Objectives
This tutorial is meant to introduce pepe package only, using a case study. The purpose of this package is to present the tables and plots with ease.
Dataset
After cleaning the data set (sample_data) for this case
study, we can visualize the summary statistics of the given data.
Summary Statistics
Note: HR stands for Household Registration. NW-HE is net-worth minus home equity. All the asset variables (e.g. income, net-worth, NW-HE, and liquid assets are in Chinese renminbi (CNY).
Overview
Package pepe runs Plot.by.Factr,
df4.Plot.by.Factr, and Pvot.by.Factr.
This package is useful when you need to
do descriptive stats and plotting for different data splits.
Building plots
Plotting
Plot.by.Factr function will create plotting by two level
factor variables (var).
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 with `by = join_by(Variable, Mean)`
#> Warning in scale_x_log10(): log-10 transformation introduced infinite values.
#> log-10 transformation introduced infinite values.
#> Selecting by Mean
#> Joining with `by = join_by(Variable, Mean)`
#> Warning in scale_x_log10(): log-10 transformation introduced infinite values.
#> log-10 transformation introduced infinite values.
#> Selecting by Mean
#> Joining with `by = join_by(Variable, Mean)`
#> Warning in scale_x_log10(): log-10 transformation introduced infinite values.
#> log-10 transformation introduced infinite values.
#> Selecting by Mean
#> Joining with `by = join_by(Variable, Mean)`
#> Warning in scale_x_log10(): log-10 transformation introduced infinite values.
#> log-10 transformation introduced infinite values.
#> Selecting by Mean
#> Joining with `by = join_by(Variable, Mean)`
#> Warning in scale_x_log10(): log-10 transformation introduced infinite values.
#> log-10 transformation introduced infinite values.
Building tables
Summary statistics
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.intermdiariesPercantage table
Pvot.by.Factr function will create a percentage table of
the selected factor variables.
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%Have Fun!