Analyze New Beneficiary Data
1 Overview
This notebook explores the relationship between the share of a country’s population receiving covid cash transfers and various variables which proxy for the severity of the covid crisis in the country and the capacity to deliver cash transfers.
2 Analyze missing data
The figure below graphically shows which variables have missing values. Before doing this analysis, I first removed all countries for which we don’t have data on cash transfer coverage from the latest WB SP spreadsheet. (Otherwise, there are tons of countries with missing data for a lot of variables and it becomes difficult to read the output.)
Note that the variable new_id is equal to the Findex value for share of the population with IDs times a binary for whether or not the country has a digital ID from the DGSS dataset (where missing values in that dataset are assumed to be 0).
3 Regressions
3.1 Kitchen sink regression
In the regressions below, I regress the share of the total population receiving covid cash transfers, spending on covid cash transfers per capita, the share of the population receiving cash transfers pre covid, and total spending on covid response as a share of GDP on various variables.
| Dependent variable: | ||||
| m21_bens | m21_spending | m21_precovid | total_fiscal | |
| (1) | (2) | (3) | (4) | |
| m21_precovid | 0.213 | 120.268*** | ||
| (0.336) | (41.103) | |||
| gdpg_2020e | -0.009 | -2.196 | -0.002 | 0.016 |
| (0.013) | (1.642) | (0.009) | (0.136) | |
| gdp_per_capita | -0.00001 | 0.002 | 0.00000 | -0.00005 |
| (0.00001) | (0.001) | (0.00001) | (0.0001) | |
| tax_to_gdp | -0.002 | 0.160 | -0.0001 | 0.163* |
| (0.008) | (1.034) | (0.005) | (0.086) | |
| new_id | 0.045 | 0.826 | -0.075 | 0.843 |
| (0.145) | (17.731) | (0.091) | (1.446) | |
| soc_ins | -0.090 | -64.871 | 0.567 | 2.098 |
| (0.565) | (69.091) | (0.337) | (5.385) | |
| soc_reg_coverage | -0.048 | -16.435 | 0.140 | 0.865 |
| (0.153) | (18.742) | (0.093) | (1.476) | |
| i_branches_A1_pop | 0.006 | 0.525 | -0.002 | 0.034 |
| (0.005) | (0.642) | (0.003) | (0.053) | |
| i_ATMs_pop | 0.006** | 0.458 | 0.001 | 0.011 |
| (0.002) | (0.271) | (0.001) | (0.022) | |
| has_account | -0.479 | -42.427 | 0.698** | 2.205 |
| (0.464) | (56.724) | (0.254) | (4.051) | |
| april_ld_severity | 0.001 | 0.481 | 0.017 | 0.049 |
| (0.017) | (2.076) | (0.010) | (0.162) | |
| deaths | -0.0001 | -0.040 | 0.0001 | 0.002 |
| (0.0002) | (0.024) | (0.0001) | (0.002) | |
| gov_effect | 0.104 | -14.877 | 0.070 | 0.679 |
| (0.128) | (15.624) | (0.080) | (1.272) | |
| tsa | -0.048 | -2.108 | 0.060 | -0.035 |
| (0.061) | (7.510) | (0.037) | (0.587) | |
| unpan_egov | -0.001 | -0.137 | 0.004** | 0.015 |
| (0.004) | (0.443) | (0.002) | (0.033) | |
| regionChina | -0.620 | -16.083 | -0.262 | -0.733 |
| (0.414) | (50.695) | (0.257) | (4.101) | |
| regionEAP | 0.060 | -6.363 | 0.333* | 1.184 |
| (0.278) | (34.058) | (0.162) | (2.581) | |
| regionIndia | 0.164 | 12.502 | -0.288 | -1.794 |
| (0.343) | (41.986) | (0.209) | (3.335) | |
| regionLAC | 0.146 | -1.804 | -0.040 | -0.891 |
| (0.203) | (24.817) | (0.128) | (2.050) | |
| regionMNA | 0.130 | -15.574 | -0.067 | -2.912 |
| (0.213) | (26.047) | (0.134) | (2.144) | |
| regionSAR | 0.139 | -21.100 | 0.129 | -0.235 |
| (0.223) | (27.227) | (0.139) | (2.211) | |
| Constant | 0.663 | 15.210 | -1.295** | -3.838 |
| (0.919) | (112.441) | (0.514) | (8.199) | |
| Observations | 43 | 43 | 43 | 43 |
| R2 | 0.742 | 0.755 | 0.748 | 0.537 |
| Adjusted R2 | 0.485 | 0.510 | 0.519 | 0.116 |
| Residual Std. Error | 0.227 (df = 21) | 27.789 (df = 21) | 0.144 (df = 22) | 2.301 (df = 22) |
| F Statistic | 2.883*** (df = 21; 21) | 3.078*** (df = 21; 21) | 3.270*** (df = 20; 22) | 1.276 (df = 20; 22) |
| Note: | p<0.1; p<0.05; p<0.01 | |||
3.2 Regression of beneficiary numbers on infrastructure alone and need alone
In the two regressions below, I regress the share of the population receving covid cash transfers on a) just the capacity variables and b) just the pandemic severity variables. The adjusted R squared for each regression gives a rough sense of the relative importance of the two sets of variables for predicting the cash transfer response.
| Dependent variable: | ||
| m21_bens | ||
| (1) | (2) | |
| gdp_per_capita | 0.00001 | |
| (0.00001) | ||
| tax_to_gdp | -0.005 | |
| (0.008) | ||
| new_id | 0.041 | |
| (0.121) | ||
| soc_ins | -0.499 | |
| (0.427) | ||
| soc_reg_coverage | 0.085 | |
| (0.149) | ||
| i_branches_A1_pop | 0.013** | |
| (0.005) | ||
| i_ATMs_pop | 0.003 | |
| (0.002) | ||
| has_account | -0.126 | |
| (0.303) | ||
| gov_effect | -0.040 | |
| (0.115) | ||
| factor(tsa)1 | -0.153 | |
| (0.162) | ||
| factor(tsa)2 | -0.154 | |
| (0.160) | ||
| factor(tsa)3 | -0.198 | |
| (0.164) | ||
| unpan_egov | -0.002 | |
| (0.002) | ||
| gdpg_2020e | -0.011 | |
| (0.008) | ||
| april_ld_severity | -0.001 | |
| (0.011) | ||
| deaths | 0.0003** | |
| (0.0001) | ||
| Constant | 0.725 | 0.274 |
| (0.517) | (0.193) | |
| Observations | 51 | 64 |
| R2 | 0.506 | 0.221 |
| Adjusted R2 | 0.333 | 0.182 |
| Residual Std. Error | 0.260 (df = 37) | 0.272 (df = 60) |
| F Statistic | 2.920*** (df = 13; 37) | 5.664*** (df = 3; 60) |
| Note: | p<0.1; p<0.05; p<0.01 | |
3.3 Select additional regressions
Additional regressions specified in the email.
| Dependent variable: | ||||||||
| total_fiscal | m21_bens | m21_spending | m21_bens | |||||
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
| gdpg_2020e | -0.099* | -0.013* | -1.401 | -0.022** | ||||
| (0.056) | (0.007) | (6.061) | (0.009) | |||||
| deaths | 0.001** | 0.0002** | -0.078 | 0.0002** | ||||
| (0.001) | (0.0001) | (0.078) | (0.0001) | |||||
| tax_to_gdp | 0.077 | 0.110** | -0.003 | -0.001 | 6.580 | 7.196 | -0.008 | -0.007 |
| (0.047) | (0.043) | (0.006) | (0.006) | (5.565) | (5.106) | (0.007) | (0.007) | |
| gov_effect | 0.742* | 0.515 | 0.145** | 0.101* | 67.590 | 81.392* | 0.167** | 0.090 |
| (0.416) | (0.424) | (0.057) | (0.058) | (48.767) | (48.357) | (0.070) | (0.074) | |
| new_id | -0.005 | 0.047 | ||||||
| (0.129) | (0.128) | |||||||
| Constant | 1.672** | 1.008 | 0.446*** | 0.418*** | -33.825 | -9.568 | 0.541*** | 0.526*** |
| (0.828) | (0.836) | (0.113) | (0.118) | (97.614) | (98.213) | (0.126) | (0.137) | |
| Observations | 65 | 66 | 69 | 70 | 69 | 70 | 53 | 54 |
| R2 | 0.237 | 0.251 | 0.163 | 0.159 | 0.078 | 0.089 | 0.229 | 0.156 |
| Adjusted R2 | 0.199 | 0.215 | 0.125 | 0.121 | 0.036 | 0.047 | 0.165 | 0.087 |
| Residual Std. Error | 2.011 (df = 61) | 1.995 (df = 62) | 0.281 (df = 65) | 0.289 (df = 66) | 242.919 (df = 65) | 239.916 (df = 66) | 0.279 (df = 48) | 0.298 (df = 49) |
| F Statistic | 6.315*** (df = 3; 61) | 6.941*** (df = 3; 62) | 4.226*** (df = 3; 65) | 4.170*** (df = 3; 66) | 1.837 (df = 3; 65) | 2.138 (df = 3; 66) | 3.564** (df = 4; 48) | 2.269* (df = 4; 49) |
| Note: | p<0.1; p<0.05; p<0.01 | |||||||
4 Pairwise correlation table
The two tables below give the pairwise correlations between pretty much all the variables in the dataset. The first table gives the Spearman correlation (i.e. the standard correlation) while the second table gives the Kendall correlation (this is a rank correlation measure which helps pick up relationships which aren’t linear).
cor(select(infra, -region, -code), use = "pairwise.complete.obs", method = "spearman")## m21_bens m21_spending m21_precovid total_fiscal gdpg_2020e
## m21_bens 1.0000000 0.48400447 0.37839406 0.35158303 -0.38781792
## m21_spending 0.4840045 1.00000000 0.18477734 0.21884488 -0.42955982
## m21_precovid 0.3783941 0.18477734 1.00000000 0.23234981 -0.13786249
## total_fiscal 0.3515830 0.21884488 0.23234981 1.00000000 -0.45089073
## gdpg_2020e -0.3878179 -0.42955982 -0.13786249 -0.45089073 1.00000000
## gdp_per_capita 0.4903688 0.38898997 0.40176755 0.29228760 -0.47406596
## tax_to_gdp 0.1835457 0.15103800 0.14173510 0.32556017 -0.42049132
## new_id 0.0888584 -0.01926129 0.01816643 0.07284908 0.04218961
## soc_ins 0.4967546 0.26039433 0.29594943 0.22683387 -0.42669152
## soc_reg_coverage 0.2359823 0.18343324 0.30429551 0.15944631 -0.26954940
## i_branches_A1_pop 0.5654868 0.29351549 0.27879177 0.44410414 -0.48185318
## i_ATMs_pop 0.5450257 0.32332177 0.37699709 0.49741834 -0.50160063
## has_account 0.3118875 0.18609274 0.40309054 0.35650342 -0.29981485
## april_ld_severity 0.1596843 0.12476677 0.19483817 0.07809769 -0.32999893
## deaths 0.4639797 0.30146996 0.15933712 0.34848690 -0.54590949
## gov_effect 0.4015805 0.31844175 0.49250185 0.39418239 -0.30818364
## tsa 0.2118039 0.05595771 0.25997172 0.13681255 -0.10969969
## unpan_egov -0.5096790 -0.33965979 -0.47167271 -0.41708883 0.44668417
## gdp_per_capita tax_to_gdp new_id soc_ins
## m21_bens 0.4903688 0.1835457 0.08885840 0.4967546
## m21_spending 0.3889900 0.1510380 -0.01926129 0.2603943
## m21_precovid 0.4017675 0.1417351 0.01816643 0.2959494
## total_fiscal 0.2922876 0.3255602 0.07284908 0.2268339
## gdpg_2020e -0.4740660 -0.4204913 0.04218961 -0.4266915
## gdp_per_capita 1.0000000 0.2855168 0.25425585 0.7757870
## tax_to_gdp 0.2855168 1.0000000 0.13186406 0.2305200
## new_id 0.2542558 0.1318641 1.00000000 0.2941031
## soc_ins 0.7757870 0.2305200 0.29410314 1.0000000
## soc_reg_coverage 0.2851356 0.2383625 0.09231259 0.2814438
## i_branches_A1_pop 0.7073767 0.3221326 0.20358017 0.5920626
## i_ATMs_pop 0.8779986 0.3655502 0.23523866 0.6850353
## has_account 0.6073479 0.3046401 0.35232140 0.5082626
## april_ld_severity 0.1631705 0.2521550 0.10582052 0.1396027
## deaths 0.5396801 0.2927565 0.06282861 0.4249956
## gov_effect 0.7277945 0.3662727 0.32607632 0.6424739
## tsa 0.3632331 0.2127751 0.19157900 0.3298830
## unpan_egov -0.8764762 -0.2935653 -0.36326772 -0.7900440
## soc_reg_coverage i_branches_A1_pop i_ATMs_pop has_account
## m21_bens 0.23598227 0.5654868 0.54502573 0.311887516
## m21_spending 0.18343324 0.2935155 0.32332177 0.186092736
## m21_precovid 0.30429551 0.2787918 0.37699709 0.403090535
## total_fiscal 0.15944631 0.4441041 0.49741834 0.356503423
## gdpg_2020e -0.26954940 -0.4818532 -0.50160063 -0.299814848
## gdp_per_capita 0.28513555 0.7073767 0.87799859 0.607347876
## tax_to_gdp 0.23836253 0.3221326 0.36555024 0.304640117
## new_id 0.09231259 0.2035802 0.23523866 0.352321400
## soc_ins 0.28144382 0.5920626 0.68503531 0.508262628
## soc_reg_coverage 1.00000000 0.2128371 0.25762741 0.033949897
## i_branches_A1_pop 0.21283710 1.0000000 0.78714956 0.527062374
## i_ATMs_pop 0.25762741 0.7871496 1.00000000 0.647652582
## has_account 0.03394990 0.5270624 0.64765258 1.000000000
## april_ld_severity 0.11570864 0.1550180 0.06979175 -0.009244329
## deaths 0.50311488 0.5957053 0.51857337 0.273449392
## gov_effect 0.25570082 0.5702986 0.72626009 0.581154858
## tsa 0.10619978 0.4399063 0.42677930 0.258795021
## unpan_egov -0.30160156 -0.6443812 -0.84708393 -0.702388242
## april_ld_severity deaths gov_effect tsa
## m21_bens 0.159684310 0.46397974 0.4015805 0.21180392
## m21_spending 0.124766767 0.30146996 0.3184417 0.05595771
## m21_precovid 0.194838169 0.15933712 0.4925018 0.25997172
## total_fiscal 0.078097693 0.34848690 0.3941824 0.13681255
## gdpg_2020e -0.329998931 -0.54590949 -0.3081836 -0.10969969
## gdp_per_capita 0.163170469 0.53968012 0.7277945 0.36323315
## tax_to_gdp 0.252154976 0.29275654 0.3662727 0.21277508
## new_id 0.105820520 0.06282861 0.3260763 0.19157900
## soc_ins 0.139602749 0.42499559 0.6424739 0.32988296
## soc_reg_coverage 0.115708641 0.50311488 0.2557008 0.10619978
## i_branches_A1_pop 0.155018021 0.59570529 0.5702986 0.43990635
## i_ATMs_pop 0.069791747 0.51857337 0.7262601 0.42677930
## has_account -0.009244329 0.27344939 0.5811549 0.25879502
## april_ld_severity 1.000000000 0.26629438 0.1182923 0.14442207
## deaths 0.266294378 1.00000000 0.3190892 0.31824038
## gov_effect 0.118292296 0.31908924 1.0000000 0.38839617
## tsa 0.144422065 0.31824038 0.3883962 1.00000000
## unpan_egov -0.220734016 -0.50373014 -0.7768344 -0.45845897
## unpan_egov
## m21_bens -0.5096790
## m21_spending -0.3396598
## m21_precovid -0.4716727
## total_fiscal -0.4170888
## gdpg_2020e 0.4466842
## gdp_per_capita -0.8764762
## tax_to_gdp -0.2935653
## new_id -0.3632677
## soc_ins -0.7900440
## soc_reg_coverage -0.3016016
## i_branches_A1_pop -0.6443812
## i_ATMs_pop -0.8470839
## has_account -0.7023882
## april_ld_severity -0.2207340
## deaths -0.5037301
## gov_effect -0.7768344
## tsa -0.4584590
## unpan_egov 1.0000000cor(select(infra, -region, -code), use = "pairwise.complete.obs", method = "kendall")## m21_bens m21_spending m21_precovid total_fiscal
## m21_bens 1.00000000 0.37690187 0.27563296 0.25567798
## m21_spending 0.37690187 1.00000000 0.13623961 0.14824922
## m21_precovid 0.27563296 0.13623961 1.00000000 0.16542557
## total_fiscal 0.25567798 0.14824922 0.16542557 1.00000000
## gdpg_2020e -0.23578814 -0.28142951 -0.07089975 -0.31413434
## gdp_per_capita 0.34644751 0.27812524 0.28509717 0.19818294
## tax_to_gdp 0.12673339 0.10169452 0.10015468 0.22881020
## new_id 0.07084136 -0.01734593 0.01179604 0.05510541
## soc_ins 0.33862963 0.17161838 0.18504541 0.16680890
## soc_reg_coverage 0.17354091 0.14014527 0.25220309 0.10983846
## i_branches_A1_pop 0.38847248 0.20162902 0.21792423 0.31103509
## i_ATMs_pop 0.37847748 0.22357503 0.27434474 0.35523042
## has_account 0.21091763 0.12345435 0.30109163 0.23965732
## april_ld_severity 0.11356608 0.08686278 0.13955277 0.06576773
## deaths 0.32660619 0.21171689 0.11272549 0.25895399
## gov_effect 0.27053684 0.21108928 0.36272905 0.28171608
## tsa 0.16117761 0.04878113 0.21581251 0.09767467
## unpan_egov -0.35351945 -0.22326865 -0.34481695 -0.29330208
## gdpg_2020e gdp_per_capita tax_to_gdp new_id soc_ins
## m21_bens -0.23578814 0.3464475 0.1267334 0.07084136 0.33862963
## m21_spending -0.28142951 0.2781252 0.1016945 -0.01734593 0.17161838
## m21_precovid -0.07089975 0.2850972 0.1001547 0.01179604 0.18504541
## total_fiscal -0.31413434 0.1981829 0.2288102 0.05510541 0.16680890
## gdpg_2020e 1.00000000 -0.3263570 -0.2633070 0.03399556 -0.28745773
## gdp_per_capita -0.32635700 1.0000000 0.1830986 0.20656944 0.57362358
## tax_to_gdp -0.26330699 0.1830986 1.0000000 0.11549917 0.15132722
## new_id 0.03399556 0.2065694 0.1154992 1.00000000 0.24069784
## soc_ins -0.28745773 0.5736236 0.1513272 0.24069784 1.00000000
## soc_reg_coverage -0.20032233 0.2130989 0.1666997 0.07898350 0.19383867
## i_branches_A1_pop -0.31763654 0.5078247 0.2140351 0.15633956 0.40138925
## i_ATMs_pop -0.32983867 0.6917058 0.2652632 0.18809603 0.48474325
## has_account -0.19357724 0.4331002 0.2020460 0.29069387 0.35851474
## april_ld_severity -0.24066881 0.1023074 0.1823335 0.08657152 0.08354919
## deaths -0.37585629 0.3977173 0.1935614 0.04906024 0.30301428
## gov_effect -0.19584200 0.5267762 0.2586277 0.27305804 0.45636647
## tsa -0.08772976 0.2695070 0.1679199 0.17166474 0.23430529
## unpan_egov 0.29502138 -0.6853002 -0.2032581 -0.30046509 -0.59001282
## soc_reg_coverage i_branches_A1_pop i_ATMs_pop has_account
## m21_bens 0.17354091 0.3884725 0.37847748 0.21091763
## m21_spending 0.14014527 0.2016290 0.22357503 0.12345435
## m21_precovid 0.25220309 0.2179242 0.27434474 0.30109163
## total_fiscal 0.10983846 0.3110351 0.35523042 0.23965732
## gdpg_2020e -0.20032233 -0.3176365 -0.32983867 -0.19357724
## gdp_per_capita 0.21309891 0.5078247 0.69170579 0.43310023
## tax_to_gdp 0.16669973 0.2140351 0.26526316 0.20204604
## new_id 0.07898350 0.1563396 0.18809603 0.29069387
## soc_ins 0.19383867 0.4013892 0.48474325 0.35851474
## soc_reg_coverage 1.00000000 0.1419241 0.17868754 0.02868227
## i_branches_A1_pop 0.14192407 1.0000000 0.58175158 0.36740443
## i_ATMs_pop 0.17868754 0.5817516 1.00000000 0.46961771
## has_account 0.02868227 0.3674044 0.46961771 1.00000000
## april_ld_severity 0.08762050 0.1087326 0.02119365 -0.03748373
## deaths 0.36992781 0.4414003 0.42085236 0.18426501
## gov_effect 0.17796022 0.3853386 0.52618397 0.41402271
## tsa 0.07991116 0.3346238 0.32346963 0.18981710
## unpan_egov -0.21368285 -0.4479279 -0.65189189 -0.50641822
## april_ld_severity deaths gov_effect tsa
## m21_bens 0.11356608 0.32660619 0.27053684 0.16117761
## m21_spending 0.08686278 0.21171689 0.21108928 0.04878113
## m21_precovid 0.13955277 0.11272549 0.36272905 0.21581251
## total_fiscal 0.06576773 0.25895399 0.28171608 0.09767467
## gdpg_2020e -0.24066881 -0.37585629 -0.19584200 -0.08772976
## gdp_per_capita 0.10230742 0.39771730 0.52677623 0.26950704
## tax_to_gdp 0.18233353 0.19356137 0.25862773 0.16791989
## new_id 0.08657152 0.04906024 0.27305804 0.17166474
## soc_ins 0.08354919 0.30301428 0.45636647 0.23430529
## soc_reg_coverage 0.08762050 0.36992781 0.17796022 0.07991116
## i_branches_A1_pop 0.10873263 0.44140030 0.38533857 0.33462375
## i_ATMs_pop 0.02119365 0.42085236 0.52618397 0.32346963
## has_account -0.03748373 0.18426501 0.41402271 0.18981710
## april_ld_severity 1.00000000 0.19304104 0.07736307 0.11856298
## deaths 0.19304104 1.00000000 0.22152571 0.22542472
## gov_effect 0.07736307 0.22152571 1.00000000 0.28991194
## tsa 0.11856298 0.22542472 0.28991194 1.00000000
## unpan_egov -0.12883364 -0.38106416 -0.57876359 -0.34258228
## unpan_egov
## m21_bens -0.3535194
## m21_spending -0.2232686
## m21_precovid -0.3448170
## total_fiscal -0.2933021
## gdpg_2020e 0.2950214
## gdp_per_capita -0.6853002
## tax_to_gdp -0.2032581
## new_id -0.3004651
## soc_ins -0.5900128
## soc_reg_coverage -0.2136828
## i_branches_A1_pop -0.4479279
## i_ATMs_pop -0.6518919
## has_account -0.5064182
## april_ld_severity -0.1288336
## deaths -0.3810642
## gov_effect -0.5787636
## tsa -0.3425823
## unpan_egov 1.00000005 Selected bivariate scatter plots
It’s a bit hard to grasp the relationship between two variables from correlation coefficients alone which is why I have graphed bivariate scatterplots for a couple of pairs of variables.
library(rlang)##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## %@%, as_function, flatten, flatten_chr, flatten_dbl, flatten_int,
## flatten_lgl, flatten_raw, invoke, list_along, modify, prepend,
## splicecreate_scatter <- function(x, y) {
p <- ggplot(infra, aes({{x}}, {{y}}, colour = .data[["region"]], label = .data[["code"]])) + geom_point()+ geom_text_repel()
}
# Gdpg_2020e vs total_fiscal
print(create_scatter(gdpg_2020e, total_fiscal))## Warning: Removed 13 rows containing missing values (geom_point).## Warning: Removed 13 rows containing missing values (geom_text_repel).## Warning: ggrepel: 9 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Gdpg_2020e vs M21_bens
print(create_scatter(gdpg_2020e, m21_bens))## Warning: Removed 6 rows containing missing values (geom_point).## Warning: Removed 6 rows containing missing values (geom_text_repel).## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Gdpg_2020e vs M21_spending
print(create_scatter(gdpg_2020e, m21_spending))## Warning: Removed 6 rows containing missing values (geom_point).## Warning: Removed 6 rows containing missing values (geom_text_repel).## Warning: ggrepel: 64 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# M21_spending vs total_fiscal
print(create_scatter(m21_spending, total_fiscal))## Warning: Removed 8 rows containing missing values (geom_point).## Warning: Removed 8 rows containing missing values (geom_text_repel).## Warning: ggrepel: 55 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# Gdpg_2020e vs deaths
print(create_scatter(gdpg_2020e, deaths))## Warning: Removed 9 rows containing missing values (geom_point).## Warning: Removed 9 rows containing missing values (geom_text_repel).## Warning: ggrepel: 31 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Some more scatterplots.
ggplot(infra, aes(m21_precovid, gdp_per_capita, colour = region, label = code)) + geom_point()+ geom_text_repel() + labs(title = "Pre-covid vs GDP per capita")## Warning: Removed 7 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_text_repel).## Warning: ggrepel: 26 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ggplot(infra, aes(m21_bens, m21_precovid, colour = region, label = code)) + geom_point()+ geom_text_repel() + labs(title = "Post covid bens vs pre-covid bens")## Warning: ggrepel: 29 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ggplot(infra, aes(m21_bens, i_ATMs_pop, colour = region, label = code)) + geom_point()+ geom_text_repel() + labs(title = "Post covid bens vs ATM desnsity")## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_text_repel).## Warning: ggrepel: 16 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps