01-HUD_Data.Rmd

```{r include=FALSE, warning=FALSE, message=FALSE}
library(plm) 
library(tidycensus)
library(tidyverse)
library(scales)
library(lubridate)
library(assertive)
library(visdat)
library(stringdist)
library(fuzzyjoin)
library(reclin)
library(sf)
library(pander)
library(stargazer)
library(shiny)
library(readxl)
library(formattable)
library(naniar)
library(knitr)
library(reactable)
library(crosstalk)
library(wbstats) # To get data from the World Bank API
options(tigris_use_cache = TRUE)
## turn off scientific notation
options(scipen = 5)
```


```{r include=FALSE, eval=FALSE, warning=FALSE, message=FALSE}
CoC_Awards <- read_excel("1st project CoC Awards.xlsx")
PIT_Chronic_Data <- read_excel("1st project 2015-2020 PIT, Chronic.xlsx", sheet =2)

```


```{r include=FALSE, eval=FALSE, warning=FALSE, message=FALSE}
CoC_Awards <- CoC_Awards %>%
  group_by(Year, State) %>%
    summarise(`Award Amount` = sum(`Award Amount`)) %>%
      arrange(desc(Year)) %>%
         filter(State != "MP") %>%
          mutate(Year = as.factor(Year), State = as.factor(State))

PIT_Chronic_Data <- PIT_Chronic_Data %>% 
          mutate(Year = as.factor(Year), State = as.factor(State))

```


```{r include=FALSE, eval=FALSE, warning=FALSE, message=FALSE}
PIT_Chronic_Data2020 <- PIT_Chronic_Data %>%
  filter(Year == 2020) %>%
  select(everything(), -Year)

CoC_Awards2020 <- CoC_Awards %>%
  filter(Year == 2020) 

Trend_Data2020 <- PIT_Chronic_Data2020 %>%
  inner_join(CoC_Awards2020, by = "State") %>%
    select(Year, everything())

# Checking matches between datasets
# PIT_Chronic_Data2020 %>%
#  anti_join(CoC_Awards2020, by = "State")

PIT_Chronic_Data2019 <- PIT_Chronic_Data %>%
  filter(Year == 2019) %>%
  select(everything(), -Year)

CoC_Awards2019 <- CoC_Awards %>%
  filter(Year == 2019) 

Trend_Data2019 <- PIT_Chronic_Data2019 %>%
  inner_join(CoC_Awards2019, by = "State") %>%
    select(Year, everything())

# Checking matches between datasets
# PIT_Chronic_Data2019 %>%
#  anti_join(CoC_Awards2019, by = "State")

PIT_Chronic_Data2018 <- PIT_Chronic_Data %>%
  filter(Year == 2018) %>%
  select(everything(), -Year)

CoC_Awards2018 <- CoC_Awards %>%
  filter(Year == 2018) 

Trend_Data2018 <- PIT_Chronic_Data2018 %>%
  inner_join(CoC_Awards2018, by = "State") %>%
    select(Year, everything())

# Checking matches between datasets
# PIT_Chronic_Data2018 %>%
#  anti_join(CoC_Awards2018, by = "State")

PIT_Chronic_Data2017 <- PIT_Chronic_Data %>%
  filter(Year == 2017) %>%
  select(everything(), -Year)

CoC_Awards2017 <- CoC_Awards %>%
  filter(Year == 2017) 

Trend_Data2017 <- PIT_Chronic_Data2017 %>%
  inner_join(CoC_Awards2017, by = "State") %>%
    select(Year, everything())

# Checking matches between datasets
# PIT_Chronic_Data2017 %>%
#  anti_join(CoC_Awards2017, by = "State")

PIT_Chronic_Data2016 <- PIT_Chronic_Data %>%
  filter(Year == 2016) %>%
  select(everything(), -Year)

CoC_Awards2016 <- CoC_Awards %>%
  filter(Year == 2016) 

Trend_Data2016 <- PIT_Chronic_Data2016 %>%
  inner_join(CoC_Awards2016, by = "State") %>%
    select(Year, everything())

# Checking matches between datasets
# PIT_Chronic_Data2016 %>%
#  anti_join(CoC_Awards2016, by = "State")

PIT_Chronic_Data2015 <- PIT_Chronic_Data %>%
  filter(Year == 2015) %>%
  select(everything(), -Year)

CoC_Awards2015 <- CoC_Awards %>%
  filter(Year == 2015) 

Trend_Data2015 <- PIT_Chronic_Data2015 %>%
  inner_join(CoC_Awards2015, by = "State") %>%
    select(Year, everything())

# Checking matches between datasets
# PIT_Chronic_Data2015 %>%
#  anti_join(CoC_Awards2015, by = "State")

```


```{r include=FALSE, eval=FALSE, warning=FALSE, message=FALSE}
# Joining Data
Trend_Data <- rbind(Trend_Data2015, Trend_Data2016)
Trend_Data <- rbind(Trend_Data, Trend_Data2017)
Trend_Data <- rbind(Trend_Data, Trend_Data2018)
Trend_Data <- rbind(Trend_Data, Trend_Data2019)
Trend_Data <- rbind(Trend_Data, Trend_Data2020)

# get vector of TRUE and FALSE where FALSE indicates there's one or more NA
noNAs <- complete.cases(Trend_Data)
# sum(noNAs)
## out of how many rows:
# nrow(noNAs)

# keep rows without any NA
Trend_Data <- Trend_Data[noNAs,]

# Trend_Data %>% count(State) %>% filter(n < 7)

Trend_Data <- Trend_Data %>%
  as.data.frame(Trend_Data) %>%
    select(Year, State, `Award Amount`, `Overall Homeless`, `Sheltered Homeless`, `Unsheltered Homeless`, `Overall Chronically Homeless`)

```


```{r include=FALSE, eval=FALSE, warning=FALSE, message=FALSE}
## Export Trend_Data.csv file into pc and input MI and SUB variables manually due to HUD not making these two varaibles available in their datasets

# write.csv(Trend_Data, "Trend_Data.csv")

```


```{r include=FALSE, eval=FALSE, warning=FALSE, message=FALSE}
Trend_Data_2.0 <- read_excel("1st project Trend_Data.xlsx")

```


```{r include=FALSE, eval=FALSE, warning=FALSE, message=FALSE}
Trend_Data_2.0 <- Trend_Data_2.0 %>%
 mutate(Award_Pct_Change = (`Award Amount`/lag(`Award Amount`) - 1),
        PIT_Pct_Change = (`Overall Homeless`/lag(`Overall Homeless`) - 1),
        Per_Capita = (`Award Amount`/`Overall Homeless`),
        PerCap_Pct_Change = (Per_Capita/lag(Per_Capita) - 1),
        Chronic_Pct_Change = (`Overall Chronically Homeless`/lag(`Overall Chronically Homeless`) - 1),
        MI_Pct_Change = (`Severely Mentally Ill`/lag(`Severely Mentally Ill`) - 1),
        SUD_Pct_Change = (`Chronic Substance Abuse`/lag(`Chronic Substance Abuse`) - 1)
        ) %>%
  mutate(Award_Pct_Change = Award_Pct_Change*100,
         PIT_Pct_Change = PIT_Pct_Change*100,
         PerCap_Pct_Change = PerCap_Pct_Change*100,
         Chronic_Pct_Change = Chronic_Pct_Change*100,
         MI_Pct_Change = MI_Pct_Change*100,
         SUD_Pct_Change = SUD_Pct_Change*100,
         ) 

Trend_Data_2.0_State <- Trend_Data_2.0 %>%
  mutate(Award_Pct_Change = ifelse(Year == "2015", "0", Award_Pct_Change),
         PIT_Pct_Change = ifelse(Year == "2015", "0", PIT_Pct_Change),
         PerCap_Pct_Change = ifelse(Year == "2015", "0", PerCap_Pct_Change),
         Chronic_Pct_Change = ifelse(Year == "2015", "0", Chronic_Pct_Change),
         MI_Pct_Change = ifelse(Year == "2015", "0", MI_Pct_Change),
         SUD_Pct_Change = ifelse(Year == "2015", "0", SUD_Pct_Change)
         ) %>%
  mutate(Award_Pct_Change = as.numeric(Award_Pct_Change),
         PIT_Pct_Change = as.numeric(PIT_Pct_Change),
         PerCap_Pct_Change = as.numeric(PerCap_Pct_Change),
         Chronic_Pct_Change = as.numeric(Chronic_Pct_Change),
         MI_Pct_Change = as.numeric(MI_Pct_Change),
         SUD_Pct_Change = as.numeric(SUD_Pct_Change)
         )

Trend_Data_2.0 <- Trend_Data_2.0 %>%
  mutate(Award_Pct_Change = ifelse(Year == "2015", "NA", Award_Pct_Change),
         PIT_Pct_Change = ifelse(Year == "2015", "NA", PIT_Pct_Change),
         PerCap_Pct_Change = ifelse(Year == "2015", "NA", PerCap_Pct_Change),
         Chronic_Pct_Change = ifelse(Year == "2015", "NA", Chronic_Pct_Change),
         MI_Pct_Change = ifelse(Year == "2015", "NA", MI_Pct_Change),
         SUD_Pct_Change = ifelse(Year == "2015", "NA", SUD_Pct_Change)
         ) %>%
  mutate(Award_Pct_Change = as.numeric(Award_Pct_Change),
         PIT_Pct_Change = as.numeric(PIT_Pct_Change),
         PerCap_Pct_Change = as.numeric(PerCap_Pct_Change),
         Chronic_Pct_Change = as.numeric(Chronic_Pct_Change),
         MI_Pct_Change = as.numeric(MI_Pct_Change),
         SUD_Pct_Change = as.numeric(SUD_Pct_Change)
         )


Trend_Data_2.0 <- Trend_Data_2.0 %>%
  select(Year, State, `Award Amount`, Award_Pct_Change, `Sheltered Homeless`, `Unsheltered Homeless`, `Overall Homeless`, PIT_Pct_Change, Per_Capita, 
         PerCap_Pct_Change, `Overall Chronically Homeless`, Chronic_Pct_Change, `Severely Mentally Ill`, MI_Pct_Change, `Chronic Substance Abuse`, SUD_Pct_Change)

Trend_Data_2.0_State <- Trend_Data_2.0_State %>%
  select(Year, State, `Award Amount`, Award_Pct_Change, `Sheltered Homeless`, `Unsheltered Homeless`, `Overall Homeless`, PIT_Pct_Change, Per_Capita,
         PerCap_Pct_Change, `Overall Chronically Homeless`, Chronic_Pct_Change, `Severely Mentally Ill`, MI_Pct_Change, `Chronic Substance Abuse`, SUD_Pct_Change)

```


```{r include=FALSE, eval=FALSE, warning=FALSE, message=FALSE}
Trend_Data_2.0_Nation <- Trend_Data_2.0_State %>%
  group_by(Year) %>%
    summarise(`Award Amount` = sum(`Award Amount`),
              `Sheltered Homeless` = sum(`Sheltered Homeless`), 
              `Unsheltered Homeless` = sum(`Unsheltered Homeless`), 
              `Overall Homeless` = sum(`Overall Homeless`), 
              `Overall Chronically Homeless` = sum(`Overall Chronically Homeless`), 
              `Severely Mentally Ill` = sum(`Severely Mentally Ill`), 
              `Chronic Substance Abuse` = sum(`Chronic Substance Abuse`)
              ) %>%
   mutate(Award_Pct_Change = (`Award Amount`/lag(`Award Amount`) - 1),
        PIT_Pct_Change = (`Overall Homeless`/lag(`Overall Homeless`) - 1),
        Per_Capita = (`Award Amount`/`Overall Homeless`),
        PerCap_Pct_Change = (Per_Capita/lag(Per_Capita) - 1),
        Chronic_Pct_Change = (`Overall Chronically Homeless`/lag(`Overall Chronically Homeless`) - 1),
        MI_Pct_Change = (`Severely Mentally Ill`/lag(`Severely Mentally Ill`) - 1),
        SUD_Pct_Change = (`Chronic Substance Abuse`/lag(`Chronic Substance Abuse`) - 1)
        ) %>%
  mutate(Award_Pct_Change = Award_Pct_Change*100,
         PIT_Pct_Change = PIT_Pct_Change*100,
         PerCap_Pct_Change = PerCap_Pct_Change*100,
         Chronic_Pct_Change = Chronic_Pct_Change*100,
         MI_Pct_Change = MI_Pct_Change*100,
         SUD_Pct_Change = SUD_Pct_Change*100,
         ) %>%
  select(Year, `Award Amount`, Award_Pct_Change, `Sheltered Homeless`, `Unsheltered Homeless`, `Overall Homeless`, PIT_Pct_Change, Per_Capita, 
         PerCap_Pct_Change, `Overall Chronically Homeless`, Chronic_Pct_Change, `Severely Mentally Ill`, MI_Pct_Change, `Chronic Substance Abuse`, 
         SUD_Pct_Change)

Trend_Data_2.0_State <- Trend_Data_2.0_State %>%
  group_by(State) %>%
    summarise(`Award Amount` = sum(`Award Amount`),
              Award_Pct_Change = sum(Award_Pct_Change),
              PIT_Pct_Change = sum(PIT_Pct_Change), 
              PerCap_Pct_Change = sum(PerCap_Pct_Change),
              Chronic_Pct_Change = sum(Chronic_Pct_Change),
              MI_Pct_Change= sum(MI_Pct_Change),
              SUD_Pct_Change = sum(SUD_Pct_Change)
              )

```


```{r include=FALSE, eval=FALSE, warning=FALSE, message=FALSE}
# Reformat columns for better visual output
# Adding dollar sign and comma for separation 
Trend_Data_2.0$`Award Amount` <- currency(Trend_Data_2.0$`Award Amount`, digits = 0L)
Trend_Data_2.0$Per_Capita <- currency(Trend_Data_2.0$Per_Capita, digits = 2L)
# Adding percentage sign
Trend_Data_2.0$Award_Pct_Change <- percent(Trend_Data_2.0$Award_Pct_Change, digits = 2L)
Trend_Data_2.0$PIT_Pct_Change <- percent(Trend_Data_2.0$PIT_Pct_Change, digits = 2L)
Trend_Data_2.0$PerCap_Pct_Change <- percent(Trend_Data_2.0$PerCap_Pct_Change, digits = 2L)
Trend_Data_2.0$Chronic_Pct_Change <- percent(Trend_Data_2.0$Chronic_Pct_Change, digits = 2L)
Trend_Data_2.0$MI_Pct_Change <- percent(Trend_Data_2.0$MI_Pct_Change, digits = 2L)
Trend_Data_2.0$SUD_Pct_Change <- percent(Trend_Data_2.0$SUD_Pct_Change, digits = 2L)
# Adding comma sign for digits separation every 3 units
Trend_Data_2.0$`Sheltered Homeless` <- format(Trend_Data_2.0$`Sheltered Homeless`,big.mark=",", trim=TRUE)
Trend_Data_2.0$`Unsheltered Homeless` <- format(Trend_Data_2.0$`Unsheltered Homeless`,big.mark=",", trim=TRUE)
Trend_Data_2.0$`Overall Homeless` <- format(Trend_Data_2.0$`Overall Homeless`,big.mark=",", trim=TRUE)
Trend_Data_2.0$`Overall Chronically Homeless` <- format(Trend_Data_2.0$`Overall Chronically Homeless`,big.mark=",", trim=TRUE)
Trend_Data_2.0$`Severely Mentally Ill` <- format(Trend_Data_2.0$`Severely Mentally Ill`,big.mark=",", trim=TRUE)
Trend_Data_2.0$`Chronic Substance Abuse` <- format(Trend_Data_2.0$`Chronic Substance Abuse`,big.mark=",", trim=TRUE)

Trend_Data_2.0_Nation$`Award Amount` <- currency(Trend_Data_2.0_Nation$`Award Amount`, digits = 0L)
Trend_Data_2.0_Nation$Per_Capita <- currency(Trend_Data_2.0_Nation$Per_Capita, digits = 2L)

Trend_Data_2.0_Nation$Award_Pct_Change <- percent(Trend_Data_2.0_Nation$Award_Pct_Change, digits = 2L)
Trend_Data_2.0_Nation$PIT_Pct_Change <- percent(Trend_Data_2.0_Nation$PIT_Pct_Change, digits = 2L)
Trend_Data_2.0_Nation$Chronic_Pct_Change <- percent(Trend_Data_2.0_Nation$Chronic_Pct_Change, digits = 2L)
Trend_Data_2.0_Nation$MI_Pct_Change <- percent(Trend_Data_2.0_Nation$MI_Pct_Change, digits = 2L)
Trend_Data_2.0_Nation$SUD_Pct_Change <- percent(Trend_Data_2.0_Nation$SUD_Pct_Change, digits = 2L)

Trend_Data_2.0_Nation$`Sheltered Homeless` <- format(Trend_Data_2.0_Nation$`Sheltered Homeless`,big.mark=",", trim=TRUE)
Trend_Data_2.0_Nation$`Unsheltered Homeless` <- format(Trend_Data_2.0_Nation$`Unsheltered Homeless`,big.mark=",", trim=TRUE)
Trend_Data_2.0_Nation$`Overall Homeless` <- format(Trend_Data_2.0_Nation$`Overall Homeless`,big.mark=",", trim=TRUE)
Trend_Data_2.0_Nation$`Overall Chronically Homeless` <- format(Trend_Data_2.0_Nation$`Overall Chronically Homeless`,big.mark=",", trim=TRUE)
Trend_Data_2.0_Nation$`Severely Mentally Ill` <- format(Trend_Data_2.0_Nation$`Severely Mentally Ill`,big.mark=",", trim=TRUE)
Trend_Data_2.0_Nation$`Chronic Substance Abuse` <- format(Trend_Data_2.0_Nation$`Chronic Substance Abuse`,big.mark=",", trim=TRUE)

Trend_Data_2.0_State$`Award Amount` <- currency(Trend_Data_2.0_State$`Award Amount`, digits = 0L)

Trend_Data_2.0_State$Award_Pct_Change <- percent(Trend_Data_2.0_State$Award_Pct_Change, digits = 2L)
Trend_Data_2.0_State$PIT_Pct_Change <- percent(Trend_Data_2.0_State$PIT_Pct_Change, digits = 2L)
Trend_Data_2.0_State$PerCap_Pct_Change <- percent(Trend_Data_2.0_State$PerCap_Pct_Change, digits = 2L)
Trend_Data_2.0_State$Chronic_Pct_Change <- percent(Trend_Data_2.0_State$Chronic_Pct_Change, digits = 2L)
Trend_Data_2.0_State$MI_Pct_Change <- percent(Trend_Data_2.0_State$MI_Pct_Change, digits = 2L)
Trend_Data_2.0_State$SUD_Pct_Change <- percent(Trend_Data_2.0_State$SUD_Pct_Change, digits = 2L)

```


```{r include=FALSE, eval=FALSE, warning=FALSE, message=FALSE}
Trend_Data_2.0_Înflation <- read_excel("1st project Trend_Data_Inflation.xlsx")
```


```{r include=FALSE, eval=FALSE, warning=FALSE, message=FALSE}
Trend_Data_2.0_Înflation <- Trend_Data_2.0_Înflation %>%
 mutate(Award_Pct_Change = (`Award (2015 $)`/lag(`Award (2015 $)`) - 1),
        PIT_Pct_Change = (`Overall Homeless`/lag(`Overall Homeless`) - 1),
        Per_Capita = (`Award (2015 $)`/`Overall Homeless`),
        PerCap_Pct_Change = (Per_Capita/lag(Per_Capita) - 1),
        Chronic_Pct_Change = (`Overall Chronically Homeless`/lag(`Overall Chronically Homeless`) - 1),
        MI_Pct_Change = (`Severely Mentally Ill`/lag(`Severely Mentally Ill`) - 1),
        SUD_Pct_Change = (`Chronic Substance Abuse`/lag(`Chronic Substance Abuse`) - 1)
        ) %>%
  mutate(Award_Pct_Change = Award_Pct_Change*100,
         PIT_Pct_Change = PIT_Pct_Change*100,
         PerCap_Pct_Change = PerCap_Pct_Change*100,
         Chronic_Pct_Change = Chronic_Pct_Change*100,
         MI_Pct_Change = MI_Pct_Change*100,
         SUD_Pct_Change = SUD_Pct_Change*100,
         ) 

Trend_Data_2.0_Inflation_State <- Trend_Data_2.0_Înflation %>%
  mutate(Award_Pct_Change = ifelse(Year == "2015", "0", Award_Pct_Change),
         PIT_Pct_Change = ifelse(Year == "2015", "0", PIT_Pct_Change),
         PerCap_Pct_Change = ifelse(Year == "2015", "0", PerCap_Pct_Change),
         Chronic_Pct_Change = ifelse(Year == "2015", "0", Chronic_Pct_Change),
         MI_Pct_Change = ifelse(Year == "2015", "0", MI_Pct_Change),
         SUD_Pct_Change = ifelse(Year == "2015", "0", SUD_Pct_Change)
         ) %>%
  mutate(Award_Pct_Change = as.numeric(Award_Pct_Change),
         PIT_Pct_Change = as.numeric(PIT_Pct_Change),
         PerCap_Pct_Change = as.numeric(PerCap_Pct_Change),
         Chronic_Pct_Change = as.numeric(Chronic_Pct_Change),
         MI_Pct_Change = as.numeric(MI_Pct_Change),
         SUD_Pct_Change = as.numeric(SUD_Pct_Change)
         )

Trend_Data_2.0_Înflation <- Trend_Data_2.0_Înflation %>%
  mutate(Award_Pct_Change = ifelse(Year == "2015", "NA", Award_Pct_Change),
         PIT_Pct_Change = ifelse(Year == "2015", "NA", PIT_Pct_Change),
         PerCap_Pct_Change = ifelse(Year == "2015", "NA", PerCap_Pct_Change),
         Chronic_Pct_Change = ifelse(Year == "2015", "NA", Chronic_Pct_Change),
         MI_Pct_Change = ifelse(Year == "2015", "NA", MI_Pct_Change),
         SUD_Pct_Change = ifelse(Year == "2015", "NA", SUD_Pct_Change)
         ) %>%
  mutate(Award_Pct_Change = as.numeric(Award_Pct_Change),
         PIT_Pct_Change = as.numeric(PIT_Pct_Change),
         PerCap_Pct_Change = as.numeric(PerCap_Pct_Change),
         Chronic_Pct_Change = as.numeric(Chronic_Pct_Change),
         MI_Pct_Change = as.numeric(MI_Pct_Change),
         SUD_Pct_Change = as.numeric(SUD_Pct_Change)
         )


Trend_Data_2.0_Înflation <- Trend_Data_2.0_Înflation %>%
  select(Year, State, `Award (2015 $)`, Award_Pct_Change, `Sheltered Homeless`, `Unsheltered Homeless`, `Overall Homeless`, PIT_Pct_Change, Per_Capita, 
         PerCap_Pct_Change, `Overall Chronically Homeless`, Chronic_Pct_Change, `Severely Mentally Ill`, MI_Pct_Change, `Chronic Substance Abuse`, SUD_Pct_Change)

Trend_Data_2.0_Inflation_State <- Trend_Data_2.0_Inflation_State %>%
  select(Year, State, `Award (2015 $)`, Award_Pct_Change, `Sheltered Homeless`, `Unsheltered Homeless`, `Overall Homeless`, PIT_Pct_Change, Per_Capita, 
         PerCap_Pct_Change, `Overall Chronically Homeless`, Chronic_Pct_Change, `Severely Mentally Ill`, MI_Pct_Change, `Chronic Substance Abuse`, SUD_Pct_Change)

```


```{r include=FALSE, eval=FALSE, warning=FALSE, message=FALSE}
Trend_Data_2.0_Inflation_Nation <- Trend_Data_2.0_Inflation_State %>%
  group_by(Year) %>%
    summarise(`Award (2015 $)` = sum(`Award (2015 $)`),
              `Sheltered Homeless` = sum(`Sheltered Homeless`), 
              `Unsheltered Homeless` = sum(`Unsheltered Homeless`), 
              `Overall Homeless` = sum(`Overall Homeless`), 
              `Overall Chronically Homeless` = sum(`Overall Chronically Homeless`), 
              `Severely Mentally Ill` = sum(`Severely Mentally Ill`), 
              `Chronic Substance Abuse` = sum(`Chronic Substance Abuse`)
              ) %>%
   mutate(Award_Pct_Change = (`Award (2015 $)`/lag(`Award (2015 $)`) - 1),
        PIT_Pct_Change = (`Overall Homeless`/lag(`Overall Homeless`) - 1),
        Per_Capita = (`Award (2015 $)`/`Overall Homeless`),
        PerCap_Pct_Change = (Per_Capita/lag(Per_Capita) - 1),
        Chronic_Pct_Change = (`Overall Chronically Homeless`/lag(`Overall Chronically Homeless`) - 1),
        MI_Pct_Change = (`Severely Mentally Ill`/lag(`Severely Mentally Ill`) - 1),
        SUD_Pct_Change = (`Chronic Substance Abuse`/lag(`Chronic Substance Abuse`) - 1)
        ) %>%
  mutate(Award_Pct_Change = Award_Pct_Change*100,
         PIT_Pct_Change = PIT_Pct_Change*100,
         PerCap_Pct_Change = PerCap_Pct_Change*100,
         Chronic_Pct_Change = Chronic_Pct_Change*100,
         MI_Pct_Change = MI_Pct_Change*100,
         SUD_Pct_Change = SUD_Pct_Change*100,
         ) %>%
  select(Year, `Award (2015 $)`, Award_Pct_Change, `Sheltered Homeless`, `Unsheltered Homeless`, `Overall Homeless`, PIT_Pct_Change, Per_Capita, 
         PerCap_Pct_Change, `Overall Chronically Homeless`, Chronic_Pct_Change, `Severely Mentally Ill`, MI_Pct_Change, `Chronic Substance Abuse`, 
         SUD_Pct_Change)

Trend_Data_2.0_Inflation_State <- Trend_Data_2.0_Inflation_State %>%
  group_by(State) %>%
    summarise(`Award (2015 $)` = sum(`Award (2015 $)`),
              Award_Pct_Change = sum(Award_Pct_Change),
              PIT_Pct_Change = sum(PIT_Pct_Change),
              PerCap_Pct_Change = sum(PerCap_Pct_Change),
              Chronic_Pct_Change = sum(Chronic_Pct_Change),
              MI_Pct_Change= sum(MI_Pct_Change),
              SUD_Pct_Change = sum(SUD_Pct_Change)
              )

```


```{r include=FALSE, eval=FALSE, warning=FALSE, message=FALSE}
Trend_Data_2.0_Înflation$`Award (2015 $)` <- currency(Trend_Data_2.0_Înflation$`Award (2015 $)`, digits = 0L)
Trend_Data_2.0_Înflation$Per_Capita <- currency(Trend_Data_2.0_Înflation$Per_Capita, digits = 2L)

Trend_Data_2.0_Înflation$Award_Pct_Change <- percent(Trend_Data_2.0_Înflation$Award_Pct_Change, digits = 2L)
Trend_Data_2.0_Înflation$PIT_Pct_Change <- percent(Trend_Data_2.0_Înflation$PIT_Pct_Change, digits = 2L)
Trend_Data_2.0_Înflation$PerCap_Pct_Change <- percent(Trend_Data_2.0_Înflation$PIT_Pct_Change, digits = 2L)
Trend_Data_2.0_Înflation$Chronic_Pct_Change <- percent(Trend_Data_2.0_Înflation$PerCap_Pct_Change, digits = 2L)
Trend_Data_2.0_Înflation$MI_Pct_Change <- percent(Trend_Data_2.0_Înflation$MI_Pct_Change, digits = 2L)
Trend_Data_2.0_Înflation$SUD_Pct_Change <- percent(Trend_Data_2.0_Înflation$SUD_Pct_Change, digits = 2L)

Trend_Data_2.0_Înflation$`Sheltered Homeless` <- format(Trend_Data_2.0_Înflation$`Sheltered Homeless`,big.mark=",", trim=TRUE)
Trend_Data_2.0_Înflation$`Unsheltered Homeless` <- format(Trend_Data_2.0_Înflation$`Unsheltered Homeless`,big.mark=",", trim=TRUE)
Trend_Data_2.0_Înflation$`Overall Homeless` <- format(Trend_Data_2.0_Înflation$`Overall Homeless`,big.mark=",", trim=TRUE)
Trend_Data_2.0_Înflation$`Overall Chronically Homeless` <- format(Trend_Data_2.0_Înflation$`Overall Chronically Homeless`,big.mark=",", trim=TRUE)
Trend_Data_2.0_Înflation$`Severely Mentally Ill` <- format(Trend_Data_2.0_Înflation$`Severely Mentally Ill`,big.mark=",", trim=TRUE)
Trend_Data_2.0_Înflation$`Chronic Substance Abuse` <- format(Trend_Data_2.0_Înflation$`Chronic Substance Abuse`,big.mark=",", trim=TRUE)

Trend_Data_2.0_Inflation_Nation$`Award (2015 $)` <- currency(Trend_Data_2.0_Inflation_Nation$`Award (2015 $)`, digits = 0L)
Trend_Data_2.0_Inflation_Nation$Per_Capita <- currency(Trend_Data_2.0_Inflation_Nation$Per_Capita, digits = 2L)

Trend_Data_2.0_Inflation_Nation$Award_Pct_Change <- percent(Trend_Data_2.0_Inflation_Nation$Award_Pct_Change, digits = 2L)
Trend_Data_2.0_Inflation_Nation$PIT_Pct_Change <- percent(Trend_Data_2.0_Inflation_Nation$PIT_Pct_Change, digits = 2L)
Trend_Data_2.0_Inflation_Nation$Chronic_Pct_Change <- percent(Trend_Data_2.0_Inflation_Nation$Chronic_Pct_Change, digits = 2L)
Trend_Data_2.0_Inflation_Nation$MI_Pct_Change <- percent(Trend_Data_2.0_Inflation_Nation$MI_Pct_Change, digits = 2L)
Trend_Data_2.0_Inflation_Nation$SUD_Pct_Change <- percent(Trend_Data_2.0_Inflation_Nation$SUD_Pct_Change, digits = 2L)

Trend_Data_2.0_Inflation_Nation$`Sheltered Homeless` <- format(Trend_Data_2.0_Inflation_Nation$`Sheltered Homeless`,big.mark=",", trim=TRUE)
Trend_Data_2.0_Inflation_Nation$`Unsheltered Homeless` <- format(Trend_Data_2.0_Inflation_Nation$`Unsheltered Homeless`,big.mark=",", trim=TRUE)
Trend_Data_2.0_Inflation_Nation$`Overall Homeless` <- format(Trend_Data_2.0_Inflation_Nation$`Overall Homeless`,big.mark=",", trim=TRUE)
Trend_Data_2.0_Inflation_Nation$`Overall Chronically Homeless` <- format(Trend_Data_2.0_Inflation_Nation$`Overall Chronically Homeless`,big.mark=",", trim=TRUE)
Trend_Data_2.0_Inflation_Nation$`Severely Mentally Ill` <- format(Trend_Data_2.0_Inflation_Nation$`Severely Mentally Ill`,big.mark=",", trim=TRUE)
Trend_Data_2.0_Inflation_Nation$`Chronic Substance Abuse` <- format(Trend_Data_2.0_Inflation_Nation$`Chronic Substance Abuse`,big.mark=",", trim=TRUE)

```


```{r include=FALSE, eval=FALSE, warning=FALSE, message=FALSE}
Trend_Data_2.0_Inflation_State$`Award (2015 $)` <- currency(Trend_Data_2.0_Inflation_State$`Award (2015 $)`, digits = 0L)

Trend_Data_2.0_Inflation_State$Award_Pct_Change <- percent(Trend_Data_2.0_Inflation_State$Award_Pct_Change, digits = 2L)
Trend_Data_2.0_Inflation_State$PIT_Pct_Change <- percent(Trend_Data_2.0_Inflation_State$PIT_Pct_Change, digits = 2L)
Trend_Data_2.0_Inflation_State$PerCap_Pct_Change <- percent(Trend_Data_2.0_Inflation_State$PIT_Pct_Change, digits = 2L)
Trend_Data_2.0_Inflation_State$Chronic_Pct_Change <- percent(Trend_Data_2.0_Inflation_State$Chronic_Pct_Change, digits = 2L)
Trend_Data_2.0_Inflation_State$MI_Pct_Change <- percent(Trend_Data_2.0_Inflation_State$MI_Pct_Change, digits = 2L)
Trend_Data_2.0_Inflation_State$SUD_Pct_Change <- percent(Trend_Data_2.0_Inflation_State$SUD_Pct_Change, digits = 2L)

```