Managing personal finances is important for maintaining financial health and achieving financial goals. Building a personal finance tracker dashboard in R can provide valuable insights into income, expenses, savings and investment performance. In this article, we'll outline the steps to create a simple yet effective personal finance tracker dashboard using R and popular visualization libraries like ggplot2 and shiny.
1. Data Collection and Preparation
Start by gathering your financial data from sources such as bank statements, credit card statements, investment accounts and budgeting apps. Organize the data into a structured format, such as a CSV file, with columns for date, category, amount and description.
Dataset Link: Personal Finance
2. Data Analysis and Visualization
Use R and ggplot2 to analyze and visualize your financial data. Create visualizations such as:
2.1. Creating dashboard templet
We are using R studio, so the first thing we have to do as follows:
Go to File --> New file --> R markdown --> Templet --> Flex Dashboard --> Click ok
After clicking on that we get this window.
Then you get a template look like as follows:
Dashboard layout
Now we will create the Dashboard layout and pass the arguments in a chunks.
1. YAML (Header Defines):
This section describes the dashboard's title and layout choices. The dashboard will have social sharing buttons and rows as its main layout style.
---
title: "Personal Finance"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: fill
social: [ "twitter", "facebook", "menu"]
---
2. Setup Chunk
We load all the necessary libraries in this chunk
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(highcharter)
library(gt)
library(DT)
library(htmltools)
library(plotly)
library(ggplot2)
library(plyr)
library(rpivotTable)
library(lubridate)
```
3. Loading the Dataset
We will load our dataset using read_csv() function.
```{r}
# Import data
library(readxl)
df = read_csv("/content/personal-finance-dataxlsx---Sheet1.csv")
```
The dashboard is divided into rows and columns, each of containing tables or different visuals.
Creating value box and gauge chart
This section computes the mean salary by filtering the data for salary portions. The average monthly salary is shown visually on a gauge chart.
### **Salary per Month**
```{r}
df_salary = df %>%
select(Category,`Debit/Credit`) %>%
filter(tolower(Category) == "salary")
gauge(round(mean(df_salary$`Debit/Credit`),
digits = 2),
min = 0,
max = 100000,
gaugeSectors(colors = "yellow"))
```
Output:
Similarly we made:
### Others Income
```{r}
df_other = df %>%
select(Category,`Income/Expense`,`Debit/Credit`) %>%
filter(tolower(Category) == "other") %>%
filter(tolower(`Income/Expense`) == "income")
valueBox(sum(df_other$`Debit/Credit`),
icon = "fa-building")
```
### Total Spending
```{r}
df_ = df %>%
select(Category,`Income/Expense`,`Debit/Credit`) %>%
filter(tolower(`Income/Expense`) != "income")
valueBox(sum(df_$`Debit/Credit`),
icon = "fa-building")
```
### Total Savings
```{r}
df_ = df %>%
select(Category,`Income/Expense`,`Debit/Credit`) %>%
filter(tolower(`Income/Expense`) != "income")
df_salary = df %>%
select(Category,`Income/Expense`,`Debit/Credit`) %>%
filter(tolower(`Income/Expense`) == "income")
valueBox(sum(df_salary$`Debit/Credit`) - sum(df_$`Debit/Credit`),
icon = "fa-building")
```
### Maximum Expense
```{r}
df_date = df %>%
select(`Date / Time`, Category, `Income/Expense`, `Debit/Credit`) %>%
mutate(`Date / Time` = mdy(`Date / Time`)) %>%
filter(tolower(`Income/Expense`) != "income")
df_date$Category = tolower(df_date$Category)
valueBox(max(df_date$`Debit/Credit`), icon = "fa-user")
```
### Average Expense
```{r}
df_date = df %>%
select(`Date / Time`, Category, `Income/Expense`, `Debit/Credit`) %>%
mutate(`Date / Time` = mdy(`Date / Time`)) %>%
filter(tolower(`Income/Expense`) != "income")
df_date$Category = tolower(df_date$Category)
valueBox(round(mean(df_date$`Debit/Credit`), 2), icon = "fa-user")
```
### Minimum Expense
```{r}
df_date = df %>%
select(`Date / Time`, Category, `Income/Expense`, `Debit/Credit`) %>%
mutate(`Date / Time` = mdy(`Date / Time`)) %>%
filter(tolower(`Income/Expense`) != "income")
df_date$Category = tolower(df_date$Category)
valueBox(min(df_date$`Debit/Credit`), icon = "fa-user")
```
Creating plots using ggplot2 and make them interactive with Plotly
Combining the power of ggplot2 for static visualizations with the interactivity of Plotly creates dynamic and engaging plots that enhance data exploration and presentation.
### **Most Frequently Spending On A Category**
```{r}
df_ = df %>%
select(Category, `Income/Expense`, `Debit/Credit`) %>%
filter(tolower(`Income/Expense`) != "income")
df_$Category = tolower(df_$Category)
bar = ggplot(data= df_) +
geom_bar(aes(x = Category, fill = Category)) +
guides(fill = F)
ggplotly(bar)
```
Output:
Similarly we made:
### **Monthly Spending On Each Category**
```{r}
df_date = df %>%
select(`Date / Time`, Category, `Income/Expense`, `Debit/Credit`) %>%
mutate(`Date / Time` = mdy(`Date / Time`)) %>%
filter(tolower(`Income/Expense`) != "income")
df_date$Category = tolower(df_date$Category)
l1 = ggplot(df_date, aes(x = `Date / Time`, y = `Debit/Credit`, color = Category)) +
geom_line() +
labs(x = "Date", y = "Debit/Credit", color = "Category")
ggplotly(l1)
```
Create a new page for the pivot table
Applies PivotTable to create a pivot table to summarize the spending of a person by category.
Table Spending In Each Category
==========================================================
```{r}
df_11 = df %>%
select(`Date / Time`, Category, `Income/Expense`, `Debit/Credit`) %>%
mutate(`Date / Time` = mdy(`Date / Time`)) %>%
filter(tolower(`Income/Expense`) != "income")
df_11$Category = tolower(df_11$Category)
rpivotTable(df_11,
aggregatorName = "sum",
cols= "`Debit/Credit`",
rows = "Category" ,
rendererName = "Table Barchart")
```
Output:
There are lots of options you can use in the pivot table.
Creating Data Table
Create a new page and using 'DT::datatable' to display the data in an interactive table in the new page.
Data Table
=============================================================
```{r}
datatable(df,
caption = "Personal Finance Data",
rownames = T,
filter = "top",
options = list(pageLength = 25))
```
Output:
It will show the complete visualization in the R Dashboard.
Complete Code Block
Here is the whole code for the process of creating the Personal Finance Tracker Dashboard.
---
title: "Personal Finance"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: fill
social: [ "twitter", "facebook", "menu"]
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(highcharter)
library(gt)
library(DT)
library(htmltools)
library(plotly)
library(ggplot2)
library(plyr)
library(rpivotTable)
library(lubridate)
```
```{r}
# Import data
library(readxl)
df = read_csv("/content/personal-finance-dataxlsx---Sheet1.csv")
```
Personal Finance
=======================================================================
Column
-----------------------------------------------------------------------
### **Salary per Month**
```{r}
df_salary = df %>%
select(Category,`Debit/Credit`) %>%
filter(tolower(Category) == "salary")
gauge(round(mean(df_salary$`Debit/Credit`),
digits = 2),
min = 0,
max = 100000,
gaugeSectors(colors = "yellow"))
```
### Total Allowences Get
```{r}
df_all = df %>%
select(Category,`Debit/Credit`) %>%
filter(tolower(Category) == "allowance")
valueBox(sum(df_all$`Debit.Credit`),
icon = "fa-building")
```
### Others Income
```{r}
df_other = df %>%
select(Category,`Income/Expense`,`Debit/Credit`) %>%
filter(tolower(Category) == "other") %>%
filter(tolower(`Income/Expense`) == "income")
valueBox(sum(df_other$`Debit/Credit`),
icon = "fa-building")
```
### Total Spending
```{r}
df_ = df %>%
select(Category,`Income/Expense`,`Debit/Credit`) %>%
filter(tolower(`Income/Expense`) != "income")
valueBox(sum(df_$`Debit/Credit`),
icon = "fa-building")
```
### Total Savings
```{r}
df_ = df %>%
select(Category,`Income/Expense`,`Debit/Credit`) %>%
filter(tolower(`Income/Expense`) != "income")
df_salary = df %>%
select(Category,`Income/Expense`,`Debit/Credit`) %>%
filter(tolower(`Income/Expense`) == "income")
valueBox(sum(df_salary$`Debit/Credit`) - sum(df_$`Debit/Credit`),
icon = "fa-building")
```
Row
-----------------------------------------------------------
### **Most Frequently Spending On A Category**
```{r}
df_ = df %>%
select(Category, `Income/Expense`, `Debit/Credit`) %>%
filter(tolower(`Income/Expense`) != "income")
df_$Category = tolower(df_$Category)
bar = ggplot(data= df_) +
geom_bar(aes(x = Category, fill = Category)) +
guides(fill = F)
ggplotly(bar)
```
### **Monthly Spending On Each Category**
```{r}
df_date = df %>%
select(`Date / Time`, Category, `Income/Expense`, `Debit/Credit`) %>%
mutate(`Date / Time` = mdy(`Date / Time`)) %>%
filter(tolower(`Income/Expense`) != "income")
df_date$Category = tolower(df_date$Category)
l1 = ggplot(df_date, aes(x = `Date / Time`, y = `Debit/Credit`, color = Category)) +
geom_line() +
labs(x = "Date", y = "Debit/Credit", color = "Category")
ggplotly(l1)
```
Row
-----------------------------------------------------------------------
### Maximum Expense
```{r}
df_date = df %>%
select(`Date / Time`, Category, `Income/Expense`, `Debit/Credit`) %>%
mutate(`Date / Time` = mdy(`Date / Time`)) %>%
filter(tolower(`Income/Expense`) != "income")
df_date$Category = tolower(df_date$Category)
valueBox(max(df_date$`Debit/Credit`), icon = "fa-user")
```
### Average Expense
```{r}
df_date = df %>%
select(`Date / Time`, Category, `Income/Expense`, `Debit/Credit`) %>%
mutate(`Date / Time` = mdy(`Date / Time`)) %>%
filter(tolower(`Income/Expense`) != "income")
df_date$Category = tolower(df_date$Category)
valueBox(round(mean(df_date$`Debit/Credit`), 2), icon = "fa-user")
```
### Minimum Expense
```{r}
df_date = df %>%
select(`Date / Time`, Category, `Income/Expense`, `Debit/Credit`) %>%
mutate(`Date / Time` = mdy(`Date / Time`)) %>%
filter(tolower(`Income/Expense`) != "income")
df_date$Category = tolower(df_date$Category)
valueBox(min(df_date$`Debit/Credit`), icon = "fa-user")
```
Table Spending In Each Category
==========================================================
```{r}
df_11 = df %>%
select(`Date / Time`, Category, `Income/Expense`, `Debit/Credit`) %>%
mutate(`Date / Time` = mdy(`Date / Time`)) %>%
filter(tolower(`Income/Expense`) != "income")
df_11$Category = tolower(df_11$Category)
rpivotTable(df_11,
aggregatorName = "sum",
cols= "`Debit/Credit`",
rows = "Category" ,
rendererName = "Table Barchart")
```
Data Table
=============================================================
```{r}
datatable(df,
caption = "Personal Finance Data",
rownames = T,
filter = "top",
options = list(pageLength = 25))
```
Output:
After knitting the current file we get the below output and you can then publish your dash board on the web.
After knitting the current file we get the below output and you can then publish your dash board on the web.
Overall, this flex dashboard provides a comprehensive overview of personal finance data, allowing users to analyze income, expenses, savings and spending patterns effectively. It offers a combination of visualizations and summary statistics for better understanding and decision-making regarding personal finances.