---
title: "Personal Finance Dashboard"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: fill
social: []
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)
library(highcharter)
library(ggvis)
```
```{r}
# The plyr library will be used to help us remap values
library(plyr)
# Load the data in to a dataframe for later use, please enter the appropriate path for the CSV.
df = read.csv('https://raw.githubusercontent.com/crarnouts/CUNY-MSDS/master/NFWBS_PUF_2016_data.csv')
# This code maps values from the dummy variables to the descriptors from the codebook.
df$sample = revalue(factor(df$sample), c(
`1` = "General population",
`2` = "Age 62+ oversample",
`3` = "Race/ethnicity and poverty oversample"
))
df$fpl = revalue(factor(df$fpl), c(
`1` = "<100% FPL",
`2` = "100%-199% FPL",
`3` = "200%+ FPL"
))
df$SWB_1 = revalue(factor(df$SWB_1), c(
`-4` = "Response not written to database",
`-1` = "Refused",
`1` = "1 Strongly disagree",
`2` = "2",
`3` = "3",
`4` = "4",
`5` = "5",
`6` = "6",
`7` = "7 Strongly agree"
))
df$SWB_2 = revalue(factor(df$SWB_2), c(
`-4` = "Response not written to database",
`-1` = "Refused",
`1` = "1 Strongly disagree",
`2` = "2",
`3` = "3",
`4` = "4",
`5` = "5",
`6` = "6",
`7` = "7 Strongly agree"
))
df$SWB_3 = revalue(factor(df$SWB_3), c(
`-4` = "Response not written to database",
`-1` = "Refused",
`1` = "1 Strongly disagree",
`2` = "2",
`3` = "3",
`4` = "4",
`5` = "5",
`6` = "6",
`7` = "7 Strongly agree"
))
df$FWBscore = revalue(factor(df$FWBscore), c(
`-4` = "Response not written to database",
`-1` = "Refused"
))
df$FWB1_1 = revalue(factor(df$FWB1_1), c(
`-4` = "Response not written to database",
`-1` = "Refused",
`1` = "Not at all",
`2` = "Very little",
`3` = "Somewhat",
`4` = "Very well",
`5` = "Completely"
))
df$FWB1_2 = revalue(factor(df$FWB1_2), c(
`-4` = "Response not written to database",
`-1` = "Refused",
`1` = "Not at all",
`2` = "Very little",
`3` = "Somewhat",
`4` = "Very well",
`5` = "Completely"
))
df$FWB1_3 = revalue(factor(df$FWB1_3), c(
`-4` = "Response not written to database",
`-1` = "Refused",
`1` = "Not at all",
`2` = "Very little",
`3` = "Somewhat",
`4` = "Very well",
`5` = "Completely"
))
df$FWB1_4 = revalue(factor(df$FWB1_4), c(
`-4` = "Response not written to database",
`-1` = "Refused",
`1` = "Not at all",
`2` = "Very little",
`3` = "Somewhat",
`4` = "Very well",
`5` = "Completely"
))
df$FWB1_5 = revalue(factor(df$FWB1_5), c(
`-4` = "Response not written to database",
`-1` = "Refused",
`1` = "Not at all",
`2` = "Very little",
`3` = "Somewhat",
`4` = "Very well",
`5` = "Completely"
))
df$FWB1_6 = revalue(factor(df$FWB1_6), c(
`-4` = "Response not written to database",
`-1` = "Refused",
`1` = "Not at all",
`2` = "Very little",
`3` = "Somewhat",
`4` = "Very well",
`5` = "Completely"
))
df$FWB2_1 = revalue(factor(df$FWB2_1), c(
`-4` = "Response not written to database",
`-1` = "Refused",
`1` = "Never",
`2` = "Rarely",
`3` = "Sometimes",
`4` = "Often",
`5` = "Always"
))
df$FWB2_2 = revalue(factor(df$FWB2_2), c(
`-4` = "Response not written to database",
`-1` = "Refused",
`1` = "Never",
`2` = "Rarely",
`3` = "Sometimes",
`4` = "Often",
`5` = "Always"
))
df$FWB2_3 = revalue(factor(df$FWB2_3), c(
`-4` = "Response not written to database",
`-1` = "Refused",
`1` = "Never",
`2` = "Rarely",
`3` = "Sometimes",
`4` = "Often",
`5` = "Always"
))
df$FWB2_4 = revalue(factor(df$FWB2_4), c(
`-4` = "Response not written to database",
`-1` = "Refused",
`1` = "Never",
`2` = "Rarely",
`3` = "Sometimes",
`4` = "Often",
`5` = "Always"
))
df$FSscore = revalue(factor(df$FSscore), c(
`-1` = "Refused"
))
df$FS1_1 = revalue(factor(df$FS1_1), c(
`-1` = "Refused",
`1` = "Not at all",
`2` = "Very little",
`3` = "Somewhat",
`4` = "Very well",
`5` = "Completely"
))
df$FS1_2 = revalue(factor(df$FS1_2), c(
`-1` = "Refused",
`1` = "Not at all",
`2` = "Very little",
`3` = "Somewhat",
`4` = "Very well",
`5` = "Completely"
))
df$FS1_3 = revalue(factor(df$FS1_3), c(
`-1` = "Refused",
`1` = "Not at all",
`2` = "Very little",
`3` = "Somewhat",
`4` = "Very well",
`5` = "Completely"
))
df$FS1_4 = revalue(factor(df$FS1_4), c(
`-1` = "Refused",
`1` = "Not at all",
`2` = "Very little",
`3` = "Somewhat",
`4` = "Very well",
`5` = "Completely"
))
df$FS1_5 = revalue(factor(df$FS1_5), c(
`-1` = "Refused",
`1` = "Not at all",
`2` = "Very little",
`3` = "Somewhat",
`4` = "Very well",
`5` = "Completely"
))
df$FS1_6 = revalue(factor(df$FS1_6), c(
`-1` = "Refused",
`1` = "Not at all",
`2` = "Very little",
`3` = "Somewhat",
`4` = "Very well",
`5` = "Completely"
))
df$FS1_7 = revalue(factor(df$FS1_7), c(
`-1` = "Refused",
`1` = "Not at all",
`2` = "Very little",
`3` = "Somewhat",
`4` = "Very well",
`5` = "Completely"
))
df$FS2_1 = revalue(factor(df$FS2_1), c(
`-1` = "Refused",
`1` = "Never",
`2` = "Rarely",
`3` = "Sometimes",
`4` = "Often",
`5` = "Always"
))
df$FS2_2 = revalue(factor(df$FS2_2), c(
`-1` = "Refused",
`1` = "Never",
`2` = "Rarely",
`3` = "Sometimes",
`4` = "Often",
`5` = "Always"
))
df$FS2_3 = revalue(factor(df$FS2_3), c(
`-1` = "Refused",
`1` = "Never",
`2` = "Rarely",
`3` = "Sometimes",
`4` = "Often",
`5` = "Always"
))
df$SUBKNOWL1 = revalue(factor(df$SUBKNOWL1), c(
`-1` = "Refused",
`1` = "1 - Very low",
`2` = "2",
`3` = "3",
`4` = "4",
`5` = "5",
`6` = "6",
`7` = "7 - Very high"
))
df$ACT1_1 = revalue(factor(df$ACT1_1), c(
`-1` = "Refused",
`1` = "Not at all",
`2` = "Very little",
`3` = "Somewhat",
`4` = "Very well",
`5` = "Completely"
))
df$ACT1_2 = revalue(factor(df$ACT1_2), c(
`-1` = "Refused",
`1` = "Not at all",
`2` = "Very little",
`3` = "Somewhat",
`4` = "Very well",
`5` = "Completely"
))
df$FINGOALS = revalue(factor(df$FINGOALS), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$PROPPLAN_1 = revalue(factor(df$PROPPLAN_1), c(
`-1` = "Refused",
`1` = "Strongly disagree",
`2` = "Disagree",
`3` = "Neither agree nor disagree",
`4` = "Agree",
`5` = "Strongly agree"
))
df$PROPPLAN_2 = revalue(factor(df$PROPPLAN_2), c(
`-1` = "Refused",
`1` = "Strongly disagree",
`2` = "Disagree",
`3` = "Neither agree nor disagree",
`4` = "Agree",
`5` = "Strongly agree"
))
df$PROPPLAN_3 = revalue(factor(df$PROPPLAN_3), c(
`-1` = "Refused",
`1` = "Strongly disagree",
`2` = "Disagree",
`3` = "Neither agree nor disagree",
`4` = "Agree",
`5` = "Strongly agree"
))
df$PROPPLAN_4 = revalue(factor(df$PROPPLAN_4), c(
`-1` = "Refused",
`1` = "Strongly disagree",
`2` = "Disagree",
`3` = "Neither agree nor disagree",
`4` = "Agree",
`5` = "Strongly agree"
))
df$MANAGE1_1 = revalue(factor(df$MANAGE1_1), c(
`-1` = "Refused",
`1` = "Not applicable or never",
`2` = "Seldom",
`3` = "Sometimes",
`4` = "Often",
`5` = "Always"
))
df$MANAGE1_2 = revalue(factor(df$MANAGE1_2), c(
`-1` = "Refused",
`1` = "Not applicable or never",
`2` = "Seldom",
`3` = "Sometimes",
`4` = "Often",
`5` = "Always"
))
df$MANAGE1_3 = revalue(factor(df$MANAGE1_3), c(
`-1` = "Refused",
`1` = "Not applicable or never",
`2` = "Seldom",
`3` = "Sometimes",
`4` = "Often",
`5` = "Always"
))
df$MANAGE1_4 = revalue(factor(df$MANAGE1_4), c(
`-1` = "Refused",
`1` = "Not applicable or never",
`2` = "Seldom",
`3` = "Sometimes",
`4` = "Often",
`5` = "Always"
))
df$SAVEHABIT = revalue(factor(df$SAVEHABIT), c(
`-1` = "Refused",
`1` = "Strongly disagree",
`2` = "Disagree",
`3` = "Disagree slightly",
`4` = "Agree slightly",
`5` = "Agree",
`6` = "Strongly agree"
))
df$FRUGALITY = revalue(factor(df$FRUGALITY), c(
`-1` = "Refused",
`1` = "Strongly disagree",
`2` = "Disagree",
`3` = "Disagree slightly",
`4` = "Agree slightly",
`5` = "Agree",
`6` = "Strongly agree"
))
df$AUTOMATED_1 = revalue(factor(df$AUTOMATED_1), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes",
`7` = "I do not have this type of account"
))
df$AUTOMATED_2 = revalue(factor(df$AUTOMATED_2), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes",
`7` = "I do not have this type of account"
))
df$ASK1_1 = revalue(factor(df$ASK1_1), c(
`-1` = "Refused",
`1` = "Never",
`2` = "Seldom",
`3` = "Sometimes",
`4` = "Often",
`5` = "Always"
))
df$ASK1_2 = revalue(factor(df$ASK1_2), c(
`-1` = "Refused",
`1` = "Never",
`2` = "Seldom",
`3` = "Sometimes",
`4` = "Often",
`5` = "Always"
))
df$SUBNUMERACY2 = revalue(factor(df$SUBNUMERACY2), c(
`-1` = "Refused",
`1` = "1 - Always prefer words",
`2` = "2",
`3` = "3",
`4` = "4",
`5` = "5",
`6` = "6 - Always prefer numbers"
))
df$SUBNUMERACY1 = revalue(factor(df$SUBNUMERACY1), c(
`-1` = "Refused",
`1` = "1 - Not good at all",
`2` = "2",
`3` = "3",
`4` = "4",
`5` = "5",
`6` = "6 - Extremely good"
))
df$CHANGEABLE = revalue(factor(df$CHANGEABLE), c(
`-1` = "Refused",
`1` = "Strongly disagree",
`2` = "Disagree",
`3` = "Somewhat disagree",
`4` = "Neither agree nor disagree",
`5` = "Somewhat agree",
`6` = "Agree",
`7` = "Strongly agree"
))
df$GOALCONF = revalue(factor(df$GOALCONF), c(
`-1` = "Refused",
`1` = "Not at all confident",
`2` = "Not very confident",
`3` = "Somewhat confident",
`4` = "Very confident"
))
df$FINKNOWL1 = revalue(factor(df$FINKNOWL1), c(
`-1` = "Refused",
`1` = "More than $102",
`2` = "Exactly $102",
`3` = "Less than $102"
))
df$FINKNOWL2 = revalue(factor(df$FINKNOWL2), c(
`-1` = "Refused",
`1` = "More than today",
`2` = "Exactly the same",
`3` = "Less than today"
))
df$FINKNOWL3 = revalue(factor(df$FINKNOWL3), c(
`-1` = "Refused",
`1` = "True",
`2` = "False"
))
# df$FK1correct = revalue(factor(df$FK1correct), c(
# `0` = "No",
# `1` = "Yes"
# ))
# df$FK2correct = revalue(factor(df$FK2correct), c(
# `0` = "No",
# `1` = "Yes"
# ))
# df$FK3correct = revalue(factor(df$FK3correct), c(
# `0` = "No",
# `1` = "Yes"
# ))
df$KHKNOWL1 = revalue(factor(df$KHKNOWL1), c(
`-1` = "Refused",
`1` = "Savings accounts",
`2` = "Bonds",
`3` = "Stocks"
))
df$KHKNOWL2 = revalue(factor(df$KHKNOWL2), c(
`-1` = "Refused",
`1` = "Savings accounts",
`2` = "Bonds",
`3` = "Stocks"
))
df$KHKNOWL3 = revalue(factor(df$KHKNOWL3), c(
`-1` = "Refused",
`1` = "Increase",
`2` = "Decrease",
`3` = "Stay the same"
))
df$KHKNOWL4 = revalue(factor(df$KHKNOWL4), c(
`-1` = "Refused",
`1` = "True",
`2` = "False"
))
df$KHKNOWL5 = revalue(factor(df$KHKNOWL5), c(
`-1` = "Refused",
`1` = "True",
`2` = "False"
))
df$KHKNOWL6 = revalue(factor(df$KHKNOWL6), c(
`-1` = "Refused",
`1` = "True",
`2` = "False"
))
df$KHKNOWL7 = revalue(factor(df$KHKNOWL7), c(
`-1` = "Refused",
`1` = "Less than 5 years",
`2` = "Between 5 and 10 years",
`3` = "Between 10 and 15 years",
`4` = "Never, you will continue to be in debt"
))
df$KHKNOWL8 = revalue(factor(df$KHKNOWL8), c(
`-1` = "Refused",
`1` = "They will rise",
`2` = "They will fall",
`3` = "They will stay the same",
`4` = "There is no relationship between bond prices and the interest rate"
))
df$KHKNOWL9 = revalue(factor(df$KHKNOWL9), c(
`-1` = "Refused",
`1` = "True",
`2` = "False"
))
# df$KH1correct = revalue(factor(df$KH1correct), c(
# `0` = "No",
# `1` = "Yes"
# ))
# df$KH2correct = revalue(factor(df$KH2correct), c(
# `0` = "No",
# `1` = "Yes"
# ))
# df$KH3correct = revalue(factor(df$KH3correct), c(
# `0` = "No",
# `1` = "Yes"
# ))
# df$KH4correct = revalue(factor(df$KH4correct), c(
# `0` = "No",
# `1` = "Yes"
# ))
# df$KH5correct = revalue(factor(df$KH5correct), c(
# `0` = "No",
# `1` = "Yes"
# ))
# df$KH6correct = revalue(factor(df$KH6correct), c(
# `0` = "No",
# `1` = "Yes"
# ))
# df$KH7correct = revalue(factor(df$KH7correct), c(
# `0` = "No",
# `1` = "Yes"
# ))
# df$KH8correct = revalue(factor(df$KH8correct), c(
# `0` = "No",
# `1` = "Yes"
# ))
# df$KH9correct = revalue(factor(df$KH9correct), c(
# `0` = "No",
# `1` = "Yes"
# ))
df$ENDSMEET = revalue(factor(df$ENDSMEET), c(
`-1` = "Refused",
`1` = "Not at all difficult",
`2` = "Somewhat difficult",
`3` = "Very difficult"
))
df$HOUSING = revalue(factor(df$HOUSING), c(
`-1` = "Refused",
`1` = "I own my home",
`2` = "I rent",
`3` = "I do not currently own or rent"
))
df$LIVINGARRANGEMENT = revalue(factor(df$LIVINGARRANGEMENT), c(
`-1` = "Refused",
`1` = "I am the only adult in the household",
`2` = "I live with my spouse/partner/significant other",
`3` = "I live in my parents' home",
`4` = "I live with other family, friends, or roommates",
`5` = "Some other arrangement"
))
df$HOUSERANGES = revalue(factor(df$HOUSERANGES), c(
`-1` = "Refused",
`1` = "Less than $300",
`2` = "$300-499",
`3` = "$500-749",
`4` = "$750-999",
`5` = "$1,000-1,499",
`6` = "$1,500-1,999",
`7` = "$2,000 or more",
`98` = "I don't know",
`99` = "Prefer not to say"
))
df$IMPUTATION_FLAG = revalue(factor(df$IMPUTATION_FLAG), c(
`0` = "Not imputed",
`1` = "Imputed"
))
df$VALUERANGES = revalue(factor(df$VALUERANGES), c(
`-2` = "Question not asked because respondent not in item base",
`-1` = "Refused",
`1` = "Less than $150,000",
`2` = "$150,000-249,999",
`3` = "$250,000-399,999",
`4` = "$400,000 or more",
`98` = "I don't know",
`99` = "Prefer not to say"
))
df$MORTGAGE = revalue(factor(df$MORTGAGE), c(
`-2` = "Question not asked because respondent not in item base",
`-1` = "Refused",
`1` = "Less than $50,000",
`2` = "$50,000-199,999",
`3` = "$200,000 or more",
`98` = "I don't know",
`99` = "Prefer not to say"
))
df$SAVINGSRANGES = revalue(factor(df$SAVINGSRANGES), c(
`-1` = "Refused",
`1` = "0",
`2` = "$1-99",
`3` = "$100-999",
`4` = "$1,000-4,999",
`5` = "$5,000-19,999",
`6` = "$20,000-74,999",
`7` = "$75,000 or more",
`98` = "I don't know",
`99` = "Prefer not to say"
))
df$PRODHAVE_1 = revalue(factor(df$PRODHAVE_1), c(
`0` = "No",
`1` = "Yes"
))
df$PRODHAVE_2 = revalue(factor(df$PRODHAVE_2), c(
`0` = "No",
`1` = "Yes"
))
df$PRODHAVE_3 = revalue(factor(df$PRODHAVE_3), c(
`0` = "No",
`1` = "Yes"
))
df$PRODHAVE_4 = revalue(factor(df$PRODHAVE_4), c(
`0` = "No",
`1` = "Yes"
))
df$PRODHAVE_5 = revalue(factor(df$PRODHAVE_5), c(
`0` = "No",
`1` = "Yes"
))
df$PRODHAVE_6 = revalue(factor(df$PRODHAVE_6), c(
`0` = "No",
`1` = "Yes"
))
df$PRODHAVE_7 = revalue(factor(df$PRODHAVE_7), c(
`0` = "No",
`1` = "Yes"
))
df$PRODHAVE_8 = revalue(factor(df$PRODHAVE_8), c(
`0` = "No",
`1` = "Yes"
))
df$PRODHAVE_9 = revalue(factor(df$PRODHAVE_9), c(
`0` = "No",
`1` = "Yes"
))
df$PRODUSE_1 = revalue(factor(df$PRODUSE_1), c(
`0` = "No",
`1` = "Yes"
))
df$PRODUSE_2 = revalue(factor(df$PRODUSE_2), c(
`0` = "No",
`1` = "Yes"
))
df$PRODUSE_3 = revalue(factor(df$PRODUSE_3), c(
`0` = "No",
`1` = "Yes"
))
df$PRODUSE_4 = revalue(factor(df$PRODUSE_4), c(
`0` = "No",
`1` = "Yes"
))
df$PRODUSE_5 = revalue(factor(df$PRODUSE_5), c(
`0` = "No",
`1` = "Yes"
))
df$PRODUSE_6 = revalue(factor(df$PRODUSE_6), c(
`0` = "No",
`1` = "Yes"
))
df$CONSPROTECT1 = revalue(factor(df$CONSPROTECT1), c(
`-1` = "Refused",
`1` = "Never",
`2` = "Rarely",
`3` = "Sometimes",
`4` = "Often"
))
df$CONSPROTECT2 = revalue(factor(df$CONSPROTECT2), c(
`-1` = "Refused",
`1` = "Not at all familiar",
`2` = "Somewhat familiar",
`3` = "Very familiar"
))
df$CONSPROTECT3 = revalue(factor(df$CONSPROTECT3), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$EARNERS = revalue(factor(df$EARNERS), c(
`-1` = "Refused",
`1` = "One",
`2` = "Two",
`3` = "More than two"
))
df$VOLATILITY = revalue(factor(df$VOLATILITY), c(
`-1` = "Refused",
`1` = "Roughly the same each month",
`2` = "Roughly the same most months, but some unusually high or low months during the year",
`3` = "Often varies quite a bit from one month to the next"
))
df$SNAP = revalue(factor(df$SNAP), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes",
`8` = "Not sure"
))
df$MATHARDSHIP_1 = revalue(factor(df$MATHARDSHIP_1), c(
`-1` = "Refused",
`1` = "Never",
`2` = "Sometimes",
`3` = "Often"
))
df$MATHARDSHIP_2 = revalue(factor(df$MATHARDSHIP_2), c(
`-1` = "Refused",
`1` = "Never",
`2` = "Sometimes",
`3` = "Often"
))
df$MATHARDSHIP_3 = revalue(factor(df$MATHARDSHIP_3), c(
`-1` = "Refused",
`1` = "Never",
`2` = "Sometimes",
`3` = "Often"
))
df$MATHARDSHIP_4 = revalue(factor(df$MATHARDSHIP_4), c(
`-1` = "Refused",
`1` = "Never",
`2` = "Sometimes",
`3` = "Often"
))
df$MATHARDSHIP_5 = revalue(factor(df$MATHARDSHIP_5), c(
`-1` = "Refused",
`1` = "Never",
`2` = "Sometimes",
`3` = "Often"
))
df$MATHARDSHIP_6 = revalue(factor(df$MATHARDSHIP_6), c(
`-1` = "Refused",
`1` = "Never",
`2` = "Sometimes",
`3` = "Often"
))
df$COLLECT = revalue(factor(df$COLLECT), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes",
`8` = "Not sure"
))
df$REJECTED_1 = revalue(factor(df$REJECTED_1), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$REJECTED_2 = revalue(factor(df$REJECTED_2), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$ABSORBSHOCK = revalue(factor(df$ABSORBSHOCK), c(
`-1` = "Refused",
`1` = "I am certain I could not come up with $2,000",
`2` = "I could probably not come up with $2,000",
`3` = "I could probably come up with $2,000",
`4` = "I am certain I could come up with the full $2,000",
`8` = "I don't know"
))
df$BENEFITS_1 = revalue(factor(df$BENEFITS_1), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$BENEFITS_2 = revalue(factor(df$BENEFITS_2), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$BENEFITS_3 = revalue(factor(df$BENEFITS_3), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$BENEFITS_4 = revalue(factor(df$BENEFITS_4), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$BENEFITS_5 = revalue(factor(df$BENEFITS_5), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$FRAUD2 = revalue(factor(df$FRAUD2), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes",
`8` = "Not sure"
))
df$COVERCOSTS = revalue(factor(df$COVERCOSTS), c(
`-1` = "Refused",
`1` = "Use savings or sell something you own",
`2` = "Cut back or do without",
`3` = "Earn more money",
`4` = "Borrow money"
))
df$BORROW_1 = revalue(factor(df$BORROW_1), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$BORROW_2 = revalue(factor(df$BORROW_2), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$SHOCKS_1 = revalue(factor(df$SHOCKS_1), c(
`0` = "No",
`1` = "Yes"
))
df$SHOCKS_2 = revalue(factor(df$SHOCKS_2), c(
`0` = "No",
`1` = "Yes"
))
df$SHOCKS_3 = revalue(factor(df$SHOCKS_3), c(
`0` = "No",
`1` = "Yes"
))
df$SHOCKS_4 = revalue(factor(df$SHOCKS_4), c(
`0` = "No",
`1` = "Yes"
))
df$SHOCKS_5 = revalue(factor(df$SHOCKS_5), c(
`0` = "No",
`1` = "Yes"
))
df$SHOCKS_6 = revalue(factor(df$SHOCKS_6), c(
`0` = "No",
`1` = "Yes"
))
df$SHOCKS_7 = revalue(factor(df$SHOCKS_7), c(
`0` = "No",
`1` = "Yes"
))
df$SHOCKS_8 = revalue(factor(df$SHOCKS_8), c(
`0` = "No",
`1` = "Yes"
))
df$SHOCKS_9 = revalue(factor(df$SHOCKS_9), c(
`0` = "No",
`1` = "Yes"
))
df$SHOCKS_10 = revalue(factor(df$SHOCKS_10), c(
`0` = "No",
`1` = "Yes"
))
df$SHOCKS_11 = revalue(factor(df$SHOCKS_11), c(
`0` = "No",
`1` = "Yes"
))
df$SHOCKS_12 = revalue(factor(df$SHOCKS_12), c(
`0` = "No",
`1` = "Yes"
))
df$MANAGE2 = revalue(factor(df$MANAGE2), c(
`-1` = "Refused",
`1` = "Someone else takes care of all or most money matters in my household.",
`2` = "Someone else and I take care of money matters in my household about the same.",
`3` = "I take care of all or most money matters in my household."
))
df$PAIDHELP = revalue(factor(df$PAIDHELP), c(
`-2` = "Question not asked because respondent not in item base",
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$HSLOC = revalue(factor(df$HSLOC), c(
`-1` = "Refused",
`1` = "U.S. and territories",
`2` = "Outside the U.S.",
`8` = "I can't recall"
))
df$PAREDUC = revalue(factor(df$PAREDUC), c(
`-1` = "Refused",
`1` = "Less than high school",
`2` = "High school degree/GED",
`3` = "Some college/Associate",
`4` = "Bachelor's degree",
`5` = "Graduate/professional degree"
))
df$FINSOC2_1 = revalue(factor(df$FINSOC2_1), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$FINSOC2_2 = revalue(factor(df$FINSOC2_2), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$FINSOC2_3 = revalue(factor(df$FINSOC2_3), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$FINSOC2_4 = revalue(factor(df$FINSOC2_4), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$FINSOC2_5 = revalue(factor(df$FINSOC2_5), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$FINSOC2_6 = revalue(factor(df$FINSOC2_6), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$FINSOC2_7 = revalue(factor(df$FINSOC2_7), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$OBJNUMERACY1 = revalue(factor(df$OBJNUMERACY1), c(
`-1` = "Refused",
`1` = "1%",
`2` = "10%",
`3` = "5%"
))
# df$ON2correct = revalue(factor(df$ON2correct), c(
# `0` = "No",
# `1` = "Yes"
# ))
# df$ON1correct = revalue(factor(df$ON1correct), c(
# `0` = "No",
# `1` = "Yes"
# ))
df$MATERIALISM_1 = revalue(factor(df$MATERIALISM_1), c(
`-1` = "Refused",
`1` = "Strongly disagree",
`2` = "Disagree",
`3` = "Neither agree nor disagree",
`4` = "Agree",
`5` = "Strongly agree"
))
df$MATERIALISM_2 = revalue(factor(df$MATERIALISM_2), c(
`-1` = "Refused",
`1` = "Strongly disagree",
`2` = "Disagree",
`3` = "Neither agree nor disagree",
`4` = "Agree",
`5` = "Strongly agree"
))
df$MATERIALISM_3 = revalue(factor(df$MATERIALISM_3), c(
`-1` = "Refused",
`1` = "Strongly disagree",
`2` = "Disagree",
`3` = "Neither agree nor disagree",
`4` = "Agree",
`5` = "Strongly agree"
))
df$CONNECT = revalue(factor(df$CONNECT), c(
`-1` = "Refused"
))
df$HEALTH = revalue(factor(df$HEALTH), c(
`-1` = "Refused",
`1` = "Poor",
`2` = "Fair",
`3` = "Good",
`4` = "Very good",
`5` = "Excellent"
))
df$SCFHORIZON = revalue(factor(df$SCFHORIZON), c(
`-1` = "Refused",
`1` = "The next few months",
`2` = "The next year",
`3` = "The next few years",
`4` = "The next 5 to 10 years",
`5` = "Longer than 10 years"
))
df$DISCOUNT = revalue(factor(df$DISCOUNT), c(
`-1` = "Refused",
`1` = "$816 now",
`2` = "$860 in three months"
))
df$MEMLOSS = revalue(factor(df$MEMLOSS), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$DISTRESS = revalue(factor(df$DISTRESS), c(
`-1` = "Refused",
`1` = "Strongly disagree",
`2` = "Disagree",
`3` = "Neither agree nor disagree",
`4` = "Agree",
`5` = "Strongly agree"
))
df$SELFCONTROL_1 = revalue(factor(df$SELFCONTROL_1), c(
`-1` = "Refused",
`1` = "Not at all",
`2` = "Not very well",
`3` = "Very well",
`4` = "Completely well"
))
df$SELFCONTROL_2 = revalue(factor(df$SELFCONTROL_2), c(
`-1` = "Refused",
`1` = "Not at all",
`2` = "Not very well",
`3` = "Very well",
`4` = "Completely well"
))
df$SELFCONTROL_3 = revalue(factor(df$SELFCONTROL_3), c(
`-1` = "Refused",
`1` = "Not at all",
`2` = "Not very well",
`3` = "Very well",
`4` = "Completely well"
))
df$OUTLOOK_1 = revalue(factor(df$OUTLOOK_1), c(
`-1` = "Refused",
`1` = "Strongly disagree",
`2` = "Somewhat disagree",
`3` = "Neither agree nor disagree",
`4` = "Somewhat agree",
`5` = "Strongly agree"
))
df$OUTLOOK_2 = revalue(factor(df$OUTLOOK_2), c(
`-1` = "Refused",
`1` = "Strongly disagree",
`2` = "Somewhat disagree",
`3` = "Neither agree nor disagree",
`4` = "Somewhat agree",
`5` = "Strongly agree"
))
df$INTERCONNECTIONS_1 = revalue(factor(df$INTERCONNECTIONS_1), c(
`0` = "No",
`1` = "Yes"
))
df$INTERCONNECTIONS_2 = revalue(factor(df$INTERCONNECTIONS_2), c(
`0` = "No",
`1` = "Yes"
))
df$INTERCONNECTIONS_3 = revalue(factor(df$INTERCONNECTIONS_3), c(
`0` = "No",
`1` = "Yes"
))
df$INTERCONNECTIONS_4 = revalue(factor(df$INTERCONNECTIONS_4), c(
`0` = "No",
`1` = "Yes"
))
df$INTERCONNECTIONS_5 = revalue(factor(df$INTERCONNECTIONS_5), c(
`0` = "No",
`1` = "Yes"
))
df$INTERCONNECTIONS_6 = revalue(factor(df$INTERCONNECTIONS_6), c(
`0` = "No",
`1` = "Yes"
))
df$INTERCONNECTIONS_7 = revalue(factor(df$INTERCONNECTIONS_7), c(
`0` = "No",
`1` = "Yes"
))
df$INTERCONNECTIONS_8 = revalue(factor(df$INTERCONNECTIONS_8), c(
`0` = "No",
`1` = "Yes"
))
df$INTERCONNECTIONS_9 = revalue(factor(df$INTERCONNECTIONS_9), c(
`0` = "No",
`1` = "Yes"
))
df$INTERCONNECTIONS_10 = revalue(factor(df$INTERCONNECTIONS_10), c(
`0` = "No",
`1` = "Yes"
))
df$PEM = revalue(factor(df$PEM), c(
`-1` = "Refused",
`1` = "Strongly disagree",
`2` = "Disagree",
`3` = "Somewhat disagree",
`4` = "Neither agree nor disagree",
`5` = "Somewhat agree",
`6` = "Agree",
`7` = "Strongly agree"
))
df$HOUSESAT = revalue(factor(df$HOUSESAT), c(
`-1` = "Refused",
`1` = "Not at all satisfied",
`2` = "Not very satisfied",
`3` = "Somewhat satisfied",
`4` = "Very satisfied"
))
df$SOCSEC1 = revalue(factor(df$SOCSEC1), c(
`-2` = "Question not asked because respondent not in item base",
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$SOCSEC2 = revalue(factor(df$SOCSEC2), c(
`-3` = "Invalid Response",
`-2` = "Question not asked because respondent not in item base",
`-1` = "Refused",
`62` = "62",
`63` = "63",
`64` = "64",
`65` = "65",
`66` = "66",
`67` = "67",
`68` = "68",
`69` = "69",
`70` = "70 and above"
))
df$SOCSEC3 = revalue(factor(df$SOCSEC3), c(
`-3` = "Invalid Response",
`-2` = "Question not asked because respondent not in item base",
`-1` = "Refused",
`61` = "61 and below",
`62` = "62",
`63` = "63",
`64` = "64",
`65` = "65",
`66` = "66",
`67` = "67",
`68` = "68",
`69` = "69",
`70` = "70",
`71` = "71 and above"
))
df$LIFEEXPECT = revalue(factor(df$LIFEEXPECT), c(
`-2` = "Question not asked because respondent not in item base",
`-1` = "Refused",
`0` = "0",
`1` = "1",
`2` = "2",
`3` = "3",
`4` = "4",
`5` = "5",
`6` = "6",
`7` = "7",
`8` = "8",
`9` = "9",
`10` = "10",
`11` = "11",
`12` = "12",
`15` = "15",
`20` = "20",
`22` = "22",
`24` = "24",
`25` = "25",
`26` = "26",
`28` = "28",
`29` = "29",
`30` = "30",
`33` = "33",
`35` = "35",
`36` = "36",
`37` = "37",
`40` = "40",
`41` = "41",
`42` = "42",
`43` = "43",
`44` = "44",
`45` = "45",
`47` = "47",
`50` = "50",
`52` = "52",
`54` = "54",
`55` = "55",
`56` = "56",
`57` = "57",
`59` = "59",
`60` = "60",
`62` = "62",
`63` = "63",
`64` = "64",
`65` = "65",
`66` = "66",
`67` = "67",
`68` = "68",
`69` = "69",
`70` = "70",
`71` = "71",
`72` = "72",
`73` = "73",
`75` = "75",
`76` = "76",
`77` = "77",
`78` = "78",
`79` = "79",
`80` = "80",
`81` = "81",
`82` = "82",
`83` = "83",
`84` = "84",
`85` = "85",
`86` = "86",
`87` = "87",
`88` = "88",
`89` = "89",
`90` = "90",
`91` = "91",
`92` = "92",
`93` = "93",
`94` = "94",
`95` = "95",
`96` = "96",
`97` = "97",
`98` = "98",
`99` = "99",
`100` = "100"
))
df$HHEDUC = revalue(factor(df$HHEDUC), c(
`-1` = "Refused",
`1` = "Less than high school",
`2` = "High school degree/GED",
`3` = "Some college/Associate",
`4` = "Bachelor's degree",
`5` = "Graduate/professional degree"
))
df$KIDS_NoChildren = revalue(factor(df$KIDS_NoChildren), c(
`-1` = "Refused",
`0` = "Respondent financially supports children",
`1` = "I have no children that I financially support"
))
df$KIDS_1 = revalue(factor(df$KIDS_1), c(
`-1` = "Refused",
`0` = "0",
`1` = "1",
`2` = "2+"
))
df$KIDS_2 = revalue(factor(df$KIDS_2), c(
`-3` = "Invalid Response",
`-1` = "Refused",
`0` = "0",
`1` = "1",
`2` = "2+"
))
df$KIDS_3 = revalue(factor(df$KIDS_3), c(
`-1` = "Refused",
`0` = "0",
`1` = "1",
`2` = "2+"
))
df$KIDS_4 = revalue(factor(df$KIDS_4), c(
`-1` = "Refused",
`0` = "0",
`1` = "1",
`2` = "2+"
))
df$EMPLOY = revalue(factor(df$EMPLOY), c(
`1` = "Self-employed",
`2` = "Work full-time for an employer or the military",
`3` = "Work part-time for an employer or the military",
`4` = "Homemaker",
`5` = "Full-time student",
`6` = "Permanently sick, disabled or unable to work",
`7` = "Unemployed or temporarily laid off",
`8` = "Retired",
`99` = "Refused"
))
df$EMPLOY1_1 = revalue(factor(df$EMPLOY1_1), c(
`0` = "No",
`1` = "Yes"
))
df$EMPLOY1_2 = revalue(factor(df$EMPLOY1_2), c(
`0` = "No",
`1` = "Yes"
))
df$EMPLOY1_3 = revalue(factor(df$EMPLOY1_3), c(
`0` = "No",
`1` = "Yes"
))
df$EMPLOY1_4 = revalue(factor(df$EMPLOY1_4), c(
`0` = "No",
`1` = "Yes"
))
df$EMPLOY1_5 = revalue(factor(df$EMPLOY1_5), c(
`0` = "No",
`1` = "Yes"
))
df$EMPLOY1_6 = revalue(factor(df$EMPLOY1_6), c(
`0` = "No",
`1` = "Yes"
))
df$EMPLOY1_7 = revalue(factor(df$EMPLOY1_7), c(
`0` = "No",
`1` = "Yes"
))
df$EMPLOY1_8 = revalue(factor(df$EMPLOY1_8), c(
`0` = "No",
`1` = "Yes"
))
df$EMPLOY1_9 = revalue(factor(df$EMPLOY1_9), c(
`0` = "No",
`1` = "Yes"
))
df$RETIRE = revalue(factor(df$RETIRE), c(
`-2` = "Question not asked because respondent not in item base",
`-1` = "Refused",
`1` = "Earlier than planned",
`2` = "About when planned",
`3` = "Later than planned"
))
df$MILITARY = revalue(factor(df$MILITARY), c(
`-1` = "Refused",
`0` = "No",
`1` = "Yes"
))
df$Military_Status = revalue(factor(df$Military_Status), c(
`-1` = "Refused",
`1` = "Active military (active, reserve, natl guard)",
`2` = "Veteran (veteran or retired)",
`3` = "Spouse/dependent Active",
`4` = "Spouse/dependent Veteran",
`5` = "Neither Active nor Veteran"
))
df$agecat = revalue(factor(df$agecat), c(
`1` = "18-24",
`2` = "25-34",
`3` = "35-44",
`4` = "45-54",
`5` = "55-61",
`6` = "62-69",
`7` = "70-74",
`8` = "75+"
))
df$generation = revalue(factor(df$generation), c(
`1` = "Pre-Boomer",
`2` = "Boomer",
`3` = "Gen X",
`4` = "Millennial"
))
df$PPEDUC = revalue(factor(df$PPEDUC), c(
`1` = "Less than high school",
`2` = "High school degree/GED",
`3` = "Some college/Associate",
`4` = "Bachelor's degree",
`5` = "Graduate/professional degree"
))
df$PPETHM = revalue(factor(df$PPETHM), c(
`1` = "White, Non-Hispanic",
`2` = "Black, Non-Hispanic",
`3` = "Other, Non-Hispanic",
`4` = "Hispanic"
))
df$PPGENDER = revalue(factor(df$PPGENDER), c(
`1` = "Male",
`2` = "Female"
))
df$PPHHSIZE = revalue(factor(df$PPHHSIZE), c(
`1` = "1",
`2` = "2",
`3` = "3",
`4` = "4",
`5` = "5+"
))
df$PPINCIMP = revalue(factor(df$PPINCIMP), c(
`1` = "Less than $20,000",
`2` = "$20,000 to $29,999",
`3` = "$30,000 to $39,999",
`4` = "$40,000 to $49,999",
`5` = "$50,000 to $59,999",
`6` = "$60,000 to $74,999",
`7` = "$75,000 to $99,999",
`8` = "$100,000 to $149,999",
`9` = "$150,000 or more"
))
df$PPMARIT = revalue(factor(df$PPMARIT), c(
`1` = "Married",
`2` = "Widowed",
`3` = "Divorced/Separated",
`4` = "Never married",
`5` = "Living with partner"
))
df$PPMSACAT = revalue(factor(df$PPMSACAT), c(
`0` = "Non-Metro",
`1` = "Metro"
))
df$PPREG4 = revalue(factor(df$PPREG4), c(
`1` = "Northeast",
`2` = "Midwest",
`3` = "South",
`4` = "West"
))
df$PPREG9 = revalue(factor(df$PPREG9), c(
`1` = "New England",
`2` = "Mid-Atlantic",
`3` = "East-North Central",
`4` = "West-North Central",
`5` = "South Atlantic",
`6` = "East-South Central",
`7` = "West-South Central",
`8` = "Mountain",
`9` = "Pacific"
))
df$PPT01 = revalue(factor(df$PPT01), c(
`0` = "0",
`1` = "1+"
))
df$PPT25 = revalue(factor(df$PPT25), c(
`0` = "0",
`1` = "1+"
))
df$PPT612 = revalue(factor(df$PPT612), c(
`0` = "0",
`1` = "1+"
))
df$PPT1317 = revalue(factor(df$PPT1317), c(
`0` = "0",
`1` = "1+"
))
df$PPT18OV = revalue(factor(df$PPT18OV), c(
`1` = "1",
`2` = "2",
`3` = "3",
`4` = "4+"
))
df$PCTLT200FPL = revalue(factor(df$PCTLT200FPL), c(
`-5` = "County is not known",
`0` = "Less than 40% of county population below 200% of poverty level",
`1` = "40% or more of county population below 200% of poverty level"
))
########################################Analysis#######################################################################
#
# str(df)
df$FWB_score_num <- as.numeric(df$FWBscore)
df$FSscore_num <- as.numeric(df$FSscore)
df$LMscore_num <- as.numeric(df$LMscore)
# cor(df$FWB_score_num,df$FSscore_num)
# cor(df$FWB_score_num,df$LMscore_num)
# cor(df$LMscore_num,df$FSscore_num)
#
# library(ggplot2)
# # this shows the relationship between financial skill and overall financial well being with the different colors representing
# #the salary brackets
# ggplot(df, aes(x=FSscore_num, y=FWB_score_num, color=PPINCIMP)) + geom_point(shape=1) +
# scale_colour_hue(l=50) + # Use a slightly darker palette than normal
# geom_smooth(method=lm, # Add linear regression lines
# se=FALSE, # Don't add shaded confidence region
# fullrange=TRUE) # Extend regression lines
#
#
# ggplot(df, aes(x=FSscore_num, y=Knowledge, color=PPINCIMP)) + geom_point(shape=1) +
# scale_colour_hue(l=50) + # Use a slightly darker palette than normal
# geom_smooth(method=lm, # Add linear regression lines
# se=FALSE, # Don't add shaded confidence region
# fullrange=TRUE) # Extend regression lines
#
#
#
# boxplot(FWB_score_num ~ PPINCIMP, data=df)
#
#
# boxplot(FSscore_num~ PPINCIMP, data=df)
#
# boxplot(FSscore_num~ SUBKNOWL1, data=df)
# #
# boxplot(FWB_score_num ~ generation, data=df)
# boxplot(FWB_score_num ~ agecat, data=df)
#
# #Financial well being score compated to their monthly living payment - interesting that people that refused had the highest well being scores
# boxplot(FWB_score_num ~ HOUSERANGES, data=df)
#
# df$agecat
# df$generation
#
#
#
# #Distribution of financial well being score
# # Histogram overlaid with kernel density curve
# ggplot(df, aes(x=FWB_score_num)) +
# geom_histogram(aes(y=..density..), # Histogram with density instead of count on y-axis
# binwidth=.5,
# colour="black", fill="white") +
# geom_density(alpha=.2, fill="#FF6666") # Overlay with transparent density plot
#
#
# # Overlaid histograms of different income brackets and their respective financial well being
# ggplot(df, aes(x=FWB_score_num, fill=PPINCIMP)) +
# geom_histogram(binwidth=.5, alpha=.5, position="identity")
#
#
#
# # Density plots
#ggplot(df, aes(x=FWB_score_num, fill=PPINCIMP)) + geom_density(alpha=.1)
#
# # Density plots
# ggplot(df, aes(x=FSscore_num, fill=PPINCIMP)) + geom_density(alpha=.1)
#
#
#
# #Creating of new Knowledge metric
# df$Knowledge <- df$FK1correct + df$FK3correct + df$FK2correct + df$KH1correct + df$KH2correct + df$KH3correct + df$KH4correct + df$KH5correct + df$KH6correct + df$KH7correct + df$KH8correct+df$KH9correct + df$ON1correct + df$ON2correct
#
# # Bar graph of counts
# ggplot(data=df, aes(x=Knowledge)) +
# geom_bar(stat="count")
#
df <- as.data.frame(df)
df$Kids <- df$KIDS_1 + df$KIDS_2 +df$KIDS_3 + df$KIDS_4
dash_df <- subset(df,select = c(FWB_score_num,FSscore_num,PPINCIMP,fpl,ENDSMEET,HOUSERANGES,PPMARIT,PPEDUC,agecat,PPGENDER,PPETHM,Kids,HOUSING, HOUSESAT, SAVINGSRANGES,MATERIALISM_1))
names(dash_df)<- c("Financial Well Being Score", "Financial Skill Score","Income","Poverty","Ability to Cover Expenses","House Payment","Maritial Status","Education Level","Age","gender","Race","Children","Housing Situation", "Housing Satisfaction", "Amount of Savings","Materialism")
###############################################################################
library(censusapi)
library(dplyr)
library(ggmap)
library(ggplot2)
library(jsonlite)
library(zipcode)
url <- "https://api.census.gov/data/2016/zbp?get=EMP,GEO_TTL,PAYANN&for=zipcode:*&key=b59c6007b682978c337d188a809695328d22d031"
df1<- fromJSON(url, flatten = TRUE)
dat <- as.data.frame(df1)
names(dat) <- as.matrix(dat[1, ])
dat <- dat[-1, ]
dat[] <- lapply(dat, function(x) type.convert(as.character(x)))
census_2 <- dat
census_2$EMP <- as.numeric(census_2$EMP)
census_2$PAYANN <- as.numeric(census_2$PAYANN)
names(census_2)<- c("EMP","GEO_TTL","PAYANN","zipcode")
census_2 <- filter(census_2, PAYANN>0)
census_2 <- filter(census_2, EMP>0)
census_2$salary <- (census_2$PAYANN*1000)/census_2$EMP
census_2$salary <- ifelse(census_2$salary > 200000,200000,census_2$salary)
census_3 <- census_2[c(4,5)]
names(census_3) <- c("Zip","Median")
census <- census_3
census$Median <- round(census$Median)
census$Zip <- as.integer(census$Zip)
data(zipcode)
census$Zip <- clean.zipcodes(census$Zip)
census <- merge(census, zipcode, by.x='Zip', by.y='zip')
library(ggmap)
library(ggplot2)
us <- c(left = -125, bottom = 25.75, right = -67, top = 49)
map <- get_stamenmap(us, zoom = 5, maptype = "toner-lite")
#ggmap(map)
# income_map <-
# ggmap(map) + geom_point(
# aes(x=longitude, y=latitude, show_guide = TRUE, colour=Median),
# data=census, alpha=.9, na.rm = T) +
# scale_color_gradient(low="beige", high="blue")
###################################################Decision Tree Testing########################
#Decision Tree of Everything
library(plyr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(rpart) # Popular decision tree algorithm
library(rattle) # Fancy tree plot
library(rpart.plot) # Enhanced tree plots
library(RColorBrewer) # Color selection for fancy tree plot
library(party) # Alternative decision tree algorithm
library(partykit) # Convert rpart object to BinaryTree
library(caret)
library(plotly)
library(ggcorrplot)
```
Dashboard Description
=========================================
Row
-------------------------------
### The "US Distribution of Wealth" tab lists of the average income by zip code. This is pulled from the US Census API, I calculated the average Income per Worker for each Zip Code in the United States anything above 250,000 was classified at 250,000, the number above is the average Income per Worker for the entire US
```{r}
valueBox(round(mean(census$Median)))
```
Row
-------------------------------
### The "Personal Finance Dashboard" tab lets users look at the different ways to slice and dice personal finance data. The Financial Well Being score is a result of multiple different Personal Finance Questions that were provided to them on a survery. If you are interested in how your well being score stacks up you can take a shortened version of the the survey at:https://www.consumerfinance.gov/consumer-tools/financial-well-being/ The number above is the number of people that were surveyed across the United States to gather all of this data.If you are interested in what exactly goes in the Financial Well Being Score: https://www.consumerfinance.gov/consumer-tools/financial-well-being/about/
```{r}
valueBox(NROW(dash_df))
```
Row
-------------------------------
### Average Financial Well Being by Income Group
```{r}
df$Financial_Well_Being <- df$FWB_score_num
p <- ggplot(df, aes(x=Financial_Well_Being, fill=PPINCIMP)) + geom_density(alpha=.1)
p <- ggplotly(p)
p
```
US Distribution of Wealth
=========================================
```{r}
income_map <-
ggmap(map) + geom_point(
aes(x=longitude, y=latitude, show_guide = TRUE, colour=Median),
data=census, alpha=.9, na.rm = T) +
scale_color_gradient(low="beige", high="blue")
income_map
```
Personal Finance Dashboard
=========================================
```{r}
rpivotTable(dash_df,
rendererName = "Heatmap")
```
Decision Tree
=========================================
Row
-------------------------------
### These are decision trees that represent the breakdown of key variables that determine someone's Financial Well being score, the number on top of each bucket is the Financial Well Being Score for that specified bucket, the other numbers are the number of members for that bucket and the % of the whole that the specific bucket represents.
```{r}
form <- as.formula(`Financial Well Being Score`~ .)
# A fast plot
fancyRpartPlot(rpart(form,dash_df),caption ="Financial Well Being Decision Tree")
```
Final Project Workflow
=========================================
Row
-------------------------------
### This is the Workflow that I followed for this specific project, as are most Projects it was an iterative Process in which I performed analysis on a few different datasets and used multiple packages and functions in till I displayed the data in a way that made the most sense
```{r}
```
