library(pepe)This package was set for the data visualization. First thing let’s
see the str of the sample_data with
str(sample_data).
Plot.by.Factr function will create plotting.
df <- sample_data[c("Formal","Informal","L.Both","No.Loan",
"sex","educ","political.afl","married",
"havejob","rural","age","Income","Networth","Liquid.Assets",
"NW.HE","fin.knowldge","fin.intermdiaries")]
CN = colnames(df)
var <- c("educ","rural","sex","havejob","political.afl")
name.levels = c("Formal","Informal","L.Both","No.Loan",
"sex","educ","political.afl","married",
"havejob","rural","age","Income","Networth","Liquid.Assets",
"NW.HE","fin.knowldge","fin.intermdiaries")
XXX <- df4.Plot.by.Factr(var,df)$Summ.Stats.long
Plot.by.Factr(XXX, name.levels)
#> Selecting by Mean
#> Joining, by = c("Variable", "Mean")
#> Warning: Transformation introduced infinite values in continuous x-axis
#> Transformation introduced infinite values in continuous x-axis
#> Selecting by Mean
#> Joining, by = c("Variable", "Mean")
#> Warning: Transformation introduced infinite values in continuous x-axis
#> Transformation introduced infinite values in continuous x-axis
#> Selecting by Mean
#> Joining, by = c("Variable", "Mean")
#> Warning: Transformation introduced infinite values in continuous x-axis
#> Transformation introduced infinite values in continuous x-axis
#> Selecting by Mean
#> Joining, by = c("Variable", "Mean")
#> Warning: Transformation introduced infinite values in continuous x-axis
#> Transformation introduced infinite values in continuous x-axis
#> Selecting by Mean
#> Joining, by = c("Variable", "Mean")
#> Warning: Transformation introduced infinite values in continuous x-axis
#> Transformation introduced infinite values in continuous x-axis
df4.Plot.by.Factr function will create group stats.
df4.Plot.by.Factr(var,df)
#> $Summ.Stats
#> $Summ.Stats[[1]]
#> educ_0 educ_1 educ_diff
#> age 56.233 48.944 7.289
#> Income 50112.134 111281.618 61169.485
#> Networth 498209.669 1270342.194 772132.524
#> Liquid.Assets 542379.811 1343952.158 801572.347
#> NW.HE 482692.708 1187307.896 704615.189
#> Formal 0.059 0.238 0.179
#> Informal 0.172 0.071 0.101
#> L.Both 0.041 0.062 0.020
#> No.Loan 0.727 0.629 0.098
#> sex 0.778 0.730 0.049
#> educ 0.000 1.000 1.000
#> political.afl 0.122 0.341 0.219
#> married 0.859 0.861 0.002
#> havejob 0.627 0.671 0.044
#> rural 0.562 0.879 0.317
#> fin.knowldge 0.019 0.129 0.110
#> fin.intermdiaries 0.179 0.196 0.017
#>
#> $Summ.Stats[[2]]
#> rural_0 rural_1 rural_diff
#> age 55.830 52.914 2.917
#> Income 41979.507 83801.586 41822.079
#> Networth 283621.530 980214.349 696592.819
#> Liquid.Assets 320888.314 1042114.177 721225.863
#> NW.HE 274315.470 928913.998 654598.528
#> Formal 0.047 0.152 0.104
#> Informal 0.216 0.101 0.114
#> L.Both 0.049 0.047 0.002
#> No.Loan 0.688 0.700 0.012
#> sex 0.878 0.704 0.174
#> educ 0.116 0.425 0.309
#> political.afl 0.125 0.226 0.100
#> married 0.886 0.847 0.039
#> havejob 0.773 0.574 0.198
#> rural 0.000 1.000 1.000
#> fin.knowldge 0.017 0.074 0.057
#> fin.intermdiaries 0.195 0.180 0.015
#>
#> $Summ.Stats[[3]]
#> sex_0 sex_1 sex_diff
#> age 54.226 53.792 0.434
#> Income 69848.240 69695.249 152.991
#> Networth 856991.073 711293.342 145697.731
#> Liquid.Assets 913497.514 764005.787 149491.727
#> NW.HE 813350.902 676132.915 137217.987
#> Formal 0.138 0.110 0.028
#> Informal 0.111 0.149 0.038
#> L.Both 0.043 0.049 0.007
#> No.Loan 0.709 0.692 0.017
#> sex 0.000 1.000 1.000
#> educ 0.366 0.307 0.059
#> political.afl 0.159 0.202 0.043
#> married 0.691 0.913 0.222
#> havejob 0.438 0.704 0.266
#> rural 0.828 0.613 0.215
#> fin.knowldge 0.067 0.050 0.017
#> fin.intermdiaries 0.176 0.187 0.011
#>
#> $Summ.Stats[[4]]
#> havejob_0 havejob_1 havejob_diff
#> age 63.576 48.475 15.101
#> Income 56781.006 76982.126 20201.120
#> Networth 757974.392 739081.250 18893.142
#> Liquid.Assets 805614.836 796037.507 9577.329
#> NW.HE 742160.748 689950.830 52209.918
#> Formal 0.058 0.149 0.092
#> Informal 0.114 0.154 0.040
#> L.Both 0.024 0.062 0.038
#> No.Loan 0.804 0.635 0.169
#> sex 0.628 0.838 0.210
#> educ 0.294 0.336 0.041
#> political.afl 0.219 0.177 0.042
#> married 0.784 0.903 0.119
#> havejob 0.000 1.000 1.000
#> rural 0.787 0.595 0.192
#> fin.knowldge 0.046 0.059 0.013
#> fin.intermdiaries 0.195 0.179 0.017
#>
#> $Summ.Stats[[5]]
#> political.afl_0 political.afl_1 political.afl_diff
#> age 53.461 55.724 2.263
#> Income 64184.651 93097.169 28912.518
#> Networth 661085.850 1102973.001 441887.150
#> Liquid.Assets 711676.724 1169314.401 457637.677
#> NW.HE 630009.072 1040123.664 410114.592
#> Formal 0.101 0.182 0.081
#> Informal 0.154 0.081 0.073
#> L.Both 0.047 0.051 0.004
#> No.Loan 0.698 0.686 0.013
#> sex 0.753 0.803 0.050
#> educ 0.262 0.569 0.308
#> political.afl 0.000 1.000 1.000
#> married 0.852 0.894 0.042
#> havejob 0.653 0.591 0.063
#> rural 0.636 0.780 0.145
#> fin.knowldge 0.040 0.116 0.076
#> fin.intermdiaries 0.188 0.171 0.017
#>
#>
#> $Summ.Stats.long
#> $Summ.Stats.long[[1]]
#> Diff Levels Mean Variable
#> 1 7.289 educ_0 56.233 age
#> 2 61169.485 educ_0 50112.134 Income
#> 3 772132.524 educ_0 498209.669 Networth
#> 4 801572.347 educ_0 542379.811 Liquid.Assets
#> 5 704615.189 educ_0 482692.708 NW.HE
#> 6 0.179 educ_0 0.059 Formal
#> 7 0.101 educ_0 0.172 Informal
#> 8 0.020 educ_0 0.041 L.Both
#> 9 0.098 educ_0 0.727 No.Loan
#> 10 0.049 educ_0 0.778 sex
#> 11 1.000 educ_0 0.000 educ
#> 12 0.219 educ_0 0.122 political.afl
#> 13 0.002 educ_0 0.859 married
#> 14 0.044 educ_0 0.627 havejob
#> 15 0.317 educ_0 0.562 rural
#> 16 0.110 educ_0 0.019 fin.knowldge
#> 17 0.017 educ_0 0.179 fin.intermdiaries
#> 18 7.289 educ_1 48.944 age
#> 19 61169.485 educ_1 111281.618 Income
#> 20 772132.524 educ_1 1270342.194 Networth
#> 21 801572.347 educ_1 1343952.158 Liquid.Assets
#> 22 704615.189 educ_1 1187307.896 NW.HE
#> 23 0.179 educ_1 0.238 Formal
#> 24 0.101 educ_1 0.071 Informal
#> 25 0.020 educ_1 0.062 L.Both
#> 26 0.098 educ_1 0.629 No.Loan
#> 27 0.049 educ_1 0.730 sex
#> 28 1.000 educ_1 1.000 educ
#> 29 0.219 educ_1 0.341 political.afl
#> 30 0.002 educ_1 0.861 married
#> 31 0.044 educ_1 0.671 havejob
#> 32 0.317 educ_1 0.879 rural
#> 33 0.110 educ_1 0.129 fin.knowldge
#> 34 0.017 educ_1 0.196 fin.intermdiaries
#>
#> $Summ.Stats.long[[2]]
#> Diff Levels Mean Variable
#> 1 2.917 rural_0 55.830 age
#> 2 41822.079 rural_0 41979.507 Income
#> 3 696592.819 rural_0 283621.530 Networth
#> 4 721225.863 rural_0 320888.314 Liquid.Assets
#> 5 654598.528 rural_0 274315.470 NW.HE
#> 6 0.104 rural_0 0.047 Formal
#> 7 0.114 rural_0 0.216 Informal
#> 8 0.002 rural_0 0.049 L.Both
#> 9 0.012 rural_0 0.688 No.Loan
#> 10 0.174 rural_0 0.878 sex
#> 11 0.309 rural_0 0.116 educ
#> 12 0.100 rural_0 0.125 political.afl
#> 13 0.039 rural_0 0.886 married
#> 14 0.198 rural_0 0.773 havejob
#> 15 1.000 rural_0 0.000 rural
#> 16 0.057 rural_0 0.017 fin.knowldge
#> 17 0.015 rural_0 0.195 fin.intermdiaries
#> 18 2.917 rural_1 52.914 age
#> 19 41822.079 rural_1 83801.586 Income
#> 20 696592.819 rural_1 980214.349 Networth
#> 21 721225.863 rural_1 1042114.177 Liquid.Assets
#> 22 654598.528 rural_1 928913.998 NW.HE
#> 23 0.104 rural_1 0.152 Formal
#> 24 0.114 rural_1 0.101 Informal
#> 25 0.002 rural_1 0.047 L.Both
#> 26 0.012 rural_1 0.700 No.Loan
#> 27 0.174 rural_1 0.704 sex
#> 28 0.309 rural_1 0.425 educ
#> 29 0.100 rural_1 0.226 political.afl
#> 30 0.039 rural_1 0.847 married
#> 31 0.198 rural_1 0.574 havejob
#> 32 1.000 rural_1 1.000 rural
#> 33 0.057 rural_1 0.074 fin.knowldge
#> 34 0.015 rural_1 0.180 fin.intermdiaries
#>
#> $Summ.Stats.long[[3]]
#> Diff Levels Mean Variable
#> 1 0.434 sex_0 54.226 age
#> 2 152.991 sex_0 69848.240 Income
#> 3 145697.731 sex_0 856991.073 Networth
#> 4 149491.727 sex_0 913497.514 Liquid.Assets
#> 5 137217.987 sex_0 813350.902 NW.HE
#> 6 0.028 sex_0 0.138 Formal
#> 7 0.038 sex_0 0.111 Informal
#> 8 0.007 sex_0 0.043 L.Both
#> 9 0.017 sex_0 0.709 No.Loan
#> 10 1.000 sex_0 0.000 sex
#> 11 0.059 sex_0 0.366 educ
#> 12 0.043 sex_0 0.159 political.afl
#> 13 0.222 sex_0 0.691 married
#> 14 0.266 sex_0 0.438 havejob
#> 15 0.215 sex_0 0.828 rural
#> 16 0.017 sex_0 0.067 fin.knowldge
#> 17 0.011 sex_0 0.176 fin.intermdiaries
#> 18 0.434 sex_1 53.792 age
#> 19 152.991 sex_1 69695.249 Income
#> 20 145697.731 sex_1 711293.342 Networth
#> 21 149491.727 sex_1 764005.787 Liquid.Assets
#> 22 137217.987 sex_1 676132.915 NW.HE
#> 23 0.028 sex_1 0.110 Formal
#> 24 0.038 sex_1 0.149 Informal
#> 25 0.007 sex_1 0.049 L.Both
#> 26 0.017 sex_1 0.692 No.Loan
#> 27 1.000 sex_1 1.000 sex
#> 28 0.059 sex_1 0.307 educ
#> 29 0.043 sex_1 0.202 political.afl
#> 30 0.222 sex_1 0.913 married
#> 31 0.266 sex_1 0.704 havejob
#> 32 0.215 sex_1 0.613 rural
#> 33 0.017 sex_1 0.050 fin.knowldge
#> 34 0.011 sex_1 0.187 fin.intermdiaries
#>
#> $Summ.Stats.long[[4]]
#> Diff Levels Mean Variable
#> 1 15.101 havejob_0 63.576 age
#> 2 20201.120 havejob_0 56781.006 Income
#> 3 18893.142 havejob_0 757974.392 Networth
#> 4 9577.329 havejob_0 805614.836 Liquid.Assets
#> 5 52209.918 havejob_0 742160.748 NW.HE
#> 6 0.092 havejob_0 0.058 Formal
#> 7 0.040 havejob_0 0.114 Informal
#> 8 0.038 havejob_0 0.024 L.Both
#> 9 0.169 havejob_0 0.804 No.Loan
#> 10 0.210 havejob_0 0.628 sex
#> 11 0.041 havejob_0 0.294 educ
#> 12 0.042 havejob_0 0.219 political.afl
#> 13 0.119 havejob_0 0.784 married
#> 14 1.000 havejob_0 0.000 havejob
#> 15 0.192 havejob_0 0.787 rural
#> 16 0.013 havejob_0 0.046 fin.knowldge
#> 17 0.017 havejob_0 0.195 fin.intermdiaries
#> 18 15.101 havejob_1 48.475 age
#> 19 20201.120 havejob_1 76982.126 Income
#> 20 18893.142 havejob_1 739081.250 Networth
#> 21 9577.329 havejob_1 796037.507 Liquid.Assets
#> 22 52209.918 havejob_1 689950.830 NW.HE
#> 23 0.092 havejob_1 0.149 Formal
#> 24 0.040 havejob_1 0.154 Informal
#> 25 0.038 havejob_1 0.062 L.Both
#> 26 0.169 havejob_1 0.635 No.Loan
#> 27 0.210 havejob_1 0.838 sex
#> 28 0.041 havejob_1 0.336 educ
#> 29 0.042 havejob_1 0.177 political.afl
#> 30 0.119 havejob_1 0.903 married
#> 31 1.000 havejob_1 1.000 havejob
#> 32 0.192 havejob_1 0.595 rural
#> 33 0.013 havejob_1 0.059 fin.knowldge
#> 34 0.017 havejob_1 0.179 fin.intermdiaries
#>
#> $Summ.Stats.long[[5]]
#> Diff Levels Mean Variable
#> 1 2.263 political.afl_0 53.461 age
#> 2 28912.518 political.afl_0 64184.651 Income
#> 3 441887.150 political.afl_0 661085.850 Networth
#> 4 457637.677 political.afl_0 711676.724 Liquid.Assets
#> 5 410114.592 political.afl_0 630009.072 NW.HE
#> 6 0.081 political.afl_0 0.101 Formal
#> 7 0.073 political.afl_0 0.154 Informal
#> 8 0.004 political.afl_0 0.047 L.Both
#> 9 0.013 political.afl_0 0.698 No.Loan
#> 10 0.050 political.afl_0 0.753 sex
#> 11 0.308 political.afl_0 0.262 educ
#> 12 1.000 political.afl_0 0.000 political.afl
#> 13 0.042 political.afl_0 0.852 married
#> 14 0.063 political.afl_0 0.653 havejob
#> 15 0.145 political.afl_0 0.636 rural
#> 16 0.076 political.afl_0 0.040 fin.knowldge
#> 17 0.017 political.afl_0 0.188 fin.intermdiaries
#> 18 2.263 political.afl_1 55.724 age
#> 19 28912.518 political.afl_1 93097.169 Income
#> 20 441887.150 political.afl_1 1102973.001 Networth
#> 21 457637.677 political.afl_1 1169314.401 Liquid.Assets
#> 22 410114.592 political.afl_1 1040123.664 NW.HE
#> 23 0.081 political.afl_1 0.182 Formal
#> 24 0.073 political.afl_1 0.081 Informal
#> 25 0.004 political.afl_1 0.051 L.Both
#> 26 0.013 political.afl_1 0.686 No.Loan
#> 27 0.050 political.afl_1 0.803 sex
#> 28 0.308 political.afl_1 0.569 educ
#> 29 1.000 political.afl_1 1.000 political.afl
#> 30 0.042 political.afl_1 0.894 married
#> 31 0.063 political.afl_1 0.591 havejob
#> 32 0.145 political.afl_1 0.780 rural
#> 33 0.076 political.afl_1 0.116 fin.knowldge
#> 34 0.017 political.afl_1 0.171 fin.intermdiariesStats.by.Factr function will create group stats.
Stats.by.Factr(var, df)
#> $educ.0
#> mean sd n median min max
#> Formal* 1.06 0.24 22256 1.0 1.0 2
#> Informal* 1.17 0.38 22256 1.0 1.0 2
#> L.Both* 1.04 0.20 22256 1.0 1.0 2
#> No.Loan* 1.73 0.45 22256 2.0 1.0 2
#> sex* 1.78 0.42 22256 2.0 1.0 2
#> educ* 1.00 0.00 22256 1.0 1.0 1
#> political.afl* 1.12 0.33 22256 1.0 1.0 2
#> married* 1.86 0.35 22256 2.0 1.0 2
#> havejob* 1.63 0.48 22256 2.0 1.0 2
#> rural* 1.56 0.50 22256 2.0 1.0 2
#> age 56.23 13.40 22256 57.0 17.0 101
#> Income 50112.13 127502.75 22256 31681.5 -800000.0 5000000
#> Networth 498209.67 1187345.68 22256 193778.0 -627904.2 19999748
#> Liquid.Assets 542379.81 1206224.03 22256 229982.1 0.0 20000000
#> NW.HE 482692.71 1143011.42 22256 189322.6 -1490700.0 19999748
#> fin.knowldge* 1.02 0.14 22256 1.0 1.0 2
#> fin.intermdiaries* 1.18 0.38 22256 1.0 1.0 2
#> skew kurtosis
#> Formal* 3.74 11.97
#> Informal* 1.74 1.02
#> L.Both* 4.60 19.21
#> No.Loan* -1.02 -0.96
#> sex* -1.34 -0.20
#> educ* NaN NaN
#> political.afl* 2.32 3.36
#> married* -2.07 2.28
#> havejob* -0.53 -1.72
#> rural* -0.25 -1.94
#> age 0.00 -0.43
#> Income 23.96 819.95
#> Networth 9.07 117.71
#> Liquid.Assets 8.97 115.38
#> NW.HE 8.93 116.05
#> fin.knowldge* 6.98 46.77
#> fin.intermdiaries* 1.67 0.80
#>
#> $educ.1
#> mean sd n median min max
#> Formal* 1.24 0.43 10509 1.0 1.0 2
#> Informal* 1.07 0.26 10509 1.0 1.0 2
#> L.Both* 1.06 0.24 10509 1.0 1.0 2
#> No.Loan* 1.63 0.48 10509 2.0 1.0 2
#> sex* 1.73 0.44 10509 2.0 1.0 2
#> educ* 2.00 0.00 10509 2.0 2.0 2
#> political.afl* 1.34 0.47 10509 1.0 1.0 2
#> married* 1.86 0.35 10509 2.0 1.0 2
#> havejob* 1.67 0.47 10509 2.0 1.0 2
#> rural* 1.88 0.33 10509 2.0 1.0 2
#> age 48.94 14.82 10509 49.0 17.0 93
#> Income 111281.62 242540.61 10509 66840.0 -800000.0 5000000
#> Networth 1270342.19 2151333.30 10509 604400.0 -277925.9 19956044
#> Liquid.Assets 1343952.16 2190862.93 10509 669550.0 0.0 20000000
#> NW.HE 1187307.90 2040298.96 10509 558724.2 -3614776.0 19956044
#> fin.knowldge* 1.13 0.34 10509 1.0 1.0 2
#> fin.intermdiaries* 1.20 0.40 10509 1.0 1.0 2
#> skew kurtosis
#> Formal* 1.23 -0.49
#> Informal* 3.34 9.16
#> L.Both* 3.64 11.23
#> No.Loan* -0.53 -1.71
#> sex* -1.03 -0.93
#> educ* NaN NaN
#> political.afl* 0.67 -1.55
#> married* -2.09 2.37
#> havejob* -0.73 -1.47
#> rural* -2.32 3.39
#> age 0.27 -0.50
#> Income 11.91 197.81
#> Networth 4.65 28.89
#> Liquid.Assets 4.63 28.72
#> NW.HE 4.71 30.01
#> fin.knowldge* 2.21 2.90
#> fin.intermdiaries* 1.53 0.34
#>
#> $rural.0
#> mean sd n median min max skew
#> Formal* 1.05 0.21 11023 1.0 1.0 2 4.27
#> Informal* 1.22 0.41 11023 1.0 1.0 2 1.38
#> L.Both* 1.05 0.22 11023 1.0 1.0 2 4.17
#> No.Loan* 1.69 0.46 11023 2.0 1.0 2 -0.81
#> sex* 1.88 0.33 11023 2.0 1.0 2 -2.31
#> educ* 1.12 0.32 11023 1.0 1.0 2 2.40
#> political.afl* 1.13 0.33 11023 1.0 1.0 2 2.26
#> married* 1.89 0.32 11023 2.0 1.0 2 -2.43
#> havejob* 1.77 0.42 11023 2.0 1.0 2 -1.30
#> rural* 1.00 0.00 11023 1.0 1.0 1 NaN
#> age 55.83 12.52 11023 55.0 17.0 99 0.05
#> Income 41979.51 113869.22 11023 23100.0 -800000.0 5000000 25.49
#> Networth 283621.53 765713.39 11023 117909.1 -315514.9 19842100 12.53
#> Liquid.Assets 320888.31 782991.65 11023 150426.4 0.0 20000000 12.32
#> NW.HE 274315.47 739872.21 11023 114235.3 -1136884.0 19842100 12.78
#> fin.knowldge* 1.02 0.13 11023 1.0 1.0 2 7.48
#> fin.intermdiaries* 1.19 0.40 11023 1.0 1.0 2 1.54
#> kurtosis
#> Formal* 16.20
#> Informal* -0.09
#> L.Both* 15.39
#> No.Loan* -1.34
#> sex* 3.36
#> educ* 3.78
#> political.afl* 3.13
#> married* 3.90
#> havejob* -0.31
#> rural* NaN
#> age -0.33
#> Income 1015.45
#> Networth 224.93
#> Liquid.Assets 218.09
#> NW.HE 237.81
#> fin.knowldge* 53.95
#> fin.intermdiaries* 0.38
#>
#> $rural.1
#> mean sd n median min max
#> Formal* 1.15 0.36 21742 1.0 1.0 2
#> Informal* 1.10 0.30 21742 1.0 1.0 2
#> L.Both* 1.05 0.21 21742 1.0 1.0 2
#> No.Loan* 1.70 0.46 21742 2.0 1.0 2
#> sex* 1.70 0.46 21742 2.0 1.0 2
#> educ* 1.42 0.49 21742 1.0 1.0 2
#> political.afl* 1.23 0.42 21742 1.0 1.0 2
#> married* 1.85 0.36 21742 2.0 1.0 2
#> havejob* 1.57 0.49 21742 2.0 1.0 2
#> rural* 2.00 0.00 21742 2.0 2.0 2
#> age 52.91 15.01 21742 52.0 17.0 101
#> Income 83801.59 197838.45 21742 51028.0 -800000.0 5000000
#> Networth 980214.35 1848058.17 21742 437612.4 -627904.2 19999748
#> Liquid.Assets 1042114.18 1880008.03 21742 494240.0 0.0 20000000
#> NW.HE 928914.00 1758036.37 21742 413127.2 -3614776.0 19999748
#> fin.knowldge* 1.07 0.26 21742 1.0 1.0 2
#> fin.intermdiaries* 1.18 0.38 21742 1.0 1.0 2
#> skew kurtosis
#> Formal* 1.94 1.77
#> Informal* 2.65 5.00
#> L.Both* 4.26 16.18
#> No.Loan* -0.87 -1.24
#> sex* -0.89 -1.20
#> educ* 0.30 -1.91
#> political.afl* 1.31 -0.28
#> married* -1.93 1.71
#> havejob* -0.30 -1.91
#> rural* NaN NaN
#> age 0.07 -0.62
#> Income 14.90 308.53
#> Networth 5.64 43.48
#> Liquid.Assets 5.61 43.02
#> NW.HE 5.66 44.32
#> fin.knowldge* 3.27 8.68
#> fin.intermdiaries* 1.67 0.79
#>
#> $sex.0
#> mean sd n median min max skew
#> Formal* 1.14 0.34 7774 1.0 1.0 2 2.10
#> Informal* 1.11 0.31 7774 1.0 1.0 2 2.48
#> L.Both* 1.04 0.20 7774 1.0 1.0 2 4.51
#> No.Loan* 1.71 0.45 7774 2.0 1.0 2 -0.92
#> sex* 1.00 0.00 7774 1.0 1.0 1 NaN
#> educ* 1.37 0.48 7774 1.0 1.0 2 0.56
#> political.afl* 1.16 0.37 7774 1.0 1.0 2 1.86
#> married* 1.69 0.46 7774 2.0 1.0 2 -0.83
#> havejob* 1.44 0.50 7774 1.0 1.0 2 0.25
#> rural* 1.83 0.38 7774 2.0 1.0 2 -1.73
#> age 54.23 15.82 7774 54.0 17.0 101 -0.02
#> Income 69848.24 162853.21 7774 41200.0 -497000.0 5000000 15.92
#> Networth 856991.07 1709612.37 7774 343647.5 -224187.3 19968200 5.79
#> Liquid.Assets 913497.51 1744638.87 7774 392846.6 0.0 20000000 5.78
#> NW.HE 813350.90 1616830.46 7774 323163.8 -1294996.0 19968200 5.69
#> fin.knowldge* 1.07 0.25 7774 1.0 1.0 2 3.45
#> fin.intermdiaries* 1.18 0.38 7774 1.0 1.0 2 1.70
#> kurtosis
#> Formal* 2.41
#> Informal* 4.16
#> L.Both* 18.31
#> No.Loan* -1.16
#> sex* NaN
#> educ* -1.69
#> political.afl* 1.47
#> married* -1.32
#> havejob* -1.94
#> rural* 1.00
#> age -0.71
#> Income 385.60
#> Networth 47.07
#> Liquid.Assets 46.88
#> NW.HE 46.02
#> fin.knowldge* 9.90
#> fin.intermdiaries* 0.90
#>
#> $sex.1
#> mean sd n median min max
#> Formal* 1.11 0.31 24991 1.0 1.0 2
#> Informal* 1.15 0.36 24991 1.0 1.0 2
#> L.Both* 1.05 0.22 24991 1.0 1.0 2
#> No.Loan* 1.69 0.46 24991 2.0 1.0 2
#> sex* 2.00 0.00 24991 2.0 2.0 2
#> educ* 1.31 0.46 24991 1.0 1.0 2
#> political.afl* 1.20 0.40 24991 1.0 1.0 2
#> married* 1.91 0.28 24991 2.0 1.0 2
#> havejob* 1.70 0.46 24991 2.0 1.0 2
#> rural* 1.61 0.49 24991 2.0 1.0 2
#> age 53.79 13.77 24991 53.0 17.0 98
#> Income 69695.25 178977.35 24991 41906.0 -800000.0 5000000
#> Networth 711293.34 1567726.43 24991 268824.5 -627904.2 19999748
#> Liquid.Assets 764005.79 1595467.69 24991 311500.0 0.0 20000000
#> NW.HE 676132.91 1496042.85 24991 256900.0 -3614776.0 19999748
#> fin.knowldge* 1.05 0.22 24991 1.0 1.0 2
#> fin.intermdiaries* 1.19 0.39 24991 1.0 1.0 2
#> skew kurtosis
#> Formal* 2.49 4.22
#> Informal* 1.97 1.90
#> L.Both* 4.15 15.25
#> No.Loan* -0.83 -1.31
#> sex* NaN NaN
#> educ* 0.84 -1.30
#> political.afl* 1.48 0.20
#> married* -2.92 6.54
#> havejob* -0.90 -1.20
#> rural* -0.46 -1.79
#> age 0.03 -0.45
#> Income 16.84 396.16
#> Networth 6.77 62.97
#> Liquid.Assets 6.71 61.89
#> NW.HE 6.85 65.14
#> fin.knowldge* 4.11 14.85
#> fin.intermdiaries* 1.60 0.57
#>
#> $havejob.0
#> mean sd n median min max
#> Formal* 1.06 0.23 11760 1.0 1.0 2
#> Informal* 1.11 0.32 11760 1.0 1.0 2
#> L.Both* 1.02 0.15 11760 1.0 1.0 2
#> No.Loan* 1.80 0.40 11760 2.0 1.0 2
#> sex* 1.63 0.48 11760 2.0 1.0 2
#> educ* 1.29 0.46 11760 1.0 1.0 2
#> political.afl* 1.22 0.41 11760 1.0 1.0 2
#> married* 1.78 0.41 11760 2.0 1.0 2
#> havejob* 1.00 0.00 11760 1.0 1.0 1
#> rural* 1.79 0.41 11760 2.0 1.0 2
#> age 63.58 13.10 11760 65.0 17.0 101
#> Income 56781.01 155653.05 11760 36600.0 -800000.0 5000000
#> Networth 757974.39 1474245.30 11760 306132.7 -627904.2 19951804
#> Liquid.Assets 805614.84 1495360.94 11760 351125.2 0.0 20000000
#> NW.HE 742160.75 1432865.69 11760 300375.0 -1017962.0 19951804
#> fin.knowldge* 1.05 0.21 11760 1.0 1.0 2
#> fin.intermdiaries* 1.20 0.40 11760 1.0 1.0 2
#> skew kurtosis
#> Formal* 3.79 12.35
#> Informal* 2.43 3.88
#> L.Both* 6.28 37.47
#> No.Loan* -1.53 0.36
#> sex* -0.53 -1.72
#> educ* 0.90 -1.18
#> political.afl* 1.36 -0.15
#> married* -1.38 -0.10
#> havejob* NaN NaN
#> rural* -1.40 -0.04
#> age -0.67 0.63
#> Income 21.44 598.68
#> Networth 6.11 56.40
#> Liquid.Assets 6.10 56.30
#> NW.HE 5.91 52.93
#> fin.knowldge* 4.32 16.66
#> fin.intermdiaries* 1.54 0.36
#>
#> $havejob.1
#> mean sd n median min max
#> Formal* 1.15 0.36 21005 1.0 1.0 2
#> Informal* 1.15 0.36 21005 1.0 1.0 2
#> L.Both* 1.06 0.24 21005 1.0 1.0 2
#> No.Loan* 1.64 0.48 21005 2.0 1.0 2
#> sex* 1.84 0.37 21005 2.0 1.0 2
#> educ* 1.34 0.47 21005 1.0 1.0 2
#> political.afl* 1.18 0.38 21005 1.0 1.0 2
#> married* 1.90 0.30 21005 2.0 1.0 2
#> havejob* 2.00 0.00 21005 2.0 2.0 2
#> rural* 1.59 0.49 21005 2.0 1.0 2
#> age 48.48 11.84 21005 49.0 17.0 96
#> Income 76982.13 184976.46 21005 44543.0 -800000.0 5000000
#> Networth 739081.25 1671801.15 21005 271012.9 -464159.8 19999748
#> Liquid.Assets 796037.51 1705697.57 21005 316000.0 0.0 20000000
#> NW.HE 689950.83 1576458.39 21005 256354.0 -3614776.0 19999748
#> fin.knowldge* 1.06 0.24 21005 1.0 1.0 2
#> fin.intermdiaries* 1.18 0.38 21005 1.0 1.0 2
#> skew kurtosis
#> Formal* 1.97 1.87
#> Informal* 1.92 1.68
#> L.Both* 3.65 11.30
#> No.Loan* -0.56 -1.69
#> sex* -1.83 1.36
#> educ* 0.70 -1.52
#> political.afl* 1.69 0.87
#> married* -2.72 5.40
#> havejob* NaN NaN
#> rural* -0.39 -1.85
#> age 0.06 -0.34
#> Income 14.98 330.37
#> Networth 6.61 57.96
#> Liquid.Assets 6.53 56.79
#> NW.HE 6.77 61.61
#> fin.knowldge* 3.74 11.97
#> fin.intermdiaries* 1.68 0.82
#>
#> $political.afl.0
#> mean sd n median min max
#> Formal* 1.10 0.30 26479 1.0 1.0 2
#> Informal* 1.15 0.36 26479 1.0 1.0 2
#> L.Both* 1.05 0.21 26479 1.0 1.0 2
#> No.Loan* 1.70 0.46 26479 2.0 1.0 2
#> sex* 1.75 0.43 26479 2.0 1.0 2
#> educ* 1.26 0.44 26479 1.0 1.0 2
#> political.afl* 1.00 0.00 26479 1.0 1.0 1
#> married* 1.85 0.36 26479 2.0 1.0 2
#> havejob* 1.65 0.48 26479 2.0 1.0 2
#> rural* 1.64 0.48 26479 2.0 1.0 2
#> age 53.46 14.07 26479 53.0 17.0 101
#> Income 64184.65 171285.19 26479 37550.0 -800000.0 5000000
#> Networth 661085.85 1499929.43 26479 246738.6 -627904.2 19999748
#> Liquid.Assets 711676.72 1526257.28 26479 289848.1 0.0 20000000
#> NW.HE 630009.07 1427490.00 26479 236061.6 -2814200.0 19999748
#> fin.knowldge* 1.04 0.20 26479 1.0 1.0 2
#> fin.intermdiaries* 1.19 0.39 26479 1.0 1.0 2
#> skew kurtosis
#> Formal* 2.65 5.02
#> Informal* 1.92 1.69
#> L.Both* 4.27 16.25
#> No.Loan* -0.86 -1.25
#> sex* -1.17 -0.62
#> educ* 1.08 -0.82
#> political.afl* NaN NaN
#> married* -1.98 1.93
#> havejob* -0.64 -1.59
#> rural* -0.56 -1.68
#> age 0.02 -0.46
#> Income 17.88 441.53
#> Networth 7.20 71.35
#> Liquid.Assets 7.12 70.04
#> NW.HE 7.20 72.41
#> fin.knowldge* 4.70 20.11
#> fin.intermdiaries* 1.60 0.55
#>
#> $political.afl.1
#> mean sd n median min max
#> Formal* 1.18 0.39 6286 1.0 1.0 2
#> Informal* 1.08 0.27 6286 1.0 1.0 2
#> L.Both* 1.05 0.22 6286 1.0 1.0 2
#> No.Loan* 1.69 0.46 6286 2.0 1.0 2
#> sex* 1.80 0.40 6286 2.0 1.0 2
#> educ* 1.57 0.50 6286 2.0 1.0 2
#> political.afl* 2.00 0.00 6286 2.0 2.0 2
#> married* 1.89 0.31 6286 2.0 1.0 2
#> havejob* 1.59 0.49 6286 2.0 1.0 2
#> rural* 1.78 0.41 6286 2.0 1.0 2
#> age 55.72 15.01 6286 56.0 17.0 98
#> Income 93097.17 189449.94 6286 61000.0 -800000.0 5000000
#> Networth 1102973.00 1941971.68 6286 497431.8 -329697.9 19918815
#> Liquid.Assets 1169314.40 1980845.88 6286 554688.7 0.0 20000000
#> NW.HE 1040123.66 1851839.13 6286 469564.3 -3614776.0 19918815
#> fin.knowldge* 1.12 0.32 6286 1.0 1.0 2
#> fin.intermdiaries* 1.17 0.38 6286 1.0 1.0 2
#> skew kurtosis
#> Formal* 1.65 0.71
#> Informal* 3.07 7.43
#> L.Both* 4.07 14.57
#> No.Loan* -0.80 -1.36
#> sex* -1.52 0.32
#> educ* -0.28 -1.92
#> political.afl* NaN NaN
#> married* -2.57 4.58
#> havejob* -0.37 -1.86
#> rural* -1.35 -0.16
#> age -0.02 -0.69
#> Income 13.23 272.15
#> Networth 4.89 32.87
#> Liquid.Assets 4.89 32.95
#> NW.HE 4.95 34.14
#> fin.knowldge* 2.40 3.74
#> fin.intermdiaries* 1.75 1.07Pvot.by.Factr function will create a percentage
tables.
df <- sample_data[c("multi.level",
"Formal","L.Both","No.Loan",
"region", "sex", "educ", "political.afl",
"married", "havejob", "rural",
"fin.knowldge", "fin.intermdiaries")]
Pvot.by.Factr(df)
#> 0 1 3 2
#> multi.level 69.59% 30.41% NA% NA%
#> Formal 88.35% 11.65% NA% NA%
#> L.Both 95.21% 4.79% NA% NA%
#> No.Loan 30.41% 69.59% NA% NA%
#> region NA% 48.26% 24.48% 27.26%
#> sex 23.73% 76.27% NA% NA%
#> educ 67.93% 32.07% NA% NA%
#> political.afl 80.81% 19.19% NA% NA%
#> married 13.99% 86.01% NA% NA%
#> havejob 35.89% 64.11% NA% NA%
#> rural 33.64% 66.36% NA% NA%
#> fin.knowldge 94.55% 5.45% NA% NA%
#> fin.intermdiaries 81.54% 18.46% NA% NA%