Has the overall wealth/poverty divide in the world changed for better or worse in the past ~30 years?
Each case is an individual recording of a country, when the poverty report was done, along with the ratio/amount of people living below a certain standard. There are 4877 cases.
The data was obtained from a public Kaggle dataset- https://www.kaggle.com/datasets/eishkaran/world-poverty-data
This is an observational study.
The data was obtained from a public Kaggle dataset- https://www.kaggle.com/datasets/eishkaran/world-poverty-data
The response variables are headcount_ratio_international_povline, headcount_ratio_lower_mid_income_povline, headcount_ratio_upper_mid_income_povline, which are the ratio headlines. Alternatively, we can look at the raw totals, rather than ratios, which are headcount_international_povline, headcount_lower_mid_income_povline, headcount_upper_mid_income_povline. There are several interesting dependent variables, and they all appear to be quantitative
country, year, reporting_level, survey_year
Provide summary statistics for each the variables. Also include appropriate visualizations related to your research question (e.g. scatter plot, boxplots, etc). This step requires the use of R, hence a code chunk is provided below. Insert more code chunks as needed.
Possibly needed libraries
library(jsonlite)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.1 ✔ tidyr 1.3.0
## ✔ readr 2.1.4## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ purrr::flatten() masks jsonlite::flatten()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(openintro)## Loading required package: airports
## Loading required package: cherryblossom
## Loading required package: usdatalibrary(psych)##
## Attaching package: 'psych'
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## The following objects are masked from 'package:ggplot2':
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## %+%, alphaLoad data and make into frame
poverty_raw <- read.csv(url("https://raw.githubusercontent.com/RonBalaban/CUNY-SPS-R/main/pip_dataset.csv"))
poverty_frame <- as.data.frame(poverty_raw)
head(poverty_frame)## country year reporting_level welfare_type ppp_version survey_year
## 1 Albania 1996 national consumption 2011 1996
## 2 Albania 2002 national consumption 2011 2002
## 3 Albania 2005 national consumption 2011 2005
## 4 Albania 2008 national consumption 2011 2008
## 5 Albania 2012 national consumption 2011 2012
## 6 Albania 2014 national consumption 2011 2014
## survey_comparability headcount_ratio_international_povline
## 1 0 0.9206690
## 2 1 1.5708434
## 3 1 0.8605271
## 4 1 0.3136496
## 5 1 0.8497544
## 6 2 1.5808972
## headcount_ratio_lower_mid_income_povline
## 1 11.174149
## 2 14.132118
## 3 8.715685
## 4 5.250542
## 5 6.182414
## 6 11.615621
## headcount_ratio_upper_mid_income_povline headcount_ratio_100
## 1 44.61842 0.05741581
## 2 49.66964 0.04637062
## 3 38.54525 0.02762022
## 4 31.11034 0.00000000
## 5 34.52891 0.11132100
## 6 37.03384 0.00000000
## headcount_ratio_1000 headcount_ratio_2000 headcount_ratio_3000
## 1 86.17521 99.63208 99.91247
## 2 85.33832 98.08006 99.63839
## 3 79.98713 97.57660 99.43446
## 4 75.85102 97.09978 99.01586
## 5 77.06984 97.77263 99.61830
## 6 71.58569 95.18214 99.12649
## headcount_ratio_4000 headcount_ratio_40_median headcount_ratio_50_median
## 1 99.95166 2.768821 7.548123
## 2 99.84308 3.212283 8.406373
## 3 99.75174 4.095002 8.991533
## 4 99.67825 2.118535 7.227714
## 5 99.88598 3.492413 7.753112
## 6 99.81976 7.514618 14.375828
## headcount_ratio_60_median headcount_international_povline
## 1 14.80251 29167
## 2 16.02055 47927
## 3 16.67577 25915
## 4 14.13715 9244
## 5 14.84282 24646
## 6 22.21602 45674
## headcount_lower_mid_income_povline headcount_upper_mid_income_povline
## 1 354001 1413526
## 2 431172 1515426
## 3 262472 1160785
## 4 154750 916920
## 5 179315 1001477
## 6 335587 1069946
## headcount_100 headcount_1000 headcount_2000 headcount_3000 headcount_4000
## 1 1819 2730059 3156377 3165260 3166501
## 2 1415 2603681 2992432 3039977 3046222
## 3 832 2408802 2938507 2994456 3004011
## 4 0 2235568 2861836 2918308 2937831
## 5 3229 2235334 2835798 2889330 2897094
## 6 0 2068185 2749911 2863867 2883897
## headcount_40_median headcount_50_median headcount_60_median
## 1 87717 239127 468949
## 2 98007 256479 488789
## 3 123320 270779 502189
## 4 62440 213023 416666
## 5 101294 224871 430501
## 6 217105 415333 641844
## avg_shortfall_international_povline avg_shortfall_lower_mid_income_povline
## 1 0.2890260 0.5999076
## 2 0.3166081 0.6466790
## 3 0.2899562 0.6467713
## 4 0.3104121 0.4577278
## 5 0.3781038 0.6586981
## 6 0.2546938 0.7016316
## avg_shortfall_upper_mid_income_povline avg_shortfall_100 avg_shortfall_1000
## 1 1.583098 0.20422681 4.459419
## 2 1.671541 0.53508932 4.719031
## 3 1.514365 0.00832818 4.228461
## 4 1.307345 NA 3.881222
## 5 1.377123 0.10741674 4.030469
## 6 1.742768 NA 4.431036
## avg_shortfall_2000 avg_shortfall_3000 avg_shortfall_4000
## 1 13.50430 23.45916 33.44882
## 2 13.70162 23.42240 33.36826
## 3 12.91856 22.61159 32.52479
## 4 12.36353 22.06975 31.89560
## 5 12.54608 22.24632 32.17559
## 6 12.47453 21.84794 31.67118
## avg_shortfall_40_median avg_shortfall_50_median avg_shortfall_60_median
## 1 0.3446150 0.4981147 0.6845159
## 2 0.3854980 0.5177960 0.6859527
## 3 0.4360586 0.6565995 0.8516646
## 4 0.4295745 0.5748611 0.8030727
## 5 0.5437887 0.7146906 0.8884043
## 6 0.5253690 0.7801093 1.0620848
## total_shortfall_international_povline
## 1 8430.020
## 2 15174.078
## 3 7514.215
## 4 2869.449
## 5 9318.746
## 6 11632.883
## total_shortfall_lower_mid_income_povline
## 1 212367.88
## 2 278829.87
## 3 169759.35
## 4 70833.38
## 5 118114.46
## 6 235458.46
## total_shortfall_upper_mid_income_povline total_shortfall_100
## 1 2237750 371.488565
## 2 2533096 757.151389
## 3 1757852 6.929046
## 4 1198731 0.000000
## 5 1379157 346.848655
## 6 1864668 0.000000
## total_shortfall_1000 total_shortfall_2000 total_shortfall_3000
## 1 12174477 42624656 74254340
## 2 12286852 41001158 71203557
## 3 10185526 37961293 67709400
## 4 8676735 35382386 64406320
## 5 9009444 35578150 64276958
## 6 9164201 34303852 62569586
## total_shortfall_4000 total_shortfall_40_median total_shortfall_50_median
## 1 105915713 30228.59 119112.7
## 2 101647115 37781.50 132803.8
## 3 97704835 53774.75 177793.4
## 4 93703870 26822.63 122458.6
## 5 93215717 55082.53 160713.2
## 6 91336415 114060.23 324005.1
## total_shortfall_60_median income_gap_ratio_international_povline
## 1 321003.0 15.21189
## 2 335286.1 16.66359
## 3 427696.6 15.26085
## 4 334613.1 16.33748
## 5 382458.9 19.90020
## 6 681692.8 13.40493
## income_gap_ratio_lower_mid_income_povline
## 1 18.74711
## 2 20.20872
## 3 20.21160
## 4 14.30399
## 5 20.58432
## 6 21.92599
## income_gap_ratio_upper_mid_income_povline income_gap_ratio_100
## 1 28.78360 20.422681
## 2 30.39165 53.508932
## 3 27.53390 0.832818
## 4 23.76991 NA
## 5 25.03859 10.741674
## 6 31.68669 NA
## income_gap_ratio_1000 income_gap_ratio_2000 income_gap_ratio_3000
## 1 44.59419 67.52149 78.19720
## 2 47.19031 68.50809 78.07467
## 3 42.28461 64.59282 75.37195
## 4 38.81222 61.81763 73.56582
## 5 40.30469 62.73040 74.15440
## 6 44.31036 62.37266 72.82646
## income_gap_ratio_4000 income_gap_ratio_40_median income_gap_ratio_50_median
## 1 83.62204 14.91890 17.25131
## 2 83.42064 17.39735 18.69432
## 3 81.31198 16.87440 20.32703
## 4 79.73899 15.43531 16.52455
## 5 80.43898 19.91816 20.94243
## 6 79.17794 19.11590 22.70784
## income_gap_ratio_60_median poverty_gap_index_international_povline
## 1 19.75582 0.14005071
## 2 20.63782 0.26176109
## 3 21.97156 0.13132549
## 4 19.23714 0.05124112
## 5 21.69394 0.16910086
## 6 25.76311 0.21191933
## poverty_gap_index_lower_mid_income_povline
## 1 2.0948318
## 2 2.8559177
## 3 1.7615814
## 4 0.7510374
## 5 1.2726091
## 6 2.5468370
## poverty_gap_index_upper_mid_income_povline poverty_gap_index_100
## 1 12.842785 0.0117261583
## 2 15.095426 0.0248164178
## 3 10.613009 0.0002300872
## 4 7.394906 0.0000000000
## 5 8.645555 0.0119586449
## 6 11.734796 0.0000000000
## poverty_gap_index_1000 poverty_gap_index_2000 poverty_gap_index_3000
## 1 38.42913 67.27306 78.12875
## 2 40.27143 67.19276 77.79233
## 3 33.82225 63.02749 74.94570
## 4 29.43947 60.02480 72.84183
## 5 31.06275 61.33316 73.87135
## 6 31.71987 59.36763 72.19030
## poverty_gap_index_4000 mean median decile1_avg decile2_avg decile3_avg
## 1 83.58161 6.570821 5.774805 2.538496 3.475535 4.191319
## 2 83.28973 6.715828 5.539607 2.346511 3.263519 3.923426
## 3 81.11013 7.591930 6.460357 2.643917 3.735312 4.537911
## 4 79.48243 8.314345 6.957659 3.104265 4.270897 5.062166
## 5 80.34727 7.882867 6.825289 2.884756 4.093648 4.842953
## 6 79.03524 8.399775 6.870837 2.396459 3.505870 4.387129
## decile4_avg decile5_avg decile6_avg decile7_avg decile8_avg decile9_avg
## 1 4.811182 5.506126 6.146939 7.111657 8.196356 9.790226
## 2 4.525262 5.139692 5.936429 6.869341 8.048536 10.023425
## 3 5.254513 6.064554 6.860879 7.874459 9.206284 11.261826
## 4 5.806393 6.578375 7.419832 8.562724 9.918245 12.085071
## 5 5.542227 6.372377 7.297124 8.289193 9.690645 11.736908
## 6 5.301601 6.310224 7.503401 8.898472 10.688325 13.549610
## decile10_avg decile1_share decile2_share decile3_share decile4_share
## 1 13.94037 3.863286 5.289347 6.378683 7.322042
## 2 17.08214 3.494002 4.859444 5.842059 6.738204
## 3 18.47964 3.482536 4.920109 5.977283 6.921183
## 4 20.33548 3.733625 5.136781 6.088472 6.983584
## 5 18.07883 3.659527 5.193095 6.143644 7.030726
## 6 21.45666 2.853004 4.173767 5.222913 6.311599
## decile5_share decile6_share decile7_share decile8_share decile9_share
## 1 8.379662 9.354903 10.82309 12.47387 14.89955
## 2 7.653102 8.839459 10.22859 11.98443 14.92508
## 3 7.988158 9.037069 10.37214 12.12641 14.83394
## 4 7.912079 8.924133 10.29873 11.92908 14.53520
## 5 8.083833 9.256943 10.51546 12.29330 14.88914
## 6 7.512373 8.932860 10.59370 12.72454 16.13092
## decile10_share decile1_thr decile2_thr decile3_thr decile4_thr decile6_thr
## 1 21.21557 3.06 3.88 4.48 5.16 6.66
## 2 25.43564 2.91 3.62 4.22 4.85 6.35
## 3 24.34117 3.30 4.18 4.93 5.63 7.32
## 4 24.45831 3.81 4.70 5.43 6.20 7.93
## 5 22.93434 3.65 4.49 5.17 5.93 7.74
## 6 25.54432 3.05 3.96 4.82 5.82 8.14
## decile7_thr decile8_thr decile9_thr gini mld polarization
## 1 7.61 8.85 10.92 0.2701034 0.1191043 0.2412933
## 2 7.38 8.83 11.58 0.3173898 0.1648116 0.2689816
## 3 8.51 10.02 12.78 0.3059566 0.1544128 0.2545287
## 4 9.24 10.74 13.62 0.2998467 0.1488934 0.2473111
## 5 8.91 10.52 13.26 0.2896048 0.1384171 0.2499879
## 6 9.71 11.74 15.78 0.3459890 0.1986616 0.3243097
## palma_ratio s80_s20_ratio p90_p10_ratio p90_p50_ratio p50_p10_ratio
## 1 0.9283351 3.945872 3.568627 1.889273 1.888889
## 2 1.2150564 4.831625 3.979381 2.090253 1.903780
## 3 1.1427183 4.662236 3.872727 1.978328 1.957576
## 4 1.1146566 4.395911 3.574803 1.956897 1.826772
## 5 1.0411926 4.272573 3.632877 1.941435 1.871233
## 6 1.3762154 5.930924 5.173770 2.296943 2.252459Simple summary statistics
summary(poverty_frame)## country year reporting_level welfare_type
## Length:4877 Min. :1967 Length:4877 Length:4877
## Class :character 1st Qu.:2000 Class :character Class :character
## Mode :character Median :2007 Mode :character Mode :character
## Mean :2006
## 3rd Qu.:2013
## Max. :2021
##
## ppp_version survey_year survey_comparability
## Min. :2011 Min. :1967 Min. :0.000
## 1st Qu.:2011 1st Qu.:2000 1st Qu.:1.000
## Median :2011 Median :2007 Median :1.000
## Mean :2014 Mean :2006 Mean :1.639
## 3rd Qu.:2017 3rd Qu.:2014 3rd Qu.:2.000
## Max. :2017 Max. :2021 Max. :6.000
## NA's :466 NA's :466
## headcount_ratio_international_povline headcount_ratio_lower_mid_income_povline
## Min. : 0.0000 Min. : 0.0000
## 1st Qu.: 0.2784 1st Qu.: 0.8079
## Median : 2.0414 Median : 9.1867
## Mean :11.0816 Mean : 21.6727
## 3rd Qu.:13.3910 3rd Qu.: 34.1667
## Max. :96.8714 Max. : 99.9990
##
## headcount_ratio_upper_mid_income_povline headcount_ratio_100
## Min. : 0.000 Min. : 0.00000
## 1st Qu.: 3.032 1st Qu.: 0.04477
## Median : 28.583 Median : 0.37176
## Mean : 36.539 Mean : 3.16472
## 3rd Qu.: 64.865 3rd Qu.: 2.15848
## Max. : 99.999 Max. :79.53262
##
## headcount_ratio_1000 headcount_ratio_2000 headcount_ratio_3000
## Min. : 0.00 Min. : 0.9202 Min. : 6.083
## 1st Qu.: 10.33 1st Qu.: 44.5759 1st Qu.: 71.017
## Median : 55.34 Median : 84.9163 Median : 93.503
## Mean : 50.35 Mean : 69.1060 Mean : 79.081
## 3rd Qu.: 85.13 3rd Qu.: 97.1794 3rd Qu.: 99.113
## Max. :100.00 Max. :100.0000 Max. :100.000
##
## headcount_ratio_4000 headcount_ratio_40_median headcount_ratio_50_median
## Min. : 14.10 Min. : 0.000 Min. : 0.8776
## 1st Qu.: 84.25 1st Qu.: 3.973 1st Qu.: 8.8660
## Median : 96.67 Median : 6.596 Median :12.6409
## Mean : 85.72 Mean : 8.015 Mean :13.4216
## 3rd Qu.: 99.61 3rd Qu.:11.559 3rd Qu.:17.9596
## Max. :100.00 Max. :36.163 Max. :38.9103
## NA's :466 NA's :466
## headcount_ratio_60_median headcount_international_povline
## Min. : 4.892 Min. :0.000e+00
## 1st Qu.:15.583 1st Qu.:2.121e+04
## Median :19.610 Median :3.615e+05
## Mean :20.071 Mean :4.456e+07
## 3rd Qu.:24.748 3rd Qu.:5.160e+06
## Max. :41.442 Max. :2.005e+09
## NA's :466
## headcount_lower_mid_income_povline headcount_upper_mid_income_povline
## Min. :0.000e+00 Min. :0.000e+00
## 1st Qu.:8.558e+04 1st Qu.:3.214e+05
## Median :1.027e+06 Median :2.863e+06
## Mean :8.632e+07 Mean :1.264e+08
## 3rd Qu.:1.099e+07 3rd Qu.:2.006e+07
## Max. :3.156e+09 Max. :4.244e+09
##
## headcount_100 headcount_1000 headcount_2000
## Min. : 0 Min. :0.000e+00 Min. :3.736e+03
## 1st Qu.: 2694 1st Qu.:8.784e+05 1st Qu.:2.499e+06
## Median : 63838 Median :4.951e+06 Median :8.313e+06
## Mean : 9676924 Mean :1.522e+08 Mean :1.787e+08
## 3rd Qu.: 895900 3rd Qu.:3.226e+07 3rd Qu.:5.391e+07
## Max. :569231470 Max. :4.833e+09 Max. :6.010e+09
##
## headcount_3000 headcount_4000 headcount_40_median
## Min. :9.913e+03 Min. :1.006e+04 Min. : 0
## 1st Qu.:3.069e+06 1st Qu.:3.674e+06 1st Qu.: 259948
## Median :1.051e+07 Median :1.216e+07 Median : 859900
## Mean :1.907e+08 Mean :1.987e+08 Mean : 4501881
## 3rd Qu.:6.368e+07 3rd Qu.:6.902e+07 3rd Qu.: 3269602
## Max. :6.544e+09 Max. :6.868e+09 Max. :176375997
## NA's :466
## headcount_50_median headcount_60_median avg_shortfall_international_povline
## Min. : 1195 Min. : 1847 Min. :0.00443
## 1st Qu.: 497663 1st Qu.: 787206 1st Qu.:0.45363
## Median : 1489600 Median : 2233892 Median :0.69507
## Mean : 8382880 Mean : 13254819 Mean :0.77819
## 3rd Qu.: 6392031 3rd Qu.: 10529900 3rd Qu.:1.00221
## Max. :268177618 Max. :358853136 Max. :2.15893
## NA's :466 NA's :466 NA's :291
## avg_shortfall_lower_mid_income_povline avg_shortfall_upper_mid_income_povline
## Min. :0.00523 Min. :0.05913
## 1st Qu.:0.92154 1st Qu.:1.96546
## Median :1.28584 Median :2.59216
## Mean :1.34847 Mean :2.67004
## 3rd Qu.:1.68015 3rd Qu.:3.30440
## Max. :3.64996 Max. :6.84993
## NA's :100 NA's :12
## avg_shortfall_100 avg_shortfall_1000 avg_shortfall_2000 avg_shortfall_3000
## Min. :0.0013 Min. : 1.290 Min. : 1.860 Min. : 4.758
## 1st Qu.:0.2183 1st Qu.: 3.507 1st Qu.: 7.188 1st Qu.:12.650
## Median :0.3542 Median : 4.541 Median :11.288 Median :19.992
## Mean :0.4186 Mean : 4.756 Mean :11.127 Mean :18.652
## 3rd Qu.:0.6007 3rd Qu.: 5.790 3rd Qu.:14.530 3rd Qu.:24.134
## Max. :1.0101 Max. :10.000 Max. :20.000 Max. :30.000
## NA's :750 NA's :6
## avg_shortfall_4000 avg_shortfall_40_median avg_shortfall_50_median
## Min. : 8.731 Min. :0.0419 Min. : 0.0498
## 1st Qu.:19.908 1st Qu.:0.4396 1st Qu.: 0.6074
## Median :29.330 Median :1.0867 Median : 1.4246
## Mean :26.875 Mean :1.8868 Mean : 2.3040
## 3rd Qu.:33.972 3rd Qu.:2.7650 3rd Qu.: 3.4442
## Max. :40.000 Max. :9.2963 Max. :11.3676
## NA's :468 NA's :466
## avg_shortfall_60_median total_shortfall_international_povline
## Min. : 0.0839 Min. :0.000e+00
## 1st Qu.: 0.8007 1st Qu.:1.521e+04
## Median : 1.8008 Median :2.314e+05
## Mean : 2.8122 Mean :2.948e+07
## 3rd Qu.: 4.1505 3rd Qu.:3.632e+06
## Max. :14.2995 Max. :1.583e+09
## NA's :466
## total_shortfall_lower_mid_income_povline
## Min. :0.000e+00
## 1st Qu.:9.872e+04
## Median :1.252e+06
## Mean :1.239e+08
## 3rd Qu.:1.598e+07
## Max. :5.480e+09
##
## total_shortfall_upper_mid_income_povline total_shortfall_100
## Min. :0.000e+00 Min. : 0
## 1st Qu.:6.856e+05 1st Qu.: 906
## Median :6.607e+06 Median : 22392
## Mean :4.265e+08 Mean : 2757883
## 3rd Qu.:6.025e+07 3rd Qu.: 324459
## Max. :1.730e+10 Max. :158089241
##
## total_shortfall_1000 total_shortfall_2000 total_shortfall_3000
## Min. :0.000e+00 Min. :9.589e+03 Min. :1.653e+05
## 1st Qu.:2.993e+06 1st Qu.:2.209e+07 1st Qu.:5.547e+07
## Median :2.332e+07 Median :9.120e+07 Median :1.914e+08
## Mean :9.689e+08 Mean :2.660e+09 Mean :4.529e+09
## 3rd Qu.:1.570e+08 3rd Qu.:6.277e+08 3rd Qu.:1.194e+09
## Max. :3.257e+10 Max. :8.363e+10 Max. :1.440e+11
##
## total_shortfall_4000 total_shortfall_40_median total_shortfall_50_median
## Min. :3.207e+05 Min. : 0 Min. : 758
## 1st Qu.:9.264e+07 1st Qu.: 213643 1st Qu.: 598908
## Median :3.330e+08 Median : 967355 Median : 2133861
## Mean :6.495e+09 Mean : 7901541 Mean : 16305022
## 3rd Qu.:1.846e+09 3rd Qu.: 3581223 3rd Qu.: 7914232
## Max. :2.112e+11 Max. :366229625 Max. :690458017
## NA's :466 NA's :466
## total_shortfall_60_median income_gap_ratio_international_povline
## Min. :1.654e+03 Min. : 0.2058
## 1st Qu.:1.220e+06 1st Qu.: 22.4091
## Median :4.266e+06 Median : 34.4059
## Mean :2.952e+07 Mean : 38.4236
## 3rd Qu.:1.553e+07 3rd Qu.: 49.8513
## Max. :1.156e+09 Max. :100.4155
## NA's :466 NA's :291
## income_gap_ratio_lower_mid_income_povline
## Min. : 0.1633
## 1st Qu.: 26.8856
## Median : 37.5422
## Mean : 39.3608
## 3rd Qu.: 49.0408
## Max. : 99.9990
## NA's :100
## income_gap_ratio_upper_mid_income_povline income_gap_ratio_100
## Min. : 1.075 Min. : 0.1339
## 1st Qu.: 32.666 1st Qu.: 21.8340
## Median : 42.025 Median : 35.4210
## Mean : 43.188 Mean : 41.8600
## 3rd Qu.: 53.368 3rd Qu.: 60.0720
## Max. : 99.999 Max. :101.0080
## NA's :12 NA's :750
## income_gap_ratio_1000 income_gap_ratio_2000 income_gap_ratio_3000
## Min. : 12.90 Min. : 9.30 Min. : 15.86
## 1st Qu.: 35.07 1st Qu.: 35.94 1st Qu.: 42.17
## Median : 45.41 Median : 56.44 Median : 66.64
## Mean : 47.56 Mean : 55.63 Mean : 62.17
## 3rd Qu.: 57.90 3rd Qu.: 72.65 3rd Qu.: 80.45
## Max. :100.00 Max. :100.00 Max. :100.00
## NA's :6
## income_gap_ratio_4000 income_gap_ratio_40_median income_gap_ratio_50_median
## Min. : 21.83 Min. : 6.377 Min. : 7.987
## 1st Qu.: 49.77 1st Qu.:19.779 1st Qu.:21.340
## Median : 73.32 Median :26.196 Median :26.726
## Mean : 67.19 Mean :27.359 Mean :27.901
## 3rd Qu.: 84.93 3rd Qu.:34.351 3rd Qu.:33.838
## Max. :100.00 Max. :74.986 Max. :67.338
## NA's :468 NA's :466
## income_gap_ratio_60_median poverty_gap_index_international_povline
## Min. :10.00 Min. : 0.0000
## 1st Qu.:23.17 1st Qu.: 0.1245
## Median :27.65 Median : 0.6422
## Mean :29.09 Mean : 4.0624
## 3rd Qu.:34.74 3rd Qu.: 4.0977
## Max. :68.85 Max. :64.0970
## NA's :466
## poverty_gap_index_lower_mid_income_povline
## Min. : 0.0000
## 1st Qu.: 0.3387
## Median : 2.5326
## Mean : 9.1830
## 3rd Qu.: 12.4506
## Max. : 99.9980
##
## poverty_gap_index_upper_mid_income_povline poverty_gap_index_100
## Min. : 0.000 Min. : 0.00000
## 1st Qu.: 1.055 1st Qu.: 0.01365
## Median : 9.779 Median : 0.14990
## Mean : 18.266 Mean : 1.13075
## 3rd Qu.: 29.319 3rd Qu.: 0.72813
## Max. : 99.998 Max. :41.98930
##
## poverty_gap_index_1000 poverty_gap_index_2000 poverty_gap_index_3000
## Min. : 0.000 Min. : 0.1239 Min. : 1.265
## 1st Qu.: 3.154 1st Qu.: 15.0396 1st Qu.: 30.222
## Median : 22.685 Median : 48.6546 Median : 62.493
## Mean : 28.252 Mean : 44.7445 Mean : 54.781
## 3rd Qu.: 46.917 3rd Qu.: 69.3674 3rd Qu.: 78.971
## Max. : 99.998 Max. : 99.9980 Max. : 99.998
##
## poverty_gap_index_4000 mean median decile1_avg
## Min. : 3.656 Min. : 0.743 Min. : 0.5282 Min. : 0.000
## 1st Qu.: 42.380 1st Qu.: 6.580 1st Qu.: 4.6592 1st Qu.: 1.391
## Median : 70.682 Median :12.846 Median : 9.1076 Median : 2.959
## Mean : 61.831 Mean :19.749 Mean :15.8058 Mean : 5.671
## 3rd Qu.: 84.150 3rd Qu.:26.632 3rd Qu.:21.6472 3rd Qu.: 7.463
## Max. : 99.998 Max. :85.875 Max. :69.4836 Max. :26.906
## NA's :482
## decile2_avg decile3_avg decile4_avg decile5_avg
## Min. : 0.1529 Min. : 0.2382 Min. : 0.3429 Min. : 0.4755
## 1st Qu.: 2.2762 1st Qu.: 2.9703 1st Qu.: 3.6998 1st Qu.: 4.5085
## Median : 4.7560 Median : 6.1963 Median : 7.5307 Median : 8.9686
## Mean : 8.9950 Mean :11.1980 Mean :13.2337 Mean :15.3432
## 3rd Qu.:12.4957 3rd Qu.:15.6662 3rd Qu.:18.4650 3rd Qu.:21.2789
## Max. :40.5614 Max. :49.1261 Max. :56.0456 Max. :64.6635
## NA's :482 NA's :482 NA's :482 NA's :482
## decile6_avg decile7_avg decile8_avg decile9_avg
## Min. : 0.5807 Min. : 0.7132 Min. : 0.8907 Min. : 1.196
## 1st Qu.: 5.3870 1st Qu.: 6.4734 1st Qu.: 7.9534 1st Qu.: 10.491
## Median :10.7406 Median :12.7560 Median : 15.7568 Median : 21.003
## Mean :17.7250 Mean :20.6274 Mean : 24.5551 Mean : 30.903
## 3rd Qu.:24.6651 3rd Qu.:28.7787 3rd Qu.: 34.2894 3rd Qu.: 43.572
## Max. :74.9081 Max. :88.7781 Max. :106.7388 Max. :139.220
## NA's :482 NA's :482 NA's :482 NA's :482
## decile10_avg decile1_share decile2_share decile3_share
## Min. : 1.995 Min. :0.000 Min. :0.760 Min. :1.587
## 1st Qu.: 20.327 1st Qu.:1.877 1st Qu.:3.339 1st Qu.:4.379
## Median : 42.430 Median :2.718 Median :4.253 Median :5.369
## Mean : 55.409 Mean :2.631 Mean :4.096 Mean :5.134
## 3rd Qu.: 79.494 3rd Qu.:3.394 3rd Qu.:4.971 3rd Qu.:6.018
## Max. :264.254 Max. :5.494 Max. :6.876 Max. :7.690
## NA's :482 NA's :482 NA's :482 NA's :482
## decile4_share decile5_share decile6_share decile7_share
## Min. :2.549 Min. :3.310 Min. : 4.204 Min. : 5.248
## 1st Qu.:5.414 1st Qu.:6.530 1st Qu.: 7.836 1st Qu.: 9.449
## Median :6.388 Median :7.450 Median : 8.628 Median :10.085
## Mean :6.121 Mean :7.165 Mean : 8.361 Mean : 9.844
## 3rd Qu.:6.961 3rd Qu.:7.963 3rd Qu.: 9.101 3rd Qu.:10.423
## Max. :8.420 Max. :9.142 Max. :10.044 Max. :11.636
## NA's :482 NA's :482 NA's :482 NA's :482
## decile8_share decile9_share decile10_share decile1_thr
## Min. : 6.501 Min. : 8.778 Min. :16.99 Min. : 0.01
## 1st Qu.:11.614 1st Qu.:14.672 1st Qu.:24.48 1st Qu.: 1.79
## Median :11.998 Median :15.287 Median :27.49 Median : 3.57
## Mean :11.891 Mean :15.322 Mean :29.43 Mean : 7.34
## 3rd Qu.:12.248 3rd Qu.:15.918 3rd Qu.:32.90 3rd Qu.: 9.85
## Max. :13.843 Max. :18.950 Max. :61.49 Max. :35.50
## NA's :482 NA's :482 NA's :482
## decile2_thr decile3_thr decile4_thr decile6_thr
## Min. : 0.010 Min. : 0.15 Min. : 0.39 Min. : 0.64
## 1st Qu.: 2.480 1st Qu.: 3.13 1st Qu.: 3.85 1st Qu.: 5.61
## Median : 5.010 Median : 6.30 Median : 7.62 Median :10.96
## Mean : 9.677 Mean :11.67 Mean :13.65 Mean :18.33
## 3rd Qu.:13.300 3rd Qu.:15.88 3rd Qu.:18.64 3rd Qu.:25.12
## Max. :45.400 Max. :52.45 Max. :60.15 Max. :81.50
##
## decile7_thr decile8_thr decile9_thr gini
## Min. : 0.79 Min. : 1.01 Min. : 1.46 Min. :0.1779
## 1st Qu.: 6.82 1st Qu.: 8.68 1st Qu.: 11.96 1st Qu.:0.3087
## Median :13.32 Median : 16.98 Median : 24.56 Median :0.3556
## Mean :21.56 Mean : 26.27 Mean : 35.11 Mean :0.3756
## 3rd Qu.:29.48 3rd Qu.: 35.55 3rd Qu.: 48.35 3rd Qu.:0.4277
## Max. :96.85 Max. :120.10 Max. :164.70 Max. :0.6576
## NA's :476
## mld polarization palma_ratio s80_s20_ratio
## Min. :0.0536 Min. :0.1466 Min. :0.5964 Min. : 2.430
## 1st Qu.:0.1631 1st Qu.:0.2524 1st Qu.:1.1541 1st Qu.: 4.721
## Median :0.2210 Median :0.3004 Median :1.4658 Median : 6.172
## Mean :0.2644 Mean :0.3274 Mean :1.8865 Mean : 8.220
## 3rd Qu.:0.3209 3rd Qu.:0.3802 3rd Qu.:2.1521 3rd Qu.: 9.043
## Max. :0.9370 Max. :0.8157 Max. :8.3436 Max. :72.682
## NA's :476 NA's :476 NA's :482 NA's :482
## p90_p10_ratio p90_p50_ratio p50_p10_ratio
## Min. : 2.191 Min. : 1.475 Min. : 1.485
## 1st Qu.: 3.938 1st Qu.: 2.018 1st Qu.: 1.942
## Median : 5.150 Median : 2.290 Median : 2.208
## Mean : 7.425 Mean : 2.532 Mean : 2.660
## 3rd Qu.: 7.458 3rd Qu.: 2.781 3rd Qu.: 2.740
## Max. :2892.000 Max. :11.492 Max. :809.000
## #-------------------------------------------------------------------------------
# The variables I'm most interested in
describe(poverty_frame$headcount_ratio_international_povline)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 4877 11.08 18.19 2.04 6.62 3 0 96.87 96.87 2.12 3.94 0.26describe(poverty_frame$headcount_ratio_lower_mid_income_povline)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 4877 21.67 27.1 9.19 16.77 13.25 0 100 100 1.25 0.32 0.39describe(poverty_frame$headcount_ratio_upper_mid_income_povline)## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 4877 36.54 33.67 28.58 33.76 40.06 0 100 100 0.48 -1.2 0.48Cleaning data, as some elements are messy
for (i in poverty_frame\(headcount_ratio_international_povline) { if (poverty_frame\)headcount_ratio_international_povline > 1){ poverty_frame\(headcount_ratio_international_povline <- poverty_frame\)headcount_ratio_international_povline/100 }}