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Asitav Sen
2022-08-22 15:10:36 +02:00
parent 00b045ee8e
commit 55a713de03
8 changed files with 240 additions and 236 deletions
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4
.Rproj.user/178A6739/sources/prop/48F98022
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@@ -1,6 +1,6 @@
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"source_window_id": "", "source_window_id": "",
"Source": "Source", "Source": "Source",
"cursorPosition": "50,4", "cursorPosition": "89,22",
"scrollLine": "12" "scrollLine": "71"
} }

2
.Rproj.user/178A6739/sources/prop/9D91FFC1
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@@ -1,6 +1,6 @@
{ {
"source_window_id": "", "source_window_id": "",
"Source": "Source", "Source": "Source",
"cursorPosition": "16,0", "cursorPosition": "27,17",
"scrollLine": "0" "scrollLine": "0"
} }

10
.Rproj.user/178A6739/sources/session-99529da2/58AD392C
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@@ -3,7 +3,7 @@
"path": "~/Projects/LoanRisk/panel.R", "path": "~/Projects/LoanRisk/panel.R",
"project_path": "panel.R", "project_path": "panel.R",
"type": "r_source", "type": "r_source",
"hash": "2913767970", "hash": "0",
"contents": "", "contents": "",
"dirty": false, "dirty": false,
"created": 1686047069391.0, "created": 1686047069391.0,
@@ -12,15 +12,15 @@
"properties": { "properties": {
"source_window_id": "", "source_window_id": "",
"Source": "Source", "Source": "Source",
"cursorPosition": "50,4", "cursorPosition": "89,22",
"scrollLine": "12" "scrollLine": "71"
}, },
"folds": "", "folds": "",
"lastKnownWriteTime": 1686047587, "lastKnownWriteTime": 1686050840,
"encoding": "UTF-8", "encoding": "UTF-8",
"collab_server": "", "collab_server": "",
"source_window": "", "source_window": "",
"last_content_update": 1686047587539, "last_content_update": 1686050840851,
"read_only": false, "read_only": false,
"read_only_alternatives": [] "read_only_alternatives": []
} }

38
.Rproj.user/178A6739/sources/session-99529da2/58AD392C-contents
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@@ -1,14 +1,13 @@
countries <- read.csv("./data/countries.csv") countries <- read.csv("./data/countries.csv")
panel1 <- panel1 <-
tabPanel( tabPanel(
"Home", "Home",
fluidRow(column( fluidRow(column(width = 11,
width = 11,
fluidRow( fluidRow(
column( column(width = 8,
width = 8,
tags$div( tags$div(
tags$blockquote( tags$blockquote(
"LoanRisk is being built to provide convenience to Finance and Accounting consultants and SMEs in finance industry." "LoanRisk is being built to provide convenience to Finance and Accounting consultants and SMEs in finance industry."
@@ -18,13 +17,17 @@ panel1<-
Naturally, it is time consuming and has its share of hassles. This app attempts to avoid all the hassles of setting up environment and tools to perform the multistep analysis. Naturally, it is time consuming and has its share of hassles. This app attempts to avoid all the hassles of setting up environment and tools to perform the multistep analysis.
This app will calculate the provisioning requirements in a few clicks and one can download the report with a single click!" This app will calculate the provisioning requirements in a few clicks and one can download the report with a single click!"
), ),
p("The basic steps are mentioned in the diagram on the left hand side. The app is not in its most evolved form yet. p(
There is a huge list of features and functions that I personally want to include and implement in future."), "The basic steps are mentioned in the diagram on the left hand side. The app is not in its most evolved form yet.
There is a huge list of features and functions that I personally want to include and implement in future."
),
h6("How to use?"), h6("How to use?"),
p("Using the tool is very simple. You can upload two data sets. One that shows a certain number of transactions of each asset, along with some dates, parameters and event outcome. p(
"Using the tool is very simple. You can upload two data sets. One that shows a certain number of transactions of each asset, along with some dates, parameters and event outcome.
And the other that contains value of collateral or estimated value of sales of the asset/hypothecated asset. Then proceed further with the clicks and in between select some parameters. And the other that contains value of collateral or estimated value of sales of the asset/hypothecated asset. Then proceed further with the clicks and in between select some parameters.
For e.g. the discount rate to be applied, the most probable, maximum possible and minimum possible depreciation of value of the collateral. For e.g. the discount rate to be applied, the most probable, maximum possible and minimum possible depreciation of value of the collateral.
Once the simulation is done, you can download the report in pdf format.")#, Once the simulation is done, you can download the report in pdf format."
)#,
# "If you are a R coder, you are welcome to contribute and help improve. Please visit the ", # "If you are a R coder, you are welcome to contribute and help improve. Please visit the ",
# tags$a(href = "https://github.com/asitav-sen/LoanRisk", "github page"), # tags$a(href = "https://github.com/asitav-sen/LoanRisk", "github page"),
# " or ", # " or ",
@@ -34,21 +37,21 @@ panel1<-
# " to report issues and/or request new features/functions.", "For general discussions, please use this",tags$a(href = "https://github.com/asitav-sen/LoanRisk/discussions", "link"), # " to report issues and/or request new features/functions.", "For general discussions, please use this",tags$a(href = "https://github.com/asitav-sen/LoanRisk/discussions", "link"),
# tags$br(), # tags$br(),
# tags$image(height=100, width=100,src="logo3.png") # tags$image(height=100, width=100,src="logo3.png")
) )),
),
column( column(
width = 4, width = 4,
mermaid(" mermaid(
"
graph TB graph TB
A[Data Upload]-->B[Downloading macroeconomic data] A[Data Upload]-->B[Downloading macroeconomic data]
B[Downloading macroeconomic data from IMF]-->C[Forecasting macroeconomic parameters] B[Downloading macroeconomic data from IMF]-->C[Forecasting macroeconomic parameters]
C[Forecasting macroeconomic parameters]-->D[Fit Survival Model] C[Forecasting macroeconomic parameters]-->D[Fit Survival Model]
D[Fit Survival Model]-->E[Monte Carlo Simulation] D[Fit Survival Model]-->E[Monte Carlo Simulation]
E[Monte Carlo Simulation]-->F[Report] E[Monte Carlo Simulation]-->F[Report]
") "
) )
), )
)), ),)),
tags$hr(), tags$hr(),
fluidRow( fluidRow(
column( column(
@@ -59,8 +62,11 @@ panel1<-
p("This section shows the data (uploaded or inbuilt sample).") p("This section shows the data (uploaded or inbuilt sample).")
), ),
br(), br(),
withSpinner(dataTableOutput("up_data"),type = 7, withSpinner(
color = "black"), dataTableOutput("up_data"),
type = 7,
color = "black"
),
br(), br(),
actionButton("uploadnew", "Upload New Data", class = "btn-light") actionButton("uploadnew", "Upload New Data", class = "btn-light")
), ),

8
.Rproj.user/178A6739/sources/session-99529da2/FEF97A26
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@@ -3,7 +3,7 @@
"path": "~/Projects/LoanRisk/mod_basic.R", "path": "~/Projects/LoanRisk/mod_basic.R",
"project_path": "mod_basic.R", "project_path": "mod_basic.R",
"type": "r_source", "type": "r_source",
"hash": "1648399213", "hash": "0",
"contents": "", "contents": "",
"dirty": false, "dirty": false,
"created": 1686047786220.0, "created": 1686047786220.0,
@@ -12,15 +12,15 @@
"properties": { "properties": {
"source_window_id": "", "source_window_id": "",
"Source": "Source", "Source": "Source",
"cursorPosition": "16,0", "cursorPosition": "27,17",
"scrollLine": "0" "scrollLine": "0"
}, },
"folds": "", "folds": "",
"lastKnownWriteTime": 1686049500, "lastKnownWriteTime": 1686050855,
"encoding": "UTF-8", "encoding": "UTF-8",
"collab_server": "", "collab_server": "",
"source_window": "", "source_window": "",
"last_content_update": 1686049500334, "last_content_update": 1686050855732,
"read_only": false, "read_only": false,
"read_only_alternatives": [] "read_only_alternatives": []
} }

10
.Rproj.user/178A6739/sources/session-99529da2/FEF97A26-contents
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@@ -6,9 +6,7 @@ basicstatUI <- function(id) {
fluidRow( fluidRow(
h3("No of Assets"), h3("No of Assets"),
br(), br(),
p( p("This section shows some basic information about the portfolio."),
"This section shows some basic information about the portfolio."
),
plotOutput(ns("statplot")), plotOutput(ns("statplot")),
verbatimTextOutput(ns("balancetext")) verbatimTextOutput(ns("balancetext"))
) )
@@ -90,8 +88,7 @@ basicstatServer <- function(id, dt) {
group_by(customer_type, loan_status) %>% group_by(customer_type, loan_status) %>%
summarise(no_of_loans = n()) %>% summarise(no_of_loans = n()) %>%
ungroup() %>% ungroup() %>%
ggplot( ggplot(aes(
aes(
x = customer_type, x = customer_type,
y = no_of_loans, y = no_of_loans,
label = no_of_loans, label = no_of_loans,
@@ -100,8 +97,7 @@ basicstatServer <- function(id, dt) {
levels = c("0", "1"), levels = c("0", "1"),
labels = c("Good", "Bad") labels = c("Good", "Bad")
) )
) )) +
) +
geom_col(position = "dodge") + geom_col(position = "dodge") +
geom_text(aes(y = no_of_loans + 20), position = position_dodge(width = 1)) + geom_text(aes(y = no_of_loans + 20), position = position_dodge(width = 1)) +
labs(title = "No. of assets by customer type", labs(title = "No. of assets by customer type",

10
mod_basic.R
View File

@@ -6,9 +6,7 @@ basicstatUI <- function(id) {
fluidRow( fluidRow(
h3("No of Assets"), h3("No of Assets"),
br(), br(),
p( p("This section shows some basic information about the portfolio."),
"This section shows some basic information about the portfolio."
),
plotOutput(ns("statplot")), plotOutput(ns("statplot")),
verbatimTextOutput(ns("balancetext")) verbatimTextOutput(ns("balancetext"))
) )
@@ -90,8 +88,7 @@ basicstatServer <- function(id, dt) {
group_by(customer_type, loan_status) %>% group_by(customer_type, loan_status) %>%
summarise(no_of_loans = n()) %>% summarise(no_of_loans = n()) %>%
ungroup() %>% ungroup() %>%
ggplot( ggplot(aes(
aes(
x = customer_type, x = customer_type,
y = no_of_loans, y = no_of_loans,
label = no_of_loans, label = no_of_loans,
@@ -100,8 +97,7 @@ basicstatServer <- function(id, dt) {
levels = c("0", "1"), levels = c("0", "1"),
labels = c("Good", "Bad") labels = c("Good", "Bad")
) )
) )) +
) +
geom_col(position = "dodge") + geom_col(position = "dodge") +
geom_text(aes(y = no_of_loans + 20), position = position_dodge(width = 1)) + geom_text(aes(y = no_of_loans + 20), position = position_dodge(width = 1)) +
labs(title = "No. of assets by customer type", labs(title = "No. of assets by customer type",

38
panel.R
View File

@@ -1,14 +1,13 @@
countries <- read.csv("./data/countries.csv") countries <- read.csv("./data/countries.csv")
panel1 <- panel1 <-
tabPanel( tabPanel(
"Home", "Home",
fluidRow(column( fluidRow(column(width = 11,
width = 11,
fluidRow( fluidRow(
column( column(width = 8,
width = 8,
tags$div( tags$div(
tags$blockquote( tags$blockquote(
"LoanRisk is being built to provide convenience to Finance and Accounting consultants and SMEs in finance industry." "LoanRisk is being built to provide convenience to Finance and Accounting consultants and SMEs in finance industry."
@@ -18,13 +17,17 @@ panel1<-
Naturally, it is time consuming and has its share of hassles. This app attempts to avoid all the hassles of setting up environment and tools to perform the multistep analysis. Naturally, it is time consuming and has its share of hassles. This app attempts to avoid all the hassles of setting up environment and tools to perform the multistep analysis.
This app will calculate the provisioning requirements in a few clicks and one can download the report with a single click!" This app will calculate the provisioning requirements in a few clicks and one can download the report with a single click!"
), ),
p("The basic steps are mentioned in the diagram on the left hand side. The app is not in its most evolved form yet. p(
There is a huge list of features and functions that I personally want to include and implement in future."), "The basic steps are mentioned in the diagram on the left hand side. The app is not in its most evolved form yet.
There is a huge list of features and functions that I personally want to include and implement in future."
),
h6("How to use?"), h6("How to use?"),
p("Using the tool is very simple. You can upload two data sets. One that shows a certain number of transactions of each asset, along with some dates, parameters and event outcome. p(
"Using the tool is very simple. You can upload two data sets. One that shows a certain number of transactions of each asset, along with some dates, parameters and event outcome.
And the other that contains value of collateral or estimated value of sales of the asset/hypothecated asset. Then proceed further with the clicks and in between select some parameters. And the other that contains value of collateral or estimated value of sales of the asset/hypothecated asset. Then proceed further with the clicks and in between select some parameters.
For e.g. the discount rate to be applied, the most probable, maximum possible and minimum possible depreciation of value of the collateral. For e.g. the discount rate to be applied, the most probable, maximum possible and minimum possible depreciation of value of the collateral.
Once the simulation is done, you can download the report in pdf format.")#, Once the simulation is done, you can download the report in pdf format."
)#,
# "If you are a R coder, you are welcome to contribute and help improve. Please visit the ", # "If you are a R coder, you are welcome to contribute and help improve. Please visit the ",
# tags$a(href = "https://github.com/asitav-sen/LoanRisk", "github page"), # tags$a(href = "https://github.com/asitav-sen/LoanRisk", "github page"),
# " or ", # " or ",
@@ -34,21 +37,21 @@ panel1<-
# " to report issues and/or request new features/functions.", "For general discussions, please use this",tags$a(href = "https://github.com/asitav-sen/LoanRisk/discussions", "link"), # " to report issues and/or request new features/functions.", "For general discussions, please use this",tags$a(href = "https://github.com/asitav-sen/LoanRisk/discussions", "link"),
# tags$br(), # tags$br(),
# tags$image(height=100, width=100,src="logo3.png") # tags$image(height=100, width=100,src="logo3.png")
) )),
),
column( column(
width = 4, width = 4,
mermaid(" mermaid(
"
graph TB graph TB
A[Data Upload]-->B[Downloading macroeconomic data] A[Data Upload]-->B[Downloading macroeconomic data]
B[Downloading macroeconomic data from IMF]-->C[Forecasting macroeconomic parameters] B[Downloading macroeconomic data from IMF]-->C[Forecasting macroeconomic parameters]
C[Forecasting macroeconomic parameters]-->D[Fit Survival Model] C[Forecasting macroeconomic parameters]-->D[Fit Survival Model]
D[Fit Survival Model]-->E[Monte Carlo Simulation] D[Fit Survival Model]-->E[Monte Carlo Simulation]
E[Monte Carlo Simulation]-->F[Report] E[Monte Carlo Simulation]-->F[Report]
") "
) )
), )
)), ),)),
tags$hr(), tags$hr(),
fluidRow( fluidRow(
column( column(
@@ -59,8 +62,11 @@ panel1<-
p("This section shows the data (uploaded or inbuilt sample).") p("This section shows the data (uploaded or inbuilt sample).")
), ),
br(), br(),
withSpinner(dataTableOutput("up_data"),type = 7, withSpinner(
color = "black"), dataTableOutput("up_data"),
type = 7,
color = "black"
),
br(), br(),
actionButton("uploadnew", "Upload New Data", class = "btn-light") actionButton("uploadnew", "Upload New Data", class = "btn-light")
), ),