Financial Institutions Survey Analysis Part 1

Financial Institutions Survey Analysis

 1. Data    

Survey data consist of the 99 observations and 73 variables.

This study focuses on assessing the internal control systems and their impact on the operational 

efficiency of financial institutions in Nigeria.

2. Clean data, convert data

3. Data summary

4. Plot distribution

5.Multi linear regression model statistics

Use the multi linear model statistics

In this case will use operational efficiency as dependent variable, and the independent 

variables, as example:

Variables need to be abbreviated to make it easier to create a model.

CV:  "section_a_control_environment_the_following_statements_pertain_to_how_the_control_environment_influences_the_financial_performance_of_the_microfinance_bank_please_rate_each_statement_according_to_your_understanding_by_selecting_the_appropriate_option_communication_and_enforcement_of_integrity_and_ethical_values_within_our_microfinance_bank_are_adequate",

me:  "our_microfinance_bank_management_is_dedicated_to_ensuring_employee_competence_in_financial_matters",                                                                                                                                                        org:  "the_managements_philosophy_and_operating_style_promote_the_microfinance_banks_growth_while_maintaining_adherence_to_rules_and_regulations",                                                                                  author:  "the_microfinance_bank_s_organizational_structure_has_clear_lines_for_reporting_and_decision_making_hierarchies",                                                                                                                                              mngm:  "our_microfinance_banks_management_appropriately_assigns_authority_and_responsibility_to_qualified_individuals",                                                                                                                                              hrd:  "we_have_well_designed_human_resource_policies_that_are_easy_to_implement_and_practice_within_our_microfinance_bank",                                                                                                                            compet:  "the_microfinance_bank_is_committed_to_ensuring_competence_in_job_specific_requirements_and_the_translation_of_ those_requirements_into_necessary_knowledge_and_skills",                                                                                                                                                                                                   org_author:  "the_organization_assigns_authority_and_responsibility_to_foster_accountability_and_control",                                                                                                                                                                                    entity:  "the_entity_assigns_authority_and_responsibility_to_provide_a_basis_for_accountability_and_control",                                                                                                                                                                      effect_accounting:  "the_microfinance_bank_has_an_effective_accounting_and_financial_management_system_in_place",                                                                                                                                                                          App_disciplinary:  "appropriate_disciplinary_actions_are_taken_when_employees_fail_to_comply_with_policies_procedures_or_behavioral_standards", 

 #---Dependent variable

operational efficiency: "section_f_operational_efficiency_please_indicate_your_level_of_agreement_with_the_following_statements_regarding_the_performance_of_your_organization_microfinance_bank_s_financial_statements_aim_to_provide_a_comprehensive_overview_of_the_organization_s_performance_and_position_at_a_specific_point_in_time"

 6.1 Control environment 

To assess the effect of the control environment on the operational efficiency of financial institutions.

We can see from the table of the multi linier statistics model regression:

#---

# Coefficients:

#                                               Estimate Std. Error t value Pr(>|t|)    

# (Intercept)                    2.7653917  0.7311077   3.782 0.000286 ***

# X                                 -0.0011205  0.0040307  -0.278 0.781693    

# CV                                0.0008834  0.1259978   0.007 0.994422    

# me                               -0.0411602  0.1206962  -0.341 0.733918    

# org                               -0.1852678  0.1055421  -1.755 0.082753 .  

# author                          -0.1898709  0.1128253  -1.683 0.096027 .  

# mngm                           0.0548492  0.1015565   0.540 0.590534    

# hrd                                0.2139908  0.0952287   2.247 0.027192 *  

# compet                          0.2940369  0.1283810   2.290 0.024448 *  

# org_author                   -0.0739613  0.1143838  -0.647 0.519609    

# entity                           -0.0863652  0.1106345  -0.781 0.437160    

# effect_accounting          0.2746892  0.1316306   2.087 0.039865 *  

#  App_disciplinary          0.0099777  0.1087175   0.092 0.927089    

#---

#  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

# Residual standard error: 1.079 on 86 degrees of freedom

# Multiple R-squared:  0.1715, Adjusted R-squared:  0.05595 

# F-statistic: 1.484 on 12 and 86 DF,  p-value: 0.1459

The three variables hrd(0.027192 *),compet( 0.024448 *) and effect_accounting(0.039865 *) have weak effects on the operational efficiency of financial institutions. 

The CV variable(0.994422) have no statistical influence to operational efficiency of financial.

Based on the  F-statistic of the model 1.484 and the corresponding p-value: 0.1459. This indicates that the overall model is not statistically significant because the p-value: 0.1459 is greater than 0.05.

6.2. Risk assessment

#----

# Coefficients:

#                                            Estimate Std. Error t value Pr(>|t|)    

# (Intercept)                   2.334e+00  5.144e-01   4.536 1.69e-05 ***

#                                      6.325e-05  3.532e-03   0.018   0.9857    

# risk_assessment           2.588e-01  1.025e-01   2.524   0.0133 *  

# financial_management 1.458e-01  9.261e-02   1.574   0.1188    

# staff                              2.319e-02  7.997e-02   0.290   0.7725    

#---

#  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

# Residual standard error: 0.9959 on 94 degrees of freedom

# Multiple R-squared:  0.131, Adjusted R-squared:  0.09401 

# F-statistic: 3.542 on 4 and 94 DF,  p-value: 0.009747

#---

Variable "risk_assessment"(0.0133 *) affect the operational_efficiency of financial institutions.

Based on the  F-statistic of the model 3.542 and the corresponding p-value: 0.009747. This indicates that the model is statistically significant because the p-value: 0.009747 is less than 0.05

6.3. Control activities

To determine the effect of control activities on the operational efficiency of financial institution

Let see the table of multilinear regression model:

# Coefficients:

#                                                               Estimate Std. Error t value Pr(>|t|)    

# (Intercept)                                    2.1433888  0.4896671   4.377 3.11e-05 ***

# X                                                -0.0009338  0.0034575  -0.270  0.78768    

# control_activities                         0.2386995  0.0966352   2.470  0.01531 *  

# frequently_rotates_employees     0.2625027  0.0936821   2.802  0.00617 ** 

# sufficient_procedures                 -0.0172609  0.0781796  -0.221  0.82574    

#---

#  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

# Residual standard error: 0.9693 on 94 degrees of freedom

# Multiple R-squared:  0.1768, Adjusted R-squared:  0.1418 

# F-statistic: 5.048 on 4 and 94 DF,  p-value: 0.0009929

#---

From the above table:

The variable frequently_rotates_employees(0.00617 ** ) has stronger influence to the operational efficiency than the variable control_activities(0.01531 *)

Based on the  F-statistic of the model 5.048 and the corresponding p-value: 0.0009929. This indicates that overall the model is statistically significant because the p-value: 0.0009929 is much smaller than 0.05

6.4. Information and communication

Information and communication systems have a significant positive effect on the operational efficiency of financial institutions.

#---

#Coefficients:

#                                                                          Estimate Std. Error t value Pr(>|t|)    

#( Intercept)                                               1.8123028  0.4972051   3.645 0.000438 ***

# X                                                           -0.0005646  0.0033737  -0.167 0.867456    

# information_and_communication           0.2293080  0.0962420   2.383 0.019203 *  

# communicated_in_an_accurate_clear     0.2965128  0.0953769   3.109 0.002485 ** 

# relevant_information                  0.0661351  0.0857489   0.771 0.442484    

#---

#   Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

# Residual standard error: 0.9504 on 94 degrees of freedom

# Multiple R-squared:  0.2087, Adjusted R-squared:  0.175 

# F-statistic: 6.197 on 4 and 94 DF,  p-value: 0.0001804

#---

From the above table we can see that:

The communicated_in_an_accurate_clear (0.002485 **) has the stronger influence to the operational efficiency than the variable information_and_communication(0.019203 *).

Based on the  F-statistic of the model 6.197 and the corresponding p-value: 0.0001804. This indicates that overall the model is statistically significant because the p-value: 0.0001804 is much smaller than 0.05

6.5. Monitoring 

To investigate the result of information and communication systems on the operational efficiency of financial institutions.

#---

#Coefficients:

#                                                      Estimate Std. Error t value Pr(>|t|)    

# (Intercept)                            2.3775144  0.5244241   4.534 1.71e-05 ***

# X                                            0.0005766  0.0035582   0.162   0.8716    

# monitoring_activities             0.2666477  0.1023902   2.604   0.0107 *  

# assign_different_personnel    0.1478683  0.1035997   1.427   0.1568    

# replace_redundant                -0.0140751  0.0935202  -0.151   0.8807    

#---

#  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

# Residual standard error: 0.9982 on 94 degrees of freedom

# Multiple R-squared:  0.127, Adjusted R-squared:  0.08986 

# F-statistic: 3.419 on 4 and 94 DF,  p-value: 0.01177

#---

From the above table we can see:

The variable monitoring_activities (0.0107 *) has influence to the efficiency operation financial institutions. 

The variables fulfill the statistical model with p-value: 0.01177 is less than 0.05.

Based on the  F-statistic of the model 3.419 and the corresponding p-value: 0.01177. This indicates that the model is statistically significant because the p-value: 0.01177 is less than 0.05

Financial Institutions Survey Analysis

1. Data Survey data consist of the 99 observations and 73 variables. This study focuses on assessing the internal control systems and their impact on the operational efficiency of financial institutions in Nigeria. 2. Clean data, convert data head(data) CV me org author mngm hrd compet org_author entity effect_accounting App_disciplinary operational_efficiency 1 4 3 4 2 3 2 3 3 4 3 3 3 2 5 3 4 4 1 4 3 3 1 4 5 4 3 4 3 1 3 2 3 2 2 2 2 2 2 3. Data summary ── Data Summary ──────────────────────── Values Name data Number of rows 99 Number of columns 13 _______________________ Column type frequency: numeric 13 ________________________ Group variables None ── Variable type: numeric ──────────────────────────────────────────────────────────────────── skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist 1 X 0 1 50 28.7 1 25.5 50 74.5 99 ▇▇▇▇▇ 2 CV 0 1 3.87 1.05 1 3 4 5 5 ▁▁▅▇▆ 3 me 0 1 3.55 1.07 1 3 4 4 5 ▁▃▇▇▅ 4 org 0 1 2.68 1.19 1 1 3 4 4 ▇▂▁▇▇ 5 author 0 1 3.44 1.12 1 3 4 4 5 ▁▃▇▇▅ 6 mngm 0 1 2.97 1.16 1 2 3 4 4 ▃▂▁▅▇ 7 hrd 0 1 2.77 1.23 1 1 3 4 4 ▆▂▁▆▇ 8 compet 0 1 3.58 1.01 1 3 4 4 5 ▁▁▆▇▃ 9 org_author 0 1 3.35 1.08 1 3 3 4 5 ▂▂▇▇▃ 10 entity 0 1 2.80 1.11 1 2 3 4 4 ▅▂▁▇▆ 11 effect_accounting 0 1 3.38 0.997 1 3 3 4 5 ▁▂▇▇▂ 12 App_disciplinary 0 1 3.37 1.17 1 3 3 4 5 ▂▃▇▇▅ 13 operational_efficiency 0 1 3.70 1.11 1 3 4 5 5 ▁▂▇▇▇ 4. Plot data distribution

5.Multi linear model statistics Use the multi linear model statistics In this case will use operational efficiency as dependent variable, and the independent variables, as example: Variables need to be abbreviated to make it easier to create a model. #---Independent variables CV: "section_a_control_environment_the_following_statements_pertain_to_how_the_control_environment_influences_the_financial_performance_of_the_microfinance_bank_please_rate_each_statement_according_to_your_understanding_by_selecting_the_appropriate_option_communication_and_enforcement_of_integrity_and_ethical_values_within_our_microfinance_bank_are_adequate", me: "our_microfinance_bank_management_is_dedicated_to_ensuring_employee_competence_in_financial_matters", org: "the_managements_philosophy_and_operating_style_promote_the_microfinance_banks_growth_while_maintaining_adherence_to_rules_and_regulations", author: "the_microfinance_bank_s_organizational_structure_has_clear_lines_for_reporting_and_decision_making_hierarchies", mngm: "our_microfinance_banks_management_appropriately_assigns_authority_and_responsibility_to_qualified_individuals", hrd: "we_have_well_designed_human_resource_policies_that_are_easy_to_implement_and_practice_within_our_microfinance_bank", compet: "the_microfinance_bank_is_committed_to_ensuring_competence_in_job_specific_requirements_and_the_translation_of_ those_requirements_into_necessary_knowledge_and_skills", org_author: "the_organization_assigns_authority_and_responsibility_to_foster_accountability_and_control", entity: "the_entity_assigns_authority_and_responsibility_to_provide_a_basis_for_accountability_and_control", effect_accounting: "the_microfinance_bank_has_an_effective_accounting_and_financial_management_system_in_place", App_disciplinary: "appropriate_disciplinary_actions_are_taken_when_employees_fail_to_comply_with_policies_procedures_or_behavioral_standards", "employees_at_the_microfinance_bank_are_dedicated_to_following_organizational_policies_procedures_and_ethical_standards") #---Dependent variable operational efficiency: "section_f_operational_efficiency_please_indicate_your_level_of_agreement_with_the_following_statements_regarding_the_performance_of_your_organization_microfinance_bank_s_financial_statements_aim_to_provide_a_comprehensive_overview_of_the_organization_s_performance_and_position_at_a_specific_point_in_time", 6.1 Control environment To assess the effect of the control environment on the operational efficiency of financial institutions. We can see from the table of the multi linier statistics model regression: #--- # Coefficients: # Estimate Std. Error t value Pr(>|t|) # (Intercept) 2.7653917 0.7311077 3.782 0.000286 *** # X -0.0011205 0.0040307 -0.278 0.781693 # CV 0.0008834 0.1259978 0.007 0.994422 # me -0.0411602 0.1206962 -0.341 0.733918 # org -0.1852678 0.1055421 -1.755 0.082753 . # author -0.1898709 0.1128253 -1.683 0.096027 . # mngm 0.0548492 0.1015565 0.540 0.590534 # hrd 0.2139908 0.0952287 2.247 0.027192 * # compet 0.2940369 0.1283810 2.290 0.024448 * # org_author -0.0739613 0.1143838 -0.647 0.519609 # entity -0.0863652 0.1106345 -0.781 0.437160 # effect_accounting 0.2746892 0.1316306 2.087 0.039865 * # App_disciplinary 0.0099777 0.1087175 0.092 0.927089 #--- # Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # Residual standard error: 1.079 on 86 degrees of freedom # Multiple R-squared: 0.1715, Adjusted R-squared: 0.05595 # F-statistic: 1.484 on 12 and 86 DF, p-value: 0.1459 The three variables hrd(0.027192 *),compet( 0.024448 *) and effect_accounting(0.039865 *) have weak effects on the operational efficiency of financial institutions. The CV variable(0.994422) have no statistical influence to operational efficiency of financial. Based on the F-statistic of the model 1.484 and the corresponding p-value: 0.1459. This indicates that the overall model is not statistically significant because the p-value: 0.1459 is greater than 0.05. 6.2. Risk assessment #---- # Coefficients: # Estimate Std. Error t value Pr(>|t|) # (Intercept) 2.334e+00 5.144e-01 4.536 1.69e-05 *** # 6.325e-05 3.532e-03 0.018 0.9857 # risk_assessment 2.588e-01 1.025e-01 2.524 0.0133 * # financial_management 1.458e-01 9.261e-02 1.574 0.1188 # staff 2.319e-02 7.997e-02 0.290 0.7725 #--- # Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # Residual standard error: 0.9959 on 94 degrees of freedom # Multiple R-squared: 0.131, Adjusted R-squared: 0.09401 # F-statistic: 3.542 on 4 and 94 DF, p-value: 0.009747 #--- Variable "risk_assessment"(0.0133 *) affect the operational_efficiency of financial institutions. Based on the F-statistic of the model 3.542 and the corresponding p-value: 0.009747. This indicates that the model is statistically significant because the p-value: 0.009747 is less than 0.05 6.3. Control activities To determine the effect of control activities on the operational efficiency of financial institution Let see the table of multilinear regression model: # Coefficients: # Estimate Std. Error t value Pr(>|t|) # (Intercept) 2.1433888 0.4896671 4.377 3.11e-05 *** # X -0.0009338 0.0034575 -0.270 0.78768 # control_activities 0.2386995 0.0966352 2.470 0.01531 * # frequently_rotates_employees 0.2625027 0.0936821 2.802 0.00617 ** # sufficient_procedures -0.0172609 0.0781796 -0.221 0.82574 #--- # Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # Residual standard error: 0.9693 on 94 degrees of freedom # Multiple R-squared: 0.1768, Adjusted R-squared: 0.1418 # F-statistic: 5.048 on 4 and 94 DF, p-value: 0.0009929 #--- From the above table: The variable frequently_rotates_employees(0.00617 ** ) has stronger influence to the operational efficiency than the variable control_activities(0.01531 *) Based on the F-statistic of the model 5.048 and the corresponding p-value: 0.0009929. This indicates that overall the model is statistically significant because the p-value: 0.0009929 is much smaller than 0.05 6.3. Control activities To determine the effect of control activities on the operational efficiency of financial institution Let see the table of multilinear regression model: # Coefficients: # Estimate Std. Error t value Pr(>|t|) # (Intercept) 2.1433888 0.4896671 4.377 3.11e-05 *** # X -0.0009338 0.0034575 -0.270 0.78768 # control_activities 0.2386995 0.0966352 2.470 0.01531 * # frequently_rotates_employees 0.2625027 0.0936821 2.802 0.00617 ** # sufficient_procedures -0.0172609 0.0781796 -0.221 0.82574 #--- # Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # Residual standard error: 0.9693 on 94 degrees of freedom # Multiple R-squared: 0.1768, Adjusted R-squared: 0.1418 # F-statistic: 5.048 on 4 and 94 DF, p-value: 0.0009929 #--- From the above table: The variable frequently_rotates_employees(0.00617 ** ) has stronger influence to the operational efficiency than the variable control_activities(0.01531 *) Based on the F-statistic of the model 5.048 and the corresponding p-value: 0.0009929. This indicates that overall the model is statistically significant because the p-value: 0.0009929 is much smaller than 0.05 6.4. Information and communication Information and communication systems have a significant positive effect on the operational efficiency of financial institutions. #--- #Coefficients: # Estimate Std. Error t value Pr(>|t|) #( Intercept) 1.8123028 0.4972051 3.645 0.000438 *** # X -0.0005646 0.0033737 -0.167 0.867456 # information_and_communication 0.2293080 0.0962420 2.383 0.019203 * # communicated_in_an_accurate_clear 0.2965128 0.0953769 3.109 0.002485 ** # relevant_information 0.0661351 0.0857489 0.771 0.442484 #--- # Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # Residual standard error: 0.9504 on 94 degrees of freedom # Multiple R-squared: 0.2087, Adjusted R-squared: 0.175 # F-statistic: 6.197 on 4 and 94 DF, p-value: 0.0001804 #--- From the above table we can see that: The communicated_in_an_accurate_clear (0.002485 **) has the stronger influence to the operational efficiency than the variable information_and_communication(0.019203 *). Based on the F-statistic of the model 6.197 and the corresponding p-value: 0.0001804. This indicates that overall the model is statistically significant because the p-value: 0.0001804 is much smaller than 0.05 6.5. Monitoring To investigate the result of information and communication systems on the operational efficiency of financial institutions. #--- #Coefficients: # Estimate Std. Error t value Pr(>|t|) # (Intercept) 2.3775144 0.5244241 4.534 1.71e-05 *** # X 0.0005766 0.0035582 0.162 0.8716 # monitoring_activities 0.2666477 0.1023902 2.604 0.0107 * # assign_different_personnel 0.1478683 0.1035997 1.427 0.1568 # replace_redundant -0.0140751 0.0935202 -0.151 0.8807 #--- # Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 # Residual standard error: 0.9982 on 94 degrees of freedom # Multiple R-squared: 0.127, Adjusted R-squared: 0.08986 # F-statistic: 3.419 on 4 and 94 DF, p-value: 0.01177 #--- From the above table we can see: The variable monitoring_activities (0.0107 *) has influence to the efficiency operation financial institutions. The variables fulfill the statistical model with p-value: 0.01177 is less than 0.05. Based on the F-statistic of the model 3.419 and the corresponding p-value: 0.01177. This indicates that the model is statistically significant because the p-value: 0.01177 is less than 0.05

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Selamat Idhul Fitri from Home, menjawab pertanyaan kapan covid19 mencapai puncaknya? Atau kapan grafiknya melandai? Kalau ada pejabat yang mengatakan, kita sudah mulai lega karena R0(RNol) sudah mendekati 1(satu). Apa artinya? Dan benarkah itu?

Mbak Mona waspada Covid19

Apa itu R0?

Grafik covid19 akan melandai atau mencapai puncaknya, jika factor R0 mendekati satu atau kurang dari satu. Sebagai contoh mudahnya jika factor R0 =3, artinya satu orang pertama akan menulari 3 orang lain, dan seterusnya berantai.  Begitu juga jika R0=2, nah jika R0 lebih kecil dari satu atau negative  itu artinya covid19 berhenti.

Grafik Prediksi Covid19, 150 hr ke depan

Gejala utama infeksi Covid19

Gejala utama infeksi Covid19

Gejala apa yang paling dominan menentukan bahwa seseorang terjangkit covid19? apakah demam?mual?pusing?. untuk menjawab pertanyaan ini, saya coba menganalisa
data  yang dikumpulkan dari sekitar 1.700 respondent dari sebuah aplikasi Covid Tracker.

Diagram Venn

Data dalam file excel "symptoms.xlsx". Dengan metode klustering biasa tentu akan susah melihatnya meskipun bisa digambarkan dengan diagram VEN. Dengan coding R dihasilkan  grafik yang lebih mudah dilihat dan dianalisa.

Ranking gejala Covid19

library(tidyverse)
library(here)
library(janitor)
library(socviz)
library(ggrepel)

## --------------------------------------------------------------------
## Custom font and theme, omit if you don't have the myriad library
## (https://github.com/kjhealy/myriad) and associated Adobe fonts.
## --------------------------------------------------------------------
library(showtext)
showtext_auto()
library(myriad)

#import_myriad_semi()
#theme_set(theme_myriad_semi())
symptoms <- c("Anosmia", "Cough", "Fatigue", "Diarrhea", "Breath", "Fever")
names(symptoms) <- symptoms

dat <- readxl::read_xlsx("D:/R-BLOGGER/covid_symptoms-master/data/symptoms.xlsx")
dat %>% print(n = nrow(dat))

##head(dat)
## A tibble: 6 x 2
##  combination count
##  <chr>       <dbl>
##1 Anosmia       140
##2 Cough          57
##3 Fatigue       198
##4 Diarrhea       12
##5 Breath          5
##6 Fever          11

subsets <- dat$combination
##subsets
## [1] "Anosmia"                                     "Cough"                                   
## [3] "Fatigue"                                     "Diarrhea"                                 
## [5] "Breath"                                      "Fever"                                   
## [7] "Cough&Fatigue"                               "Fatigue&Fever"                           
## [9] "Breath&Fatigue"                              "Diarrhea&Fatigue"                         
##[11] "Anosmia&Fatigue"                             "Breath&Cough"
##............................
##[31] "Anosmia&Breath&Cough&Diarrhea&Fatigue&Fever" ##"Anosmia&Cough&Diarrhea&Fatigue&Fever" 

symptom_mat <- map_dfc(subsets, str_detect, symptoms) %>%
    data.frame() %>%
    t() %>%
    as_tibble()

##symptom_mat
##A tibble: 32 x 6
##   V1    V2    V3    V4    V5    V6 
##   <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
## 1 TRUE  FALSE FALSE FALSE FALSE FALSE
## 2 FALSE TRUE  FALSE FALSE FALSE FALSE
## 3 FALSE FALSE TRUE  FALSE FALSE FALSE
## 4 FALSE FALSE FALSE TRUE  FALSE FALSE
## 5 FALSE FALSE FALSE FALSE TRUE  FALSE

colnames(symptom_mat)  <- symptoms
symptom_mat$count <- dat$count
symptom_mat %>% print(n = nrow(symptom_mat))

indvs <- symptom_mat %>%
    uncount(count)

##head(indvs)
## A tibble: 1,764 x 6
##   Anosmia Cough Fatigue Diarrhea Breath Fever
##   <lgl>   <lgl> <lgl>   <lgl>    <lgl>  <lgl>
## 1 TRUE    FALSE FALSE   FALSE    FALSE  FALSE
## 2 TRUE    FALSE FALSE   FALSE    FALSE  FALSE
##3 TRUE    FALSE FALSE   FALSE    FALSE  FALSE
##4 TRUE    FALSE FALSE   FALSE    FALSE  FALSE
##5 TRUE    FALSE FALSE   FALSE    FALSE  FALSE
## 6 TRUE    FALSE FALSE   FALSE    FALSE  FALSE
## 7 TRUE    FALSE FALSE   FALSE    FALSE  FALSE
## 8 TRUE    FALSE FALSE   FALSE    FALSE  FALSE
## 9 TRUE    FALSE FALSE   FALSE    FALSE  FALSE
##10 TRUE    FALSE FALSE   FALSE    FALSE  FALSE

library(ComplexUpset)
upset(indvs, symptoms,
      name="Frekuensi Gejala Utama COVID19. Data total 1,764 individuals.",
      min_size = 0,
      width_ratio = 0.100) +
    labs(title = "Kombinasi COVID-19 Symptoms",
         caption = "File data: covid.joinzoe.com/us, recrafted by Bambangpe" )

Kesimpulan:
Mengamati grafik batang di atas, dengan mudah disimpulkan bahwa gejala utama terjangkit covid19 dpt dirangking sbb:
1. Kehilangan indera penciuman (atau "Anosmia") + fatigue(lemah) tampaknya menjadi gejala umum Covid19 total ada 281
2. Gejala fatigue(lemah)+Anosmia+Cough(batuk2) menempati urutan kedua dengan total 259
3. Gejala tubuh lemah juga sudah mengindikasi terkena covid19 dengan total 198
4. Dst

link:
https://rpubs.com/bambangpe/601238
https://bambangpe.shinyapps.io/covid19-prediction/

Dampak saham Boeing akibat jatuhnya Lion Air JT610

Dampak saham Boeing akibat jatuhnya Lion Air JT610.

Setelah Lion Air JT610 jatuh di perairan Krawang sekitar Jakarta hari Senin 29 Oktober 2018
, saya mencoba menengok saham Boeing, apa dampaknya?.

Lion Air JT 610 crash

Selanjutnya saya mencoba membandingkan dengan Saham Airbus sebagai saingannya, berikut saya tuliskan R scriptnya dalam file RMarkdown dan saya upload di web Rpubs.

Chart Sham Boeing

R Gadget

R programming mudah di aplikasikan di gadget, di sini akan diberikan contoh sederhana aplikasi  R programming untuk gadget.

Berikut script R nya:

Script ui.R

library(shiny)
library(miniUI)
library(leaflet)
library(ggplot2)

ui <- miniPage(
  gadgetTitleBar("Example R in Gadget"),
  miniTabstripPanel(
    miniTabPanel("Parameters", icon = icon("sliders"),
      miniContentPanel(
        sliderInput("year", "Year", 1978, 2010, c(2000, 2010), sep = "")
      )
    ),
    miniTabPanel("Visualize", icon = icon("area-chart"),
      miniContentPanel(
        plotOutput("cars", height = "100%")
      )
    ),
    miniTabPanel("Map", icon = icon("map-o"),
      miniContentPanel(padding = 0,
        leafletOutput("map", height = "100%")
      ),
      miniButtonBlock(
        actionButton("resetMap", "Reset")
      )
    ),
    miniTabPanel("Data", icon = icon("table"),
      miniContentPanel(
        DT::dataTableOutput("table")
      )
    )
  )
)

Script server.R

library(shiny)
library(miniUI)
library(leaflet)
library(ggplot2)

server <- function(input, output, session) {
  output$cars <- renderPlot({
    require(ggplot2)
    ggplot(cars, aes(speed, dist)) + geom_point()
  })

  output$map <- renderLeaflet({
    force(input$resetMap)

    leaflet(quakes, height = "100%") %>% addTiles() %>%
      addMarkers(lng = ~long, lat = ~lat)
  })

  output$table <- DT::renderDataTable({
    diamonds
  })

  observeEvent(input$done, {
    stopApp(TRUE)
  })
}

Setelah di upload di server shiny.io, maka hasilnya dapat dilihat di gadget

Referensi:
Health prediction
Education
Rstudio
Kursus R

Gadged dan Machine Learning

Melanjutkan R gadget sebelumnya, kali ini akan diberikan contoh Analisa Modeling menggunakan R programming yang mudah dibaca lewat smartphone, analisa menggunakan data  'Housing Values in Suburbs of Boston' menentukan variable yang paling berpengaruh(Variable Important) terhadap nilai perumahan.

Sebuah contoh penggunaan Machine Learning dengan R programming yang mudah dipelajari dan mudah di aplikasi. 

Seperti biasa file terdiri dari file ui dan file server, scriptnya sbb:

ui.R

 ui <-
    miniPage(
    gadgetTitleBar("Boston Analytic, simple way finding Variable Importance"),
  miniTabstripPanel(
    miniTabPanel("Data", icon = icon("table"),
      miniContentPanel(     
       dataTableOutput("data"))),
    #---keterangan
    miniTabPanel("About Boston Data", icon = icon("list-alt"),
      miniContentPanel(     
       dataTableOutput("About"),
         h3(strong('Housing Values in Suburbs of Boston')),
               # p('Housing Values in Suburbs of Boston',align = 'Justify'),
p('Description : The Boston data frame has 506 rows and 14 columns.',align = 'Justify'),
p('Usage : Boston',align = 'Justify'),
p('Format : This data frame contains the following columns:',align = 'Justify'),
p('1. crim : per capita crime rate by town.',align = 'Justify'),
p('2. zn : proportion of residential land zoned for lots over 25,000 sq.ft.',align = 'Justify'),
p('3. indus : proportion of non-retail business acres per town.',align = 'Justify'),
p('4. chas : Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).',align = 'Justify'),
p('5. nox : nitrogen oxides concentration (parts per 10 million).',align = 'Justify'),
p('6. rm : average number of rooms per dwelling.',align = 'Justify'),
p('7. age : proportion of owner-occupied units built prior to 1940.',align = 'Justify'),
p('8. dis : weighted mean of distances to five Boston employment centres.',align = 'Justify'),
p('9. rad : index of accessibility to radial highways.',align = 'Justify'),
p('10. tax : full-value property-tax rate per 10,000 dollar.',align = 'Justify'),
p('11. ptratio : pupil-teacher ratio by town.',align = 'Justify'),
p('12. black : 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town.',align = 'Justify'),
p('13. lstat : lower status of the population (percent).',align = 'Justify'),
p('14. medv : median value of owner-occupied homes per 1000s dollar.',align = 'Justify'))),

    #
     miniTabPanel("Plot Model",icon = icon("area-chart"),
    miniContentPanel(
       plotOutput("model"))),

    miniTabPanel("Variable Important",icon = icon("area-chart"),
       miniContentPanel(     
        verbatimTextOutput(("var_importance")))),
   
    miniTabPanel("Variable imp",icon = icon("table"),
       miniContentPanel(     
    plotOutput("pg")))
  ))

server.R

library(shiny)
library(miniUI)
library(ggplot2)
library(dplyr)
library(MASS)
library(randomForest)

data <- Boston
data
dt1 <- data[ ,-4]
dt1
spl <- sample(nrow(dt1),nrow(dt1)*0.7)
train <- dt1[spl, ]
test <- dt1[-spl, ]

model <- randomForest(medv~., data = train)

pmod <- plot(model)
pmod+theme_linedraw()
pmod

pm <- summary(model)
pm

pv <- varImpPlot(model)
pv

var_importance <- data_frame(variable = setdiff(colnames(train), "medv"),
                             importance = as.vector(importance(model)))
var_importance <- arrange(var_importance, desc(importance))
#var_importance
var_importance$variable <- factor(var_importance$variable, levels=var_importance$variable)
pg <- ggplot(data = var_importance, aes(x = variable, y = importance))+
  geom_bar(stat = "identity")+ggtitle("Varible Importance")+theme_linedraw()
pg

server <- function(input, output, session) {
   
  output$data <- renderDataTable({
    data
  })
     output$model <- renderPlot({
    plot(model)
  })
    output$var_importance<- renderPrint({
    var_importance
  })
   output$pg <- renderPlot({
    pg
  })
    observeEvent(input$done, {
    stopApp(TRUE)
  })
}

Selanjutnya tinggal di upload di server shiny, file dibuat untuk versi gadget.

Referensi:
Kursus R programming