Look at Mint transaction data via R

mint.R

## Load libraries
if (!require(readr)) { install.packages("readr") }; library(readr)
if (!require(magrittr)) { install.packages("magrittr") }; library(magrittr)
if (!require(dplyr)) { install.packages("dplyr") }; library(dplyr)
if (!require(tibbletime)) { install.packages("tibbletime") }; library(tibbletime)
if (!require(lubridate)) { install.packages("lubridate") }; library(lubridate)
if (!require(devtools)) { install.packages("devtools") }; library(devtools)
if (!require(shinyview)) { install_github("peterhurford/shinyview") }; library(shinyview)

## Load Mint transactions
trans <- read_csv("~/Downloads/transactions.csv")

## Parse it into dataframe
trans <- trans %>%
  mutate(Date = as.Date(Date, "%m/%d/%Y")) %>%
  arrange(Date) %>%
  as_tbl_time(index = Date) %>%
  filter_time("2018-01" ~ "2018-03") %>% # Change this the appropriate date range
  mutate(moy = month(Date))

## Look at amount by category by month
view_trans_by_month <- function(trans) {
  trans %>%
    filter(`Transaction Type` == "debit") %>%
    select(Date, moy, Amount, Description, Category) %>%
    distinct %>%
    group_by(moy, Category) %>%
    summarise(total = sum(Amount)) %>%
    arrange(-moy) %>%
    shiny_view
}

## Look at individual transactions
view_trans <- function(trans) {
  trans %>%
    filter(`Transaction Type` == "debit") %>%
    select(Date, moy, Amount, Description, Category) %>%
    distinct %>%
    arrange(moy, Category) %>%
    shiny_view
}

view_trans_by_month(trans)

view_trans(trans)