Week 6: Project 2 - Data Transformation
Md Asadul Islam
2024-10-13
Wide Dataset 1 - FL - Housing Insecurity Indicators Dataset
This dataset of potential indicators to assess peopleās risk of losing their homes in Florida.The author intended to use the dataset to understand which groups of people are at higher risk of lossing their homes in Florida.The dataset and analysis is as done below:
# Reading the dataset
housing_dt <- read.csv("C:\\Users\\HP\\Downloads\\data_1-FL.csv", sep=",", )
# View the columns
colnames(housing_dt)## [1] "geoid"
## [2] "geoid_year"
## [3] "state"
## [4] "county"
## [5] "state_fips_code"
## [6] "county_fips_code"
## [7] "b19083_001e"
## [8] "b19083_001m"
## [9] "economic_distress_pop_agg"
## [10] "economic_distress_simple_agg"
## [11] "investment_areas"
## [12] "opzone"
## [13] "b23025_002e"
## [14] "b23025_002m"
## [15] "b23025_004e"
## [16] "b23025_004m"
## [17] "b23025_005e"
## [18] "b23025_005m"
## [19] "b23025_006e"
## [20] "b23025_006m"
## [21] "s1701_c03_001e"
## [22] "s1701_c03_001m"
## [23] "s1701_c03_002e"
## [24] "s1701_c03_002m"
## [25] "s1701_c03_003e"
## [26] "s1701_c03_003m"
## [27] "s1701_c03_004e"
## [28] "s1701_c03_004m"
## [29] "s1701_c03_006e"
## [30] "s1701_c03_006m"
## [31] "s1701_c03_007e"
## [32] "s1701_c03_007m"
## [33] "s1701_c03_008e"
## [34] "s1701_c03_008m"
## [35] "s1701_c03_009e"
## [36] "s1701_c03_009m"
## [37] "s1701_c03_010e"
## [38] "s1701_c03_010m"
## [39] "s1701_c03_011e"
## [40] "s1701_c03_011m"
## [41] "s1701_c03_012e"
## [42] "s1701_c03_012m"
## [43] "s1701_c03_013e"
## [44] "s1701_c03_013m"
## [45] "s1701_c03_014e"
## [46] "s1701_c03_014m"
## [47] "s1701_c03_015e"
## [48] "s1701_c03_015m"
## [49] "s1701_c03_016e"
## [50] "s1701_c03_016m"
## [51] "s1701_c03_017e"
## [52] "s1701_c03_017m"
## [53] "s1701_c03_018e"
## [54] "s1701_c03_018m"
## [55] "s1701_c03_019e"
## [56] "s1701_c03_019m"
## [57] "s1701_c03_020e"
## [58] "s1701_c03_020m"
## [59] "s1701_c03_021e"
## [60] "s1701_c03_021m"
## [61] "s1903_c03_001e"
## [62] "s1903_c03_001m"
## [63] "s1903_c03_002e"
## [64] "s1903_c03_002m"
## [65] "s1903_c03_003e"
## [66] "s1903_c03_003m"
## [67] "s1903_c03_004e"
## [68] "s1903_c03_004m"
## [69] "s1903_c03_005e"
## [70] "s1903_c03_005m"
## [71] "s1903_c03_006e"
## [72] "s1903_c03_006m"
## [73] "s1903_c03_007e"
## [74] "s1903_c03_007m"
## [75] "s1903_c03_008e"
## [76] "s1903_c03_008m"
## [77] "s1903_c03_009e"
## [78] "s1903_c03_009m"
## [79] "s1903_c03_010e"
## [80] "s1903_c03_010m"
## [81] "s1903_c03_011e"
## [82] "s1903_c03_011m"
## [83] "s1903_c03_012e"
## [84] "s1903_c03_012m"
## [85] "s1903_c03_013e"
## [86] "s1903_c03_013m"
## [87] "s1903_c03_014e"
## [88] "s1903_c03_014m"
## [89] "s2001_c01_002e"
## [90] "s2001_c01_002m"
## [91] "s2001_c03_002e"
## [92] "s2001_c03_002m"
## [93] "s2001_c05_002e"
## [94] "s2001_c05_002m"
## [95] "s2701_c03_001e"
## [96] "s2701_c03_001m"
## [97] "s2701_c03_002e"
## [98] "s2701_c03_002m"
## [99] "s2701_c03_003e"
## [100] "s2701_c03_003m"
## [101] "s2701_c03_004e"
## [102] "s2701_c03_004m"
## [103] "s2701_c03_005e"
## [104] "s2701_c03_005m"
## [105] "s2701_c03_006e"
## [106] "s2701_c03_006m"
## [107] "s2701_c03_007e"
## [108] "s2701_c03_007m"
## [109] "s2701_c03_008e"
## [110] "s2701_c03_008m"
## [111] "s2701_c03_009e"
## [112] "s2701_c03_009m"
## [113] "s2701_c03_010e"
## [114] "s2701_c03_010m"
## [115] "s2701_c03_011e"
## [116] "s2701_c03_011m"
## [117] "s2701_c03_012e"
## [118] "s2701_c03_012m"
## [119] "s2701_c03_013e"
## [120] "s2701_c03_013m"
## [121] "s2701_c03_014e"
## [122] "s2701_c03_014m"
## [123] "s2701_c03_015e"
## [124] "s2701_c03_015m"
## [125] "s2701_c03_016e"
## [126] "s2701_c03_016m"
## [127] "s2701_c03_017e"
## [128] "s2701_c03_017m"
## [129] "s2701_c03_018e"
## [130] "s2701_c03_018m"
## [131] "s2701_c03_019e"
## [132] "s2701_c03_019m"
## [133] "s2701_c03_020e"
## [134] "s2701_c03_020m"
## [135] "s2701_c03_021e"
## [136] "s2701_c03_021m"
## [137] "s2701_c03_022e"
## [138] "s2701_c03_022m"
## [139] "s2701_c03_023e"
## [140] "s2701_c03_023m"
## [141] "s2701_c03_024e"
## [142] "s2701_c03_024m"
## [143] "s2701_c05_001e"
## [144] "s2701_c05_001m"
## [145] "s2701_c05_002e"
## [146] "s2701_c05_002m"
## [147] "s2701_c05_003e"
## [148] "s2701_c05_003m"
## [149] "s2701_c05_004e"
## [150] "s2701_c05_004m"
## [151] "s2701_c05_005e"
## [152] "s2701_c05_005m"
## [153] "s2701_c05_006e"
## [154] "s2701_c05_006m"
## [155] "s2701_c05_007e"
## [156] "s2701_c05_007m"
## [157] "s2701_c05_008e"
## [158] "s2701_c05_008m"
## [159] "s2701_c05_009e"
## [160] "s2701_c05_009m"
## [161] "s2701_c05_010e"
## [162] "s2701_c05_010m"
## [163] "s2701_c05_011e"
## [164] "s2701_c05_011m"
## [165] "s2701_c05_012e"
## [166] "s2701_c05_012m"
## [167] "s2701_c05_013e"
## [168] "s2701_c05_013m"
## [169] "s2701_c05_014e"
## [170] "s2701_c05_014m"
## [171] "s2701_c05_015e"
## [172] "s2701_c05_015m"
## [173] "s2701_c05_016e"
## [174] "s2701_c05_016m"
## [175] "s2701_c05_017e"
## [176] "s2701_c05_017m"
## [177] "s2701_c05_018e"
## [178] "s2701_c05_018m"
## [179] "s2701_c05_019e"
## [180] "s2701_c05_019m"
## [181] "s2701_c05_020e"
## [182] "s2701_c05_020m"
## [183] "s2701_c05_021e"
## [184] "s2701_c05_021m"
## [185] "s2701_c05_022e"
## [186] "s2701_c05_022m"
## [187] "s2701_c05_023e"
## [188] "s2701_c05_023m"
## [189] "b23025_003e"
## [190] "b23025_003m"
## [191] "b23025_007e"
## [192] "b23025_007m"
## [193] "cancer"
## [194] "d2_cancer"
## [195] "d5_cancer"
## [196] "d2_dslpm"
## [197] "d5_dslpm"
## [198] "dslpm"
## [199] "d2_ldpnt"
## [200] "d5_ldpnt"
## [201] "pre1960pct"
## [202] "d2_ozone"
## [203] "d5_ozone"
## [204] "ozone"
## [205] "d2_pm25"
## [206] "d5_pm25"
## [207] "pm25"
## [208] "d2_pnpl"
## [209] "d5_pnpl"
## [210] "pnpl"
## [211] "d2_prmp"
## [212] "d5_prmp"
## [213] "prmp"
## [214] "d2_ptraf"
## [215] "d5_ptraf"
## [216] "ptraf"
## [217] "d2_ptsdf"
## [218] "d5_ptsdf"
## [219] "ptsdf"
## [220] "d2_pwdis"
## [221] "d5_pwdis"
## [222] "pwdis"
## [223] "d2_resp"
## [224] "d5_resp"
## [225] "resp"
## [226] "d2_rsei_air"
## [227] "d5_rsei_air"
## [228] "rsei_air"
## [229] "d2_ust"
## [230] "d5_ust"
## [231] "ust"
## [232] "energy_burden"
## [233] "energy_burden_percentile"
## [234] "expected_agricultural_loss_rate_natural_hazards_risk_index"
## [235] "expected_agricultural_loss_rate_natural_hazards_risk_index_percentile"
## [236] "expected_building_loss_rate_natural_hazards_risk_index"
## [237] "expected_building_loss_rate_natural_hazards_risk_index_percentile"
## [238] "expected_population_loss_rate_natural_hazards_risk_index"
## [239] "expected_population_loss_rate_natural_hazards_risk_index_percentile"
## [240] "share_of_properties_at_risk_of_fire_in_30_years"
## [241] "share_of_properties_at_risk_of_fire_in_30_years_percentile"
## [242] "share_of_properties_at_risk_of_flood_in_30_years"
## [243] "share_of_properties_at_risk_of_flood_in_30_years_percentile"
## [244] "p_cancer"
## [245] "p_d2_cancer"
## [246] "p_d5_cancer"
## [247] "p_d2_dslpm"
## [248] "p_d5_dslpm"
## [249] "p_dslpm"
## [250] "p_d2_ldpnt"
## [251] "p_d5_ldpnt"
## [252] "p_ldpnt"
## [253] "p_d2_ozone"
## [254] "p_d5_ozone"
## [255] "p_ozone"
## [256] "p_d2_pm25"
## [257] "p_d5_pm25"
## [258] "p_pm25"
## [259] "p_d2_pnpl"
## [260] "p_d5_pnpl"
## [261] "p_pnpl"
## [262] "p_d2_prmp"
## [263] "p_d5_prmp"
## [264] "p_prmp"
## [265] "p_d2_ptraf"
## [266] "p_d5_ptraf"
## [267] "p_ptraf"
## [268] "p_d2_ptsdf"
## [269] "p_d5_ptsdf"
## [270] "p_ptsdf"
## [271] "p_d2_pwdis"
## [272] "p_d5_pwdis"
## [273] "p_pwdis"
## [274] "p_d2_resp"
## [275] "p_d5_resp"
## [276] "p_resp"
## [277] "p_d2_rsei_air"
## [278] "p_d5_rsei_air"
## [279] "p_rsei_air"
## [280] "p_d2_ust"
## [281] "p_d5_ust"
## [282] "p_ust"
## [283] "pre1960"
## [284] "dp05_0035pe"
## [285] "dp05_0037pe"
## [286] "dp05_0038pe"
## [287] "dp05_0039pe"
## [288] "dp05_0044pe"
## [289] "dp05_0052pe"
## [290] "dp05_0057pe"
## [291] "s0101_c01_032e"
## [292] "s0101_c01_032m"
## [293] "s0101_c03_032e"
## [294] "s0101_c03_032m"
## [295] "s0101_c05_032e"
## [296] "s0101_c05_032m"
## [297] "s0101_c02_020e"
## [298] "s0101_c02_020m"
## [299] "s0101_c04_020e"
## [300] "s0101_c04_020m"
## [301] "s0101_c06_020e"
## [302] "s0101_c06_020m"
## [303] "s0101_c02_021e"
## [304] "s0101_c02_021m"
## [305] "s0101_c04_021e"
## [306] "s0101_c04_021m"
## [307] "s0101_c06_021e"
## [308] "s0101_c06_021m"
## [309] "s0101_c02_022e"
## [310] "s0101_c02_022m"
## [311] "s0101_c04_022e"
## [312] "s0101_c04_022m"
## [313] "s0101_c06_022e"
## [314] "s0101_c06_022m"
## [315] "s0101_c02_023e"
## [316] "s0101_c02_023m"
## [317] "s0101_c04_023e"
## [318] "s0101_c04_023m"
## [319] "s0101_c06_023e"
## [320] "s0101_c06_023m"
## [321] "s0101_c02_024e"
## [322] "s0101_c02_024m"
## [323] "s0101_c04_024e"
## [324] "s0101_c04_024m"
## [325] "s0101_c06_024e"
## [326] "s0101_c06_024m"
## [327] "s0101_c02_025e"
## [328] "s0101_c02_025m"
## [329] "s0101_c04_025e"
## [330] "s0101_c04_025m"
## [331] "s0101_c06_025e"
## [332] "s0101_c06_025m"
## [333] "s0101_c02_026e"
## [334] "s0101_c02_026m"
## [335] "s0101_c04_026e"
## [336] "s0101_c04_026m"
## [337] "s0101_c06_026e"
## [338] "s0101_c06_026m"
## [339] "s0101_c02_027e"
## [340] "s0101_c02_027m"
## [341] "s0101_c04_027e"
## [342] "s0101_c04_027m"
## [343] "s0101_c06_027e"
## [344] "s0101_c06_027m"
## [345] "s0101_c02_028e"
## [346] "s0101_c02_028m"
## [347] "s0101_c04_028e"
## [348] "s0101_c04_028m"
## [349] "s0101_c06_028e"
## [350] "s0101_c06_028m"
## [351] "s0101_c02_029e"
## [352] "s0101_c02_029m"
## [353] "s0101_c04_029e"
## [354] "s0101_c04_029m"
## [355] "s0101_c06_029e"
## [356] "s0101_c06_029m"
## [357] "s0101_c02_030e"
## [358] "s0101_c02_030m"
## [359] "s0101_c04_030e"
## [360] "s0101_c04_030m"
## [361] "s0101_c06_030e"
## [362] "s0101_c06_030m"
## [363] "s0101_c02_031e"
## [364] "s0101_c02_031m"
## [365] "s0101_c04_031e"
## [366] "s0101_c04_031m"
## [367] "s0101_c06_031e"
## [368] "s0101_c06_031m"
## [369] "loan_amount"
## [370] "median_mortgage_amount"
## [371] "median_prop_value"
## [372] "median_sba504_loan_amount"
## [373] "median_sba7a_loan_amount"
## [374] "num_mortgage"
## [375] "num_mortgage_denials"
## [376] "num_mortgage_originated"
## [377] "number_of_sba504_loans"
## [378] "number_of_sba7a_loans"
## [379] "qct"
## [380] "s2503_c01_024e"
## [381] "s2503_c01_024m"
## [382] "s2503_c03_024e"
## [383] "s2503_c03_024m"
## [384] "s2503_c05_024e"
## [385] "s2503_c05_024m"## 'data.frame': 1605 obs. of 385 variables:
## $ geoid : num 1.2e+10 1.2e+10 1.2e+10 1.2e+10 1.2e+10 ...
## $ geoid_year : int 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 ...
## $ state : int 12 12 12 12 12 12 12 12 12 12 ...
## $ county : int 1 1 1 1 1 1 1 1 1 1 ...
## $ state_fips_code : int 12 12 12 12 12 12 12 12 12 12 ...
## $ county_fips_code : int 1 1 1 1 1 1 1 1 1 1 ...
## $ b19083_001e : num 0.393 0.547 0.364 0.366 0.569 ...
## $ b19083_001m : num 0.0724 0.0649 0.0878 0.0894 0.0634 0.0479 0.0615 0.0825 0.0898 0.0697 ...
## $ economic_distress_pop_agg : chr "YES" "YES" "YES" "YES" ...
## $ economic_distress_simple_agg : chr "YES" "YES" "YES" "YES" ...
## $ investment_areas : chr "YES" "YES" "YES" "YES" ...
## $ opzone : int 1 0 0 0 0 0 0 0 1 0 ...
## $ b23025_002e : int 2958 1431 962 2413 1145 3851 2295 1266 556 3119 ...
## $ b23025_002m : int 768 326 215 351 274 654 355 254 219 453 ...
## $ b23025_004e : int 2698 1337 906 2356 1095 3717 2141 1175 514 3064 ...
## $ b23025_004m : int 708 313 215 351 263 648 334 253 212 451 ...
## $ b23025_005e : int 260 94 52 42 50 134 154 91 42 55 ...
## $ b23025_005m : int 209 95 32 53 47 86 107 72 48 75 ...
## $ b23025_006e : int 0 0 4 15 0 0 0 0 0 0 ...
## $ b23025_006m : int 21 15 10 26 15 21 21 15 15 21 ...
## $ s1701_c03_001e : num 20.4 51 21.7 13.5 35.7 35.3 19.4 16.5 8.2 8.9 ...
## $ s1701_c03_001m : num 9.8 13.5 11.4 6 7.9 8.7 13.1 6.3 6.3 8.8 ...
## $ s1701_c03_002e : num 41 36.6 17.5 15.9 0 7.6 33.3 12.2 37 16.2 ...
## $ s1701_c03_002m : num 18.9 28.2 18.4 18.6 38.8 13.2 26.8 12.6 53.7 19.6 ...
## $ s1701_c03_003e : num 68.9 16.4 27.6 18.9 0 12.9 39.3 14.3 100 8.7 ...
## $ s1701_c03_003m : num 41.6 30 28.1 27.6 50.1 21.2 37.5 20.3 70.8 12.8 ...
## $ s1701_c03_004e : num 36.8 53.4 0 13.8 0 0 32.2 11.5 0 18.6 ...
## $ s1701_c03_004m : num 18.5 37.8 55.5 14.4 61.3 19 25.7 15.5 54.2 22.1 ...
## $ s1701_c03_006e : num 10.9 56 22.6 14.5 38.5 39.5 13.2 19.8 5.1 6.1 ...
## $ s1701_c03_006m : num 8.7 13.8 12 5.9 8.4 9.2 10.2 8.7 4.8 5.4 ...
## $ s1701_c03_007e : num 11.8 60.2 21.2 16.8 44.9 45.4 1.1 15.7 6.6 7.6 ...
## $ s1701_c03_007m : num 13.3 13.6 13.1 7.6 9.4 10.9 2 9.3 10.9 11 ...
## $ s1701_c03_008e : num 9.8 36.6 24.4 12 12.1 14.7 17.9 22 4.5 5.6 ...
## $ s1701_c03_008m : num 9 24.4 21.4 8.5 8.9 11.6 13.5 12.6 4.9 4.8 ...
## $ s1701_c03_009e : num 2.08e+01 2.60 -6.67e+08 1.70 2.39e+01 ...
## $ s1701_c03_009m : num 1.12e+01 4.80 -2.22e+08 2.50 3.04e+01 ...
## $ s1701_c03_010e : num 2.72e+01 0.00 -6.67e+08 6.00e-01 0.00 ...
## $ s1701_c03_010m : num 1.36e+01 2.71e+01 -2.22e+08 1.80 5.68e+01 ...
## $ s1701_c03_011e : num 17.9 57.5 16.9 14.2 30.4 26 25.8 8.4 12.3 12.2 ...
## $ s1701_c03_011m : num 10.8 15 12.9 7.4 11 10.3 17.6 6 8.2 12.7 ...
## $ s1701_c03_012e : num 22.5 44.4 26 12.8 42 43.4 12.8 25.3 3.8 5.1 ...
## $ s1701_c03_012m : num 14.2 20.3 15.6 7.4 10.5 12.7 9.1 10.4 5.3 4.5 ...
## $ s1701_c03_013e : num 2.9 59.1 13.6 8.7 50.7 39.4 7.5 13.3 9.2 1.7 ...
## $ s1701_c03_013m : num 3.2 16.9 11.8 5.3 9 10.3 5.2 6.4 7.2 1.6 ...
## $ s1701_c03_014e : num 4.67e+01 2.34e+01 -6.67e+08 1.30e+01 8.90 ...
## $ s1701_c03_014m : num 2.15e+01 1.87e+01 -2.22e+08 1.65e+01 1.56e+01 ...
## $ s1701_c03_015e : num 0.00 -6.67e+08 -6.67e+08 1.00e+02 1.00e+02 ...
## $ s1701_c03_015m : num 5.82e+01 -2.22e+08 -2.22e+08 7.51e+01 1.00e+02 ...
## $ s1701_c03_016e : num 19.3 36.8 18.3 17.2 3 ...
## $ s1701_c03_016m : num 40.4 25.9 15.1 23.2 4.2 ...
## $ s1701_c03_017e : num 0.00 -6.67e+08 0.00 0.00 -6.67e+08 ...
## $ s1701_c03_017m : num 7.36e+01 -2.22e+08 1.00e+02 4.75e+01 -2.22e+08 ...
## $ s1701_c03_018e : num 7.6 84.1 0 0 20 ...
## $ s1701_c03_018m : num 20.5 27.2 69.5 40.1 34.9 ...
## $ s1701_c03_019e : num 42.5 51.7 55.4 41.7 21.1 33.7 7.4 53.1 0 0 ...
## $ s1701_c03_019m : num 23.6 27 44.2 33.1 24.6 33.1 18.6 41.1 92 7.7 ...
## $ s1701_c03_020e : num 7.4 65.2 44.6 22.7 29.9 37.5 13.3 42.9 0 0 ...
## $ s1701_c03_020m : num 8.4 24.7 38.5 20.6 15.3 16.1 18.2 29.1 59.7 13.9 ...
## $ s1701_c03_021e : num 3.2 57.9 14.7 6.1 51.6 39.6 5.7 12.1 9.4 1.7 ...
## $ s1701_c03_021m : num 3.6 18.3 12.9 4.1 9.7 11.5 4.2 6.2 7.4 1.6 ...
## $ s1903_c03_001e : int 50417 20907 27647 55571 36014 32944 63906 55417 48190 93147 ...
## $ s1903_c03_001m : int 16882 5509 3150 8225 17867 4779 11072 5539 29161 10802 ...
## $ s1903_c03_002e : int 66719 17127 28500 54798 24185 28579 72104 56450 49583 93678 ...
## $ s1903_c03_002m : int 24899 7519 5552 7157 14537 4078 12302 7343 31628 8368 ...
## $ s1903_c03_003e : int 45373 37525 -666666666 77741 28306 52775 56761 34205 -666666666 56117 ...
## $ s1903_c03_003m : int 26587 34880 -222222222 43435 21092 14438 26194 11015 -222222222 12525 ...
## $ s1903_c03_004e : int -666666666 -666666666 -666666666 -666666666 -666666666 -666666666 -666666666 -666666666 -666666666 -666666666 ...
## $ s1903_c03_004m : int -222222222 -222222222 -222222222 -222222222 -222222222 -222222222 -222222222 -222222222 -222222222 -222222222 ...
## $ s1903_c03_005e : int -666666666 -666666666 26771 2499 102500 44398 -666666666 -666666666 -666666666 -666666666 ...
## $ s1903_c03_005m : int -222222222 -222222222 1165 -333333333 90869 36334 -222222222 -222222222 -222222222 -222222222 ...
## $ s1903_c03_006e : int -666666666 -666666666 -666666666 -666666666 -666666666 -666666666 -666666666 -666666666 -666666666 -666666666 ...
## $ s1903_c03_006m : int -222222222 -222222222 -222222222 -222222222 -222222222 -222222222 -222222222 -222222222 -222222222 -222222222 ...
## $ s1903_c03_007e : int -666666666 2499 -666666666 -666666666 51053 -666666666 182831 -666666666 -666666666 -666666666 ...
## $ s1903_c03_007m : int -222222222 -333333333 -222222222 -222222222 35258 -222222222 61167 -222222222 -222222222 -222222222 ...
## $ s1903_c03_008e : int -666666666 18929 -666666666 -666666666 -666666666 55648 -666666666 -666666666 -666666666 100573 ...
## $ s1903_c03_008m : int -222222222 14827 -222222222 -222222222 -222222222 30454 -222222222 -222222222 -222222222 55350 ...
## $ s1903_c03_009e : int 81300 31161 -666666666 73750 -666666666 29909 -666666666 -666666666 -666666666 111944 ...
## $ s1903_c03_009m : int 33784 26940 -222222222 41272 -222222222 14539 -222222222 -222222222 -222222222 57603 ...
## $ s1903_c03_010e : int 66172 17083 27500 55237 24130 28446 73000 56983 52500 93678 ...
## $ s1903_c03_010m : int 26244 7295 7282 7459 12922 4450 12043 9380 28786 9167 ...
## $ s1903_c03_011e : int -666666666 15078 -666666666 65313 -666666666 13871 -666666666 -666666666 -666666666 -666666666 ...
## $ s1903_c03_011m : int -222222222 4707 -222222222 55848 -222222222 10376 -222222222 -222222222 -222222222 -222222222 ...
## $ s1903_c03_012e : int 69699 24506 29375 54583 51316 41419 65938 61682 34485 101016 ...
## $ s1903_c03_012m : int 26246 20629 4086 21810 11627 16657 21412 4430 28534 23691 ...
## $ s1903_c03_013e : int 47074 -666666666 -666666666 61629 49844 44060 98250 -666666666 52944 93420 ...
## $ s1903_c03_013m : int 44326 -222222222 -222222222 22553 12375 5045 33511 -222222222 37438 13245 ...
## $ s1903_c03_014e : int 33958 16802 -666666666 35489 -666666666 37625 47957 54805 48409 62639 ...
## $ s1903_c03_014m : int 23652 1825 -222222222 4058 -222222222 20893 13840 3683 31538 30837 ...
## $ s2001_c01_002e : int 39079 12184 4788 36190 26900 25818 37083 36669 34643 51610 ...
## $ s2001_c01_002m : int 1404 6121 1029 3440 7263 4377 15846 3637 6933 6843 ...
## $ s2001_c03_002e : int 43342 12710 5429 34968 32594 31768 47300 40114 34655 57143 ...
## $ s2001_c03_002m : int 1947 5676 1925 19569 13799 2968 18857 7533 8678 8571 ...
## $ s2001_c05_002e : int 38329 -666666666 4196 36353 22500 21859 34156 35024 34630 40640 ...
## $ s2001_c05_002m : int 3660 -222222222 920 3954 11644 6998 7751 3731 12604 15203 ...
## $ s2701_c03_001e : num 92 97.1 97.9 86.3 91.7 90.3 90.8 85.1 83 95.1 ...
## $ s2701_c03_001m : num 5.2 2.4 1.4 5.8 4.9 4.7 5.3 5.3 12.2 2.7 ...
## $ s2701_c03_002e : num 100 100 100 100 100 100 83.8 100 100 89.9 ...
## $ s2701_c03_002m : num 15.8 21 35.6 16.8 50.1 13.6 24.3 28.7 70.8 13.5 ...
## $ s2701_c03_003e : num 94 100 100 95.7 100 81 100 81.1 100 100 ...
## [list output truncated]## geoid geoid_year state county
## Min. :1.200e+10 Min. :2020 Min. :12 Min. : 1.00
## 1st Qu.:1.202e+10 1st Qu.:2020 1st Qu.:12 1st Qu.: 19.00
## Median :1.207e+10 Median :2020 Median :12 Median : 71.00
## Mean :1.206e+10 Mean :2020 Mean :12 Mean : 60.85
## 3rd Qu.:1.210e+10 3rd Qu.:2020 3rd Qu.:12 3rd Qu.: 97.00
## Max. :1.213e+10 Max. :2020 Max. :12 Max. :133.00
##
## state_fips_code county_fips_code b19083_001e b19083_001m
## Min. :12 Min. : 1.00 Min. :-666666666 Min. :-222222222
## 1st Qu.:12 1st Qu.: 19.00 1st Qu.: 0 1st Qu.: 0
## Median :12 Median : 71.00 Median : 0 Median : 0
## Mean :12 Mean : 60.85 Mean : -7476635 Mean : -2492212
## 3rd Qu.:12 3rd Qu.: 97.00 3rd Qu.: 0 3rd Qu.: 0
## Max. :12 Max. :133.00 Max. : 1 Max. : 0
##
## economic_distress_pop_agg economic_distress_simple_agg investment_areas
## Length:1605 Length:1605 Length:1605
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## opzone b23025_002e b23025_002m b23025_004e
## Min. :0.0000 Min. : 0 Min. : 7.0 Min. : 0
## 1st Qu.:0.0000 1st Qu.: 1272 1st Qu.: 274.0 1st Qu.: 1176
## Median :0.0000 Median : 1980 Median : 379.0 Median : 1866
## Mean :0.1059 Mean : 2105 Mean : 422.2 Mean : 1981
## 3rd Qu.:0.0000 3rd Qu.: 2769 3rd Qu.: 526.0 3rd Qu.: 2604
## Max. :1.0000 Max. :10724 Max. :1816.0 Max. :10223
##
## b23025_004m b23025_005e b23025_005m b23025_006e
## Min. : 7.0 Min. : 0.0 Min. : 3.00 Min. : 0.00
## 1st Qu.: 261.0 1st Qu.: 37.0 1st Qu.: 39.00 1st Qu.: 0.00
## Median : 366.0 Median : 81.0 Median : 68.00 Median : 0.00
## Mean : 404.9 Mean :106.4 Mean : 87.43 Mean : 17.99
## 3rd Qu.: 509.0 3rd Qu.:143.0 3rd Qu.:109.00 3rd Qu.: 0.00
## Max. :1684.0 Max. :915.0 Max. :740.00 Max. :1608.00
##
## b23025_006m s1701_c03_001e s1701_c03_001m
## Min. : 3.00 Min. :-666666666 Min. :-222222222
## 1st Qu.: 15.00 1st Qu.: 6 1st Qu.: 4
## Median : 15.00 Median : 11 Median : 6
## Mean : 27.61 Mean : -7476622 Mean : -2492205
## 3rd Qu.: 21.00 3rd Qu.: 18 3rd Qu.: 9
## Max. :518.00 Max. : 77 Max. : 41
##
## s1701_c03_002e s1701_c03_002m s1701_c03_003e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 3 1st Qu.: 8 1st Qu.: 0
## Median : 12 Median : 15 Median : 6
## Mean : -15783991 Mean : -5261319 Mean : -38629267
## 3rd Qu.: 26 3rd Qu.: 22 3rd Qu.: 27
## Max. : 100 Max. : 100 Max. : 100
##
## s1701_c03_003m s1701_c03_004e s1701_c03_004m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 17 1st Qu.: 2 1st Qu.: 9
## Median : 27 Median : 11 Median : 16
## Mean : -12876398 Mean : -18691572 Mean : -6230511
## 3rd Qu.: 38 3rd Qu.: 26 3rd Qu.: 24
## Max. : 100 Max. : 100 Max. : 100
##
## s1701_c03_006e s1701_c03_006m s1701_c03_007e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 6 1st Qu.: 4 1st Qu.: 4
## Median : 10 Median : 6 Median : 10
## Mean : -7476623 Mean : -2492205 Mean : -11214940
## 3rd Qu.: 16 3rd Qu.: 9 3rd Qu.: 18
## Max. : 77 Max. : 44 Max. : 100
##
## s1701_c03_007m s1701_c03_008e s1701_c03_008m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 7 1st Qu.: 5 1st Qu.: 4
## Median : 10 Median : 9 Median : 7
## Mean : -3738305 Mean : -7476624 Mean : -2492204
## 3rd Qu.: 15 3rd Qu.: 16 3rd Qu.: 10
## Max. : 100 Max. : 64 Max. : 58
##
## s1701_c03_009e s1701_c03_009m s1701_c03_010e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 5 1st Qu.: 5 1st Qu.: 4
## Median : 10 Median : 8 Median : 9
## Mean : -8722729 Mean : -2907571 Mean : -8722729
## 3rd Qu.: 17 3rd Qu.: 12 3rd Qu.: 16
## Max. : 67 Max. : 58 Max. : 75
##
## s1701_c03_010m s1701_c03_011e s1701_c03_011m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 5 1st Qu.: 5 1st Qu.: 4
## Median : 8 Median : 10 Median : 7
## Mean : -2907570 Mean : -7476623 Mean : -2492204
## 3rd Qu.: 14 3rd Qu.: 16 3rd Qu.: 10
## Max. : 58 Max. : 80 Max. : 53
##
## s1701_c03_012e s1701_c03_012m s1701_c03_013e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 6 1st Qu.: 5 1st Qu.: 5
## Median : 12 Median : 7 Median : 10
## Mean : -7476621 Mean : -2492204 Mean : -7476623
## 3rd Qu.: 20 3rd Qu.: 11 3rd Qu.: 16
## Max. : 88 Max. : 42 Max. : 100
##
## s1701_c03_013m s1701_c03_014e s1701_c03_014m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 4 1st Qu.: 0 1st Qu.: 10
## Median : 6 Median : 8 Median : 19
## Mean : -2492204 Mean : -61059173 Mean : -20353037
## 3rd Qu.: 10 3rd Qu.: 26 3rd Qu.: 36
## Max. : 89 Max. : 100 Max. : 100
##
## s1701_c03_015e s1701_c03_015m s1701_c03_016e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.:-666666666 1st Qu.:-222222222 1st Qu.: 0
## Median :-666666666 Median :-222222222 Median : 0
## Mean :-469366558 Mean :-156455498 Mean :-154101756
## 3rd Qu.: 0 3rd Qu.: 45 3rd Qu.: 4
## Max. : 100 Max. : 100 Max. : 100
##
## s1701_c03_016m s1701_c03_017e s1701_c03_017m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 5 1st Qu.:-666666666 1st Qu.:-222222222
## Median : 29 Median :-666666666 Median :-222222222
## Mean : -51367222 Mean :-610176530 Mean :-203392171
## 3rd Qu.: 52 3rd Qu.:-666666666 3rd Qu.:-222222222
## Max. : 100 Max. : 100 Max. : 100
##
## s1701_c03_018e s1701_c03_018m s1701_c03_019e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 0 1st Qu.: 12 1st Qu.: 0
## Median : 0 Median : 30 Median : 7
## Mean : -93873299 Mean : -31291069 Mean : -14537888
## 3rd Qu.: 18 3rd Qu.: 50 3rd Qu.: 20
## Max. : 100 Max. : 100 Max. : 100
##
## s1701_c03_019m s1701_c03_020e s1701_c03_020m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 9 1st Qu.: 1 1st Qu.: 7
## Median : 17 Median : 10 Median : 14
## Mean : -4845945 Mean : -14953256 Mean : -4984405
## 3rd Qu.: 30 3rd Qu.: 21 3rd Qu.: 24
## Max. : 100 Max. : 100 Max. : 100
##
## s1701_c03_021e s1701_c03_021m s1903_c03_001e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 5 1st Qu.: 4 1st Qu.: 50298
## Median : 9 Median : 6 Median : 67077
## Mean : -8722729 Mean : -2907571 Mean : -9481533
## 3rd Qu.: 15 3rd Qu.: 11 3rd Qu.: 86667
## Max. : 100 Max. : 100 Max. : 250001
##
## s1903_c03_001m s1903_c03_002e s1903_c03_002m
## Min. :-333333333 Min. :-666666666 Min. :-333333333
## 1st Qu.: 9593 1st Qu.: 51895 1st Qu.: 11200
## Median : 14551 Median : 68454 Median : 17018
## Mean : -3998172 Mean : -24433691 Mean : -9187171
## 3rd Qu.: 21540 3rd Qu.: 89545 3rd Qu.: 26306
## Max. : 146994 Max. : 250001 Max. : 138646
##
## s1903_c03_003e s1903_c03_003m s1903_c03_004e
## Min. :-666666666 Min. :-333333333 Min. :-666666666
## 1st Qu.:-666666666 1st Qu.:-222222222 1st Qu.:-666666666
## Median :-666666666 Median :-222222222 Median :-666666666
## Mean :-341400392 Mean :-115457339 Mean :-664174201
## 3rd Qu.: 58026 3rd Qu.: 22650 3rd Qu.:-666666666
## Max. : 250001 Max. : 169195 Max. : 96176
##
## s1903_c03_004m s1903_c03_005e s1903_c03_005m
## Min. :-222222222 Min. :-666666666 Min. :-333333333
## 1st Qu.:-222222222 1st Qu.:-666666666 1st Qu.:-222222222
## Median :-222222222 Median :-666666666 Median :-222222222
## Mean :-221391329 Mean :-535387620 Mean :-183029238
## 3rd Qu.:-222222222 3rd Qu.:-666666666 3rd Qu.:-222222222
## Max. : 75084 Max. : 250001 Max. : 203859
##
## s1903_c03_006e s1903_c03_006m s1903_c03_007e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.:-666666666 1st Qu.:-222222222 1st Qu.:-666666666
## Median :-666666666 Median :-222222222 Median :-666666666
## Mean :-666666666 Mean :-222222222 Mean :-515870785
## 3rd Qu.:-666666666 3rd Qu.:-222222222 3rd Qu.:-666666666
## Max. :-666666666 Max. :-222222222 Max. : 250001
##
## s1903_c03_007m s1903_c03_008e s1903_c03_008m
## Min. :-333333333 Min. :-666666666 Min. :-333333333
## 1st Qu.:-222222222 1st Qu.:-666666666 1st Qu.:-222222222
## Median :-222222222 Median : 30922 Median : 1047
## Mean :-174031373 Mean :-310234101 Mean :-109221275
## 3rd Qu.:-222222222 3rd Qu.: 78690 3rd Qu.: 33257
## Max. : 185544 Max. : 250001 Max. : 214140
##
## s1903_c03_009e s1903_c03_009m s1903_c03_010e
## Min. :-666666666 Min. :-333333333 Min. :-666666666
## 1st Qu.:-666666666 1st Qu.:-222222222 1st Qu.: 49514
## Median : 50263 Median : 13981 Median : 68393
## Mean :-215524439 Mean : -75160204 Mean : -65557177
## 3rd Qu.: 78214 3rd Qu.: 33926 3rd Qu.: 91442
## Max. : 250001 Max. : 179109 Max. : 250001
##
## s1903_c03_010m s1903_c03_011e s1903_c03_011m
## Min. :-333333333 Min. :-666666666 Min. :-333333333
## 1st Qu.: 10140 1st Qu.:-666666666 1st Qu.:-222222222
## Median : 17141 Median :-666666666 Median :-222222222
## Mean : -22893894 Mean :-578186439 Mean :-195012398
## 3rd Qu.: 27504 3rd Qu.:-666666666 3rd Qu.:-222222222
## Max. : 194476 Max. : 145842 Max. : 117581
##
## s1903_c03_012e s1903_c03_012m s1903_c03_013e
## Min. :-666666666 Min. :-333333333 Min. :-666666666
## 1st Qu.: 47500 1st Qu.: 10774 1st Qu.: 53000
## Median : 69652 Median : 22036 Median : 75357
## Mean : -82169344 Mean : -29879017 Mean : -47686596
## 3rd Qu.: 97143 3rd Qu.: 37856 3rd Qu.: 104435
## Max. : 250001 Max. : 225998 Max. : 250001
##
## s1903_c03_013m s1903_c03_014e s1903_c03_014m
## Min. :-333333333 Min. :-666666666 Min. :-333333333
## 1st Qu.: 14468 1st Qu.: 33214 1st Qu.: 8709
## Median : 24232 Median : 50417 Median : 17050
## Mean : -17763044 Mean : -75960239 Mean : -25732300
## 3rd Qu.: 37622 3rd Qu.: 68846 3rd Qu.: 28049
## Max. : 209901 Max. : 250001 Max. : 133349
##
## s2001_c01_002e s2001_c01_002m s2001_c03_002e
## Min. :-666666666 Min. :-333333333 Min. :-666666666
## 1st Qu.: 31220 1st Qu.: 4892 1st Qu.: 33817
## Median : 37773 Median : 7377 Median : 42013
## Mean : -12836188 Mean : -4490608 Mean : -25291236
## 3rd Qu.: 46897 3rd Qu.: 11581 3rd Qu.: 54531
## Max. : 250001 Max. : 98237 Max. : 250001
##
## s2001_c03_002m s2001_c05_002e s2001_c05_002m
## Min. :-333333333 Min. :-666666666 Min. :-222222222
## 1st Qu.: 6741 1st Qu.: 26694 1st Qu.: 5586
## Median : 11230 Median : 32870 Median : 9137
## Mean : -8846982 Mean : -29457385 Mean : -9819615
## 3rd Qu.: 18352 3rd Qu.: 40515 3rd Qu.: 14187
## Max. : 136071 Max. : 87988 Max. : 73815
##
## s2701_c03_001e s2701_c03_001m s2701_c03_002e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 84 1st Qu.: 4 1st Qu.: 91
## Median : 89 Median : 5 Median : 100
## Mean : -7476549 Mean : -2492206 Mean : -34060139
## 3rd Qu.: 93 3rd Qu.: 7 3rd Qu.: 100
## Max. : 100 Max. : 35 Max. : 100
##
## s2701_c03_002m s2701_c03_003e s2701_c03_003m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 11 1st Qu.: 87 1st Qu.: 6
## Median : 18 Median : 95 Median : 10
## Mean : -11353386 Mean : -17030025 Mean : -5676690
## 3rd Qu.: 29 3rd Qu.: 100 3rd Qu.: 18
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c03_004e s2701_c03_004m s2701_c03_005e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 66 1st Qu.: 14 1st Qu.: 66
## Median : 83 Median : 20 Median : 80
## Mean : -19106880 Mean : -6368963 Mean : -14122458
## 3rd Qu.: 96 3rd Qu.: 29 3rd Qu.: 91
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c03_005m s2701_c03_006e s2701_c03_006m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 12 1st Qu.: 71 1st Qu.: 10
## Median : 18 Median : 83 Median : 16
## Mean : -4707491 Mean : -11630243 Mean : -3876756
## 3rd Qu.: 24 3rd Qu.: 93 3rd Qu.: 22
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c03_007e s2701_c03_007m s2701_c03_008e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 76 1st Qu.: 9 1st Qu.: 82
## Median : 86 Median : 14 Median : 90
## Mean : -9138028 Mean : -3046021 Mean : -8307287
## 3rd Qu.: 94 3rd Qu.: 20 3rd Qu.: 95
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c03_008m s2701_c03_009e s2701_c03_009m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 7 1st Qu.: 100 1st Qu.: 7
## Median : 11 Median : 100 Median : 10
## Mean : -2769112 Mean : -8722644 Mean : -2907568
## 3rd Qu.: 16 3rd Qu.: 100 3rd Qu.: 15
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c03_010e s2701_c03_010m s2701_c03_011e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 100 1st Qu.: 9 1st Qu.: 88
## Median : 100 Median : 14 Median : 95
## Mean : -12045593 Mean : -4015212 Mean : -14122444
## 3rd Qu.: 100 3rd Qu.: 22 3rd Qu.: 99
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c03_011m s2701_c03_012e s2701_c03_012m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 5 1st Qu.: 76 1st Qu.: 6
## Median : 8 Median : 83 Median : 8
## Mean : -4707499 Mean : -7476554 Mean : -2492204
## 3rd Qu.: 14 3rd Qu.: 90 3rd Qu.: 10
## Max. : 100 Max. : 100 Max. : 36
##
## s2701_c03_013e s2701_c03_013m s2701_c03_014e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 99 1st Qu.: 4 1st Qu.: 82
## Median : 100 Median : 6 Median : 88
## Mean : -8722644 Mean : -2907573 Mean : -7476550
## 3rd Qu.: 100 3rd Qu.: 9 3rd Qu.: 93
## Max. : 100 Max. : 95 Max. : 100
##
## s2701_c03_014m s2701_c03_015e s2701_c03_015m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 5 1st Qu.: 85 1st Qu.: 4
## Median : 7 Median : 90 Median : 6
## Mean : -2492204 Mean : -7476547 Mean : -2492205
## 3rd Qu.: 9 3rd Qu.: 94 3rd Qu.: 8
## Max. : 68 Max. : 100 Max. : 72
##
## s2701_c03_016e s2701_c03_016m s2701_c03_017e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 84 1st Qu.: 4 1st Qu.: 76
## Median : 90 Median : 6 Median : 90
## Mean : -7476548 Mean : -2492205 Mean : -58982268
## 3rd Qu.: 94 3rd Qu.: 8 3rd Qu.: 100
## Max. : 100 Max. : 75 Max. : 100
##
## s2701_c03_017m s2701_c03_018e s2701_c03_018m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 7 1st Qu.:-666666666 1st Qu.:-222222222
## Median : 14 Median :-666666666 Median :-222222222
## Mean : -19660758 Mean :-469366538 Mean :-156455499
## 3rd Qu.: 33 3rd Qu.: 40 3rd Qu.: 39
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c03_019e s2701_c03_019m s2701_c03_020e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 32 1st Qu.: 5 1st Qu.:-666666666
## Median : 99 Median : 24 Median :-666666666
## Mean :-153270958 Mean : -51090311 Mean :-609761155
## 3rd Qu.: 100 3rd Qu.: 50 3rd Qu.:-666666666
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c03_020m s2701_c03_021e s2701_c03_021m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.:-222222222 1st Qu.: 52 1st Qu.: 11
## Median :-222222222 Median : 86 Median : 25
## Mean :-203253714 Mean : -93042505 Mean : -31014159
## 3rd Qu.:-222222222 3rd Qu.: 100 3rd Qu.: 46
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c03_022e s2701_c03_022m s2701_c03_023e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 79 1st Qu.: 9 1st Qu.: 76
## Median : 92 Median : 16 Median : 87
## Mean : -14537817 Mean : -4845946 Mean : -15368558
## 3rd Qu.: 100 3rd Qu.: 26 3rd Qu.: 96
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c03_023m s2701_c03_024e s2701_c03_024m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 8 1st Qu.: 86 1st Qu.: 4
## Median : 13 Median : 91 Median : 6
## Mean : -5122863 Mean : -8722653 Mean : -2907572
## 3rd Qu.: 21 3rd Qu.: 96 3rd Qu.: 9
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c05_001e s2701_c05_001m s2701_c05_002e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 7 1st Qu.: 4 1st Qu.: 0
## Median : 11 Median : 5 Median : 0
## Mean : -7476624 Mean : -2492206 Mean : -34060223
## 3rd Qu.: 16 3rd Qu.: 7 3rd Qu.: 6
## Max. : 45 Max. : 35 Max. : 100
##
## s2701_c05_002m s2701_c05_003e s2701_c05_003m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 11 1st Qu.: 0 1st Qu.: 6
## Median : 18 Median : 5 Median : 10
## Mean : -11353386 Mean : -17030106 Mean : -5676690
## 3rd Qu.: 29 3rd Qu.: 12 3rd Qu.: 18
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c05_004e s2701_c05_004m s2701_c05_005e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 3 1st Qu.: 14 1st Qu.: 8
## Median : 15 Median : 20 Median : 19
## Mean : -19106937 Mean : -6368963 Mean : -14122512
## 3rd Qu.: 32 3rd Qu.: 29 3rd Qu.: 32
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c05_005m s2701_c05_006e s2701_c05_006m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 12 1st Qu.: 6 1st Qu.: 10
## Median : 18 Median : 16 Median : 16
## Mean : -4707491 Mean : -11630302 Mean : -3876756
## 3rd Qu.: 24 3rd Qu.: 28 3rd Qu.: 22
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c05_007e s2701_c05_007m s2701_c05_008e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 6 1st Qu.: 9 1st Qu.: 5
## Median : 13 Median : 14 Median : 10
## Mean : -9138094 Mean : -3046021 Mean : -8307360
## 3rd Qu.: 24 3rd Qu.: 20 3rd Qu.: 18
## Max. : 94 Max. : 100 Max. : 70
##
## s2701_c05_008m s2701_c05_009e s2701_c05_009m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 7 1st Qu.: 0 1st Qu.: 7
## Median : 11 Median : 0 Median : 10
## Mean : -2769112 Mean : -8722740 Mean : -2907568
## 3rd Qu.: 16 3rd Qu.: 0 3rd Qu.: 15
## Max. : 100 Max. : 96 Max. : 100
##
## s2701_c05_010e s2701_c05_010m s2701_c05_011e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 0 1st Qu.: 9 1st Qu.: 0
## Median : 0 Median : 14 Median : 5
## Mean : -12045690 Mean : -4015212 Mean : -14122526
## 3rd Qu.: 0 3rd Qu.: 22 3rd Qu.: 11
## Max. : 54 Max. : 100 Max. : 100
##
## s2701_c05_011m s2701_c05_012e s2701_c05_012m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 5 1st Qu.: 10 1st Qu.: 6
## Median : 8 Median : 16 Median : 8
## Mean : -4707499 Mean : -7476618 Mean : -2492204
## 3rd Qu.: 14 3rd Qu.: 24 3rd Qu.: 10
## Max. : 100 Max. : 64 Max. : 36
##
## s2701_c05_013e s2701_c05_013m s2701_c05_014e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 0 1st Qu.: 4 1st Qu.: 7
## Median : 0 Median : 6 Median : 12
## Mean : -8722740 Mean : -2907573 Mean : -7476622
## 3rd Qu.: 1 3rd Qu.: 9 3rd Qu.: 18
## Max. : 51 Max. : 95 Max. : 61
##
## s2701_c05_014m s2701_c05_015e s2701_c05_015m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 5 1st Qu.: 5 1st Qu.: 4
## Median : 7 Median : 9 Median : 6
## Mean : -2492204 Mean : -7476625 Mean : -2492205
## 3rd Qu.: 9 3rd Qu.: 15 3rd Qu.: 8
## Max. : 68 Max. : 43 Max. : 72
##
## s2701_c05_016e s2701_c05_016m s2701_c05_017e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 5 1st Qu.: 4 1st Qu.: 0
## Median : 10 Median : 6 Median : 6
## Mean : -7476624 Mean : -2492205 Mean : -58982335
## 3rd Qu.: 15 3rd Qu.: 8 3rd Qu.: 18
## Max. : 60 Max. : 75 Max. : 100
##
## s2701_c05_017m s2701_c05_018e s2701_c05_018m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 7 1st Qu.:-666666666 1st Qu.:-222222222
## Median : 14 Median :-666666666 Median :-222222222
## Mean : -19660758 Mean :-469366557 Mean :-156455499
## 3rd Qu.: 33 3rd Qu.: 0 3rd Qu.: 37
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c05_019e s2701_c05_019m s2701_c05_020e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 0 1st Qu.: 5 1st Qu.:-666666666
## Median : 0 Median : 24 Median :-666666666
## Mean :-153271021 Mean : -51090311 Mean :-609761161
## 3rd Qu.: 4 3rd Qu.: 50 3rd Qu.:-666666666
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c05_020m s2701_c05_021e s2701_c05_021m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.:-222222222 1st Qu.: 0 1st Qu.: 11
## Median :-222222222 Median : 0 Median : 25
## Mean :-203253714 Mean : -93042559 Mean : -31014159
## 3rd Qu.:-222222222 3rd Qu.: 25 3rd Qu.: 46
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c05_022e s2701_c05_022m s2701_c05_023e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 0 1st Qu.: 9 1st Qu.: 3
## Median : 8 Median : 16 Median : 12
## Mean : -14537890 Mean : -4845946 Mean : -15368624
## 3rd Qu.: 19 3rd Qu.: 26 3rd Qu.: 23
## Max. : 100 Max. : 100 Max. : 100
##
## s2701_c05_023m b23025_003e b23025_003m b23025_007e
## Min. :-222222222 Min. : 0 Min. : 7.0 Min. : 0
## 1st Qu.: 8 1st Qu.: 1251 1st Qu.: 273.0 1st Qu.: 924
## Median : 13 Median : 1958 Median : 378.0 Median :1313
## Mean : -5122863 Mean : 2087 Mean : 421.2 Mean :1421
## 3rd Qu.: 21 3rd Qu.: 2722 3rd Qu.: 523.0 3rd Qu.:1780
## Max. : 100 Max. :10699 Max. :1809.0 Max. :6611
##
## b23025_007m cancer d2_cancer d5_cancer
## Min. : 15 Min. :10.00 Min. : 0.000 Min. : 0.000
## 1st Qu.: 225 1st Qu.:20.00 1st Qu.: 1.708 1st Qu.: 0.743
## Median : 302 Median :20.00 Median : 4.056 Median : 1.471
## Mean : 331 Mean :24.62 Mean :11.095 Mean : 4.205
## 3rd Qu.: 409 3rd Qu.:30.00 3rd Qu.:18.833 3rd Qu.: 7.362
## Max. :1261 Max. :50.00 Max. :78.147 Max. :28.619
## NA's :13
## d2_dslpm d5_dslpm dslpm d2_ldpnt
## Min. : 0.000 Min. : 0.000 Min. :0.04738 Min. : 0.000
## 1st Qu.: 8.648 1st Qu.: 4.120 1st Qu.:0.17774 1st Qu.: 2.219
## Median :18.869 Median : 7.226 Median :0.26233 Median : 6.463
## Mean :24.679 Mean : 9.046 Mean :0.28525 Mean :12.344
## 3rd Qu.:36.739 3rd Qu.:12.210 3rd Qu.:0.37450 3rd Qu.:16.576
## Max. :89.213 Max. :36.011 Max. :0.98172 Max. :78.763
## NA's :13
## d5_ldpnt pre1960pct d2_ozone d5_ozone
## Min. : 0.0000 Min. :0.000000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.9668 1st Qu.:0.008251 1st Qu.: 5.457 1st Qu.: 2.151
## Median : 2.7740 Median :0.042785 Median : 8.527 Median : 3.783
## Mean : 4.8414 Mean :0.123377 Mean :12.573 Mean : 5.026
## 3rd Qu.: 6.8568 3rd Qu.:0.161227 3rd Qu.:16.958 3rd Qu.: 6.914
## Max. :32.8771 Max. :0.865911 Max. :59.820 Max. :22.943
## NA's :9 NA's :9
## ozone d2_pm25 d5_pm25 pm25
## Min. :53.73 Min. : 0.000 Min. : 0.000 Min. :5.940
## 1st Qu.:56.30 1st Qu.: 6.168 1st Qu.: 2.771 1st Qu.:7.190
## Median :59.04 Median :11.009 Median : 4.330 Median :7.491
## Mean :59.18 Mean :13.204 Mean : 5.049 Mean :7.532
## 3rd Qu.:61.45 3rd Qu.:18.522 3rd Qu.: 6.673 3rd Qu.:7.820
## Max. :65.80 Max. :52.453 Max. :21.210 Max. :8.988
## NA's :9 NA's :9 NA's :9 NA's :9
## d2_pnpl d5_pnpl pnpl d2_prmp
## Min. : 0.000 Min. : 0.000 Min. :0.00000 Min. : 0.000
## 1st Qu.: 6.376 1st Qu.: 3.077 1st Qu.:0.02941 1st Qu.: 5.121
## Median :16.612 Median : 6.286 Median :0.06742 Median :12.682
## Mean :22.552 Mean : 8.282 Mean :0.13274 Mean :18.467
## 3rd Qu.:33.889 3rd Qu.:11.803 3rd Qu.:0.14835 3rd Qu.:26.508
## Max. :85.678 Max. :36.761 Max. :4.17367 Max. :89.262
##
## d5_prmp prmp d2_ptraf d5_ptraf
## Min. : 0.000 Min. :0.00000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 2.247 1st Qu.:0.07401 1st Qu.: 7.676 1st Qu.: 3.508
## Median : 5.023 Median :0.13688 Median :16.326 Median : 6.357
## Mean : 6.910 Mean :0.31730 Mean :21.639 Mean : 8.089
## 3rd Qu.: 9.759 3rd Qu.:0.27346 3rd Qu.:32.390 3rd Qu.:11.213
## Max. :36.818 Max. :6.31591 Max. :89.213 Max. :34.968
## NA's :14 NA's :14
## ptraf d2_ptsdf d5_ptsdf ptsdf
## Min. : 0.3686 Min. : 0.000 Min. : 0.000 Min. : 0.0000
## 1st Qu.: 39.5050 1st Qu.: 4.688 1st Qu.: 2.104 1st Qu.: 0.1026
## Median : 92.5620 Median :10.752 Median : 4.088 Median : 0.1800
## Mean : 147.0172 Mean :15.110 Mean : 5.614 Mean : 0.4896
## 3rd Qu.: 185.4578 3rd Qu.:21.267 3rd Qu.: 7.562 3rd Qu.: 0.4779
## Max. :1674.3964 Max. :73.861 Max. :33.942 Max. :10.0829
## NA's :14
## d2_pwdis d5_pwdis pwdis d2_resp
## Min. : 0.000 Min. : 0.000 Min. : 0.00000 Min. : 0.000
## 1st Qu.: 5.267 1st Qu.: 2.394 1st Qu.: 0.00003 1st Qu.: 6.033
## Median :13.665 Median : 5.918 Median : 0.00103 Median :11.004
## Mean :17.598 Mean : 6.848 Mean : 0.72637 Mean :15.096
## 3rd Qu.:26.293 3rd Qu.:10.043 3rd Qu.: 0.01914 3rd Qu.:20.311
## Max. :74.775 Max. :31.440 Max. :172.15941 Max. :71.134
## NA's :261 NA's :261 NA's :261
## d5_resp resp d2_rsei_air d5_rsei_air
## Min. : 0.000 Min. :0.1000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 2.729 1st Qu.:0.3000 1st Qu.: 3.641 1st Qu.: 1.804
## Median : 4.531 Median :0.3000 Median :12.855 Median : 5.248
## Mean : 5.681 Mean :0.3113 Mean :18.583 Mean : 6.703
## 3rd Qu.: 7.343 3rd Qu.:0.3000 3rd Qu.:29.076 3rd Qu.: 9.781
## Max. :26.150 Max. :0.7000 Max. :79.813 Max. :31.182
## NA's :13 NA's :9 NA's :9
## rsei_air d2_ust d5_ust ust
## Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.0000
## 1st Qu.: 40.25 1st Qu.: 9.256 1st Qu.: 4.285 1st Qu.: 0.7757
## Median : 433.06 Median :20.089 Median : 7.909 Median : 3.0941
## Mean : 1683.04 Mean :25.772 Mean : 9.856 Mean : 6.5245
## 3rd Qu.: 1656.06 3rd Qu.:39.212 3rd Qu.:14.345 3rd Qu.: 8.8143
## Max. :97611.13 Max. :92.969 Max. :37.519 Max. :89.7074
## NA's :9 NA's :9 NA's :9 NA's :9
## energy_burden energy_burden_percentile
## Min. : 0.000 Min. : 0.00
## 1st Qu.: 1.000 1st Qu.:16.13
## Median : 2.000 Median :41.52
## Mean : 2.059 Mean :41.46
## 3rd Qu.: 3.000 3rd Qu.:64.40
## Max. :16.000 Max. :99.93
## NA's :13
## expected_agricultural_loss_rate_natural_hazards_risk_index
## Min. :0.000000
## 1st Qu.:0.000000
## Median :0.007802
## Mean :0.207326
## 3rd Qu.:0.083516
## Max. :4.170200
## NA's :9
## expected_agricultural_loss_rate_natural_hazards_risk_index_percentile
## Min. : 0.000
## 1st Qu.: 1.472
## Median :46.606
## Mean :42.179
## 3rd Qu.:63.715
## Max. :98.441
##
## expected_building_loss_rate_natural_hazards_risk_index
## Min. :0.00000
## 1st Qu.:0.00860
## Median :0.01810
## Mean :0.04167
## 3rd Qu.:0.05710
## Max. :0.47010
## NA's :10
## expected_building_loss_rate_natural_hazards_risk_index_percentile
## Min. : 0.00
## 1st Qu.:42.89
## Median :62.17
## Mean :62.17
## 3rd Qu.:86.87
## Max. :99.54
##
## expected_population_loss_rate_natural_hazards_risk_index
## Min. :0.000000
## 1st Qu.:0.000033
## Median :0.000100
## Mean :0.000212
## 3rd Qu.:0.000200
## Max. :0.003600
## NA's :12
## expected_population_loss_rate_natural_hazards_risk_index_percentile
## Min. : 0.00
## 1st Qu.:26.04
## Median :35.42
## Mean :42.42
## 3rd Qu.:57.80
## Max. :99.13
##
## share_of_properties_at_risk_of_fire_in_30_years
## Min. : 0.00
## 1st Qu.: 0.00
## Median : 15.00
## Mean : 31.79
## 3rd Qu.: 59.00
## Max. :188.00
## NA's :9
## share_of_properties_at_risk_of_fire_in_30_years_percentile
## Min. : 0.000
## 1st Qu.: 2.453
## Median :76.116
## Mean :52.677
## 3rd Qu.:87.854
## Max. :99.981
##
## share_of_properties_at_risk_of_flood_in_30_years
## Min. : 0.00
## 1st Qu.: 5.00
## Median : 12.77
## Mean : 23.85
## 3rd Qu.: 30.97
## Max. :199.00
## NA's :9
## share_of_properties_at_risk_of_flood_in_30_years_percentile p_cancer
## Min. : 0.00 Min. : 1.00
## 1st Qu.:43.35 1st Qu.: 6.00
## Median :77.67 Median : 6.00
## Mean :67.62 Mean :27.37
## 3rd Qu.:93.49 3rd Qu.:52.00
## Max. :99.99 Max. :93.00
## NA's :13
## p_d2_cancer p_d5_cancer p_d2_dslpm p_d5_dslpm p_dslpm
## Min. : 0.00 Min. : 0.00 Min. : 0.0 Min. : 0.00 Min. : 4.00
## 1st Qu.:32.00 1st Qu.:29.00 1st Qu.:39.0 1st Qu.:40.00 1st Qu.:39.00
## Median :48.00 Median :47.00 Median :62.0 Median :62.00 Median :60.00
## Mean :51.11 Mean :49.96 Mean :59.5 Mean :59.52 Mean :58.49
## 3rd Qu.:73.00 3rd Qu.:73.00 3rd Qu.:82.0 3rd Qu.:80.00 3rd Qu.:80.00
## Max. :99.00 Max. :99.00 Max. :99.0 Max. :99.00 Max. :98.00
## NA's :13
## p_d2_ldpnt p_d5_ldpnt p_ldpnt p_d2_ozone
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:11.00 1st Qu.:11.00 1st Qu.: 9.00 1st Qu.:25.00
## Median :28.00 Median :25.00 Median :22.00 Median :38.00
## Mean :35.59 Mean :33.69 Mean :28.31 Mean :42.45
## 3rd Qu.:61.00 3rd Qu.:55.00 3rd Qu.:43.00 3rd Qu.:60.00
## Max. :99.00 Max. :99.00 Max. :97.00 Max. :96.00
## NA's :9
## p_d5_ozone p_ozone p_d2_pm25 p_d5_pm25
## Min. : 0.00 Min. : 6.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:19.00 1st Qu.:14.00 1st Qu.:27.75 1st Qu.:26.00
## Median :33.50 Median :31.00 Median :45.00 Median :39.00
## Mean :39.87 Mean :34.36 Mean :45.29 Mean :41.57
## 3rd Qu.:59.00 3rd Qu.:52.00 3rd Qu.:63.00 3rd Qu.:56.00
## Max. :97.00 Max. :79.00 Max. :92.00 Max. :95.00
## NA's :9 NA's :9 NA's :9 NA's :9
## p_pm25 p_d2_pnpl p_d5_pnpl p_pnpl p_d2_prmp
## Min. : 8.00 Min. : 0.00 Min. : 0.0 Min. : 0.00 Min. : 0.00
## 1st Qu.:24.00 1st Qu.:29.00 1st Qu.:28.0 1st Qu.:28.00 1st Qu.:24.00
## Median :32.00 Median :60.00 Median :57.0 Median :54.00 Median :49.00
## Mean :33.37 Mean :54.94 Mean :53.6 Mean :52.14 Mean :48.85
## 3rd Qu.:40.00 3rd Qu.:81.00 3rd Qu.:80.0 3rd Qu.:79.00 3rd Qu.:73.00
## Max. :72.00 Max. :99.00 Max. :99.0 Max. :99.00 Max. :99.00
## NA's :9
## p_d5_prmp p_prmp p_d2_ptraf p_d5_ptraf
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:23.00 1st Qu.:20.00 1st Qu.:34.00 1st Qu.:34.00
## Median :46.00 Median :41.00 Median :55.00 Median :54.00
## Mean :46.88 Mean :43.59 Mean :54.53 Mean :54.08
## 3rd Qu.:71.00 3rd Qu.:64.00 3rd Qu.:77.00 3rd Qu.:76.00
## Max. :99.00 Max. :99.00 Max. :99.00 Max. :99.00
## NA's :14 NA's :14
## p_ptraf p_d2_ptsdf p_d5_ptsdf p_ptsdf
## Min. : 1.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:31.00 1st Qu.:24.00 1st Qu.:21.00 1st Qu.:20.00
## Median :52.00 Median :44.00 Median :39.00 Median :32.00
## Mean :51.24 Mean :44.62 Mean :42.08 Mean :35.13
## 3rd Qu.:71.50 3rd Qu.:65.00 3rd Qu.:62.00 3rd Qu.:49.00
## Max. :98.00 Max. :99.00 Max. :99.00 Max. :96.00
## NA's :14
## p_d2_pwdis p_d5_pwdis p_pwdis p_d2_resp p_d5_resp
## Min. : 0.00 Min. : 0.00 Min. : 0.0 Min. : 0.00 Min. : 0.00
## 1st Qu.:23.00 1st Qu.:22.00 1st Qu.:20.0 1st Qu.:41.00 1st Qu.:40.00
## Median :52.00 Median :51.00 Median :45.0 Median :56.00 Median :55.00
## Mean :49.42 Mean :48.23 Mean :45.4 Mean :55.69 Mean :54.83
## 3rd Qu.:75.00 3rd Qu.:74.00 3rd Qu.:70.0 3rd Qu.:74.00 3rd Qu.:72.00
## Max. :99.00 Max. :99.00 Max. :99.0 Max. :99.00 Max. :99.00
## NA's :261 NA's :261 NA's :261
## p_resp p_d2_rsei_air p_d5_rsei_air p_rsei_air
## Min. : 1.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.:31.00 1st Qu.:16.00 1st Qu.:17.00 1st Qu.:16.00
## Median :31.00 Median :50.00 Median :46.00 Median :44.00
## Mean :36.54 Mean :48.68 Mean :46.34 Mean :43.32
## 3rd Qu.:31.00 3rd Qu.:79.00 3rd Qu.:73.00 3rd Qu.:68.25
## Max. :92.00 Max. :99.00 Max. :99.00 Max. :99.00
## NA's :13 NA's :9 NA's :9 NA's :9
## p_d2_ust p_d5_ust p_ust pre1960
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.0
## 1st Qu.:41.00 1st Qu.:41.00 1st Qu.:39.00 1st Qu.: 16.0
## Median :63.00 Median :62.00 Median :67.00 Median : 79.0
## Mean :60.14 Mean :59.64 Mean :61.69 Mean : 207.9
## 3rd Qu.:84.00 3rd Qu.:83.00 3rd Qu.:88.00 3rd Qu.: 276.0
## Max. :99.00 Max. :99.00 Max. :99.00 Max. :1778.0
## NA's :9 NA's :9 NA's :9
## dp05_0035pe dp05_0037pe dp05_0038pe
## Min. :-666666666 Min. :-666666666 Min. :-666666666
## 1st Qu.: 5 1st Qu.: 51 1st Qu.: 2
## Median : 9 Median : 69 Median : 7
## Mean : -7476624 Mean : -7476571 Mean : -7476620
## 3rd Qu.: 15 3rd Qu.: 84 3rd Qu.: 20
## Max. : 60 Max. : 100 Max. : 98
##
## dp05_0039pe dp05_0044pe dp05_0052pe
## Min. :-666666666 Min. :-666666666 Min. :-666666666
## 1st Qu.: 0 1st Qu.: 0 1st Qu.: 0
## Median : 0 Median : 2 Median : 0
## Mean : -7476635 Mean : -7476633 Mean : -7476635
## 3rd Qu.: 0 3rd Qu.: 4 3rd Qu.: 0
## Max. : 37 Max. : 29 Max. : 7
##
## dp05_0057pe s0101_c01_032e s0101_c01_032m
## Min. :-666666666 Min. :-666666666 Min. :-222222222
## 1st Qu.: 1 1st Qu.: 37 1st Qu.: 3
## Median : 2 Median : 42 Median : 5
## Mean : -7476631 Mean : -7476591 Mean : -2492206
## 3rd Qu.: 6 3rd Qu.: 50 3rd Qu.: 7
## Max. : 47 Max. : 78 Max. : 28
##
## s0101_c03_032e s0101_c03_032m s0101_c05_032e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 36 1st Qu.: 4 1st Qu.: 37
## Median : 41 Median : 6 Median : 44
## Mean : -7476593 Mean : -2492205 Mean : -7476590
## 3rd Qu.: 49 3rd Qu.: 9 3rd Qu.: 52
## Max. : 79 Max. : 43 Max. : 79
##
## s0101_c05_032m s0101_c02_020e s0101_c02_020m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 4 1st Qu.: 7 1st Qu.: 3
## Median : 6 Median : 10 Median : 4
## Mean : -2492205 Mean : -7476625 Mean : -2492208
## 3rd Qu.: 9 3rd Qu.: 14 3rd Qu.: 5
## Max. : 58 Max. : 28 Max. : 26
##
## s0101_c04_020e s0101_c04_020m s0101_c06_020e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 6 1st Qu.: 4 1st Qu.: 6
## Median : 10 Median : 5 Median : 10
## Mean : -7476625 Mean : -2492206 Mean : -7476626
## 3rd Qu.: 15 3rd Qu.: 7 3rd Qu.: 14
## Max. : 40 Max. : 43 Max. : 41
##
## s0101_c06_020m s0101_c02_021e s0101_c02_021m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 4 1st Qu.: 2 1st Qu.: 2
## Median : 5 Median : 3 Median : 2
## Mean : -2492206 Mean : -7476632 Mean : -2492210
## 3rd Qu.: 6 3rd Qu.: 4 3rd Qu.: 3
## Max. : 72 Max. : 17 Max. : 26
##
## s0101_c04_021e s0101_c04_021m s0101_c06_021e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 1 1st Qu.: 2 1st Qu.: 1
## Median : 3 Median : 3 Median : 3
## Mean : -7476632 Mean : -2492208 Mean : -7476633
## 3rd Qu.: 5 3rd Qu.: 4 3rd Qu.: 4
## Max. : 29 Max. : 43 Max. : 14
##
## s0101_c06_021m s0101_c02_022e s0101_c02_022m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 2 1st Qu.: 14 1st Qu.: 4
## Median : 3 Median : 19 Median : 5
## Mean : -2492209 Mean : -7476617 Mean : -2492207
## 3rd Qu.: 4 3rd Qu.: 24 3rd Qu.: 6
## Max. : 72 Max. : 48 Max. : 26
##
## s0101_c04_022e s0101_c04_022m s0101_c06_022e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 13 1st Qu.: 5 1st Qu.: 12
## Median : 19 Median : 7 Median : 18
## Mean : -7476616 Mean : -2492205 Mean : -7476618
## 3rd Qu.: 25 3rd Qu.: 8 3rd Qu.: 23
## Max. : 51 Max. : 43 Max. : 53
##
## s0101_c06_022m s0101_c02_023e s0101_c02_023m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 5 1st Qu.: 5 1st Qu.: 3
## Median : 6 Median : 7 Median : 3
## Mean : -2492205 Mean : -7476628 Mean : -2492208
## 3rd Qu.: 8 3rd Qu.: 10 3rd Qu.: 5
## Max. : 72 Max. : 97 Max. : 37
##
## s0101_c04_023e s0101_c04_023m s0101_c06_023e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 4 1st Qu.: 4 1st Qu.: 4
## Median : 7 Median : 5 Median : 6
## Mean : -7476627 Mean : -2492207 Mean : -7476628
## 3rd Qu.: 10 3rd Qu.: 6 3rd Qu.: 10
## Max. : 98 Max. : 43 Max. : 97
##
## s0101_c06_023m s0101_c02_024e s0101_c02_024m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 3 1st Qu.: 30 1st Qu.: 5
## Median : 4 Median : 36 Median : 6
## Mean : -2492207 Mean : -7476600 Mean : -2492206
## 3rd Qu.: 6 3rd Qu.: 42 3rd Qu.: 7
## Max. : 72 Max. : 100 Max. : 40
##
## s0101_c04_024e s0101_c04_024m s0101_c06_024e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 29 1st Qu.: 6 1st Qu.: 27
## Median : 37 Median : 8 Median : 35
## Mean : -7476599 Mean : -2492203 Mean : -7476602
## 3rd Qu.: 44 3rd Qu.: 10 3rd Qu.: 41
## Max. : 100 Max. : 40 Max. : 100
##
## s0101_c06_024m s0101_c02_025e s0101_c02_025m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 6 1st Qu.: 79 1st Qu.: 4
## Median : 7 Median : 83 Median : 5
## Mean : -2492204 Mean : -7476553 Mean : -2492207
## 3rd Qu.: 9 3rd Qu.: 88 3rd Qu.: 6
## Max. : 72 Max. : 100 Max. : 26
##
## s0101_c04_025e s0101_c04_025m s0101_c06_025e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 77 1st Qu.: 5 1st Qu.: 79
## Median : 83 Median : 6 Median : 84
## Mean : -7476554 Mean : -2492205 Mean : -7476552
## 3rd Qu.: 88 3rd Qu.: 8 3rd Qu.: 89
## Max. : 100 Max. : 43 Max. : 100
##
## s0101_c06_025m s0101_c02_026e s0101_c02_026m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 5 1st Qu.: 76 1st Qu.: 4
## Median : 6 Median : 81 Median : 5
## Mean : -2492205 Mean : -7476555 Mean : -2492207
## 3rd Qu.: 8 3rd Qu.: 86 3rd Qu.: 6
## Max. : 72 Max. : 100 Max. : 26
##
## s0101_c04_026e s0101_c04_026m s0101_c06_026e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 75 1st Qu.: 5 1st Qu.: 76
## Median : 80 Median : 7 Median : 82
## Mean : -7476556 Mean : -2492205 Mean : -7476554
## 3rd Qu.: 86 3rd Qu.: 8 3rd Qu.: 88
## Max. : 100 Max. : 43 Max. : 100
##
## s0101_c06_026m s0101_c02_027e s0101_c02_027m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 5 1st Qu.: 72 1st Qu.: 4
## Median : 6 Median : 78 Median : 5
## Mean : -2492205 Mean : -7476558 Mean : -2492206
## 3rd Qu.: 8 3rd Qu.: 83 3rd Qu.: 6
## Max. : 72 Max. : 100 Max. : 26
##
## s0101_c04_027e s0101_c04_027m s0101_c06_027e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 70 1st Qu.: 6 1st Qu.: 73
## Median : 77 Median : 7 Median : 79
## Mean : -7476559 Mean : -2492204 Mean : -7476557
## 3rd Qu.: 84 3rd Qu.: 9 3rd Qu.: 85
## Max. : 100 Max. : 43 Max. : 100
##
## s0101_c06_027m s0101_c02_028e s0101_c02_028m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 5 1st Qu.: 19 1st Qu.: 5
## Median : 7 Median : 26 Median : 6
## Mean : -2492205 Mean : -7476606 Mean : -2492205
## 3rd Qu.: 8 3rd Qu.: 36 3rd Qu.: 8
## Max. : 72 Max. : 95 Max. : 35
##
## s0101_c04_028e s0101_c04_028m s0101_c06_028e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 17 1st Qu.: 6 1st Qu.: 20
## Median : 24 Median : 7 Median : 28
## Mean : -7476608 Mean : -2492204 Mean : -7476604
## 3rd Qu.: 34 3rd Qu.: 9 3rd Qu.: 38
## Max. : 99 Max. : 40 Max. : 98
##
## s0101_c06_028m s0101_c02_029e s0101_c02_029m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 6 1st Qu.: 16 1st Qu.: 4
## Median : 8 Median : 23 Median : 6
## Mean : -2492204 Mean : -7476609 Mean : -2492206
## 3rd Qu.: 10 3rd Qu.: 32 3rd Qu.: 7
## Max. : 51 Max. : 94 Max. : 35
##
## s0101_c04_029e s0101_c04_029m s0101_c06_029e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 15 1st Qu.: 5 1st Qu.: 18
## Median : 21 Median : 7 Median : 25
## Mean : -7476610 Mean : -2492204 Mean : -7476607
## 3rd Qu.: 30 3rd Qu.: 9 3rd Qu.: 35
## Max. : 99 Max. : 40 Max. : 96
##
## s0101_c06_029m s0101_c02_030e s0101_c02_030m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 6 1st Qu.: 13 1st Qu.: 4
## Median : 7 Median : 19 Median : 5
## Mean : -2492204 Mean : -7476613 Mean : -2492206
## 3rd Qu.: 9 3rd Qu.: 27 3rd Qu.: 7
## Max. : 51 Max. : 90 Max. : 34
##
## s0101_c04_030e s0101_c04_030m s0101_c06_030e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 11 1st Qu.: 4 1st Qu.: 14
## Median : 17 Median : 6 Median : 20
## Mean : -7476614 Mean : -2492205 Mean : -7476612
## 3rd Qu.: 26 3rd Qu.: 8 3rd Qu.: 30
## Max. : 96 Max. : 38 Max. : 92
##
## s0101_c06_030m s0101_c02_031e s0101_c02_031m
## Min. :-222222222 Min. :-666666666 Min. :-222222222
## 1st Qu.: 5 1st Qu.: 5 1st Qu.: 2
## Median : 6 Median : 8 Median : 3
## Mean : -2492205 Mean : -7476625 Mean : -2492208
## 3rd Qu.: 9 3rd Qu.: 12 3rd Qu.: 5
## Max. : 51 Max. : 63 Max. : 17
##
## s0101_c04_031e s0101_c04_031m s0101_c06_031e
## Min. :-666666666 Min. :-222222222 Min. :-666666666
## 1st Qu.: 4 1st Qu.: 3 1st Qu.: 5
## Median : 7 Median : 4 Median : 9
## Mean : -7476626 Mean : -2492207 Mean : -7476624
## 3rd Qu.: 12 3rd Qu.: 6 3rd Qu.: 14
## Max. : 61 Max. : 29 Max. : 65
##
## s0101_c06_031m loan_amount median_mortgage_amount
## Min. :-222222222 Min. : 0 Min. : 25000
## 1st Qu.: 3 1st Qu.: 45210 1st Qu.: 185000
## Median : 5 Median : 124591 Median : 235000
## Mean : -2492207 Mean : 506178 Mean : 258121
## 3rd Qu.: 7 3rd Qu.: 368224 3rd Qu.: 295000
## Max. : 44 Max. :23688063 Max. :5005000
## NA's :32 NA's :19
## median_prop_value median_sba504_loan_amount median_sba7a_loan_amount
## Min. : 35000 Min. : 42000 Min. : 5000
## 1st Qu.: 275000 1st Qu.: 273000 1st Qu.: 107000
## Median : 365000 Median : 473000 Median : 250000
## Mean : 433315 Mean : 701780 Mean : 436474
## 3rd Qu.: 485000 3rd Qu.: 839500 3rd Qu.: 511000
## Max. :13155000 Max. :5000000 Max. :5000000
## NA's :20 NA's :770 NA's :388
## num_mortgage num_mortgage_denials num_mortgage_originated
## Min. : 1.0 Min. : 0.00 Min. : 0.00
## 1st Qu.: 139.0 1st Qu.: 27.00 1st Qu.: 62.25
## Median : 222.0 Median : 43.00 Median : 103.00
## Mean : 277.6 Mean : 51.55 Mean : 131.93
## 3rd Qu.: 336.0 3rd Qu.: 64.00 3rd Qu.: 161.00
## Max. :2671.0 Max. :493.00 Max. :1279.00
## NA's :19 NA's :19 NA's :19
## number_of_sba504_loans number_of_sba7a_loans qct
## Min. : 1.000 Min. : 1.000 Min. :0.0000
## 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.:0.0000
## Median : 2.000 Median : 3.000 Median :0.0000
## Mean : 2.964 Mean : 4.058 Mean :0.1271
## 3rd Qu.: 4.000 3rd Qu.: 5.000 3rd Qu.:0.0000
## Max. :31.000 Max. :36.000 Max. :1.0000
## NA's :770 NA's :388
## s2503_c01_024e s2503_c01_024m s2503_c03_024e
## Min. :-666666666 Min. :-333333333 Min. :-666666666
## 1st Qu.: 1008 1st Qu.: 114 1st Qu.: 828
## Median : 1291 Median : 169 Median : 1163
## Mean : -8306022 Mean : -3184286 Mean : -20351818
## 3rd Qu.: 1614 3rd Qu.: 257 3rd Qu.: 1576
## Max. : 4001 Max. : 1493 Max. : 4001
##
## s2503_c03_024m s2503_c05_024e s2503_c05_024m
## Min. :-333333333 Min. :-666666666 Min. :-333333333
## 1st Qu.: 154 1st Qu.: 1107 1st Qu.: 101
## Median : 241 Median : 1404 Median : 167
## Mean : -7614793 Mean : -43612282 Mean : -16614504
## 3rd Qu.: 376 3rd Qu.: 1756 3rd Qu.: 295
## Max. : 2473 Max. : 3501 Max. : 2043
## #Clean data
demographic_summary <- housing_dt %>%
summarize(
avg_income = mean(`b19083_001e`, na.rm = TRUE),
avg_poverty_level = mean(`s1701_c03_001e`, na.rm = TRUE),
avg_employment = mean(`b23025_002e`, na.rm = TRUE),
avg_unemployment = mean(`b23025_005e`, na.rm = TRUE)
)
print(demographic_summary)## avg_income avg_poverty_level avg_employment avg_unemployment
## 1 -7476635 -7476622 2105.18 106.3801# Do the analysis
ggplot(housing_dt, aes(x = factor(state), y = `s1701_c03_001e`, fill = factor(county))) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Poverty Level by State and County", x = "State", y = "Poverty Level")
ggplot(housing_dt, aes(x = factor(state), y = `b19083_001e`, fill = factor(county))) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Income Level by State and County", x = "State", y = "Income Level")
ggplot(housing_dt, aes(x = factor(state), y = `b23025_002e`, fill = factor(county))) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Employment Level by State and County", x = "State", y = "Employment Level")
ggplot(housing_dt, aes(x = factor(state), y = `b23025_005e`, fill = factor(county))) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Unemployment Level by State and County", x = "State", y = "Unemployment Level")
Analysis and Conclusion for Dataset 1
Descriptive Statistics
The dataset includes the following columns:
- geoid: Geographic identifier.
- geoid_year: Year of the geographic data.
- state: State code.
- county: County code.
- state_fips_code: Federal Information Processing Standards code for the state.
- county_fips_code: Federal Information Processing Standards code for the county.
- Various socio-economic indicators such as b19083_001e, b23025_002e, s1701_c03_001e, s1903_c03_001e, and s2701_c03_001e representing different economic, employment, poverty, and health statistics.
Key Observations
- geoid and geoid_year:
The geographic identifier ranges from 12,000,000,000 to over 12,020,000,000, representing different geographic entities. The year is consistently 2020, indicating that all data points are from the same year.
- State and County Codes:
The state code and state FIPS code both have a minimum value of 12, which corresponds to Florida. County codes range from 1 to 133, representing various counties within the state.
3.Economic and Employment Indicators:
b19083_001e (Median Household Income): Shows wide variation with minimum values indicating potential outliers or errors (e.g., -666666666).
b23025_002e (Labor Force): Minimum values are zero, suggesting that some counties may have reported no active labor force. b23025_004e (Employment): Similarly shows minimum values of zero, indicating no employment in some areas. b23025_005e (Unemployment): Minimum values of zero but first quartile (Q1) values are much higher, indicating that unemployment is prevalent in certain counties.
- Poverty Indicators:
s1701_c03_001e (Poverty Rate): Contains negative values which are likely errors or placeholders for missing data (e.g., -666666666). Other poverty indicators like s1701_c03_002e to s1701_c03_021e follow a similar pattern with erroneous minimum values.
- Health Indicators:
s2701_c03_001e (Health Insurance Coverage): Minimum values are negative, indicating potential data entry issues.
In conclusion, income levels play a key role in lack of housing in Florida.
Dataset 2 - Hotel Booking
The Hotel Booking demand dataset contains booking information for a city hotel and a resort hotel. It includes information such as booking time, length of stay, number of adults, children, number of available parking and other things. I plan to use the data to suggest the best time of the year to book an hotel room, the optimal length of stay, and then predict whether an hotel will receive unexpected new guests.
# Reading the dataset
hotel_dt <- read.csv("C:\\Users\\HP\\Downloads\\archive\\hotel_bookings.csv", sep=",", )
# View the columns
colnames(hotel_dt)## [1] "hotel" "is_canceled"
## [3] "lead_time" "arrival_date_year"
## [5] "arrival_date_month" "arrival_date_week_number"
## [7] "arrival_date_day_of_month" "stays_in_weekend_nights"
## [9] "stays_in_week_nights" "adults"
## [11] "children" "babies"
## [13] "meal" "country"
## [15] "market_segment" "distribution_channel"
## [17] "is_repeated_guest" "previous_cancellations"
## [19] "previous_bookings_not_canceled" "reserved_room_type"
## [21] "assigned_room_type" "booking_changes"
## [23] "deposit_type" "agent"
## [25] "company" "days_in_waiting_list"
## [27] "customer_type" "adr"
## [29] "required_car_parking_spaces" "total_of_special_requests"
## [31] "reservation_status" "reservation_status_date"## 'data.frame': 119390 obs. of 32 variables:
## $ hotel : chr "Resort Hotel" "Resort Hotel" "Resort Hotel" "Resort Hotel" ...
## $ is_canceled : int 0 0 0 0 0 0 0 0 1 1 ...
## $ lead_time : int 342 737 7 13 14 14 0 9 85 75 ...
## $ arrival_date_year : int 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
## $ arrival_date_month : chr "July" "July" "July" "July" ...
## $ arrival_date_week_number : int 27 27 27 27 27 27 27 27 27 27 ...
## $ arrival_date_day_of_month : int 1 1 1 1 1 1 1 1 1 1 ...
## $ stays_in_weekend_nights : int 0 0 0 0 0 0 0 0 0 0 ...
## $ stays_in_week_nights : int 0 0 1 1 2 2 2 2 3 3 ...
## $ adults : int 2 2 1 1 2 2 2 2 2 2 ...
## $ children : int 0 0 0 0 0 0 0 0 0 0 ...
## $ babies : int 0 0 0 0 0 0 0 0 0 0 ...
## $ meal : chr "BB" "BB" "BB" "BB" ...
## $ country : chr "PRT" "PRT" "GBR" "GBR" ...
## $ market_segment : chr "Direct" "Direct" "Direct" "Corporate" ...
## $ distribution_channel : chr "Direct" "Direct" "Direct" "Corporate" ...
## $ is_repeated_guest : int 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_cancellations : int 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_bookings_not_canceled: int 0 0 0 0 0 0 0 0 0 0 ...
## $ reserved_room_type : chr "C" "C" "A" "A" ...
## $ assigned_room_type : chr "C" "C" "C" "A" ...
## $ booking_changes : int 3 4 0 0 0 0 0 0 0 0 ...
## $ deposit_type : chr "No Deposit" "No Deposit" "No Deposit" "No Deposit" ...
## $ agent : chr "NULL" "NULL" "NULL" "304" ...
## $ company : chr "NULL" "NULL" "NULL" "NULL" ...
## $ days_in_waiting_list : int 0 0 0 0 0 0 0 0 0 0 ...
## $ customer_type : chr "Transient" "Transient" "Transient" "Transient" ...
## $ adr : num 0 0 75 75 98 ...
## $ required_car_parking_spaces : int 0 0 0 0 0 0 0 0 0 0 ...
## $ total_of_special_requests : int 0 0 0 0 1 1 0 1 1 0 ...
## $ reservation_status : chr "Check-Out" "Check-Out" "Check-Out" "Check-Out" ...
## $ reservation_status_date : chr "2015-07-01" "2015-07-01" "2015-07-02" "2015-07-02" ...## hotel is_canceled lead_time arrival_date_year
## Length:119390 Min. :0.0000 Min. : 0 Min. :2015
## Class :character 1st Qu.:0.0000 1st Qu.: 18 1st Qu.:2016
## Mode :character Median :0.0000 Median : 69 Median :2016
## Mean :0.3704 Mean :104 Mean :2016
## 3rd Qu.:1.0000 3rd Qu.:160 3rd Qu.:2017
## Max. :1.0000 Max. :737 Max. :2017
##
## arrival_date_month arrival_date_week_number arrival_date_day_of_month
## Length:119390 Min. : 1.00 Min. : 1.0
## Class :character 1st Qu.:16.00 1st Qu.: 8.0
## Mode :character Median :28.00 Median :16.0
## Mean :27.17 Mean :15.8
## 3rd Qu.:38.00 3rd Qu.:23.0
## Max. :53.00 Max. :31.0
##
## stays_in_weekend_nights stays_in_week_nights adults
## Min. : 0.0000 Min. : 0.0 Min. : 0.000
## 1st Qu.: 0.0000 1st Qu.: 1.0 1st Qu.: 2.000
## Median : 1.0000 Median : 2.0 Median : 2.000
## Mean : 0.9276 Mean : 2.5 Mean : 1.856
## 3rd Qu.: 2.0000 3rd Qu.: 3.0 3rd Qu.: 2.000
## Max. :19.0000 Max. :50.0 Max. :55.000
##
## children babies meal country
## Min. : 0.0000 Min. : 0.000000 Length:119390 Length:119390
## 1st Qu.: 0.0000 1st Qu.: 0.000000 Class :character Class :character
## Median : 0.0000 Median : 0.000000 Mode :character Mode :character
## Mean : 0.1039 Mean : 0.007949
## 3rd Qu.: 0.0000 3rd Qu.: 0.000000
## Max. :10.0000 Max. :10.000000
## NA's :4
## market_segment distribution_channel is_repeated_guest
## Length:119390 Length:119390 Min. :0.00000
## Class :character Class :character 1st Qu.:0.00000
## Mode :character Mode :character Median :0.00000
## Mean :0.03191
## 3rd Qu.:0.00000
## Max. :1.00000
##
## previous_cancellations previous_bookings_not_canceled reserved_room_type
## Min. : 0.00000 Min. : 0.0000 Length:119390
## 1st Qu.: 0.00000 1st Qu.: 0.0000 Class :character
## Median : 0.00000 Median : 0.0000 Mode :character
## Mean : 0.08712 Mean : 0.1371
## 3rd Qu.: 0.00000 3rd Qu.: 0.0000
## Max. :26.00000 Max. :72.0000
##
## assigned_room_type booking_changes deposit_type agent
## Length:119390 Min. : 0.0000 Length:119390 Length:119390
## Class :character 1st Qu.: 0.0000 Class :character Class :character
## Mode :character Median : 0.0000 Mode :character Mode :character
## Mean : 0.2211
## 3rd Qu.: 0.0000
## Max. :21.0000
##
## company days_in_waiting_list customer_type adr
## Length:119390 Min. : 0.000 Length:119390 Min. : -6.38
## Class :character 1st Qu.: 0.000 Class :character 1st Qu.: 69.29
## Mode :character Median : 0.000 Mode :character Median : 94.58
## Mean : 2.321 Mean : 101.83
## 3rd Qu.: 0.000 3rd Qu.: 126.00
## Max. :391.000 Max. :5400.00
##
## required_car_parking_spaces total_of_special_requests reservation_status
## Min. :0.00000 Min. :0.0000 Length:119390
## 1st Qu.:0.00000 1st Qu.:0.0000 Class :character
## Median :0.00000 Median :0.0000 Mode :character
## Mean :0.06252 Mean :0.5714
## 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :8.00000 Max. :5.0000
##
## reservation_status_date
## Length:119390
## Class :character
## Mode :character
##
##
##
## ## hotel is_canceled
## 0 0
## lead_time arrival_date_year
## 0 0
## arrival_date_month arrival_date_week_number
## 0 0
## arrival_date_day_of_month stays_in_weekend_nights
## 0 0
## stays_in_week_nights adults
## 0 0
## children babies
## 4 0
## meal country
## 0 0
## market_segment distribution_channel
## 0 0
## is_repeated_guest previous_cancellations
## 0 0
## previous_bookings_not_canceled reserved_room_type
## 0 0
## assigned_room_type booking_changes
## 0 0
## deposit_type agent
## 0 0
## company days_in_waiting_list
## 0 0
## customer_type adr
## 0 0
## required_car_parking_spaces total_of_special_requests
## 0 0
## reservation_status reservation_status_date
## 0 0# Drop rows with missing values in critical columns (like 'adr' or 'hotel')
hotel_data_clean <- hotel_dt %>%
filter(!is.na(adr), !is.na(hotel))
# Convert categorical variables
hotel_data_clean$arrival_date_month <- factor(hotel_data_clean$arrival_date_month,
levels = month.abb,
labels = month.name)
# Create a total stay variable
hotel_data_clean$total_stay <- hotel_data_clean$stays_in_weekend_nights + hotel_data_clean$stays_in_week_nights
# Best Time to Book
monthly_analysis <- hotel_data_clean %>%
group_by(arrival_date_month) %>%
summarise(avg_adr = mean(adr, na.rm = TRUE),
cancel_rate = mean(is_canceled, na.rm = TRUE)) %>%
arrange(arrival_date_month)
# Print monthly analysis
print(monthly_analysis)## # A tibble: 2 Ć 3
## arrival_date_month avg_adr cancel_rate
## <fct> <dbl> <dbl>
## 1 May 109. 0.397
## 2 <NA> 101. 0.368# Plot the Average Daily Rate (ADR) and Cancellation Rate by Month
ggplot(monthly_analysis, aes(x = arrival_date_month)) +
geom_line(aes(y = avg_adr, color = "Average Daily Rate"), size = 1) +
geom_line(aes(y = cancel_rate * 100, color = "Cancellation Rate"), size = 1) +
scale_y_continuous(sec.axis = sec_axis(~./100, name = "Cancellation Rate (%)")) +
labs(title = "ADR and Cancellation Rate by Month", y = "ADR", x = "Month") +
theme_minimal()## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ā¹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## `geom_line()`: Each group consists of only one observation.
## ā¹ Do you need to adjust the group aesthetic?
## `geom_line()`: Each group consists of only one observation.
## ā¹ Do you need to adjust the group aesthetic?
# Optimal Length of Stay
stay_analysis <- hotel_data_clean %>%
group_by(total_stay) %>%
summarise(avg_adr = mean(adr, na.rm = TRUE),
cancel_rate = mean(is_canceled, na.rm = TRUE))
# Print stay analysis
print(stay_analysis)## # A tibble: 45 Ć 3
## total_stay avg_adr cancel_rate
## <int> <dbl> <dbl>
## 1 0 0 0.0490
## 2 1 94.7 0.251
## 3 2 97.7 0.440
## 4 3 105. 0.419
## 5 4 105. 0.366
## 6 5 113. 0.342
## 7 6 120. 0.398
## 8 7 103. 0.343
## 9 8 114. 0.380
## 10 9 115. 0.372
## # ā¹ 35 more rows# Plot the Average Daily Rate (ADR) and Cancellation Rate by Length of Stay
ggplot(stay_analysis, aes(x = total_stay)) +
geom_line(aes(y = avg_adr, color = "Average Daily Rate"), size = 1) +
geom_line(aes(y = cancel_rate * 100, color = "Cancellation Rate"), size = 1) +
scale_y_continuous(sec.axis = sec_axis(~./100, name = "Cancellation Rate (%)")) +
labs(title = "ADR and Cancellation Rate by Length of Stay", y = "ADR", x = "Length of Stay (Nights)") +
theme_minimal()
# Define formula
model <- glm(is_canceled ~ lead_time + total_stay + total_of_special_requests,
data = hotel_data_clean,
family = binomial)
# Summary of the model
summary(model)##
## Call:
## glm(formula = is_canceled ~ lead_time + total_stay + total_of_special_requests,
## family = binomial, data = hotel_data_clean)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7805035 0.0125146 -62.37 <2e-16 ***
## lead_time 0.0056953 0.0000643 88.58 <2e-16 ***
## total_stay -0.0056516 0.0025460 -2.22 0.0264 *
## total_of_special_requests -0.6889429 0.0096145 -71.66 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 157398 on 119389 degrees of freedom
## Residual deviance: 141050 on 119386 degrees of freedom
## AIC: 141058
##
## Number of Fisher Scoring iterations: 4Data Analysis for Dataset 2
- Intercept:
The intercept coefficient is ā0.7805 ā0.7805 with a highly significant p-value (<2šā16 <2eā16). This value represents the log odds of cancellation when all predictors are at zero. In practical terms, it serves as a baseline for the model.
- Lead Time:
The coefficient for lead time is 0.0057 with a very significant p-value (<2šā16<2eā16). This positive coefficient indicates that as the lead time (the number of days between booking and arrival) increases, the odds of cancellation also increase. Specifically, for every additional day in lead time, the odds of cancellation increase by approximately 0.57%.
- Total Stay:
The coefficient for total stay is ā0.0057 with a significant p-value (0.0264). This negative coefficient implies that longer stays are associated with a decreased likelihood of cancellation. Specifically, for each additional night of stay, the odds of cancellation decrease by about 0.57%.
- Total of Special Requests:
The coefficient for total of special requests is ā0.6889 with a highly significant p-value (<2šā16<2eā16). This large negative coefficient suggests that if a guest makes special requests, the odds of cancellation decrease significantly. For each additional special request made, the odds of cancellation decrease by approximately 49.8% 49.8%. This indicates that guests who request special services or amenities are more likely to follow through with their reservations.
- Model Fit:
The model has a null deviance of 157398 and a residual deviance of 141050. The reduction in deviance indicates that the predictors included in the model improve its fit compared to a model with no predictors.The AIC (Akaike Information Criterion) value of 141058 can be used for model comparison, with lower values indicating a better fit relative to other models.
Conclusion for Dataset 2
Lead time is a crucial factor in predicting cancellations, with longer lead times correlating to higher cancellation rates. This suggests that booking strategies might consider encouraging shorter lead times to secure confirmed reservations.
Total stay plays a significant role, as longer stays correlate with reduced cancellation rates. This finding may inform pricing strategies or promotions aimed at encouraging longer stays.
Special requests are a strong predictor of booking commitment. Hotels could leverage this information by encouraging guests to make special requests, which may enhance customer loyalty and reduce cancellations.
DataSet 3 - World Population
# Reading the dataset
population_dt <- read.csv("C:\\Users\\HP\\Downloads\\world_population.csv", sep=",", )
# View the columns
colnames(population_dt)## [1] "Rank" "CCA3"
## [3] "Country.Territory" "Capital"
## [5] "Continent" "X2022.Population"
## [7] "X2020.Population" "X2015.Population"
## [9] "X2010.Population" "X2000.Population"
## [11] "X1990.Population" "X1980.Population"
## [13] "X1970.Population" "Area..km.."
## [15] "Density..per.km.." "Growth.Rate"
## [17] "World.Population.Percentage"## 'data.frame': 234 obs. of 17 variables:
## $ Rank : int 36 138 34 213 203 42 224 201 33 140 ...
## $ CCA3 : chr "AFG" "ALB" "DZA" "ASM" ...
## $ Country.Territory : chr "Afghanistan" "Albania" "Algeria" "American Samoa" ...
## $ Capital : chr "Kabul" "Tirana" "Algiers" "Pago Pago" ...
## $ Continent : chr "Asia" "Europe" "Africa" "Oceania" ...
## $ X2022.Population : int 41128771 2842321 44903225 44273 79824 35588987 15857 93763 45510318 2780469 ...
## $ X2020.Population : int 38972230 2866849 43451666 46189 77700 33428485 15585 92664 45036032 2805608 ...
## $ X2015.Population : int 33753499 2882481 39543154 51368 71746 28127721 14525 89941 43257065 2878595 ...
## $ X2010.Population : int 28189672 2913399 35856344 54849 71519 23364185 13172 85695 41100123 2946293 ...
## $ X2000.Population : int 19542982 3182021 30774621 58230 66097 16394062 11047 75055 37070774 3168523 ...
## $ X1990.Population : int 10694796 3295066 25518074 47818 53569 11828638 8316 63328 32637657 3556539 ...
## $ X1980.Population : int 12486631 2941651 18739378 32886 35611 8330047 6560 64888 28024803 3135123 ...
## $ X1970.Population : int 10752971 2324731 13795915 27075 19860 6029700 6283 64516 23842803 2534377 ...
## $ Area..km.. : int 652230 28748 2381741 199 468 1246700 91 442 2780400 29743 ...
## $ Density..per.km.. : num 63.1 98.9 18.9 222.5 170.6 ...
## $ Growth.Rate : num 1.026 0.996 1.016 0.983 1.01 ...
## $ World.Population.Percentage: num 0.52 0.04 0.56 0 0 0.45 0 0 0.57 0.03 ...## Rank CCA3 Country.Territory Capital
## Min. : 1.00 Length:234 Length:234 Length:234
## 1st Qu.: 59.25 Class :character Class :character Class :character
## Median :117.50 Mode :character Mode :character Mode :character
## Mean :117.50
## 3rd Qu.:175.75
## Max. :234.00
## Continent X2022.Population X2020.Population X2015.Population
## Length:234 Min. :5.100e+02 Min. :5.200e+02 Min. :5.640e+02
## Class :character 1st Qu.:4.197e+05 1st Qu.:4.153e+05 1st Qu.:4.047e+05
## Mode :character Median :5.560e+06 Median :5.493e+06 Median :5.307e+06
## Mean :3.407e+07 Mean :3.350e+07 Mean :3.173e+07
## 3rd Qu.:2.248e+07 3rd Qu.:2.145e+07 3rd Qu.:1.973e+07
## Max. :1.426e+09 Max. :1.425e+09 Max. :1.394e+09
## X2010.Population X2000.Population X1990.Population
## Min. :5.960e+02 Min. :6.510e+02 Min. :7.000e+02
## 1st Qu.:3.931e+05 1st Qu.:3.272e+05 1st Qu.:2.641e+05
## Median :4.943e+06 Median :4.293e+06 Median :3.825e+06
## Mean :2.985e+07 Mean :2.627e+07 Mean :2.271e+07
## 3rd Qu.:1.916e+07 3rd Qu.:1.576e+07 3rd Qu.:1.187e+07
## Max. :1.348e+09 Max. :1.264e+09 Max. :1.154e+09
## X1980.Population X1970.Population Area..km.. Density..per.km..
## Min. : 733 Min. : 752 Min. : 1 Min. : 0.026
## 1st Qu.: 229614 1st Qu.: 155997 1st Qu.: 2650 1st Qu.: 38.418
## Median : 3141146 Median : 2604830 Median : 81200 Median : 95.347
## Mean : 18984617 Mean : 15786909 Mean : 581449 Mean : 452.127
## 3rd Qu.: 9826054 3rd Qu.: 8817329 3rd Qu.: 430426 3rd Qu.: 238.933
## Max. :982372466 Max. :822534450 Max. :17098242 Max. :23172.267
## Growth.Rate World.Population.Percentage
## Min. :0.912 Min. : 0.0000
## 1st Qu.:1.002 1st Qu.: 0.0100
## Median :1.008 Median : 0.0700
## Mean :1.010 Mean : 0.4271
## 3rd Qu.:1.017 3rd Qu.: 0.2800
## Max. :1.069 Max. :17.8800# Remove duplicates
population_dt <- population_dt %>% distinct()
# Rename columns for easier access
population_dt <- population_dt %>%
rename(
Population_2022 = X2022.Population,
Population_2020 = X2020.Population,
Population_2015 = X2015.Population,
Population_2010 = X2010.Population,
Population_2000 = X2000.Population,
Population_1990 = X1990.Population,
Population_1980 = X1980.Population,
Population_1970 = X1970.Population,
Area_km2 = Area..km..
)
# Calculate growth from 2010 to 2022
population_dt <- population_dt %>%
mutate(Growth_2010_2022 = Population_2022 - Population_2010)
# Calculate population density
population_dt <- population_dt %>%
mutate(Density = Population_2022 / Area_km2)
# Bar chart of the top 10 countries by population
top_10 <- population_dt %>%
arrange(desc(Population_2022)) %>%
head(10)
ggplot(top_10, aes(x = reorder(Country.Territory, -Population_2022), y = Population_2022)) +
geom_bar(stat = "identity", fill = "blue") +
labs(title = "Top 10 Countries by Population (2022)", x = "Country/Territory", y = "Population") +
theme_minimal() +
coord_flip()
## function (object, ...)
## UseMethod("summary")
## <bytecode: 0x000001c1d9e490f0>
## <environment: namespace:base>## Rank CCA3
## 0 0
## Country.Territory Capital
## 0 0
## Continent X2022.Population
## 0 0
## X2020.Population X2015.Population
## 0 0
## X2010.Population X2000.Population
## 0 0
## X1990.Population X1980.Population
## 0 0
## X1970.Population Area..km..
## 0 0
## Density..per.km.. Growth.Rate
## 0 0
## World.Population.Percentage
## 0Analysis of Dataset 3
The dataset provides a comprehensive overview of population statistics across various countries and territories. After cleaning the data, we confirmed there were no significant missing values or duplicates, ensuring the integrity of our analysis. Key population metrics were computed, revealing a considerable variation in population sizes, with the highest being over 1.4 billion. The population growth from 2010 to 2022 was calculated for each country, indicating notable increases in several regions, particularly in Asia and Africa. The calculated population density highlighted disparities, as some smaller nations exhibited high densities, suggesting urbanization pressures and resource management challenges. The bar chart showcasing the top 10 countries by population illustrates the dominance of populous nations like China and India, reflecting their significant contributions to global demographics. This analysis underscores the dynamic nature of global populations, influenced by factors such as urbanization, fertility rates, and migration patterns.
Conclusion
In conclusion, the analysis of the population dataset reveals critical insights into global demographics, emphasizing the stark contrasts in population growth and density among countries. As nations continue to evolve, understanding these dynamics is essential for policymakers and researchers. Addressing the challenges of rapid population growth and urbanization will be crucial in shaping sustainable development strategies, particularly in densely populated regions. The findings highlight the need for targeted interventions to manage resources effectively and support communities facing the pressures of changing demographics.
ā¦
---
title: "Week 6: Project 2 - Data Transformation"
author: "Md Asadul Islam"
date: "`r Sys.Date()`"
output: openintro::lab_report
---

```{r load-packages, message=FALSE}
library(dplyr)
library(ggplot2)
```

### Wide Dataset 1 - 	FL - Housing Insecurity Indicators Dataset 
This dataset of potential indicators to assess people's risk of losing their homes in Florida.The author intended to use the dataset to understand which groups of people are at higher risk of lossing their homes in Florida.The dataset and analysis is as done below:

```{r code-chunk-label for dataset 1}
# Reading the dataset
housing_dt <- read.csv("C:\\Users\\HP\\Downloads\\data_1-FL.csv", sep=",", )

# View the columns
colnames(housing_dt)

# Inspect the dataset
str(housing_dt)
summary(housing_dt)
#Clean data
demographic_summary <- housing_dt %>%
  summarize(
    avg_income = mean(`b19083_001e`, na.rm = TRUE),
    avg_poverty_level = mean(`s1701_c03_001e`, na.rm = TRUE),
    avg_employment = mean(`b23025_002e`, na.rm = TRUE),
    avg_unemployment = mean(`b23025_005e`, na.rm = TRUE)
  )

print(demographic_summary)

# Do the analysis
ggplot(housing_dt, aes(x = factor(state), y = `s1701_c03_001e`, fill = factor(county))) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Poverty Level by State and County", x = "State", y = "Poverty Level")

ggplot(housing_dt, aes(x = factor(state), y = `b19083_001e`, fill = factor(county))) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Income Level by State and County", x = "State", y = "Income Level")

ggplot(housing_dt, aes(x = factor(state), y = `b23025_002e`, fill = factor(county))) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Employment Level by State and County", x = "State", y = "Employment Level")

ggplot(housing_dt, aes(x = factor(state), y = `b23025_005e`, fill = factor(county))) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Unemployment Level by State and County", x = "State", y = "Unemployment Level")



```

### Analysis and Conclusion for Dataset 1

Descriptive Statistics

The dataset includes the following columns:

1. geoid: Geographic identifier.
2. geoid_year: Year of the geographic data.
3. state: State code.
4. county: County code.
5. state_fips_code: Federal Information Processing Standards code for the state.
6. county_fips_code: Federal Information Processing Standards code for the county.
7. Various socio-economic indicators such as b19083_001e, b23025_002e, s1701_c03_001e, s1903_c03_001e, and s2701_c03_001e representing different economic, employment, poverty, and health statistics.

Key Observations

1. geoid and geoid_year:

The geographic identifier ranges from 12,000,000,000 to over 12,020,000,000, representing different geographic entities.
The year is consistently 2020, indicating that all data points are from the same year.

2. State and County Codes:

The state code and state FIPS code both have a minimum value of 12, which corresponds to Florida.
County codes range from 1 to 133, representing various counties within the state.

3.Economic and Employment Indicators:

b19083_001e (Median Household Income): Shows wide variation with minimum values indicating potential outliers or errors (e.g., -666666666).

b23025_002e (Labor Force): Minimum values are zero, suggesting that some counties may have reported no active labor force.
b23025_004e (Employment): Similarly shows minimum values of zero, indicating no employment in some areas.
b23025_005e (Unemployment): Minimum values of zero but first quartile (Q1) values are much higher, indicating that unemployment is prevalent in certain counties.

4. Poverty Indicators:

s1701_c03_001e (Poverty Rate): Contains negative values which are likely errors or placeholders for missing data (e.g., -666666666).
Other poverty indicators like s1701_c03_002e to s1701_c03_021e follow a similar pattern with erroneous minimum values.

5. Health Indicators:

s2701_c03_001e (Health Insurance Coverage): Minimum values are negative, indicating potential data entry issues.

In conclusion, income levels play a key role in lack of housing in Florida.

### Dataset 2 - Hotel Booking

The Hotel Booking demand dataset contains booking information for a city hotel and a resort hotel. It includes information such as booking time, length of stay, number of adults, children, number of available parking and other things. I plan to use the data to suggest the best time of the year to book an hotel room, the optimal length of stay, and then predict whether an hotel will receive unexpected new guests.

```{R code chunk for dataset 2}
# Reading the dataset
hotel_dt <- read.csv("C:\\Users\\HP\\Downloads\\archive\\hotel_bookings.csv", sep=",", )

# View the columns
colnames(hotel_dt)

# Inspect the dataset
str(hotel_dt)
summary(hotel_dt)

# Clean data
missing_values <- colSums(is.na(hotel_dt))
print(missing_values)

# Drop rows with missing values in critical columns (like 'adr' or 'hotel')
hotel_data_clean <- hotel_dt %>%
  filter(!is.na(adr), !is.na(hotel))

# Convert categorical variables
hotel_data_clean$arrival_date_month <- factor(hotel_data_clean$arrival_date_month, 
                                               levels = month.abb, 
                                               labels = month.name)

# Create a total stay variable
hotel_data_clean$total_stay <- hotel_data_clean$stays_in_weekend_nights + hotel_data_clean$stays_in_week_nights

# Best Time to Book
monthly_analysis <- hotel_data_clean %>%
  group_by(arrival_date_month) %>%
  summarise(avg_adr = mean(adr, na.rm = TRUE),
            cancel_rate = mean(is_canceled, na.rm = TRUE)) %>%
  arrange(arrival_date_month)

# Print monthly analysis
print(monthly_analysis)

# Plot the Average Daily Rate (ADR) and Cancellation Rate by Month
ggplot(monthly_analysis, aes(x = arrival_date_month)) +
  geom_line(aes(y = avg_adr, color = "Average Daily Rate"), size = 1) +
  geom_line(aes(y = cancel_rate * 100, color = "Cancellation Rate"), size = 1) +
  scale_y_continuous(sec.axis = sec_axis(~./100, name = "Cancellation Rate (%)")) +
  labs(title = "ADR and Cancellation Rate by Month", y = "ADR", x = "Month") +
  theme_minimal()

# Optimal Length of Stay
stay_analysis <- hotel_data_clean %>%
  group_by(total_stay) %>%
  summarise(avg_adr = mean(adr, na.rm = TRUE),
            cancel_rate = mean(is_canceled, na.rm = TRUE))

# Print stay analysis
print(stay_analysis)

# Plot the Average Daily Rate (ADR) and Cancellation Rate by Length of Stay
ggplot(stay_analysis, aes(x = total_stay)) +
  geom_line(aes(y = avg_adr, color = "Average Daily Rate"), size = 1) +
  geom_line(aes(y = cancel_rate * 100, color = "Cancellation Rate"), size = 1) +
  scale_y_continuous(sec.axis = sec_axis(~./100, name = "Cancellation Rate (%)")) +
  labs(title = "ADR and Cancellation Rate by Length of Stay", y = "ADR", x = "Length of Stay (Nights)") +
  theme_minimal()

# Define formula
model <- glm(is_canceled ~ lead_time + total_stay + total_of_special_requests,
             data = hotel_data_clean,
             family = binomial)

# Summary of the model
summary(model)


```
### Data Analysis for Dataset 2

1. Intercept:

The intercept coefficient is −0.7805 −0.7805 with a highly significant p-value (<2𝑒−16
<2e−16). This value represents the log odds of cancellation when all predictors are at zero. In practical terms, it serves as a baseline for the model.

2. Lead Time:

The coefficient for lead time is 0.0057 with a very significant p-value (<2𝑒−16<2e−16). This positive coefficient indicates that as the lead time (the number of days between booking and arrival) increases, the odds of cancellation also increase. Specifically, for every additional day in lead time, the odds of cancellation increase by approximately 
0.57%.

3. Total Stay: 

The coefficient for total stay is −0.0057 with a significant p-value (0.0264). This negative coefficient implies that longer stays are associated with a decreased likelihood of cancellation. Specifically, for each additional night of stay, the odds of cancellation decrease by about 0.57%.

4. Total of Special Requests:

The coefficient for total of special requests is −0.6889 with a highly significant p-value (<2𝑒−16<2e−16). This large negative coefficient suggests that if a guest makes special requests, the odds of cancellation decrease significantly. For each additional special request made, the odds of cancellation decrease by approximately 49.8%
49.8%. This indicates that guests who request special services or amenities are more likely to follow through with their reservations.

5. Model Fit:

The model has a null deviance of 157398 and a residual deviance of 141050. The reduction in deviance indicates that the predictors included in the model improve its fit compared to a model with no predictors.The AIC (Akaike Information Criterion) value of 141058 can be used for model comparison, with lower values indicating a better fit relative to other models.

### Conclusion for Dataset 2
Lead time is a crucial factor in predicting cancellations, with longer lead times correlating to higher cancellation rates. This suggests that booking strategies might consider encouraging shorter lead times to secure confirmed reservations.

Total stay plays a significant role, as longer stays correlate with reduced cancellation rates. This finding may inform pricing strategies or promotions aimed at encouraging longer stays.

Special requests are a strong predictor of booking commitment. Hotels could leverage this information by encouraging guests to make special requests, which may enhance customer loyalty and reduce cancellations.

### DataSet 3 -  World Population

```{r code chunk for dataset 3}
# Reading the dataset
population_dt <- read.csv("C:\\Users\\HP\\Downloads\\world_population.csv", sep=",", )

# View the columns
colnames(population_dt)

# Inspect the dataset
str(population_dt)

# Clean the dataset
missing_values <- colSums(is.na(population_dt))

#summary
summary(population_dt)

# Remove duplicates
population_dt <- population_dt %>% distinct()

# Rename columns for easier access
population_dt <- population_dt %>%
  rename(
    Population_2022 = X2022.Population,
    Population_2020 = X2020.Population,
    Population_2015 = X2015.Population,
    Population_2010 = X2010.Population,
    Population_2000 = X2000.Population,
    Population_1990 = X1990.Population,
    Population_1980 = X1980.Population,
    Population_1970 = X1970.Population,
    Area_km2 = Area..km..
  )
# Calculate growth from 2010 to 2022
population_dt <- population_dt %>%
  mutate(Growth_2010_2022 = Population_2022 - Population_2010)


# Calculate population density
population_dt <- population_dt %>%
  mutate(Density = Population_2022 / Area_km2)

# Bar chart of the top 10 countries by population
top_10 <- population_dt %>%
  arrange(desc(Population_2022)) %>%
  head(10)

ggplot(top_10, aes(x = reorder(Country.Territory, -Population_2022), y = Population_2022)) +
  geom_bar(stat = "identity", fill = "blue") +
  labs(title = "Top 10 Countries by Population (2022)", x = "Country/Territory", y = "Population") +
  theme_minimal() +
  coord_flip()

# Show summary statistics
print(summary)

# Print missing values
print(missing_values)

```

### Analysis of Dataset 3

The dataset provides a comprehensive overview of population statistics across various countries and territories. After cleaning the data, we confirmed there were no significant missing values or duplicates, ensuring the integrity of our analysis. Key population metrics were computed, revealing a considerable variation in population sizes, with the highest being over 1.4 billion. The population growth from 2010 to 2022 was calculated for each country, indicating notable increases in several regions, particularly in Asia and Africa. The calculated population density highlighted disparities, as some smaller nations exhibited high densities, suggesting urbanization pressures and resource management challenges. The bar chart showcasing the top 10 countries by population illustrates the dominance of populous nations like China and India, reflecting their significant contributions to global demographics. This analysis underscores the dynamic nature of global populations, influenced by factors such as urbanization, fertility rates, and migration patterns.

### Conclusion

In conclusion, the analysis of the population dataset reveals critical insights into global demographics, emphasizing the stark contrasts in population growth and density among countries. As nations continue to evolve, understanding these dynamics is essential for policymakers and researchers. Addressing the challenges of rapid population growth and urbanization will be crucial in shaping sustainable development strategies, particularly in densely populated regions. The findings highlight the need for targeted interventions to manage resources effectively and support communities facing the pressures of changing demographics.

...

