library(PrjThaiHFID)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tidyr)
library(readr)
library(forcats)
library(ggplot2)
library(kableExtra)
#>
#> Attaching package: 'kableExtra'
#> The following object is masked from 'package:dplyr':
#>
#> group_rowsShared parameters
bl_save_figs <- FALSE
cbp1 <- c(
"#999999", "#E69F00", "#56B4E9", "#009E73",
"#F0E442", "#0072B2", "#D55E00", "#CC79A7"
)Categories and tabulations
# code to prepare `tstm_loans` dataset goes here
# 1000. Generate Loan IDs and MISC ----
# tstm_loans <- PrjThaiHFID::tstm_loans
# Unique ID for each loan
tstm_loans <- tstm_loans %>%
select(
S_region, provid_Num, vilid_Num, hhid_Num, surveymonth,
formal, institutional, G_LenderType,
everything()
) %>%
drop_na(G_Loan_Repaid_Length) %>%
arrange(hhid_Num, surveymonth, G_Loan_Repaid_Length) %>%
mutate(loan_id = row_number())
# Tally loan types and drop NA
tstm_loans %>%
group_by(G_LenderType) %>%
tally()
#> # A tibble: 10 × 2
#> G_LenderType n
#> <chr> <int>
#> 1 Agri Coop 577
#> 2 BAAC 3396
#> 3 Commercial Bank 64
#> 4 MoneyLender 625
#> 5 Neighbor 998
#> 6 Other Non-Indi Formal or Informal 4659
#> 7 Others 1349
#> 8 PCG 501
#> 9 Relatives 1363
#> 10 Village Fund 7408
tstm_loans %>%
group_by(G_LenderType, G_Location) %>%
tally() %>%
spread(G_Location, n)
#> # A tibble: 10 × 5
#> # Groups: G_LenderType [10]
#> G_LenderType `Changwat and Out` `Tambon or Amphoe` Village `<NA>`
#> <chr> <int> <int> <int> <int>
#> 1 Agri Coop 29 547 1 NA
#> 2 BAAC 757 2636 2 1
#> 3 Commercial Bank 17 46 1 NA
#> 4 MoneyLender 162 201 260 2
#> 5 Neighbor 3 20 975 NA
#> 6 Other Non-Indi Formal o… 773 1146 2732 8
#> 7 Others 555 506 272 16
#> 8 PCG 3 9 489 NA
#> 9 Relatives 220 433 705 5
#> 10 Village Fund 9 251 7148 NAReview formal, informal, and joint loan categories.
tstm_loans %>%
group_by(G_LenderType, formal) %>%
tally() %>%
spread(formal, n)
#> # A tibble: 10 × 3
#> # Groups: G_LenderType [10]
#> G_LenderType `FALSE` `TRUE`
#> <chr> <int> <int>
#> 1 Agri Coop 577 NA
#> 2 BAAC NA 3396
#> 3 Commercial Bank NA 64
#> 4 MoneyLender 625 NA
#> 5 Neighbor 998 NA
#> 6 Other Non-Indi Formal or Informal 2732 1927
#> 7 Others 1349 NA
#> 8 PCG 501 NA
#> 9 Relatives 1363 NA
#> 10 Village Fund NA 7408
tstm_loans %>%
group_by(institutional, G_LenderType, formal) %>%
tally() %>%
spread(formal, n)
#> # A tibble: 10 × 4
#> # Groups: institutional, G_LenderType [10]
#> institutional G_LenderType `FALSE` `TRUE`
#> <lgl> <chr> <int> <int>
#> 1 FALSE MoneyLender 625 NA
#> 2 FALSE Neighbor 998 NA
#> 3 FALSE Others 1349 NA
#> 4 FALSE Relatives 1363 NA
#> 5 TRUE Agri Coop 577 NA
#> 6 TRUE BAAC NA 3396
#> 7 TRUE Commercial Bank NA 64
#> 8 TRUE Other Non-Indi Formal or Informal 2732 1927
#> 9 TRUE PCG 501 NA
#> 10 TRUE Village Fund NA 7408
tstm_loans %>%
group_by(institutional, G_LenderType, formalqf) %>%
tally() %>%
spread(formalqf, n)
#> # A tibble: 10 × 4
#> # Groups: institutional, G_LenderType [10]
#> institutional G_LenderType `FALSE` `TRUE`
#> <lgl> <chr> <int> <int>
#> 1 FALSE MoneyLender 625 NA
#> 2 FALSE Neighbor 998 NA
#> 3 FALSE Others 1349 NA
#> 4 FALSE Relatives 1363 NA
#> 5 TRUE Agri Coop NA 577
#> 6 TRUE BAAC NA 3396
#> 7 TRUE Commercial Bank NA 64
#> 8 TRUE Other Non-Indi Formal or Informal 2732 1927
#> 9 TRUE PCG NA 501
#> 10 TRUE Village Fund NA 7408
tstm_loans %>%
group_by(institutional, G_LenderType, forinfm3) %>%
tally() %>%
spread(forinfm3, n)
#> # A tibble: 10 × 5
#> # Groups: institutional, G_LenderType [10]
#> institutional G_LenderType Formal Informal `Quasi-formal`
#> <lgl> <chr> <int> <int> <int>
#> 1 FALSE MoneyLender NA 625 NA
#> 2 FALSE Neighbor NA 998 NA
#> 3 FALSE Others NA 1349 NA
#> 4 FALSE Relatives NA 1363 NA
#> 5 TRUE Agri Coop NA NA 577
#> 6 TRUE BAAC 3396 NA NA
#> 7 TRUE Commercial Bank 64 NA NA
#> 8 TRUE Other Non-Indi Formal or Inform… NA NA 4659
#> 9 TRUE PCG NA NA 501
#> 10 TRUE Village Fund 7408 NA NALoan terms tables
Show in table across loan types loan terms percentiles. This implements PrjThaiHFID-#14.
# Choose latex for paper output
# st_kableformat <- "latex"
st_kableformat <- "html"
# Percentiles of interest
# ar_fl_percentiles <- seq(0.01, 0.99, length.out = 99)
# ar_fl_percentiles <- seq(0.01, 0.99, length.out = 50)
# ar_fl_percentiles <- seq(0.02, 0.98, length.out = 33)
# ar_fl_percentiles <- seq(0.05, 0.95, length.out = 19)
# ar_fl_percentiles <- c(0.1, 0.25, 0.50, 0.75, 0.9, 0.95, 0.99)
ar_fl_percentiles <- c(
0.05, 0.10, 0.20, 0.30, 0.40,
0.25, 0.50, 0.75,
0.60, 0.70, 0.80, 0.9, 0.95)
# ar_fl_percentiles <- seq(0.10, 0.90, length.out=9)Data prep part 1: rename and reshape
We loaded in tstm_loans already.
First select loanID, forinfm3, term, and do so separately for each term, separate files.
# Select files
tstm_loans_sel <- tstm_loans %>%
select(
loan_id, forinfm3,
G_Loan_Init_Length, S_Init_Amount, G_Loan_Init_IntMthlyRat
) %>%
rename(
id = loan_id,
loan_1length = G_Loan_Init_Length,
loan_2amount = S_Init_Amount,
loan_3interest = G_Loan_Init_IntMthlyRat
)
# rename formal, quasi, informal
ls_recode_forinfm3 <- c(
"Aformal" = "Formal",
"Bquasiformal" = "Quasi-formal",
"Cinformal" = "Informal"
)
tstm_loans_sel <- tstm_loans_sel %>%
mutate(forinfm3 = as.character(
fct_recode(forinfm3, !!!ls_recode_forinfm3)
))Second, reshape wide to long.
# Select files
tstm_loans_sel_long <- tstm_loans_sel %>%
pivot_longer(
cols = starts_with("loan"),
names_to = c("terms"),
names_pattern = paste0("loan_(.*)"),
values_to = "value"
)
# Drop NA
tstm_loans_sel_long <- tstm_loans_sel_long %>%
drop_na(value)Data prep part 2: percentiles
First, compute CDF.
# Generate within-group CDF
tstm_loans_sel_long_pct <- tstm_loans_sel_long %>%
arrange(terms, forinfm3, value) %>%
group_by(terms, forinfm3) %>%
mutate(cdf = row_number() / n())Second, compute percentiles and moments.
# Define strings
st_var_prefix <- "val"
st_var_prefix_perc <- paste0(st_var_prefix, "_p")
svr_mean <- paste0(st_var_prefix, "_mean")
svr_std <- paste0(st_var_prefix, "_std")
# Generate within-group percentiles
for (it_percentile_ctr in seq(1, length(ar_fl_percentiles))) {
# Current within group percentile to compute
fl_percentile <- ar_fl_percentiles[it_percentile_ctr]
# Percentile and mean stats
svr_percentile <- paste0(st_var_prefix_perc, round(fl_percentile * 100))
# Frame with specific percentile
df_within_percentiles_cur <- tstm_loans_sel_long_pct %>%
group_by(terms, forinfm3) %>%
filter(cdf >= fl_percentile) %>%
slice(1) %>%
mutate(!!sym(svr_percentile) := value) %>%
select(terms, forinfm3, one_of(svr_percentile))
# Merge percentile frames together
if (it_percentile_ctr > 1) {
df_within_percentiles <- df_within_percentiles %>%
left_join(df_within_percentiles_cur,
by = c("forinfm3" = "forinfm3", "terms" = "terms")
)
} else {
df_within_percentiles <- df_within_percentiles_cur
}
}
# Add in within group mean
df_within_percentiles_mean <- tstm_loans_sel_long_pct %>%
group_by(terms, forinfm3) %>%
mutate(!!sym(svr_mean) := mean(value, na.rm = TRUE)) %>%
mutate(!!sym(svr_std) := sqrt(mean((value - !!sym(svr_mean))^2))) %>%
slice(1)
# Join to file
df_within_percentiles <- df_within_percentiles %>%
left_join(df_within_percentiles_mean,
by = c("forinfm3" = "forinfm3", "terms" = "terms")
) %>%
select(
terms, forinfm3,
contains(st_var_prefix_perc),
one_of(svr_mean)
# ,
# one_of(svr_std)
)
# display
print(df_within_percentiles)
#> # A tibble: 9 × 16
#> # Groups: terms, forinfm3 [9]
#> terms forinfm3 val_p5 val_p10 val_p20 val_p30 val_p40 val_p25 val_p50 val_p75
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1len… Aformal 3 e+0 4 e+0 1.1 e+1 1.2 e+1 1.2 e+1 1.2 e+1 1.2 e+1 1.3 e+1
#> 2 1len… Bquasif… 3 e+0 5 e+0 7 e+0 1 e+1 1.2 e+1 8 e+0 1.2 e+1 1.3 e+1
#> 3 1len… Cinform… 0 0 1 e+0 1 e+0 2 e+0 1 e+0 3 e+0 8 e+0
#> 4 2amo… Aformal 5 e+3 5 e+3 1 e+4 1.3 e+4 1.6 e+4 1.10e+4 2 e+4 3 e+4
#> 5 2amo… Bquasif… 1 e+3 1.4 e+3 2 e+3 2.38e+3 3 e+3 2 e+3 5 e+3 1.5 e+4
#> 6 2amo… Cinform… 6.3 e+2 1 e+3 1.8 e+3 2.7 e+3 5 e+3 2 e+3 6 e+3 2 e+4
#> 7 3int… Aformal 2.31e-3 3.08e-3 4.62e-3 4.62e-3 5.00e-3 4.62e-3 5.00e-3 6.67e-3
#> 8 3int… Bquasif… 0 0 0 1.16e-9 3.33e-3 0 4.47e-3 1.00e-2
#> 9 3int… Cinform… 0 0 0 0 0 0 7.08e-3 3.00e-2
#> # ℹ 6 more variables: val_p60 <dbl>, val_p70 <dbl>, val_p80 <dbl>,
#> # val_p90 <dbl>, val_p95 <dbl>, val_mean <dbl>Data prep part 3: transpose percentiles
First, combine all rows together and combine blnc_vars
and ivars. Do not resort, preserve existing orders. Drop
the mean, not informative.
# Bind rows for selected key results:
df_within_percentiles <- df_within_percentiles %>%
ungroup() %>%
mutate(
cate_jnt = paste0(terms, "_j_", forinfm3)
) %>%
select(
cate_jnt, -terms, -forinfm3,
contains(st_var_prefix)
)Second, reshape wide to long then to wide again to transpose.
df_within_percentiles_trans <- df_within_percentiles %>%
pivot_longer(
cols = starts_with(st_var_prefix),
names_to = c("percentile"),
names_pattern = paste0(st_var_prefix, "_(.*)"),
values_to = "value"
) %>%
pivot_wider(
names_from = cate_jnt,
values_from = value
)Third, formatting. Length, levels, percentages. percentage formatting.
# format percentiles
df_within_percentiles_trans <- df_within_percentiles_trans %>%
mutate(percentile = gsub(
percentile, pattern="p", replace=""))
# format length
df_within_percentiles_trans <- df_within_percentiles_trans %>%
mutate_at(
vars(contains("1length_")),
list(~ paste0(
format(round(., 1),
nsmall = 1,
big.mark = ","
)
))
)
# format length
df_within_percentiles_trans <- df_within_percentiles_trans %>%
mutate_at(
vars(contains("2amount_")),
list(~ paste0(
format(round(., 0),
nsmall = 0,
big.mark = ","
)
))
)
# format interest rates
df_within_percentiles_trans <- df_within_percentiles_trans %>%
mutate_at(
vars(contains("3interest_")),
list(~ paste0(
format(round(. *100, 2),
nsmall = 2,
big.mark = ","
),
"%"
))
)Table display (full table)
We present full information with lower and upper deciles, formal, informal, and quasiformal information jointly.
# First, we define column names, which correspond to previously defined variable selection list.
ar_st_col_names <- c(
"Percentiles",
"Formal", "Quasi-formal", "Informal",
"Formal", "Quasi-formal", "Informal",
"Formal", "Quasi-formal", "Informal"
)
# Define column groups, grouping the names above
ar_st_col_groups <- c(
" " = 1,
"Length (months)" = 3,
"Amount (baht)" = 3,
"Interest (monthly)" = 3
)
# Define column groups, grouping the names above
ar_st_col_groups_super <- c(
" " = 1,
"Loan terms" = 9
)
# Second, we construct main table, and add styling.
bk_loan_terms <- kbl(
df_within_percentiles_trans,
format = st_kableformat,
# escape = F,
linesep = "",
booktabs = T,
align = "c",
caption = "Loan terms distributions.",
col.names = ar_st_col_names
) %>%
# see https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html#Bootstrap_table_classes
kable_styling(
bootstrap_options = c("striped", "hover", "condensed", "responsive"),
full_width = F, position = "left"
)
# Third, we add in row groups
bk_loan_terms <- bk_loan_terms %>%
add_header_above(ar_st_col_groups)
# add_header_above(ar_st_col_groups_super)
# Fourth, we add in column groups.
bk_loan_terms <- bk_loan_terms %>%
pack_rows(
"Below median deciles",
1, 5,
latex_gap_space = "0.5em"
) %>%
pack_rows(
"Quartiles",
6, 8,
latex_gap_space = "0.5em", hline_before = F
) %>%
pack_rows(
"Above median deciles",
9, 13,
latex_gap_space = "0.5em", hline_before = F
) %>%
pack_rows(
"Mean",
14, 14,
latex_gap_space = "0.5em", hline_before = F
)
# Fifth, column formatting.
bk_loan_terms <- bk_loan_terms %>%
column_spec(1, width = "2.0cm") %>%
column_spec(2:10, width = "1.6cm")
# Final adjustments
st_texcmd <- "frac"
bk_loan_terms <- gsub(bk_loan_terms,
pattern = paste0("\\textbackslash{}", st_texcmd, "\\"),
replacement = paste0("\\", st_texcmd), fixed = TRUE
)
st_texcmd <- "text"
bk_loan_terms <- gsub(bk_loan_terms,
pattern = paste0("\\textbackslash{}", st_texcmd, "\\"),
replacement = paste0("\\", st_texcmd), fixed = TRUE
)
bk_loan_terms <- gsub(bk_loan_terms,
pattern = "\\}\\{",
replacement = "}{", fixed = TRUE
)
bk_loan_terms <- gsub(bk_loan_terms,
pattern = "\\}",
replacement = "}", fixed = TRUE
)
bk_loan_terms <- gsub(bk_loan_terms,
pattern = "\\$",
replacement = "$", fixed = TRUE
)
# Sixth, display.
# pl_bk_asset_count <- bk_loan_terms %>% as_image()
bk_loan_terms| Percentiles | Formal | Quasi-formal | Informal | Formal | Quasi-formal | Informal | Formal | Quasi-formal | Informal |
|---|---|---|---|---|---|---|---|---|---|
| Below median deciles | |||||||||
| 5 | 3.0 | 3.0 | 0.0 | 5,000 | 1,000 | 630 | 0.23% | 0.00% | 0.00% |
| 10 | 4.0 | 5.0 | 0.0 | 5,000 | 1,400 | 1,000 | 0.31% | 0.00% | 0.00% |
| 20 | 11.0 | 7.0 | 1.0 | 10,000 | 2,000 | 1,800 | 0.46% | 0.00% | 0.00% |
| 30 | 12.0 | 10.0 | 1.0 | 13,000 | 2,380 | 2,700 | 0.46% | 0.00% | 0.00% |
| 40 | 12.0 | 12.0 | 2.0 | 16,000 | 3,000 | 5,000 | 0.50% | 0.33% | 0.00% |
| Quartiles | |||||||||
| 25 | 12.0 | 8.0 | 1.0 | 11,000 | 2,000 | 2,000 | 0.46% | 0.00% | 0.00% |
| 50 | 12.0 | 12.0 | 3.0 | 20,000 | 5,000 | 6,000 | 0.50% | 0.45% | 0.71% |
| 75 | 13.0 | 13.0 | 8.0 | 30,000 | 15,000 | 20,000 | 0.67% | 1.00% | 3.00% |
| Above median deciles | |||||||||
| 60 | 12.0 | 12.0 | 5.0 | 20,000 | 6,000 | 10,000 | 0.58% | 0.69% | 2.00% |
| 70 | 13.0 | 13.0 | 7.0 | 20,000 | 10,000 | 15,000 | 0.62% | 0.92% | 2.62% |
| 80 | 13.0 | 18.0 | 10.0 | 30,000 | 23,520 | 20,000 | 0.75% | 1.00% | 4.00% |
| 90 | 13.0 | 26.0 | 13.0 | 50,000 | 50,000 | 45,000 | 1.00% | 1.85% | 6.25% |
| 95 | 13.0 | 60.0 | 13.0 | 90,000 | 108,750 | 110,000 | 1.27% | 3.00% | 9.23% |
| Mean | |||||||||
| mean | 12.8 | 15.5 | 5.8 | 31,876 | 27,271 | 22,628 | 0.80% | 0.86% | 2.36% |
Table display (partial table)
We now present a subset of information contained in the prior table, we focus on just the quartiles and exclude quasi-formal.
First, we select a subset of the table to consider only quartiles and exclude quasi-formal.
df_within_percentiles_trans_sel <- df_within_percentiles_trans %>%
filter(percentile %in% c("25", "50", "75","mean")) %>%
select(-contains("quasi"))Second, we generate the plot.
# First, we define column names, which correspond to previously defined variable selection list.
ar_st_col_names <- c(
"",
"Formal", "Informal",
"Formal", "Informal",
"Formal", "Informal"
)
# Define column groups, grouping the names above
ar_st_col_groups <- c(
" " = 1,
"Length (months)" = 2,
"Amount (baht)" = 2,
"Interest (monthly)" = 2
)
# Define column groups, grouping the names above
ar_st_col_groups_super <- c(
" " = 1,
"Loan terms" = 6
)
# Second, we construct main table, and add styling.
bk_loan_qrt_terms <- kbl(
df_within_percentiles_trans_sel,
format = st_kableformat,
# escape = F,
linesep = "",
booktabs = T,
align = "c",
caption = "Loan terms distributions.",
col.names = ar_st_col_names
) %>%
# see https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html#Bootstrap_table_classes
kable_styling(
bootstrap_options = c("striped", "hover", "condensed", "responsive"),
full_width = F, position = "left"
)
# Third, we add in row groups
bk_loan_qrt_terms <- bk_loan_qrt_terms %>%
add_header_above(ar_st_col_groups)
# add_header_above(ar_st_col_groups_super)
# Fourth, we add in column groups.
bk_loan_qrt_terms <- bk_loan_qrt_terms %>%
pack_rows(
"Quartiles",
1, 3,
latex_gap_space = "0.5em", hline_before = F
) %>%
pack_rows(
"Mean",
4, 4,
latex_gap_space = "0.5em", hline_before = F
)
# Fifth, column formatting.
bk_loan_qrt_terms <- bk_loan_qrt_terms %>%
column_spec(1, width = "2.0cm") %>%
column_spec(2:7, width = "1.6cm")
# Final adjustments
st_texcmd <- "frac"
bk_loan_qrt_terms <- gsub(bk_loan_qrt_terms,
pattern = paste0("\\textbackslash{}", st_texcmd, "\\"),
replacement = paste0("\\", st_texcmd), fixed = TRUE
)
st_texcmd <- "text"
bk_loan_qrt_terms <- gsub(bk_loan_qrt_terms,
pattern = paste0("\\textbackslash{}", st_texcmd, "\\"),
replacement = paste0("\\", st_texcmd), fixed = TRUE
)
bk_loan_qrt_terms <- gsub(bk_loan_qrt_terms,
pattern = "\\}\\{",
replacement = "}{", fixed = TRUE
)
bk_loan_qrt_terms <- gsub(bk_loan_qrt_terms,
pattern = "\\}",
replacement = "}", fixed = TRUE
)
bk_loan_qrt_terms <- gsub(bk_loan_qrt_terms,
pattern = "\\$",
replacement = "$", fixed = TRUE
)
# Sixth, display.
# pl_bk_asset_count <- bk_loan_qrt_terms %>% as_image()
bk_loan_qrt_terms| Formal | Informal | Formal | Informal | Formal | Informal | |
|---|---|---|---|---|---|---|
| Quartiles | ||||||
| 25 | 12.0 | 1.0 | 11,000 | 2,000 | 0.46% | 0.00% |
| 50 | 12.0 | 3.0 | 20,000 | 6,000 | 0.50% | 0.71% |
| 75 | 13.0 | 8.0 | 30,000 | 20,000 | 0.67% | 3.00% |
| Mean | ||||||
| mean | 12.8 | 5.8 | 31,876 | 22,628 | 0.80% | 2.36% |
Loan length distribution
Loan length all loan type violin plot
We show the distribution of all loan types as violins.
pl_violin_loan_length_all <- tstm_loans %>%
filter(G_Loan_Init_Length <= 36) %>%
ggplot(aes(
x = factor(G_LenderType,
levels = rev(c(
"BAAC",
"Village Fund",
"Commercial Bank",
"Agri Coop",
"PCG",
"Other Non-Indi Formal or Informal",
"MoneyLender",
"Neighbor",
"Relatives",
"Others"
))
), y = G_Loan_Init_Length,
fill = forinfm3
)) +
geom_violin(
width = 2.1, size = 0.2
) +
scale_fill_manual(values = cbp1, name = "") +
theme_minimal() +
coord_flip() +
labs(
y = paste0("Months"),
x = paste0("Lender types"),
title = paste(
"Violin plot of loan length distribution, capping at 36 months",
sep = " "
),
caption = paste(
"Townsend Thai Monthly data up 1998 to 2011",
sep = ""
)
)
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
print(pl_violin_loan_length_all)
#> Warning: `position_dodge()` requires non-overlapping x
#> intervals.
Loan length eight key categories
Loan length distribution among eight selected key categories.
First, in shares.
# We will first count the number of loans by type and month
pl_bar_loan_length_1a <- tstm_loans %>%
ungroup() %>%
filter(
G_LenderType != "Other Non-Indi Formal or Informal",
G_LenderType != "Commercial Bank",
) %>%
group_by(forinfm3, G_LenderType, G_Loan_Init_Length) %>%
summarize(group_len_count = n()) %>%
ungroup() %>%
group_by(forinfm3, G_LenderType) %>%
mutate(group_count = sum(group_len_count)) %>%
mutate(group_len_share = group_len_count / group_count) %>%
filter(G_Loan_Init_Length <= 36) %>%
ggplot(aes(
x = G_Loan_Init_Length, y = group_len_share,
fill = forinfm3, color = forinfm3
)) +
geom_bar(stat = "identity") +
facet_wrap(factor(forinfm3, c(
"Formal",
"Quasi-formal",
"Informal"
)) ~
factor(G_LenderType,
levels = c(
"MoneyLender",
"Neighbor",
"Relatives",
"Others",
"BAAC",
"Village Fund",
# 'Commercial Bank',
"Agri Coop",
"PCG"
)
), scales = "free_y", ncol = 4) +
scale_fill_manual(values = cbp1) +
scale_color_manual(values = cbp1) +
guides(color = FALSE, fill = FALSE) +
theme_minimal() +
labs(
x = paste0("Number of months by which all principal + interests are due"),
y = paste0("Share of loans with this duration"),
title = paste(
"Loan length distribution, capping at 36 months (share of loans)",
sep = " "
),
caption = paste(
"Townsend Thai Monthly data up 1998 to 2011",
sep = ""
)
)
#> `summarise()` has grouped output by 'forinfm3', 'G_LenderType'. You can
#> override using the `.groups` argument.
#> Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
#> of ggplot2 3.3.4.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
print(pl_bar_loan_length_1a)
Second, in levels (frequency of loans).
# We will first count the number of loans by type and month
pl_bar_loan_length_1b <- tstm_loans %>%
ungroup() %>%
filter(
G_LenderType != "Other Non-Indi Formal or Informal",
G_LenderType != "Commercial Bank",
) %>%
group_by(forinfm3, G_LenderType, G_Loan_Init_Length) %>%
summarize(group_len_count = n()) %>%
ungroup() %>%
group_by(forinfm3, G_LenderType) %>%
mutate(group_count = sum(group_len_count)) %>%
mutate(group_len_share = group_len_count / group_count) %>%
filter(G_Loan_Init_Length <= 36) %>%
ggplot(aes(
x = G_Loan_Init_Length, y = group_len_count,
fill = forinfm3, color = forinfm3
)) +
geom_bar(stat = "identity") +
facet_wrap(factor(forinfm3, c(
"Formal",
"Quasi-formal",
"Informal"
)) ~
factor(G_LenderType,
levels = c(
"MoneyLender",
"Neighbor",
"Relatives",
"Others",
"BAAC",
"Village Fund",
# 'Commercial Bank',
"Agri Coop",
"PCG"
)
), scales = "free_y", ncol = 4) +
scale_fill_manual(values = cbp1) +
scale_color_manual(values = cbp1) +
guides(color = FALSE, fill = FALSE) +
theme_minimal() +
labs(
x = paste0("Number of months by which all principal + interests are due"),
y = paste0("Number of loans with this duration"),
title = paste(
"Loan length distribution, capping at 36 months (number of loans)",
sep = " "
),
caption = paste(
"Townsend Thai Monthly data up 1998 to 2011",
sep = ""
)
)
#> `summarise()` has grouped output by 'forinfm3', 'G_LenderType'. You can
#> override using the `.groups` argument.
print(pl_bar_loan_length_1b)
Loan length three categories
Formal, informal, and quasi-formal laon length distribution break-down.
First, we visualize with share of loans.
# We will first count the number of loans by type and month
pl_bar_loan_length_2a <- tstm_loans %>%
ungroup() %>%
# filter(G_LenderType != "Other Non-Indi Formal or Informal",
# G_LenderType != "Commercial Bank",) %>%
group_by(forinfm3, G_Loan_Init_Length) %>%
summarize(group_len_count = n()) %>%
ungroup() %>%
group_by(forinfm3) %>%
mutate(group_count = sum(group_len_count)) %>%
mutate(group_len_share = group_len_count / group_count) %>%
filter(G_Loan_Init_Length <= 24) %>%
ggplot(aes(
x = G_Loan_Init_Length, y = group_len_share,
fill = forinfm3
)) +
geom_bar(stat = "identity") +
facet_wrap(~ factor(forinfm3,
levels = c(
"Formal",
"Quasi-formal",
"Informal"
)
), scales = "free_y", ncol = 3) +
scale_fill_manual(values = cbp1) +
scale_color_manual(values = cbp1) +
guides(color = FALSE, fill = FALSE) +
theme_minimal() +
labs(
x = paste0("Number of months by which all principal + interests are due"),
y = paste0("Share of loans with this duration"),
title = paste(
"Loan length distribution, capping at 24 months (share of loans)",
sep = " "
),
caption = paste(
"Townsend Thai Monthly data up 1998 to 2011",
sep = ""
)
)
#> `summarise()` has grouped output by 'forinfm3'. You can override using the
#> `.groups` argument.
print(pl_bar_loan_length_2a)
Second, we visualize with number of loans.
# Frequencies
pl_bar_loan_length_2b <- tstm_loans %>%
ungroup() %>%
# filter(G_LenderType != "Other Non-Indi Formal or Informal",
# G_LenderType != "Commercial Bank",) %>%
group_by(forinfm3, G_Loan_Init_Length) %>%
summarize(group_len_count = n()) %>%
ungroup() %>%
group_by(forinfm3) %>%
mutate(group_count = sum(group_len_count)) %>%
mutate(group_len_share = group_len_count / group_count) %>%
filter(G_Loan_Init_Length <= 24) %>%
ggplot(aes(
x = G_Loan_Init_Length, y = group_len_count,
fill = forinfm3
)) +
geom_bar(stat = "identity") +
facet_wrap(~ factor(forinfm3,
levels = c(
"Formal",
"Quasi-formal",
"Informal"
)
), scales = "free_y", ncol = 3) +
scale_fill_manual(values = cbp1) +
scale_color_manual(values = cbp1) +
guides(color = FALSE, fill = FALSE) +
theme_minimal() +
labs(
x = paste0("Number of months by which all principal + interests are due"),
y = paste0("Number of loans with this duration"),
title = paste(
"Loan length distribution, capping at 24 months (number of loans)",
sep = " "
),
caption = paste(
"Townsend Thai Monthly data up 1998 to 2011",
sep = ""
)
)
#> `summarise()` has grouped output by 'forinfm3'. You can override using the
#> `.groups` argument.
print(pl_bar_loan_length_2b)
Loan length overall formal and informal
First, fOrmal and informal, density plot.
# We will first count the number of loans by type and month
pl_bar_loan_length_3 <- tstm_loans %>%
filter(G_Loan_Init_Length <= 36) %>%
ggplot(aes(G_Loan_Init_Length,
fill = formal, color = formal
)) +
geom_density(alpha = 0.5, stat = "density", position = "identity") +
scale_fill_manual(values = cbp1) +
scale_color_manual(values = cbp1) +
guides(color = FALSE, fill = FALSE) +
theme_minimal()
print(pl_bar_loan_length_3)
Second, formal vs informal bifurcation violin plot.
pl_loan_length <- tstm_loans %>%
filter(G_Loan_Init_Length <= 36) %>%
ggplot(aes(x = formal, y = G_Loan_Init_Length)) +
geom_violin(
width = 2.1, size = 0.2,
fill = "#a4a4a4", color = "darkred"
) +
theme_minimal() +
coord_flip() +
labs(
y = paste0("Months"),
x = paste0("Formal vs Informal"),
title = paste(
"Violin plot of loan length distribution, capping at 36 months",
sep = " "
),
caption = paste(
"Townsend Thai Monthly data up 1998 to 2011",
sep = ""
)
)
print(pl_loan_length)
#> Warning: `position_dodge()` requires non-overlapping x
#> intervals.
Loan amount distribution
Loan amount all loan type violin plot
Overall and all types of lenders:
# Figure B: Loan Amount by Lender Types
pl_violin_loan_size_all <- tstm_loans %>%
mutate(S_Init_Amount_Log = log(S_Init_Amount)) %>%
# filter(G_Loan_Init_Length <= 24) %>%
ggplot(aes(x = factor(G_LenderType,
levels = rev(c(
"BAAC",
"Village Fund",
"Commercial Bank",
"Agri Coop",
"PCG",
"Other Non-Indi Formal or Informal",
"MoneyLender",
"Neighbor",
"Relatives",
"Others"
))
), y = S_Init_Amount_Log, fill = forinfm3)) +
geom_violin(
width = 2.1, size = 0.2,
) +
scale_fill_manual(values = cbp1, name = "") +
theme_minimal() +
coord_flip() +
labs(
y = paste0("Natural log of loan size (amount in baht)"),
x = paste0("Lender types"),
title = paste(
"Violin plot of log loan size (amount in Baht) distribution",
sep = " "
),
caption = paste(
"Townsend Thai Monthly data up 1998 to 2011",
sep = ""
)
)
print(pl_violin_loan_size_all)
#> Warning: Removed 12 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).
#> Warning: `position_dodge()` requires non-overlapping x
#> intervals.
Loan amount key categories distributions
Compare eight key categories.
# # Density plot
# pl_loan_amount_density <- tstm_loans %>%
# mutate(S_Init_Amount_Log = log(S_Init_Amount)) %>%
# ggplot(aes(S_Init_Amount_Log,
# fill=formal, color=formal)) +
# geom_density(alpha=0.5, stat = "density", position = "identity") +
# facet_wrap(~ G_LenderType + formal, scales = "free_y") +
# theme_minimal()
# print(pl_loan_amount_density)
# Density plot
pl_loan_amount_density_1 <- tstm_loans %>%
filter(
G_LenderType != "Other Non-Indi Formal or Informal",
G_LenderType != "Commercial Bank",
) %>%
mutate(S_Init_Amount_Log = log(S_Init_Amount)) %>%
ggplot(aes(S_Init_Amount_Log,
fill = forinfm3, color = forinfm3
)) +
geom_density(alpha = 0.5, stat = "density", position = "identity") +
facet_wrap(factor(forinfm3, c(
"Formal",
"Quasi-formal",
"Informal"
)) ~
factor(G_LenderType,
levels = c(
"MoneyLender",
"Neighbor",
"Relatives",
"Others",
"BAAC",
"Village Fund",
# 'Commercial Bank',
"Agri Coop",
"PCG"
)
), scales = "free_y", ncol = 4) +
scale_fill_manual(values = cbp1) +
scale_color_manual(values = cbp1) +
guides(color = FALSE, fill = FALSE) +
theme_minimal()
print(pl_loan_amount_density_1)
#> Warning: Removed 6 rows containing non-finite outside the scale range
#> (`stat_density()`).
Compare distributions by formal and informal groupings, individual categories overlapping.
pl_loan_amount_density_2 <- tstm_loans %>%
filter(
G_LenderType != "Other Non-Indi Formal or Informal",
G_LenderType != "Commercial Bank",
) %>%
mutate(S_Init_Amount_Log = log(S_Init_Amount)) %>%
ggplot(aes(S_Init_Amount_Log,
fill = G_LenderType, color = G_LenderType
)) +
geom_density(alpha = 0.5, stat = "density", position = "identity") +
facet_wrap(~formal, scales = "free_y", ncol = 1) +
scale_fill_manual(values = cbp1) +
scale_color_manual(values = cbp1) +
theme_minimal()
print(pl_loan_amount_density_2)
#> Warning: Removed 6 rows containing non-finite outside the scale range
#> (`stat_density()`).
Loan amount formal vs informal vs quasi
pl_density_loan_size_forinfm3 <- tstm_loans %>%
mutate(S_Init_Amount_Log = log(S_Init_Amount)) %>%
ggplot(aes(S_Init_Amount_Log,
fill = forinfm3, color = forinfm3
)) +
geom_density(alpha = 0.5, stat = "density", position = "identity") +
scale_fill_manual(values = cbp1, name = "") +
scale_color_manual(values = cbp1, name = "") +
theme_minimal() +
labs(
x = paste0("Natural log of loan size (amount in baht)"),
y = paste0("Density"),
title = paste(
"Density of log loan size (amount in Baht) distribution",
sep = " "
),
caption = paste(
"Townsend Thai Monthly data up 1998 to 2011",
sep = ""
)
)
print(pl_density_loan_size_forinfm3)
#> Warning: Removed 12 rows containing non-finite outside the scale range
#> (`stat_density()`).
Loan amount formal vs informal distribution
Formal vs informal, density.
pl_density_loan_size_3 <- tstm_loans %>%
mutate(S_Init_Amount_Log = log(S_Init_Amount)) %>%
ggplot(aes(S_Init_Amount_Log,
fill = formal, color = formal
)) +
geom_density(alpha = 0.5, stat = "density", position = "identity") +
scale_fill_manual(values = cbp1) +
scale_color_manual(values = cbp1) +
theme_minimal() +
labs(
x = paste0("Natural log of loan size (amount in baht)"),
y = paste0("Density"),
title = paste(
"Density of log loan size (amount in Baht) distribution",
sep = " "
),
caption = paste(
"Townsend Thai Monthly data up 1998 to 2011",
sep = ""
)
)
print(pl_density_loan_size_3)
#> Warning: Removed 12 rows containing non-finite outside the scale range
#> (`stat_density()`).
Formal vs informal bifurcation, violin.
# Figure B: Loan Amount by Lender Types
pl_loan_size <- tstm_loans %>%
mutate(S_Init_Amount_Log = log(S_Init_Amount)) %>%
# filter(G_Loan_Init_Length <= 24) %>%
ggplot(aes(x = formal, y = S_Init_Amount_Log)) +
geom_violin(
width = 2.1, size = 0.2,
fill = "#A4A4A4", color = "darkred"
) +
theme_minimal() +
coord_flip() +
labs(
y = paste0("Natural log of loan size (amount in baht)"),
x = paste0("Formal vs informal"),
title = paste(
"Violin plot: log loan size (amount in Baht) dist",
sep = " "
),
caption = paste(
"Townsend Thai Monthly data up 1998 to 2011",
sep = ""
)
)
print(pl_loan_size)
#> Warning: Removed 12 rows containing non-finite outside the scale range
#> (`stat_ydensity()`).
#> Warning: `position_dodge()` requires non-overlapping x
#> intervals.
Loan interest rate distribution
Interest rate all loan type violin plot
Overall and all types of lenders.
# Figure C: Interest Rate By Loan Types
pl_violin_loan_rate_all <- tstm_loans %>%
mutate(
G_Loan_Init_IntMthlyRat_Log =
log((G_Loan_Init_IntMthlyRat) * 100 + 1)
) %>%
filter(G_Loan_Init_IntMthlyRat_Log <= 3) %>%
# filter(G_Loan_Init_IntMthlyRat <= 0.15) %>%
ggplot(aes(x = factor(G_LenderType,
levels = rev(c(
"BAAC",
"Village Fund",
"Commercial Bank",
"Agri Coop",
"PCG",
"Other Non-Indi Formal or Informal",
"MoneyLender",
"Neighbor",
"Relatives",
"Others"
))
), y = G_Loan_Init_IntMthlyRat_Log, fill = forinfm3)) +
geom_violin(
width = 2.1, size = 0.2
) +
scale_fill_manual(values = cbp1, name = "") +
theme_minimal() +
coord_flip() +
labs(
y = paste0("Natural log of (monthly loan interest x 100 + 1)"),
# y = paste0("Monthly loan interest"),
x = paste0("Lender types"),
title = paste(
# "Monthly interest distribution (annual/weekly interest rates converted to monthly)",
"Log of (monthly interest x 100 + 1) dist.\n",
"Annual and weekly loan interest rates converted to monthly",
sep = " "
),
caption = paste(
"Townsend Thai Monthly data up 1998 to 2011",
sep = ""
)
)
print(pl_violin_loan_rate_all)
#> Warning: `position_dodge()` requires non-overlapping x
#> intervals.
### Interest rate key categories distribution
Eight categories separate plots.
# Density plot
pl_density_loan_rate_forinfm3_bym8 <- tstm_loans %>%
filter(
G_LenderType != "Other Non-Indi Formal or Informal",
G_LenderType != "Commercial Bank",
) %>%
mutate(
G_Loan_Init_IntMthlyRat_Log =
log((G_Loan_Init_IntMthlyRat) * 100 + 1)
) %>%
filter(G_Loan_Init_IntMthlyRat <= 0.15) %>%
ggplot(aes(G_Loan_Init_IntMthlyRat,
fill = forinfm3, color = forinfm3
)) +
geom_density(alpha = 0.5, stat = "density", position = "identity") +
# facet_wrap(~ institutional + G_LenderType, scales = "free_y") +
facet_wrap(factor(forinfm3, c(
"Formal",
"Quasi-formal",
"Informal"
)) ~ factor(G_LenderType,
levels = c(
"MoneyLender",
"Neighbor",
"Relatives",
"Others",
"BAAC",
"Village Fund",
# 'Commercial Bank',
"Agri Coop",
"PCG"
)
), scales = "free_y", ncol = 4) +
scale_fill_manual(values = cbp1) +
scale_color_manual(values = cbp1) +
guides(color = FALSE, fill = FALSE) +
theme_minimal() +
labs(
x = paste0("Monthly loan interest"),
y = "Density",
title = paste(
"Monthly interest distribution (annual/weekly interest rates converted to monthly)",
sep = " "
),
caption = paste(
"Townsend Thai Monthly data up 1998 to 2011",
sep = ""
)
)
print(pl_density_loan_rate_forinfm3_bym8)
Overlapping distributions grouped by formal and informal.
# Density plot
pl_density_loan_rate_2 <- tstm_loans %>%
filter(
G_LenderType != "Other Non-Indi Formal or Informal",
G_LenderType != "Commercial Bank",
) %>%
mutate(
G_Loan_Init_IntMthlyRat_Log =
log((G_Loan_Init_IntMthlyRat) * 100 + 1)
) %>%
filter(G_Loan_Init_IntMthlyRat <= 0.15) %>%
ggplot(aes(G_Loan_Init_IntMthlyRat_Log,
fill = G_LenderType, color = G_LenderType
)) +
geom_density(alpha = 0.5, stat = "density", position = "identity") +
# facet_wrap(~ institutional + G_LenderType, scales = "free_y") +
facet_wrap(~formal, scales = "free_y", ncol = 1) +
scale_fill_manual(values = cbp1) +
scale_color_manual(values = cbp1) +
theme_minimal()
print(pl_density_loan_rate_2)
### Interest rate formal vs informal vs quasi
# Density plot,
pl_density_loan_rate_4 <- tstm_loans %>%
filter(G_Loan_Init_IntMthlyRat <= 0.15) %>%
ggplot(aes(G_Loan_Init_IntMthlyRat,
fill = forinfm3, color = forinfm3
)) +
geom_density(alpha = 0.5, stat = "density", position = "identity") +
scale_fill_manual(values = cbp1) +
scale_color_manual(values = cbp1) +
theme_minimal()
print(pl_density_loan_rate_4)
Interest rate formal vs informal
Formal vs informal density.
# Density plot
pl_density_loan_rate_3 <- tstm_loans %>%
mutate(
G_Loan_Init_IntMthlyRat_Log =
log((G_Loan_Init_IntMthlyRat) * 100 + 1)
) %>%
filter(G_Loan_Init_IntMthlyRat_Log <= 3) %>%
ggplot(aes(G_Loan_Init_IntMthlyRat_Log,
fill = formal, color = formal
)) +
geom_density(alpha = 0.5, stat = "density", position = "identity") +
scale_fill_manual(values = cbp1) +
scale_color_manual(values = cbp1) +
theme_minimal()
print(pl_density_loan_rate_3)
Formal vs informal bifurcation via violin plots.
# Figure C: Interest Rate By Loan Types
pl_loan_rate <- tstm_loans %>%
mutate(
G_Loan_Init_IntMthlyRat_Log =
log((G_Loan_Init_IntMthlyRat) * 100 + 1)
) %>%
filter(G_Loan_Init_IntMthlyRat_Log <= 3) %>%
ggplot(aes(x = formal, y = G_Loan_Init_IntMthlyRat_Log)) +
geom_violin(
width = 2.1, size = 0.2,
fill = "#A4A4A4", color = "darkred"
) +
theme_minimal() +
coord_flip() +
labs(
y = paste0("Natural log of (monthly loan interest x 100 + 1)"),
x = paste0("Formal vs informal"),
title = paste(
"Violin plot: log of (monthly interest x 100 + 1) dist.\n",
"Annual and weekly loan interest rates converted to monthly",
sep = " "
),
caption = paste(
"Townsend Thai Monthly data up 1998 to 2011",
sep = ""
)
)
print(pl_loan_rate)
#> Warning: `position_dodge()` requires non-overlapping x
#> intervals.
Saving Figures
Now we save the figures we have generates
if (bl_save_figs) {
# Project root
spt_root_prj <- ffs_hfid_path()$spt_root_prj_main_dropbox
ar_it_save_group <- c(1, 2, 3)
for (it_grp in ar_it_save_group) {
# Path
spt_img_folder <- file.path(
spt_root_prj,
"summ", "summ_loan_terms_dist", "res_2023_1211",
fsep = .Platform$file.sep
)
if (it_grp == 1) {
# Plots and names
# plots
ls_plts <- list(
pl_violin_loan_length_all,
pl_bar_loan_length_1a, pl_bar_loan_length_1b,
pl_bar_loan_length_2a, pl_bar_loan_length_2b
)
# Names
ls_plts_names <- c(
"pl_violin_loan_length_all",
"pl_bar_loan_length_1a", "pl_bar_loan_length_1b",
"pl_bar_loan_length_2a", "pl_bar_loan_length_2b"
)
}
if (it_grp == 2) {
# Plots and names
# plots
ls_plts <- list(
pl_violin_loan_size_all,
pl_density_loan_size_forinfm3
)
# Names
ls_plts_names <- c(
"pl_violin_loan_size_all",
"pl_density_loan_size_forinfm3"
)
}
if (it_grp == 3) {
# Plots and names
# plots
ls_plts <- list(
pl_violin_loan_rate_all,
pl_density_loan_rate_forinfm3_bym8
)
# Names
ls_plts_names <- c(
"pl_violin_loan_rate_all",
"pl_density_loan_rate_forinfm3_bym8"
)
}
# Save plot one by one
it_len_figs <- length(ls_plts_names)
for (it_fig in 1:1:it_len_figs) {
# Get plot
pl_fig <- ls_plts[[it_fig]]
st_fig_name <- ls_plts_names[it_fig]
# Image save path
spn_graph_png <- file.path(spt_img_folder, paste0(st_fig_name, ".png"),
fsep = .Platform$file.sep
)
spn_graph_eps <- file.path(spt_img_folder, paste0(st_fig_name, ".eps"),
fsep = .Platform$file.sep
)
# Plot
print(pl_fig)
png(spn_graph_png,
width = 250,
height = 150, units = "mm",
res = 150, pointsize = 7
)
ggsave(spn_graph_eps,
plot = last_plot(),
device = "eps",
path = NULL,
scale = 1,
width = 250,
height = 150,
units = c("mm"),
dpi = 150,
limitsize = TRUE
)
print(pl_fig)
dev.off()
}
}
}