install.packages(“flexdashboard”) set.seed(1) install.packages(“dplyr”) install.packages(“ggplot2”) install.packages(“data.table”) install.packages(“bit64”) install.packages(“corrplot”) install.packages(“ggthemes”) install.packages(“car”) install.packages(“tidyverse”) install.packages(“ggcorrplot”) install.packages(“plotrix”) install.packages(“reshape2”) install.packages(“/Users/JLu/Desktop/512/plotly_4.7.1.tar.gz”, repos = NULL, type=“source”) install.packages(“/Users/JLu/Desktop/512/httpuv_1.4.4.1.tar.gz”, repos = NULL, type=“source”)

Column

Pie Chart to understand spending in different categories over the past 6 months

[1] 1998
[1] 4080
[1] 2914.67
[1] 2005.53
[1] 184.82
[1] 1326.86
[1] 558.48
[1] 415.8
[1] 227.23
[1] 6150.05

Column

Time Series for total spending vs total income

  Date_Asof Total_Income Total_Expense Credit_Total Car_Financing Rent
1   1/12/18      3329.24       2632.29   $1,619.29            333  680
2   2/12/18      3114.84       3383.25   $2,370.25            333  680
3   3/12/18      3222.06       2871.42   $1,858.42            333  680
4   4/12/18      3222.06       3110.62   $2,097.62            333  680
5   5/12/18      3330.17       3748.47   $2,735.47            333  680
6   6/12/18      2931.79       4865.63   $3,852.63            333  680
  Travel Dining_Out   Gas Grocery Cosmetics Coffee Subscriptions   Other
1 355.00     226.00 23.14  272.27    187.86  44.33         32.09  510.69
2 659.67     235.11 42.92  394.64     81.90  81.89         32.09  842.03
3 311.00     248.01 20.53  187.34      0.00  43.66        133.08  914.80
4 113.67     269.34 39.19  239.76      0.00 131.66          9.99  511.69
5 716.00     431.67 37.12   69.63    288.72  95.33          9.99 1087.01
6 759.33     595.40 21.92  163.22      0.00  18.93          9.99 2283.83

Stacked Bar plot for each catergory in each month

We can see that leisure spending and fine dinning were increasing dramatically. Which was due to the travelling for fun.