TABLE ASSIGNMENT

Author

GABA FOLLY NAPO

Quarto

Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org.

Running Code

When you click the Render button a document will be generated that includes both content and the output of embedded code. You can embed code like this:

library(tidyverse)
library(ggplot2)
library(ggthemes)
library(dplyr)
library(anytime)
library(knitr)
library(kableExtra)
library(sparkline)
library(DT)

Description of the data and data source

For this assignment we use the data produces by the National Association of Realtors (NAR). NAR produces housing statistics on the national, regional, and metro-marketlevel where data is available. we use the national historical data (view US data) on https://www.realtor.com/research/data/ (Data from 2016 to 2022)

what you want to convey in the table

We are going to make a table that shows the median listing price, the median square feet, the price increased count, price reduced count, and the average price by state.

what functionality you put into the table and why For the functionalty, we make a Tables with kable and DT. we also add Monthly patterns in Ohio State.

what formatting you put into the table and why kable_styling, kable_paper are the only use in our table.

library(tidyverse)
house=read.csv("C:/Users/L/OneDrive/Bureau/STAA 566/TABLE/RDC_Inventory_Core_Metrics_State_History.csv",header = TRUE)
house=house%>% rename(Date=month_date_yyyymm)
house$Date=anydate(house$Date)
house$Date=as.Date(house$Date,"%y-%m-%d")
house=house %>% drop_na()

Make a Table with kbl

cases <- house%>% 
  filter(Date == max(Date)) %>%
  drop_na()

# Select variable that going to be use for the table.
house_table_1 = cases %>% 
  select(state, median_listing_price,median_square_feet,price_increased_count,price_reduced_count,average_listing_price)
  

# make table
Table_1 = house_table_1 %>%
  select(state, median_listing_price,price_increased_count,price_reduced_count,average_listing_price, median_square_feet) %>%
  kbl(escape = FALSE,
      col.names = c("State",
                    "Median price",
                    "Price increased",
                    "Price reduced",
                    "Average Price",
                    "Median  feet"),
      align = c("l","r","r","r","r","r"),
      digits = 1) %>%
  add_header_above(c(" ","Price Evolution" = 3,  "Square Feet Evolution" = 2)) %>%
  kable_styling(font_size=14, fixed_thead = TRUE)
Table_1 %>% kable_paper(lightable_options = "hover", full_width = FALSE)
Price Evolution
Square Feet Evolution
State Median price Price increased Price reduced Average Price Median feet
alaska 399700 40 784 465011 1900
alabama 325000 348 4604 426049 2005
arkansas 274500 112 2628 375364 1887
arizona 492000 568 17224 678743 1895
california 725000 1616 27988 1297386 1756
colorado 609000 364 11368 1088739 2249
connecticut 500000 80 1792 1199332 2113
district of columbia 599450 16 464 888295 1213
delaware 467000 184 688 580810 2171
florida 477000 2444 38336 916818 1740
georgia 399950 1292 20252 543587 2203
hawaii 879500 36 912 1613733 1298
iowa 279950 528 2836 331226 1611
idaho 572245 120 6040 841647 2121
illinois 299900 328 9196 436583 1695
indiana 275000 264 5880 344575 1935
kansas 279000 180 1796 366173 2015
kentucky 282450 124 3436 358553 1866
louisiana 288300 164 3972 378393 1919
massachusetts 677950 136 3256 1203262 1826
maryland 400000 648 4336 540716 1880
maine 389000 44 1200 567862 1680
michigan 275000 344 9652 385986 1629
minnesota 375000 180 5148 481725 1986
missouri 279950 288 5204 369032 1792
mississippi 277000 116 1728 347090 2049
montana 637450 32 1468 1081090 2138
north carolina 410640 636 10360 558855 2090
north dakota 289635 44 620 346422 2111
nebraska 329850 36 952 383791 2086
new hampshire 499450 36 884 669336 1969
new jersey 489950 404 5036 734871 1728
new mexico 375000 84 1424 555448 2016
nevada 485500 220 7892 743007 1859
new york 579500 520 8464 1234330 1623
ohio 232400 328 8348 319085 1716
oklahoma 280245 168 3780 379209 1869
oregon 552450 1444 6980 721979 1879
pennsylvania 289450 756 8680 412497 1729
rhode island 479450 12 472 807265 1600
south carolina 359450 728 6736 534512 1999
south dakota 355000 32 520 457537 1947
tennessee 432500 408 9836 609266 2087
texas 397500 2592 39344 546701 2082
utah 599900 336 7232 933727 2434
virginia 425492 452 6168 602607 2016
vermont 437000 12 356 670529 2033
washington 646450 508 8836 862161 2027
wisconsin 349900 108 3316 442877 1822
west virginia 217000 36 996 292698 1797
wyoming 450000 16 692 972443 2335 
Table_1
Price Evolution
Square Feet Evolution
State Median price Price increased Price reduced Average Price Median feet
alaska 399700 40 784 465011 1900
alabama 325000 348 4604 426049 2005
arkansas 274500 112 2628 375364 1887
arizona 492000 568 17224 678743 1895
california 725000 1616 27988 1297386 1756
colorado 609000 364 11368 1088739 2249
connecticut 500000 80 1792 1199332 2113
district of columbia 599450 16 464 888295 1213
delaware 467000 184 688 580810 2171
florida 477000 2444 38336 916818 1740
georgia 399950 1292 20252 543587 2203
hawaii 879500 36 912 1613733 1298
iowa 279950 528 2836 331226 1611
idaho 572245 120 6040 841647 2121
illinois 299900 328 9196 436583 1695
indiana 275000 264 5880 344575 1935
kansas 279000 180 1796 366173 2015
kentucky 282450 124 3436 358553 1866
louisiana 288300 164 3972 378393 1919
massachusetts 677950 136 3256 1203262 1826
maryland 400000 648 4336 540716 1880
maine 389000 44 1200 567862 1680
michigan 275000 344 9652 385986 1629
minnesota 375000 180 5148 481725 1986
missouri 279950 288 5204 369032 1792
mississippi 277000 116 1728 347090 2049
montana 637450 32 1468 1081090 2138
north carolina 410640 636 10360 558855 2090
north dakota 289635 44 620 346422 2111
nebraska 329850 36 952 383791 2086
new hampshire 499450 36 884 669336 1969
new jersey 489950 404 5036 734871 1728
new mexico 375000 84 1424 555448 2016
nevada 485500 220 7892 743007 1859
new york 579500 520 8464 1234330 1623
ohio 232400 328 8348 319085 1716
oklahoma 280245 168 3780 379209 1869
oregon 552450 1444 6980 721979 1879
pennsylvania 289450 756 8680 412497 1729
rhode island 479450 12 472 807265 1600
south carolina 359450 728 6736 534512 1999
south dakota 355000 32 520 457537 1947
tennessee 432500 408 9836 609266 2087
texas 397500 2592 39344 546701 2082
utah 599900 336 7232 933727 2434
virginia 425492 452 6168 602607 2016
vermont 437000 12 356 670529 2033
washington 646450 508 8836 862161 2027
wisconsin 349900 108 3316 442877 1822
west virginia 217000 36 996 292698 1797
wyoming 450000 16 692 972443 2335

Monthly patterns for Median listing price and Median square feet

N = house %>% group_by(Date)
cases_1 <- N%>% 
  filter(Date == max(Date)) %>%
  drop_na()

cases_trend_1 = N%>%
   filter(Date > max(Date)-31)

## Only in Ohio
co_recent_1 <- cases_trend_1 %>%
  filter(state=="ohio")

## Monthly patterns in Ohio
par(mfrow=c(1,1))
p_listing <- ggplot(co_recent_1, aes(x=Date, y=median_listing_price)) +
  geom_point() + geom_line() +
  theme_tufte() +
  ylab("Monthly count") +
  ggtitle("Ohio Median listing price")

p_feet <- ggplot(co_recent_1, aes(x=Date, y=median_square_feet)) + 
   geom_point() + geom_line() + 
   theme_tufte() +
   ylab("Monthly count") + 
  ggtitle("Ohio Median Square feet")

p_listing  

p_feet

cases_trend = house %>%
   filter(Date > max(Date)-31)
# one row per state with more recent data
house_table = cases%>% 
  select(state, median_listing_price,median_square_feet,price_increased_count,price_reduced_count,average_listing_price)%>%
  mutate(plt_MP=NA, plt_MF=NA) %>%  # this adds a column to store the plot
  as.data.frame()

Table with DT

# make table
DT_table = house_table %>%
  select(state, median_listing_price,price_increased_count,price_reduced_count,average_listing_price, median_square_feet) %>%
  datatable(colnames = c("State","Median price", "Price increased", "Price reduced","Average Price","Median  feet")) %>%
  formatRound(columns = c(2,3,5,6),
              digits = 1)
DT_table