SBerbank Housing price exploratory data analysis
Edward Cooper
May 17, 2017
The goal of this notebook is exploratory data analysis.
In the blog post from Jason Brownlee, he summarised four steps to improve model performance.
- Improve Performance With Data.
- Improve Performance With Algorithms.
- Improve Performance With Algorithm Tuning.
- Improve Performance With Ensembles.
1 Import libraries, test data and training data
1.1 import libraries
library(data.table)
library(tidyverse)## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr## Conflicts with tidy packages ----------------------------------------------## between(): dplyr, data.table
## filter(): dplyr, stats
## first(): dplyr, data.table
## lag(): dplyr, stats
## last(): dplyr, data.table
## transpose(): purrr, data.tablelibrary(Amelia)## Loading required package: Rcpp## ##
## ## Amelia II: Multiple Imputation
## ## (Version 1.7.4, built: 2015-12-05)
## ## Copyright (C) 2005-2017 James Honaker, Gary King and Matthew Blackwell
## ## Refer to http://gking.harvard.edu/amelia/ for more information
## ##1.2 Load the test and training data
sberbankTr=fread("train.csv", header=TRUE)##
Read 98.5% of 30471 rows
Read 30471 rows and 292 (of 292) columns from 0.043 GB file in 00:00:03sberbankTe=fread("test.csv", header = TRUE)2 Exploring the percentage of missing data and data entry error
2.1 Rough missing value plot using Amelia package
# rough plot of the percentage of missing data
missmap(sberbankTr)## Warning in if (class(obj) == "amelia") {: the condition has length > 1 and
## only the first element will be used
Since there are 291 features and 1 target variable in the plot, we are going to find out which variables have the missing values
2.2 Percentage plot of missing value
# Define a function to calculate percentage of missing value in each feature
miss_pct=function(data){
pct=sum(is.na(data))/length(data)
}
# apply the function define above to every columns of the data set
missing=sapply(sberbankTr,miss_pct)%>%as.data.frame()
colnames(missing)="miss_pct"
missing=data.frame(missing_pct=missing,names=row.names(missing))
# Select only the features with missing values and their missing value percentage
missing=missing%>%filter(miss_pct>0)%>%arrange(desc(miss_pct))
missing%>%ggplot(aes(x=reorder(names,-miss_pct),y=miss_pct))+
geom_bar(stat = "identity",fill="blue")+
theme(axis.text.x = element_text(angle = 60,hjust=1))+
labs(x="Features with missing value", y="Percentage of missing value")
There are 51 features with missing values.
2.3 Maddening visualization!
2.3.1 Data quality examination of main features of house
sberbankTr=sberbankTr%>%as.data.frame()
colnames(sberbankTr)## [1] "id"
## [2] "timestamp"
## [3] "full_sq"
## [4] "life_sq"
## [5] "floor"
## [6] "max_floor"
## [7] "material"
## [8] "build_year"
## [9] "num_room"
## [10] "kitch_sq"
## [11] "state"
## [12] "product_type"
## [13] "sub_area"
## [14] "area_m"
## [15] "raion_popul"
## [16] "green_zone_part"
## [17] "indust_part"
## [18] "children_preschool"
## [19] "preschool_quota"
## [20] "preschool_education_centers_raion"
## [21] "children_school"
## [22] "school_quota"
## [23] "school_education_centers_raion"
## [24] "school_education_centers_top_20_raion"
## [25] "hospital_beds_raion"
## [26] "healthcare_centers_raion"
## [27] "university_top_20_raion"
## [28] "sport_objects_raion"
## [29] "additional_education_raion"
## [30] "culture_objects_top_25"
## [31] "culture_objects_top_25_raion"
## [32] "shopping_centers_raion"
## [33] "office_raion"
## [34] "thermal_power_plant_raion"
## [35] "incineration_raion"
## [36] "oil_chemistry_raion"
## [37] "radiation_raion"
## [38] "railroad_terminal_raion"
## [39] "big_market_raion"
## [40] "nuclear_reactor_raion"
## [41] "detention_facility_raion"
## [42] "full_all"
## [43] "male_f"
## [44] "female_f"
## [45] "young_all"
## [46] "young_male"
## [47] "young_female"
## [48] "work_all"
## [49] "work_male"
## [50] "work_female"
## [51] "ekder_all"
## [52] "ekder_male"
## [53] "ekder_female"
## [54] "0_6_all"
## [55] "0_6_male"
## [56] "0_6_female"
## [57] "7_14_all"
## [58] "7_14_male"
## [59] "7_14_female"
## [60] "0_17_all"
## [61] "0_17_male"
## [62] "0_17_female"
## [63] "16_29_all"
## [64] "16_29_male"
## [65] "16_29_female"
## [66] "0_13_all"
## [67] "0_13_male"
## [68] "0_13_female"
## [69] "raion_build_count_with_material_info"
## [70] "build_count_block"
## [71] "build_count_wood"
## [72] "build_count_frame"
## [73] "build_count_brick"
## [74] "build_count_monolith"
## [75] "build_count_panel"
## [76] "build_count_foam"
## [77] "build_count_slag"
## [78] "build_count_mix"
## [79] "raion_build_count_with_builddate_info"
## [80] "build_count_before_1920"
## [81] "build_count_1921-1945"
## [82] "build_count_1946-1970"
## [83] "build_count_1971-1995"
## [84] "build_count_after_1995"
## [85] "ID_metro"
## [86] "metro_min_avto"
## [87] "metro_km_avto"
## [88] "metro_min_walk"
## [89] "metro_km_walk"
## [90] "kindergarten_km"
## [91] "school_km"
## [92] "park_km"
## [93] "green_zone_km"
## [94] "industrial_km"
## [95] "water_treatment_km"
## [96] "cemetery_km"
## [97] "incineration_km"
## [98] "railroad_station_walk_km"
## [99] "railroad_station_walk_min"
## [100] "ID_railroad_station_walk"
## [101] "railroad_station_avto_km"
## [102] "railroad_station_avto_min"
## [103] "ID_railroad_station_avto"
## [104] "public_transport_station_km"
## [105] "public_transport_station_min_walk"
## [106] "water_km"
## [107] "water_1line"
## [108] "mkad_km"
## [109] "ttk_km"
## [110] "sadovoe_km"
## [111] "bulvar_ring_km"
## [112] "kremlin_km"
## [113] "big_road1_km"
## [114] "ID_big_road1"
## [115] "big_road1_1line"
## [116] "big_road2_km"
## [117] "ID_big_road2"
## [118] "railroad_km"
## [119] "railroad_1line"
## [120] "zd_vokzaly_avto_km"
## [121] "ID_railroad_terminal"
## [122] "bus_terminal_avto_km"
## [123] "ID_bus_terminal"
## [124] "oil_chemistry_km"
## [125] "nuclear_reactor_km"
## [126] "radiation_km"
## [127] "power_transmission_line_km"
## [128] "thermal_power_plant_km"
## [129] "ts_km"
## [130] "big_market_km"
## [131] "market_shop_km"
## [132] "fitness_km"
## [133] "swim_pool_km"
## [134] "ice_rink_km"
## [135] "stadium_km"
## [136] "basketball_km"
## [137] "hospice_morgue_km"
## [138] "detention_facility_km"
## [139] "public_healthcare_km"
## [140] "university_km"
## [141] "workplaces_km"
## [142] "shopping_centers_km"
## [143] "office_km"
## [144] "additional_education_km"
## [145] "preschool_km"
## [146] "big_church_km"
## [147] "church_synagogue_km"
## [148] "mosque_km"
## [149] "theater_km"
## [150] "museum_km"
## [151] "exhibition_km"
## [152] "catering_km"
## [153] "ecology"
## [154] "green_part_500"
## [155] "prom_part_500"
## [156] "office_count_500"
## [157] "office_sqm_500"
## [158] "trc_count_500"
## [159] "trc_sqm_500"
## [160] "cafe_count_500"
## [161] "cafe_sum_500_min_price_avg"
## [162] "cafe_sum_500_max_price_avg"
## [163] "cafe_avg_price_500"
## [164] "cafe_count_500_na_price"
## [165] "cafe_count_500_price_500"
## [166] "cafe_count_500_price_1000"
## [167] "cafe_count_500_price_1500"
## [168] "cafe_count_500_price_2500"
## [169] "cafe_count_500_price_4000"
## [170] "cafe_count_500_price_high"
## [171] "big_church_count_500"
## [172] "church_count_500"
## [173] "mosque_count_500"
## [174] "leisure_count_500"
## [175] "sport_count_500"
## [176] "market_count_500"
## [177] "green_part_1000"
## [178] "prom_part_1000"
## [179] "office_count_1000"
## [180] "office_sqm_1000"
## [181] "trc_count_1000"
## [182] "trc_sqm_1000"
## [183] "cafe_count_1000"
## [184] "cafe_sum_1000_min_price_avg"
## [185] "cafe_sum_1000_max_price_avg"
## [186] "cafe_avg_price_1000"
## [187] "cafe_count_1000_na_price"
## [188] "cafe_count_1000_price_500"
## [189] "cafe_count_1000_price_1000"
## [190] "cafe_count_1000_price_1500"
## [191] "cafe_count_1000_price_2500"
## [192] "cafe_count_1000_price_4000"
## [193] "cafe_count_1000_price_high"
## [194] "big_church_count_1000"
## [195] "church_count_1000"
## [196] "mosque_count_1000"
## [197] "leisure_count_1000"
## [198] "sport_count_1000"
## [199] "market_count_1000"
## [200] "green_part_1500"
## [201] "prom_part_1500"
## [202] "office_count_1500"
## [203] "office_sqm_1500"
## [204] "trc_count_1500"
## [205] "trc_sqm_1500"
## [206] "cafe_count_1500"
## [207] "cafe_sum_1500_min_price_avg"
## [208] "cafe_sum_1500_max_price_avg"
## [209] "cafe_avg_price_1500"
## [210] "cafe_count_1500_na_price"
## [211] "cafe_count_1500_price_500"
## [212] "cafe_count_1500_price_1000"
## [213] "cafe_count_1500_price_1500"
## [214] "cafe_count_1500_price_2500"
## [215] "cafe_count_1500_price_4000"
## [216] "cafe_count_1500_price_high"
## [217] "big_church_count_1500"
## [218] "church_count_1500"
## [219] "mosque_count_1500"
## [220] "leisure_count_1500"
## [221] "sport_count_1500"
## [222] "market_count_1500"
## [223] "green_part_2000"
## [224] "prom_part_2000"
## [225] "office_count_2000"
## [226] "office_sqm_2000"
## [227] "trc_count_2000"
## [228] "trc_sqm_2000"
## [229] "cafe_count_2000"
## [230] "cafe_sum_2000_min_price_avg"
## [231] "cafe_sum_2000_max_price_avg"
## [232] "cafe_avg_price_2000"
## [233] "cafe_count_2000_na_price"
## [234] "cafe_count_2000_price_500"
## [235] "cafe_count_2000_price_1000"
## [236] "cafe_count_2000_price_1500"
## [237] "cafe_count_2000_price_2500"
## [238] "cafe_count_2000_price_4000"
## [239] "cafe_count_2000_price_high"
## [240] "big_church_count_2000"
## [241] "church_count_2000"
## [242] "mosque_count_2000"
## [243] "leisure_count_2000"
## [244] "sport_count_2000"
## [245] "market_count_2000"
## [246] "green_part_3000"
## [247] "prom_part_3000"
## [248] "office_count_3000"
## [249] "office_sqm_3000"
## [250] "trc_count_3000"
## [251] "trc_sqm_3000"
## [252] "cafe_count_3000"
## [253] "cafe_sum_3000_min_price_avg"
## [254] "cafe_sum_3000_max_price_avg"
## [255] "cafe_avg_price_3000"
## [256] "cafe_count_3000_na_price"
## [257] "cafe_count_3000_price_500"
## [258] "cafe_count_3000_price_1000"
## [259] "cafe_count_3000_price_1500"
## [260] "cafe_count_3000_price_2500"
## [261] "cafe_count_3000_price_4000"
## [262] "cafe_count_3000_price_high"
## [263] "big_church_count_3000"
## [264] "church_count_3000"
## [265] "mosque_count_3000"
## [266] "leisure_count_3000"
## [267] "sport_count_3000"
## [268] "market_count_3000"
## [269] "green_part_5000"
## [270] "prom_part_5000"
## [271] "office_count_5000"
## [272] "office_sqm_5000"
## [273] "trc_count_5000"
## [274] "trc_sqm_5000"
## [275] "cafe_count_5000"
## [276] "cafe_sum_5000_min_price_avg"
## [277] "cafe_sum_5000_max_price_avg"
## [278] "cafe_avg_price_5000"
## [279] "cafe_count_5000_na_price"
## [280] "cafe_count_5000_price_500"
## [281] "cafe_count_5000_price_1000"
## [282] "cafe_count_5000_price_1500"
## [283] "cafe_count_5000_price_2500"
## [284] "cafe_count_5000_price_4000"
## [285] "cafe_count_5000_price_high"
## [286] "big_church_count_5000"
## [287] "church_count_5000"
## [288] "mosque_count_5000"
## [289] "leisure_count_5000"
## [290] "sport_count_5000"
## [291] "market_count_5000"
## [292] "price_doc"sberbankTr%>%filter(full_sq>300)%>%arrange(desc(full_sq))%>%head## id timestamp full_sq life_sq floor max_floor material build_year
## 1 3530 2012-09-07 5326 22 13 NA NA NA
## 2 2783 2012-07-06 729 44 12 NA NA NA
## 3 22788 2014-09-23 637 637 18 19 4 2016
## 4 5947 2013-02-07 634 38 3 NA NA NA
## 5 18344 2014-05-13 634 NA 3 17 1 NA
## 6 23718 2014-10-20 603 NA 16 18 1 NA
## num_room kitch_sq state product_type sub_area area_m
## 1 NA NA NA OwnerOccupier Birjulevo Vostochnoe 14795571
## 2 NA NA NA Investment Troparevo-Nikulino 11275074
## 3 2 10 1 OwnerOccupier Tverskoe 7307411
## 4 NA NA NA Investment Lianozovo 5646405
## 5 2 0 NA OwnerOccupier Nekrasovka 11391678
## 6 2 1 NA OwnerOccupier Krjukovo 10842310
## raion_popul green_zone_part indust_part children_preschool
## 1 145088 0.30805683 0.0509000580 9223
## 2 112804 0.33107580 0.0009913630 6945
## 3 75377 0.06544431 0.0000781528 4237
## 4 79576 0.25866338 0.1018724650 4857
## 5 19940 0.05564356 0.2432045190 1706
## 6 85219 0.06217241 0.1615317410 5767
## preschool_quota preschool_education_centers_raion children_school
## 1 4519 6 10621
## 2 3587 8 6783
## 3 1874 4 6398
## 4 2703 5 4583
## 5 2395 5 1564
## 6 5278 6 5648
## school_quota school_education_centers_raion
## 1 10053 6
## 2 11286 9
## 3 6772 4
## 4 7236 5
## 5 7377 5
## 6 10529 6
## school_education_centers_top_20_raion hospital_beds_raion
## 1 0 30
## 2 2 NA
## 3 1 1046
## 4 0 NA
## 5 0 540
## 6 0 30
## healthcare_centers_raion university_top_20_raion sport_objects_raion
## 1 2 0 8
## 2 1 1 14
## 3 3 2 29
## 4 3 0 4
## 5 0 0 0
## 6 2 0 4
## additional_education_raion culture_objects_top_25
## 1 3 no
## 2 2 no
## 3 16 yes
## 4 3 no
## 5 4 no
## 6 1 no
## culture_objects_top_25_raion shopping_centers_raion office_raion
## 1 0 3 1
## 2 0 9 3
## 3 10 23 141
## 4 0 3 3
## 5 0 0 0
## 6 0 4 1
## thermal_power_plant_raion incineration_raion oil_chemistry_raion
## 1 no no no
## 2 no no no
## 3 no no no
## 4 no no no
## 5 no yes no
## 6 no no no
## radiation_raion railroad_terminal_raion big_market_raion
## 1 yes no no
## 2 yes no no
## 3 yes yes no
## 4 yes no no
## 5 no no no
## 6 no no no
## nuclear_reactor_raion detention_facility_raion full_all male_f female_f
## 1 no no 1716730 774585 942145
## 2 no no 113897 52357 61540
## 3 no yes 116742 52836 63906
## 4 no no 68630 33005 35625
## 5 no no 247469 112902 134567
## 6 no no 221709 101426 120283
## young_all young_male young_female work_all work_male work_female
## 1 21130 10962 10168 93008 48690 44318
## 2 14587 7484 7103 68234 34276 33958
## 3 11272 5470 5802 43921 21901 22020
## 4 10019 5071 4948 51295 28679 22616
## 5 3459 1782 1677 13331 6670 6661
## 6 12194 6155 6039 58114 28761 29353
## ekder_all ekder_male ekder_female 0_6_all 0_6_male 0_6_female 7_14_all
## 1 30950 9206 21744 9223 4754 4469 10621
## 2 29983 9871 20112 6945 3523 3422 6783
## 3 20184 6644 13540 4237 2079 2158 6398
## 4 18262 5511 12751 4857 2424 2433 4583
## 5 3150 948 2202 1706 862 844 1564
## 6 14911 4222 10689 5767 2919 2848 5648
## 7_14_male 7_14_female 0_17_all 0_17_male 0_17_female 16_29_all
## 1 5550 5071 23495 12204 11291 367659
## 2 3509 3274 16521 8492 8029 25781
## 3 3094 3304 12508 6065 6443 23480
## 4 2341 2242 11158 5690 5468 15292
## 5 821 743 3831 1973 1858 55710
## 6 2830 2818 13872 7020 6852 49415
## 16_29_male 16_29_female 0_13_all 0_13_male 0_13_female
## 1 172958 194701 18567 9643 8924
## 2 12410 13371 12892 6592 6300
## 3 11491 11989 9955 4835 5120
## 4 7613 7679 8865 4433 4432
## 5 27242 28468 3112 1600 1512
## 6 25109 24306 10654 5374 5280
## raion_build_count_with_material_info build_count_block build_count_wood
## 1 217 52 0
## 2 179 13 0
## 3 651 19 27
## 4 301 9 71
## 5 43 3 0
## 6 372 8 146
## build_count_frame build_count_brick build_count_monolith
## 1 0 43 3
## 2 0 1 18
## 3 4 529 25
## 4 47 71 13
## 5 0 10 2
## 6 11 65 9
## build_count_panel build_count_foam build_count_slag build_count_mix
## 1 119 0 0 0
## 2 147 0 0 0
## 3 41 0 5 1
## 4 84 0 4 2
## 5 28 0 0 0
## 6 111 1 17 4
## raion_build_count_with_builddate_info build_count_before_1920
## 1 217 0
## 2 178 0
## 3 650 263
## 4 300 0
## 5 41 0
## 6 373 2
## build_count_1921-1945 build_count_1946-1970 build_count_1971-1995
## 1 2 32 170
## 2 0 28 90
## 3 105 154 71
## 4 52 44 108
## 5 1 7 10
## 6 20 184 131
## build_count_after_1995 ID_metro metro_min_avto metro_km_avto
## 1 13 30 4.907833 3.1156478
## 2 60 54 2.685885 1.9653855
## 3 57 120 1.482746 1.0365678
## 4 96 8 1.746993 0.7600703
## 5 23 108 4.721045 3.7768360
## 6 36 29 23.437470 19.5713576
## metro_min_walk metro_km_walk kindergarten_km school_km park_km
## 1 28.439791 2.3699826 0.78019099 0.6992566 1.1118741
## 2 22.127146 1.8439288 0.13648544 0.4268706 0.3017664
## 3 13.459068 1.1215890 1.04896162 0.2697164 0.2842998
## 4 8.067791 0.6723159 0.02517233 0.3533901 0.6510777
## 5 45.322032 3.7768360 0.28671120 0.2141965 4.6161764
## 6 226.917425 18.9097854 1.16281155 0.6483438 15.8662115
## green_zone_km industrial_km water_treatment_km cemetery_km
## 1 0.49939754 0.21450349 5.631573 1.767978
## 2 0.02373995 1.41522607 13.350400 1.661585
## 3 0.18908921 2.64080293 10.378040 4.242627
## 4 0.12164087 1.06352380 22.120720 0.894321
## 5 0.01757241 0.39410779 0.387777 5.618100
## 6 0.30657320 0.01149424 4.375824 2.785590
## incineration_km railroad_station_walk_km railroad_station_walk_min
## 1 1.640725 2.678152 32.13782
## 2 13.221530 3.577648 42.93177
## 3 12.180740 3.378717 40.54460
## 4 4.289169 3.622119 43.46543
## 5 3.194229 1.923495 23.08194
## 6 27.518110 1.072802 12.87362
## ID_railroad_station_walk railroad_station_avto_km
## 1 29 3.131005
## 2 33 3.699104
## 3 5 4.060430
## 4 8 3.938316
## 5 75 1.923495
## 6 28 1.108640
## railroad_station_avto_min ID_railroad_station_avto
## 1 4.927029 29
## 2 4.879621 33
## 3 5.143798 32
## 4 5.639924 8
## 5 3.050727 75
## 6 1.534438 28
## public_transport_station_km public_transport_station_min_walk water_km
## 1 0.22810888 2.7373066 0.7039874
## 2 0.11997785 1.4397342 0.3918505
## 3 0.32603485 3.9124182 0.5248394
## 4 0.20462420 2.4554904 0.7194951
## 5 0.03598649 0.4318379 1.1103287
## 6 0.14613224 1.7535869 0.3769553
## water_1line mkad_km ttk_km sadovoe_km bulvar_ring_km kremlin_km
## 1 no 3.666678 10.849470 13.996607 15.6237719 16.13980920
## 2 no 2.556467 7.599183 10.299658 11.4702043 12.30789689
## 3 no 13.917815 4.081283 2.185333 0.5069192 0.07289655
## 4 no 1.053626 12.034140 14.226101 14.8023900 16.29257878
## 5 no 5.946908 14.298225 18.418929 19.2725365 20.54946417
## 6 no 18.223918 31.728754 34.369783 35.1477763 36.39579596
## big_road1_km ID_big_road1 big_road1_1line big_road2_km ID_big_road2
## 1 3.329267 2 no 3.519118 31
## 2 2.214797 22 no 2.556467 1
## 3 4.081283 4 no 4.273395 34
## 4 1.053626 1 no 2.304882 36
## 5 1.905125 11 no 2.657336 55
## 6 4.686737 14 no 7.642579 49
## railroad_km railroad_1line zd_vokzaly_avto_km ID_railroad_terminal
## 1 0.8418268 no 16.95461 32
## 2 1.3830851 no 13.68898 50
## 3 2.5660444 no 4.06043 32
## 4 1.4265934 no 14.31170 101
## 5 1.2148613 no 24.06121 5
## 6 0.3148071 no 36.83612 83
## bus_terminal_avto_km ID_bus_terminal oil_chemistry_km nuclear_reactor_km
## 1 1.433444 12 10.430876 4.543918
## 2 13.699962 8 20.368617 7.754401
## 3 3.983721 13 8.750185 6.663317
## 4 22.313721 1 19.705969 4.463365
## 5 7.253743 14 8.483391 16.372510
## 6 26.092359 1 43.606687 22.386228
## radiation_km power_transmission_line_km thermal_power_plant_km ts_km
## 1 0.5239026 1.159781 3.639973 1.927394
## 2 2.0629608 2.413072 2.608128 2.324525
## 3 0.8900007 5.444976 4.308531 3.757130
## 4 1.2350510 1.002005 4.889237 5.354205
## 5 4.3177439 6.380918 9.046319 5.013100
## 6 21.0352953 16.977934 22.249194 18.058129
## big_market_km market_shop_km fitness_km swim_pool_km ice_rink_km
## 1 7.614379 3.1698353 1.5359363 3.381655 8.0760437
## 2 7.103961 0.6857908 0.3833312 2.488351 6.7761918
## 3 7.836658 1.0928965 0.2697164 1.421099 0.5018559
## 4 8.835486 2.5579304 0.9078184 2.029415 3.7898597
## 5 10.264218 5.4397341 2.4932355 8.106936 8.6237895
## 6 41.683695 2.3363622 1.3618674 4.726813 4.6684379
## stadium_km basketball_km hospice_morgue_km detention_facility_km
## 1 1.572132 0.419198 0.7333308 9.302295
## 2 3.740085 1.287224 1.0915228 12.277656
## 3 4.018205 1.171506 0.6156576 3.939382
## 4 10.899425 2.830028 0.9967077 5.425558
## 5 13.591774 5.211201 1.4518978 12.239172
## 6 26.743261 14.720309 2.0165901 28.463205
## public_healthcare_km university_km workplaces_km shopping_centers_km
## 1 2.1493844 7.517728 0.4000439 1.8967305
## 2 0.5648586 1.614626 5.7624512 0.2692872
## 3 2.6081621 2.180440 1.0915065 0.1073595
## 4 1.5663208 6.794769 0.3533901 0.1922365
## 5 5.4225513 8.837248 5.5990713 2.2151038
## 6 2.3486038 2.330873 4.1545843 0.8623378
## office_km additional_education_km preschool_km big_church_km
## 1 3.1292338 0.6927833 0.6992566 1.7539602
## 2 0.6369076 0.5116921 0.4268706 1.3776332
## 3 0.1821943 0.0000000 0.2697164 0.1818966
## 4 0.9078184 0.7157017 0.3533901 0.8755519
## 5 4.7787665 0.2034657 0.2141965 3.4682894
## 6 0.9310161 2.1239513 0.6483438 4.5615768
## church_synagogue_km mosque_km theater_km museum_km exhibition_km
## 1 1.7595762 5.461636 12.085723 1.7240298 2.859378
## 2 0.5452511 3.917901 3.217170 0.3506165 4.959816
## 3 0.1846815 1.827838 2.370385 0.6952508 1.184340
## 4 0.8573049 4.915372 12.074362 8.6044999 8.558098
## 5 0.9121684 11.891506 9.836242 7.2887853 7.402244
## 6 1.7922356 28.710811 5.525778 1.6499361 20.676510
## catering_km ecology green_part_500 prom_part_500 office_count_500
## 1 0.3513902 poor 0.00 14.36 0
## 2 0.1374399 satisfactory 11.53 0.00 0
## 3 0.1077591 excellent 12.24 0.00 10
## 4 0.3274383 poor 14.07 0.00 0
## 5 1.8360732 good 42.63 5.76 0
## 6 1.0761931 no data 3.95 12.74 0
## office_sqm_500 trc_count_500 trc_sqm_500 cafe_count_500
## 1 0 0 0 1
## 2 0 2 65500 10
## 3 131844 6 467600 71
## 4 0 2 10071 3
## 5 0 0 0 0
## 6 0 0 0 0
## cafe_sum_500_min_price_avg cafe_sum_500_max_price_avg cafe_avg_price_500
## 1 500.00 1000.00 750.00
## 2 711.11 1166.67 938.89
## 3 954.84 1564.52 1259.68
## 4 533.33 833.33 683.33
## 5 NA NA NA
## 6 NA NA NA
## cafe_count_500_na_price cafe_count_500_price_500
## 1 0 0
## 2 1 3
## 3 9 19
## 4 0 2
## 5 0 0
## 6 0 0
## cafe_count_500_price_1000 cafe_count_500_price_1500
## 1 1 0
## 2 2 3
## 3 11 13
## 4 0 1
## 5 0 0
## 6 0 0
## cafe_count_500_price_2500 cafe_count_500_price_4000
## 1 0 0
## 2 1 0
## 3 14 4
## 4 0 0
## 5 0 0
## 6 0 0
## cafe_count_500_price_high big_church_count_500 church_count_500
## 1 0 0 0
## 2 0 0 0
## 3 1 8 15
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## mosque_count_500 leisure_count_500 sport_count_500 market_count_500
## 1 0 0 1 0
## 2 0 1 2 1
## 3 0 0 9 1
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## green_part_1000 prom_part_1000 office_count_1000 office_sqm_1000
## 1 13.74 19.14 0 0
## 2 29.14 0.00 2 10795
## 3 7.51 0.00 47 649057
## 4 30.65 0.00 1 4000
## 5 21.22 28.43 0 0
## 6 8.86 15.22 1 6000
## trc_count_1000 trc_sqm_1000 cafe_count_1000 cafe_sum_1000_min_price_avg
## 1 0 0 2 1000.00
## 2 3 73500 13 809.09
## 3 20 842476 316 914.53
## 4 4 18071 8 712.50
## 5 0 0 0 NA
## 6 1 1700 0 NA
## cafe_sum_1000_max_price_avg cafe_avg_price_1000 cafe_count_1000_na_price
## 1 1750.00 1375.00 0
## 2 1318.18 1063.64 2
## 3 1500.00 1207.26 20
## 4 1125.00 918.75 0
## 5 NA NA 0
## 6 NA NA 0
## cafe_count_1000_price_500 cafe_count_1000_price_1000
## 1 0 1
## 2 3 2
## 3 94 56
## 4 4 0
## 5 0 0
## 6 0 0
## cafe_count_1000_price_1500 cafe_count_1000_price_2500
## 1 0 1
## 2 4 2
## 3 68 50
## 4 3 1
## 5 0 0
## 6 0 0
## cafe_count_1000_price_4000 cafe_count_1000_price_high
## 1 0 0
## 2 0 0
## 3 27 1
## 4 0 0
## 5 0 0
## 6 0 0
## big_church_count_1000 church_count_1000 mosque_count_1000
## 1 0 0 0
## 2 0 1 0
## 3 16 35 0
## 4 1 2 0
## 5 0 1 0
## 6 0 0 0
## leisure_count_1000 sport_count_1000 market_count_1000 green_part_1500
## 1 0 2 0 30.29
## 2 1 3 1 20.65
## 3 11 16 1 5.70
## 4 0 1 0 28.41
## 5 0 0 0 9.43
## 6 0 0 0 7.25
## prom_part_1500 office_count_1500 office_sqm_1500 trc_count_1500
## 1 17.09 0 0 0
## 2 0.77 6 41744 6
## 3 0.00 120 1259653 25
## 4 4.56 4 104500 7
## 5 34.86 0 0 0
## 6 19.25 1 6000 1
## trc_sqm_1500 cafe_count_1500 cafe_sum_1500_min_price_avg
## 1 0 2 1000.00
## 2 118500 18 713.33
## 3 1075476 643 899.17
## 4 231071 17 737.50
## 5 0 0 NA
## 6 1700 5 480.00
## cafe_sum_1500_max_price_avg cafe_avg_price_1500 cafe_count_1500_na_price
## 1 1750.00 1375.00 0
## 2 1200.00 956.67 3
## 3 1476.03 1187.60 38
## 4 1187.50 962.50 1
## 5 NA NA 0
## 6 800.00 640.00 0
## cafe_count_1500_price_500 cafe_count_1500_price_1000
## 1 0 1
## 2 4 5
## 3 165 143
## 4 6 2
## 5 0 0
## 6 3 1
## cafe_count_1500_price_1500 cafe_count_1500_price_2500
## 1 0 1
## 2 4 2
## 3 160 87
## 4 6 2
## 5 0 0
## 6 1 0
## cafe_count_1500_price_4000 cafe_count_1500_price_high
## 1 0 0
## 2 0 0
## 3 45 5
## 4 0 0
## 5 0 0
## 6 0 0
## big_church_count_1500 church_count_1500 mosque_count_1500
## 1 0 0 0
## 2 1 1 0
## 3 44 66 0
## 4 1 3 0
## 5 0 2 0
## 6 0 0 0
## leisure_count_1500 sport_count_1500 market_count_1500 green_part_2000
## 1 0 2 0 31.94
## 2 1 7 1 14.50
## 3 20 25 2 6.05
## 4 0 7 0 20.20
## 5 0 0 0 5.30
## 6 0 2 0 12.49
## prom_part_2000 office_count_2000 office_sqm_2000 trc_count_2000
## 1 16.48 0 0 1
## 2 5.99 7 79744 10
## 3 0.00 232 2210580 34
## 4 8.05 5 106254 7
## 5 26.61 0 0 0
## 6 14.30 1 6000 4
## trc_sqm_2000 cafe_count_2000 cafe_sum_2000_min_price_avg
## 1 5900 9 828.57
## 2 258195 34 706.90
## 3 1240332 1058 891.63
## 4 231071 26 648.00
## 5 0 2 650.00
## 6 15600 5 480.00
## cafe_sum_2000_max_price_avg cafe_avg_price_2000 cafe_count_2000_na_price
## 1 1428.57 1128.57 2
## 2 1206.90 956.90 5
## 3 1468.75 1180.19 66
## 4 1080.00 864.00 1
## 5 1000.00 825.00 0
## 6 800.00 640.00 0
## cafe_count_2000_price_500 cafe_count_2000_price_1000
## 1 1 3
## 2 5 13
## 3 255 257
## 4 9 7
## 5 1 0
## 6 3 1
## cafe_count_2000_price_1500 cafe_count_2000_price_2500
## 1 1 2
## 2 8 3
## 3 257 150
## 4 7 2
## 5 1 0
## 6 1 0
## cafe_count_2000_price_4000 cafe_count_2000_price_high
## 1 0 0
## 2 0 0
## 3 63 10
## 4 0 0
## 5 0 0
## 6 0 0
## big_church_count_2000 church_count_2000 mosque_count_2000
## 1 2 4 0
## 2 1 4 0
## 3 67 104 1
## 4 3 4 0
## 5 0 2 0
## 6 0 1 0
## leisure_count_2000 sport_count_2000 market_count_2000 green_part_3000
## 1 1 6 2 26.30
## 2 1 15 1 19.82
## 3 47 42 2 6.96
## 4 0 7 2 14.42
## 5 0 0 0 2.36
## 6 1 3 1 27.59
## prom_part_3000 office_count_3000 office_sqm_3000 trc_count_3000
## 1 21.24 0 0 2
## 2 12.38 14 315414 12
## 3 0.99 486 5082992 54
## 4 11.58 12 340554 25
## 5 13.06 0 0 2
## 6 9.51 2 31000 7
## trc_sqm_3000 cafe_count_3000 cafe_sum_3000_min_price_avg
## 1 7600 19 758.82
## 2 351195 77 678.87
## 3 1702619 1815 882.31
## 4 1767234 59 636.36
## 5 41100 5 760.00
## 6 49700 8 550.00
## cafe_sum_3000_max_price_avg cafe_avg_price_3000 cafe_count_3000_na_price
## 1 1294.12 1026.47 2
## 2 1147.89 913.38 6
## 3 1453.00 1167.66 113
## 4 1072.73 854.55 4
## 5 1300.00 1030.00 0
## 6 937.50 743.75 0
## cafe_count_3000_price_500 cafe_count_3000_price_1000
## 1 3 7
## 2 24 22
## 3 449 432
## 4 20 18
## 5 1 2
## 6 3 3
## cafe_count_3000_price_1500 cafe_count_3000_price_2500
## 1 4 3
## 2 15 10
## 3 446 255
## 4 13 3
## 5 1 1
## 6 2 0
## cafe_count_3000_price_4000 cafe_count_3000_price_high
## 1 0 0
## 2 0 0
## 3 105 15
## 4 1 0
## 5 0 0
## 6 0 0
## big_church_count_3000 church_count_3000 mosque_count_3000
## 1 3 7 0
## 2 3 10 0
## 3 94 162 2
## 4 4 5 0
## 5 0 2 0
## 6 0 2 0
## leisure_count_3000 sport_count_3000 market_count_3000 green_part_5000
## 1 2 13 3 14.17
## 2 2 27 1 23.36
## 3 85 88 6 6.80
## 4 0 16 2 18.65
## 5 0 1 0 4.46
## 6 1 5 1 33.49
## prom_part_5000 office_count_5000 office_sqm_5000 trc_count_5000
## 1 16.84 15 386151 43
## 2 10.07 31 750760 32
## 3 5.73 774 9997846 101
## 4 12.37 20 671096 41
## 5 6.54 1 26950 4
## 6 7.96 3 51038 7
## trc_sqm_5000 cafe_count_5000 cafe_sum_5000_min_price_avg
## 1 2299621 114 668.27
## 2 728050 215 782.67
## 3 3346565 2625 880.53
## 4 2136111 131 728.69
## 5 44437 16 662.50
## 6 49700 14 507.14
## cafe_sum_5000_max_price_avg cafe_avg_price_5000 cafe_count_5000_na_price
## 1 1134.62 901.44 10
## 2 1304.46 1043.56 13
## 3 1451.32 1165.93 170
## 4 1217.21 972.95 9
## 5 1156.25 909.38 0
## 6 857.14 682.14 0
## cafe_count_5000_price_500 cafe_count_5000_price_1000
## 1 30 40
## 2 52 63
## 3 639 642
## 4 33 41
## 5 2 9
## 6 7 4
## cafe_count_5000_price_1500 cafe_count_5000_price_2500
## 1 25 7
## 2 54 27
## 3 636 371
## 4 35 9
## 5 4 1
## 6 3 0
## cafe_count_5000_price_4000 cafe_count_5000_price_high
## 1 2 0
## 2 5 1
## 3 141 26
## 4 4 0
## 5 0 0
## 6 0 0
## big_church_count_5000 church_count_5000 mosque_count_5000
## 1 5 16 0
## 2 5 24 1
## 3 150 249 2
## 4 8 12 1
## 5 2 3 0
## 6 1 3 0
## leisure_count_5000 sport_count_5000 market_count_5000 price_doc
## 1 2 43 6 6868818
## 2 4 63 6 13250000
## 3 105 203 13 4725142
## 4 0 40 5 10200000
## 5 0 6 1 6213200
## 6 2 10 2 6572700sberbankTr%>%filter(full_sq<300)%>%ggplot()+geom_histogram(aes(x=full_sq),binwidth = 5,fill="red")
This are some strange data here since full_sq is 5326 while life_sq is just 22.
sberbankTr%>%filter(life_sq>1000)%>%head## id timestamp full_sq life_sq floor max_floor material build_year
## 1 13549 2013-12-30 79 7478 8 17 1 2014
## num_room kitch_sq state product_type sub_area area_m
## 1 3 1 1 OwnerOccupier Poselenie Voskresenskoe 21494095
## raion_popul green_zone_part indust_part children_preschool
## 1 7122 0.2624591 0.01764705 489
## preschool_quota preschool_education_centers_raion children_school
## 1 NA 0 469
## school_quota school_education_centers_raion
## 1 NA 0
## school_education_centers_top_20_raion hospital_beds_raion
## 1 0 NA
## healthcare_centers_raion university_top_20_raion sport_objects_raion
## 1 0 0 0
## additional_education_raion culture_objects_top_25
## 1 2 no
## culture_objects_top_25_raion shopping_centers_raion office_raion
## 1 0 0 0
## thermal_power_plant_raion incineration_raion oil_chemistry_raion
## 1 no no no
## radiation_raion railroad_terminal_raion big_market_raion
## 1 no no no
## nuclear_reactor_raion detention_facility_raion full_all male_f female_f
## 1 no no 9553 4529 5024
## young_all young_male young_female work_all work_male work_female
## 1 1021 529 493 4568 2414 2155
## ekder_all ekder_male ekder_female 0_6_all 0_6_male 0_6_female 7_14_all
## 1 1533 435 1099 489 254 236 469
## 7_14_male 7_14_female 0_17_all 0_17_male 0_17_female 16_29_all
## 1 242 228 1150 597 553 2155
## 16_29_male 16_29_female 0_13_all 0_13_male 0_13_female
## 1 1206 950 900 465 435
## raion_build_count_with_material_info build_count_block build_count_wood
## 1 NA NA NA
## build_count_frame build_count_brick build_count_monolith
## 1 NA NA NA
## build_count_panel build_count_foam build_count_slag build_count_mix
## 1 NA NA NA NA
## raion_build_count_with_builddate_info build_count_before_1920
## 1 NA NA
## build_count_1921-1945 build_count_1946-1970 build_count_1971-1995
## 1 NA NA NA
## build_count_after_1995 ID_metro metro_min_avto metro_km_avto
## 1 NA 45 5.595301 2.254916
## metro_min_walk metro_km_walk kindergarten_km school_km park_km
## 1 27.059 2.254916 0.6472391 0.8260521 1.799915
## green_zone_km industrial_km water_treatment_km cemetery_km
## 1 0.3143817 1.006133 0.970986 1.63075
## incineration_km railroad_station_walk_km railroad_station_walk_min
## 1 11.43591 5.784237 69.41085
## ID_railroad_station_walk railroad_station_avto_km
## 1 39 5.784237
## railroad_station_avto_min ID_railroad_station_avto
## 1 9.879409 39
## public_transport_station_km public_transport_station_min_walk water_km
## 1 0.3273572 3.928286 0.1381677
## water_1line mkad_km ttk_km sadovoe_km bulvar_ring_km kremlin_km
## 1 yes 7.229763 20.48348 23.61361 24.89313 25.59597
## big_road1_km ID_big_road1 big_road1_1line big_road2_km ID_big_road2
## 1 4.540854 2 no 5.58599 38
## railroad_km railroad_1line zd_vokzaly_avto_km ID_railroad_terminal
## 1 1.128409 no 29.07249 32
## bus_terminal_avto_km ID_bus_terminal oil_chemistry_km nuclear_reactor_km
## 1 9.943967 9 23.46345 17.10374
## radiation_km power_transmission_line_km thermal_power_plant_km ts_km
## 1 7.436487 4.79895 9.793209 9.53014
## big_market_km market_shop_km fitness_km swim_pool_km ice_rink_km
## 1 14.70376 6.287163 0.6800168 5.19295 13.32985
## stadium_km basketball_km hospice_morgue_km detention_facility_km
## 1 21.91143 9.98917 4.096246 26.98175
## public_healthcare_km university_km workplaces_km shopping_centers_km
## 1 3.262483 18.73388 8.802153 3.231528
## office_km additional_education_km preschool_km big_church_km
## 1 5.072389 1.210613 0.8260521 1.582378
## church_synagogue_km mosque_km theater_km museum_km exhibition_km
## 1 0.7561186 2.797455 21.47242 14.91733 13.02996
## catering_km ecology green_part_500 prom_part_500 office_count_500
## 1 1.314915 no data 9.88 0 0
## office_sqm_500 trc_count_500 trc_sqm_500 cafe_count_500
## 1 0 0 0 0
## cafe_sum_500_min_price_avg cafe_sum_500_max_price_avg cafe_avg_price_500
## 1 NA NA NA
## cafe_count_500_na_price cafe_count_500_price_500
## 1 0 0
## cafe_count_500_price_1000 cafe_count_500_price_1500
## 1 0 0
## cafe_count_500_price_2500 cafe_count_500_price_4000
## 1 0 0
## cafe_count_500_price_high big_church_count_500 church_count_500
## 1 0 0 0
## mosque_count_500 leisure_count_500 sport_count_500 market_count_500
## 1 0 0 0 0
## green_part_1000 prom_part_1000 office_count_1000 office_sqm_1000
## 1 13.93 0 0 0
## trc_count_1000 trc_sqm_1000 cafe_count_1000 cafe_sum_1000_min_price_avg
## 1 0 0 0 NA
## cafe_sum_1000_max_price_avg cafe_avg_price_1000 cafe_count_1000_na_price
## 1 NA NA 0
## cafe_count_1000_price_500 cafe_count_1000_price_1000
## 1 0 0
## cafe_count_1000_price_1500 cafe_count_1000_price_2500
## 1 0 0
## cafe_count_1000_price_4000 cafe_count_1000_price_high
## 1 0 0
## big_church_count_1000 church_count_1000 mosque_count_1000
## 1 0 1 0
## leisure_count_1000 sport_count_1000 market_count_1000 green_part_1500
## 1 0 1 0 13.83
## prom_part_1500 office_count_1500 office_sqm_1500 trc_count_1500
## 1 1.42 0 0 0
## trc_sqm_1500 cafe_count_1500 cafe_sum_1500_min_price_avg
## 1 0 1 1000
## cafe_sum_1500_max_price_avg cafe_avg_price_1500 cafe_count_1500_na_price
## 1 1500 1250 0
## cafe_count_1500_price_500 cafe_count_1500_price_1000
## 1 0 0
## cafe_count_1500_price_1500 cafe_count_1500_price_2500
## 1 1 0
## cafe_count_1500_price_4000 cafe_count_1500_price_high
## 1 0 0
## big_church_count_1500 church_count_1500 mosque_count_1500
## 1 0 1 0
## leisure_count_1500 sport_count_1500 market_count_1500 green_part_2000
## 1 0 1 0 12.39
## prom_part_2000 office_count_2000 office_sqm_2000 trc_count_2000
## 1 1.55 0 0 0
## trc_sqm_2000 cafe_count_2000 cafe_sum_2000_min_price_avg
## 1 0 2 1000
## cafe_sum_2000_max_price_avg cafe_avg_price_2000 cafe_count_2000_na_price
## 1 1500 1250 0
## cafe_count_2000_price_500 cafe_count_2000_price_1000
## 1 0 0
## cafe_count_2000_price_1500 cafe_count_2000_price_2500
## 1 2 0
## cafe_count_2000_price_4000 cafe_count_2000_price_high
## 1 0 0
## big_church_count_2000 church_count_2000 mosque_count_2000
## 1 1 2 0
## leisure_count_2000 sport_count_2000 market_count_2000 green_part_3000
## 1 0 3 0 10.54
## prom_part_3000 office_count_3000 office_sqm_3000 trc_count_3000
## 1 2.14 0 0 0
## trc_sqm_3000 cafe_count_3000 cafe_sum_3000_min_price_avg
## 1 0 3 1000
## cafe_sum_3000_max_price_avg cafe_avg_price_3000 cafe_count_3000_na_price
## 1 1500 1250 0
## cafe_count_3000_price_500 cafe_count_3000_price_1000
## 1 0 0
## cafe_count_3000_price_1500 cafe_count_3000_price_2500
## 1 3 0
## cafe_count_3000_price_4000 cafe_count_3000_price_high
## 1 0 0
## big_church_count_3000 church_count_3000 mosque_count_3000
## 1 1 3 1
## leisure_count_3000 sport_count_3000 market_count_3000 green_part_5000
## 1 0 5 0 17.92
## prom_part_5000 office_count_5000 office_sqm_5000 trc_count_5000
## 1 4.75 0 0 5
## trc_sqm_5000 cafe_count_5000 cafe_sum_5000_min_price_avg
## 1 262000 18 700
## cafe_sum_5000_max_price_avg cafe_avg_price_5000 cafe_count_5000_na_price
## 1 1125 912.5 2
## cafe_count_5000_price_500 cafe_count_5000_price_1000
## 1 4 4
## cafe_count_5000_price_1500 cafe_count_5000_price_2500
## 1 8 0
## cafe_count_5000_price_4000 cafe_count_5000_price_high
## 1 0 0
## big_church_count_5000 church_count_5000 mosque_count_5000
## 1 1 7 1
## leisure_count_5000 sport_count_5000 market_count_5000 price_doc
## 1 0 12 1 7705000sberbankTr%>%ggplot()+geom_histogram(aes(x=life_sq),binwidth = 5,fill="red")## Warning: Removed 6383 rows containing non-finite values (stat_bin).
This is also strange that full_sq is just 79 while life_sq is 7478
As we could read from the data_Description.txt, 1.full_sq: total area in square meters, including loggias, balconies and other non-residential areas 2.life_sq: living area in square meters, excluding loggias, balconies and other non-residential areas
so it is natural to assume that full_sq>=life_sq
But not all data follow this pattern.
sberbankTr%>%filter(life_sq>full_sq)%>%head## id timestamp full_sq life_sq floor max_floor material build_year
## 1 1085 2012-02-06 44 281 6 NA NA NA
## 2 1189 2012-02-14 9 44 3 NA NA NA
## 3 1825 2012-04-09 18 38 NA NA NA NA
## 4 1866 2012-04-11 30 178 4 NA NA NA
## 5 2012 2012-04-25 5 40 5 NA NA NA
## 6 4388 2012-10-25 73 426 17 NA NA NA
## num_room kitch_sq state product_type sub_area area_m raion_popul
## 1 NA NA NA Investment Bibirevo 6407578 155572
## 2 NA NA NA Investment Veshnjaki 10518367 118945
## 3 NA NA NA Investment Ljublino 17881914 165727
## 4 NA NA NA Investment Presnenskoe 11638050 123280
## 5 NA NA NA Investment Gol'janovo 14286991 157010
## 6 NA NA NA OwnerOccupier Vojkovskoe 5333221 64931
## green_zone_part indust_part children_preschool preschool_quota
## 1 0.18972712 0.0000699893 9576 5001
## 2 0.33490531 0.0123388860 5208 3494
## 3 0.26065264 0.1332153330 10809 5153
## 4 0.06820217 0.0420315870 7125 3240
## 5 0.38935357 0.1944892650 7751 5041
## 6 0.07407671 0.1690910900 3905 1579
## preschool_education_centers_raion children_school school_quota
## 1 5 10309 11065
## 2 6 5776 6766
## 3 10 11395 11887
## 4 7 6856 10602
## 5 6 8004 11081
## 6 6 3856 5838
## school_education_centers_raion school_education_centers_top_20_raion
## 1 5 0
## 2 7 0
## 3 13 0
## 4 9 0
## 5 7 0
## 6 8 0
## hospital_beds_raion healthcare_centers_raion university_top_20_raion
## 1 240 1 0
## 2 2078 2 0
## 3 1406 3 0
## 4 1940 2 1
## 5 125 3 0
## 6 NA 2 0
## sport_objects_raion additional_education_raion culture_objects_top_25
## 1 7 3 no
## 2 7 0 no
## 3 13 1 no
## 4 29 2 yes
## 5 5 3 no
## 6 5 2 no
## culture_objects_top_25_raion shopping_centers_raion office_raion
## 1 0 16 1
## 2 0 4 1
## 3 0 4 3
## 4 3 5 84
## 5 0 5 3
## 6 0 5 10
## thermal_power_plant_raion incineration_raion oil_chemistry_raion
## 1 no no no
## 2 no no no
## 3 no no no
## 4 no no no
## 5 no no no
## 6 no no no
## radiation_raion railroad_terminal_raion big_market_raion
## 1 no no no
## 2 no no no
## 3 yes no yes
## 4 yes no no
## 5 yes no no
## 6 no no no
## nuclear_reactor_raion detention_facility_raion full_all male_f female_f
## 1 no no 86206 40477 45729
## 2 no no 104410 49293 55117
## 3 no yes 89971 44031 45940
## 4 no no 57999 25611 32388
## 5 no no 12327 5588 6739
## 6 no yes 73148 32972 40176
## young_all young_male young_female work_all work_male work_female
## 1 21154 11007 10147 98207 52277 45930
## 2 11729 6059 5670 74032 36686 37346
## 3 23651 12084 11567 107626 53883 53743
## 4 14906 7789 7117 75357 38841 36516
## 5 16831 8637 8194 98260 47405 50855
## 6 8240 4062 4178 38266 19019 19247
## ekder_all ekder_male ekder_female 0_6_all 0_6_male 0_6_female 7_14_all
## 1 36211 10580 25631 9576 4899 4677 10309
## 2 33184 10307 22877 5208 2686 2522 5776
## 3 34450 9536 24914 10809 5581 5228 11395
## 4 33017 10594 22423 7125 3725 3400 6856
## 5 41919 12424 29495 7751 3941 3810 8004
## 6 18425 5728 12697 3905 1897 2008 3856
## 7_14_male 7_14_female 0_17_all 0_17_male 0_17_female 16_29_all
## 1 5463 4846 23603 12286 11317 17508
## 2 3000 2776 13481 6960 6521 21542
## 3 5778 5617 26333 13469 12864 23273
## 4 3580 3276 16727 8746 7981 10184
## 5 4152 3852 18912 9716 9196 2780
## 6 1923 1933 9251 4545 4706 11699
## 16_29_male 16_29_female 0_13_all 0_13_male 0_13_female
## 1 9425 8083 18654 9709 8945
## 2 10971 10571 10230 5290 4940
## 3 13683 9590 20866 10654 10212
## 4 4834 5350 13121 6880 6241
## 5 1351 1429 14694 7551 7143
## 6 5634 6065 7282 3577 3705
## raion_build_count_with_material_info build_count_block build_count_wood
## 1 211 25 0
## 2 234 45 0
## 3 462 58 0
## 4 836 99 6
## 5 371 88 0
## 6 268 52 0
## build_count_frame build_count_brick build_count_monolith
## 1 0 0 2
## 2 0 26 2
## 3 0 209 4
## 4 1 664 33
## 5 0 68 8
## 6 0 156 9
## build_count_panel build_count_foam build_count_slag build_count_mix
## 1 184 0 0 0
## 2 161 0 0 0
## 3 190 0 1 0
## 4 30 0 2 1
## 5 207 0 0 0
## 6 46 0 5 0
## raion_build_count_with_builddate_info build_count_before_1920
## 1 211 0
## 2 234 0
## 3 461 0
## 4 831 240
## 5 371 0
## 6 268 0
## build_count_1921-1945 build_count_1946-1970 build_count_1971-1995
## 1 0 0 206
## 2 0 47 171
## 3 20 193 154
## 4 114 332 76
## 5 1 221 129
## 6 14 170 59
## build_count_after_1995 ID_metro metro_min_avto metro_km_avto
## 1 5 1 2.590241 1.131260
## 2 16 31 2.737264 1.318052
## 3 94 43 2.334253 1.803020
## 4 69 14 2.236106 1.432811
## 5 20 20 1.882826 1.336681
## 6 25 88 2.932925 2.230348
## metro_min_walk metro_km_walk kindergarten_km school_km park_km
## 1 13.57512 1.1312599 0.1456996 0.1779754 2.1585871
## 2 15.24058 1.2700483 0.1496372 0.1436858 1.2291872
## 3 21.63624 1.8030196 0.2284757 0.3135958 1.2517388
## 4 14.07404 1.1728370 0.2693281 0.7943346 0.6758806
## 5 11.98157 0.9984645 0.2446896 0.3894621 2.5058484
## 6 26.01308 2.1677570 0.3940469 0.8396451 1.2785187
## green_zone_km industrial_km water_treatment_km cemetery_km
## 1 0.60097310 1.0809343 23.683460 1.804127
## 2 0.85379830 0.2477636 7.557858 1.823077
## 3 0.07592649 1.0830778 5.165015 1.239101
## 4 0.34655797 0.3516305 13.670260 0.899464
## 5 0.75308178 0.2886056 16.576700 1.220085
## 6 0.20690844 0.2065454 21.541710 0.590766
## incineration_km railroad_station_walk_km railroad_station_walk_min
## 1 3.633334 5.419893 65.03872
## 2 6.439648 2.672106 32.06527
## 3 10.651390 4.358451 52.30141
## 4 11.662270 1.221875 14.66250
## 5 13.792130 3.757634 45.09161
## 6 5.552801 1.228906 14.74687
## ID_railroad_station_walk railroad_station_avto_km
## 1 1 5.419893
## 2 11 2.672106
## 3 17 4.358451
## 4 14 1.481849
## 5 18 3.563059
## 6 77 1.287436
## railroad_station_avto_min ID_railroad_station_avto
## 1 6.905893 1
## 2 4.033396 11
## 3 5.528542 17
## 4 3.095000 14
## 5 4.363100 18
## 6 1.843228 77
## public_transport_station_km public_transport_station_min_walk water_km
## 1 0.27498514 3.2998217 0.9926311
## 2 0.25189905 3.0227886 0.9770803
## 3 0.10391550 1.2469860 0.8770446
## 4 0.08223107 0.9867728 0.3600820
## 5 0.10450528 1.2540634 1.6008222
## 6 0.35520346 4.2624415 0.4912729
## water_1line mkad_km ttk_km sadovoe_km bulvar_ring_km kremlin_km
## 1 no 1.4223914 10.9185867 13.100618 13.675657 15.156211
## 2 no 0.6827515 7.2649599 11.206231 11.965528 13.319043
## 3 no 3.3885784 7.6968438 11.529895 12.700914 13.635361
## 4 no 10.5676436 0.2707612 2.836297 3.728604 4.751068
## 5 no 3.4339677 7.3651189 9.527256 10.273956 12.070524
## 6 no 6.1465486 6.3005946 9.046109 9.770605 11.098499
## big_road1_km ID_big_road1 big_road1_1line big_road2_km ID_big_road2
## 1 1.4223914 1 no 3.8309514 5
## 2 0.6827515 1 no 1.0430949 21
## 3 3.3885784 1 no 3.4998780 48
## 4 0.2707611 4 no 0.8991133 32
## 5 3.1040893 12 no 3.1273845 41
## 6 0.9195790 14 no 2.9178648 28
## railroad_km railroad_1line zd_vokzaly_avto_km ID_railroad_terminal
## 1 1.3051595 no 14.231961 101
## 2 0.5394000 no 14.161685 5
## 3 2.4168472 no 15.051574 5
## 4 0.3155071 no 5.594799 83
## 5 1.1592340 no 9.111791 97
## 6 0.2392208 no 10.263330 83
## bus_terminal_avto_km ID_bus_terminal oil_chemistry_km nuclear_reactor_km
## 1 24.292406 1 18.152338 5.718519
## 2 3.578125 3 5.795800 12.448587
## 3 9.534786 6 2.488769 7.052142
## 4 6.289391 5 13.896360 5.827708
## 5 1.407617 7 6.540371 16.315448
## 6 7.487896 5 18.287998 2.745715
## radiation_km power_transmission_line_km thermal_power_plant_km ts_km
## 1 1.210027 1.0625130 5.814135 4.3081270
## 2 1.812571 1.2334431 7.346294 2.5377707
## 3 1.049354 0.9372905 4.284588 5.9854341
## 4 1.545985 1.4504783 2.823265 0.7293254
## 5 1.016013 1.9119972 1.407261 8.0286073
## 6 3.651884 0.2664288 5.517886 4.0084806
## big_market_km market_shop_km fitness_km swim_pool_km ice_rink_km
## 1 10.814172 1.676258 0.4858414 3.0650471 1.107594
## 2 8.425031 1.289596 1.4362459 0.5509071 3.486314
## 3 1.218309 2.989881 0.7251435 3.2170995 3.641982
## 4 16.980582 3.526191 0.5170108 1.2122283 3.032966
## 5 17.389836 1.343389 1.0257389 2.1594887 4.133274
## 6 21.732418 1.448470 0.7015471 3.7060721 2.895562
## stadium_km basketball_km hospice_morgue_km detention_facility_km
## 1 8.148591 3.5165129 2.3923530 4.248036
## 2 11.752587 0.8271273 1.4726555 10.478191
## 3 4.691729 2.1086360 2.8808257 2.682508
## 4 3.055364 1.8545360 0.3716244 3.312893
## 5 3.438991 1.4937322 1.9183063 7.457830
## 6 3.226160 0.8429688 1.5173949 1.711932
## public_healthcare_km university_km workplaces_km shopping_centers_km
## 1 0.9747428 6.7150258 0.8843500 0.6484876
## 2 1.4767817 2.2718505 2.1667937 0.4306164
## 3 1.6249776 2.2763077 0.8403917 0.7794709
## 4 0.7676782 0.5499595 1.0874751 1.0116462
## 5 1.1489140 2.5310238 1.2987571 0.3195774
## 6 1.0740269 1.9959509 1.2873755 0.8877283
## office_km additional_education_km preschool_km big_church_km
## 1 0.6371888 0.9479617 0.1779754 0.6257834
## 2 0.5345507 1.2618871 0.1436858 1.8402006
## 3 0.7246359 1.3738145 0.3135958 1.5283851
## 4 0.2035600 2.6255834 0.7943346 1.2451645
## 5 0.8008764 0.7664432 0.3894621 1.4114014
## 6 0.3818757 1.1858798 0.8396451 0.9746479
## church_synagogue_km mosque_km theater_km museum_km exhibition_km
## 1 0.6281865 3.932040 14.053047 7.389498 7.023705
## 2 1.0757908 10.500816 1.245313 2.201734 1.233578
## 3 1.4736039 10.716884 7.977639 3.035901 4.910758
## 4 0.3681969 4.476644 3.675552 2.180548 3.651333
## 5 0.5865218 9.506840 6.635464 6.470112 2.354902
## 6 1.4420903 6.204222 9.775627 7.006796 3.996847
## catering_km ecology green_part_500 prom_part_500 office_count_500
## 1 0.51683808 good 0.00 0.00 0
## 2 0.58342327 poor 0.00 12.21 0
## 3 0.76363372 excellent 3.16 0.00 0
## 4 0.00368080 poor 4.43 6.18 1
## 5 0.06781785 good 0.00 15.46 0
## 6 0.40232813 excellent 4.80 46.60 2
## office_sqm_500 trc_count_500 trc_sqm_500 cafe_count_500
## 1 0 0 0 0
## 2 0 1 8500 0
## 3 0 0 0 0
## 4 32065 0 0 3
## 5 0 1 3164 2
## 6 6558 0 0 1
## cafe_sum_500_min_price_avg cafe_sum_500_max_price_avg cafe_avg_price_500
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 700 1166.67 933.33
## 5 400 750.00 575.00
## 6 1000 1500.00 1250.00
## cafe_count_500_na_price cafe_count_500_price_500
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 2
## 5 0 1
## 6 0 0
## cafe_count_500_price_1000 cafe_count_500_price_1500
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 1 0
## 6 0 1
## cafe_count_500_price_2500 cafe_count_500_price_4000
## 1 0 0
## 2 0 0
## 3 0 0
## 4 1 0
## 5 0 0
## 6 0 0
## cafe_count_500_price_high big_church_count_500 church_count_500
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 1
## 5 0 0 0
## 6 0 0 0
## mosque_count_500 leisure_count_500 sport_count_500 market_count_500
## 1 0 0 1 0
## 2 0 0 1 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## green_part_1000 prom_part_1000 office_count_1000 office_sqm_1000
## 1 7.36 0.00 1 30500
## 2 2.83 9.72 1 111700
## 3 3.83 0.00 1 50000
## 4 6.30 14.85 16 1251253
## 5 2.10 32.37 1 6600
## 6 7.59 43.32 8 90027
## trc_count_1000 trc_sqm_1000 cafe_count_1000 cafe_sum_1000_min_price_avg
## 1 3 55600 19 527.78
## 2 4 53423 3 600.00
## 3 1 1500 1 500.00
## 4 0 0 24 780.95
## 5 2 23732 8 637.50
## 6 4 96236 7 683.33
## cafe_sum_1000_max_price_avg cafe_avg_price_1000 cafe_count_1000_na_price
## 1 888.89 708.33 1
## 2 1000.00 800.00 0
## 3 1000.00 750.00 0
## 4 1309.52 1045.24 3
## 5 1062.50 850.00 0
## 6 1083.33 883.33 1
## cafe_count_1000_price_500 cafe_count_1000_price_1000
## 1 10 4
## 2 1 1
## 3 0 1
## 4 8 5
## 5 2 3
## 6 2 1
## cafe_count_1000_price_1500 cafe_count_1000_price_2500
## 1 3 1
## 2 1 0
## 3 0 0
## 4 3 4
## 5 3 0
## 6 3 0
## cafe_count_1000_price_4000 cafe_count_1000_price_high
## 1 0 0
## 2 0 0
## 3 0 0
## 4 1 0
## 5 0 0
## 6 0 0
## big_church_count_1000 church_count_1000 mosque_count_1000
## 1 1 2 0
## 2 0 0 0
## 3 0 0 0
## 4 0 1 0
## 5 0 1 0
## 6 1 0 0
## leisure_count_1000 sport_count_1000 market_count_1000 green_part_1500
## 1 0 6 1 14.27
## 2 1 2 2 3.41
## 3 0 1 0 7.18
## 4 0 6 0 6.25
## 5 0 0 3 4.54
## 6 0 3 1 17.78
## prom_part_1500 office_count_1500 office_sqm_1500 trc_count_1500
## 1 6.92 3 39554 9
## 2 4.33 1 111700 5
## 3 6.93 1 50000 3
## 4 17.02 37 2536232 4
## 5 32.93 2 9100 4
## 6 23.85 18 740065 9
## trc_sqm_1500 cafe_count_1500 cafe_sum_1500_min_price_avg
## 1 171420 34 566.67
## 2 63153 4 700.00
## 3 376500 2 750.00
## 4 357500 96 1024.71
## 5 61032 19 705.26
## 6 448091 51 606.67
## cafe_sum_1500_max_price_avg cafe_avg_price_1500 cafe_count_1500_na_price
## 1 969.70 768.18 1
## 2 1125.00 912.50 0
## 3 1250.00 1000.00 0
## 4 1688.24 1356.47 11
## 5 1184.21 944.74 0
## 6 1011.11 808.89 6
## cafe_count_1500_price_500 cafe_count_1500_price_1000
## 1 14 11
## 2 1 1
## 3 0 1
## 4 17 17
## 5 3 8
## 6 21 11
## cafe_count_1500_price_1500 cafe_count_1500_price_2500
## 1 6 2
## 2 2 0
## 3 1 0
## 4 20 24
## 5 7 1
## 6 10 2
## cafe_count_1500_price_4000 cafe_count_1500_price_high
## 1 0 0
## 2 0 0
## 3 0 0
## 4 7 0
## 5 0 0
## 6 1 0
## big_church_count_1500 church_count_1500 mosque_count_1500
## 1 1 2 0
## 2 0 2 0
## 3 0 1 0
## 4 2 4 0
## 5 1 3 0
## 6 2 2 0
## leisure_count_1500 sport_count_1500 market_count_1500 green_part_2000
## 1 0 7 1 11.77
## 2 2 3 2 4.90
## 3 0 3 0 16.33
## 4 0 12 0 5.63
## 5 0 8 3 11.07
## 6 0 4 4 19.33
## prom_part_2000 office_count_2000 office_sqm_2000 trc_count_2000
## 1 15.97 9 188854 19
## 2 4.35 1 111700 8
## 3 11.55 1 50000 7
## 4 19.69 54 2977247 7
## 5 28.08 5 86300 5
## 6 15.91 20 752817 10
## trc_sqm_2000 cafe_count_2000 cafe_sum_2000_min_price_avg
## 1 1244891 36 614.29
## 2 123392 7 614.29
## 3 391220 17 600.00
## 4 696200 178 1092.07
## 5 67467 34 658.06
## 6 451691 66 603.39
## cafe_sum_2000_max_price_avg cafe_avg_price_2000 cafe_count_2000_na_price
## 1 1042.86 828.57 1
## 2 1071.43 842.86 0
## 3 1000.00 800.00 2
## 4 1777.44 1434.76 14
## 5 1096.77 877.42 3
## 6 1008.47 805.93 7
## cafe_count_2000_price_500 cafe_count_2000_price_1000
## 1 15 11
## 2 1 4
## 3 5 5
## 4 32 33
## 5 8 11
## 6 27 15
## cafe_count_2000_price_1500 cafe_count_2000_price_2500
## 1 6 2
## 2 2 0
## 3 5 0
## 4 43 36
## 5 11 1
## 6 13 3
## cafe_count_2000_price_4000 cafe_count_2000_price_high
## 1 1 0
## 2 0 0
## 3 0 0
## 4 16 4
## 5 0 0
## 6 1 0
## big_church_count_2000 church_count_2000 mosque_count_2000
## 1 1 2 0
## 2 2 6 0
## 3 1 3 0
## 4 5 6 0
## 5 1 3 0
## 6 3 4 0
## leisure_count_2000 sport_count_2000 market_count_2000 green_part_3000
## 1 0 10 1 11.98
## 2 2 8 3 14.42
## 3 0 8 1 30.75
## 4 2 18 1 5.89
## 5 0 13 5 23.86
## 6 0 14 5 22.87
## prom_part_3000 office_count_3000 office_sqm_3000 trc_count_3000
## 1 13.55 12 251554 23
## 2 6.06 1 111700 10
## 3 13.40 3 65323 13
## 4 20.91 102 3938290 21
## 5 18.25 8 121139 12
## 6 14.18 39 1041489 16
## trc_sqm_3000 cafe_count_3000 cafe_sum_3000_min_price_avg
## 1 1419204 68 639.68
## 2 155072 27 655.56
## 3 762298 49 667.39
## 4 1419941 393 1044.11
## 5 122066 54 724.00
## 6 602291 118 635.24
## cafe_sum_3000_max_price_avg cafe_avg_price_3000 cafe_count_3000_na_price
## 1 1079.37 859.52 5
## 2 1074.07 864.81 0
## 3 1130.43 898.91 3
## 4 1702.74 1373.42 28
## 5 1220.00 972.00 4
## 6 1071.43 853.33 13
## cafe_count_3000_price_500 cafe_count_3000_price_1000
## 1 21 22
## 2 9 7
## 3 14 17
## 4 82 82
## 5 14 18
## 6 39 34
## cafe_count_3000_price_1500 cafe_count_3000_price_2500
## 1 16 3
## 2 10 1
## 3 11 3
## 4 91 64
## 5 12 4
## 6 24 6
## cafe_count_3000_price_4000 cafe_count_3000_price_high
## 1 1 0
## 2 0 0
## 3 1 0
## 4 37 9
## 5 2 0
## 6 2 0
## big_church_count_3000 church_count_3000 mosque_count_3000
## 1 2 4 0
## 2 3 9 0
## 3 5 8 0
## 4 13 15 0
## 5 4 8 0
## 6 4 7 0
## leisure_count_3000 sport_count_3000 market_count_3000 green_part_5000
## 1 0 21 1 13.09
## 2 4 20 5 21.94
## 3 0 22 1 26.28
## 4 7 47 3 11.60
## 5 1 32 5 41.28
## 6 0 29 5 15.32
## prom_part_5000 office_count_5000 office_sqm_5000 trc_count_5000
## 1 13.31 29 807385 52
## 2 5.56 6 226450 30
## 3 9.05 7 106573 36
## 4 11.82 421 7947937 56
## 5 9.82 20 398612 20
## 6 13.63 82 2038109 45
## trc_sqm_5000 cafe_count_5000 cafe_sum_5000_min_price_avg
## 1 4036616 152 708.57
## 2 452982 102 680.00
## 3 1484860 105 625.26
## 4 2623000 1498 922.93
## 5 719166 103 641.24
## 6 1388959 304 682.55
## cafe_sum_5000_max_price_avg cafe_avg_price_5000 cafe_count_5000_na_price
## 1 1185.71 947.14 12
## 2 1150.00 915.00 2
## 3 1063.16 844.21 10
## 4 1518.57 1220.75 98
## 5 1087.63 864.43 6
## 6 1145.45 914.00 29
## cafe_count_5000_price_500 cafe_count_5000_price_1000
## 1 39 48
## 2 30 37
## 3 33 34
## 4 352 360
## 5 34 34
## 6 89 91
## cafe_count_5000_price_1500 cafe_count_5000_price_2500
## 1 40 9
## 2 24 6
## 3 21 6
## 4 347 223
## 5 21 6
## 6 70 17
## cafe_count_5000_price_4000 cafe_count_5000_price_high
## 1 4 0
## 2 3 0
## 3 1 0
## 4 98 20
## 5 2 0
## 6 8 0
## big_church_count_5000 church_count_5000 mosque_count_5000
## 1 13 22 1
## 2 9 18 0
## 3 8 23 0
## 4 48 91 1
## 5 10 14 0
## 6 14 25 0
## leisure_count_5000 sport_count_5000 market_count_5000 price_doc
## 1 0 52 4 6200000
## 2 10 53 9 6300000
## 3 3 55 9 5200000
## 4 68 155 7 5900000
## 5 2 50 5 5770000
## 6 3 88 14 11918400Plus, floor should be smaller than max_floor
sberbankTr%>%filter(floor>max_floor)%>%head## id timestamp full_sq life_sq floor max_floor material build_year
## 1 8219 2013-05-29 58 30 13 0 1 NA
## 2 8271 2013-05-31 93 93 3 1 1 2013
## 3 8502 2013-06-14 37 18 2 0 1 1979
## 4 8534 2013-06-17 34 15 7 0 6 NA
## 5 8915 2013-07-03 51 30 5 0 1 1997
## 6 9164 2013-07-12 83 42 8 3 2 1961
## num_room kitch_sq state product_type sub_area area_m
## 1 2 0 NA OwnerOccupier Poselenie Voskresenskoe 21494095
## 2 3 1 1 OwnerOccupier Poselenie Pervomajskoe 118663840
## 3 1 0 2 Investment Bibirevo 6407578
## 4 1 8 1 OwnerOccupier Poselenie Voskresenskoe 21494095
## 5 2 8 1 Investment Beskudnikovskoe 3292112
## 6 3 9 2 Investment Tverskoe 7307411
## raion_popul green_zone_part indust_part children_preschool
## 1 7122 0.26245914 0.0176470530 489
## 2 7538 0.55188258 0.0140729610 477
## 3 155572 0.18972712 0.0000699893 9576
## 4 7122 0.26245914 0.0176470530 489
## 5 73148 0.06374725 0.0922906420 4449
## 6 75377 0.06544431 0.0000781528 4237
## preschool_quota preschool_education_centers_raion children_school
## 1 NA 0 469
## 2 NA 0 475
## 3 5001 5 10309
## 4 NA 0 469
## 5 2757 5 4346
## 6 1874 4 6398
## school_quota school_education_centers_raion
## 1 NA 0
## 2 NA 0
## 3 11065 5
## 4 NA 0
## 5 7327 5
## 6 6772 4
## school_education_centers_top_20_raion hospital_beds_raion
## 1 0 NA
## 2 0 NA
## 3 0 240
## 4 0 NA
## 5 0 165
## 6 1 1046
## healthcare_centers_raion university_top_20_raion sport_objects_raion
## 1 0 0 0
## 2 0 0 0
## 3 1 0 7
## 4 0 0 0
## 5 1 0 3
## 6 3 2 29
## additional_education_raion culture_objects_top_25
## 1 2 no
## 2 0 no
## 3 3 no
## 4 2 no
## 5 2 no
## 6 16 yes
## culture_objects_top_25_raion shopping_centers_raion office_raion
## 1 0 0 0
## 2 0 0 0
## 3 0 16 1
## 4 0 0 0
## 5 0 2 2
## 6 10 23 141
## thermal_power_plant_raion incineration_raion oil_chemistry_raion
## 1 no no no
## 2 no no no
## 3 no no no
## 4 no no no
## 5 no no no
## 6 no no no
## radiation_raion railroad_terminal_raion big_market_raion
## 1 no no no
## 2 no no no
## 3 no no no
## 4 no no no
## 5 no no no
## 6 yes yes no
## nuclear_reactor_raion detention_facility_raion full_all male_f female_f
## 1 no no 9553 4529 5024
## 2 no no 5740 2824 2917
## 3 no no 86206 40477 45729
## 4 no no 9553 4529 5024
## 5 no no 41504 18905 22599
## 6 no yes 116742 52836 63906
## young_all young_male young_female work_all work_male work_female
## 1 1021 529 493 4568 2414 2155
## 2 1016 525 491 4880 2727 2154
## 3 21154 11007 10147 98207 52277 45930
## 4 1021 529 493 4568 2414 2155
## 5 9308 4847 4461 44449 22170 22279
## 6 11272 5470 5802 43921 21901 22020
## ekder_all ekder_male ekder_female 0_6_all 0_6_male 0_6_female 7_14_all
## 1 1533 435 1099 489 254 236 469
## 2 1642 457 1185 477 246 231 475
## 3 36211 10580 25631 9576 4899 4677 10309
## 4 1533 435 1099 489 254 236 469
## 5 19391 5955 13436 4449 2281 2168 4346
## 6 20184 6644 13540 4237 2079 2158 6398
## 7_14_male 7_14_female 0_17_all 0_17_male 0_17_female 16_29_all
## 1 242 228 1150 597 553 2155
## 2 247 228 1138 588 551 1432
## 3 5463 4846 23603 12286 11317 17508
## 4 242 228 1150 597 553 2155
## 5 2283 2063 10271 5360 4911 9620
## 6 3094 3304 12508 6065 6443 23480
## 16_29_male 16_29_female 0_13_all 0_13_male 0_13_female
## 1 1206 950 900 465 435
## 2 877 556 893 463 430
## 3 9425 8083 18654 9709 8945
## 4 1206 950 900 465 435
## 5 4575 5045 8322 4322 4000
## 6 11491 11989 9955 4835 5120
## raion_build_count_with_material_info build_count_block build_count_wood
## 1 NA NA NA
## 2 1 1 0
## 3 211 25 0
## 4 NA NA NA
## 5 222 97 0
## 6 651 19 27
## build_count_frame build_count_brick build_count_monolith
## 1 NA NA NA
## 2 0 0 0
## 3 0 0 2
## 4 NA NA NA
## 5 0 4 13
## 6 4 529 25
## build_count_panel build_count_foam build_count_slag build_count_mix
## 1 NA NA NA NA
## 2 0 0 0 0
## 3 184 0 0 0
## 4 NA NA NA NA
## 5 108 0 0 0
## 6 41 0 5 1
## raion_build_count_with_builddate_info build_count_before_1920
## 1 NA NA
## 2 1 0
## 3 211 0
## 4 NA NA
## 5 222 0
## 6 650 263
## build_count_1921-1945 build_count_1946-1970 build_count_1971-1995
## 1 NA NA NA
## 2 0 1 0
## 3 0 0 206
## 4 NA NA NA
## 5 0 144 25
## 6 105 154 71
## build_count_after_1995 ID_metro metro_min_avto metro_km_avto
## 1 NA 45 5.595301 2.254916
## 2 0 206 22.617277 25.516583
## 3 5 8 1.639245 1.394360
## 4 NA 45 3.121542 2.436882
## 5 53 90 2.559909 1.192569
## 6 57 212 1.756117 1.404894
## metro_min_walk metro_km_walk kindergarten_km school_km park_km
## 1 27.058996 2.2549163 0.6472391 0.8260521 1.7999148
## 2 330.483158 27.5402632 7.2532768 11.3922174 18.6527788
## 3 16.732319 1.3943599 0.2459102 0.4096068 1.7753905
## 4 29.242588 2.4368823 0.7452858 0.9363236 1.7737590
## 5 5.767462 0.4806218 0.4094077 0.3137615 1.4680926
## 6 8.595866 0.7163222 0.3916439 0.8925907 0.3736273
## green_zone_km industrial_km water_treatment_km cemetery_km
## 1 0.31438174 1.00613309 0.970986 1.630750
## 2 0.29735594 0.54285648 20.093970 2.790259
## 3 0.14038006 0.60959772 23.229580 1.852694
## 4 0.45670428 1.01813706 1.014447 1.765269
## 5 0.16753390 0.03326611 22.104300 1.901829
## 6 0.04099266 2.01779737 12.262590 2.606584
## incineration_km railroad_station_walk_km railroad_station_walk_min
## 1 11.435910 5.784237 69.41085
## 2 28.358180 10.579845 126.95815
## 3 3.270613 4.490452 53.88543
## 4 11.571550 5.966203 71.59444
## 5 0.924072 1.445479 17.34575
## 6 10.278240 2.513238 30.15886
## ID_railroad_station_walk railroad_station_avto_km
## 1 39 5.784237
## 2 108 10.579845
## 3 1 4.490452
## 4 39 5.966203
## 5 64 2.157427
## 6 83 3.547253
## railroad_station_avto_min ID_railroad_station_avto
## 1 9.879409 39
## 2 12.558241 108
## 3 4.789005 1
## 4 7.405651 39
## 5 3.765981 64
## 6 4.861863 80
## public_transport_station_km public_transport_station_min_walk
## 1 0.32735719 3.9282862
## 2 0.98895285 11.8674342
## 3 0.07068581 0.8482297
## 4 0.46757568 5.6109082
## 5 0.03522273 0.4226727
## 6 0.09988011 1.1985614
## water_km water_1line mkad_km ttk_km sadovoe_km bulvar_ring_km
## 1 0.138167727 yes 7.229763 20.483476 23.6136144 24.8931303
## 2 0.326167197 no 20.414777 31.073663 33.7727652 34.9422760
## 3 1.295901892 no 1.800545 10.802549 12.9839065 13.5546830
## 4 0.006707311 yes 7.371716 20.624073 23.7533881 25.0321097
## 5 0.234796467 no 5.878261 7.063888 9.4900001 10.1009633
## 6 1.022373933 no 13.996703 2.500557 0.3127259 0.2342427
## kremlin_km big_road1_km ID_big_road1 big_road1_1line big_road2_km
## 1 25.595974 4.540854 2 no 5.585990
## 2 35.770687 4.532678 13 no 8.674693
## 3 15.041282 1.800545 1 no 3.264185
## 4 25.735256 4.586082 2 no 5.602182
## 5 11.580949 0.151530 5 no 5.079906
## 6 1.725258 2.500557 34 no 2.500557
## ID_big_road2 railroad_km railroad_1line zd_vokzaly_avto_km
## 1 38 1.1284093 no 29.072494
## 2 38 7.5925500 no 49.711909
## 3 5 1.3428188 no 13.302520
## 4 38 1.2661420 no 29.254460
## 5 30 0.4749633 no 8.760325
## 6 4 2.0177974 no 3.209214
## ID_railroad_terminal bus_terminal_avto_km ID_bus_terminal
## 1 32 9.943967 9
## 2 50 34.053817 8
## 3 101 23.362965 1
## 4 32 10.125933 9
## 5 121 11.605691 5
## 6 83 3.398231 4
## oil_chemistry_km nuclear_reactor_km radiation_km
## 1 23.463447 17.103736 7.4364868
## 2 40.553843 30.662722 21.5514723
## 3 18.333611 5.174899 0.6311975
## 4 23.590580 17.243125 7.5762254
## 5 16.639902 2.078564 0.9432842
## 6 9.450249 8.282563 0.6467738
## power_transmission_line_km thermal_power_plant_km ts_km
## 1 4.798950 9.793209 9.530140
## 2 17.948822 24.321468 17.944250
## 3 1.490064 5.490035 4.120848
## 4 4.926551 9.920165 9.658805
## 5 1.014119 5.369908 1.948918
## 6 4.354573 5.073351 2.510430
## big_market_km market_shop_km fitness_km swim_pool_km ice_rink_km
## 1 14.703756 6.287163 0.6800168 5.192950 13.329847
## 2 32.233184 12.495152 7.9161156 23.836031 24.030191
## 3 9.884731 1.663206 0.1962435 2.135606 1.698589
## 4 14.350258 6.469129 0.8223239 5.374915 13.511813
## 5 16.583547 3.452315 0.3836134 2.515825 4.979982
## 6 9.616704 1.495449 0.2842626 1.193776 1.469792
## stadium_km basketball_km hospice_morgue_km detention_facility_km
## 1 21.911435 9.9891704 4.0962459 26.9817488
## 2 30.585763 22.6212304 9.6096165 47.9521421
## 3 9.233697 3.1682968 2.2925068 4.4513485
## 4 22.093400 10.1313384 4.2176080 27.1637148
## 5 4.864658 0.8192178 3.3214938 7.4749182
## 6 4.198491 0.3679491 0.2546149 0.1764217
## public_healthcare_km university_km workplaces_km shopping_centers_km
## 1 3.262483 18.733878 8.802153 3.2315278
## 2 17.599690 29.580840 21.617931 8.5666844
## 3 1.680239 5.785585 1.053419 0.4588378
## 4 2.992295 18.915844 8.941513 3.1723338
## 5 3.107655 4.026318 2.482394 0.1239680
## 6 1.905216 1.983626 1.896555 0.5029871
## office_km additional_education_km preschool_km big_church_km
## 1 5.07238942 1.2106132 0.8260521 1.5823780
## 2 8.18896578 7.4773415 11.3922174 9.4821332
## 3 0.09866115 0.6697903 0.4096068 0.3727119
## 4 5.08710599 1.3340193 0.9363236 1.7149830
## 5 0.32238773 0.4857496 0.3137615 1.3538009
## 6 0.24514390 0.8789289 0.8925907 0.1848937
## church_synagogue_km mosque_km theater_km museum_km exhibition_km
## 1 0.7561186 2.797455 21.472421 14.917332 13.029959
## 2 0.7738169 20.876846 30.146749 23.720853 18.517204
## 3 0.3766381 3.710017 13.123606 7.307488 7.184673
## 4 0.8223935 2.938594 21.654387 15.051278 13.149344
## 5 0.3383902 1.719862 8.026071 4.567166 3.515528
## 6 0.1782223 1.333860 1.485872 0.399813 2.489026
## catering_km ecology green_part_500 prom_part_500 office_count_500
## 1 1.31491481 no data 9.88 0.00 0
## 2 7.77651804 no data 21.14 0.00 0
## 3 0.10547629 good 6.22 0.00 1
## 4 1.45291864 no data 1.48 0.00 0
## 5 0.18218197 poor 7.25 54.58 3
## 6 0.06423368 excellent 11.25 0.00 20
## office_sqm_500 trc_count_500 trc_sqm_500 cafe_count_500
## 1 0 0 0 0
## 2 0 0 0 0
## 3 30500 2 84000 6
## 4 0 0 0 0
## 5 68633 1 19000 2
## 6 130314 0 0 58
## cafe_sum_500_min_price_avg cafe_sum_500_max_price_avg cafe_avg_price_500
## 1 NA NA NA
## 2 NA NA NA
## 3 766.67 1250.00 1008.33
## 4 NA NA NA
## 5 750.00 1250.00 1000.00
## 6 996.23 1632.08 1314.15
## cafe_count_500_na_price cafe_count_500_price_500
## 1 0 0
## 2 0 0
## 3 0 2
## 4 0 0
## 5 0 0
## 6 5 11
## cafe_count_500_price_1000 cafe_count_500_price_1500
## 1 0 0
## 2 0 0
## 3 1 2
## 4 0 0
## 5 1 1
## 6 12 14
## cafe_count_500_price_2500 cafe_count_500_price_4000
## 1 0 0
## 2 0 0
## 3 1 0
## 4 0 0
## 5 0 0
## 6 12 3
## cafe_count_500_price_high big_church_count_500 church_count_500
## 1 0 0 0
## 2 0 0 0
## 3 0 1 1
## 4 0 0 0
## 5 0 0 1
## 6 1 5 6
## mosque_count_500 leisure_count_500 sport_count_500 market_count_500
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 2 0
## 4 0 0 0 0
## 5 0 0 2 0
## 6 0 5 8 0
## green_part_1000 prom_part_1000 office_count_1000 office_sqm_1000
## 1 13.93 0.00 0 0
## 2 31.39 0.52 0 0
## 3 5.55 6.20 3 39554
## 4 10.96 0.00 0 0
## 5 8.79 51.34 4 81690
## 6 9.36 0.00 90 750476
## trc_count_1000 trc_sqm_1000 cafe_count_1000 cafe_sum_1000_min_price_avg
## 1 0 0 0 NA
## 2 0 0 0 NA
## 3 8 152800 21 623.81
## 4 0 0 0 NA
## 5 1 19000 6 800.00
## 6 5 227500 302 922.50
## cafe_sum_1000_max_price_avg cafe_avg_price_1000 cafe_count_1000_na_price
## 1 NA NA 0
## 2 NA NA 0
## 3 1071.43 847.62 0
## 4 NA NA 0
## 5 1300.00 1050.00 1
## 6 1514.29 1218.39 22
## cafe_count_1000_price_500 cafe_count_1000_price_1000
## 1 0 0
## 2 0 0
## 3 7 8
## 4 0 0
## 5 0 2
## 6 76 65
## cafe_count_1000_price_1500 cafe_count_1000_price_2500
## 1 0 0
## 2 0 0
## 3 4 2
## 4 0 0
## 5 3 0
## 6 69 47
## cafe_count_1000_price_4000 cafe_count_1000_price_high
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 19 4
## big_church_count_1000 church_count_1000 mosque_count_1000
## 1 0 1 0
## 2 0 1 0
## 3 1 1 0
## 4 0 1 0
## 5 0 2 0
## 6 10 13 0
## leisure_count_1000 sport_count_1000 market_count_1000 green_part_1500
## 1 0 1 0 13.83
## 2 0 0 0 25.93
## 3 0 3 1 11.41
## 4 0 1 0 12.58
## 5 0 4 0 8.06
## 6 10 13 1 8.33
## prom_part_1500 office_count_1500 office_sqm_1500 trc_count_1500
## 1 1.42 0 0 0
## 2 2.84 0 0 0
## 3 11.13 5 109554 11
## 4 1.45 0 0 0
## 5 39.26 8 110853 3
## 6 0.00 162 1402693 16
## trc_sqm_1500 cafe_count_1500 cafe_sum_1500_min_price_avg
## 1 0 1 1000.00
## 2 0 0 NA
## 3 302291 34 633.33
## 4 0 1 1000.00
## 5 45790 20 713.33
## 6 443600 624 929.59
## cafe_sum_1500_max_price_avg cafe_avg_price_1500 cafe_count_1500_na_price
## 1 1500.00 1250.00 0
## 2 NA NA 0
## 3 1075.76 854.55 1
## 4 1500.00 1250.00 0
## 5 1166.67 940.00 5
## 6 1529.76 1229.68 36
## cafe_count_1500_price_500 cafe_count_1500_price_1000
## 1 0 0
## 2 0 0
## 3 13 11
## 4 0 0
## 5 4 4
## 6 157 142
## cafe_count_1500_price_1500 cafe_count_1500_price_2500
## 1 1 0
## 2 0 0
## 3 6 2
## 4 1 0
## 5 6 1
## 6 137 99
## cafe_count_1500_price_4000 cafe_count_1500_price_high
## 1 0 0
## 2 0 0
## 3 1 0
## 4 0 0
## 5 0 0
## 6 46 7
## big_church_count_1500 church_count_1500 mosque_count_1500
## 1 0 1 0
## 2 0 1 0
## 3 1 2 0
## 4 0 1 0
## 5 1 5 0
## 6 19 32 1
## leisure_count_1500 sport_count_1500 market_count_1500 green_part_2000
## 1 0 1 0 12.39
## 2 0 0 0 30.13
## 3 0 6 1 14.13
## 4 0 1 0 11.82
## 5 0 8 0 7.15
## 6 34 22 2 7.19
## prom_part_2000 office_count_2000 office_sqm_2000 trc_count_2000
## 1 1.55 0 0 0
## 2 4.24 0 0 0
## 3 14.50 8 176554 12
## 4 1.64 0 0 0
## 5 34.89 16 273378 7
## 6 0.00 250 2669813 30
## trc_sqm_2000 cafe_count_2000 cafe_sum_2000_min_price_avg
## 1 0 2 1000.00
## 2 0 0 NA
## 3 305634 39 621.62
## 4 0 2 1000.00
## 5 138430 38 631.25
## 6 1094355 1030 903.21
## cafe_sum_2000_max_price_avg cafe_avg_price_2000 cafe_count_2000_na_price
## 1 1500.00 1250.00 0
## 2 NA NA 0
## 3 1054.05 837.84 2
## 4 1500.00 1250.00 0
## 5 1062.50 846.88 6
## 6 1489.64 1196.42 65
## cafe_count_2000_price_500 cafe_count_2000_price_1000
## 1 0 0
## 2 0 0
## 3 15 12
## 4 0 0
## 5 9 12
## 6 252 249
## cafe_count_2000_price_1500 cafe_count_2000_price_2500
## 1 2 0
## 2 0 0
## 3 7 2
## 4 2 0
## 5 10 1
## 6 231 160
## cafe_count_2000_price_4000 cafe_count_2000_price_high
## 1 0 0
## 2 0 0
## 3 1 0
## 4 0 0
## 5 0 0
## 6 61 12
## big_church_count_2000 church_count_2000 mosque_count_2000
## 1 1 2 0
## 2 0 1 0
## 3 1 2 0
## 4 1 2 0
## 5 2 9 1
## 6 31 56 1
## leisure_count_2000 sport_count_2000 market_count_2000 green_part_3000
## 1 0 3 0 10.54
## 2 0 0 0 35.89
## 3 0 11 1 11.93
## 4 0 3 0 10.62
## 5 0 10 0 11.71
## 6 44 44 3 6.13
## prom_part_3000 office_count_3000 office_sqm_3000 trc_count_3000
## 1 2.14 0 0 0
## 2 2.72 0 0 0
## 3 17.18 14 363854 24
## 4 1.98 0 0 0
## 5 25.83 22 376420 17
## 6 4.62 413 4813397 47
## trc_sqm_3000 cafe_count_3000 cafe_sum_3000_min_price_avg
## 1 0 3 1000.00
## 2 0 0 NA
## 3 1457204 75 641.43
## 4 0 3 1000.00
## 5 436735 80 669.44
## 6 1766369 1583 877.71
## cafe_sum_3000_max_price_avg cafe_avg_price_3000 cafe_count_3000_na_price
## 1 1500.00 1250.00 0
## 2 NA NA 0
## 3 1085.71 863.57 5
## 4 1500.00 1250.00 0
## 5 1138.89 904.17 8
## 6 1448.80 1163.25 89
## cafe_count_3000_price_500 cafe_count_3000_price_1000
## 1 0 0
## 2 0 0
## 3 23 25
## 4 0 0
## 5 19 30
## 6 406 396
## cafe_count_3000_price_1500 cafe_count_3000_price_2500
## 1 3 0
## 2 0 0
## 3 17 4
## 4 3 0
## 5 18 3
## 6 361 224
## cafe_count_3000_price_4000 cafe_count_3000_price_high
## 1 0 0
## 2 0 0
## 3 1 0
## 4 0 0
## 5 2 0
## 6 89 18
## big_church_count_3000 church_count_3000 mosque_count_3000
## 1 1 3 1
## 2 0 1 0
## 3 3 4 0
## 4 1 3 1
## 5 4 13 1
## 6 73 128 1
## leisure_count_3000 sport_count_3000 market_count_3000 green_part_5000
## 1 0 5 0 17.92
## 2 0 0 0 42.79
## 3 0 21 2 13.60
## 4 0 5 0 17.74
## 5 0 26 1 17.63
## 6 61 88 6 7.98
## prom_part_5000 office_count_5000 office_sqm_5000 trc_count_5000
## 1 4.75 0 0 5
## 2 1.80 0 0 0
## 3 15.35 29 805983 49
## 4 4.75 0 0 5
## 5 18.20 48 956899 41
## 6 10.73 747 9814494 104
## trc_sqm_5000 cafe_count_5000 cafe_sum_5000_min_price_avg
## 1 262000 18 700.00
## 2 0 0 NA
## 3 2280401 151 720.14
## 4 262000 18 700.00
## 5 1182332 190 694.41
## 6 3657379 2551 877.90
## cafe_sum_5000_max_price_avg cafe_avg_price_5000 cafe_count_5000_na_price
## 1 1125.00 912.50 2
## 2 NA NA 0
## 3 1205.04 962.59 12
## 4 1125.00 912.50 2
## 5 1170.39 932.40 11
## 6 1447.38 1162.64 166
## cafe_count_5000_price_500 cafe_count_5000_price_1000
## 1 4 4
## 2 0 0
## 3 37 48
## 4 4 4
## 5 51 65
## 6 636 621
## cafe_count_5000_price_1500 cafe_count_5000_price_2500
## 1 8 0
## 2 0 0
## 3 40 10
## 4 8 0
## 5 46 12
## 6 605 359
## cafe_count_5000_price_4000 cafe_count_5000_price_high
## 1 0 0
## 2 0 0
## 3 4 0
## 4 0 0
## 5 5 0
## 6 138 26
## big_church_count_5000 church_count_5000 mosque_count_5000
## 1 1 7 1
## 2 0 3 0
## 3 11 23 1
## 4 1 7 1
## 5 13 29 1
## 6 134 233 2
## leisure_count_5000 sport_count_5000 market_count_5000 price_doc
## 1 0 12 1 5813760
## 2 0 0 0 5427640
## 3 0 52 5 6000000
## 4 0 12 1 3842500
## 5 2 74 11 3850000
## 6 99 196 13 24435000The kitch_sq should be smaller than full_sq or life_sq.
sberbankTr%>%filter(kitch_sq>full_sq | kitch_sq>life_sq)## id timestamp full_sq life_sq floor max_floor material build_year
## 1 8059 2013-05-21 11 11 2 5 2 1907
## 2 9175 2013-07-12 37 19 9 10 1 2006
## 3 10051 2013-08-24 38 20 13 16 1 1983
## 4 10371 2013-09-10 79 41 5 17 6 2013
## 5 10542 2013-09-16 59 13 8 40 4 2009
## 6 10644 2013-09-21 41 1 2 1 6 NA
## 7 10731 2013-09-25 63 30 16 16 4 NA
## 8 10958 2013-10-03 81 1 3 10 6 2016
## 9 11093 2013-10-08 79 1 15 17 1 NA
## 10 11160 2013-10-10 43 1 13 13 6 NA
## 11 11190 2013-10-11 58 1 13 22 6 NA
## 12 11244 2013-10-15 41 10 15 17 2 2004
## 13 11249 2013-10-15 48 42 20 1 1 2013
## 14 11523 2013-10-24 38 23 11 14 5 1971
## 15 11865 2013-11-06 54 1 22 22 6 2013
## 16 12248 2013-11-18 22 14 3 5 2 1970
## 17 12426 2013-11-23 75 46 7 12 1 NA
## 18 13120 2013-12-14 31 19 5 1 1 NA
## 19 13220 2013-12-18 61 0 16 17 1 2013
## 20 14700 2014-02-07 59 18 2 6 2 0
## 21 15591 2014-03-01 35 19 4 14 1 1970
## 22 15732 2014-03-05 84 22 5 19 4 1984
## 23 15973 2014-03-13 62 33 1 7 1 2014
## 24 16415 2014-03-24 74 51 1 9 1 1985
## 25 16891 2014-04-02 53 32 7 17 1 1994
## 26 17933 2014-04-28 37 0 5 0 1 2015
## 27 18407 2014-05-14 67 47 5 6 2 1929
## 28 18795 2014-05-23 66 14 2 20 6 2006
## 29 19399 2014-06-05 59 13 7 30 4 2011
## 30 20744 2014-07-10 55 0 6 0 1 0
## 31 21335 2014-08-07 73 0 3 16 4 NA
## 32 21418 2014-08-11 43 43 3 1 1 2014
## 33 21641 2014-08-19 84 0 8 0 1 0
## 34 21680 2014-08-20 42 0 15 24 1 0
## 35 22140 2014-09-02 75 43 11 16 1 1986
## 36 22194 2014-09-03 60 33 4 17 4 2011
## 37 22461 2014-09-12 66 62 2 2 1 2014
## 38 22982 2014-09-27 32 10 7 25 4 2013
## 39 23219 2014-10-02 43 29 5 5 2 1962
## 40 23247 2014-10-03 51 9 2 3 4 2010
## 41 23792 2014-10-21 63 31 5 17 1 2016
## 42 26170 2014-12-09 56 18 4 6 2 2014
## 43 26239 2014-12-11 34 16 2 17 2 2016
## 44 26339 2014-12-12 78 23 12 40 4 2012
## 45 26783 2014-12-18 39 20 1 10 1 2010
## 46 26836 2014-12-19 39 11 1 15 1 2010
## 47 26853 2014-12-19 38 20 4 12 1 1977
## 48 27121 2014-12-24 78 23 6 40 4 2014
## 49 27432 2015-01-21 78 22 34 40 1 2013
## 50 27654 2015-01-31 77 0 4 24 4 2016
## 51 28271 2015-03-10 93 29 2 20 4 2006
## 52 28737 2015-03-31 31 16 2 12 5 1972
## 53 29000 2015-04-09 50 30 4 17 1 2014
## 54 29228 2015-04-18 50 11 6 9 6 1910
## 55 29591 2015-05-07 65 46 5 9 1 1978
## 56 30272 2015-06-16 34 12 17 25 4 2014
## num_room kitch_sq state product_type sub_area
## 1 1 12 3 Investment Hamovniki
## 2 1 73 2 Investment Kosino-Uhtomskoe
## 3 1 37 3 Investment Juzhnoe Tushino
## 4 3 2013 1 OwnerOccupier Poselenie Voskresenskoe
## 5 2 20 4 Investment Sokol
## 6 1 41 1 OwnerOccupier Strogino
## 7 2 63 NA OwnerOccupier Poselenie Voskresenskoe
## 8 3 14 2 OwnerOccupier Mitino
## 9 3 12 1 OwnerOccupier Nagornoe
## 10 1 40 1 OwnerOccupier Mitino
## 11 2 58 1 OwnerOccupier Mitino
## 12 2 19 NA Investment Troickij okrug
## 13 1 48 1 OwnerOccupier Nekrasovka
## 14 2 620 2 Investment Novogireevo
## 15 2 54 1 OwnerOccupier Mitino
## 16 1 33 1 Investment Bogorodskoe
## 17 3 75 2 Investment Juzhnoe Butovo
## 18 1 1970 3 Investment Izmajlovo
## 19 2 10 1 OwnerOccupier Nekrasovka
## 20 1 19 1 OwnerOccupier Troickij okrug
## 21 1 35 1 Investment Chertanovo Central'noe
## 22 2 30 3 Investment Taganskoe
## 23 1 62 1 OwnerOccupier Poselenie Krasnopahorskoe
## 24 4 74 3 Investment Danilovskoe
## 25 2 53 3 Investment Donskoe
## 26 1 11 3 OwnerOccupier Poselenie Voskresenskoe
## 27 3 65 2 Investment Presnenskoe
## 28 1 30 4 Investment Prospekt Vernadskogo
## 29 1 21 1 Investment Sokol
## 30 2 12 1 OwnerOccupier Poselenie Sosenskoe
## 31 2 1 1 OwnerOccupier Tverskoe
## 32 1 2014 1 OwnerOccupier Poselenie Sosenskoe
## 33 3 1 1 OwnerOccupier Poselenie Sosenskoe
## 34 1 1 1 OwnerOccupier Poselenie Vnukovskoe
## 35 3 72 3 Investment Mozhajskoe
## 36 2 60 3 Investment Poselenie Sosenskoe
## 37 2 66 1 OwnerOccupier Poselenie Voskresenskoe
## 38 1 13 1 Investment Poselenie Sosenskoe
## 39 2 44 2 Investment Metrogorodok
## 40 1 20 4 Investment Poselenie Pervomajskoe
## 41 2 63 NA OwnerOccupier Poselenie Filimonkovskoe
## 42 1 19 1 Investment Troickij okrug
## 43 1 84 1 OwnerOccupier Poselenie Desjonovskoe
## 44 1 29 1 Investment Akademicheskoe
## 45 1 39 2 Investment Strogino
## 46 1 14 3 Investment Poselenie Sosenskoe
## 47 1 86 2 Investment Ivanovskoe
## 48 1 29 1 Investment Akademicheskoe
## 49 1 28 3 Investment Akademicheskoe
## 50 1 1 1 OwnerOccupier Hovrino
## 51 2 31 3 Investment Jakimanka
## 52 1 1974 3 Investment Matushkino
## 53 2 97 1 Investment Nekrasovka
## 54 1 20 3 Investment Kotlovka
## 55 4 61 2 Investment Jasenevo
## 56 1 13 3 Investment Kon'kovo
## area_m raion_popul green_zone_part indust_part children_preschool
## 1 10071560 102726 0.04879058 0.0000000000 6374
## 2 14883622 72131 0.02444419 0.1582490430 7567
## 3 7887684 104434 0.22264632 0.1887837660 5989
## 4 21494095 7122 0.26245914 0.0176470530 489
## 5 3496890 57107 0.06792697 0.1747390840 2982
## 6 16751119 155427 0.33815098 0.0411246340 9254
## 7 21494095 7122 0.26245914 0.0176470530 489
## 8 12583536 178473 0.19470287 0.0697533610 13087
## 9 5293465 77878 0.02346402 0.1957810530 4713
## 10 12583536 178473 0.19470287 0.0697533610 13087
## 11 12583536 178473 0.19470287 0.0697533610 13087
## 12 16315230 39873 0.37597378 0.0754236800 2870
## 13 11391678 19940 0.05564356 0.2432045190 1706
## 14 4395333 94561 0.06375521 0.0386929870 6120
## 15 12583536 178473 0.19470287 0.0697533610 13087
## 16 8659075 104410 0.41686645 0.0696603440 7103
## 17 26155137 178264 0.13784620 0.0411163540 14080
## 18 15045565 102828 0.63739894 0.0060761520 4992
## 19 11391678 19940 0.05564356 0.2432045190 1706
## 20 16315230 39873 0.37597378 0.0754236800 2870
## 21 6879020 112221 0.06147666 0.2827976690 7104
## 22 8087656 116742 0.04801103 0.0076585150 6694
## 23 86999406 4199 0.50164804 0.0140579200 294
## 24 12495435 91100 0.02765099 0.3410719450 5947
## 25 5686537 48439 0.07352096 0.0006991190 3229
## 26 21494095 7122 0.26245914 0.0176470530 489
## 27 11638050 123280 0.06820217 0.0420315870 7125
## 28 4695913 61039 0.26045964 0.0000000000 3201
## 29 3496890 57107 0.06792697 0.1747390840 2982
## 30 66772451 9553 0.33617690 0.0721575810 656
## 31 7307411 75377 0.06544431 0.0000781528 4237
## 32 66772451 9553 0.33617690 0.0721575810 656
## 33 66772451 9553 0.33617690 0.0721575810 656
## 34 25536297 4001 0.49631528 0.0071223170 275
## 35 16861533 132349 0.06967088 0.1306178060 9005
## 36 66772451 9553 0.33617690 0.0721575810 656
## 37 21494095 7122 0.26245914 0.0176470530 489
## 38 66772451 9553 0.33617690 0.0721575810 656
## 39 27456467 36154 0.85292284 0.0868852650 2153
## 40 118663840 7538 0.55188258 0.0140729610 477
## 41 35747948 2546 0.54899161 0.0346560820 175
## 42 16315230 39873 0.37597378 0.0754236800 2870
## 43 52995275 13890 0.34993529 0.0116541270 953
## 44 5704502 106445 0.04312710 0.1513462160 5506
## 45 16751119 155427 0.33815098 0.0411246340 9254
## 46 66772451 9553 0.33617690 0.0721575810 656
## 47 10207215 122862 0.51270747 0.0001696760 6027
## 48 5704502 106445 0.04312710 0.1513462160 5506
## 49 5704502 106445 0.04312710 0.1513462160 5506
## 50 5264734 80791 0.18428294 0.0507555690 5195
## 51 4800968 26578 0.29471752 0.0000000000 1792
## 52 4708040 38075 0.27170152 0.3101991300 2448
## 53 11391678 19940 0.05564356 0.2432045190 1706
## 54 3998216 64317 0.25871247 0.0144231140 3977
## 55 24813850 174831 0.68384440 0.0371778180 10712
## 56 7811375 153248 0.12700238 0.1356497890 7517
## preschool_quota preschool_education_centers_raion children_school
## 1 165 5 7538
## 2 3848 4 5731
## 3 2707 3 6137
## 4 NA 0 469
## 5 1744 4 3379
## 6 4606 8 9515
## 7 NA 0 469
## 8 6839 9 13670
## 9 2279 4 5212
## 10 6839 9 13670
## 11 6839 9 13670
## 12 NA 0 3097
## 13 2395 5 1564
## 14 2215 4 6533
## 15 6839 9 13670
## 16 3107 7 6119
## 17 11926 11 14892
## 18 1313 4 5285
## 19 2395 5 1564
## 20 NA 0 3097
## 21 2546 5 8667
## 22 3318 5 7077
## 23 NA 0 290
## 24 2235 5 5664
## 25 0 1 3369
## 26 NA 0 469
## 27 3240 7 6856
## 28 866 5 3374
## 29 1744 4 3379
## 30 NA 0 629
## 31 1874 4 6398
## 32 NA 0 629
## 33 NA 0 629
## 34 NA 0 264
## 35 4182 6 10418
## 36 NA 0 629
## 37 NA 0 469
## 38 NA 0 629
## 39 725 2 2277
## 40 NA 0 475
## 41 NA 0 168
## 42 NA 0 3097
## 43 NA 0 915
## 44 926 6 5889
## 45 4606 8 9515
## 46 NA 0 629
## 47 2697 7 5992
## 48 926 6 5889
## 49 926 6 5889
## 50 3082 4 5469
## 51 0 1 1660
## 52 2080 3 2748
## 53 2395 5 1564
## 54 1094 1 3806
## 55 4172 7 11217
## 56 2627 5 7960
## school_quota school_education_centers_raion
## 1 9337 8
## 2 8687 5
## 3 6340 4
## 4 NA 0
## 5 4960 6
## 6 11032 9
## 7 NA 0
## 8 17063 10
## 9 10027 8
## 10 17063 10
## 11 17063 10
## 12 NA 0
## 13 7377 5
## 14 5824 4
## 15 17063 10
## 16 7277 9
## 17 24750 13
## 18 4339 6
## 19 7377 5
## 20 NA 0
## 21 6009 5
## 22 9748 10
## 23 NA 0
## 24 6073 9
## 25 2022 2
## 26 NA 0
## 27 10602 9
## 28 4385 5
## 29 4960 6
## 30 NA 0
## 31 6772 4
## 32 NA 0
## 33 NA 0
## 34 NA 0
## 35 8658 6
## 36 NA 0
## 37 NA 0
## 38 NA 0
## 39 2837 3
## 40 NA 0
## 41 NA 0
## 42 NA 0
## 43 NA 0
## 44 9501 6
## 45 11032 9
## 46 NA 0
## 47 9439 8
## 48 9501 6
## 49 9501 6
## 50 7123 4
## 51 2223 1
## 52 3885 4
## 53 7377 5
## 54 2204 1
## 55 10559 7
## 56 9650 5
## school_education_centers_top_20_raion hospital_beds_raion
## 1 1 4702
## 2 0 NA
## 3 0 NA
## 4 0 NA
## 5 0 100
## 6 0 NA
## 7 0 NA
## 8 0 NA
## 9 1 NA
## 10 0 NA
## 11 0 NA
## 12 0 NA
## 13 0 540
## 14 0 1015
## 15 0 NA
## 16 0 NA
## 17 1 NA
## 18 0 645
## 19 0 540
## 20 0 NA
## 21 0 NA
## 22 0 2301
## 23 0 NA
## 24 0 2300
## 25 0 4129
## 26 0 NA
## 27 0 1940
## 28 0 620
## 29 0 100
## 30 0 NA
## 31 1 1046
## 32 0 NA
## 33 0 NA
## 34 0 NA
## 35 0 900
## 36 0 NA
## 37 0 NA
## 38 0 NA
## 39 0 1547
## 40 0 NA
## 41 0 NA
## 42 0 NA
## 43 0 NA
## 44 0 830
## 45 0 NA
## 46 0 NA
## 47 1 NA
## 48 0 830
## 49 0 830
## 50 0 145
## 51 1 1786
## 52 0 NA
## 53 0 540
## 54 0 NA
## 55 0 2300
## 56 0 350
## healthcare_centers_raion university_top_20_raion sport_objects_raion
## 1 5 1 23
## 2 1 0 4
## 3 0 0 10
## 4 0 0 0
## 5 0 0 5
## 6 1 0 6
## 7 0 0 0
## 8 1 0 17
## 9 3 0 2
## 10 1 0 17
## 11 1 0 17
## 12 0 0 3
## 13 0 0 0
## 14 2 0 7
## 15 1 0 17
## 16 1 0 8
## 17 1 0 13
## 18 6 0 16
## 19 0 0 0
## 20 0 0 3
## 21 0 0 5
## 22 3 1 24
## 23 0 0 0
## 24 3 0 12
## 25 0 0 10
## 26 0 0 0
## 27 2 1 29
## 28 1 1 9
## 29 0 0 5
## 30 0 0 1
## 31 3 2 29
## 32 0 0 1
## 33 0 0 1
## 34 0 0 0
## 35 1 0 12
## 36 0 0 1
## 37 0 0 0
## 38 0 0 1
## 39 0 0 1
## 40 0 0 0
## 41 0 0 0
## 42 0 0 3
## 43 0 0 0
## 44 4 0 7
## 45 1 0 6
## 46 0 0 1
## 47 1 0 5
## 48 4 0 7
## 49 4 0 7
## 50 0 0 10
## 51 1 1 20
## 52 0 0 0
## 53 0 0 0
## 54 0 0 2
## 55 1 0 7
## 56 3 0 11
## additional_education_raion culture_objects_top_25
## 1 2 yes
## 2 2 no
## 3 1 no
## 4 2 no
## 5 10 no
## 6 2 no
## 7 2 no
## 8 6 no
## 9 2 no
## 10 6 no
## 11 6 no
## 12 2 no
## 13 4 no
## 14 1 no
## 15 6 no
## 16 4 no
## 17 4 no
## 18 3 no
## 19 4 no
## 20 2 no
## 21 3 no
## 22 1 yes
## 23 1 no
## 24 5 no
## 25 3 yes
## 26 2 no
## 27 2 yes
## 28 5 no
## 29 10 no
## 30 0 no
## 31 16 yes
## 32 0 no
## 33 0 no
## 34 0 no
## 35 2 no
## 36 0 no
## 37 2 no
## 38 0 no
## 39 1 no
## 40 0 no
## 41 0 no
## 42 2 no
## 43 1 no
## 44 0 no
## 45 2 no
## 46 0 no
## 47 0 no
## 48 0 no
## 49 0 no
## 50 2 no
## 51 1 no
## 52 0 no
## 53 4 no
## 54 1 no
## 55 7 no
## 56 3 no
## culture_objects_top_25_raion shopping_centers_raion office_raion
## 1 2 5 87
## 2 0 0 0
## 3 0 1 4
## 4 0 0 0
## 5 0 3 9
## 6 0 10 5
## 7 0 0 0
## 8 0 11 4
## 9 0 2 6
## 10 0 11 4
## 11 0 11 4
## 12 0 0 0
## 13 0 0 0
## 14 0 5 1
## 15 0 11 4
## 16 0 2 4
## 17 0 4 4
## 18 0 6 0
## 19 0 0 0
## 20 0 0 0
## 21 0 6 2
## 22 1 19 56
## 23 0 0 0
## 24 0 11 48
## 25 1 3 24
## 26 0 0 0
## 27 3 5 84
## 28 0 2 5
## 29 0 3 9
## 30 0 0 1
## 31 10 23 141
## 32 0 0 1
## 33 0 0 1
## 34 0 1 0
## 35 0 9 9
## 36 0 0 1
## 37 0 0 0
## 38 0 0 1
## 39 0 0 2
## 40 0 0 0
## 41 0 0 0
## 42 0 0 0
## 43 0 0 0
## 44 0 1 10
## 45 0 10 5
## 46 0 0 1
## 47 0 1 0
## 48 0 1 10
## 49 0 1 10
## 50 0 1 1
## 51 0 5 39
## 52 0 0 1
## 53 0 0 0
## 54 0 3 4
## 55 0 7 1
## 56 0 11 6
## thermal_power_plant_raion incineration_raion oil_chemistry_raion
## 1 no no no
## 2 no yes no
## 3 no no no
## 4 no no no
## 5 no no no
## 6 no no no
## 7 no no no
## 8 no no no
## 9 no no no
## 10 no no no
## 11 no no no
## 12 no no no
## 13 no yes no
## 14 no no no
## 15 no no no
## 16 no no no
## 17 no no no
## 18 no no no
## 19 no yes no
## 20 no no no
## 21 no no no
## 22 no no no
## 23 no no no
## 24 yes no no
## 25 no no no
## 26 no no no
## 27 no no no
## 28 no no no
## 29 no no no
## 30 no no no
## 31 no no no
## 32 no no no
## 33 no no no
## 34 no no no
## 35 no no no
## 36 no no no
## 37 no no no
## 38 no no no
## 39 yes no no
## 40 no no no
## 41 no no no
## 42 no no no
## 43 no no no
## 44 yes no no
## 45 no no no
## 46 no no no
## 47 no no no
## 48 yes no no
## 49 yes no no
## 50 no no no
## 51 no no no
## 52 no no no
## 53 no yes no
## 54 no no no
## 55 no no no
## 56 no no no
## radiation_raion railroad_terminal_raion big_market_raion
## 1 yes no no
## 2 yes no no
## 3 no no no
## 4 no no no
## 5 no no no
## 6 no no no
## 7 no no no
## 8 no no no
## 9 no no no
## 10 no no no
## 11 no no no
## 12 no no no
## 13 no no no
## 14 yes no no
## 15 no no no
## 16 yes no no
## 17 no no no
## 18 yes no no
## 19 no no no
## 20 no no no
## 21 no no no
## 22 yes no no
## 23 no no no
## 24 no no no
## 25 yes no no
## 26 no no no
## 27 yes no no
## 28 no no no
## 29 no no no
## 30 no no yes
## 31 yes yes no
## 32 no no yes
## 33 no no yes
## 34 no no no
## 35 yes no no
## 36 no no yes
## 37 no no no
## 38 no no yes
## 39 no no no
## 40 no no no
## 41 no no no
## 42 no no no
## 43 no no no
## 44 yes no no
## 45 no no no
## 46 no no yes
## 47 yes no no
## 48 yes no no
## 49 yes no no
## 50 no no no
## 51 no no no
## 52 no no no
## 53 no no no
## 54 no no no
## 55 yes no no
## 56 yes no no
## nuclear_reactor_raion detention_facility_raion full_all male_f female_f
## 1 no no 75377 34015 41362
## 2 no no 102828 47783 55045
## 3 no no 105663 49025 56638
## 4 no no 9553 4529 5024
## 5 no no 57995 26205 31790
## 6 no yes 156377 71846 84531
## 7 no no 9553 4529 5024
## 8 no no 21155 9828 11327
## 9 no no 115352 52813 62539
## 10 no no 21155 9828 11327
## 11 no no 21155 9828 11327
## 12 no no 143661 65525 78136
## 13 no no 247469 112902 134567
## 14 no no 72131 34296 37835
## 15 no no 21155 9828 11327
## 16 no no 1452550 655329 797221
## 17 no no 102618 47681 54937
## 18 no no 122862 54990 67872
## 19 no no 247469 112902 134567
## 20 no no 143661 65525 78136
## 21 no no 111874 46887 64987
## 22 no no 123280 57224 66056
## 23 no no 12061 5934 6127
## 24 no no 102590 48106 54484
## 25 no no 91100 39227 51873
## 26 no no 9553 4529 5024
## 27 no no 57999 25611 32388
## 28 no no 118843 60240 58603
## 29 no no 57995 26205 31790
## 30 no no 13890 6584 7307
## 31 no yes 116742 52836 63906
## 32 no no 13890 6584 7307
## 33 no no 13890 6584 7307
## 34 no no 17790 8350 9443
## 35 no no 142462 64715 77747
## 36 no no 13890 6584 7307
## 37 no no 9553 4529 5024
## 38 no no 13890 6584 7307
## 39 no no 103746 46999 56747
## 40 no no 5740 2824 2917
## 41 no no 2942 1381 1562
## 42 no no 143661 65525 78136
## 43 no no 21819 10343 11477
## 44 yes no 1362363 637906 724457
## 45 no yes 156377 71846 84531
## 46 no no 13890 6584 7307
## 47 no no 157010 68466 88544
## 48 yes no 1362363 637906 724457
## 49 yes no 1362363 637906 724457
## 50 no no 81887 37070 44817
## 51 no no 102726 46180 56546
## 52 no no 85219 39138 46081
## 53 no no 247469 112902 134567
## 54 no no 153248 76265 76983
## 55 no no 178264 82799 95465
## 56 no no 123000 58226 64774
## young_all young_male young_female work_all work_male work_female
## 1 14868 7239 7629 61102 30166 30936
## 2 13954 7202 6752 49242 24606 24636
## 3 12906 6652 6254 65729 34908 30821
## 4 1021 529 493 4568 2414 2155
## 5 6807 3590 3217 36496 18719 17777
## 6 20003 10461 9542 97920 53042 44878
## 7 1021 529 493 4568 2414 2155
## 8 28563 14680 13883 120381 60040 60341
## 9 10600 5422 5178 49882 24603 25279
## 10 28563 14680 13883 120381 60040 60341
## 11 28563 14680 13883 120381 60040 60341
## 12 6363 3275 3088 24009 12128 11881
## 13 3459 1782 1677 13331 6670 6661
## 14 13523 6724 6799 56908 27219 29689
## 15 28563 14680 13883 120381 60040 60341
## 16 13897 6937 6960 59366 30856 28510
## 17 30808 16251 14557 121369 59138 62231
## 18 10988 5655 5333 65499 34167 31332
## 19 3459 1782 1677 13331 6670 6661
## 20 6363 3275 3088 24009 12128 11881
## 21 16719 7542 9177 68254 33075 35179
## 22 14715 7597 7118 71253 35672 35581
## 23 622 318 304 2615 1387 1229
## 24 12370 6137 6233 54478 26382 28096
## 25 7033 3519 3514 28737 14086 14651
## 26 1021 529 493 4568 2414 2155
## 27 14906 7789 7117 75357 38841 36516
## 28 7029 3580 3449 41554 20567 20987
## 29 6807 3590 3217 36496 18719 17777
## 30 1370 709 661 6127 3237 2890
## 31 11272 5470 5802 43921 21901 22020
## 32 1370 709 661 6127 3237 2890
## 33 1370 709 661 6127 3237 2890
## 34 574 297 277 2566 1356 1211
## 35 20725 10613 10112 82808 44728 38080
## 36 1370 709 661 6127 3237 2890
## 37 1021 529 493 4568 2414 2155
## 38 1370 709 661 6127 3237 2890
## 39 4741 2439 2302 22573 11154 11419
## 40 1016 525 491 4880 2727 2154
## 41 365 189 177 1633 863 771
## 42 6363 3275 3088 24009 12128 11881
## 43 1991 1030 962 8910 4707 4203
## 44 12074 6198 5876 68518 34132 34386
## 45 20003 10461 9542 97920 53042 44878
## 46 1370 709 661 6127 3237 2890
## 47 12919 6578 6341 77612 38637 38975
## 48 12074 6198 5876 68518 34132 34386
## 49 12074 6198 5876 68518 34132 34386
## 50 11356 5819 5537 47628 23527 24101
## 51 3639 1734 1905 16642 8329 8313
## 52 5572 2978 2594 22413 11273 11140
## 53 3459 1782 1677 13331 6670 6661
## 54 8284 4260 4024 41476 20890 20586
## 55 23483 12083 11400 105171 52848 52323
## 56 16468 8851 7617 100062 55456 44606
## ekder_all ekder_male ekder_female 0_6_all 0_6_male 0_6_female 7_14_all
## 1 26756 8775 17981 6374 3205 3169 7538
## 2 8935 2488 6447 7567 3867 3700 5731
## 3 25799 7471 18328 5989 3061 2928 6137
## 4 1533 435 1099 489 254 236 469
## 5 13804 4608 9196 2982 1549 1433 3379
## 6 37504 12102 25402 9254 4856 4398 9515
## 7 1533 435 1099 489 254 236 469
## 8 29529 9083 20446 13087 6645 6442 13670
## 9 17396 5130 12266 4713 2390 2323 5212
## 10 29529 9083 20446 13087 6645 6442 13670
## 11 29529 9083 20446 13087 6645 6442 13670
## 12 9501 2903 6598 2870 1442 1428 3097
## 13 3150 948 2202 1706 862 844 1564
## 14 24130 7105 17025 6120 3096 3024 6533
## 15 29529 9083 20446 13087 6645 6442 13670
## 16 31147 11500 19647 7103 3598 3505 6119
## 17 26087 7410 18677 14080 7457 6623 14892
## 18 26341 7961 18380 4992 2602 2390 5285
## 19 3150 948 2202 1706 862 844 1564
## 20 9501 2903 6598 2870 1442 1428 3097
## 21 27248 8428 18820 7104 3262 3842 8667
## 22 30774 9567 21207 6694 3466 3228 7077
## 23 962 267 696 294 152 143 290
## 24 24252 6708 17544 5947 2950 2997 5664
## 25 12669 3966 8703 3229 1612 1617 3369
## 26 1533 435 1099 489 254 236 469
## 27 33017 10594 22423 7125 3725 3400 6856
## 28 12456 3676 8780 3201 1631 1570 3374
## 29 13804 4608 9196 2982 1549 1433 3379
## 30 2056 583 1473 656 340 316 629
## 31 20184 6644 13540 4237 2079 2158 6398
## 32 2056 583 1473 656 340 316 629
## 33 2056 583 1473 656 340 316 629
## 34 861 244 617 275 143 133 264
## 35 28816 8173 20643 9005 4632 4373 10418
## 36 2056 583 1473 656 340 316 629
## 37 1533 435 1099 489 254 236 469
## 38 2056 583 1473 656 340 316 629
## 39 8840 2629 6211 2153 1095 1058 2277
## 40 1642 457 1185 477 246 231 475
## 41 548 156 393 175 91 85 168
## 42 9501 2903 6598 2870 1442 1428 3097
## 43 2989 847 2142 953 495 459 915
## 44 25853 8422 17431 5506 2861 2645 5889
## 45 37504 12102 25402 9254 4856 4398 9515
## 46 2056 583 1473 656 340 316 629
## 47 32331 9775 22556 6027 3092 2935 5992
## 48 25853 8422 17431 5506 2861 2645 5889
## 49 25853 8422 17431 5506 2861 2645 5889
## 50 21807 6668 15139 5195 2621 2574 5469
## 51 6297 2129 4168 1792 840 952 1660
## 52 10090 2911 7179 2448 1294 1154 2748
## 53 3150 948 2202 1706 862 844 1564
## 54 14557 3963 10594 3977 2026 1951 3806
## 55 46177 14748 31429 10712 5502 5210 11217
## 56 36718 11958 24760 7517 4062 3455 7960
## 7_14_male 7_14_female 0_17_all 0_17_male 0_17_female 16_29_all
## 1 3585 3953 16584 8083 8501 14705
## 2 3000 2731 15057 7784 7273 26154
## 3 3196 2941 14403 7428 6975 20306
## 4 242 228 1150 597 553 2155
## 5 1804 1575 7887 4153 3734 12073
## 6 4991 4524 22087 11541 10546 32847
## 7 242 228 1150 597 553 2155
## 8 7126 6544 32063 16513 15550 3292
## 9 2677 2535 11749 6001 5748 22625
## 10 7126 6544 32063 16513 15550 3292
## 11 7126 6544 32063 16513 15550 3292
## 12 1618 1479 7060 3655 3405 31367
## 13 821 743 3831 1973 1858 55710
## 14 3192 3341 14994 7422 7572 17070
## 15 7126 6544 32063 16513 15550 3292
## 16 2984 3135 15630 7783 7847 311210
## 17 7839 7053 34341 18094 16247 19906
## 18 2693 2592 13203 6882 6321 24605
## 19 821 743 3831 1973 1858 55710
## 20 1618 1479 7060 3655 3405 31367
## 21 3861 4806 18927 8599 10328 22027
## 22 3623 3454 16586 8587 7999 24466
## 23 147 144 693 355 339 3009
## 24 2798 2866 13855 6851 7004 24296
## 25 1701 1668 7917 3962 3955 17864
## 26 242 228 1150 597 553 2155
## 27 3580 3276 16727 8746 7981 10184
## 28 1728 1646 8184 4225 3959 25783
## 29 1804 1575 7887 4153 3734 12073
## 30 325 305 1542 801 742 3134
## 31 3094 3304 12508 6065 6443 23480
## 32 325 305 1542 801 742 3134
## 33 325 305 1542 801 742 3134
## 34 136 128 646 336 311 3796
## 35 5300 5118 23155 11841 11314 27514
## 36 325 305 1542 801 742 3134
## 37 242 228 1150 597 553 2155
## 38 325 305 1542 801 742 3134
## 39 1184 1093 5492 2833 2659 26633
## 40 247 228 1138 588 551 1432
## 41 87 82 411 214 198 628
## 42 1618 1479 7060 3655 3405 31367
## 43 472 444 2243 1165 1078 4923
## 44 2990 2899 14297 7209 7088 291222
## 45 4991 4524 22087 11541 10546 32847
## 46 325 305 1542 801 742 3134
## 47 3035 2957 14586 7366 7220 30912
## 48 2990 2899 14297 7209 7088 291222
## 49 2990 2899 14297 7209 7088 291222
## 50 2842 2627 12655 6470 6185 21910
## 51 789 871 3959 1892 2067 20788
## 52 1478 1270 6332 3374 2958 21787
## 53 821 743 3831 1973 1858 55710
## 54 1983 1823 9216 4729 4487 34556
## 55 5750 5467 26209 13455 12754 45669
## 56 4280 3680 19439 10398 9041 24753
## 16_29_male 16_29_female 0_13_all 0_13_male 0_13_female
## 1 7343 7362 13042 6343 6699
## 2 13689 12465 12659 6564 6095
## 3 10071 10235 11355 5854 5501
## 4 1206 950 900 465 435
## 5 5873 6200 5981 3138 2843
## 6 16042 16805 17622 9249 8373
## 7 1206 950 900 465 435
## 8 1450 1842 24934 12782 12152
## 9 11064 11561 9319 4737 4582
## 10 1450 1842 24934 12782 12152
## 11 1450 1842 24934 12782 12152
## 12 15559 15808 5569 2857 2712
## 13 27242 28468 3112 1600 1512
## 14 7717 9353 11903 5928 5975
## 15 1450 1842 24934 12782 12152
## 16 150254 160956 12479 6183 6296
## 17 9676 10230 27123 14340 12783
## 18 11742 12863 9633 4966 4667
## 19 27242 28468 3112 1600 1512
## 20 15559 15808 5569 2857 2712
## 21 10067 11960 14733 6684 8049
## 22 12304 12162 12941 6673 6268
## 23 1842 1167 549 281 268
## 24 11769 12527 10869 5377 5492
## 25 8474 9390 6237 3131 3106
## 26 1206 950 900 465 435
## 27 4834 5350 13121 6880 6241
## 28 14387 11396 6146 3149 2997
## 29 5873 6200 5981 3138 2843
## 30 1753 1381 1207 623 584
## 31 11491 11989 9955 4835 5120
## 32 1753 1381 1207 623 584
## 33 1753 1381 1207 623 584
## 34 2035 1762 506 261 245
## 35 13369 14145 18047 9244 8803
## 36 1753 1381 1207 623 584
## 37 1206 950 900 465 435
## 38 1753 1381 1207 623 584
## 39 12714 13919 4152 2134 2018
## 40 877 556 893 463 430
## 41 337 292 322 166 156
## 42 15559 15808 5569 2857 2712
## 43 2754 2169 1755 906 849
## 44 143474 147748 10653 5506 5147
## 45 16042 16805 17622 9249 8373
## 46 1753 1381 1207 623 584
## 47 14269 16643 11227 5734 5493
## 48 143474 147748 10653 5506 5147
## 49 143474 147748 10653 5506 5147
## 50 10290 11620 10012 5135 4877
## 51 9936 10852 3255 1535 1720
## 52 10891 10896 4828 2603 2225
## 53 27242 28468 3112 1600 1512
## 54 17958 16598 7321 3780 3541
## 55 22276 23393 20530 10531 9999
## 56 13005 11748 14528 7865 6663
## raion_build_count_with_material_info build_count_block build_count_wood
## 1 641 19 4
## 2 1204 12 793
## 3 332 58 0
## 4 NA NA NA
## 5 335 17 42
## 6 453 14 161
## 7 NA NA NA
## 8 458 9 51
## 9 244 71 0
## 10 458 9 51
## 11 458 9 51
## 12 2 0 1
## 13 43 3 0
## 14 304 108 2
## 15 458 9 51
## 16 413 104 4
## 17 1681 173 607
## 18 531 85 39
## 19 43 3 0
## 20 2 0 1
## 21 175 47 0
## 22 502 70 31
## 23 NA NA NA
## 24 360 47 0
## 25 191 36 0
## 26 NA NA NA
## 27 836 99 6
## 28 216 34 0
## 29 335 17 42
## 30 NA NA NA
## 31 651 19 27
## 32 NA NA NA
## 33 NA NA NA
## 34 NA NA NA
## 35 454 64 15
## 36 NA NA NA
## 37 NA NA NA
## 38 NA NA NA
## 39 142 39 0
## 40 1 1 0
## 41 NA NA NA
## 42 2 0 1
## 43 NA NA NA
## 44 374 81 0
## 45 453 14 161
## 46 NA NA NA
## 47 244 72 0
## 48 374 81 0
## 49 374 81 0
## 50 254 58 1
## 51 180 17 4
## 52 107 23 0
## 53 43 3 0
## 54 249 102 0
## 55 175 16 5
## 56 314 44 0
## build_count_frame build_count_brick build_count_monolith
## 1 0 550 48
## 2 36 179 14
## 3 0 184 6
## 4 NA NA NA
## 5 14 250 5
## 6 25 73 9
## 7 NA NA NA
## 8 12 124 50
## 9 0 105 4
## 10 12 124 50
## 11 12 124 50
## 12 0 1 0
## 13 0 10 2
## 14 0 105 4
## 15 12 124 50
## 16 0 215 7
## 17 19 245 116
## 18 3 356 13
## 19 0 10 2
## 20 0 1 0
## 21 0 0 5
## 22 1 296 19
## 23 NA NA NA
## 24 0 288 3
## 25 0 135 3
## 26 NA NA NA
## 27 1 664 33
## 28 0 11 47
## 29 14 250 5
## 30 NA NA NA
## 31 4 529 25
## 32 NA NA NA
## 33 NA NA NA
## 34 NA NA NA
## 35 1 225 8
## 36 NA NA NA
## 37 NA NA NA
## 38 NA NA NA
## 39 0 48 0
## 40 0 0 0
## 41 NA NA NA
## 42 0 1 0
## 43 NA NA NA
## 44 0 205 9
## 45 25 73 9
## 46 NA NA NA
## 47 0 22 7
## 48 0 205 9
## 49 0 205 9
## 50 0 18 14
## 51 0 134 16
## 52 0 10 16
## 53 0 10 2
## 54 0 106 1
## 55 0 2 0
## 56 0 7 3
## build_count_panel build_count_foam build_count_slag build_count_mix
## 1 8 0 11 1
## 2 97 0 64 9
## 3 84 0 0 0
## 4 NA NA NA NA
## 5 5 0 2 0
## 6 160 0 11 0
## 7 NA NA NA NA
## 8 201 0 9 2
## 9 64 0 0 0
## 10 201 0 9 2
## 11 201 0 9 2
## 12 0 0 0 0
## 13 28 0 0 0
## 14 85 0 0 0
## 15 201 0 9 2
## 16 63 0 20 0
## 17 431 1 84 5
## 18 32 0 3 0
## 19 28 0 0 0
## 20 0 0 0 0
## 21 123 0 0 0
## 22 76 0 6 3
## 23 NA NA NA NA
## 24 22 0 0 0
## 25 17 0 0 0
## 26 NA NA NA NA
## 27 30 0 2 1
## 28 124 0 0 0
## 29 5 0 2 0
## 30 NA NA NA NA
## 31 41 0 5 1
## 32 NA NA NA NA
## 33 NA NA NA NA
## 34 NA NA NA NA
## 35 135 0 6 0
## 36 NA NA NA NA
## 37 NA NA NA NA
## 38 NA NA NA NA
## 39 55 0 0 0
## 40 0 0 0 0
## 41 NA NA NA NA
## 42 0 0 0 0
## 43 NA NA NA NA
## 44 78 0 1 0
## 45 160 0 11 0
## 46 NA NA NA NA
## 47 143 0 0 0
## 48 78 0 1 0
## 49 78 0 1 0
## 50 162 0 1 0
## 51 8 0 1 0
## 52 58 0 0 0
## 53 28 0 0 0
## 54 40 0 0 0
## 55 150 0 2 0
## 56 260 0 0 0
## raion_build_count_with_builddate_info build_count_before_1920
## 1 637 206
## 2 1204 298
## 3 331 3
## 4 NA NA
## 5 336 0
## 6 453 47
## 7 NA NA
## 8 459 13
## 9 243 0
## 10 459 13
## 11 459 13
## 12 2 0
## 13 41 0
## 14 303 1
## 15 459 13
## 16 413 4
## 17 1680 34
## 18 530 2
## 19 41 0
## 20 2 0
## 21 175 0
## 22 501 65
## 23 NA NA
## 24 357 21
## 25 190 6
## 26 NA NA
## 27 831 240
## 28 216 0
## 29 336 0
## 30 NA NA
## 31 650 263
## 32 NA NA
## 33 NA NA
## 34 NA NA
## 35 456 0
## 36 NA NA
## 37 NA NA
## 38 NA NA
## 39 142 1
## 40 1 0
## 41 NA NA
## 42 2 0
## 43 NA NA
## 44 374 0
## 45 453 47
## 46 NA NA
## 47 244 0
## 48 374 0
## 49 374 0
## 50 253 0
## 51 177 63
## 52 107 0
## 53 41 0
## 54 249 1
## 55 175 0
## 56 315 0
## build_count_1921-1945 build_count_1946-1970 build_count_1971-1995
## 1 122 176 42
## 2 382 306 42
## 3 4 211 93
## 4 NA NA NA
## 5 110 199 10
## 6 42 99 175
## 7 NA NA NA
## 8 24 40 130
## 9 1 153 56
## 10 24 40 130
## 11 24 40 130
## 12 1 0 1
## 13 1 7 10
## 14 2 220 66
## 15 24 40 130
## 16 14 316 61
## 17 299 439 109
## 18 4 442 59
## 19 1 7 10
## 20 1 0 1
## 21 0 60 99
## 22 58 219 102
## 23 NA NA NA
## 24 86 198 44
## 25 27 116 27
## 26 NA NA NA
## 27 114 332 76
## 28 0 144 18
## 29 110 199 10
## 30 NA NA NA
## 31 105 154 71
## 32 NA NA NA
## 33 NA NA NA
## 34 NA NA NA
## 35 37 263 105
## 36 NA NA NA
## 37 NA NA NA
## 38 NA NA NA
## 39 1 116 17
## 40 0 1 0
## 41 NA NA NA
## 42 1 0 1
## 43 NA NA NA
## 44 0 304 21
## 45 42 99 175
## 46 NA NA NA
## 47 0 46 185
## 48 0 304 21
## 49 0 304 21
## 50 0 113 52
## 51 25 44 13
## 52 0 46 15
## 53 1 7 10
## 54 2 203 38
## 55 2 7 159
## 56 0 156 126
## build_count_after_1995 ID_metro metro_min_avto metro_km_avto
## 1 91 70 1.79877598 1.29187565
## 2 176 19 1.67793719 0.71910881
## 3 20 22 1.20666403 0.81373918
## 4 NA 45 3.12154245 2.43688229
## 5 17 113 1.60583187 1.02663308
## 6 90 27 3.13117599 2.01330351
## 7 NA 45 3.12154245 2.43688229
## 8 252 4 0.05964088 0.03180847
## 9 33 18 0.68215051 0.54572041
## 10 252 4 0.05964088 0.03180847
## 11 252 4 0.05964088 0.03180847
## 12 0 45 26.90806838 21.16469868
## 13 23 108 4.72104496 3.77683596
## 14 14 49 1.86134924 1.40483126
## 15 252 4 0.05964088 0.03180847
## 16 18 25 2.39301154 1.61521827
## 17 799 62 1.47178991 1.17743193
## 18 23 76 2.14033561 1.48376086
## 19 23 108 4.72104496 3.77683596
## 20 0 45 25.73224939 21.31947799
## 21 16 142 3.00612466 1.54716771
## 22 57 205 1.82350170 1.23317195
## 23 NA 45 31.11145090 25.70357472
## 24 8 2 2.66388091 2.03192448
## 25 14 168 1.69735612 1.14822148
## 26 NA 45 5.59530067 2.25491634
## 27 69 146 2.08704008 1.36784833
## 28 54 68 1.37700251 0.63972721
## 29 17 113 1.60583187 1.02663308
## 30 NA 45 7.29034784 5.85003266
## 31 57 120 1.48274610 1.03656781
## 32 NA 132 7.45049460 6.13856957
## 33 NA 45 8.23395456 5.80157054
## 34 NA 21 2.86878548 2.29502838
## 35 51 10 5.06733749 4.29927321
## 36 NA 45 8.03762319 6.51397956
## 37 NA 45 5.59530067 2.25491634
## 38 NA 45 6.94174356 5.75028240
## 39 7 25 3.73579706 2.56967022
## 40 0 206 18.15380978 21.70500430
## 41 NA 206 23.23793271 13.73967005
## 42 0 45 23.63581542 19.62289197
## 43 NA 45 15.83945056 12.71856773
## 44 49 95 0.79193340 0.32104080
## 45 90 27 1.34651201 0.47791688
## 46 NA 132 7.16875571 6.18260733
## 47 13 49 5.93797318 3.44653958
## 48 49 95 0.79193340 0.32104080
## 49 49 95 0.79193340 0.32104080
## 50 88 77 3.06716612 1.73816565
## 51 32 115 1.55952744 0.73396405
## 52 46 12 28.67124408 25.20511095
## 53 23 108 1.28047351 0.80158869
## 54 5 168 2.26388018 1.36612872
## 55 7 79 4.00890359 2.25074492
## 56 33 35 1.15524805 0.75085150
## metro_min_walk metro_km_walk kindergarten_km school_km park_km
## 1 6.9902811 0.58252342 0.37742825 0.1858094 0.98527942
## 2 8.6293057 0.71910881 0.08942255 0.2870845 0.91963593
## 3 9.7648702 0.81373918 0.40776972 0.1198119 1.02283155
## 4 29.2425875 2.43688229 0.74528577 0.9363236 1.77375901
## 5 12.3195970 1.02663308 0.59751697 0.4168259 0.08444745
## 6 23.6809631 1.97341360 0.96493997 0.1992599 2.25326020
## 7 29.2425875 2.43688229 0.74528577 0.9363236 1.77375901
## 8 0.3817016 0.03180847 0.55606750 0.2491313 0.52876379
## 9 6.4754572 0.53962143 0.87356505 0.5520190 0.85373008
## 10 0.3817016 0.03180847 0.55606750 0.2491313 0.52876379
## 11 0.3817016 0.03180847 0.55606750 0.2491313 0.52876379
## 12 253.9763841 21.16469868 0.52726173 14.4001433 15.52143786
## 13 45.3220315 3.77683596 0.28671120 0.2141965 4.61617640
## 14 14.5830767 1.21525639 0.18729109 0.5285028 0.54102266
## 15 0.3817016 0.03180847 0.55606750 0.2491313 0.52876379
## 16 19.3194203 1.60995169 0.30613182 0.3089892 3.15440214
## 17 14.1291831 1.17743193 0.13517446 0.2375697 2.44842774
## 18 17.8051303 1.48376086 0.65102026 0.6247492 2.23893042
## 19 45.3220315 3.77683596 0.28671120 0.2141965 4.61617640
## 20 255.8337358 21.31947799 0.49463478 14.9548095 15.91383305
## 21 19.1001538 1.59167949 0.87602029 0.6967814 3.42294109
## 22 4.1845095 0.34870913 0.47617418 0.2925003 0.39254871
## 23 308.4428966 25.70357472 4.10934022 14.6562917 14.89008084
## 24 15.5066292 1.29221910 0.52289740 1.0023900 2.21124846
## 25 13.7786577 1.14822148 0.78186964 0.0000000 2.99615478
## 26 27.0589961 2.25491634 0.64723911 0.8260521 1.79991481
## 27 16.4141800 1.36784833 0.12604029 0.3185563 0.63392229
## 28 8.0277799 0.66898166 0.45817885 0.7204284 0.11206486
## 29 12.3195970 1.02663308 0.59751697 0.4168259 0.08444745
## 30 70.2003919 5.85003266 3.39566077 3.6571872 4.54398868
## 31 13.4590681 1.12158901 1.04896162 0.2697164 0.28429980
## 32 73.6628348 6.13856957 3.80396829 3.8408413 4.19469803
## 33 69.6188465 5.80157054 2.85199717 3.4199706 5.40607767
## 34 27.5403405 2.29502838 1.33039045 1.6920607 5.03465682
## 35 42.1267463 3.51056219 0.22309820 0.1725066 2.49134260
## 36 78.1677547 6.51397956 3.38379480 3.8026649 4.80923702
## 37 27.0589961 2.25491634 0.64723911 0.8260521 1.79991481
## 38 69.0033888 5.75028240 3.24877911 3.5208549 4.67164605
## 39 30.8360426 2.56967022 0.03859735 0.5948017 3.42382262
## 40 318.7157649 26.55964708 10.49965319 12.3749154 17.35387652
## 41 152.5884044 12.71570037 3.25386835 7.8729471 10.01352057
## 42 235.4747036 19.62289197 0.20253016 13.1851970 14.47106306
## 43 144.2363376 12.01969480 4.41520507 7.6713221 9.68419652
## 44 3.8524896 0.32104080 0.39152673 0.2923793 1.19617862
## 45 5.7350026 0.47791688 0.44389333 0.1826003 1.37426862
## 46 74.1912879 6.18260733 3.36773322 3.3411744 3.85984362
## 47 40.0292081 3.33576734 0.21789873 0.1263145 2.70941634
## 48 3.8524896 0.32104080 0.39152673 0.2923793 1.19617862
## 49 3.8524896 0.32104080 0.39152673 0.2923793 1.19617862
## 50 20.8579877 1.73816565 0.41521198 0.3637173 0.82260631
## 51 8.8075685 0.73396405 0.05864544 0.5414264 0.80130831
## 52 297.0449460 24.75374550 1.86570204 0.6450113 17.35295318
## 53 9.6190643 0.80158869 1.20568036 0.3594434 3.00957184
## 54 16.3935446 1.36612872 0.46314729 1.0883454 1.58334062
## 55 24.8357695 2.06964746 0.40872474 0.6215194 1.61508120
## 56 9.0102181 0.75085150 0.12149422 0.2310540 2.21990937
## green_zone_km industrial_km water_treatment_km cemetery_km
## 1 0.46598133 1.65943692 9.671627 2.325364
## 2 0.78506733 0.29604401 3.645498 2.521188
## 3 0.21602347 0.10327417 18.697520 3.375666
## 4 0.45670428 1.01813706 1.014447 1.765269
## 5 0.52148482 0.13325625 19.411360 3.165124
## 6 0.33033108 0.08684521 22.075470 0.089826
## 7 0.45670428 1.01813706 1.014447 1.765269
## 8 0.55797958 0.62470691 15.893560 1.667690
## 9 0.30392798 0.00000000 4.247497 3.534524
## 10 0.55797958 0.62470691 15.893560 1.667690
## 11 0.55797958 0.62470691 15.893560 1.667690
## 12 0.10768683 0.37829588 15.369920 1.976714
## 13 0.01757241 0.39410779 0.387777 5.618100
## 14 0.11413725 1.07345051 9.514084 0.842494
## 15 0.55797958 0.62470691 15.893560 1.667690
## 16 0.12102387 0.46554185 16.906400 1.736341
## 17 0.40055220 0.88401630 3.382356 1.853113
## 18 0.10098408 0.12675144 12.405830 1.962503
## 19 0.01757241 0.39410779 0.387777 5.618100
## 20 0.00000000 0.17217782 15.815090 2.194207
## 21 0.16366924 1.66703827 6.676815 2.470831
## 22 0.13316336 1.50686537 8.133319 1.833183
## 23 0.14520975 0.76551418 15.007450 1.385175
## 24 0.09418038 0.23238703 3.510912 3.297574
## 25 0.11907367 0.61088336 5.250525 0.654322
## 26 0.31438174 1.00613309 0.970986 1.630750
## 27 0.26706900 0.31497235 13.195940 0.681325
## 28 0.04909445 2.88982044 11.317640 3.629507
## 29 0.52148482 0.13325625 19.411360 3.165124
## 30 0.76191025 0.28434357 5.590707 1.815053
## 31 0.18908921 2.64080293 10.378040 4.242627
## 32 0.49026948 0.12008887 6.094327 1.931266
## 33 0.19345679 0.30929978 5.304559 1.017887
## 34 0.03851822 0.28822474 16.953350 1.790982
## 35 0.23379164 0.35917148 17.681130 0.826807
## 36 0.33958363 0.28133303 5.723694 1.409903
## 37 0.31438174 1.00613309 0.970986 1.630750
## 38 0.70133566 0.19118912 5.453613 1.759670
## 39 0.13729805 0.18065050 17.780120 2.401344
## 40 0.17967754 0.05863082 23.245310 3.102444
## 41 0.00000000 0.89959751 10.210550 1.273208
## 42 0.13422682 0.41060096 14.262990 1.800897
## 43 0.99182392 1.35192172 9.211348 1.252875
## 44 0.03869514 1.01682812 7.197647 2.557302
## 45 0.19934528 1.11042561 21.102170 1.152619
## 46 0.34725168 0.14454109 5.841077 2.533017
## 47 0.15374449 0.87329546 11.673480 0.782756
## 48 0.03869514 1.01682812 7.197647 2.557302
## 49 0.03869514 1.01682812 7.197647 2.557302
## 50 0.11079589 0.42383780 20.120080 1.433284
## 51 0.46614824 1.09213078 7.684227 1.161209
## 52 0.01685750 0.29075128 4.647790 2.464853
## 53 1.41947705 0.99108801 1.497270 3.877562
## 54 0.20391422 0.16418540 4.602744 2.198106
## 55 0.09174928 2.47971497 9.136225 1.143626
## 56 0.14965269 0.53447281 9.384977 4.895735
## incineration_km railroad_station_walk_km railroad_station_walk_min
## 1 14.002650 2.4474688 29.36963
## 2 2.299242 3.2930111 39.51613
## 3 8.638527 3.3862833 40.63540
## 4 11.571550 5.9662032 71.59444
## 5 7.334530 1.4261696 17.11403
## 6 13.453250 6.2326785 74.79214
## 7 11.571550 5.9662032 71.59444
## 8 13.383420 3.2138657 38.56639
## 9 7.637877 2.9326861 35.19223
## 10 13.383420 3.2138657 38.56639
## 11 13.383420 3.2138657 38.56639
## 12 25.603450 20.4821458 245.78575
## 13 3.194229 1.9234949 23.08194
## 14 7.323802 0.9409324 11.29119
## 15 13.383420 3.2138657 38.56639
## 16 9.580035 1.9419425 23.30331
## 17 7.383157 1.4313656 17.17639
## 18 13.350080 2.9010254 34.81231
## 19 3.194229 1.9234949 23.08194
## 20 26.221160 23.1592094 277.91051
## 21 3.654044 4.7469681 56.96362
## 22 14.329970 2.0872143 25.04657
## 23 25.944310 22.2654381 267.18526
## 24 10.845470 2.2037191 26.44463
## 25 10.627330 1.8456828 22.14819
## 26 11.435910 5.7842372 69.41085
## 27 11.498420 2.4535010 29.44201
## 28 11.954750 4.7770453 57.32454
## 29 7.334530 1.4261696 17.11403
## 30 10.367990 9.7374362 116.84923
## 31 12.180740 3.3787169 40.54460
## 32 10.403630 10.2952904 123.54349
## 33 11.131470 9.6889741 116.26769
## 34 19.538730 4.3084608 51.70153
## 35 18.830430 2.2674512 27.20941
## 36 10.802200 10.4013831 124.81660
## 37 11.435910 5.7842372 69.41085
## 38 10.397880 9.6376860 115.65223
## 39 12.229940 4.3537194 52.24463
## 40 31.037700 6.7682666 81.21920
## 41 17.188930 14.7291328 176.74959
## 42 24.314070 18.8185169 225.82220
## 43 18.367510 18.4495738 221.39489
## 44 10.251430 3.2355395 38.82647
## 45 12.897110 6.0554418 72.66530
## 46 9.716588 10.4996184 125.99542
## 47 8.910399 4.0864962 49.03795
## 48 10.251430 3.2355395 38.82647
## 49 10.251430 3.2355395 38.82647
## 50 5.039484 2.7942163 33.53060
## 51 13.525740 2.7103290 32.52395
## 52 28.410730 5.1333676 61.60041
## 53 1.642403 3.6090871 43.30904
## 54 8.947672 2.5465740 30.55889
## 55 5.067761 12.1567307 145.88077
## 56 8.422361 8.6192828 103.43139
## ID_railroad_station_walk railroad_station_avto_km
## 1 50 4.238200
## 2 35 3.618454
## 3 4 3.497706
## 4 39 5.966203
## 5 40 2.056616
## 6 26 5.885532
## 7 39 5.966203
## 8 4 2.738478
## 9 2 4.562884
## 10 4 2.738478
## 11 4 2.738478
## 12 108 20.420873
## 13 75 1.923495
## 14 31 2.384729
## 15 4 2.738478
## 16 38 1.941942
## 17 47 1.431366
## 18 59 2.726093
## 19 75 1.923495
## 20 108 23.159209
## 21 21 4.841322
## 22 44 2.144076
## 23 123 22.265438
## 24 65 4.240327
## 25 2 1.902001
## 26 39 5.784237
## 27 71 2.065489
## 28 33 4.997573
## 29 40 2.056616
## 30 47 9.741436
## 31 5 4.060430
## 32 47 13.388035
## 33 47 9.692974
## 34 24 4.308461
## 35 88 2.106156
## 36 47 14.036596
## 37 39 5.784237
## 38 47 9.641686
## 39 38 4.879462
## 40 108 6.768267
## 41 24 15.631091
## 42 108 18.757244
## 43 24 21.279217
## 44 42 3.257138
## 45 26 4.709332
## 46 47 12.765767
## 47 55 2.577788
## 48 42 3.257138
## 49 42 3.257138
## 50 54 2.766565
## 51 32 2.823866
## 52 28 5.477223
## 53 75 3.504841
## 54 2 2.602892
## 55 42 11.337572
## 56 33 8.342431
## railroad_station_avto_min ID_railroad_station_avto
## 1 5.481681 49
## 2 5.224698 11
## 3 4.833423 4
## 4 7.405651 39
## 5 3.098664 40
## 6 5.658055 4
## 7 7.405651 39
## 8 3.532968 4
## 9 5.851418 42
## 10 3.532968 4
## 11 3.532968 4
## 12 26.088223 108
## 13 3.050727 75
## 14 4.127622 31
## 15 3.532968 4
## 16 3.884100 38
## 17 2.599279 47
## 18 3.693251 59
## 19 3.050727 75
## 20 27.066602 108
## 21 7.268420 21
## 22 2.979169 44
## 23 26.139001 123
## 24 6.423012 137
## 25 2.545528 2
## 26 9.879409 39
## 27 3.756687 14
## 28 6.546481 33
## 29 3.098664 40
## 30 12.837131 47
## 31 5.143798 32
## 32 12.265966 105
## 33 13.780737 47
## 34 5.498317 24
## 35 3.985800 88
## 36 13.288299 105
## 37 9.879409 39
## 38 12.488526 47
## 39 6.363637 18
## 40 8.094774 108
## 41 26.714890 19
## 42 22.397185 108
## 43 21.765343 105
## 44 4.462055 42
## 45 5.371899 4
## 46 11.787907 73
## 47 4.757350 136
## 48 4.462055 42
## 49 4.462055 42
## 50 4.188107 12
## 51 3.917618 32
## 52 7.291221 28
## 53 4.814000 75
## 54 3.715782 2
## 55 12.837314 73
## 56 10.644722 42
## public_transport_station_km public_transport_station_min_walk
## 1 0.21998179 2.6397815
## 2 0.17206160 2.0647391
## 3 0.04925213 0.5910255
## 4 0.46757568 5.6109082
## 5 0.14209249 1.7051098
## 6 0.16734145 2.0080974
## 7 0.46757568 5.6109082
## 8 0.12173702 1.4608442
## 9 0.06239673 0.7487608
## 10 0.12173702 1.4608442
## 11 0.12173702 1.4608442
## 12 0.06204344 0.7445212
## 13 0.03598649 0.4318379
## 14 0.13399851 1.6079821
## 15 0.12173702 1.4608442
## 16 0.20670189 2.4804227
## 17 0.06395816 0.7674980
## 18 0.14886976 1.7864371
## 19 0.03598649 0.4318379
## 20 0.18277325 2.1932791
## 21 0.05369068 0.6442882
## 22 0.06307949 0.7569539
## 23 3.07957403 36.9548884
## 24 0.15541302 1.8649563
## 25 0.07165763 0.8598916
## 26 0.32735719 3.9282862
## 27 0.03535641 0.4242769
## 28 0.10894202 1.3073042
## 29 0.14209249 1.7051098
## 30 0.06727427 0.8072913
## 31 0.32603485 3.9124182
## 32 0.16269833 1.9523800
## 33 0.84405384 10.1286460
## 34 0.17300028 2.0760034
## 35 0.09935667 1.1922800
## 36 0.36965895 4.4359074
## 37 0.32735719 3.9282862
## 38 0.14616663 1.7539995
## 39 0.20734857 2.4881828
## 40 1.77836598 21.3403918
## 41 2.72381503 32.6857804
## 42 0.04097125 0.4916550
## 43 1.79121215 21.4945458
## 44 0.05252654 0.6303184
## 45 0.22361206 2.6833447
## 46 0.72993920 8.7592704
## 47 0.16794442 2.0153331
## 48 0.05252654 0.6303184
## 49 0.05252654 0.6303184
## 50 0.20311808 2.4374170
## 51 0.13179712 1.5815655
## 52 0.22392366 2.6870839
## 53 0.17485882 2.0983058
## 54 0.17241856 2.0690228
## 55 0.04669383 0.5603259
## 56 0.25524493 3.0629392
## water_km water_1line mkad_km ttk_km sadovoe_km bulvar_ring_km
## 1 0.514685186 no 12.1147256 2.3010371 0.1892945 1.3100013
## 2 1.189032273 no 3.5706000 11.5485113 15.4962457 16.2264781
## 3 0.729121280 no 2.8896942 10.0643097 12.6707699 13.4526040
## 4 0.006707311 yes 7.3717161 20.6240727 23.7533881 25.0321097
## 5 1.432948320 no 7.1253584 4.6312016 7.2180217 8.0086687
## 6 0.700752529 no 1.2022104 9.2351802 12.3024232 13.1467148
## 7 0.006707311 yes 7.3717161 20.6240727 23.7533881 25.0321097
## 8 0.749486176 no 1.7448559 13.9259363 16.7241740 17.5664625
## 9 0.329814955 no 9.8656844 3.4925200 6.6821732 8.1367491
## 10 0.749486176 no 1.7448559 13.9259363 16.7241740 17.5664625
## 11 0.749486176 no 1.7448559 13.9259363 16.7241740 17.5664625
## 12 1.542479455 no 19.2939171 31.8375299 34.6581370 35.7882472
## 13 1.110328656 no 5.9469083 14.2982251 18.4189285 19.2725365
## 14 0.959898233 no 0.9301928 7.6150109 10.6809129 11.2302384
## 15 0.749486176 no 1.7448559 13.9259363 16.7241740 17.5664625
## 16 0.938168933 no 5.5841565 4.8617238 7.0376018 7.7631018
## 17 0.397478239 no 3.7966116 17.4828145 20.6786819 22.0522056
## 18 0.390671528 no 6.0724556 3.4516537 5.7469902 6.4804307
## 19 1.110328656 no 5.9469083 14.2982251 18.4189285 19.2725365
## 20 0.752413160 no 20.0721914 32.7060739 35.5498993 36.6765602
## 21 0.574418312 no 4.6525019 8.7979372 11.9963935 13.3871320
## 22 0.754106676 no 11.4367563 2.4083033 0.3401321 1.4265126
## 23 0.543667639 no 20.6868461 33.7550493 36.7148324 37.8299626
## 24 0.172205682 no 10.2640923 0.7940695 4.1181893 5.7586911
## 25 0.435357748 no 11.9515164 0.6019457 3.6102474 5.1170157
## 26 0.138167727 yes 7.2297626 20.4834757 23.6136144 24.8931303
## 27 0.337348153 no 11.3266015 1.0234554 2.0698033 2.9502857
## 28 0.507205930 no 4.6135160 5.8813777 8.5245882 9.6779606
## 29 1.432948320 no 7.1253584 4.6312016 7.2180217 8.0086687
## 30 0.092803188 yes 3.6945684 16.8277743 19.7984612 20.9695114
## 31 0.524839432 no 13.9178146 4.0812826 2.1853330 0.5069192
## 32 0.534079880 no 3.4446275 16.4981015 19.4619015 20.6186432
## 33 0.888206787 no 4.5859506 17.7039973 20.6702056 21.8336968
## 34 0.852330964 no 7.5956880 17.6121356 20.3325878 21.5017378
## 35 0.247638594 no 1.7721597 8.0358414 11.0382857 12.0871107
## 36 0.501459529 no 4.0326600 17.1119550 20.0762184 21.2343406
## 37 0.138167727 yes 7.2297626 20.4834757 23.6136144 24.8931303
## 38 0.098459060 yes 3.8000467 16.9476020 19.9205517 21.0946039
## 39 0.518581898 no 3.2717334 7.2897357 9.3441315 10.0737826
## 40 0.113256517 yes 22.3333215 32.5540295 35.2704660 36.4421497
## 41 0.752826532 no 9.6206325 21.4631942 24.2012708 25.3355255
## 42 0.889732740 no 17.8927585 30.3998442 33.2169145 34.3472366
## 43 0.215267903 no 11.7512766 24.3821341 27.2713964 28.3851042
## 44 1.249133557 no 8.8898796 2.3658597 5.3258853 6.5098283
## 45 1.135082140 no 0.7368622 9.6289650 12.6728428 13.5113910
## 46 0.554398632 no 2.9706925 16.1280591 19.1061102 20.2869774
## 47 0.435573472 no 0.6490415 8.7994182 11.0787457 11.7333100
## 48 1.249133557 no 8.8898796 2.3658597 5.3258853 6.5098283
## 49 1.249133557 no 8.8898796 2.3658597 5.3258853 6.5098283
## 50 0.340919055 no 3.6533651 8.5341545 11.3209374 12.0102830
## 51 0.911709562 no 13.0367974 1.9779308 0.7399605 2.2147187
## 52 0.141561466 yes 20.1748579 33.5232647 36.2637473 36.9953738
## 53 0.404347602 no 5.4811095 13.4200563 17.4526298 18.2331571
## 54 0.820865288 no 10.6601484 2.1595871 5.3429394 6.8033420
## 55 0.122956478 yes 2.6964423 10.4947009 13.6285853 14.9254836
## 56 0.640935065 no 4.2314979 7.4376324 10.3970555 11.5164228
## kremlin_km big_road1_km ID_big_road1 big_road1_1line big_road2_km
## 1 2.10956097 2.30103716 4 no 2.9025230
## 2 17.60373258 2.70971159 11 no 2.7430588
## 3 14.69651397 2.16038644 14 no 2.8270634
## 4 25.73525569 4.58608204 2 no 5.6021821
## 5 9.24067776 0.12133182 14 no 0.5319186
## 6 14.29423798 0.92554890 29 no 1.2022104
## 7 25.73525569 4.58608204 2 no 5.6021821
## 8 18.75284320 1.74485586 1 no 2.0916030
## 9 8.74565560 0.75300213 2 no 3.4925200
## 10 18.75284320 1.74485586 1 no 2.0916030
## 11 18.75284320 1.74485586 1 no 2.0916030
## 12 36.54046337 0.92601208 38 no 9.0677420
## 13 20.54946417 1.90512505 11 no 2.6573363
## 14 12.79861360 0.93019277 1 no 2.6899648
## 15 18.75284320 1.74485586 1 no 2.0916030
## 16 9.50179525 3.86134772 20 no 4.0164909
## 17 22.71535183 1.31668618 2 no 2.6560469
## 18 8.22065906 2.01484309 10 no 3.4516537
## 19 20.54946417 1.90512505 11 no 2.6573363
## 20 37.42680814 0.46951912 38 no 8.0498800
## 21 14.03969049 1.62882262 2 no 4.6525019
## 22 2.47019958 2.40830330 4 no 2.6877770
## 23 38.57270610 2.87050726 38 no 5.4458522
## 24 6.37001195 0.79406949 4 no 2.8300677
## 25 5.68317097 0.06956116 2 yes 0.6019457
## 26 25.59597356 4.54085394 2 no 5.5859897
## 27 4.00151428 1.02345538 4 no 1.2031070
## 28 10.47594973 1.50623594 16 no 4.2891768
## 29 9.24067776 0.12133182 14 no 0.5319186
## 30 21.70183576 1.41641551 38 no 3.6945684
## 31 0.07289655 4.08128258 4 no 4.2733950
## 32 21.35376728 0.96900298 38 no 3.4446274
## 33 22.56765524 1.44409766 38 no 4.5859506
## 34 22.37012991 2.80862792 13 no 3.6065635
## 35 12.96233461 0.09557911 9 yes 1.5489075
## 36 21.96927419 1.15447591 38 no 4.0326600
## 37 25.59597356 4.54085394 2 no 5.5859897
## 38 21.82631611 1.52555187 38 no 3.8000466
## 39 11.86971525 3.27173339 1 no 4.5928753
## 40 37.29751265 2.04821562 13 no 8.7905088
## 41 26.09730228 2.72764223 38 no 5.5638587
## 42 35.09976600 1.03538196 38 no 10.3580709
## 43 29.13376481 0.46467935 38 no 9.1520014
## 44 7.23335576 1.24362584 16 no 2.3658597
## 45 14.67552240 0.73686218 1 no 1.5512369
## 46 21.01715884 1.47141967 38 no 2.9706925
## 47 13.43876450 0.15374449 10 no 0.3752031
## 48 7.23335576 1.24362584 16 no 2.3658597
## 49 7.23335576 1.24362584 16 no 2.3658597
## 50 13.38346366 1.47325475 14 no 3.1513804
## 51 2.79904943 1.97793083 4 no 1.9791296
## 52 38.31191026 0.32292867 14 no 4.4353686
## 53 19.57296713 2.82983175 11 no 3.9846477
## 54 7.40705669 0.67213543 2 no 2.1595870
## 55 15.62029186 2.69644230 1 no 3.3760273
## 56 12.25585212 1.95249149 16 no 4.2050306
## ID_big_road2 railroad_km railroad_1line zd_vokzaly_avto_km
## 1 16 1.62234578 no 4.742795
## 2 7 2.31127614 no 22.946310
## 3 17 0.03951401 yes 13.794143
## 4 38 1.26614201 no 29.254460
## 5 28 0.68355661 no 6.457222
## 6 1 3.55932198 no 14.226267
## 7 38 1.26614201 no 29.254460
## 8 17 1.66327497 no 16.400814
## 9 4 0.69642523 no 8.584593
## 10 17 1.66327497 no 16.400814
## 11 17 1.66327497 no 16.400814
## 12 46 14.63228528 no 38.641485
## 13 55 1.21486132 no 24.061214
## 14 10 0.14691400 no 13.456231
## 15 17 1.66327497 no 16.400814
## 16 15 0.46554185 no 7.129767
## 17 39 0.47458967 no 23.848103
## 18 4 0.10338967 no 7.048509
## 19 55 1.21486132 no 24.061214
## 20 46 15.64705543 no 38.796265
## 21 1 2.56575323 no 14.453489
## 22 10 1.37649010 no 3.243669
## 23 46 14.04021804 no 43.180361
## 24 2 0.21620919 no 9.158034
## 25 4 0.21716063 no 6.243637
## 26 38 1.12840928 no 29.072494
## 27 32 1.06380206 no 3.773720
## 28 22 2.69369610 no 11.584767
## 29 28 0.68355661 no 6.457222
## 30 1 4.12861456 no 22.791377
## 31 34 2.56604438 no 4.060430
## 32 1 4.63327096 no 22.304173
## 33 1 3.85231998 no 24.613188
## 34 27 1.87225839 no 26.272257
## 35 47 1.08902811 no 10.675826
## 36 1 4.25905295 no 22.952734
## 37 38 1.12840928 no 29.072494
## 38 1 3.99060135 no 22.814310
## 39 12 0.67567460 no 9.756576
## 40 46 4.81659655 no 45.900330
## 41 13 7.41548090 no 37.934996
## 42 46 13.43711745 no 37.099679
## 43 13 8.60758958 no 30.195354
## 44 4 1.75182207 no 7.745523
## 45 44 2.67517400 no 14.292050
## 46 1 4.37770108 no 22.348211
## 47 25 1.42535910 no 12.667687
## 48 4 1.75182207 no 7.745523
## 49 4 1.75182207 no 7.745523
## 50 8 1.54571354 no 13.218420
## 51 19 1.72721950 no 2.823866
## 52 49 1.85617148 no 36.857094
## 53 7 2.39519876 no 25.676259
## 54 4 0.31263817 no 6.719765
## 55 2 4.46371303 no 17.841654
## 56 51 5.88838984 no 12.830815
## ID_railroad_terminal bus_terminal_avto_km ID_bus_terminal
## 1 50 5.587676 13
## 2 5 7.896744 3
## 3 83 5.385547 1
## 4 32 10.125933 9
## 5 83 3.681787 5
## 6 83 7.741019 1
## 7 32 10.125933 9
## 8 83 5.657051 1
## 9 32 4.304500 2
## 10 83 5.657051 1
## 11 83 5.657051 1
## 12 32 23.612353 8
## 13 5 7.253743 14
## 14 5 5.992097 3
## 15 83 5.657051 1
## 16 97 4.580760 10
## 17 32 5.558192 9
## 18 97 3.457547 10
## 19 5 7.253743 14
## 20 32 23.767133 8
## 21 32 8.376382 9
## 22 5 4.761619 13
## 23 32 28.151229 8
## 24 32 8.907089 13
## 25 32 5.992692 13
## 26 32 9.943967 9
## 27 83 6.111069 5
## 28 50 10.419617 8
## 29 83 3.681787 5
## 30 32 7.762245 8
## 31 32 3.983721 13
## 32 32 7.275041 8
## 33 32 12.909037 9
## 34 50 17.698835 8
## 35 50 18.309894 1
## 36 32 7.923602 8
## 37 32 9.943967 9
## 38 32 7.785178 8
## 39 97 3.661649 7
## 40 50 30.242239 8
## 41 50 15.425839 8
## 42 32 22.070547 8
## 43 32 15.166222 8
## 44 32 7.668813 13
## 45 83 6.564819 1
## 46 32 7.319079 8
## 47 5 8.720511 7
## 48 32 7.668813 13
## 49 32 7.668813 13
## 50 83 10.442986 5
## 51 32 2.747157 13
## 52 83 31.664017 1
## 53 5 7.139147 14
## 54 32 6.468819 13
## 55 32 4.541429 8
## 56 32 5.191718 8
## oil_chemistry_km nuclear_reactor_km radiation_km
## 1 10.902881 4.169488 0.6244607
## 2 8.754915 15.507787 1.5739184
## 3 22.304752 5.624696 1.7641117
## 4 23.590580 17.243125 7.5762254
## 5 17.184480 2.334670 3.4512219
## 6 23.123997 4.804399 3.4892059
## 7 23.590580 17.243125 7.5762254
## 8 26.847323 8.840886 5.5092768
## 9 12.552390 3.614315 1.3903317
## 10 26.847323 8.840886 5.5092768
## 11 26.847323 8.840886 5.5092768
## 12 37.871031 30.812678 20.1797711
## 13 8.483391 16.372510 4.3177439
## 14 4.276125 14.728939 1.9090730
## 15 26.847323 8.840886 5.5092768
## 16 8.015637 11.778377 1.2058877
## 17 19.232942 13.189199 4.7221256
## 18 2.688324 13.407973 1.7397300
## 19 8.483391 16.372510 4.3177439
## 20 38.460063 31.501281 20.8930099
## 21 14.162490 6.157288 2.2575818
## 22 6.507790 6.686853 0.1375765
## 23 37.940707 31.517036 21.1788152
## 24 8.542574 5.186081 3.3932516
## 25 10.817783 2.257140 0.6175020
## 26 23.463447 17.103736 7.4364868
## 27 13.119055 6.368255 0.8725378
## 28 18.273232 5.617810 1.4054785
## 29 17.184480 2.334670 3.4512219
## 30 22.642612 15.175759 4.5245014
## 31 8.750185 6.663317 0.8900007
## 32 22.644305 15.085021 4.4711072
## 33 23.424518 16.024061 5.3689754
## 34 30.411171 17.871173 9.4914922
## 35 22.088057 9.552864 1.3979411
## 36 23.073667 15.586587 4.9414637
## 37 23.463447 17.103736 7.4364868
## 38 22.679658 15.238958 4.5844188
## 39 7.825375 14.650480 1.2011440
## 40 43.117502 32.461703 23.4656418
## 41 29.384164 20.526331 11.2067987
## 42 36.599452 29.429507 18.8213040
## 43 30.671952 23.376473 12.7214878
## 44 13.876460 1.347791 0.6577390
## 45 23.358759 4.860774 3.5377900
## 46 21.971300 14.461561 3.8196199
## 47 5.193000 17.474502 1.8200465
## 48 13.876460 1.347791 0.6577390
## 49 13.876460 1.347791 0.6577390
## 50 19.988179 2.363390 2.6766549
## 51 9.831446 3.353369 0.8831434
## 52 44.925710 23.048577 22.4373931
## 53 8.994354 16.461379 3.2044115
## 54 12.210833 2.587349 1.2838636
## 55 16.528936 8.612384 2.5633087
## 56 17.604998 6.434551 0.9219433
## power_transmission_line_km thermal_power_plant_km ts_km
## 1 5.1626086 1.8461881 4.7975086
## 2 3.8606868 9.9859771 2.6952447
## 3 0.4292212 6.7285015 1.0468889
## 4 4.9265505 9.9201648 9.6588050
## 5 1.1509836 2.9404264 5.5100119
## 6 4.4934401 6.6910702 1.3575134
## 7 4.9265505 9.9201648 9.6588050
## 8 0.8334625 10.3102133 0.6787003
## 9 0.6816355 3.7771350 2.8961773
## 10 0.8334625 10.3102133 0.6787003
## 11 0.8334625 10.3102133 0.6787003
## 12 16.6595921 24.2840312 20.2839704
## 13 6.3809180 9.0463193 5.0131003
## 14 0.8420921 6.2428501 0.4914742
## 15 0.8334625 10.3102133 0.6787003
## 16 0.1231422 3.5091155 4.3504835
## 17 1.6804349 5.5682506 7.0107197
## 18 0.5169535 2.8379680 4.7748442
## 19 6.3809180 9.0463193 5.0131003
## 20 17.4187836 24.8367035 21.2590330
## 21 2.0194160 5.7555507 0.1862722
## 22 3.5469022 2.9675074 4.5816235
## 23 15.2115182 24.2826045 22.1641604
## 24 0.5330315 1.2404695 2.4563872
## 25 2.4186813 2.1207987 3.5446889
## 26 4.7989499 9.7932089 9.5301399
## 27 2.2303215 2.5813834 1.4571749
## 28 0.4884141 3.8232006 3.4879518
## 29 1.1509836 2.9404264 5.5100119
## 30 1.0425029 9.7651505 5.2191364
## 31 5.4449756 4.3085307 3.7571298
## 32 0.7877515 9.9276848 4.8817217
## 33 1.9210224 10.3935185 6.0878382
## 34 5.9337731 10.5492140 4.1881742
## 35 1.0025986 3.2089821 0.7312517
## 36 1.3556912 10.1972505 5.4948835
## 37 4.7989499 9.7932089 9.5301399
## 38 1.1686276 9.7577435 5.3440330
## 39 0.4501373 0.5956686 6.9783963
## 40 19.7623034 25.5447246 19.1728421
## 41 7.2837894 16.0113143 9.9253109
## 42 15.2733517 23.0527273 18.8684457
## 43 9.1370230 17.2935483 13.0111387
## 44 0.6266758 1.6610581 4.9609963
## 45 3.5786380 6.9703666 2.3639287
## 46 0.4305527 9.2238973 4.5399826
## 47 0.9686679 6.3138891 3.6167122
## 48 0.6266758 1.6610581 4.9609963
## 49 0.6266758 1.6610581 4.9609963
## 50 0.9324236 4.1056802 1.5642113
## 51 3.8885121 3.0403312 6.0606838
## 52 19.2497399 23.0267861 21.0171857
## 53 5.5459851 9.8668260 4.1590950
## 54 0.8135817 2.6754940 3.1127254
## 55 2.3433373 6.2322915 2.3250642
## 56 1.7512421 6.7477381 4.1474016
## big_market_km market_shop_km fitness_km swim_pool_km ice_rink_km
## 1 9.892177 3.6095792 0.77305896 1.2551658 3.5265292
## 2 12.743650 1.4302631 0.83824702 4.4986578 4.4986578
## 3 22.662194 2.3637236 0.34415693 1.2379559 9.4421669
## 4 14.350258 6.4691293 0.82232392 5.3749155 13.5118133
## 5 17.240628 2.4598959 0.72059954 1.8257847 2.7526677
## 6 25.902751 7.4775302 0.10936304 5.0594915 6.9256485
## 7 14.350258 6.4691293 0.82232392 5.3749155 13.5118133
## 8 26.815150 0.9526907 0.34101203 1.1243969 6.9571702
## 9 10.144266 1.5036527 0.62260824 3.1727067 9.8574616
## 10 26.815150 0.9526907 0.34101203 1.1243969 6.9571702
## 11 26.815150 0.9526907 0.34101203 1.1243969 6.9571702
## 12 20.356573 0.5029435 0.28041429 24.2962091 24.8332122
## 13 10.264218 5.4397341 2.49323550 8.1069365 8.6237895
## 14 14.375087 3.3616440 0.31663378 2.7177640 2.0295490
## 15 26.815150 0.9526907 0.34101203 1.1243969 6.9571702
## 16 15.301332 5.3692546 0.49459369 3.0479705 5.8630284
## 17 13.249850 5.3561225 0.37395753 2.6016330 9.5767767
## 18 13.536643 3.0698288 1.00808188 2.5800188 1.5749141
## 19 10.264218 5.4397341 2.49323550 8.1069365 8.6237895
## 20 20.511352 1.4447080 1.20620067 24.4509884 24.5063871
## 21 10.288381 4.6874936 0.28924697 2.5392783 8.5476075
## 22 4.518210 1.8298283 0.37792474 2.0136453 0.2053025
## 23 24.895449 7.1684453 4.64083954 28.8350851 18.9768409
## 24 5.103772 2.8667953 0.23450945 1.3040820 7.1660275
## 25 8.755830 5.3064860 0.09615559 0.3067061 8.3251666
## 26 14.703756 6.2871634 0.68001683 5.1929495 13.3298473
## 27 17.324047 2.8020519 0.23467273 0.5327820 2.1441085
## 28 7.476915 2.6379944 0.60727588 1.4170895 9.2591475
## 29 17.240628 2.4598959 0.72059954 1.8257847 2.7526677
## 30 4.506465 6.6367385 0.19394265 8.8509370 7.9433719
## 31 7.836658 1.0928965 0.26971642 1.4210995 0.5018559
## 32 4.019261 6.1495343 0.39507676 7.9588968 8.4958999
## 33 6.328276 10.5332175 1.08022653 9.7908606 8.8832956
## 34 15.878202 7.0065889 1.99237127 3.0944857 3.2886452
## 35 16.955567 6.0111215 0.52327173 0.9416821 7.8442799
## 36 4.667821 6.7980951 0.57342221 8.6074577 8.6073189
## 37 14.703756 6.2871634 0.68001683 5.1929495 13.3298473
## 38 4.529398 6.6596713 0.32846858 8.7511867 7.8436217
## 39 17.849471 3.5974211 0.88703639 1.4468760 5.0413969
## 40 28.421605 8.6835731 8.28824721 20.0244523 20.2186119
## 41 12.170059 14.3003326 3.47058951 13.5151577 13.7093172
## 42 18.814766 1.2391696 0.16192752 22.7544024 23.2914055
## 43 11.910442 7.4805137 5.98037245 15.8500781 16.3870813
## 44 7.442870 0.3834291 0.44861960 0.9587745 7.5575853
## 45 27.007065 6.3013300 0.32642779 4.8802148 6.2571584
## 46 4.272730 6.1935721 0.55545137 8.0116883 7.1041232
## 47 15.040888 3.5723147 0.40904877 7.3011252 3.1780132
## 48 7.442870 0.3834291 0.44861960 0.9587745 7.5575853
## 49 7.442870 0.3834291 0.44861960 0.9587745 7.5575853
## 50 18.463965 2.5664735 0.12130992 1.3564763 3.8236941
## 51 7.051658 2.6435042 0.16794780 0.7931862 4.5710518
## 52 39.033842 3.2424063 2.36046274 4.0678741 4.0094992
## 53 10.149622 4.4208756 2.91667907 7.9923401 7.7817811
## 54 9.231957 3.5630628 0.25175067 2.6293205 8.9071236
## 55 4.811109 2.6282267 0.24612542 3.4784792 3.3042526
## 56 2.249017 0.9183308 0.49576301 1.3103715 2.3637318
## stadium_km basketball_km hospice_morgue_km detention_facility_km
## 1 2.0694583 1.7284514 0.5510812 5.8505479
## 2 16.0712054 4.1574688 3.1489805 14.7968096
## 3 3.4712649 0.4747733 0.4956477 9.4017203
## 4 22.0934005 10.1313384 4.2176080 27.1637148
## 5 2.6078773 1.8256909 1.9654289 5.3359115
## 6 2.7963751 4.0820182 3.7099472 0.3213367
## 7 22.0934005 10.1313384 4.2176080 27.1637148
## 8 6.3079539 4.1021993 2.6259252 7.7123275
## 9 5.7598568 2.0549682 1.9627697 12.6779560
## 10 6.3079539 4.1021993 2.6259252 7.7123275
## 11 6.3079539 4.1021993 2.6259252 7.7123275
## 12 28.1929366 22.9997263 9.7392540 45.5615331
## 13 13.5917741 5.2112008 1.4518978 12.2391718
## 14 4.9974405 1.1094563 1.2399052 10.0809940
## 15 6.3079539 4.1021993 2.6259252 7.7123275
## 16 1.6701404 0.5960016 0.6632980 5.4758062
## 17 15.6544069 6.9365618 0.1548413 21.6272669
## 18 2.8527627 1.4029419 0.7106302 5.4597590
## 19 13.5917741 5.2112008 1.4518978 12.2391718
## 20 28.3477159 23.7180103 10.7212932 45.7163124
## 21 10.8008744 1.9458364 3.7301053 22.2252720
## 22 4.2947060 0.2821207 0.8327377 5.6315151
## 23 32.7318127 23.9799301 13.0449154 50.1004091
## 24 4.4114926 1.1360884 1.4720768 10.8430834
## 25 0.3067061 0.8140372 0.5197055 14.4951406
## 26 21.9114345 9.9891704 4.0962459 26.9817488
## 27 2.1665065 1.1482539 0.4444265 3.8965325
## 28 3.8938752 1.5672883 1.6671928 12.9369457
## 29 2.6078773 1.8256909 1.9654289 5.3359115
## 30 12.3428285 7.3149034 5.0614500 27.1760670
## 31 4.0182055 1.1715061 0.6156576 3.9393817
## 32 11.8556244 7.2111183 5.2895153 27.7339212
## 33 14.1646398 8.1759548 5.6327734 28.1159906
## 34 14.2307814 8.7950552 7.3961832 25.6576078
## 35 7.7349849 3.4525927 1.1994729 6.8428205
## 36 12.5041852 7.7198723 5.4837030 27.8400139
## 37 21.9114345 9.9891704 4.0962459 26.9817488
## 38 12.3657614 7.3844256 5.0374472 27.0763167
## 39 4.3471146 3.0158632 2.3572192 7.2878259
## 40 26.7741844 23.7837005 12.2904415 44.1405633
## 41 18.8088502 13.6104166 1.4972334 37.3203472
## 42 26.6511299 21.6359821 8.3320941 44.0197264
## 43 19.7468057 15.5236584 4.0928899 37.1154022
## 44 6.2359110 2.0394195 0.9053048 11.1928443
## 45 2.0586568 3.4456630 3.8473854 1.8663591
## 46 11.8996621 6.5949069 4.6013049 26.3368183
## 47 4.0385423 1.9832178 1.8330486 9.2924504
## 48 6.2359110 2.0394195 0.9053048 11.1928443
## 49 6.2359110 2.0394195 0.9053048 11.1928443
## 50 5.2470169 2.0258843 1.0353176 3.5175217
## 51 3.8580088 1.1839429 0.5497716 6.5229944
## 52 29.6077250 18.6779227 2.4494867 28.5381010
## 53 13.4771777 5.9732552 1.1520308 12.1245753
## 54 3.6042519 1.0375509 1.6400891 14.9712682
## 55 6.9641261 1.1443296 1.4788853 24.9086234
## 56 2.9441316 3.3060988 1.1604096 16.2781367
## public_healthcare_km university_km workplaces_km shopping_centers_km
## 1 1.2228912 1.519220 0.1858094 0.30063664
## 2 1.0189113 6.590469 3.2992104 2.11514489
## 3 2.1828813 7.218924 0.1198119 0.83696357
## 4 2.9922953 18.915844 8.9415127 3.17233376
## 5 2.8961058 2.241247 0.4885847 0.34907106
## 6 0.6932738 7.651449 0.1992599 0.83103123
## 7 2.9922953 18.915844 8.9415127 3.17233376
## 8 2.6377337 9.825595 0.2491313 0.17954508
## 9 2.2207987 1.448005 1.6200513 0.81409727
## 10 2.6377337 9.825595 0.2491313 0.17954508
## 11 2.6377337 9.825595 0.2491313 0.17954508
## 12 20.9242103 25.015381 21.1053428 13.79981407
## 13 5.4225513 8.837248 5.5990713 2.21510380
## 14 2.1581872 3.078368 2.0931352 0.77245821
## 15 2.6377337 9.825595 0.2491313 0.17954508
## 16 1.4624737 2.591160 0.6174441 1.42369240
## 17 4.7995597 14.980808 5.0169638 1.02942731
## 18 1.2407440 1.498504 1.2908121 0.03480446
## 19 5.4225513 8.837248 5.5990713 2.21510380
## 20 21.0789897 25.170160 21.9537969 14.08199307
## 21 3.4205991 5.407014 1.2823179 0.98293571
## 22 2.1816288 1.888613 0.2925003 0.10622148
## 23 25.4630864 29.554257 22.9882974 10.86552327
## 24 2.4087431 1.777485 1.4922579 0.23450945
## 25 4.2092338 1.630518 2.0370736 1.29624741
## 26 3.2624832 18.733878 8.8021526 3.23152778
## 27 1.3942468 1.400826 0.3185563 0.76131083
## 28 1.8854738 1.252018 3.7018487 0.41666609
## 29 2.8961058 2.241247 0.4885847 0.34907106
## 30 5.1382871 9.165272 6.2466935 4.66248710
## 31 2.6081621 2.180440 1.0915065 0.10735951
## 32 5.6961413 8.678068 5.8735898 4.25582807
## 33 5.0898250 10.987084 7.0982320 4.90921058
## 34 2.6257034 13.225858 9.8480535 1.35086804
## 35 3.1894841 9.381687 0.9801590 0.14220501
## 36 5.8022340 9.326629 6.4860938 4.86922272
## 37 3.2624832 18.733878 8.8021526 3.23152778
## 38 5.0385368 9.188205 6.3268720 4.62140944
## 39 2.7204918 4.381519 0.5948017 1.22102055
## 40 13.7881114 25.769261 23.8334496 7.73875501
## 41 13.3589297 16.828867 10.9155017 4.15214429
## 42 19.3824036 23.473574 19.6739089 12.41810799
## 43 12.4780794 16.569250 13.6113445 7.93700763
## 44 1.2691167 1.327606 0.3728827 0.00000000
## 45 1.6317117 7.472173 1.0597084 0.26162426
## 46 5.9004693 8.722106 5.5180619 4.05828281
## 47 1.3459307 3.555751 1.8386030 1.35305875
## 48 1.2691167 1.327606 0.3728827 0.00000000
## 49 1.2691167 1.327606 0.3728827 0.00000000
## 50 2.0086616 1.447544 0.5301283 1.06536114
## 51 1.5678957 1.541967 0.9524289 0.16936253
## 52 4.6422736 4.092086 1.2340507 2.27202083
## 53 4.4036928 9.320418 4.7955882 1.74929985
## 54 2.8041953 2.106646 1.0883454 0.83090304
## 55 3.5122886 4.794209 0.6566527 1.05154536
## 56 1.5653504 1.804877 3.3526643 0.62221773
## office_km additional_education_km preschool_km big_church_km
## 1 0.1122761 0.6832289 0.1974508 0.37322593
## 2 3.8096505 0.5077345 0.2870845 2.53900572
## 3 0.8650926 1.2776620 0.6087751 2.08420999
## 4 5.0871060 1.3340193 0.9363236 1.71498300
## 5 0.1629766 0.4226844 0.4168259 0.37287059
## 6 0.6688532 1.9694764 0.4515807 0.48579755
## 7 5.0871060 1.3340193 0.9363236 1.71498300
## 8 0.0000000 0.7128863 0.2491313 0.17143787
## 9 0.5835539 0.7344453 0.5520190 1.22487248
## 10 0.0000000 0.7128863 0.2491313 0.17143787
## 11 0.0000000 0.7128863 0.2491313 0.17143787
## 12 13.7998141 0.1888586 14.4001433 16.14677334
## 13 4.7787665 0.2034657 0.2141965 3.46828939
## 14 2.8832504 0.7892929 0.5285028 1.33630961
## 15 0.0000000 0.7128863 0.2491313 0.17143787
## 16 1.4229921 0.9562256 0.3089892 1.29465764
## 17 4.2332062 0.9302495 0.2375697 2.70137620
## 18 0.5516383 1.4467130 0.6247492 1.08069015
## 19 4.7787665 0.2034657 0.2141965 3.46828939
## 20 14.7972701 1.0727458 14.9548095 16.65750749
## 21 1.7206184 0.3160589 0.6967814 0.49135287
## 22 0.1449860 0.3852984 0.5210732 0.19321305
## 23 17.1854575 3.9558766 14.6562917 16.10213167
## 24 0.2345095 0.7803362 1.0023900 1.65752094
## 25 0.3525202 0.7075094 0.0000000 0.09656283
## 26 5.0723894 1.2106132 0.8260521 1.58237803
## 27 0.1054564 1.9552795 0.3185563 0.86909991
## 28 0.5046082 0.3653901 0.7204284 2.80305963
## 29 0.1629766 0.4226844 0.4168259 0.37287059
## 30 3.8142936 4.6717573 3.6571872 4.73655095
## 31 0.1821943 0.0000000 0.2697164 0.18189660
## 32 3.3094813 4.1832795 3.8408413 5.16642787
## 33 4.2672673 3.9782449 3.4199706 4.60111134
## 34 4.2872948 1.7193533 1.6920607 1.51693189
## 35 0.5678277 2.1296331 0.1725066 0.60995469
## 36 3.7354710 4.5776663 3.8026649 4.93327378
## 37 5.0723894 1.2106132 0.8260521 1.58237803
## 38 3.9536274 4.7330477 3.5208549 4.60939895
## 39 0.7787630 0.6116418 0.5948017 2.60059379
## 40 7.2483445 5.4003104 12.3749154 8.93609276
## 41 4.1521443 4.4260766 7.8729471 9.07328484
## 42 12.4181080 0.4195093 13.1851970 14.96970093
## 43 7.9370076 3.9780969 7.6713221 9.55984641
## 44 0.1948941 0.9582792 0.2923793 1.32714576
## 45 0.3293621 1.2490003 0.1826003 1.39205858
## 46 3.6459370 4.0418662 3.3411744 4.89137722
## 47 1.9066722 2.0247694 0.1263145 0.88442028
## 48 0.1948941 0.9582792 0.2923793 1.32714576
## 49 0.1948941 0.9582792 0.2923793 1.32714576
## 50 0.2793878 0.9368386 0.3637173 1.05821824
## 51 0.1990344 0.2482201 0.5414264 0.08645220
## 52 1.4709271 5.6395988 0.6450113 2.88197618
## 53 4.8588721 1.4929979 0.3594434 3.08730869
## 54 0.6525095 0.6054113 1.2991487 0.52198764
## 55 2.9697566 1.3037489 0.6215194 1.36835759
## 56 0.6568807 0.7202851 0.2310540 2.21990937
## church_synagogue_km mosque_km theater_km museum_km exhibition_km
## 1 0.26410696 2.761752 2.9857506 0.2244479 1.5923827
## 2 2.52783527 9.199579 6.3018488 5.6454372 4.7714017
## 3 2.75112801 9.567520 13.3064402 11.0302671 1.0235606
## 4 0.82239348 2.938594 21.6543866 15.0512784 13.1493445
## 5 0.83212875 7.676960 5.9695188 6.5588900 1.8461270
## 6 0.46546849 9.771815 16.2516779 9.9118231 5.9306613
## 7 0.82239348 2.938594 21.6543866 15.0512784 13.1493445
## 8 0.85476317 14.366596 15.9131106 15.5089160 4.0956061
## 9 0.55008751 2.480613 2.9820798 3.3680740 1.0463220
## 10 0.85476317 14.366596 15.9131106 15.5089160 4.0956061
## 11 0.85476317 14.366596 15.9131106 15.5089160 4.0956061
## 12 0.20846269 17.069924 27.7539228 25.1597565 20.7286561
## 13 0.91216838 11.891506 9.8362421 7.2887853 7.4022436
## 14 1.39524154 8.320671 1.5861227 0.9022409 3.5019635
## 15 0.85476317 14.366596 15.9131106 15.5089160 4.0956061
## 16 1.29283726 7.136099 4.8534425 5.6947977 1.4917028
## 17 2.45708503 2.041410 17.7193500 10.7934814 8.7823998
## 18 0.62556976 7.449762 3.8228036 2.2948836 2.2485266
## 19 0.91216838 11.891506 9.8362421 7.2887853 7.4022436
## 20 0.02204374 17.628356 27.9087021 26.1051594 21.7148945
## 21 0.82474123 3.252772 5.8515077 5.8017829 4.2998385
## 22 0.09967921 1.648905 0.2053025 0.5371775 0.6553407
## 23 3.14425643 16.138528 32.2927988 27.7970290 23.8605662
## 24 0.54289538 5.039404 5.6736115 2.8624856 1.8758898
## 25 0.79249069 4.734423 4.5983234 3.3547107 1.8373995
## 26 0.75611855 2.797455 21.4724206 14.9173324 13.0299590
## 27 0.78744442 4.994609 2.7669247 1.4394621 2.9212214
## 28 0.86737306 2.356306 3.6426036 2.1128945 4.1041067
## 29 0.83212875 7.676960 5.9695188 6.5588900 1.8461270
## 30 0.27390942 4.148704 11.9038146 12.1673239 8.9779068
## 31 0.18468146 1.827838 2.3703848 0.6952508 1.1843399
## 32 0.31213093 4.590479 11.4166105 11.6915447 8.6106472
## 33 0.92070918 4.263268 13.7256259 12.8583196 9.8306132
## 34 1.51224804 13.121289 13.7917675 10.3613126 5.0278604
## 35 0.90408356 5.301737 7.8925002 5.5027959 1.4049474
## 36 0.32883257 4.441236 12.0651713 12.2576102 9.2267996
## 37 0.75611855 2.797455 21.4724206 14.9173324 13.0299590
## 38 0.39005215 4.046196 11.9267475 12.3134682 9.1073040
## 39 1.70365232 7.687396 7.2646520 6.8413905 2.0738906
## 40 0.28329053 23.881933 26.3351706 25.2229621 19.9009375
## 41 1.44237770 10.227318 18.3698363 14.6511209 10.4915465
## 42 1.32601461 15.844271 26.2121160 23.7249388 19.3263756
## 43 0.78179094 10.199456 19.3077918 18.1042176 14.2482557
## 44 1.10305064 3.956116 2.3846207 0.7743376 2.1479930
## 45 0.28358132 10.619401 13.8043466 10.6016630 5.5087684
## 46 0.81982547 4.117628 11.4606483 11.6776987 8.3148701
## 47 0.88543186 6.663935 4.3575641 3.1257112 4.1502765
## 48 1.10305064 3.956116 2.3846207 0.7743376 2.1479930
## 49 1.10305064 3.956116 2.3846207 0.7743376 2.1479930
## 50 0.71332164 6.034122 10.7915468 8.1167847 4.2516141
## 51 0.09626400 2.177374 1.4998774 1.1938906 1.1416073
## 52 2.71814100 29.601291 4.4466652 3.5184378 22.7643556
## 53 0.88029348 10.239373 9.0317976 6.8726272 6.5115772
## 54 1.23308511 3.808852 5.0744509 3.3921720 0.9842616
## 55 1.36307399 5.552658 8.2264011 7.9343085 4.0955771
## 56 0.41218573 1.600462 3.7174238 5.0191818 0.5017999
## catering_km ecology green_part_500 prom_part_500 office_count_500
## 1 0.05742955 excellent 0.17 0.00 14
## 2 0.16322276 good 0.00 6.61 0
## 3 0.29969017 poor 3.22 22.02 0
## 4 1.45291864 no data 1.48 0.00 0
## 5 0.08953638 poor 0.00 28.68 3
## 6 1.04186485 good 1.51 24.49 0
## 7 1.45291864 no data 1.48 0.00 0
## 8 0.08043102 good 0.00 0.00 2
## 9 0.46210658 poor 18.23 45.41 0
## 10 0.08043102 good 0.00 0.00 2
## 11 0.08043102 good 0.00 0.00 2
## 12 0.60558578 no data 49.00 3.56 0
## 13 1.83607316 good 42.63 5.76 0
## 14 0.66683763 poor 3.49 0.00 0
## 15 0.08043102 good 0.00 0.00 2
## 16 0.57087531 poor 27.16 1.07 0
## 17 0.03312999 good 1.02 0.00 0
## 18 0.62183981 poor 37.30 23.83 0
## 19 1.83607316 good 42.63 5.76 0
## 20 0.50241121 no data 20.50 7.40 0
## 21 0.51508312 poor 11.42 0.00 0
## 22 0.08793326 excellent 4.29 0.00 12
## 23 3.75712419 no data 10.33 0.00 0
## 24 0.22782964 excellent 40.49 13.97 2
## 25 0.13442509 excellent 5.08 0.00 6
## 26 1.31491481 no data 9.88 0.00 0
## 27 0.02624315 poor 17.78 4.01 7
## 28 0.34885645 satisfactory 38.28 0.00 0
## 29 0.08953638 poor 0.00 28.68 3
## 30 0.34192135 no data 0.00 6.86 0
## 31 0.10775908 excellent 12.24 0.00 10
## 32 0.84391624 no data 0.02 17.40 0
## 33 0.85549290 no data 17.94 1.51 0
## 34 0.44120737 no data 31.13 2.83 0
## 35 0.31951368 poor 4.40 4.22 0
## 36 0.64525502 no data 1.87 7.96 0
## 37 1.31491481 no data 9.88 0.00 0
## 38 0.22046116 no data 0.00 9.09 0
## 39 0.96552193 good 4.98 27.49 0
## 40 5.69423497 no data 5.23 14.43 0
## 41 2.51680571 no data 86.86 0.00 0
## 42 1.70902471 no data 15.29 2.98 0
## 43 0.04682983 no data 0.00 0.00 0
## 44 0.08243620 poor 7.96 0.00 2
## 45 0.54439759 good 14.15 0.00 3
## 46 0.86480420 no data 6.08 13.69 0
## 47 1.00595720 good 6.50 0.00 0
## 48 0.08243620 poor 7.96 0.00 2
## 49 0.08243620 poor 7.96 0.00 2
## 50 0.11911950 poor 12.09 1.17 2
## 51 0.07842638 excellent 0.15 0.00 4
## 52 0.49902879 no data 38.24 13.28 0
## 53 1.99972418 good 0.00 0.00 0
## 54 0.52821133 poor 5.24 30.42 0
## 55 0.50170576 good 60.22 0.00 0
## 56 0.25178176 satisfactory 3.89 0.00 0
## office_sqm_500 trc_count_500 trc_sqm_500 cafe_count_500
## 1 242192 1 2720 42
## 2 0 0 0 2
## 3 0 0 0 3
## 4 0 0 0 0
## 5 103363 3 135000 8
## 6 0 0 0 0
## 7 0 0 0 0
## 8 11000 5 77480 7
## 9 0 0 0 1
## 10 11000 5 77480 7
## 11 11000 5 77480 7
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 11000 5 77480 7
## 16 0 0 0 0
## 17 0 0 0 1
## 18 0 1 128000 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 80902 8 66601 58
## 23 0 0 0 0
## 24 410000 2 144500 1
## 25 234985 0 0 8
## 26 0 0 0 0
## 27 93514 0 0 21
## 28 0 1 18000 6
## 29 103363 3 135000 8
## 30 0 0 0 1
## 31 131844 6 467600 71
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 1
## 35 0 3 105580 2
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 1
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## 42 0 0 0 0
## 43 0 0 0 1
## 44 33000 1 22000 10
## 45 175395 2 15000 0
## 46 0 0 0 0
## 47 0 0 0 0
## 48 33000 1 22000 10
## 49 33000 1 22000 10
## 50 35000 0 0 7
## 51 63722 2 20000 19
## 52 0 0 0 1
## 53 0 0 0 0
## 54 0 0 0 0
## 55 0 0 0 0
## 56 0 0 0 5
## cafe_sum_500_min_price_avg cafe_sum_500_max_price_avg
## 1 985.37 1597.56
## 2 750.00 1250.00
## 3 500.00 1000.00
## 4 NA NA
## 5 457.14 785.71
## 6 NA NA
## 7 NA NA
## 8 485.71 857.14
## 9 500.00 1000.00
## 10 485.71 857.14
## 11 485.71 857.14
## 12 NA NA
## 13 NA NA
## 14 NA NA
## 15 485.71 857.14
## 16 NA NA
## 17 NA NA
## 18 NA NA
## 19 NA NA
## 20 NA NA
## 21 NA NA
## 22 858.49 1452.83
## 23 NA NA
## 24 300.00 500.00
## 25 942.86 1500.00
## 26 NA NA
## 27 861.11 1444.44
## 28 560.00 1000.00
## 29 457.14 785.71
## 30 500.00 1000.00
## 31 954.84 1564.52
## 32 NA NA
## 33 NA NA
## 34 1500.00 2500.00
## 35 750.00 1250.00
## 36 NA NA
## 37 NA NA
## 38 500.00 1000.00
## 39 NA NA
## 40 NA NA
## 41 NA NA
## 42 NA NA
## 43 1000.00 1500.00
## 44 760.00 1250.00
## 45 NA NA
## 46 NA NA
## 47 NA NA
## 48 760.00 1250.00
## 49 760.00 1250.00
## 50 716.67 1166.67
## 51 700.00 1187.50
## 52 300.00 500.00
## 53 NA NA
## 54 NA NA
## 55 NA NA
## 56 650.00 1000.00
## cafe_avg_price_500 cafe_count_500_na_price cafe_count_500_price_500
## 1 1291.46 1 8
## 2 1000.00 0 0
## 3 750.00 0 0
## 4 NA 0 0
## 5 621.43 1 4
## 6 NA 0 0
## 7 NA 0 0
## 8 671.43 0 3
## 9 750.00 0 0
## 10 671.43 0 3
## 11 671.43 0 3
## 12 NA 0 0
## 13 NA 0 0
## 14 NA 0 0
## 15 671.43 0 3
## 16 NA 0 0
## 17 NA 1 0
## 18 NA 0 0
## 19 NA 0 0
## 20 NA 0 0
## 21 NA 0 0
## 22 1155.66 5 15
## 23 NA 0 0
## 24 400.00 0 1
## 25 1221.43 1 2
## 26 NA 0 0
## 27 1152.78 3 5
## 28 780.00 1 1
## 29 621.43 1 4
## 30 750.00 0 0
## 31 1259.68 9 19
## 32 NA 0 0
## 33 NA 0 0
## 34 2000.00 0 0
## 35 1000.00 0 0
## 36 NA 0 0
## 37 NA 0 0
## 38 750.00 0 0
## 39 NA 0 0
## 40 NA 0 0
## 41 NA 0 0
## 42 NA 0 0
## 43 1250.00 0 0
## 44 1005.00 0 2
## 45 NA 0 0
## 46 NA 0 0
## 47 NA 0 0
## 48 1005.00 0 2
## 49 1005.00 0 2
## 50 941.67 1 1
## 51 943.75 3 4
## 52 400.00 0 1
## 53 NA 0 0
## 54 NA 0 0
## 55 NA 0 0
## 56 825.00 1 2
## cafe_count_500_price_1000 cafe_count_500_price_1500
## 1 7 15
## 2 1 1
## 3 3 0
## 4 0 0
## 5 2 1
## 6 0 0
## 7 0 0
## 8 3 1
## 9 1 0
## 10 3 1
## 11 3 1
## 12 0 0
## 13 0 0
## 14 0 0
## 15 3 1
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 17 6
## 23 0 0
## 24 0 0
## 25 0 3
## 26 0 0
## 27 5 3
## 28 3 1
## 29 2 1
## 30 1 0
## 31 11 13
## 32 0 0
## 33 0 0
## 34 0 0
## 35 1 1
## 36 0 0
## 37 0 0
## 38 1 0
## 39 0 0
## 40 0 0
## 41 0 0
## 42 0 0
## 43 0 1
## 44 3 4
## 45 0 0
## 46 0 0
## 47 0 0
## 48 3 4
## 49 3 4
## 50 2 3
## 51 6 4
## 52 0 0
## 53 0 0
## 54 0 0
## 55 0 0
## 56 0 2
## cafe_count_500_price_2500 cafe_count_500_price_4000
## 1 8 3
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 11 4
## 23 0 0
## 24 0 0
## 25 2 0
## 26 0 0
## 27 4 1
## 28 0 0
## 29 0 0
## 30 0 0
## 31 14 4
## 32 0 0
## 33 0 0
## 34 1 0
## 35 0 0
## 36 0 0
## 37 0 0
## 38 0 0
## 39 0 0
## 40 0 0
## 41 0 0
## 42 0 0
## 43 0 0
## 44 1 0
## 45 0 0
## 46 0 0
## 47 0 0
## 48 1 0
## 49 1 0
## 50 0 0
## 51 2 0
## 52 0 0
## 53 0 0
## 54 0 0
## 55 0 0
## 56 0 0
## cafe_count_500_price_high big_church_count_500 church_count_500
## 1 0 1 4
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 1 0
## 6 0 1 1
## 7 0 0 0
## 8 0 1 0
## 9 0 0 0
## 10 0 1 0
## 11 0 1 0
## 12 0 0 1
## 13 0 0 0
## 14 0 0 0
## 15 0 1 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 1
## 21 0 1 0
## 22 0 3 4
## 23 0 0 0
## 24 0 0 0
## 25 0 2 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 1 0
## 30 0 0 1
## 31 1 8 15
## 32 0 0 1
## 33 0 0 0
## 34 0 0 0
## 35 0 0 0
## 36 0 0 1
## 37 0 0 0
## 38 0 0 1
## 39 0 0 0
## 40 0 0 1
## 41 0 0 0
## 42 0 0 0
## 43 0 0 0
## 44 0 0 0
## 45 0 0 1
## 46 0 0 0
## 47 0 0 0
## 48 0 0 0
## 49 0 0 0
## 50 0 0 0
## 51 0 1 2
## 52 0 0 0
## 53 0 0 0
## 54 0 0 0
## 55 0 0 0
## 56 0 0 1
## mosque_count_500 leisure_count_500 sport_count_500 market_count_500
## 1 0 9 0 0
## 2 0 0 0 0
## 3 0 0 2 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 1 0
## 7 0 0 0 0
## 8 0 0 1 0
## 9 0 0 0 0
## 10 0 0 1 0
## 11 0 0 1 0
## 12 0 0 2 1
## 13 0 0 0 0
## 14 0 0 1 0
## 15 0 0 1 0
## 16 0 0 1 0
## 17 0 0 1 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 1 0
## 22 0 3 5 1
## 23 0 0 0 0
## 24 0 0 1 0
## 25 0 0 5 0
## 26 0 0 0 0
## 27 0 0 4 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 1 0
## 31 0 0 9 1
## 32 0 0 1 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 1 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 1 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## 42 0 0 1 0
## 43 0 0 0 0
## 44 0 0 1 1
## 45 0 0 2 0
## 46 0 0 0 0
## 47 0 0 1 0
## 48 0 0 1 1
## 49 0 0 1 1
## 50 0 0 7 0
## 51 0 0 2 0
## 52 0 0 0 0
## 53 0 0 0 0
## 54 0 0 1 0
## 55 0 0 1 0
## 56 0 0 1 0
## green_part_1000 prom_part_1000 office_count_1000 office_sqm_1000
## 1 12.07 0.00 44 363707
## 2 4.49 8.08 0 0
## 3 4.52 28.44 1 9881
## 4 10.96 0.00 0 0
## 5 3.34 17.10 9 186319
## 6 15.49 19.15 1 6798
## 7 10.96 0.00 0 0
## 8 21.70 4.06 2 11000
## 9 19.28 25.35 3 274683
## 10 21.70 4.06 2 11000
## 11 21.70 4.06 2 11000
## 12 55.74 3.34 0 0
## 13 21.22 28.43 0 0
## 14 2.45 0.00 0 0
## 15 21.70 4.06 2 11000
## 16 28.13 9.85 0 0
## 17 9.63 0.46 0 0
## 18 43.61 13.61 4 21900
## 19 21.22 28.43 0 0
## 20 37.92 13.17 0 0
## 21 31.00 0.00 0 0
## 22 3.46 0.00 33 249676
## 23 27.82 0.96 0 0
## 24 29.07 24.25 3 472600
## 25 6.69 15.86 11 333481
## 26 13.93 0.00 0 0
## 27 11.65 5.74 17 376435
## 28 24.77 0.00 3 49161
## 29 3.34 17.10 9 186319
## 30 1.68 11.54 0 0
## 31 7.51 0.00 47 649057
## 32 1.85 11.46 0 0
## 33 23.34 3.11 0 0
## 34 43.01 1.93 0 0
## 35 3.37 7.23 2 85000
## 36 4.77 7.27 0 0
## 37 13.93 0.00 0 0
## 38 3.63 11.14 0 0
## 39 22.99 37.89 1 2500
## 40 6.88 5.89 0 0
## 41 65.34 0.52 0 0
## 42 26.63 6.52 0 0
## 43 0.00 0.00 0 0
## 44 5.57 0.00 8 133306
## 45 9.92 0.00 3 175395
## 46 15.40 11.66 0 0
## 47 17.97 0.39 0 0
## 48 5.57 0.00 8 133306
## 49 5.57 0.00 8 133306
## 50 14.98 1.74 2 35000
## 51 11.61 0.00 19 269472
## 52 20.27 23.99 0 0
## 53 0.00 0.02 0 0
## 54 5.53 28.68 4 154183
## 55 59.81 0.00 0 0
## 56 1.78 8.18 6 284103
## trc_count_1000 trc_sqm_1000 cafe_count_1000 cafe_sum_1000_min_price_avg
## 1 2 10220 99 866.67
## 2 0 0 2 750.00
## 3 2 124078 12 827.27
## 4 0 0 0 NA
## 5 4 141000 30 639.29
## 6 2 3000 0 NA
## 7 0 0 0 NA
## 8 6 80780 13 592.31
## 9 1 15970 5 520.00
## 10 6 80780 13 592.31
## 11 6 80780 13 592.31
## 12 0 0 2 1250.00
## 13 0 0 0 NA
## 14 3 25016 10 470.00
## 15 6 80780 13 592.31
## 16 0 0 2 500.00
## 17 0 0 8 657.14
## 18 1 128000 6 833.33
## 19 0 0 0 NA
## 20 0 0 2 1250.00
## 21 1 40000 8 850.00
## 22 11 90101 124 853.98
## 23 0 0 0 NA
## 24 2 144500 3 600.00
## 25 0 0 11 877.78
## 26 0 0 0 NA
## 27 1 10000 82 1061.43
## 28 2 25295 16 486.67
## 29 4 141000 30 639.29
## 30 0 0 1 500.00
## 31 20 842476 316 914.53
## 32 0 0 1 500.00
## 33 0 0 1 500.00
## 34 0 0 2 1000.00
## 35 3 105580 8 728.57
## 36 0 0 1 500.00
## 37 0 0 0 NA
## 38 0 0 1 500.00
## 39 0 0 1 NA
## 40 0 0 0 NA
## 41 0 0 0 NA
## 42 0 0 0 NA
## 43 0 0 1 1000.00
## 44 1 22000 30 743.33
## 45 3 15000 8 1142.86
## 46 0 0 1 500.00
## 47 0 0 0 NA
## 48 1 22000 30 743.33
## 49 1 22000 30 743.33
## 50 0 0 15 607.14
## 51 3 40000 76 773.53
## 52 0 0 2 300.00
## 53 0 0 0 NA
## 54 2 71470 10 588.89
## 55 0 0 2 300.00
## 56 4 83500 19 658.82
## cafe_sum_1000_max_price_avg cafe_avg_price_1000
## 1 1416.67 1141.67
## 2 1250.00 1000.00
## 3 1409.09 1118.18
## 4 NA NA
## 5 1071.43 855.36
## 6 NA NA
## 7 NA NA
## 8 1000.00 796.15
## 9 900.00 710.00
## 10 1000.00 796.15
## 11 1000.00 796.15
## 12 2000.00 1625.00
## 13 NA NA
## 14 850.00 660.00
## 15 1000.00 796.15
## 16 1000.00 750.00
## 17 1071.43 864.29
## 18 1416.67 1125.00
## 19 NA NA
## 20 2000.00 1625.00
## 21 1333.33 1091.67
## 22 1398.23 1126.11
## 23 NA NA
## 24 1000.00 800.00
## 25 1388.89 1133.33
## 26 NA NA
## 27 1728.57 1395.00
## 28 866.67 676.67
## 29 1071.43 855.36
## 30 1000.00 750.00
## 31 1500.00 1207.26
## 32 1000.00 750.00
## 33 1000.00 750.00
## 34 1750.00 1375.00
## 35 1142.86 935.71
## 36 1000.00 750.00
## 37 NA NA
## 38 1000.00 750.00
## 39 NA NA
## 40 NA NA
## 41 NA NA
## 42 NA NA
## 43 1500.00 1250.00
## 44 1250.00 996.67
## 45 1928.57 1535.71
## 46 1000.00 750.00
## 47 NA NA
## 48 1250.00 996.67
## 49 1250.00 996.67
## 50 1000.00 803.57
## 51 1301.47 1037.50
## 52 500.00 400.00
## 53 NA NA
## 54 1111.11 850.00
## 55 500.00 400.00
## 56 1117.65 888.24
## cafe_count_1000_na_price cafe_count_1000_price_500
## 1 9 25
## 2 0 0
## 3 1 2
## 4 0 0
## 5 2 13
## 6 0 0
## 7 0 0
## 8 0 4
## 9 0 2
## 10 0 4
## 11 0 4
## 12 0 0
## 13 0 0
## 14 0 4
## 15 0 4
## 16 1 0
## 17 1 2
## 18 0 0
## 19 0 0
## 20 0 0
## 21 2 2
## 22 11 25
## 23 0 0
## 24 0 1
## 25 2 3
## 26 0 0
## 27 12 16
## 28 1 6
## 29 2 13
## 30 0 0
## 31 20 94
## 32 0 0
## 33 0 0
## 34 0 0
## 35 1 2
## 36 0 0
## 37 0 0
## 38 0 0
## 39 1 0
## 40 0 0
## 41 0 0
## 42 0 0
## 43 0 0
## 44 0 6
## 45 1 0
## 46 0 0
## 47 0 0
## 48 0 6
## 49 0 6
## 50 1 5
## 51 8 17
## 52 0 2
## 53 0 0
## 54 1 1
## 55 0 2
## 56 2 4
## cafe_count_1000_price_1000 cafe_count_1000_price_1500
## 1 17 28
## 2 1 1
## 3 5 2
## 4 0 0
## 5 7 5
## 6 0 0
## 7 0 0
## 8 5 4
## 9 2 1
## 10 5 4
## 11 5 4
## 12 0 1
## 13 0 0
## 14 5 1
## 15 5 4
## 16 1 0
## 17 2 3
## 18 3 2
## 19 0 0
## 20 0 1
## 21 0 3
## 22 27 40
## 23 0 0
## 24 1 1
## 25 0 4
## 26 0 0
## 27 9 19
## 28 7 2
## 29 7 5
## 30 1 0
## 31 56 68
## 32 1 0
## 33 1 0
## 34 1 0
## 35 1 4
## 36 1 0
## 37 0 0
## 38 1 0
## 39 0 0
## 40 0 0
## 41 0 0
## 42 0 0
## 43 0 1
## 44 11 9
## 45 2 1
## 46 1 0
## 47 0 0
## 48 11 9
## 49 11 9
## 50 4 5
## 51 24 16
## 52 0 0
## 53 0 0
## 54 7 0
## 55 0 0
## 56 7 5
## cafe_count_1000_price_2500 cafe_count_1000_price_4000
## 1 16 4
## 2 0 0
## 3 1 1
## 4 0 0
## 5 2 1
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 1 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 1 0
## 19 0 0
## 20 1 0
## 21 1 0
## 22 17 4
## 23 0 0
## 24 0 0
## 25 2 0
## 26 0 0
## 27 19 7
## 28 0 0
## 29 2 1
## 30 0 0
## 31 50 27
## 32 0 0
## 33 0 0
## 34 1 0
## 35 0 0
## 36 0 0
## 37 0 0
## 38 0 0
## 39 0 0
## 40 0 0
## 41 0 0
## 42 0 0
## 43 0 0
## 44 4 0
## 45 4 0
## 46 0 0
## 47 0 0
## 48 4 0
## 49 4 0
## 50 0 0
## 51 8 3
## 52 0 0
## 53 0 0
## 54 1 0
## 55 0 0
## 56 1 0
## cafe_count_1000_price_high big_church_count_1000 church_count_1000
## 1 0 5 12
## 2 0 0 0
## 3 0 0 0
## 4 0 0 1
## 5 0 2 2
## 6 0 1 4
## 7 0 0 1
## 8 0 1 1
## 9 0 0 1
## 10 0 1 1
## 11 0 1 1
## 12 0 0 1
## 13 0 0 1
## 14 0 0 0
## 15 0 1 1
## 16 0 0 0
## 17 0 0 0
## 18 0 0 1
## 19 0 0 1
## 20 0 0 2
## 21 0 1 2
## 22 0 10 14
## 23 0 0 0
## 24 0 0 1
## 25 0 3 3
## 26 0 0 1
## 27 0 1 2
## 28 0 0 1
## 29 0 2 2
## 30 0 0 1
## 31 1 16 35
## 32 0 0 1
## 33 0 0 1
## 34 0 0 0
## 35 0 2 1
## 36 0 0 1
## 37 0 0 1
## 38 0 0 1
## 39 0 0 0
## 40 0 0 1
## 41 0 0 0
## 42 0 0 0
## 43 0 0 1
## 44 0 0 0
## 45 0 0 2
## 46 0 0 1
## 47 0 1 1
## 48 0 0 0
## 49 0 0 0
## 50 0 0 1
## 51 0 7 10
## 52 0 0 0
## 53 0 0 1
## 54 0 1 0
## 55 0 0 0
## 56 0 0 2
## mosque_count_1000 leisure_count_1000 sport_count_1000 market_count_1000
## 1 0 12 7 0
## 2 0 0 1 1
## 3 0 0 4 0
## 4 0 0 1 0
## 5 0 0 1 0
## 6 0 0 2 0
## 7 0 0 1 0
## 8 0 0 5 1
## 9 0 0 2 1
## 10 0 0 5 1
## 11 0 0 5 1
## 12 0 0 2 2
## 13 0 0 0 0
## 14 0 4 2 0
## 15 0 0 5 1
## 16 0 0 5 0
## 17 0 0 5 0
## 18 0 0 1 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 1 0
## 22 0 7 8 2
## 23 0 0 0 0
## 24 0 0 2 0
## 25 0 0 8 0
## 26 0 0 1 0
## 27 0 0 9 0
## 28 0 0 5 0
## 29 0 0 1 0
## 30 0 0 1 0
## 31 0 11 16 1
## 32 0 0 1 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 7 0
## 36 0 0 1 0
## 37 0 0 1 0
## 38 0 0 1 0
## 39 0 0 2 0
## 40 0 0 0 0
## 41 0 0 0 0
## 42 0 0 2 0
## 43 0 0 0 0
## 44 0 1 3 1
## 45 0 0 5 0
## 46 0 0 1 0
## 47 0 0 2 0
## 48 0 1 3 1
## 49 0 1 3 1
## 50 0 0 8 0
## 51 0 2 10 0
## 52 0 0 0 0
## 53 0 0 0 0
## 54 0 1 1 0
## 55 0 0 1 0
## 56 0 1 6 1
## green_part_1500 prom_part_1500 office_count_1500 office_sqm_1500
## 1 15.03 0.00 81 677136
## 2 6.75 7.34 0 0
## 3 5.93 23.31 1 9881
## 4 12.58 1.45 0 0
## 5 10.24 9.58 20 500355
## 6 26.63 9.75 5 207193
## 7 12.58 1.45 0 0
## 8 27.68 7.30 2 11000
## 9 11.53 17.78 10 536315
## 10 27.68 7.30 2 11000
## 11 27.68 7.30 2 11000
## 12 56.04 7.53 0 0
## 13 9.43 34.86 0 0
## 14 7.82 3.65 0 0
## 15 27.68 7.30 2 11000
## 16 39.21 7.40 2 150000
## 17 27.36 3.49 0 0
## 18 44.22 9.18 4 21900
## 19 9.43 34.86 0 0
## 20 48.45 9.43 0 0
## 21 34.83 0.00 0 0
## 22 5.02 0.00 74 726388
## 23 38.29 2.75 0 0
## 24 18.99 29.18 6 594662
## 25 3.78 24.67 23 527921
## 26 13.83 1.42 0 0
## 27 7.36 7.47 42 2398016
## 28 19.56 0.00 7 67116
## 29 10.24 9.58 20 500355
## 30 11.06 10.61 0 0
## 31 5.70 0.00 120 1259653
## 32 17.52 7.81 0 0
## 33 19.67 7.86 0 0
## 34 39.35 1.30 0 0
## 35 3.19 12.80 4 163740
## 36 13.91 7.47 0 0
## 37 13.83 1.42 0 0
## 38 10.62 11.61 0 0
## 39 38.51 43.20 3 14539
## 40 8.58 3.80 0 0
## 41 52.41 0.74 0 0
## 42 33.15 3.88 0 0
## 43 8.47 0.05 0 0
## 44 6.79 4.35 14 228176
## 45 14.73 3.37 4 182193
## 46 21.53 8.07 0 0
## 47 34.96 1.54 0 0
## 48 6.79 4.35 14 228176
## 49 6.79 4.35 14 228176
## 50 17.00 5.25 5 82427
## 51 17.66 1.88 51 619993
## 52 21.27 20.19 1 20038
## 53 0.16 7.14 0 0
## 54 5.14 25.62 10 348608
## 55 60.09 0.00 0 0
## 56 5.95 16.98 8 324375
## trc_count_1500 trc_sqm_1500 cafe_count_1500 cafe_sum_1500_min_price_avg
## 1 5 122060 225 901.93
## 2 0 0 2 750.00
## 3 2 124078 17 826.67
## 4 0 0 1 1000.00
## 5 5 145000 70 610.00
## 6 3 15000 4 1000.00
## 7 0 0 1 1000.00
## 8 10 128092 24 716.67
## 9 6 286770 20 700.00
## 10 10 128092 24 716.67
## 11 10 128092 24 716.67
## 12 0 0 2 1250.00
## 13 0 0 0 NA
## 14 7 167806 12 458.33
## 15 10 128092 24 716.67
## 16 1 7000 3 400.00
## 17 4 87000 12 710.00
## 18 1 128000 24 613.04
## 19 0 0 0 NA
## 20 0 0 2 1250.00
## 21 6 354000 18 662.50
## 22 14 124101 196 850.82
## 23 0 0 0 NA
## 24 7 692500 23 659.09
## 25 4 190650 36 667.65
## 26 0 0 1 1000.00
## 27 4 357500 176 1116.05
## 28 2 25295 25 613.04
## 29 5 145000 70 610.00
## 30 0 0 1 500.00
## 31 25 1075476 643 899.17
## 32 0 0 1 500.00
## 33 0 0 1 500.00
## 34 1 17000 2 1000.00
## 35 6 156795 13 658.33
## 36 0 0 1 500.00
## 37 0 0 1 1000.00
## 38 0 0 1 500.00
## 39 1 20568 2 1000.00
## 40 0 0 0 NA
## 41 0 0 0 NA
## 42 0 0 0 NA
## 43 0 0 1 1000.00
## 44 3 67940 55 814.55
## 45 10 650124 15 1038.46
## 46 0 0 1 500.00
## 47 1 175000 4 575.00
## 48 3 67940 55 814.55
## 49 3 67940 55 814.55
## 50 5 67040 32 566.67
## 51 5 55566 161 767.83
## 52 0 0 5 340.00
## 53 0 0 0 NA
## 54 5 225470 24 686.96
## 55 2 33300 6 400.00
## 56 10 175154 34 587.50
## cafe_sum_1500_max_price_avg cafe_avg_price_1500
## 1 1487.92 1194.93
## 2 1250.00 1000.00
## 3 1400.00 1113.33
## 4 1500.00 1250.00
## 5 1025.00 817.50
## 6 1625.00 1312.50
## 7 1500.00 1250.00
## 8 1208.33 962.50
## 9 1166.67 933.33
## 10 1208.33 962.50
## 11 1208.33 962.50
## 12 2000.00 1625.00
## 13 NA NA
## 14 833.33 645.83
## 15 1208.33 962.50
## 16 750.00 575.00
## 17 1150.00 930.00
## 18 1043.48 828.26
## 19 NA NA
## 20 2000.00 1625.00
## 21 1062.50 862.50
## 22 1396.17 1123.50
## 23 NA NA
## 24 1113.64 886.36
## 25 1147.06 907.35
## 26 1500.00 1250.00
## 27 1811.73 1463.89
## 28 1043.48 828.26
## 29 1025.00 817.50
## 30 1000.00 750.00
## 31 1476.03 1187.60
## 32 1000.00 750.00
## 33 1000.00 750.00
## 34 1750.00 1375.00
## 35 1083.33 870.83
## 36 1000.00 750.00
## 37 1500.00 1250.00
## 38 1000.00 750.00
## 39 1500.00 1250.00
## 40 NA NA
## 41 NA NA
## 42 NA NA
## 43 1500.00 1250.00
## 44 1345.45 1080.00
## 45 1730.77 1384.62
## 46 1000.00 750.00
## 47 1000.00 787.50
## 48 1345.45 1080.00
## 49 1345.45 1080.00
## 50 966.67 766.67
## 51 1272.73 1020.28
## 52 600.00 470.00
## 53 NA NA
## 54 1173.91 930.43
## 55 750.00 575.00
## 56 1000.00 793.75
## cafe_count_1500_na_price cafe_count_1500_price_500
## 1 18 49
## 2 0 0
## 3 2 3
## 4 0 0
## 5 10 27
## 6 0 0
## 7 0 0
## 8 0 4
## 9 2 7
## 10 0 4
## 11 0 4
## 12 0 0
## 13 0 0
## 14 0 5
## 15 0 4
## 16 1 1
## 17 2 2
## 18 1 7
## 19 0 0
## 20 0 0
## 21 2 7
## 22 13 44
## 23 0 0
## 24 1 10
## 25 2 9
## 26 0 0
## 27 14 31
## 28 2 7
## 29 10 27
## 30 0 0
## 31 38 165
## 32 0 0
## 33 0 0
## 34 0 0
## 35 1 3
## 36 0 0
## 37 0 0
## 38 0 0
## 39 1 0
## 40 0 0
## 41 0 0
## 42 0 0
## 43 0 0
## 44 0 11
## 45 2 0
## 46 0 0
## 47 0 1
## 48 0 11
## 49 0 11
## 50 2 10
## 51 18 41
## 52 0 4
## 53 0 0
## 54 1 6
## 55 0 3
## 56 2 11
## cafe_count_1500_price_1000 cafe_count_1500_price_1500
## 1 50 56
## 2 1 1
## 3 6 3
## 4 0 1
## 5 16 12
## 6 1 2
## 7 0 1
## 8 10 8
## 9 4 4
## 10 10 8
## 11 10 8
## 12 0 1
## 13 0 0
## 14 6 1
## 15 10 8
## 16 1 0
## 17 3 5
## 18 9 6
## 19 0 0
## 20 0 1
## 21 2 6
## 22 45 59
## 23 0 0
## 24 6 3
## 25 14 7
## 26 0 1
## 27 33 43
## 28 9 6
## 29 16 12
## 30 1 0
## 31 143 160
## 32 1 0
## 33 1 0
## 34 1 0
## 35 4 5
## 36 1 0
## 37 0 1
## 38 1 0
## 39 0 1
## 40 0 0
## 41 0 0
## 42 0 0
## 43 0 1
## 44 16 19
## 45 4 4
## 46 1 0
## 47 2 1
## 48 16 19
## 49 16 19
## 50 12 8
## 51 39 41
## 52 1 0
## 53 0 0
## 54 9 5
## 55 3 0
## 56 12 8
## cafe_count_1500_price_2500 cafe_count_1500_price_4000
## 1 39 13
## 2 0 0
## 3 2 1
## 4 0 0
## 5 4 1
## 6 1 0
## 7 0 0
## 8 2 0
## 9 3 0
## 10 2 0
## 11 2 0
## 12 1 0
## 13 0 0
## 14 0 0
## 15 2 0
## 16 0 0
## 17 0 0
## 18 1 0
## 19 0 0
## 20 1 0
## 21 1 0
## 22 28 6
## 23 0 0
## 24 2 1
## 25 4 0
## 26 0 0
## 27 33 17
## 28 1 0
## 29 4 1
## 30 0 0
## 31 87 45
## 32 0 0
## 33 0 0
## 34 1 0
## 35 0 0
## 36 0 0
## 37 0 0
## 38 0 0
## 39 0 0
## 40 0 0
## 41 0 0
## 42 0 0
## 43 0 0
## 44 8 1
## 45 5 0
## 46 0 0
## 47 0 0
## 48 8 1
## 49 8 1
## 50 0 0
## 51 18 4
## 52 0 0
## 53 0 0
## 54 3 0
## 55 0 0
## 56 1 0
## cafe_count_1500_price_high big_church_count_1500 church_count_1500
## 1 0 14 23
## 2 0 0 0
## 3 0 0 0
## 4 0 0 1
## 5 0 3 7
## 6 0 1 5
## 7 0 0 1
## 8 0 1 1
## 9 0 1 2
## 10 0 1 1
## 11 0 1 1
## 12 0 0 2
## 13 0 0 2
## 14 0 2 1
## 15 0 1 1
## 16 0 1 1
## 17 0 0 0
## 18 0 1 2
## 19 0 0 2
## 20 0 0 2
## 21 0 1 2
## 22 1 16 25
## 23 0 0 0
## 24 0 0 1
## 25 0 3 3
## 26 0 0 1
## 27 5 6 6
## 28 0 0 1
## 29 0 3 7
## 30 0 0 1
## 31 5 44 66
## 32 0 0 2
## 33 0 0 1
## 34 0 0 0
## 35 0 3 2
## 36 0 0 1
## 37 0 0 1
## 38 0 0 1
## 39 0 0 0
## 40 0 0 1
## 41 0 0 1
## 42 0 0 1
## 43 0 0 1
## 44 0 1 3
## 45 0 1 5
## 46 0 0 2
## 47 0 1 1
## 48 0 1 3
## 49 0 1 3
## 50 0 3 3
## 51 0 13 21
## 52 0 0 0
## 53 0 0 1
## 54 0 2 1
## 55 0 1 1
## 56 0 0 7
## mosque_count_1500 leisure_count_1500 sport_count_1500 market_count_1500
## 1 0 18 21 0
## 2 0 0 1 1
## 3 0 1 10 0
## 4 0 0 1 0
## 5 0 0 2 1
## 6 0 0 5 0
## 7 0 0 1 0
## 8 0 0 9 2
## 9 0 3 9 2
## 10 0 0 9 2
## 11 0 0 9 2
## 12 0 0 2 2
## 13 0 0 0 0
## 14 0 4 7 0
## 15 0 0 9 2
## 16 0 1 7 0
## 17 0 0 7 0
## 18 0 0 8 1
## 19 0 0 0 0
## 20 0 0 2 2
## 21 0 0 4 0
## 22 0 8 13 2
## 23 0 0 0 0
## 24 0 0 8 3
## 25 0 0 9 1
## 26 0 0 1 0
## 27 0 1 19 0
## 28 0 0 11 0
## 29 0 0 2 1
## 30 0 0 1 0
## 31 0 20 25 2
## 32 0 0 1 0
## 33 0 0 1 0
## 34 0 0 0 0
## 35 0 1 7 0
## 36 0 0 1 0
## 37 0 0 1 0
## 38 0 0 1 0
## 39 0 0 2 0
## 40 0 0 0 0
## 41 0 0 0 0
## 42 0 0 3 2
## 43 0 0 0 0
## 44 0 1 7 1
## 45 0 0 7 0
## 46 0 0 1 0
## 47 0 0 2 0
## 48 0 1 7 1
## 49 0 1 7 1
## 50 0 0 11 0
## 51 0 8 24 1
## 52 0 0 0 0
## 53 0 0 0 0
## 54 0 1 8 0
## 55 0 0 4 1
## 56 0 1 9 3
## green_part_2000 prom_part_2000 office_count_2000 office_sqm_2000
## 1 11.75 0.82 129 1394447
## 2 13.18 10.13 0 0
## 3 10.83 16.98 5 108421
## 4 11.82 1.64 0 0
## 5 13.49 7.97 30 871598
## 6 33.23 5.48 5 207193
## 7 11.82 1.64 0 0
## 8 25.52 7.03 4 167000
## 9 10.24 22.36 16 704495
## 10 25.52 7.03 4 167000
## 11 25.52 7.03 4 167000
## 12 54.09 6.10 0 0
## 13 5.30 26.61 0 0
## 14 14.67 2.92 0 0
## 15 25.52 7.03 4 167000
## 16 47.03 10.47 4 177000
## 17 34.71 5.04 0 0
## 18 43.41 12.40 8 64636
## 19 5.30 26.61 0 0
## 20 54.38 8.78 0 0
## 21 37.51 1.82 4 391160
## 22 3.86 2.38 127 1668580
## 23 43.67 1.78 0 0
## 24 11.00 31.34 15 1154912
## 25 3.57 28.96 30 717091
## 26 12.39 1.55 0 0
## 27 6.77 9.28 68 3169091
## 28 19.67 0.00 8 69911
## 29 13.49 7.97 30 871598
## 30 23.83 8.07 0 0
## 31 6.05 0.00 232 2210580
## 32 27.94 6.10 0 0
## 33 18.18 8.00 0 0
## 34 40.87 2.23 0 0
## 35 6.38 19.39 8 400740
## 36 22.17 8.58 0 0
## 37 12.39 1.55 0 0
## 38 23.21 8.07 0 0
## 39 45.19 37.53 5 41139
## 40 21.01 2.51 0 0
## 41 42.29 0.70 0 0
## 42 40.65 2.62 0 0
## 43 18.54 0.10 0 0
## 44 8.02 6.36 17 249147
## 45 20.77 5.40 5 207193
## 46 29.99 7.18 0 0
## 47 38.05 4.36 1 4337
## 48 8.02 6.36 17 249147
## 49 8.02 6.36 17 249147
## 50 18.23 10.21 8 260027
## 51 12.98 1.62 106 1593213
## 52 23.41 13.11 1 20038
## 53 1.90 11.92 0 0
## 54 9.92 24.76 17 650385
## 55 61.19 0.00 0 0
## 56 12.84 17.22 15 572826
## trc_count_2000 trc_sqm_2000 cafe_count_2000 cafe_sum_2000_min_price_avg
## 1 17 457342 474 912.41
## 2 0 0 2 750.00
## 3 5 192078 28 684.00
## 4 0 0 2 1000.00
## 5 11 533808 119 657.14
## 6 5 23200 16 966.67
## 7 0 0 2 1000.00
## 8 11 137756 30 733.33
## 9 8 337770 39 680.00
## 10 11 137756 30 733.33
## 11 11 137756 30 733.33
## 12 0 0 2 1250.00
## 13 0 0 2 650.00
## 14 10 195023 22 609.09
## 15 11 137756 30 733.33
## 16 2 27000 6 760.00
## 17 5 89600 13 736.36
## 18 4 215990 58 696.49
## 19 0 0 2 650.00
## 20 0 0 2 1250.00
## 21 12 727003 30 640.74
## 22 25 464727 320 811.74
## 23 0 0 0 NA
## 24 11 877000 41 662.50
## 25 6 518650 63 794.92
## 26 0 0 2 1000.00
## 27 8 635200 270 1046.99
## 28 7 69895 47 754.55
## 29 11 533808 119 657.14
## 30 0 0 1 500.00
## 31 34 1240332 1058 891.63
## 32 0 0 1 500.00
## 33 0 0 3 1833.33
## 34 1 17000 3 833.33
## 35 8 182795 17 668.75
## 36 0 0 2 1500.00
## 37 0 0 2 1000.00
## 38 0 0 1 500.00
## 39 4 50732 4 1000.00
## 40 0 0 0 NA
## 41 0 0 0 NA
## 42 0 0 4 1125.00
## 43 0 0 1 1000.00
## 44 7 243440 93 796.74
## 45 10 650124 25 1130.43
## 46 0 0 1 500.00
## 47 2 350000 10 540.00
## 48 7 243440 93 796.74
## 49 7 243440 93 796.74
## 50 9 138640 54 582.69
## 51 14 212076 285 867.69
## 52 0 0 5 340.00
## 53 1 36600 1 500.00
## 54 9 486270 37 705.88
## 55 5 123800 11 520.00
## 56 16 267550 62 626.67
## cafe_sum_2000_max_price_avg cafe_avg_price_2000
## 1 1503.45 1207.93
## 2 1250.00 1000.00
## 3 1180.00 932.00
## 4 1500.00 1250.00
## 5 1100.00 878.57
## 6 1600.00 1283.33
## 7 1500.00 1250.00
## 8 1233.33 983.33
## 9 1171.43 925.71
## 10 1233.33 983.33
## 11 1233.33 983.33
## 12 2000.00 1625.00
## 13 1000.00 825.00
## 14 1068.18 838.64
## 15 1233.33 983.33
## 16 1200.00 980.00
## 17 1181.82 959.09
## 18 1166.67 931.58
## 19 1000.00 825.00
## 20 2000.00 1625.00
## 21 1055.56 848.15
## 22 1337.25 1074.50
## 23 NA NA
## 24 1112.50 887.50
## 25 1338.98 1066.95
## 26 1500.00 1250.00
## 27 1702.81 1374.90
## 28 1272.73 1013.64
## 29 1100.00 878.57
## 30 1000.00 750.00
## 31 1468.75 1180.19
## 32 1000.00 750.00
## 33 3000.00 2416.67
## 34 1500.00 1166.67
## 35 1093.75 881.25
## 36 2500.00 2000.00
## 37 1500.00 1250.00
## 38 1000.00 750.00
## 39 1666.67 1333.33
## 40 NA NA
## 41 NA NA
## 42 1875.00 1500.00
## 43 1500.00 1250.00
## 44 1331.52 1064.13
## 45 1847.83 1489.13
## 46 1000.00 750.00
## 47 950.00 745.00
## 48 1331.52 1064.13
## 49 1331.52 1064.13
## 50 1000.00 791.35
## 51 1432.69 1150.19
## 52 600.00 470.00
## 53 1000.00 750.00
## 54 1176.47 941.18
## 55 900.00 710.00
## 56 1075.00 850.83
## cafe_count_2000_na_price cafe_count_2000_price_500
## 1 39 103
## 2 0 0
## 3 3 7
## 4 0 0
## 5 14 40
## 6 1 0
## 7 0 0
## 8 0 5
## 9 4 11
## 10 0 5
## 11 0 5
## 12 0 0
## 13 0 1
## 14 0 8
## 15 0 5
## 16 1 1
## 17 2 2
## 18 1 19
## 19 0 1
## 20 0 0
## 21 3 11
## 22 22 73
## 23 0 0
## 24 1 15
## 25 4 13
## 26 0 0
## 27 21 54
## 28 3 9
## 29 14 40
## 30 0 0
## 31 66 255
## 32 0 0
## 33 0 0
## 34 0 0
## 35 1 4
## 36 0 0
## 37 0 0
## 38 0 0
## 39 1 0
## 40 0 0
## 41 0 0
## 42 0 0
## 43 0 0
## 44 1 16
## 45 2 0
## 46 0 0
## 47 0 3
## 48 1 16
## 49 1 16
## 50 2 16
## 51 25 62
## 52 0 4
## 53 0 0
## 54 3 10
## 55 1 4
## 56 2 17
## cafe_count_2000_price_1000 cafe_count_2000_price_1500
## 1 115 114
## 2 1 1
## 3 11 4
## 4 0 2
## 5 30 25
## 6 5 6
## 7 0 2
## 8 12 10
## 9 14 5
## 10 12 10
## 11 12 10
## 12 0 1
## 13 0 1
## 14 10 2
## 15 12 10
## 16 1 3
## 17 3 6
## 18 16 14
## 19 0 1
## 20 0 1
## 21 6 8
## 22 83 96
## 23 0 0
## 24 12 9
## 25 21 14
## 26 0 2
## 27 52 68
## 28 16 12
## 29 30 25
## 30 1 0
## 31 257 257
## 32 1 0
## 33 1 0
## 34 2 0
## 35 5 7
## 36 1 0
## 37 0 2
## 38 1 0
## 39 1 1
## 40 0 0
## 41 0 0
## 42 1 1
## 43 0 1
## 44 32 29
## 45 8 8
## 46 1 0
## 47 5 2
## 48 32 29
## 49 32 29
## 50 22 13
## 51 72 71
## 52 1 0
## 53 1 0
## 54 10 10
## 55 4 2
## 56 25 14
## cafe_count_2000_price_2500 cafe_count_2000_price_4000
## 1 69 30
## 2 0 0
## 3 2 1
## 4 0 0
## 5 8 2
## 6 4 0
## 7 0 0
## 8 3 0
## 9 4 1
## 10 3 0
## 11 3 0
## 12 1 0
## 13 0 0
## 14 1 1
## 15 3 0
## 16 0 0
## 17 0 0
## 18 8 0
## 19 0 0
## 20 1 0
## 21 2 0
## 22 34 11
## 23 0 0
## 24 3 1
## 25 9 2
## 26 0 0
## 27 46 23
## 28 7 0
## 29 8 2
## 30 0 0
## 31 150 63
## 32 0 0
## 33 0 2
## 34 1 0
## 35 0 0
## 36 0 1
## 37 0 0
## 38 0 0
## 39 1 0
## 40 0 0
## 41 0 0
## 42 2 0
## 43 0 0
## 44 14 1
## 45 5 1
## 46 0 0
## 47 0 0
## 48 14 1
## 49 14 1
## 50 1 0
## 51 42 10
## 52 0 0
## 53 0 0
## 54 4 0
## 55 0 0
## 56 4 0
## cafe_count_2000_price_high big_church_count_2000 church_count_2000
## 1 4 24 45
## 2 0 0 0
## 3 0 0 0
## 4 0 1 2
## 5 0 3 7
## 6 0 1 5
## 7 0 1 2
## 8 0 1 2
## 9 0 1 3
## 10 0 1 2
## 11 0 1 2
## 12 0 0 2
## 13 0 0 2
## 14 0 2 4
## 15 0 1 2
## 16 0 1 2
## 17 0 0 0
## 18 0 2 3
## 19 0 0 2
## 20 0 0 2
## 21 0 2 3
## 22 1 32 42
## 23 0 0 0
## 24 0 2 3
## 25 0 9 7
## 26 0 1 2
## 27 6 10 10
## 28 0 0 3
## 29 0 3 7
## 30 0 0 3
## 31 10 67 104
## 32 0 0 2
## 33 0 0 3
## 34 0 1 3
## 35 0 3 3
## 36 0 0 3
## 37 0 1 2
## 38 0 0 3
## 39 0 0 1
## 40 0 0 1
## 41 0 0 2
## 42 0 0 2
## 43 0 0 2
## 44 0 1 4
## 45 1 1 5
## 46 0 0 3
## 47 0 1 2
## 48 0 1 4
## 49 0 1 4
## 50 0 5 3
## 51 3 29 41
## 52 0 0 0
## 53 0 0 2
## 54 0 4 5
## 55 0 2 5
## 56 0 0 9
## mosque_count_2000 leisure_count_2000 sport_count_2000 market_count_2000
## 1 0 24 36 3
## 2 0 0 1 1
## 3 0 1 14 1
## 4 0 0 3 0
## 5 0 1 16 4
## 6 0 0 6 0
## 7 0 0 3 0
## 8 0 0 15 2
## 9 0 3 10 2
## 10 0 0 15 2
## 11 0 0 15 2
## 12 0 0 3 2
## 13 0 0 0 0
## 14 0 5 9 0
## 15 0 0 15 2
## 16 0 1 8 0
## 17 0 0 8 0
## 18 0 0 14 1
## 19 0 0 0 0
## 20 0 0 2 2
## 21 0 0 12 0
## 22 1 10 29 2
## 23 0 0 0 0
## 24 0 1 11 3
## 25 0 1 15 1
## 26 0 0 3 0
## 27 0 4 29 1
## 28 0 0 20 1
## 29 0 1 16 4
## 30 0 0 1 0
## 31 1 47 42 2
## 32 0 0 1 0
## 33 0 0 1 0
## 34 0 0 1 0
## 35 0 1 12 0
## 36 0 0 1 0
## 37 0 0 3 0
## 38 0 0 1 0
## 39 0 0 2 0
## 40 0 0 0 0
## 41 0 0 0 0
## 42 0 0 3 2
## 43 0 0 0 0
## 44 0 2 16 1
## 45 0 0 7 0
## 46 0 0 1 0
## 47 0 0 4 0
## 48 0 2 16 1
## 49 0 2 16 1
## 50 0 0 13 1
## 51 0 17 34 1
## 52 0 1 0 0
## 53 0 0 0 0
## 54 0 1 20 0
## 55 0 0 4 2
## 56 1 1 15 3
## green_part_3000 prom_part_3000 office_count_3000 office_sqm_3000
## 1 9.80 1.21 258 3025460
## 2 15.95 9.15 0 0
## 3 17.48 13.62 13 376272
## 4 10.62 1.98 0 0
## 5 20.26 12.71 47 1250285
## 6 34.71 2.50 6 307672
## 7 10.62 1.98 0 0
## 8 25.12 5.28 4 167000
## 9 9.09 19.91 25 872266
## 10 25.12 5.28 4 167000
## 11 25.12 5.28 4 167000
## 12 51.02 5.16 0 0
## 13 2.36 13.06 0 0
## 14 18.73 1.67 1 85000
## 15 25.12 5.28 4 167000
## 16 54.52 10.07 10 243254
## 17 30.18 3.99 0 0
## 18 31.46 15.32 36 663083
## 19 2.36 13.06 0 0
## 20 52.99 5.36 0 0
## 21 39.73 9.39 10 606509
## 22 3.52 10.81 258 3318664
## 23 47.97 1.30 0 0
## 24 7.42 33.30 50 2043246
## 25 6.06 26.08 70 2285425
## 26 10.54 2.14 0 0
## 27 4.28 15.37 164 4700061
## 28 17.00 0.23 15 263558
## 29 20.26 12.71 47 1250285
## 30 31.84 5.08 0 0
## 31 6.96 0.99 486 5082992
## 32 31.20 6.14 0 0
## 33 25.80 4.12 0 0
## 34 44.37 1.97 0 0
## 35 9.09 17.87 15 779354
## 36 27.73 4.12 0 0
## 37 10.54 2.14 0 0
## 38 31.65 4.87 0 0
## 39 49.31 20.09 7 116139
## 40 38.71 1.72 0 0
## 41 36.39 1.57 0 0
## 42 44.66 4.35 0 0
## 43 40.25 0.43 0 0
## 44 13.26 4.43 28 623053
## 45 25.22 3.27 5 207193
## 46 38.10 7.09 0 0
## 47 33.19 2.21 2 9337
## 48 13.26 4.43 28 623053
## 49 13.26 4.43 28 623053
## 50 12.57 18.18 19 491613
## 51 9.38 3.85 250 3793037
## 52 24.28 7.21 2 45038
## 53 3.09 16.19 0 0
## 54 7.05 25.80 44 1323126
## 55 57.07 0.90 1 17700
## 56 29.62 8.38 22 868708
## trc_count_3000 trc_sqm_3000 cafe_count_3000 cafe_sum_3000_min_price_avg
## 1 28 1002718 979 933.08
## 2 1 36600 6 833.33
## 3 13 307198 62 733.33
## 4 0 0 3 1000.00
## 5 20 1250639 172 690.73
## 6 10 650124 34 946.67
## 7 0 0 3 1000.00
## 8 17 315256 37 732.43
## 9 15 629270 95 679.78
## 10 17 315256 37 732.43
## 11 17 315256 37 732.43
## 12 0 0 2 1250.00
## 13 2 41100 5 760.00
## 14 15 581446 46 624.44
## 15 17 315256 37 732.43
## 16 5 45600 19 558.82
## 17 12 150704 19 747.06
## 18 15 373586 95 667.78
## 19 2 41100 5 760.00
## 20 0 0 2 1250.00
## 21 21 925086 53 606.00
## 22 50 1167310 773 818.80
## 23 0 0 0 NA
## 24 20 1271065 88 723.26
## 25 23 1648720 160 789.33
## 26 0 0 3 1000.00
## 27 24 1364616 649 991.79
## 28 18 462795 159 749.01
## 29 20 1250639 172 690.73
## 30 0 0 4 1750.00
## 31 54 1702619 1815 882.31
## 32 0 0 4 1750.00
## 33 0 0 4 1750.00
## 34 2 22000 10 680.00
## 35 15 927866 44 785.71
## 36 0 0 4 1750.00
## 37 0 0 3 1000.00
## 38 0 0 4 1750.00
## 39 8 68432 21 611.11
## 40 0 0 0 NA
## 41 0 0 1 NA
## 42 0 0 6 1416.67
## 43 0 0 2 750.00
## 44 18 984740 199 778.42
## 45 10 650124 31 1189.66
## 46 0 0 2 1500.00
## 47 7 375106 30 627.59
## 48 18 984740 199 778.42
## 49 18 984740 199 778.42
## 50 16 334640 71 635.29
## 51 24 1317076 571 869.19
## 52 3 34100 9 522.22
## 53 2 41100 6 633.33
## 54 12 554270 97 765.22
## 55 5 123800 27 540.00
## 56 25 578150 134 699.23
## cafe_sum_3000_max_price_avg cafe_avg_price_3000
## 1 1529.12 1231.10
## 2 1333.33 1083.33
## 3 1236.84 985.09
## 4 1500.00 1250.00
## 5 1158.94 924.83
## 6 1566.67 1256.67
## 7 1500.00 1250.00
## 8 1243.24 987.84
## 9 1146.07 912.92
## 10 1243.24 987.84
## 11 1243.24 987.84
## 12 2000.00 1625.00
## 13 1300.00 1030.00
## 14 1077.78 851.11
## 15 1243.24 987.84
## 16 970.59 764.71
## 17 1205.88 976.47
## 18 1127.78 897.78
## 19 1300.00 1030.00
## 20 2000.00 1625.00
## 21 1010.00 808.00
## 22 1348.89 1083.84
## 23 NA NA
## 24 1215.12 969.19
## 25 1306.67 1048.00
## 26 1500.00 1250.00
## 27 1625.62 1308.70
## 28 1251.66 1000.33
## 29 1158.94 924.83
## 30 2875.00 2312.50
## 31 1453.00 1167.66
## 32 2875.00 2312.50
## 33 2875.00 2312.50
## 34 1200.00 940.00
## 35 1309.52 1047.62
## 36 2875.00 2312.50
## 37 1500.00 1250.00
## 38 2875.00 2312.50
## 39 1055.56 833.33
## 40 NA NA
## 41 NA NA
## 42 2333.33 1875.00
## 43 1250.00 1000.00
## 44 1292.11 1035.26
## 45 1931.03 1560.34
## 46 2500.00 2000.00
## 47 1086.21 856.90
## 48 1292.11 1035.26
## 49 1292.11 1035.26
## 50 1088.24 861.76
## 51 1432.89 1151.04
## 52 888.89 705.56
## 53 1083.33 858.33
## 54 1266.30 1015.76
## 55 920.00 730.00
## 56 1184.62 941.92
## cafe_count_3000_na_price cafe_count_3000_price_500
## 1 69 217
## 2 0 0
## 3 5 16
## 4 0 0
## 5 21 51
## 6 4 3
## 7 0 0
## 8 0 7
## 9 6 25
## 10 0 7
## 11 0 7
## 12 0 0
## 13 0 1
## 14 1 12
## 15 0 7
## 16 2 5
## 17 2 4
## 18 5 32
## 19 0 1
## 20 0 0
## 21 3 21
## 22 55 193
## 23 0 0
## 24 2 24
## 25 10 38
## 26 0 0
## 27 40 140
## 28 8 37
## 29 21 51
## 30 0 0
## 31 113 449
## 32 0 0
## 33 0 0
## 34 0 1
## 35 2 10
## 36 0 0
## 37 0 0
## 38 0 0
## 39 3 5
## 40 0 0
## 41 1 0
## 42 0 0
## 43 0 0
## 44 9 38
## 45 2 0
## 46 0 0
## 47 1 9
## 48 9 38
## 49 9 38
## 50 3 19
## 51 42 126
## 52 0 4
## 53 0 1
## 54 5 23
## 55 2 10
## 56 4 33
## cafe_count_3000_price_1000 cafe_count_3000_price_1500
## 1 222 253
## 2 2 4
## 3 20 13
## 4 0 3
## 5 47 36
## 6 10 10
## 7 0 3
## 8 15 10
## 9 32 24
## 10 15 10
## 11 15 10
## 12 0 1
## 13 2 1
## 14 21 10
## 15 15 10
## 16 8 4
## 17 4 8
## 18 28 19
## 19 2 1
## 20 0 1
## 21 13 13
## 22 189 213
## 23 0 0
## 24 29 22
## 25 45 45
## 26 0 3
## 27 154 147
## 28 52 43
## 29 47 36
## 30 1 0
## 31 432 446
## 32 1 0
## 33 1 0
## 34 6 2
## 35 13 12
## 36 1 0
## 37 0 3
## 38 1 0
## 39 8 4
## 40 0 0
## 41 0 0
## 42 1 1
## 43 1 1
## 44 65 64
## 45 8 11
## 46 1 0
## 47 13 5
## 48 65 64
## 49 65 64
## 50 29 16
## 51 142 151
## 52 3 2
## 53 3 2
## 54 27 30
## 55 9 6
## 56 49 33
## cafe_count_3000_price_2500 cafe_count_3000_price_4000
## 1 143 63
## 2 0 0
## 3 6 2
## 4 0 0
## 5 13 4
## 6 5 2
## 7 0 0
## 8 5 0
## 9 7 1
## 10 5 0
## 11 5 0
## 12 1 0
## 13 1 0
## 14 1 1
## 15 5 0
## 16 0 0
## 17 1 0
## 18 10 1
## 19 1 0
## 20 1 0
## 21 3 0
## 22 91 28
## 23 0 0
## 24 9 2
## 25 17 4
## 26 0 0
## 27 103 51
## 28 16 2
## 29 13 4
## 30 1 2
## 31 255 105
## 32 1 2
## 33 1 2
## 34 1 0
## 35 6 1
## 36 1 2
## 37 0 0
## 38 1 2
## 39 1 0
## 40 0 0
## 41 0 0
## 42 3 1
## 43 0 0
## 44 19 3
## 45 7 2
## 46 0 1
## 47 1 1
## 48 19 3
## 49 19 3
## 50 3 1
## 51 81 25
## 52 0 0
## 53 0 0
## 54 10 2
## 55 0 0
## 56 14 1
## cafe_count_3000_price_high big_church_count_3000 church_count_3000
## 1 12 60 102
## 2 0 2 3
## 3 0 6 3
## 4 0 1 3
## 5 0 4 10
## 6 0 1 8
## 7 0 1 3
## 8 0 4 4
## 9 0 4 11
## 10 0 4 4
## 11 0 4 4
## 12 0 0 4
## 13 0 0 2
## 14 0 5 7
## 15 0 4 4
## 16 0 3 6
## 17 0 1 2
## 18 0 7 9
## 19 0 0 2
## 20 0 0 3
## 21 0 2 6
## 22 4 76 118
## 23 0 0 0
## 24 0 9 7
## 25 1 17 17
## 26 0 1 3
## 27 14 19 26
## 28 1 2 10
## 29 0 4 10
## 30 0 0 5
## 31 15 94 162
## 32 0 0 4
## 33 0 0 5
## 34 0 1 3
## 35 0 4 4
## 36 0 0 5
## 37 0 1 3
## 38 0 0 5
## 39 0 1 4
## 40 0 0 1
## 41 0 0 4
## 42 0 0 5
## 43 0 0 3
## 44 1 5 12
## 45 1 1 5
## 46 0 0 3
## 47 0 2 5
## 48 1 5 12
## 49 1 5 12
## 50 0 7 6
## 51 4 54 82
## 52 0 1 2
## 53 0 0 2
## 54 0 5 9
## 55 0 3 10
## 56 0 1 17
## mosque_count_3000 leisure_count_3000 sport_count_3000 market_count_3000
## 1 1 44 79 4
## 2 0 0 2 1
## 3 0 1 22 4
## 4 1 0 5 0
## 5 0 2 38 8
## 6 0 0 8 0
## 7 1 0 5 0
## 8 0 0 19 3
## 9 1 4 30 8
## 10 0 0 19 3
## 11 0 0 19 3
## 12 0 0 3 2
## 13 0 0 1 0
## 14 0 6 19 4
## 15 0 0 19 3
## 16 0 1 18 0
## 17 1 0 14 0
## 18 0 4 42 2
## 19 0 0 1 0
## 20 0 0 3 2
## 21 0 0 20 0
## 22 1 21 69 4
## 23 0 0 0 0
## 24 0 5 32 4
## 25 0 3 32 4
## 26 1 0 5 0
## 27 0 19 47 4
## 28 1 3 38 2
## 29 0 2 38 8
## 30 0 0 1 0
## 31 2 85 88 6
## 32 0 0 1 0
## 33 0 0 1 0
## 34 0 0 6 0
## 35 0 2 19 0
## 36 0 0 1 0
## 37 1 0 5 0
## 38 0 0 1 0
## 39 0 1 16 3
## 40 0 0 0 0
## 41 0 0 0 0
## 42 0 0 3 2
## 43 0 0 0 0
## 44 0 4 33 1
## 45 0 0 7 0
## 46 0 0 1 0
## 47 0 0 13 1
## 48 0 4 33 1
## 49 0 4 33 1
## 50 0 0 25 2
## 51 1 28 56 2
## 52 0 1 3 1
## 53 0 0 1 1
## 54 0 3 28 3
## 55 0 0 11 2
## 56 1 1 33 3
## green_part_5000 prom_part_5000 office_count_5000 office_sqm_5000
## 1 9.38 4.35 672 10742760
## 2 14.43 7.61 2 138650
## 3 22.10 8.29 35 1166374
## 4 17.74 4.75 0 0
## 5 16.11 13.08 100 2529240
## 6 25.30 6.02 18 546198
## 7 17.74 4.75 0 0
## 8 17.39 4.80 10 414166
## 9 14.82 20.23 86 3262764
## 10 17.39 4.80 10 414166
## 11 17.39 4.80 10 414166
## 12 46.18 3.49 0 0
## 13 4.46 6.54 1 26950
## 14 25.31 6.36 12 332974
## 15 17.39 4.80 10 414166
## 16 42.26 12.52 80 1499491
## 17 24.81 4.43 5 78808
## 18 21.80 16.81 112 1876406
## 19 4.46 6.54 1 26950
## 20 44.51 3.16 0 0
## 21 28.24 17.96 29 1356285
## 22 5.06 17.25 635 8224755
## 23 48.94 2.60 0 0
## 24 8.82 24.92 192 4503518
## 25 12.28 14.04 259 5371006
## 26 17.92 4.75 0 0
## 27 10.61 12.01 503 8986803
## 28 19.70 9.61 58 1494253
## 29 16.11 13.08 100 2529240
## 30 34.53 7.37 1 85159
## 31 6.80 5.73 774 9997846
## 32 33.58 8.62 1 85159
## 33 32.94 6.05 1 85159
## 34 35.66 6.07 1 117300
## 35 18.81 10.47 17 849063
## 36 33.72 7.24 1 85159
## 37 17.92 4.75 0 0
## 38 34.29 7.05 1 85159
## 39 41.92 7.81 22 548867
## 40 37.23 1.83 0 0
## 41 42.14 3.96 1 117300
## 42 47.85 3.13 0 0
## 43 39.40 1.32 0 0
## 44 11.62 12.15 119 2941563
## 45 23.37 7.52 22 706388
## 46 35.94 8.57 1 85159
## 47 25.33 3.81 7 238037
## 48 11.62 12.15 119 2941563
## 49 11.62 12.15 119 2941563
## 50 15.53 16.85 43 1182939
## 51 9.19 10.12 534 7475424
## 52 19.15 6.36 3 51038
## 53 13.13 7.72 1 26950
## 54 13.64 15.90 117 3600300
## 55 40.95 9.61 21 1060187
## 56 31.20 4.59 52 1750784
## trc_count_5000 trc_sqm_5000 cafe_count_5000 cafe_sum_5000_min_price_avg
## 1 83 3434795 2295 908.42
## 2 18 372816 33 712.50
## 3 47 1847952 212 728.80
## 4 5 262000 18 700.00
## 5 43 1992370 325 760.47
## 6 25 1065744 118 970.75
## 7 5 262000 18 700.00
## 8 27 1019560 71 889.39
## 9 50 2245525 342 732.52
## 10 27 1019560 71 889.39
## 11 27 1019560 71 889.39
## 12 0 0 7 1285.71
## 13 4 44437 16 662.50
## 14 22 674915 86 667.47
## 15 27 1019560 71 889.39
## 16 31 1137771 219 737.25
## 17 15 190783 26 816.67
## 18 46 1043695 294 688.89
## 19 4 44437 16 662.50
## 20 0 0 5 1400.00
## 21 54 1559115 154 653.15
## 22 106 3849432 2049 859.83
## 23 0 0 2 1250.00
## 24 58 2416025 447 800.96
## 25 53 2490351 694 816.20
## 26 5 262000 18 700.00
## 27 75 3361226 1825 934.08
## 28 43 1030411 336 756.70
## 29 43 1992370 325 760.47
## 30 5 293076 14 1176.92
## 31 101 3346565 2625 880.53
## 32 1 189076 12 1108.33
## 33 3 73000 14 1176.92
## 34 4 201300 20 747.37
## 35 27 1158392 86 990.00
## 36 1 189076 10 1311.11
## 37 5 262000 18 700.00
## 38 6 307076 16 1120.00
## 39 21 600566 101 643.62
## 40 0 0 0 NA
## 41 1 117300 5 1875.00
## 42 0 0 7 1285.71
## 43 0 0 7 1114.29
## 44 43 2034460 473 767.41
## 45 25 1064863 123 984.55
## 46 8 331442 24 936.36
## 47 21 672267 96 635.48
## 48 43 2034460 473 767.41
## 49 43 2034460 473 767.41
## 50 43 1389188 209 707.37
## 51 88 3717826 1977 902.82
## 52 5 41900 14 507.14
## 53 5 54937 19 689.47
## 54 52 2579525 398 763.32
## 55 54 1602507 130 629.27
## 56 48 1428321 334 692.79
## cafe_sum_5000_max_price_avg cafe_avg_price_5000
## 1 1493.45 1200.94
## 2 1203.13 957.81
## 3 1219.90 974.35
## 4 1125.00 912.50
## 5 1266.89 1013.68
## 6 1603.77 1287.26
## 7 1125.00 912.50
## 8 1484.85 1187.12
## 9 1229.48 981.00
## 10 1484.85 1187.12
## 11 1484.85 1187.12
## 12 2142.86 1714.29
## 13 1156.25 909.38
## 14 1132.53 900.00
## 15 1484.85 1187.12
## 16 1245.10 991.18
## 17 1312.50 1064.58
## 18 1170.25 929.57
## 19 1156.25 909.38
## 20 2300.00 1850.00
## 21 1104.90 879.02
## 22 1420.19 1140.01
## 23 2000.00 1625.00
## 24 1336.14 1068.55
## 25 1352.62 1084.41
## 26 1125.00 912.50
## 27 1536.43 1235.25
## 28 1261.68 1009.19
## 29 1266.89 1013.68
## 30 1884.62 1530.77
## 31 1451.32 1165.93
## 32 1833.33 1470.83
## 33 1884.62 1530.77
## 34 1263.16 1005.26
## 35 1637.50 1313.75
## 36 2111.11 1711.11
## 37 1125.00 912.50
## 38 1800.00 1460.00
## 39 1095.74 869.68
## 40 NA NA
## 41 3000.00 2437.50
## 42 2142.86 1714.29
## 43 1857.14 1485.71
## 44 1279.02 1023.21
## 45 1627.27 1305.91
## 46 1545.45 1240.91
## 47 1091.40 863.44
## 48 1279.02 1023.21
## 49 1279.02 1023.21
## 50 1186.84 947.11
## 51 1484.56 1193.69
## 52 857.14 682.14
## 53 1184.21 936.84
## 54 1274.41 1018.87
## 55 1060.98 845.12
## 56 1166.14 929.47
## cafe_count_5000_na_price cafe_count_5000_price_500
## 1 157 539
## 2 1 6
## 3 21 54
## 4 2 4
## 5 29 92
## 6 12 18
## 7 2 4
## 8 5 9
## 9 13 85
## 10 5 9
## 11 5 9
## 12 0 0
## 13 0 2
## 14 3 23
## 15 5 9
## 16 15 58
## 17 2 7
## 18 15 79
## 19 0 2
## 20 0 0
## 21 11 43
## 22 132 491
## 23 0 0
## 24 32 98
## 25 46 158
## 26 2 4
## 27 123 431
## 28 15 78
## 29 29 92
## 30 1 1
## 31 170 639
## 32 0 1
## 33 1 1
## 34 1 4
## 35 6 14
## 36 1 1
## 37 2 4
## 38 1 1
## 39 7 30
## 40 0 0
## 41 1 0
## 42 0 0
## 43 0 1
## 44 25 111
## 45 13 16
## 46 2 2
## 47 3 27
## 48 25 111
## 49 25 111
## 50 19 58
## 51 131 452
## 52 0 7
## 53 0 2
## 54 19 91
## 55 7 43
## 56 15 85
## cafe_count_5000_price_1000 cafe_count_5000_price_1500
## 1 537 562
## 2 14 10
## 3 64 50
## 4 4 8
## 5 90 71
## 6 33 27
## 7 4 8
## 8 25 18
## 9 113 89
## 10 25 18
## 11 25 18
## 12 2 1
## 13 9 4
## 14 33 21
## 15 25 18
## 16 68 44
## 17 4 10
## 18 105 63
## 19 9 4
## 20 1 1
## 21 51 37
## 22 520 511
## 23 0 1
## 24 136 112
## 25 190 186
## 26 4 8
## 27 421 413
## 28 105 95
## 29 90 71
## 30 2 6
## 31 642 636
## 32 4 3
## 33 2 6
## 34 8 5
## 35 23 20
## 36 1 3
## 37 4 8
## 38 3 7
## 39 36 21
## 40 0 0
## 41 0 1
## 42 2 1
## 43 2 1
## 44 145 128
## 45 36 29
## 46 8 8
## 47 40 20
## 48 145 128
## 49 145 128
## 50 64 48
## 51 473 503
## 52 4 3
## 53 10 6
## 54 129 108
## 55 40 31
## 56 115 88
## cafe_count_5000_price_2500 cafe_count_5000_price_4000
## 1 339 135
## 2 1 1
## 3 18 4
## 4 0 0
## 5 29 12
## 6 19 7
## 7 0 0
## 8 11 2
## 9 35 7
## 10 11 2
## 11 11 2
## 12 3 1
## 13 1 0
## 14 4 2
## 15 11 2
## 16 30 4
## 17 2 1
## 18 27 5
## 19 1 0
## 20 2 1
## 21 12 0
## 22 277 105
## 23 1 0
## 24 51 17
## 25 89 22
## 26 0 0
## 27 290 124
## 28 37 5
## 29 29 12
## 30 2 2
## 31 371 141
## 32 2 2
## 33 2 2
## 34 1 1
## 35 14 9
## 36 2 2
## 37 0 0
## 38 2 2
## 39 5 2
## 40 0 0
## 41 1 2
## 42 3 1
## 43 2 1
## 44 53 9
## 45 19 8
## 46 2 2
## 47 4 2
## 48 53 9
## 49 53 9
## 50 13 7
## 51 282 117
## 52 0 0
## 53 1 0
## 54 41 8
## 55 9 0
## 56 29 1
## cafe_count_5000_price_high big_church_count_5000 church_count_5000
## 1 26 133 207
## 2 0 5 9
## 3 1 11 13
## 4 0 1 7
## 5 2 12 27
## 6 2 3 17
## 7 0 1 7
## 8 1 5 4
## 9 0 15 33
## 10 1 5 4
## 11 1 5 4
## 12 0 0 6
## 13 0 2 3
## 14 0 7 16
## 15 1 5 4
## 16 0 17 30
## 17 0 2 7
## 18 0 27 44
## 19 0 2 3
## 20 0 0 7
## 21 0 6 27
## 22 13 138 215
## 23 0 0 3
## 24 1 36 51
## 25 3 49 77
## 26 0 1 7
## 27 23 77 137
## 28 1 5 33
## 29 2 12 27
## 30 0 1 13
## 31 26 150 249
## 32 0 0 12
## 33 0 1 12
## 34 0 2 12
## 35 0 7 12
## 36 0 1 13
## 37 0 1 7
## 38 0 1 12
## 39 0 9 12
## 40 0 0 3
## 41 0 0 7
## 42 0 0 6
## 43 0 0 10
## 44 2 24 45
## 45 2 4 16
## 46 0 2 14
## 47 0 8 12
## 48 2 24 45
## 49 2 24 45
## 50 0 11 16
## 51 19 127 188
## 52 0 1 2
## 53 0 4 6
## 54 2 23 42
## 55 0 6 28
## 56 1 7 35
## mosque_count_5000 leisure_count_5000 sport_count_5000 market_count_5000
## 1 1 89 161 10
## 2 0 2 17 6
## 3 0 1 60 6
## 4 1 0 12 1
## 5 0 4 102 16
## 6 0 0 51 3
## 7 1 0 12 1
## 8 0 1 32 5
## 9 1 11 75 10
## 10 0 1 32 5
## 11 0 1 32 5
## 12 0 0 3 2
## 13 0 0 6 1
## 14 0 9 49 11
## 15 0 1 32 5
## 16 0 7 93 3
## 17 1 0 20 3
## 18 0 12 117 12
## 19 0 0 6 1
## 20 0 0 3 2
## 21 1 3 44 12
## 22 2 94 163 15
## 23 0 0 2 2
## 24 0 18 87 14
## 25 1 30 108 11
## 26 1 0 12 1
## 27 1 75 171 8
## 28 1 6 85 5
## 29 0 4 102 16
## 30 1 0 11 0
## 31 2 105 203 13
## 32 1 0 6 1
## 33 1 0 7 0
## 34 0 0 9 0
## 35 0 2 43 1
## 36 1 0 4 0
## 37 1 0 12 1
## 38 1 0 11 0
## 39 0 2 53 5
## 40 0 0 0 0
## 41 0 0 2 0
## 42 0 0 3 2
## 43 0 0 0 0
## 44 1 10 89 9
## 45 0 0 56 3
## 46 1 0 13 1
## 47 0 7 45 10
## 48 1 10 89 9
## 49 1 10 89 9
## 50 0 1 68 9
## 51 1 76 153 13
## 52 0 2 7 2
## 53 0 0 8 1
## 54 1 15 87 12
## 55 0 1 40 8
## 56 1 4 80 11
## price_doc
## 1 2750000
## 2 5600000
## 3 6900000
## 4 7281225
## 5 1000000
## 6 7529400
## 7 5813760
## 8 10844900
## 9 7221456
## 10 6600879
## 11 9069927
## 12 4900000
## 13 4986770
## 14 6300000
## 15 8383079
## 16 3600000
## 17 10150000
## 18 3650000
## 19 5944257
## 20 5285119
## 21 5700000
## 22 20300000
## 23 3662880
## 24 14000000
## 25 9000000
## 26 4651200
## 27 12400000
## 28 24000000
## 29 2000000
## 30 4725120
## 31 6640370
## 32 3801200
## 33 9377280
## 34 5591751
## 35 13000000
## 36 3840000
## 37 6546750
## 38 3100000
## 39 5900000
## 40 5400000
## 41 6716130
## 42 5942622
## 43 3281884
## 44 17409480
## 45 7100000
## 46 2844525
## 47 2000000
## 48 16321500
## 49 17924168
## 50 8758453
## 51 35250000
## 52 1000000
## 53 6500000
## 54 10020078
## 55 11000000
## 56 3800000State value has an abnomaly of 33.
sberbankTr%>%select(state)%>%table(useNA="always")## .
## 1 2 3 4 33 <NA>
## 4855 5844 5790 422 1 13559Let us check the material column. Does not see anything wrong with it.
sberbankTr$material%>%table(useNA="always")## .
## 1 2 3 4 5 6 <NA>
## 14197 2993 1 1344 1561 803 9572Let us check product_type
sberbankTr%>%select(product_type)%>%table(useNA="always")## .
## Investment OwnerOccupier <NA>
## 19448 11023 0Check on sub_area
sberbankTr%>%select(sub_area)%>%table(useNA="always")## .
## Ajeroport Akademicheskoe
## 123 211
## Alekseevskoe Altuf'evskoe
## 100 68
## Arbat Babushkinskoe
## 15 123
## Basmannoe Begovoe
## 98 60
## Beskudnikovskoe Bibirevo
## 166 230
## Birjulevo Vostochnoe Birjulevo Zapadnoe
## 268 115
## Bogorodskoe Brateevo
## 305 182
## Butyrskoe Caricyno
## 101 220
## Cheremushki Chertanovo Central'noe
## 158 196
## Chertanovo Juzhnoe Chertanovo Severnoe
## 273 200
## Danilovskoe Dmitrovskoe
## 199 174
## Donskoe Dorogomilovo
## 135 56
## Filevskij Park Fili Davydkovo
## 148 137
## Gagarinskoe Gol'janovo
## 79 295
## Golovinskoe Hamovniki
## 224 90
## Horoshevo-Mnevniki Horoshevskoe
## 262 136
## Hovrino Ivanovskoe
## 178 197
## Izmajlovo Jakimanka
## 300 81
## Jaroslavskoe Jasenevo
## 121 237
## Juzhnoe Butovo Juzhnoe Medvedkovo
## 451 143
## Juzhnoe Tushino Juzhnoportovoe
## 175 126
## Kapotnja Kon'kovo
## 49 220
## Koptevo Kosino-Uhtomskoe
## 207 237
## Kotlovka Krasnosel'skoe
## 147 37
## Krjukovo Krylatskoe
## 518 103
## Kuncevo Kurkino
## 184 62
## Kuz'minki Lefortovo
## 220 119
## Levoberezhnoe Lianozovo
## 135 126
## Ljublino Lomonosovskoe
## 297 147
## Losinoostrovskoe Mar'ina Roshha
## 177 116
## Mar'ino Marfino
## 508 85
## Matushkino Meshhanskoe
## 111 94
## Metrogorodok Mitino
## 58 679
## Molzhaninovskoe Moskvorech'e-Saburovo
## 3 99
## Mozhajskoe Nagatino-Sadovniki
## 197 158
## Nagatinskij Zaton Nagornoe
## 327 305
## Nekrasovka Nizhegorodskoe
## 1611 77
## Novo-Peredelkino Novogireevo
## 149 201
## Novokosino Obruchevskoe
## 139 185
## Ochakovo-Matveevskoe Orehovo-Borisovo Juzhnoe
## 255 208
## Orehovo-Borisovo Severnoe Ostankinskoe
## 206 79
## Otradnoe Pechatniki
## 353 192
## Perovo Pokrovskoe Streshnevo
## 247 164
## Poselenie Desjonovskoe Poselenie Filimonkovskoe
## 362 496
## Poselenie Kievskij Poselenie Klenovskoe
## 2 1
## Poselenie Kokoshkino Poselenie Krasnopahorskoe
## 20 27
## Poselenie Marushkinskoe Poselenie Mihajlovo-Jarcevskoe
## 6 1
## Poselenie Moskovskij Poselenie Mosrentgen
## 925 19
## Poselenie Novofedorovskoe Poselenie Pervomajskoe
## 148 142
## Poselenie Rjazanovskoe Poselenie Rogovskoe
## 34 31
## Poselenie Shhapovskoe Poselenie Shherbinka
## 2 443
## Poselenie Sosenskoe Poselenie Vnukovskoe
## 1776 1372
## Poselenie Voronovskoe Poselenie Voskresenskoe
## 7 713
## Preobrazhenskoe Presnenskoe
## 152 190
## Prospekt Vernadskogo Ramenki
## 100 241
## Rjazanskij Rostokino
## 180 64
## Savelki Savelovskoe
## 105 85
## Severnoe Severnoe Butovo
## 37 182
## Severnoe Izmajlovo Severnoe Medvedkovo
## 163 167
## Severnoe Tushino Shhukino
## 282 155
## Silino Sokol
## 100 72
## Sokol'niki Sokolinaja Gora
## 60 188
## Solncevo Staroe Krjukovo
## 421 92
## Strogino Sviblovo
## 301 131
## Taganskoe Tekstil'shhiki
## 173 298
## Teplyj Stan Timirjazevskoe
## 165 154
## Troickij okrug Troparevo-Nikulino
## 158 126
## Tverskoe Veshnjaki
## 678 213
## Vnukovo Vojkovskoe
## 44 131
## Vostochnoe Vostochnoe Degunino
## 7 118
## Vostochnoe Izmajlovo Vyhino-Zhulebino
## 154 264
## Zamoskvorech'e Zapadnoe Degunino
## 50 410
## Zjablikovo Zjuzino
## 127 259
## <NA>
## 0Next, we will also examine build_year and num_room.
sberbankTr%>%select(build_year)%>%table(useNA="always")## .
## 0 1 3 20 71 215 1691 1860
## 530 368 2 1 1 1 1 2
## 1876 1886 1890 1895 1896 1900 1904 1905
## 1 1 5 1 2 2 1 1
## 1906 1907 1910 1911 1912 1914 1915 1917
## 1 2 5 1 5 3 5 16
## 1920 1924 1925 1926 1927 1928 1929 1930
## 1 3 1 8 10 12 12 6
## 1931 1932 1933 1934 1935 1936 1937 1938
## 6 8 7 13 11 5 10 9
## 1939 1940 1941 1943 1946 1947 1948 1949
## 7 14 2 2 2 4 1 3
## 1950 1951 1952 1953 1954 1955 1956 1957
## 22 23 45 24 36 52 48 119
## 1958 1959 1960 1961 1962 1963 1964 1965
## 179 208 344 297 338 325 315 378
## 1966 1967 1968 1969 1970 1971 1972 1973
## 348 384 389 407 418 352 360 333
## 1974 1975 1976 1977 1978 1979 1980 1981
## 357 309 263 260 235 236 226 189
## 1982 1983 1984 1985 1986 1987 1988 1989
## 189 185 169 178 131 171 155 155
## 1990 1991 1992 1993 1994 1995 1996 1997
## 129 93 139 115 160 149 162 139
## 1998 1999 2000 2001 2002 2003 2004 2005
## 141 125 130 177 214 193 220 176
## 2006 2007 2008 2009 2010 2011 2012 2013
## 242 219 234 176 132 162 233 464
## 2014 2015 2016 2017 2018 4965 20052009 <NA>
## 919 824 375 154 1 1 1 13605sberbankTr%>%select(num_room)%>%table(useNA="always")## .
## 0 1 2 3 4 5 6 7 8 9 10 17 19 <NA>
## 14 7602 8132 4675 418 40 9 1 3 1 2 1 1 9572The house with num_room=0 is just a studio? I assume. Since num_room refers to number of living room in the dataset.
sberbankTr%>%ggplot()+geom_point(aes(x=full_sq,y=price_doc))
# change the timestamp variable from a chr to a Date object
sberbankTr$timestamp=as.Date(sberbankTr$timestamp)
sberbankTr%>%ggplot()+geom_point(aes(x=timestamp,y=price_doc))
There does not seem to be any error in timestamp.
It is kind of clear that there are some data error in the main features of the house properties. And we need to fix it. The purpose of fixing those data entry error is to improve the quality of the data, which leads to better performance of prediction model.
2.3.2 Fix data error in main features of house.
For some of the errors, there are clear ways to fix them. But for some errors, there is no clear ways to fix it and I will test the fixes with a simple random forest model. For the purpose of demonstrating that fixing the data error will improve the model performance, I will do a random forest model every time I made a data error fix.
full_sq,life_sq,kitch_sq,floor,max_floor,state,build_year,num_room are the variables that needs to be fixed.
Before we start fixing data errors, let us visualize the relationship between different variables.
main_feature=sberbankTr%>%select(price_doc,full_sq,life_sq,kitch_sq,floor,max_floor,state,material,build_year,num_room)
library(corrplot)
corrplot(cor(main_feature, use="complete.obs"))
It appears that floor and max_floor has a high correlation while num_room, full_sq and price_doc have a high correlation with each other. The variable life-sq has a small correlation with price_doc while the variable kitch_sq has almost no correlation with price_doc.
Fixing state variable for abnormaly (Use the same method as Troy Walters )
# Make a new dataframe to fix data error in it
sberbankATr=sberbankTr
# fix the state
sberbankATr$state[sberbankATr$state==33]=which.max(table(sberbankATr$state))
sberbankATr$state%>%table## .
## 1 2 3 4
## 4855 5845 5790 422Fix build_year
# Assuming that 2009 is an correction to the previous entry
sberbankATr$build_year[sberbankATr$build_year==20052009]=2009
# replace the abnormal build_year value to be the median of the build_year variable
sberbankATr$build_year[sberbankATr$build_year< 1691 | sberbankATr$build_year>2018 ]=median(sberbankATr$build_year,na.rm=TRUE)
sberbankATr$build_year%>%table(useNA="always")## .
## 1691 1860 1876 1886 1890 1895 1896 1900 1904 1905 1906 1907
## 1 2 1 1 5 1 2 2 1 1 1 2
## 1910 1911 1912 1914 1915 1917 1920 1924 1925 1926 1927 1928
## 5 1 5 3 5 16 1 3 1 8 10 12
## 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940
## 12 6 6 8 7 13 11 5 10 9 7 14
## 1941 1943 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955
## 2 2 2 4 1 3 22 23 45 24 36 52
## 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967
## 48 119 179 208 344 297 338 325 315 378 348 384
## 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979
## 389 407 418 352 360 333 357 309 263 260 235 1140
## 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991
## 226 189 189 185 169 178 131 171 155 155 129 93
## 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
## 139 115 160 149 162 139 141 125 130 177 214 193
## 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
## 220 176 242 219 234 177 132 162 233 464 919 824
## 2016 2017 2018 <NA>
## 375 154 1 13605Fix floor and max_floor
# Give max_floor the value of floor if max_floor is NA, vice versa.
sberbankATr$max_floor=ifelse(is.na(sberbankATr$max_floor), sberbankATr$floor, sberbankATr$max_floor)
sberbankATr$floor=ifelse(is.na(sberbankATr$floor),sberbankATr$max_floor, sberbankATr$floor)
# Give max_floor the value of floor if max_floor<floor
sberbankATr$max_floor=ifelse(sberbankATr$max_floor<sberbankATr$floor,sberbankATr$floor,sberbankATr$max_floor)
sberbankATr%>%filter(max_floor<floor)%>%dim## [1] 0 292kitch_sq does not seem to correlate with any other variables. We could just give the median of kitch_sq to the missing values in it.
sberbankATr$kitch_sq[is.na(sberbankATr$kitch_sq)]=median(sberbankATr$kitch_sq,na.rm = TRUE)
sum(is.na(sberbankATr$kitch_sq))## [1] 0Before we try to fix the full_sq, life_sq and num_room. Let us first visualize the relationship between them.
sberbankATr%>%ggplot()+geom_point(aes(x=full_sq,y=life_sq))## Warning: Removed 6383 rows containing missing values (geom_point).
sberbankATr%>%ggplot(aes(x=factor(num_room),y=full_sq))+geom_violin()
Let us get the missing percentage of three variables
sberbankATr%>%select(full_sq,life_sq,num_room)%>%sapply(miss_pct)%>%as.data.frame()## .
## full_sq 0.0000000
## life_sq 0.2094779
## num_room 0.3141348# Give life_sq value of full_sq if life_sq is missing.
sberbankATr$life_sq[is.na(sberbankATr$life_sq)]=sberbankATr$full_sq[is.na(sberbankATr$life_sq)]
sberbankATr%>%select(full_sq,life_sq,num_room)%>%sapply(miss_pct)%>%as.data.frame()## .
## full_sq 0.0000000
## life_sq 0.0000000
## num_room 0.3141348We will need to get rid or change some unreasonable variable values for full_sq,life_sq
# Give the full_sq the value of life_sq and life_sq the value of full_sq if life_sq>full_sq.
sberbankATr=sberbankATr%>%mutate(full_sq2=ifelse(full_sq<life_sq, life_sq,full_sq),life_sq2=ifelse(full_sq<life_sq,full_sq,life_sq) )%>%select(-full_sq,-life_sq)%>%rename(full_sq=full_sq2,life_sq=life_sq2)
# Let us check the really big house
sberbankATr%>%filter(full_sq>1000)%>%select(full_sq,life_sq,num_room,price_doc)## full_sq life_sq num_room price_doc
## 1 5326 22 NA 6868818
## 2 7478 79 3 7705000# Next, we need to get rid of some unreasonable size in full_sq.
sberbankATr=sberbankATr%>%filter(full_sq<1000)
sberbankATr%>%filter(life_sq>1000)%>%dim## [1] 0 292sberbankATr%>%filter(full_sq>1000)%>%dim## [1] 0 292Since num_room is also correlated with price_doc, it is best if we could use knn imputation to get the missing value.
library(caret)## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## liftmain_feature=sberbankATr%>%select(timestamp,full_sq,life_sq,floor,max_floor,material,build_year,num_room,kitch_sq,state,product_type,sub_area)
main_feature=main_feature%>%preProcess(c("knnImpute"))%>%predict(main_feature)
sapply(main_feature,miss_pct)## timestamp full_sq life_sq floor max_floor
## 0 0 0 0 0
## material build_year num_room kitch_sq state
## 0 0 0 0 0
## product_type sub_area
## 0 02.3.3 Compare the prediction RMSE and RMSLE (as indicated on the kaggle competition) value before and after the data cleaning using random forest.
First, I just use the cleaned main features of the data to compare with main features in the raw data.
Let us summarise the data cleaning in another file and do it here.
source('C:/Users/edward cooper/Google Drive/Learn R/housing_price/B data clean/data_clean1.R',echo=TRUE)##
## > sberbank_data_clean1 = function(data = sberbankATr,
## + cl_num = 3) {
## + library(data.table)
## + library(tidyverse)
## + library(doParallel .... [TRUNCATED]
##
## > print("The function that imported is sberbank_data_clean1(data=sberbankATr)")
## [1] "The function that imported is sberbank_data_clean1(data=sberbankATr)"main_feature=sberbank_data_clean1(data=fread("train.csv",header=TRUE) )## Loading required package: foreach##
## Attaching package: 'foreach'## The following objects are masked from 'package:purrr':
##
## accumulate, when## Loading required package: iterators## Loading required package: parallel# Check to see if all missing values are imputed
main_feature%>%sapply(miss_pct)%>%as.data.frame()## .
## timestamp 0
## full_sq 0
## life_sq 0
## floor 0
## max_floor 0
## material 0
## build_year 0
## num_room 0
## kitch_sq 0
## state 0
## product_type 0
## sub_area 0
## price_doc 0When I am using random forest on the main features of data, I realized that I changed timestamp variable to Date. Thus, random forest only treats it as a factor. There are going to be problems with prediction if there is some clear trend in the price when time changes. It is more problemetic with random forest than with just simple linear regression for a prediction with time dependence.