433000    0.0000000    0.0000000  134.3858000
##  [556]   54.9691700    0.0000000  120.5592000  320.4867000 1352.4880000
##  [561]   87.1491700   81.2650000  643.3233000  134.9183000  860.7550000
##  [566]   85.9783300  463.1517000  134.2008000  192.7208000  562.3592000
##  [571]  274.9467000  398.0475000  124.4758000  163.4175000   93.3100000
##  [576]  113.2492000    0.0000000   37.4291600  341.2450000  508.2908000
##  [581]    0.0000000  432.3592000   46.2916700  253.6292000   61.7425000
##  [586]  125.8917000 1836.9760000   15.5833300   51.7500000    0.0000000
##  [591]   77.2866700    0.0000000  240.4842000   67.3983300   69.2733300
##  [596]  172.5717000  199.0042000    0.0000000  160.4792000    0.0000000
##  [601]    0.0000000    0.0000000   44.4133300  492.2558000  401.9300000
##  [606]  179.1783000    0.0000000  182.1675000    0.0000000   61.9525000
##  [611]   80.8258400    8.3333330  169.0575000   57.9808300  203.1333000
##  [616]    0.0000000  181.0150000    0.0000000    0.0000000  483.0608000
##  [621]   15.9408300  180.6550000    4.5833330 1902.0000000  356.7817000
##  [626]    0.0000000   24.5750000  302.2467000   33.2150000   13.6858300
##  [631]  116.3525000  144.3975000  113.7500000  184.9433000   15.3325000
##  [636]  113.1808000   37.5833300    0.0000000  156.9000000    0.0000000
##  [641]  349.2017000  615.1208000  208.0833000   88.7391700   34.5208300
##  [646]  366.7450000   44.0000000    0.0000000  299.8858000  359.1225000
##  [651]  246.5708000   63.9916600   51.6450000    0.0000000  633.4133000
##  [656]  242.1283000    0.0000000  200.4933000   32.4641600    0.0000000
##  [661]   59.6191700    0.0000000    0.0000000  315.2017000  258.2908000
##  [666]    0.0000000    0.0000000  457.4875000    0.0000000    4.5833330
##  [671]  745.0383000  180.1775000  111.8392000  613.6425000  333.6117000
##  [676]  207.7133000  199.3692000   81.4350000  116.3150000    0.0000000
##  [681]  530.1800000    0.0000000    0.0000000  116.4492000  357.5908000
##  [686]  214.8600000  200.5867000   56.4633300   17.6666700    0.0000000
##  [691]    0.0000000  479.4367000    0.0000000  308.9250000  144.6417000
##  [696]    0.0000000  244.6533000   38.8333300  462.6558000  214.9342000
##  [701]    0.0000000    0.0000000  244.4233000  135.1842000  185.7383000
##  [706]  413.5975000    0.0000000   14.6666700   16.9133300    0.0000000
##  [711]    0.0000000   20.3333300    0.0000000    5.5833330    0.0000000
##  [716]  238.3650000  300.9942000  552.3417000  113.5892000   99.9166600
##  [721]  147.3333000  480.3233000  304.8950000   63.4566700  149.7000000
##  [726]    0.0000000  166.0592000  148.1900000  121.2208000  160.0283000
##  [731]  201.4825000   59.1191700    0.0000000  356.8792000  159.1958000
##  [736]    0.0000000  112.8783000  108.9775000   91.6875000    0.0000000
##  [741]    0.0000000    0.0000000  462.0208000  138.5692000  373.4167000
##  [746]  304.6225000  385.7750000  319.4300000  465.9383000   59.7866700
##  [751]  170.4625000  242.1950000    0.0000000  478.7125000    0.0000000
##  [756]   98.3450000  301.0175000  509.0542000  183.8983000   46.5233300
##  [761]  256.1867000  470.9733000 2291.1740000  128.4267000 1949.8620000
##  [766]  269.1533000   41.4816700   79.3608300  612.8633000  192.4508000
##  [771]  327.7367000  177.6158000  365.9058000   41.3333300   86.1483300
##  [776]    0.0000000    0.0000000  376.7650000    0.0000000  189.1767000
##  [781]  260.1208000  406.9792000    0.0000000  118.7217000   91.7325000
##  [786]  200.2375000  438.3667000    0.0000000  621.6600000  324.7050000
##  [791]  148.0592000  940.3925000  325.8500000   30.5483300  192.8183000
##  [796]  324.0275000   91.9891700  267.3483000    0.0000000   12.2500000
##  [801]   98.5791600   43.2066700   60.0666700    0.0000000  187.1425000
##  [806]   29.9750000  445.8058000  374.0517000    0.0000000  121.0558000
##  [811]  240.8208000   82.3425000  131.5242000  215.7392000    0.0000000
##  [816]  517.2117000   36.2050000  159.6550000    0.0000000  247.2992000
##  [821]   21.7150000   16.2500000  100.3367000   64.7558400  103.9167000
##  [826]  288.8017000    0.0000000   58.6083300    0.0000000  387.1742000
##  [831]  136.0408000  105.3250000   97.4241600  229.4933000  436.2750000
##  [836]   45.1241700  355.9683000  650.4375000  442.4458000  310.8417000
##  [841]    0.0000000    0.0000000  426.8325000   36.2500000  337.0583000
##  [846]    0.0000000 1010.5020000    0.0000000  568.2433000  461.9217000
##  [851]  140.0150000  206.3817000  719.5059000  383.3117000    0.0000000
##  [856]  265.6517000    0.0000000  282.7258000  383.5667000  494.8875000
##  [861]   12.6508300  317.4642000    9.7416670   49.4166700    0.0000000
##  [866]  821.8208000  323.9517000    0.0000000  476.3158000  112.9942000
##  [871]   44.4633300  248.5058000  319.7925000  758.9391000  412.9933000
##  [876]    0.0000000  131.9892000   52.4891700  225.2692000  298.0233000
##  [881]    0.0000000  173.4675000   79.6850000  138.5467000  565.3292000
##  [886]    0.0000000  220.8942000  527.9417000  104.7833000    0.0000000
##  [891]  466.3475000   88.8175000  437.1342000    0.0000000  114.2533000
##  [896]    0.0000000  293.0417000  683.9883000  113.0992000  288.3633000
##  [901]  111.6750000    0.0000000  151.3367000    4.5833330  199.7950000
##  [906]  308.2500000  193.2933000    0.0000000    0.0000000   59.8541700
##  [911]    0.0000000  313.4333000    0.0000000  195.0233000  162.4458000
##  [916]    0.0000000  314.4908000  292.9483000    0.0000000    0.0000000
##  [921]  533.6275000  343.6542000    0.0000000  397.0333000   71.6666600
##  [926]  503.5475000  142.5142000    8.3333330  487.4817000    0.0000000
##  [931]  352.8483000    0.0000000    0.0000000   61.1666700  553.1067000
##  [936]   59.0891600  132.4717000   89.8266700   36.3333300  108.9767000
##  [941]   64.5600000  192.6400000  131.7658000  203.8917000    0.0000000
##  [946]  390.1667000    0.0000000  270.3683000    0.0000000    0.0000000
##  [951]  352.1733000  179.7708000  156.0892000   57.9158300  205.5133000
##  [956]  149.2992000  310.7358000   97.9400000  127.1183000  220.7925000
##  [961]    0.0000000  854.7850000  602.9208000  436.2592000    0.0000000
##  [966]   67.0975000   94.2683300   84.3908300  175.4442000  132.1292000
##  [971]  305.8375000  440.6908000  634.8625000    0.0000000   92.4416700
##  [976]  286.5700000  121.0308000  136.1242000    0.0000000  510.0900000
##  [981]   42.3108300    6.4166670    7.0833330  159.7300000  124.5033000
##  [986] 1028.9610000    0.0000000    0.0000000    0.0000000   71.9591700
##  [991]  111.6175000  386.0525000    0.0000000   62.5083300  159.3600000
##  [996]  434.8775000   40.3275000   38.7083300   57.3066700   30.4458300
## [1001]  106.7208000  181.4142000  732.9792000    0.0000000    0.0000000
## [1006]   95.5283400  896.9384000   85.2408300    0.0000000  298.4783000
## [1011]    0.0000000   11.7125000   65.8183400  898.4258000  494.4875000
## [1016]    0.0000000   67.5358400  126.1033000   98.6825000  264.9658000
## [1021]   17.0833300  469.2475000  108.2958000  590.9484000    0.0000000
## [1026]   48.0833300    0.0000000  141.6883000    0.0000000  109.6092000
## [1031] 1637.5270000  114.5575000   11.6725000   22.3575000    0.0000000
## [1036]   91.4125000  239.4867000  190.4308000   66.7325000  221.8892000
## [1041]  446.4150000  830.2767000  113.6308000    0.0000000    0.0000000
## [1046]  127.3725000  166.9017000  315.9425000  171.8608000   46.6058300
## [1051]  306.4683000   31.0000000  140.2992000    0.0000000  175.6442000
## [1056]   29.5158300   67.2016700  146.2700000    4.5833330  160.4517000
## [1061]  171.5125000    8.1250000  384.1725000 1774.3560000  285.2550000
## [1066]   49.2500000   40.1991700   26.5000000  183.5608000   45.3541700
## [1071]  664.6025000   37.0150000  862.9642000    0.0000000    0.0000000
## [1076]  143.4292000  124.6325000  577.9833000  266.3433000   30.0525000
## [1081]  193.3225000  121.6333000    8.3333330  158.6017000    0.0000000
## [1086]  178.4558000  167.0400000  569.5458000    0.0000000  351.3633000
## [1091]  198.0233000  153.7517000    0.0000000    0.0000000    9.1666670
## [1096]    0.0000000  448.0233000  470.2967000  400.0708000  149.0967000
## [1101]    0.0000000  332.2392000    0.0000000    0.0000000  721.0600000
## [1106]    0.0000000    7.0833330  166.2383000    5.5208330    5.8333330
## [1111]   78.8850000   20.2500000  113.9475000  493.7333000  332.7042000
## [1116]   42.5166700  151.0325000  128.0725000    0.0000000    0.0000000
## [1121]  219.2717000   37.7133300    0.0000000    0.0000000  362.6383000
## [1126]    8.3333330    0.0000000    0.0000000  580.9725000   25.2916700
## [1131]  210.2283000  121.3367000   77.8066600    0.0000000   87.3066600
## [1136]    0.0000000    0.0000000  471.3450000   16.6341700    0.0000000
## [1141]  737.0808000  544.5208000    0.0000000    0.0000000  160.8750000
## [1146]  108.6233000  500.6158000  674.9092000  364.8192000  216.1650000
## [1151]    0.0000000    0.0000000  287.7583000  323.0633000  214.0975000
## [1156]  163.8392000  154.7400000    0.0000000  164.8100000  126.3867000
## [1161]   39.9116700  321.2108000    0.0000000  228.9658000  449.0392000
## [1166]   79.6091700  233.0417000  266.1908000  204.0875000  101.8542000
## [1171]  881.2642000  889.5850000  142.8775000  434.1250000  201.6583000
## [1176]    0.0000000    7.0833330   18.2541700  127.9833000  161.1617000
## [1181] 1786.2770000  284.2558000  426.4467000    0.0000000    0.0000000
## [1186]  435.9908000   58.0808300  103.3283000 2001.5470000    0.0000000
## [1191]    0.0000000    0.0000000    0.0000000    0.0000000  205.2542000
## [1196]  258.1667000   15.8333300  163.0392000  475.0183000  429.9050000
## [1201]   47.2416600  396.5000000    0.0000000    0.0000000   73.1725000
## [1206]   23.5833300    0.0000000  569.9183000   24.0700000  258.8783000
## [1211]  387.2183000    0.0000000  839.3008000  133.0683000    0.0000000
## [1216]   68.5458300  664.1208000  647.2067000  402.7225000   47.0816700
## [1221]  242.1450000  273.4900000  150.2917000  106.2008000  402.7875000
## [1226]    0.0000000  125.4817000  213.0000000  201.5825000   70.2850000
## [1231]  188.7275000   32.6666700  202.4700000   69.4275000    0.0000000
## [1236]    0.0000000    0.0000000    0.0000000   84.7316700    0.0000000
## [1241]   80.0550000   43.4366700  142.9508000    0.0000000    0.0000000
## [1246]  119.3017000    0.0000000    0.0000000  130.0267000  482.6575000
## [1251]    0.0000000  858.3475000    0.0000000    0.0000000   33.0133300
## [1256]   46.0558400  169.8767000   90.0633300  323.5875000  520.3417000
## [1261]    0.0000000  167.2125000  209.8442000    0.0000000  229.7975000
## [1266]    0.0000000  536.2500000    0.0000000    0.0000000  191.8358000
## [1271]    0.0000000  362.7542000  163.1800000  176.2717000   61.3333300
## [1276]   57.4408300  948.3625000  132.5883000   91.1766700  146.4275000
## [1281]  111.8183000    0.0000000  396.2717000   19.5900000   21.4650000
## [1286]    0.0000000  885.0858000    0.0000000   29.9566700   26.8783300
## [1291]  394.5717000  906.3092000    0.0000000  400.4475000  303.6742000
## [1296]  143.0158000  146.4417000    0.0000000   97.1841700   38.9975000
## [1301]    0.0000000    0.0000000   27.4166700  179.7167000   82.6591600
## [1306]  157.8442000   63.5516700  154.8592000   44.0208300  424.6292000
## [1311]    4.5833330    0.0000000    0.0000000    0.0000000    7.3333330
## [1316]    0.0000000  101.2983000   26.9966700  344.1575000

#limits <- cut(CreditCard$expenditure, seq(0,3500, by=500))
#tabelka <- freq(limits)

Task 2.1

First let’s load the data

download.file("https://raw.githubusercontent.com/kflisikowski/ds/master/netflix-dataset.csv?raw=true", destfile ="dane.csv",mode="wb")
mydata<-read.csv(file="dane.csv",encoding ="UTF-8",header=TRUE,sep = ",")
attach(mydata)

We will consider a movie or series to be polish if it is available in Poland and has Polish subtitles. Now some data wrangling.

movies <- mydata %>% 
  filter(Languages == "Polish") %>%
  filter (Country.Availability == "Poland") %>%
  select( Country.Availability,Languages, Title, IMDb.Score )

ggplot(mydata, aes(IMDb.Score))+
  geom_histogram(binwidth=0.1, fill = "cornsilk", color="navyblue")+
  facet_wrap(vars(Series.or.Movie))

Task 2.2

Now let’s take a look at density.

ggplot(mydata, aes(IMDb.Score), y=after_stat(density), bw=0.5)+
  geom_density( fill = "cornsilk", color="navyblue")+
  facet_wrap(vars(Series.or.Movie))

Task 3.3

new <- mydata %>%
  select(Languages) %>%
  separate_rows(Languages, sep=", ") %>%
  count(Languages, sort=TRUE) %>%
  top_n(10, n)


ggplot(new, aes(x=n, y=fct_reorder(Languages, n)))+
  geom_col()+
  ylab("Language")+
  xlab("How many titles available")

Extra task 1

Extra task 2

newdata <-mydata %>%
  select(IMDb.Score, Metacritic.Score, Rotten.Tomatoes.Score, Hidden.Gem.Score, Series.or.Movie) %>%

  pivot_longer(cols=c("IMDb.Score", "Metacritic.Score", "Rotten.Tomatoes.Score", "Hidden.Gem.Score"), names_to="Reviewer", values_to="Rating" )
newdata

## # A tibble: 37,700 × 3
##    Series.or.Movie Reviewer              Rating
##    <chr>           <chr>                  <dbl>
##  1 Series          IMDb.Score               7.9
##  2 Series          Metacritic.Score        82  
##  3 Series          Rotten.Tomatoes.Score   98  
##  4 Series          Hidden.Gem.Score         4.3
##  5 Movie           IMDb.Score               5.8
##  6 Movie           Metacritic.Score        69  
##  7 Movie           Rotten.Tomatoes.Score   79  
##  8 Movie           Hidden.Gem.Score         7  
##  9 Movie           IMDb.Score               7.4
## 10 Movie           Metacritic.Score        NA  
## # ℹ 37,690 more rows

ggplot(newdata, aes(x=Rating))+
  geom_histogram()+
  facet_wrap(vars(Reviewer, Series.or.Movie), scales="free")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.