library(dplyr)
library(kableExtra)
expense = read.csv("/Users/gabrieltaylor/Downloads/AccountHistory.csv")
head(expense)
## Account.Number Post.Date Check Description
## 1 01 06801121 4/27/2020 NA ACH TRANSACTION VENMO
## 2 01 06801121 4/27/2020 NA ACH TRANSACTION VENMO
## 3 01 06801121 4/27/2020 NA POS PURCHASE SOMBRERITO MEXICAN IL EAST PEORIA
## 4 01 06801121 4/26/2020 NA POS PURCHASE SUBWAY 0004 IL METAMORA
## 5 01 06801121 4/26/2020 NA POS PURCHASE METAMORA IGA IL METAMORA
## 6 01 06801121 4/25/2020 NA Woodland Knoll Il
## Debit Credit Status Balance Classification
## 1 NA 27 Posted NA
## 2 NA 75 Posted NA
## 3 23.87 NA Posted NA
## 4 7.95 NA Posted NA
## 5 8.51 NA Posted NA
## 6 5.23 NA Posted 927.51 Food & Dining
expense_stats = expense %>%
group_by(Classification) %>%
summarise(Expenses = sum(Debit),
Income = sum(Credit))
tibble(expense_stats)%>%
kable(digits = 2)%>%
kable_styling("striped", full_width = FALSE)%>%
row_spec(c(14:15, 27, 30), background = "black", color = "white")
|
Classification
|
Expenses
|
Income
|
|
|
NA
|
NA
|
|
Alcohol & Bars
|
380.47
|
NA
|
|
Auto & Transport
|
53.25
|
NA
|
|
Auto Insurance
|
NA
|
14695.56
|
|
Banking Fee
|
24.00
|
NA
|
|
Bills & Utilities
|
555.46
|
NA
|
|
Books
|
2.38
|
NA
|
|
Cash
|
100.00
|
NA
|
|
Clothing
|
NA
|
NA
|
|
Coffee Shops
|
106.08
|
NA
|
|
Credit Card Payment
|
498.02
|
NA
|
|
Electronics & Software
|
178.31
|
NA
|
|
Entertainment
|
168.23
|
NA
|
|
Fast Food
|
1392.64
|
NA
|
|
Food & Dining
|
4436.17
|
NA
|
|
Gas
|
1890.74
|
NA
|
|
Groceries
|
144.92
|
NA
|
|
Health & Fitness
|
10.00
|
NA
|
|
Home Improvement
|
1.01
|
NA
|
|
Income
|
NA
|
528.02
|
|
Movies & DVDs
|
199.49
|
NA
|
|
Music
|
70.20
|
NA
|
|
Pharmacy
|
69.33
|
NA
|
|
Rental Car & Taxi
|
127.93
|
NA
|
|
Restaurants
|
470.31
|
NA
|
|
Shipping
|
5.43
|
NA
|
|
Shopping
|
1069.41
|
NA
|
|
Transfer
|
NA
|
NA
|
|
Travel
|
27.09
|
NA
|
|
Uncategorized
|
1077.99
|
NA
|
|
Utilities
|
612.25
|
NA
|