pacman::p_load(tidyverse,Lock5Data,ISLR,nycflights13,wooldridge,expss,hablar,rio)
wooldridge::ceosal1
## salary pcsalary sales roe pcroe ros indus finance consprod utility
## 1 1095 20 27595.0 14.1 106.4 191 1 0 0 0
## 2 1001 32 9958.0 10.9 -30.6 13 1 0 0 0
## 3 1122 9 6125.9 23.5 -16.3 14 1 0 0 0
## 4 578 -9 16246.0 5.9 -25.7 -21 1 0 0 0
## 5 1368 7 21783.2 13.8 -3.0 56 1 0 0 0
## 6 1145 5 6021.4 20.0 1.0 55 1 0 0 0
## 7 1078 10 2266.7 16.4 -5.9 62 1 0 0 0
## 8 1094 7 2966.8 16.3 -1.6 44 1 0 0 0
## 9 1237 16 4570.2 10.5 -70.2 37 1 0 0 0
## 10 833 5 2830.0 26.3 -23.9 37 1 0 0 0
## 11 567 7 596.8 25.9 39.5 109 1 0 0 0
## 12 933 -3 19773.0 26.8 -26.8 -10 1 0 0 0
## 13 1339 -9 40047.0 14.8 12.1 41 1 0 0 0
## 14 937 9 2513.8 22.3 9.8 44 1 0 0 0
## 15 2011 49 1580.6 56.3 62.2 63 1 0 0 0
## 16 1585 4 6754.0 12.6 9.3 17 1 0 0 0
## 17 905 12 1066.3 20.4 33.8 141 1 0 0 0
## 18 1058 94 3199.9 1.9 -86.8 -15 1 0 0 0
## 19 922 48 1452.7 19.9 97.3 56 1 0 0 0
## 20 1220 -7 8995.0 15.4 19.5 28 1 0 0 0
## 21 1022 16 1212.3 38.7 162.9 83 1 0 0 0
## 22 759 12 2824.2 16.4 -9.4 21 1 0 0 0
## 23 1414 0 7621.0 24.4 -30.2 -10 1 0 0 0
## 24 1041 -11 4418.3 15.6 -7.5 74 1 0 0 0
## 25 1688 1 12343.0 14.4 -16.8 15 1 0 0 0
## 26 2983 13 57662.0 19.0 9.9 42 1 0 0 0
## 27 1160 0 4319.7 16.1 -23.3 -2 1 0 0 0
## 28 3844 61 20604.0 12.1 -5.9 18 1 0 0 0
## 29 476 6 611.3 16.2 -15.7 40 1 0 0 0
## 30 1492 10 12431.7 18.4 -23.3 58 1 0 0 0
## 31 1024 -3 8169.0 14.2 -36.2 -10 1 0 0 0
## 32 1593 18 20659.0 14.9 410.3 16 1 0 0 0
## 33 427 27 3072.1 12.4 -17.7 61 1 0 0 0
## 34 829 5 1669.1 17.1 -39.3 64 1 0 0 0
## 35 797 -3 2401.2 16.9 6.0 39 1 0 0 0
## 36 577 23 3043.6 18.1 -13.5 1 1 0 0 0
## 37 1342 7 6222.2 10.9 -6.0 59 1 0 0 0
## 38 1774 0 7727.9 19.3 15.3 19 1 0 0 0
## 39 709 11 719.5 18.3 4.6 65 1 0 0 0
## 40 860 28 3921.3 18.4 34.7 45 1 0 0 0
## 41 1336 20 5155.1 13.8 1.7 7 1 0 0 0
## 42 516 -2 649.2 13.7 -10.3 27 1 0 0 0
## 43 931 20 10885.0 12.7 14.5 10 1 0 0 0
## 44 815 12 1651.9 15.1 -2.4 33 1 0 0 0
## 45 1681 20 12915.0 16.5 -66.7 29 1 0 0 0
## 46 568 -36 11436.0 10.2 -48.3 -19 1 0 0 0
## 47 775 7 2210.3 19.6 18.9 34 1 0 0 0
## 48 1188 9 3737.8 12.8 32.5 15 1 0 0 0
## 49 782 -1 1976.7 15.9 -0.6 24 1 0 0 0
## 50 1170 0 2576.0 17.3 -39.1 93 1 0 0 0
## 51 1469 8 6309.1 8.5 93.9 79 1 0 0 0
## 52 916 -1 2940.5 16.4 25.8 105 1 0 0 0
## 53 1070 -9 1072.6 19.5 34.8 269 1 0 0 0
## 54 894 1 1534.0 19.2 -3.7 74 1 0 0 0
## 55 829 -4 2158.8 15.9 -27.6 -18 1 0 0 0
## 56 780 17 1736.0 19.9 50.7 23 1 0 0 0
## 57 2327 10 3598.8 28.1 -28.3 104 1 0 0 0
## 58 717 20 1413.6 25.0 -15.8 56 1 0 0 0
## 59 1368 9 13538.0 15.0 -8.8 -43 1 0 0 0
## 60 2028 185 69018.0 12.6 2.3 10 1 0 0 0
## 61 1195 2 6285.0 20.3 6.3 49 1 0 0 0
## 62 256 19 526.3 22.7 24.7 175 1 0 0 0
## 63 775 -10 3195.6 14.8 -32.6 12 1 0 0 0
## 64 1407 80 2762.8 13.2 -17.4 28 1 0 0 0
## 65 543 14 1873.1 10.3 -45.0 -58 1 0 0 0
## 66 874 -10 4648.1 17.7 -13.9 -6 1 0 0 0
## 67 1287 -3 16951.0 10.0 -1.0 -21 1 0 0 0
## 68 1248 16 3506.9 15.6 5.0 54 0 1 0 0
## 69 875 -19 5333.1 6.8 27.6 -8 0 1 0 0
## 70 925 -3 3296.2 12.4 -56.9 -21 0 1 0 0
## 71 798 -6 2584.0 13.1 -50.5 10 0 1 0 0
## 72 760 14 834.4 15.8 3.0 55 0 1 0 0
## 73 600 -41 4068.7 12.8 -32.0 -6 0 1 0 0
## 74 991 18 2518.6 15.3 13.3 65 0 1 0 0
## 75 1570 14 10465.0 0.5 977.0 36 0 1 0 0
## 76 911 1 2682.4 16.5 31.5 23 0 1 0 0
## 77 1360 12 2688.2 15.1 23.0 67 0 1 0 0
## 78 700 -53 6682.3 13.0 5.8 53 0 1 0 0
## 79 741 -50 4879.9 11.1 -77.7 -26 0 1 0 0
## 80 1097 143 2772.1 8.9 595.6 20 0 1 0 0
## 81 953 15 1320.4 17.5 -4.3 83 0 1 0 0
## 82 441 -7 3408.5 15.9 -10.0 37 0 1 0 0
## 83 595 18 1919.7 14.2 31.4 61 0 1 0 0
## 84 1067 1 19020.5 9.3 -37.8 4 0 1 0 0
## 85 1298 3 4778.3 9.5 -26.7 -2 0 1 0 0
## 86 1798 -31 24332.0 15.5 -59.5 2 0 1 0 0
## 87 4143 -10 2678.4 14.4 -12.8 42 0 1 0 0
## 88 1336 8 4481.0 11.1 10.8 15 0 1 0 0
## 89 1750 11 14932.1 15.9 -7.8 62 0 1 0 0
## 90 912 15 2626.4 16.4 0.3 70 0 1 0 0
## 91 1892 -16 2659.5 8.6 -1.4 126 0 1 0 0
## 92 833 19 1073.2 24.6 14.4 51 0 1 0 0
## 93 1142 -12 2577.3 15.4 -22.4 56 0 1 0 0
## 94 1159 13 4293.8 16.9 2.6 107 0 1 0 0
## 95 1283 16 18164.0 7.2 -47.3 14 0 1 0 0
## 96 2109 42 9944.4 11.6 -42.2 23 0 1 0 0
## 97 1039 -21 12719.0 26.4 43.4 264 0 1 0 0
## 98 992 42 1931.6 21.4 -9.2 52 0 1 0 0
## 99 1253 4 2993.2 19.2 1.9 74 0 1 0 0
## 100 721 9 2127.2 15.1 -22.3 101 0 1 0 0
## 101 1351 4 4211.9 9.0 -18.0 -4 0 1 0 0
## 102 1391 25 8489.6 9.4 -27.3 26 0 1 0 0
## 103 1245 25 12222.8 19.0 -20.9 52 0 1 0 0
## 104 1550 0 11213.4 3.5 -55.4 6 0 1 0 0
## 105 2150 -61 5869.6 22.1 -38.0 68 0 1 0 0
## 106 1846 20 6193.8 10.9 -14.7 6 0 1 0 0
## 107 573 8 3053.2 15.1 -3.8 31 0 1 0 0
## 108 6640 4 8946.0 10.2 87.6 34 0 1 0 0
## 109 959 49 4045.2 17.3 -7.8 51 0 1 0 0
## 110 612 39 3618.9 33.3 -10.5 68 0 1 0 0
## 111 1820 69 1796.1 22.8 12.7 115 0 1 0 0
## 112 1411 2 6703.1 11.1 -37.2 28 0 1 0 0
## 113 1026 6 2170.3 12.4 134.6 162 0 1 0 0
## 114 1287 -12 4674.0 20.9 84.2 -47 0 0 0 1
## 115 800 162 3372.0 6.7 29.0 60 0 0 0 1
## 116 1115 -9 8205.0 7.1 13.9 23 0 0 0 1
## 117 1631 33 4617.0 11.8 227.0 75 0 0 0 1
## 118 1910 23 6964.0 14.0 2.7 43 0 0 0 1
## 119 996 -3 6623.0 10.1 -63.6 10 0 0 1 0
## 120 918 -35 29797.0 6.4 -95.0 -28 0 0 1 0
## 121 1261 21 5225.5 12.4 -54.9 1 0 0 1 0
## 122 1053 -8 3639.0 17.6 -22.3 5 0 0 1 0
## 123 1221 -43 97649.9 15.1 -85.2 -7 0 0 1 0
## 124 1738 19 10743.6 23.6 11.5 36 0 0 1 0
## 125 3142 15 10236.3 35.7 27.2 155 0 0 1 0
## 126 1900 26 17802.7 23.2 -7.8 142 0 0 1 0
## 127 427 21 6158.7 12.4 -17.7 95 0 0 1 0
## 128 1700 8 6775.2 44.4 37.7 61 0 0 1 0
## 129 360 103 298.7 2.1 23.1 302 0 0 1 0
## 130 459 9 785.3 18.4 -43.1 5 0 0 1 0
## 131 1340 5 1368.6 16.1 12.6 115 0 0 1 0
## 132 729 25 2066.1 15.1 -8.4 52 0 0 1 0
## 133 223 -6 181.5 22.7 32.6 295 0 0 1 0
## 134 2101 25 10300.0 23.4 46.9 76 0 0 1 0
## 135 1082 28 11232.0 25.7 -2.4 101 0 0 1 0
## 136 1781 31 5191.6 27.0 141.0 98 0 0 1 0
## 137 791 18 976.7 19.9 -13.2 89 0 0 1 0
## 138 2092 -11 7671.5 43.7 8.5 80 0 0 1 0
## 139 1573 10 6406.0 16.4 -11.5 87 0 0 1 0
## 140 1045 9 2917.4 11.6 -98.9 203 0 0 1 0
## 141 1694 12 3322.9 24.8 23.9 100 0 0 1 0
## 142 453 20 175.2 26.2 35.6 418 0 0 1 0
## 143 1130 1 1686.3 44.5 3.4 99 0 0 1 0
## 144 1334 13 514.1 22.3 62.4 358 0 0 1 0
## 145 1344 13 3032.7 22.3 62.4 36 0 0 1 0
## 146 1585 5 4686.9 35.1 2.2 111 0 0 1 0
## 147 1946 45 8388.1 13.1 5.9 88 0 0 1 0
## 148 1619 9 7632.8 11.0 22.6 32 0 0 1 0
## 149 1620 12 17453.8 19.4 -12.8 128 0 0 1 0
## 150 967 -27 5781.1 28.5 -11.0 119 0 0 1 0
## 151 1431 0 6896.2 43.9 3.5 110 0 0 1 0
## 152 1231 9 6503.1 26.8 19.8 84 0 0 1 0
## 153 770 -4 2715.6 15.7 -2.5 63 0 0 1 0
## 154 1594 53 2761.9 15.0 27.8 85 0 0 1 0
## 155 1568 47 5181.4 28.2 -19.1 55 0 0 1 0
## 156 995 15 1323.0 15.4 74.6 195 0 0 1 0
## 157 1077 -16 5296.0 20.0 27.5 36 0 0 1 0
## 158 1161 9 7177.0 42.2 -2.8 68 0 0 1 0
## 159 1401 1 12183.5 19.6 -2.1 93 0 0 1 0
## 160 1127 4 7863.5 16.2 26.0 152 0 0 1 0
## 161 3068 -14 3825.6 21.5 -27.6 142 0 0 1 0
## 162 730 7 1110.6 29.5 20.5 60 0 0 1 0
## 163 729 -10 1391.3 22.6 -17.2 32 0 0 1 0
## 164 11233 17 6047.9 22.9 9.9 74 0 0 1 0
## 165 949 -11 18908.0 13.0 -46.7 -3 0 0 1 0
## 166 3646 -11 3921.5 7.8 -64.2 29 0 0 1 0
## 167 1502 212 3453.8 48.1 35.1 28 0 0 1 0
## 168 807 10 1558.5 18.0 -1.0 49 0 0 1 0
## 169 713 8 962.8 18.0 7.6 81 0 0 1 0
## 170 1489 0 25848.0 21.7 380.6 114 0 0 1 0
## 171 736 73 631.5 21.3 60.8 71 0 0 1 0
## 172 1226 53 1728.9 26.9 -7.1 84 0 0 1 0
## 173 543 112 2669.3 30.5 24.9 323 0 0 1 0
## 174 14822 1 2159.2 19.4 -37.3 17 0 0 1 0
## 175 890 -18 2612.6 15.6 -49.0 -14 0 0 1 0
## 176 1627 6 8270.3 19.4 -3.7 104 0 0 1 0
## 177 2408 28 44323.0 29.1 9.8 160 0 0 1 0
## 178 2248 6 764.7 40.8 44.4 191 0 0 1 0
## 179 787 20 5167.5 13.7 -4.0 34 0 0 0 1
## 180 474 -5 2159.3 11.1 -58.2 16 0 0 0 1
## 181 439 7 2617.1 10.8 -16.1 65 0 0 0 1
## 182 465 11 2367.7 5.1 -25.0 43 0 0 0 1
## 183 594 33 2744.0 12.3 -12.2 76 0 0 0 1
## 184 688 -6 2357.9 7.4 -9.7 33 0 0 0 1
## 185 607 12 5262.0 6.2 -94.8 59 0 0 0 1
## 186 634 44 5738.9 12.7 -7.0 38 0 0 0 1
## 187 532 2 2714.9 10.6 -21.2 35 0 0 0 1
## 188 441 39 3306.7 7.4 13.0 150 0 0 0 1
## 189 694 18 3532.5 12.6 -13.8 37 0 0 0 1
## 190 520 -6 3681.5 12.8 -7.7 64 0 0 0 1
## 191 757 12 3982.1 2.9 45.4 197 0 0 0 1
## 192 668 30 2996.0 13.5 -3.3 81 0 0 0 1
## 193 803 18 4178.6 10.7 -29.9 52 0 0 0 1
## 194 500 3 2616.3 11.9 -1.3 25 0 0 0 1
## 195 552 -5 2064.5 12.9 -16.8 37 0 0 0 1
## 196 412 -5 2225.5 10.1 -27.4 13 0 0 0 1
## 197 1100 58 9470.1 7.3 76.6 82 0 0 0 1
## 198 959 26 3783.2 14.6 5.0 65 0 0 0 1
## 199 333 34 2388.7 13.8 6.5 59 0 0 0 1
## 200 503 -19 3705.2 8.9 -96.8 29 0 0 0 1
## 201 448 -8 1411.7 14.0 -33.5 14 0 0 0 1
## 202 732 7 4800.1 12.9 -11.2 36 0 0 0 1
## 203 720 8 1771.9 14.5 13.6 78 0 0 0 1
## 204 808 -16 7198.5 14.7 7.4 49 0 0 0 1
## 205 930 10 1509.1 9.0 20.5 131 0 0 0 1
## 206 525 3 1097.1 15.5 20.1 72 0 0 0 1
## 207 658 32 4542.6 12.1 -7.8 68 0 0 0 1
## 208 555 6 2023.0 13.7 -14.6 60 0 0 0 1
## 209 626 0 1442.5 14.4 -10.2 62 0 0 0 1
## lsalary lsales
## 1 6.998509 10.225389
## 2 6.908755 9.206132
## 3 7.022868 8.720281
## 4 6.359574 9.695602
## 5 7.221105 9.988894
## 6 7.043160 8.703075
## 7 6.982863 7.726080
## 8 6.997596 7.995239
## 9 7.120444 8.427312
## 10 6.725034 7.948032
## 11 6.340359 6.391582
## 12 6.838405 9.892073
## 13 7.199678 10.597809
## 14 6.842683 7.829551
## 15 7.606388 7.365560
## 16 7.368340 8.817890
## 17 6.807935 6.971950
## 18 6.964136 8.070875
## 19 6.826545 7.281179
## 20 7.106606 9.104424
## 21 6.929517 7.100275
## 22 6.632002 7.945981
## 23 7.254178 8.938663
## 24 6.947937 8.393510
## 25 7.431300 9.420844
## 26 8.000685 10.962354
## 27 7.056175 8.370941
## 28 8.254269 9.933241
## 29 6.165418 6.415588
## 30 7.307873 9.428005
## 31 6.931472 9.008101
## 32 7.373374 9.935906
## 33 6.056784 8.030117
## 34 6.720220 7.420040
## 35 6.680855 7.783724
## 36 6.357842 8.020797
## 37 7.201916 8.735879
## 38 7.480992 8.952593
## 39 6.563856 6.578557
## 40 6.756932 8.274179
## 41 7.197435 8.547742
## 42 6.246107 6.475741
## 43 6.836259 9.295141
## 44 6.703188 7.409681
## 45 7.427144 9.466145
## 46 6.342122 9.344522
## 47 6.652863 7.700883
## 48 7.080027 8.226253
## 49 6.661855 7.589184
## 50 7.064759 7.853993
## 51 7.292337 8.749748
## 52 6.820016 7.986335
## 53 6.975414 6.977841
## 54 6.795706 7.335634
## 55 6.720220 7.677308
## 56 6.659294 7.459339
## 57 7.752335 8.188355
## 58 6.575076 7.253895
## 59 7.221105 9.513256
## 60 7.614805 11.142122
## 61 7.085901 8.745921
## 62 5.545177 6.265872
## 63 6.652863 8.069530
## 64 7.249215 7.924000
## 65 6.297109 7.535350
## 66 6.773080 8.444214
## 67 7.160069 9.738082
## 68 7.129298 8.162488
## 69 6.774224 8.581688
## 70 6.829794 8.100526
## 71 6.682108 7.857094
## 72 6.633318 6.726713
## 73 6.396930 8.311079
## 74 6.898715 7.831459
## 75 7.358831 9.255792
## 76 6.814543 7.894467
## 77 7.215240 7.896627
## 78 6.551080 8.807218
## 79 6.608001 8.492880
## 80 7.000334 7.927361
## 81 6.859615 7.185690
## 82 6.089045 8.134027
## 83 6.388561 7.559924
## 84 6.972606 9.853272
## 85 7.168580 8.471840
## 86 7.494430 10.099547
## 87 8.329175 7.892975
## 88 7.197435 8.407601
## 89 7.467371 9.611268
## 90 6.815640 7.873369
## 91 7.545390 7.885893
## 92 6.725034 6.978400
## 93 7.040536 7.854497
## 94 7.055313 8.364927
## 95 7.156956 9.807197
## 96 7.653969 9.204765
## 97 6.946014 9.450852
## 98 6.899723 7.566104
## 99 7.133296 8.004098
## 100 6.580639 7.662562
## 101 7.208601 8.345669
## 102 7.237778 9.046597
## 103 7.126891 9.411058
## 104 7.346010 9.324864
## 105 7.673223 8.677542
## 106 7.520776 8.731304
## 107 6.350886 8.023946
## 108 8.800867 9.098962
## 109 6.865891 8.305286
## 110 6.416732 8.193925
## 111 7.506592 7.493373
## 112 7.252054 8.810326
## 113 6.933423 7.682621
## 114 7.160069 8.449771
## 115 6.684612 8.123261
## 116 7.016610 9.012499
## 117 7.396949 8.437500
## 118 7.554859 8.848509
## 119 6.903747 8.798304
## 120 6.822197 10.302163
## 121 7.139660 8.561306
## 122 6.959399 8.199464
## 123 7.107426 11.489144
## 124 7.460490 9.282065
## 125 8.052615 9.233695
## 126 7.549609 9.787106
## 127 6.056784 8.725621
## 128 7.438384 8.821024
## 129 5.886104 5.699440
## 130 6.129050 6.666066
## 131 7.200425 7.221544
## 132 6.591674 7.633418
## 133 5.407172 5.201256
## 134 7.650169 9.239900
## 135 6.986567 9.326522
## 136 7.484931 8.554797
## 137 6.673298 6.884180
## 138 7.645876 8.945268
## 139 7.360740 8.764991
## 140 6.951772 7.978448
## 141 7.434848 8.108593
## 142 6.115892 5.165928
## 143 7.029973 7.430292
## 144 7.195937 6.242418
## 145 7.203405 8.017208
## 146 7.368340 8.452527
## 147 7.573531 9.034570
## 148 7.389564 8.940210
## 149 7.390182 9.767313
## 150 6.874198 8.662350
## 151 7.266129 8.838726
## 152 7.115582 8.780034
## 153 6.646390 7.906768
## 154 7.374002 7.923674
## 155 7.357556 8.552831
## 156 6.902743 7.187657
## 157 6.981935 8.574707
## 158 7.057037 8.878636
## 159 7.244942 9.407838
## 160 7.027315 8.969987
## 161 8.028781 8.249471
## 162 6.593045 7.012656
## 163 6.591674 7.237994
## 164 9.326612 8.707466
## 165 6.855409 9.847341
## 166 8.201385 8.274229
## 167 7.314553 8.147230
## 168 6.693324 7.351479
## 169 6.569481 6.869846
## 170 7.305860 10.159988
## 171 6.601230 6.448098
## 172 7.111512 7.455241
## 173 6.297109 7.889572
## 174 9.603868 7.677493
## 175 6.791222 7.868101
## 176 7.394493 9.020426
## 177 7.786552 10.699259
## 178 7.717796 6.639483
## 179 6.668228 8.550144
## 180 6.161207 7.677539
## 181 6.084499 7.869822
## 182 6.142037 7.769674
## 183 6.386879 7.917172
## 184 6.533789 7.765527
## 185 6.408529 8.568267
## 186 6.452049 8.655023
## 187 6.276643 7.906510
## 188 6.089045 8.103706
## 189 6.542472 8.169761
## 190 6.253829 8.211076
## 191 6.629363 8.289565
## 192 6.504288 8.005033
## 193 6.688354 8.337731
## 194 6.214608 7.869516
## 195 6.313548 7.632643
## 196 6.021023 7.707737
## 197 7.003066 9.155894
## 198 6.865891 8.238325
## 199 5.808143 7.778504
## 200 6.220590 8.217492
## 201 6.104793 7.252550
## 202 6.595780 8.476392
## 203 6.579251 7.479808
## 204 6.694562 8.881628
## 205 6.835185 7.319269
## 206 6.263398 7.000426
## 207 6.489205 8.421255
## 208 6.318968 7.612337
## 209 6.439351 7.274133
# 1. Count the number of firms in the finance, industrial, and consumer products industries.
ceosal1 %>%
filter(indus == 1) %>%
select(indus)
## indus
## 1 1
## 2 1
## 3 1
## 4 1
## 5 1
## 6 1
## 7 1
## 8 1
## 9 1
## 10 1
## 11 1
## 12 1
## 13 1
## 14 1
## 15 1
## 16 1
## 17 1
## 18 1
## 19 1
## 20 1
## 21 1
## 22 1
## 23 1
## 24 1
## 25 1
## 26 1
## 27 1
## 28 1
## 29 1
## 30 1
## 31 1
## 32 1
## 33 1
## 34 1
## 35 1
## 36 1
## 37 1
## 38 1
## 39 1
## 40 1
## 41 1
## 42 1
## 43 1
## 44 1
## 45 1
## 46 1
## 47 1
## 48 1
## 49 1
## 50 1
## 51 1
## 52 1
## 53 1
## 54 1
## 55 1
## 56 1
## 57 1
## 58 1
## 59 1
## 60 1
## 61 1
## 62 1
## 63 1
## 64 1
## 65 1
## 66 1
## 67 1
#ANSWER: 67 firms in the industrial industry.
ceosal1 %>%
filter(finance == 1) %>%
select(finance)
## finance
## 1 1
## 2 1
## 3 1
## 4 1
## 5 1
## 6 1
## 7 1
## 8 1
## 9 1
## 10 1
## 11 1
## 12 1
## 13 1
## 14 1
## 15 1
## 16 1
## 17 1
## 18 1
## 19 1
## 20 1
## 21 1
## 22 1
## 23 1
## 24 1
## 25 1
## 26 1
## 27 1
## 28 1
## 29 1
## 30 1
## 31 1
## 32 1
## 33 1
## 34 1
## 35 1
## 36 1
## 37 1
## 38 1
## 39 1
## 40 1
## 41 1
## 42 1
## 43 1
## 44 1
## 45 1
## 46 1
#ANSWER: 46 firms in the finance industry
ceosal1 %>%
filter(consprod==1) %>%
select(consprod)
## consprod
## 1 1
## 2 1
## 3 1
## 4 1
## 5 1
## 6 1
## 7 1
## 8 1
## 9 1
## 10 1
## 11 1
## 12 1
## 13 1
## 14 1
## 15 1
## 16 1
## 17 1
## 18 1
## 19 1
## 20 1
## 21 1
## 22 1
## 23 1
## 24 1
## 25 1
## 26 1
## 27 1
## 28 1
## 29 1
## 30 1
## 31 1
## 32 1
## 33 1
## 34 1
## 35 1
## 36 1
## 37 1
## 38 1
## 39 1
## 40 1
## 41 1
## 42 1
## 43 1
## 44 1
## 45 1
## 46 1
## 47 1
## 48 1
## 49 1
## 50 1
## 51 1
## 52 1
## 53 1
## 54 1
## 55 1
## 56 1
## 57 1
## 58 1
## 59 1
## 60 1
#ANSWER: 60 firms in the consumer products industry
#2. Does finance, industrial, or consumer product firms have higher ROE?
ceosal1 %>%
filter(indus == 1) %>%
select(roe) %>%
summarise(max=max(roe), total=sum(roe), av=mean(roe))
## max total av
## 1 56.3 1162.5 17.35075
ceosal1 %>%
filter(finance == 1) %>%
select(roe) %>%
summarise(max=max(roe), total=sum(roe), av=mean(roe))
## max total av
## 1 33.3 659.1 14.32826
ceosal1 %>%
filter(consprod == 1) %>%
select(roe) %>%
summarise(max=max(roe), total=sum(roe), av=mean(roe))
## max total av
## 1 48.1 1359.3 22.655
#ANSWER: The highest average ROE is for the consumer products industry, the highest ROE overall is in the Industrial and the highest total ROE is in the consumer products industry.
#3. How many finance firms increased the salary by more than 20%?
ceosal1 %>%
filter(finance == 1 & pcsalary > 20)
## salary pcsalary sales roe pcroe ros indus finance consprod utility
## 1 1097 143 2772.1 8.9 595.6 20 0 1 0 0
## 2 2109 42 9944.4 11.6 -42.2 23 0 1 0 0
## 3 992 42 1931.6 21.4 -9.2 52 0 1 0 0
## 4 1391 25 8489.6 9.4 -27.3 26 0 1 0 0
## 5 1245 25 12222.8 19.0 -20.9 52 0 1 0 0
## 6 959 49 4045.2 17.3 -7.8 51 0 1 0 0
## 7 612 39 3618.9 33.3 -10.5 68 0 1 0 0
## 8 1820 69 1796.1 22.8 12.7 115 0 1 0 0
## lsalary lsales
## 1 7.000334 7.927361
## 2 7.653969 9.204765
## 3 6.899723 7.566104
## 4 7.237778 9.046597
## 5 7.126891 9.411058
## 6 6.865891 8.305286
## 7 6.416732 8.193925
## 8 7.506592 7.493373
#ANSWER: 8 finance firms increased the salary by more than 20%
#4. Firms in which industry gave the highest salary hikes?
ceosal1 %>%
filter(indus == 1) %>%
select(pcsalary) %>%
summarise(max=max(pcsalary))
## max
## 1 185
ceosal1 %>%
filter(finance == 1) %>%
select(pcsalary) %>%
summarise(max=max(pcsalary))
## max
## 1 143
ceosal1 %>%
filter(consprod == 1) %>%
select(pcsalary) %>%
summarise(max=max(pcsalary))
## max
## 1 212
#ANSWER: Firms in the consumer products industry gave the highest salary hikes.
#5. Firms in which industry have the highest sales/salary ratio?
ceosal1 %>% dplyr::mutate(
ssratio=sales/salary
) %>%
filter(indus == 1) %>%
select(ssratio) %>%
summarise(max=max(ssratio))
## max
## 1 34.03254
ceosal1 %>% dplyr::mutate(
ssratio=sales/salary
) %>%
filter(finance == 1) %>%
select(ssratio) %>%
summarise(max=max(ssratio))
## max
## 1 17.82615
ceosal1 %>% dplyr::mutate(
ssratio=sales/salary
) %>%
filter(consprod == 1) %>%
select(ssratio) %>%
summarise(max=max(ssratio))
## max
## 1 79.97535
#ANSWER: Firms in the consumer products industry have the highest sales/salary ratio
#6. Filter top 20 firms by ROE, then filter top 10 firms by ROS, then filter bottom 5 firms by salary.
ceosal1 %>%
arrange(desc(roe)) %>%
slice(1:20) %>%
arrange(desc(ros)) %>%
slice(1:10) %>%
arrange(salary) %>%
slice(1:5)
## salary pcsalary sales roe pcroe ros indus finance consprod utility lsalary
## 1 543 112 2669.3 30.5 24.9 323 0 0 1 0 6.297109
## 2 967 -27 5781.1 28.5 -11.0 119 0 0 1 0 6.874198
## 3 1130 1 1686.3 44.5 3.4 99 0 0 1 0 7.029973
## 4 1431 0 6896.2 43.9 3.5 110 0 0 1 0 7.266129
## 5 1585 5 4686.9 35.1 2.2 111 0 0 1 0 7.368340
## lsales
## 1 7.889572
## 2 8.662350
## 3 7.430292
## 4 8.838726
## 5 8.452527
#7. Five number summary (sales)
ceosal1 %>% summarise(min=min(sales),Q1= quantile(sales, 0.25),median= median(sales), Q3= quantile(sales, 0.75),max=max(sales))
## min Q1 median Q3 max
## 1 175.2 2210.3 3705.2 7177 97649.9