Data_Dive3
Shresht Venkatraman
2024-01-28
Data Dive 3
Groupby and Probability:
In this Data Dive we will be exploring the use of the groupby function to explore the probabilities of various categorical values in our data set.
Preparing our Data Set:
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorstxhousing = na.omit(txhousing)
txhousing## # A tibble: 7,126 × 9
## city year month sales volume median listings inventory date
## <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Abilene 2000 1 72 5380000 71400 701 6.3 2000
## 2 Abilene 2000 2 98 6505000 58700 746 6.6 2000.
## 3 Abilene 2000 3 130 9285000 58100 784 6.8 2000.
## 4 Abilene 2000 4 98 9730000 68600 785 6.9 2000.
## 5 Abilene 2000 5 141 10590000 67300 794 6.8 2000.
## 6 Abilene 2000 6 156 13910000 66900 780 6.6 2000.
## 7 Abilene 2000 7 152 12635000 73500 742 6.2 2000.
## 8 Abilene 2000 8 131 10710000 75000 765 6.4 2001.
## 9 Abilene 2000 9 104 7615000 64500 771 6.5 2001.
## 10 Abilene 2000 10 101 7040000 59300 764 6.6 2001.
## # ℹ 7,116 more rowsChoosing 3 Categorical Variables to group_by() on:
- year
- city
- year and month
Grouping by Year to calculate Mean Sales:
In the code-block below we use the group_by function to create a new dataframe called grouped_year that summarizes the mean sales amount for all houses in Texas for a given year.
## Loading the tidyverse library as well as the TXHousing Dataset
grouped_year = txhousing |> group_by(year) |> summarize(mean_sales = mean(sales))
grouped_year## # A tibble: 16 × 2
## year mean_sales
## <int> <dbl>
## 1 2000 501.
## 2 2001 546.
## 3 2002 619.
## 4 2003 639.
## 5 2004 681.
## 6 2005 796.
## 7 2006 910.
## 8 2007 776.
## 9 2008 582.
## 10 2009 496.
## 11 2010 459.
## 12 2011 449.
## 13 2012 523.
## 14 2013 610.
## 15 2014 627.
## 16 2015 662.Dividing our mean values into Bins and Calculating Probability
We classify our mean sales into 5 categories (1-5) in order of their sales amount. We then use the count of each category to classify the probability of selecting a value from a given category.
We store these probabilities in a new dataframe called probability1.
grouped_year$sales_bins = cut(grouped_year$mean_sales, 5, labels = c('1','2','3','4','5'))
probability1 = count(grouped_year,sales_bins)
probability1$Probability = probability1$n/sum(probability1$n)
probability1## # A tibble: 5 × 3
## sales_bins n Probability
## <fct> <int> <dbl>
## 1 1 5 0.312
## 2 2 5 0.312
## 3 3 3 0.188
## 4 4 2 0.125
## 5 5 1 0.0625It appears as though category 5 has the lowest probability of just 0.0625. Category 1 and Category 2 have the same probability of 0.3125, making them have the highest probability.
From this much alone, it appears as though Years with the highest mean sales value, have the lowest probability of being selected and vice-versa
Let us give each of the probabilistic classes thier own labels in accordance with their individual probabilities.
probability1 <- probability1 |>
mutate(Probability_Class = ifelse(Probability == max(Probability), "Highest",
ifelse(Probability == min(Probability), "Lowest", "Other")))
probability1## # A tibble: 5 × 4
## sales_bins n Probability Probability_Class
## <fct> <int> <dbl> <chr>
## 1 1 5 0.312 Highest
## 2 2 5 0.312 Highest
## 3 3 3 0.188 Other
## 4 4 2 0.125 Other
## 5 5 1 0.0625 LowestNow that we have the probability classes for each ‘year’ in our dataset, we merge the probability1 dataframe with out grouped_year dataframe and plot the distribution of mean sales across years based on their probability class
m = merge(grouped_year,probability1, on = sales_bins)
ggplot(m, aes(x = Probability_Class, y = mean_sales)) +
geom_boxplot() +
labs(title = "Boxplot of Mean Sales by Probability Class",
x = "Probability Class",
y = "Mean Sales($)")
Examining our Results;
From the graph above, it appears that our initial observation, that high mean sales values have lower probability and vice versa. This generally makes sense as highly expensive houses are not likely to be sold very often.
Let us use a scatter plot to verify this one more time:
ggplot(m, aes(x = year, y = mean_sales, color = Probability_Class)) +
geom_point(size = 3) +
labs(title = "Scatter Plot of Mean Sales by Year",
x = "Year",
y = "Mean Sales",
color = "Probability Class")
As we see in the above graph as well, Lower Mean sales values (below
$620) are colored Red, indicating that they have a higher probability,
and High sales value (above $900) are colored green, indicating a lower
probability.
Having established this pattern, let us group some more columns:
Grouping by City to calculate Total Volume:
In the dataframe grouped_city we view the total volume of sales in our data set grouped by on each city in Texas:
## # A tibble: 46 × 2
## city total_volume
## <chr> <dbl>
## 1 Abilene 3178820632
## 2 Amarillo 5908116316
## 3 Arlington 11545041833
## 4 Austin 90524109737
## 5 Bay Area 16750641629
## 6 Beaumont 4473962695
## 7 Brazoria County 1544337955
## 8 Brownsville 555310375
## 9 Bryan-College Station 5768137453
## 10 Collin County 49680889869
## # ℹ 36 more rowsDividing our Volume values into Bins and Calculating their Probability
We classify our mean sales into 5 categories (10) in order of their total $ volume amount. We then use the count of each category to classify the probability of selecting a value from a given category.
We store these probabilities in a new dataframe called probability1.
breaks <- quantile(grouped_city$total_volume, probs = seq(0, 1, length.out = 10 + 1))
grouped_city$volume_bins = cut(grouped_city$total_volume, breaks = breaks, labels = FALSE)
grouped_city## # A tibble: 46 × 3
## city total_volume volume_bins
## <chr> <dbl> <int>
## 1 Abilene 3178820632 5
## 2 Amarillo 5908116316 7
## 3 Arlington 11545041833 8
## 4 Austin 90524109737 10
## 5 Bay Area 16750641629 8
## 6 Beaumont 4473962695 6
## 7 Brazoria County 1544337955 3
## 8 Brownsville 555310375 1
## 9 Bryan-College Station 5768137453 7
## 10 Collin County 49680889869 10
## # ℹ 36 more rowsLike in the previous case, we calculate the probability of belonging to a given bin and classify these probabilities in Highest, Lowest and Others respectively.
probability2 = count(grouped_city,volume_bins)
probability2$Probability = probability2$n/sum(probability2$n)
probability2 <- probability2 |>
mutate(Probability_Class = ifelse(Probability == max(Probability), "Highest",
ifelse(Probability == min(Probability), "Lowest", "Other")))
probability2## # A tibble: 11 × 4
## volume_bins n Probability Probability_Class
## <int> <int> <dbl> <chr>
## 1 1 4 0.0870 Other
## 2 2 5 0.109 Highest
## 3 3 4 0.0870 Other
## 4 4 5 0.109 Highest
## 5 5 4 0.0870 Other
## 6 6 5 0.109 Highest
## 7 7 4 0.0870 Other
## 8 8 5 0.109 Highest
## 9 9 4 0.0870 Other
## 10 10 5 0.109 Highest
## 11 NA 1 0.0217 LowestNow that we have the probability classes for each volume-bin in our dataset, we merge the probability2 dataframe with our grouped_city dataframe and plot the distribution of mean volumes across cities based on their probability class
m1 = merge(grouped_city,probability2, on = volume_bins)
ggplot(m1, aes(x = Probability_Class, y = total_volume)) +
geom_boxplot() +
labs(title = "Boxplot of Total Volume by Probability Class",
x = "Probability Class",
y = "Volume ($)")
m1## volume_bins city total_volume n Probability
## 1 1 Lufkin 542598345 4 0.08695652
## 2 1 Nacogdoches 490137622 4 0.08695652
## 3 1 South Padre Island 418820140 4 0.08695652
## 4 1 Brownsville 555310375 4 0.08695652
## 5 2 Paris 570522681 5 0.10869565
## 6 2 Laredo 1121534001 5 0.10869565
## 7 2 Texarkana 988680775 5 0.10869565
## 8 2 Kerrville 685095490 5 0.10869565
## 9 2 Harlingen 882720149 5 0.10869565
## 10 3 Port Arthur 1346078433 4 0.08695652
## 11 3 Victoria 1757221647 4 0.08695652
## 12 3 Odessa 1216128638 4 0.08695652
## 13 3 Brazoria County 1544337955 4 0.08695652
## 14 4 Sherman-Denison 2261424980 5 0.10869565
## 15 4 Temple-Belton 2728364331 5 0.10869565
## 16 4 Galveston 2638752735 5 0.10869565
## 17 4 McAllen 2124169176 5 0.10869565
## 18 4 San Angelo 2387927414 5 0.10869565
## 19 5 Abilene 3178820632 4 0.08695652
## 20 5 Waco 3097648656 4 0.08695652
## 21 5 Midland 3445619670 4 0.08695652
## 22 5 Wichita Falls 2739455360 4 0.08695652
## 23 6 Beaumont 4473962695 5 0.10869565
## 24 6 Garland 4318007608 5 0.10869565
## 25 6 Killeen-Fort Hood 4161642970 5 0.10869565
## 26 6 Irving 4080690475 5 0.10869565
## 27 6 Longview-Marshall 3750221827 5 0.10869565
## 28 7 Tyler 7219282248 4 0.08695652
## 29 7 Bryan-College Station 5768137453 4 0.08695652
## 30 7 Amarillo 5908116316 4 0.08695652
## 31 7 Lubbock 6547274620 4 0.08695652
## 32 8 Arlington 11545041833 5 0.10869565
## 33 8 El Paso 10761469007 5 0.10869565
## 34 8 Corpus Christi 9398012807 5 0.10869565
## 35 8 Bay Area 16750641629 5 0.10869565
## 36 8 Fort Worth 20381390425 5 0.10869565
## 37 9 Denton County 23469290796 4 0.08695652
## 38 9 Fort Bend 34070480996 4 0.08695652
## 39 9 Montgomery County 24114939999 4 0.08695652
## 40 9 NE Tarrant County 28266591118 4 0.08695652
## 41 10 Dallas 172254815470 5 0.10869565
## 42 10 Collin County 49680889869 5 0.10869565
## 43 10 Houston 212967342030 5 0.10869565
## 44 10 San Antonio 55176992274 5 0.10869565
## 45 10 Austin 90524109737 5 0.10869565
## 46 NA San Marcos 355312320 1 0.02173913
## Probability_Class
## 1 Other
## 2 Other
## 3 Other
## 4 Other
## 5 Highest
## 6 Highest
## 7 Highest
## 8 Highest
## 9 Highest
## 10 Other
## 11 Other
## 12 Other
## 13 Other
## 14 Highest
## 15 Highest
## 16 Highest
## 17 Highest
## 18 Highest
## 19 Other
## 20 Other
## 21 Other
## 22 Other
## 23 Highest
## 24 Highest
## 25 Highest
## 26 Highest
## 27 Highest
## 28 Other
## 29 Other
## 30 Other
## 31 Other
## 32 Highest
## 33 Highest
## 34 Highest
## 35 Highest
## 36 Highest
## 37 Other
## 38 Other
## 39 Other
## 40 Other
## 41 Highest
## 42 Highest
## 43 Highest
## 44 Highest
## 45 Highest
## 46 LowestThe above chart does not show a consistent pattern that discerns certain cities having a higher probability than others. Although the groups with the Highest probability tend to show large outliers. Perhaps using smaller categorizations are more useful in the regard.
ggplot(m1, aes(x = city, y = total_volume, fill = Probability_Class )) +
geom_bar(stat = "identity") +
labs(title = "Bar Chart of Total Volume($) by City",
x = "City",
y = "Total Value",
fill = "Category") +
scale_x_discrete(guide = guide_axis(check.overlap = TRUE))
### Evaluating our Results:
In both the above graphs, there seems to be no direct probability pattern between cities that have a High a High or Low Volume. It appears that certain cities exhibit large outliers, but there is no clear pattern in thier probability.
Grouping by both Year and City:
In our next explorations we will attempt to use the group_by() function on 2 different labels - city and year. Just like in the previous 2 explorations we will caclulate probabilities and attempt to find patterns. However, this time we will summarize the total number of listings
grouped_city_year = txhousing |> group_by(year,city ) |> summarize(total_listings = sum(listings))## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.grouped_city_year## # A tibble: 661 × 3
## # Groups: year [16]
## year city total_listings
## <int> <chr> <dbl>
## 1 2000 Abilene 9011
## 2 2000 Amarillo 13070
## 3 2000 Arlington 16656
## 4 2000 Austin 43892
## 5 2000 Bay Area 22108
## 6 2000 Beaumont 11354
## 7 2000 Brazoria County 5391
## 8 2000 Brownsville 4365
## 9 2000 Bryan-College Station 6719
## 10 2000 Collin County 37874
## # ℹ 651 more rowsThis has yielded a fairly large dataframe with a diverse range of listings.
Breaking the listings into categories and Calculating Probabilities:
Just like in the previous 2 cases, we will divide our total_listings column in 5 categories and label the probability of selecting a given category
breaks <- quantile(grouped_city$total_listings, probs = seq(0, 1, length.out = 10 + 1))## Warning: Unknown or uninitialised column: `total_listings`.grouped_city_year$listing_bins = cut(grouped_city_year$total_listings, breaks = 5, labels = FALSE)
probability3 = count(grouped_city_year,listing_bins)
probability3$Probability = probability3$n/sum(probability3$n)
probability3 <- probability3 |>
mutate(Probability_Class = ifelse(Probability == max(Probability), "Highest",
ifelse(Probability == min(Probability), "Lowest", "Other")))
probability3## # A tibble: 55 × 5
## # Groups: year [16]
## year listing_bins n Probability Probability_Class
## <int> <int> <int> <dbl> <chr>
## 1 2000 1 36 0.0545 Highest
## 2 2000 2 1 0.00151 Lowest
## 3 2000 3 1 0.00151 Lowest
## 4 2001 1 36 0.0545 Highest
## 5 2001 3 2 0.00303 Lowest
## 6 2002 1 34 0.0514 Highest
## 7 2002 2 1 0.00151 Lowest
## 8 2002 3 2 0.00303 Other
## 9 2003 1 34 0.0514 Highest
## 10 2003 2 1 0.00151 Lowest
## # ℹ 45 more rowsGraphing and Analyzing the Probabilities:
m2 = merge(grouped_city_year,probability3, on = listing_bins)
ggplot(m2, aes(x = Probability_Class, y = total_listings)) +
geom_boxplot() +
labs(title = "Boxplot of Total Listings by Probability Class",
x = "Probability Class",
y = "# Listings")
m2## year listing_bins city total_listings n Probability
## 1 2000 1 Abilene 9011 36 0.054462935
## 2 2000 1 Amarillo 13070 36 0.054462935
## 3 2000 1 Arlington 16656 36 0.054462935
## 4 2000 1 Austin 43892 36 0.054462935
## 5 2000 1 Bay Area 22108 36 0.054462935
## 6 2000 1 Beaumont 11354 36 0.054462935
## 7 2000 1 Brazoria County 5391 36 0.054462935
## 8 2000 1 Brownsville 4365 36 0.054462935
## 9 2000 1 Bryan-College Station 6719 36 0.054462935
## 10 2000 1 Collin County 37874 36 0.054462935
## 11 2000 1 Corpus Christi 27828 36 0.054462935
## 12 2000 1 Lufkin 2813 36 0.054462935
## 13 2000 1 Denton County 25924 36 0.054462935
## 14 2000 1 El Paso 29286 36 0.054462935
## 15 2000 1 Fort Bend 30650 36 0.054462935
## 16 2000 1 Fort Worth 27860 36 0.054462935
## 17 2000 1 Galveston 6065 36 0.054462935
## 18 2000 1 Garland 5569 36 0.054462935
## 19 2000 1 Harlingen 8036 36 0.054462935
## 20 2000 1 San Marcos 2592 36 0.054462935
## 21 2000 1 Irving 3627 36 0.054462935
## 22 2000 1 Killeen-Fort Hood 17161 36 0.054462935
## 23 2000 1 Longview-Marshall 7526 36 0.054462935
## 24 2000 1 Lubbock 9187 36 0.054462935
## 25 2000 1 Wichita Falls 8406 36 0.054462935
## 26 2000 1 McAllen 12592 36 0.054462935
## 27 2000 1 Montgomery County 28617 36 0.054462935
## 28 2000 1 NE Tarrant County 25564 36 0.054462935
## 29 2000 1 Paris 3596 36 0.054462935
## 30 2000 1 Port Arthur 4193 36 0.054462935
## 31 2000 1 San Angelo 6588 36 0.054462935
## 32 2000 1 San Antonio 76898 36 0.054462935
## 33 2000 1 Victoria 3598 36 0.054462935
## 34 2000 1 Sherman-Denison 6296 36 0.054462935
## 35 2000 1 Temple-Belton 7037 36 0.054462935
## 36 2000 1 Tyler 18070 36 0.054462935
## 37 2000 2 Dallas 170685 1 0.001512859
## 38 2000 3 Houston 221616 1 0.001512859
## 39 2001 1 Abilene 9236 36 0.054462935
## 40 2001 1 Amarillo 13133 36 0.054462935
## 41 2001 1 Arlington 18805 36 0.054462935
## 42 2001 1 Austin 85963 36 0.054462935
## 43 2001 1 Bay Area 21471 36 0.054462935
## 44 2001 1 Beaumont 12127 36 0.054462935
## 45 2001 1 Brazoria County 4160 36 0.054462935
## 46 2001 1 Brownsville 4600 36 0.054462935
## 47 2001 1 Bryan-College Station 8517 36 0.054462935
## 48 2001 1 Collin County 47746 36 0.054462935
## 49 2001 1 Corpus Christi 26747 36 0.054462935
## 50 2001 1 Lufkin 3297 36 0.054462935
## 51 2001 1 Denton County 31410 36 0.054462935
## 52 2001 1 El Paso 39302 36 0.054462935
## 53 2001 1 Fort Bend 31174 36 0.054462935
## 54 2001 1 Fort Worth 31817 36 0.054462935
## 55 2001 1 Galveston 1403 36 0.054462935
## 56 2001 1 Garland 7661 36 0.054462935
## 57 2001 1 Harlingen 7466 36 0.054462935
## 58 2001 1 Sherman-Denison 7900 36 0.054462935
## 59 2001 1 Irving 4397 36 0.054462935
## 60 2001 1 Killeen-Fort Hood 10549 36 0.054462935
## 61 2001 1 Longview-Marshall 8082 36 0.054462935
## 62 2001 1 Lubbock 9734 36 0.054462935
## 63 2001 1 Wichita Falls 9797 36 0.054462935
## 64 2001 1 Montgomery County 30397 36 0.054462935
## 65 2001 1 NE Tarrant County 30090 36 0.054462935
## 66 2001 1 Paris 3882 36 0.054462935
## 67 2001 1 Port Arthur 3988 36 0.054462935
## 68 2001 1 San Angelo 7435 36 0.054462935
## 69 2001 1 San Antonio 81842 36 0.054462935
## 70 2001 1 San Marcos 775 36 0.054462935
## 71 2001 1 Waco 840 36 0.054462935
## 72 2001 1 Temple-Belton 7093 36 0.054462935
## 73 2001 1 Tyler 17936 36 0.054462935
## 74 2001 1 Victoria 3682 36 0.054462935
## 75 2001 3 Houston 231002 2 0.003025719
## 76 2001 3 Dallas 211139 2 0.003025719
## 77 2002 1 Abilene 8589 34 0.051437216
## 78 2002 1 Amarillo 14076 34 0.051437216
## 79 2002 1 Arlington 21887 34 0.051437216
## 80 2002 1 Garland 10659 34 0.051437216
## 81 2002 1 Bay Area 26177 34 0.051437216
## 82 2002 1 Beaumont 12121 34 0.051437216
## 83 2002 1 Brazoria County 5694 34 0.051437216
## 84 2002 1 Brownsville 850 34 0.051437216
## 85 2002 1 Bryan-College Station 9215 34 0.051437216
## 86 2002 1 Collin County 60422 34 0.051437216
## 87 2002 1 Corpus Christi 16321 34 0.051437216
## 88 2002 1 McAllen 3830 34 0.051437216
## 89 2002 1 Denton County 36499 34 0.051437216
## 90 2002 1 El Paso 37744 34 0.051437216
## 91 2002 1 Fort Bend 34235 34 0.051437216
## 92 2002 1 Fort Worth 36211 34 0.051437216
## 93 2002 1 Port Arthur 4527 34 0.051437216
## 94 2002 1 Harlingen 4410 34 0.051437216
## 95 2002 1 San Antonio 84921 34 0.051437216
## 96 2002 1 Irving 5666 34 0.051437216
## 97 2002 1 Killeen-Fort Hood 15039 34 0.051437216
## 98 2002 1 Longview-Marshall 943 34 0.051437216
## 99 2002 1 Lubbock 8274 34 0.051437216
## 100 2002 1 Lufkin 370 34 0.051437216
## 101 2002 1 Paris 4165 34 0.051437216
## 102 2002 1 Montgomery County 38358 34 0.051437216
## 103 2002 1 NE Tarrant County 38127 34 0.051437216
## 104 2002 1 Nacogdoches 66 34 0.051437216
## 105 2002 1 Sherman-Denison 9717 34 0.051437216
## 106 2002 1 Temple-Belton 584 34 0.051437216
## 107 2002 1 San Angelo 6393 34 0.051437216
## 108 2002 1 Victoria 3389 34 0.051437216
## 109 2002 1 Wichita Falls 8753 34 0.051437216
## 110 2002 1 Tyler 18490 34 0.051437216
## 111 2002 2 Austin 105971 1 0.001512859
## 112 2002 3 Houston 291577 2 0.003025719
## 113 2002 3 Dallas 257416 2 0.003025719
## 114 2003 1 Abilene 8714 34 0.051437216
## 115 2003 1 Amarillo 15910 34 0.051437216
## 116 2003 1 Arlington 30357 34 0.051437216
## 117 2003 1 Garland 14496 34 0.051437216
## 118 2003 1 Bay Area 30646 34 0.051437216
## 119 2003 1 Beaumont 13558 34 0.051437216
## 120 2003 1 Brownsville 1020 34 0.051437216
## 121 2003 1 Bryan-College Station 11908 34 0.051437216
## 122 2003 1 Collin County 71482 34 0.051437216
## 123 2003 1 Corpus Christi 18190 34 0.051437216
## 124 2003 1 Montgomery County 43483 34 0.051437216
## 125 2003 1 Denton County 42849 34 0.051437216
## 126 2003 1 El Paso 37833 34 0.051437216
## 127 2003 1 Fort Bend 42735 34 0.051437216
## 128 2003 1 Fort Worth 46247 34 0.051437216
## 129 2003 1 Galveston 6022 34 0.051437216
## 130 2003 1 San Antonio 96270 34 0.051437216
## 131 2003 1 Sherman-Denison 11504 34 0.051437216
## 132 2003 1 Irving 7490 34 0.051437216
## 133 2003 1 Killeen-Fort Hood 11410 34 0.051437216
## 134 2003 1 Lubbock 10236 34 0.051437216
## 135 2003 1 Lufkin 2162 34 0.051437216
## 136 2003 1 McAllen 2680 34 0.051437216
## 137 2003 1 Wichita Falls 8300 34 0.051437216
## 138 2003 1 NE Tarrant County 47644 34 0.051437216
## 139 2003 1 Nacogdoches 2518 34 0.051437216
## 140 2003 1 Paris 4947 34 0.051437216
## 141 2003 1 Port Arthur 5095 34 0.051437216
## 142 2003 1 San Angelo 6697 34 0.051437216
## 143 2003 1 Tyler 18734 34 0.051437216
## 144 2003 1 Victoria 4055 34 0.051437216
## 145 2003 1 Temple-Belton 2276 34 0.051437216
## 146 2003 1 Texarkana 1357 34 0.051437216
## 147 2003 1 Waco 1805 34 0.051437216
## 148 2003 2 Austin 124080 1 0.001512859
## 149 2003 4 Houston 357570 2 0.003025719
## 150 2003 4 Dallas 308677 2 0.003025719
## 151 2004 1 Abilene 8132 35 0.052950076
## 152 2004 1 Amarillo 14440 35 0.052950076
## 153 2004 1 Arlington 33294 35 0.052950076
## 154 2004 1 Harlingen 3664 35 0.052950076
## 155 2004 1 Bay Area 31398 35 0.052950076
## 156 2004 1 Beaumont 14406 35 0.052950076
## 157 2004 1 Bryan-College Station 13884 35 0.052950076
## 158 2004 1 Collin County 71430 35 0.052950076
## 159 2004 1 Corpus Christi 20887 35 0.052950076
## 160 2004 1 Lufkin 3173 35 0.052950076
## 161 2004 1 Denton County 46553 35 0.052950076
## 162 2004 1 El Paso 16069 35 0.052950076
## 163 2004 1 Fort Bend 48638 35 0.052950076
## 164 2004 1 Fort Worth 52908 35 0.052950076
## 165 2004 1 Galveston 7023 35 0.052950076
## 166 2004 1 Garland 15175 35 0.052950076
## 167 2004 1 San Angelo 7449 35 0.052950076
## 168 2004 1 San Antonio 78956 35 0.052950076
## 169 2004 1 Irving 8935 35 0.052950076
## 170 2004 1 Killeen-Fort Hood 10114 35 0.052950076
## 171 2004 1 Longview-Marshall 3245 35 0.052950076
## 172 2004 1 Lubbock 13595 35 0.052950076
## 173 2004 1 Victoria 4015 35 0.052950076
## 174 2004 1 McAllen 4697 35 0.052950076
## 175 2004 1 Montgomery County 45638 35 0.052950076
## 176 2004 1 NE Tarrant County 48741 35 0.052950076
## 177 2004 1 Nacogdoches 234 35 0.052950076
## 178 2004 1 Paris 4931 35 0.052950076
## 179 2004 1 Port Arthur 5582 35 0.052950076
## 180 2004 1 Texarkana 4932 35 0.052950076
## 181 2004 1 Tyler 23684 35 0.052950076
## 182 2004 1 Sherman-Denison 13404 35 0.052950076
## 183 2004 1 Temple-Belton 8085 35 0.052950076
## 184 2004 1 Wichita Falls 8349 35 0.052950076
## 185 2004 1 Waco 4848 35 0.052950076
## 186 2004 2 Austin 124727 1 0.001512859
## 187 2004 4 Dallas 326358 1 0.001512859
## 188 2004 5 Houston 406073 1 0.001512859
## 189 2005 1 Abilene 7344 33 0.049924357
## 190 2005 1 Amarillo 12551 33 0.049924357
## 191 2005 1 Arlington 31576 33 0.049924357
## 192 2005 1 Harlingen 1895 33 0.049924357
## 193 2005 1 Bay Area 33306 33 0.049924357
## 194 2005 1 Beaumont 14737 33 0.049924357
## 195 2005 1 Brazoria County 3412 33 0.049924357
## 196 2005 1 Bryan-College Station 13983 33 0.049924357
## 197 2005 1 Collin County 65046 33 0.049924357
## 198 2005 1 Corpus Christi 23849 33 0.049924357
## 199 2005 1 Montgomery County 44520 33 0.049924357
## 200 2005 1 Denton County 40826 33 0.049924357
## 201 2005 1 Fort Bend 48692 33 0.049924357
## 202 2005 1 Fort Worth 56725 33 0.049924357
## 203 2005 1 Galveston 4944 33 0.049924357
## 204 2005 1 Garland 13587 33 0.049924357
## 205 2005 1 Longview-Marshall 14010 33 0.049924357
## 206 2005 1 Sherman-Denison 11899 33 0.049924357
## 207 2005 1 Irving 8662 33 0.049924357
## 208 2005 1 Killeen-Fort Hood 10114 33 0.049924357
## 209 2005 1 NE Tarrant County 43311 33 0.049924357
## 210 2005 1 Lubbock 16206 33 0.049924357
## 211 2005 1 Lufkin 720 33 0.049924357
## 212 2005 1 Wichita Falls 8760 33 0.049924357
## 213 2005 1 San Angelo 4765 33 0.049924357
## 214 2005 1 Nacogdoches 1230 33 0.049924357
## 215 2005 1 Paris 406 33 0.049924357
## 216 2005 1 Port Arthur 5115 33 0.049924357
## 217 2005 1 Texarkana 1536 33 0.049924357
## 218 2005 1 Tyler 25068 33 0.049924357
## 219 2005 1 Victoria 3988 33 0.049924357
## 220 2005 1 Temple-Belton 5012 33 0.049924357
## 221 2005 1 Waco 11126 33 0.049924357
## 222 2005 2 Austin 107577 2 0.003025719
## 223 2005 2 San Antonio 101339 2 0.003025719
## 224 2005 4 Dallas 301934 1 0.001512859
## 225 2005 5 Houston 426876 1 0.001512859
## 226 2006 1 Abilene 8071 32 0.048411498
## 227 2006 1 Amarillo 13166 32 0.048411498
## 228 2006 1 Arlington 36316 32 0.048411498
## 229 2006 1 Garland 16322 32 0.048411498
## 230 2006 1 Bay Area 45041 32 0.048411498
## 231 2006 1 Beaumont 11522 32 0.048411498
## 232 2006 1 Brazoria County 718 32 0.048411498
## 233 2006 1 Bryan-College Station 14831 32 0.048411498
## 234 2006 1 Collin County 76454 32 0.048411498
## 235 2006 1 Corpus Christi 31473 32 0.048411498
## 236 2006 1 Montgomery County 42384 32 0.048411498
## 237 2006 1 Denton County 42083 32 0.048411498
## 238 2006 1 El Paso 16638 32 0.048411498
## 239 2006 1 Fort Bend 46063 32 0.048411498
## 240 2006 1 Fort Worth 70985 32 0.048411498
## 241 2006 1 Galveston 14472 32 0.048411498
## 242 2006 1 Longview-Marshall 13257 32 0.048411498
## 243 2006 1 Harlingen 1689 32 0.048411498
## 244 2006 1 Temple-Belton 5259 32 0.048411498
## 245 2006 1 Irving 10022 32 0.048411498
## 246 2006 1 NE Tarrant County 46819 32 0.048411498
## 247 2006 1 Lubbock 18119 32 0.048411498
## 248 2006 1 Midland 758 32 0.048411498
## 249 2006 1 Port Arthur 3773 32 0.048411498
## 250 2006 1 San Angelo 624 32 0.048411498
## 251 2006 1 Nacogdoches 772 32 0.048411498
## 252 2006 1 Paris 1947 32 0.048411498
## 253 2006 1 Wichita Falls 10218 32 0.048411498
## 254 2006 1 Tyler 28011 32 0.048411498
## 255 2006 1 Victoria 3912 32 0.048411498
## 256 2006 1 Sherman-Denison 13563 32 0.048411498
## 257 2006 1 Waco 11646 32 0.048411498
## 258 2006 2 Austin 104343 2 0.003025719
## 259 2006 2 San Antonio 103795 2 0.003025719
## 260 2006 4 Dallas 349743 1 0.001512859
## 261 2006 5 Houston 429540 1 0.001512859
## 262 2007 1 Abilene 10364 35 0.052950076
## 263 2007 1 Amarillo 15664 35 0.052950076
## 264 2007 1 Arlington 36359 35 0.052950076
## 265 2007 1 Garland 16324 35 0.052950076
## 266 2007 1 Bay Area 50694 35 0.052950076
## 267 2007 1 Beaumont 13967 35 0.052950076
## 268 2007 1 Brazoria County 1409 35 0.052950076
## 269 2007 1 Bryan-College Station 14741 35 0.052950076
## 270 2007 1 Collin County 88804 35 0.052950076
## 271 2007 1 Corpus Christi 36113 35 0.052950076
## 272 2007 1 McAllen 2573 35 0.052950076
## 273 2007 1 Denton County 45145 35 0.052950076
## 274 2007 1 El Paso 41545 35 0.052950076
## 275 2007 1 Fort Bend 53171 35 0.052950076
## 276 2007 1 Fort Worth 74338 35 0.052950076
## 277 2007 1 Galveston 19959 35 0.052950076
## 278 2007 1 Port Arthur 5445 35 0.052950076
## 279 2007 1 Harlingen 2596 35 0.052950076
## 280 2007 1 Lubbock 20921 35 0.052950076
## 281 2007 1 Irving 11667 35 0.052950076
## 282 2007 1 Killeen-Fort Hood 14774 35 0.052950076
## 283 2007 1 Longview-Marshall 14954 35 0.052950076
## 284 2007 1 NE Tarrant County 46090 35 0.052950076
## 285 2007 1 Victoria 4634 35 0.052950076
## 286 2007 1 Midland 3301 35 0.052950076
## 287 2007 1 Montgomery County 45693 35 0.052950076
## 288 2007 1 San Angelo 4021 35 0.052950076
## 289 2007 1 Nacogdoches 1295 35 0.052950076
## 290 2007 1 Paris 4194 35 0.052950076
## 291 2007 1 Temple-Belton 8871 35 0.052950076
## 292 2007 1 Texarkana 530 35 0.052950076
## 293 2007 1 Tyler 33011 35 0.052950076
## 294 2007 1 Sherman-Denison 14047 35 0.052950076
## 295 2007 1 Waco 10421 35 0.052950076
## 296 2007 1 Wichita Falls 11107 35 0.052950076
## 297 2007 2 Austin 117995 2 0.003025719
## 298 2007 2 San Antonio 143617 2 0.003025719
## 299 2007 4 Dallas 377951 1 0.001512859
## 300 2007 5 Houston 487485 1 0.001512859
## 301 2008 1 Abilene 11088 38 0.057488654
## 302 2008 1 Amarillo 17519 38 0.057488654
## 303 2008 1 Arlington 29260 38 0.057488654
## 304 2008 1 Garland 12625 38 0.057488654
## 305 2008 1 Bay Area 45824 38 0.057488654
## 306 2008 1 Beaumont 16503 38 0.057488654
## 307 2008 1 Brazoria County 7503 38 0.057488654
## 308 2008 1 Bryan-College Station 14423 38 0.057488654
## 309 2008 1 Collin County 77403 38 0.057488654
## 310 2008 1 Corpus Christi 33594 38 0.057488654
## 311 2008 1 Lubbock 18511 38 0.057488654
## 312 2008 1 Denton County 41118 38 0.057488654
## 313 2008 1 El Paso 57924 38 0.057488654
## 314 2008 1 Fort Bend 54008 38 0.057488654
## 315 2008 1 Fort Worth 63303 38 0.057488654
## 316 2008 1 Galveston 11093 38 0.057488654
## 317 2008 1 Odessa 91 38 0.057488654
## 318 2008 1 Harlingen 12170 38 0.057488654
## 319 2008 1 Port Arthur 6161 38 0.057488654
## 320 2008 1 Irving 10578 38 0.057488654
## 321 2008 1 Killeen-Fort Hood 18501 38 0.057488654
## 322 2008 1 Laredo 7759 38 0.057488654
## 323 2008 1 Longview-Marshall 16461 38 0.057488654
## 324 2008 1 Temple-Belton 11280 38 0.057488654
## 325 2008 1 McAllen 32742 38 0.057488654
## 326 2008 1 Midland 4591 38 0.057488654
## 327 2008 1 Montgomery County 45543 38 0.057488654
## 328 2008 1 NE Tarrant County 43531 38 0.057488654
## 329 2008 1 Nacogdoches 3269 38 0.057488654
## 330 2008 1 Texarkana 6935 38 0.057488654
## 331 2008 1 Paris 3883 38 0.057488654
## 332 2008 1 Sherman-Denison 12915 38 0.057488654
## 333 2008 1 San Angelo 5919 38 0.057488654
## 334 2008 1 Wichita Falls 11174 38 0.057488654
## 335 2008 1 San Marcos 657 38 0.057488654
## 336 2008 1 Victoria 5127 38 0.057488654
## 337 2008 1 Waco 20400 38 0.057488654
## 338 2008 1 Tyler 34397 38 0.057488654
## 339 2008 2 Austin 139018 2 0.003025719
## 340 2008 2 San Antonio 159559 2 0.003025719
## 341 2008 4 Dallas 342368 1 0.001512859
## 342 2008 5 Houston 465098 1 0.001512859
## 343 2009 1 Abilene 9738 39 0.059001513
## 344 2009 1 Amarillo 17728 39 0.059001513
## 345 2009 1 Arlington 23074 39 0.059001513
## 346 2009 1 Garland 9306 39 0.059001513
## 347 2009 1 Bay Area 39058 39 0.059001513
## 348 2009 1 Beaumont 17184 39 0.059001513
## 349 2009 1 Brazoria County 8070 39 0.059001513
## 350 2009 1 Bryan-College Station 14546 39 0.059001513
## 351 2009 1 Collin County 60179 39 0.059001513
## 352 2009 1 Corpus Christi 35373 39 0.059001513
## 353 2009 1 Longview-Marshall 17065 39 0.059001513
## 354 2009 1 Denton County 36313 39 0.059001513
## 355 2009 1 El Paso 46777 39 0.059001513
## 356 2009 1 Fort Bend 42177 39 0.059001513
## 357 2009 1 Fort Worth 55863 39 0.059001513
## 358 2009 1 Galveston 13489 39 0.059001513
## 359 2009 1 Nacogdoches 2946 39 0.059001513
## 360 2009 1 Harlingen 24493 39 0.059001513
## 361 2009 1 Paris 3891 39 0.059001513
## 362 2009 1 Irving 9371 39 0.059001513
## 363 2009 1 Kerrville 6219 39 0.059001513
## 364 2009 1 Killeen-Fort Hood 19807 39 0.059001513
## 365 2009 1 Laredo 7178 39 0.059001513
## 366 2009 1 Sherman-Denison 11915 39 0.059001513
## 367 2009 1 Lubbock 16961 39 0.059001513
## 368 2009 1 McAllen 26228 39 0.059001513
## 369 2009 1 Midland 6554 39 0.059001513
## 370 2009 1 Montgomery County 41975 39 0.059001513
## 371 2009 1 NE Tarrant County 39498 39 0.059001513
## 372 2009 1 Wichita Falls 11181 39 0.059001513
## 373 2009 1 Odessa 5255 39 0.059001513
## 374 2009 1 San Marcos 2063 39 0.059001513
## 375 2009 1 Port Arthur 7981 39 0.059001513
## 376 2009 1 San Angelo 6583 39 0.059001513
## 377 2009 1 Texarkana 6943 39 0.059001513
## 378 2009 1 Tyler 33803 39 0.059001513
## 379 2009 1 Victoria 5333 39 0.059001513
## 380 2009 1 Temple-Belton 12129 39 0.059001513
## 381 2009 1 Waco 16919 39 0.059001513
## 382 2009 2 San Antonio 146326 2 0.003025719
## 383 2009 2 Austin 129637 2 0.003025719
## 384 2009 3 Dallas 290567 1 0.001512859
## 385 2009 4 Houston 380839 1 0.001512859
## 386 2010 1 Abilene 10551 42 0.063540091
## 387 2010 1 Amarillo 16078 42 0.063540091
## 388 2010 1 Arlington 23969 42 0.063540091
## 389 2010 1 Victoria 5110 42 0.063540091
## 390 2010 1 Bay Area 50945 42 0.063540091
## 391 2010 1 Beaumont 20773 42 0.063540091
## 392 2010 1 Brazoria County 8858 42 0.063540091
## 393 2010 1 Brownsville 740 42 0.063540091
## 394 2010 1 Bryan-College Station 18749 42 0.063540091
## 395 2010 1 Collin County 61850 42 0.063540091
## 396 2010 1 Corpus Christi 32870 42 0.063540091
## 397 2010 1 Longview-Marshall 19308 42 0.063540091
## 398 2010 1 Denton County 38544 42 0.063540091
## 399 2010 1 El Paso 36204 42 0.063540091
## 400 2010 1 Fort Bend 51402 42 0.063540091
## 401 2010 1 Fort Worth 59411 42 0.063540091
## 402 2010 1 Galveston 14914 42 0.063540091
## 403 2010 1 Garland 10660 42 0.063540091
## 404 2010 1 Harlingen 20026 42 0.063540091
## 405 2010 1 Odessa 5202 42 0.063540091
## 406 2010 1 Irving 10289 42 0.063540091
## 407 2010 1 Kerrville 8988 42 0.063540091
## 408 2010 1 Killeen-Fort Hood 19952 42 0.063540091
## 409 2010 1 Laredo 7844 42 0.063540091
## 410 2010 1 San Marcos 2561 42 0.063540091
## 411 2010 1 Lubbock 19698 42 0.063540091
## 412 2010 1 Lufkin 3201 42 0.063540091
## 413 2010 1 McAllen 24282 42 0.063540091
## 414 2010 1 Midland 7613 42 0.063540091
## 415 2010 1 Montgomery County 45977 42 0.063540091
## 416 2010 1 NE Tarrant County 41445 42 0.063540091
## 417 2010 1 Nacogdoches 2597 42 0.063540091
## 418 2010 1 Wichita Falls 11513 42 0.063540091
## 419 2010 1 Paris 4065 42 0.063540091
## 420 2010 1 Port Arthur 7538 42 0.063540091
## 421 2010 1 San Angelo 7484 42 0.063540091
## 422 2010 1 Temple-Belton 13064 42 0.063540091
## 423 2010 1 Texarkana 7705 42 0.063540091
## 424 2010 1 Sherman-Denison 12320 42 0.063540091
## 425 2010 1 South Padre Island 10619 42 0.063540091
## 426 2010 1 Waco 16287 42 0.063540091
## 427 2010 1 Tyler 36613 42 0.063540091
## 428 2010 2 Austin 138949 2 0.003025719
## 429 2010 2 San Antonio 152690 2 0.003025719
## 430 2010 4 Dallas 300022 1 0.001512859
## 431 2010 5 Houston 439576 1 0.001512859
## 432 2011 1 Abilene 9600 42 0.063540091
## 433 2011 1 Amarillo 16983 42 0.063540091
## 434 2011 1 Arlington 20972 42 0.063540091
## 435 2011 1 Victoria 3997 42 0.063540091
## 436 2011 1 Bay Area 48388 42 0.063540091
## 437 2011 1 Beaumont 20975 42 0.063540091
## 438 2011 1 Brazoria County 9003 42 0.063540091
## 439 2011 1 Brownsville 9301 42 0.063540091
## 440 2011 1 Bryan-College Station 19274 42 0.063540091
## 441 2011 1 Collin County 53084 42 0.063540091
## 442 2011 1 Corpus Christi 29525 42 0.063540091
## 443 2011 1 Longview-Marshall 18231 42 0.063540091
## 444 2011 1 Denton County 34710 42 0.063540091
## 445 2011 1 El Paso 37386 42 0.063540091
## 446 2011 1 Fort Bend 51272 42 0.063540091
## 447 2011 1 Fort Worth 52222 42 0.063540091
## 448 2011 1 Galveston 12924 42 0.063540091
## 449 2011 1 Garland 9034 42 0.063540091
## 450 2011 1 Harlingen 17829 42 0.063540091
## 451 2011 1 Odessa 4464 42 0.063540091
## 452 2011 1 Irving 8266 42 0.063540091
## 453 2011 1 Kerrville 10086 42 0.063540091
## 454 2011 1 Killeen-Fort Hood 17261 42 0.063540091
## 455 2011 1 Laredo 7222 42 0.063540091
## 456 2011 1 San Marcos 2044 42 0.063540091
## 457 2011 1 Lubbock 20464 42 0.063540091
## 458 2011 1 Lufkin 4839 42 0.063540091
## 459 2011 1 McAllen 26647 42 0.063540091
## 460 2011 1 Midland 7448 42 0.063540091
## 461 2011 1 Montgomery County 43523 42 0.063540091
## 462 2011 1 NE Tarrant County 36405 42 0.063540091
## 463 2011 1 Nacogdoches 3204 42 0.063540091
## 464 2011 1 Wichita Falls 11698 42 0.063540091
## 465 2011 1 Paris 4181 42 0.063540091
## 466 2011 1 Port Arthur 7787 42 0.063540091
## 467 2011 1 San Angelo 6635 42 0.063540091
## 468 2011 1 Temple-Belton 13659 42 0.063540091
## 469 2011 1 Texarkana 8485 42 0.063540091
## 470 2011 1 Sherman-Denison 11129 42 0.063540091
## 471 2011 1 South Padre Island 10731 42 0.063540091
## 472 2011 1 Waco 18213 42 0.063540091
## 473 2011 1 Tyler 36836 42 0.063540091
## 474 2011 2 Austin 116808 2 0.003025719
## 475 2011 2 San Antonio 142642 2 0.003025719
## 476 2011 3 Dallas 262693 1 0.001512859
## 477 2011 5 Houston 414543 1 0.001512859
## 478 2012 1 Abilene 11635 43 0.065052950
## 479 2012 1 Amarillo 15865 43 0.065052950
## 480 2012 1 Arlington 14755 43 0.065052950
## 481 2012 1 Austin 92226 43 0.065052950
## 482 2012 1 Bay Area 38278 43 0.065052950
## 483 2012 1 Beaumont 20296 43 0.065052950
## 484 2012 1 Brazoria County 7382 43 0.065052950
## 485 2012 1 Brownsville 9506 43 0.065052950
## 486 2012 1 Bryan-College Station 19314 43 0.065052950
## 487 2012 1 Collin County 39244 43 0.065052950
## 488 2012 1 Corpus Christi 26527 43 0.065052950
## 489 2012 1 Longview-Marshall 19834 43 0.065052950
## 490 2012 1 Denton County 26000 43 0.065052950
## 491 2012 1 El Paso 38553 43 0.065052950
## 492 2012 1 Fort Bend 40928 43 0.065052950
## 493 2012 1 Fort Worth 42051 43 0.065052950
## 494 2012 1 Galveston 11162 43 0.065052950
## 495 2012 1 Garland 5820 43 0.065052950
## 496 2012 1 Harlingen 19626 43 0.065052950
## 497 2012 1 Odessa 2576 43 0.065052950
## 498 2012 1 Irving 6054 43 0.065052950
## 499 2012 1 Kerrville 10132 43 0.065052950
## 500 2012 1 Killeen-Fort Hood 15381 43 0.065052950
## 501 2012 1 Laredo 5907 43 0.065052950
## 502 2012 1 San Marcos 1774 43 0.065052950
## 503 2012 1 Lubbock 18626 43 0.065052950
## 504 2012 1 Lufkin 5063 43 0.065052950
## 505 2012 1 McAllen 25933 43 0.065052950
## 506 2012 1 Midland 5720 43 0.065052950
## 507 2012 1 Montgomery County 36185 43 0.065052950
## 508 2012 1 NE Tarrant County 27122 43 0.065052950
## 509 2012 1 Nacogdoches 2879 43 0.065052950
## 510 2012 1 Wichita Falls 10752 43 0.065052950
## 511 2012 1 Paris 4287 43 0.065052950
## 512 2012 1 Port Arthur 7157 43 0.065052950
## 513 2012 1 San Angelo 5351 43 0.065052950
## 514 2012 1 Temple-Belton 12985 43 0.065052950
## 515 2012 1 Texarkana 6707 43 0.065052950
## 516 2012 1 Sherman-Denison 9806 43 0.065052950
## 517 2012 1 South Padre Island 8852 43 0.065052950
## 518 2012 1 Waco 17640 43 0.065052950
## 519 2012 1 Tyler 34925 43 0.065052950
## 520 2012 1 Victoria 3393 43 0.065052950
## 521 2012 2 San Antonio 124196 1 0.001512859
## 522 2012 3 Dallas 195543 1 0.001512859
## 523 2012 4 Houston 324204 1 0.001512859
## 524 2013 1 Abilene 11505 43 0.065052950
## 525 2013 1 Amarillo 15247 43 0.065052950
## 526 2013 1 Arlington 11111 43 0.065052950
## 527 2013 1 Austin 73242 43 0.065052950
## 528 2013 1 Bay Area 32668 43 0.065052950
## 529 2013 1 Beaumont 19675 43 0.065052950
## 530 2013 1 Brazoria County 5779 43 0.065052950
## 531 2013 1 Brownsville 9896 43 0.065052950
## 532 2013 1 Bryan-College Station 16873 43 0.065052950
## 533 2013 1 Collin County 29072 43 0.065052950
## 534 2013 1 Corpus Christi 23206 43 0.065052950
## 535 2013 1 Longview-Marshall 21121 43 0.065052950
## 536 2013 1 Denton County 19326 43 0.065052950
## 537 2013 1 El Paso 44158 43 0.065052950
## 538 2013 1 Fort Bend 28669 43 0.065052950
## 539 2013 1 Fort Worth 35900 43 0.065052950
## 540 2013 1 Galveston 9945 43 0.065052950
## 541 2013 1 Garland 3882 43 0.065052950
## 542 2013 1 Harlingen 17669 43 0.065052950
## 543 2013 1 Odessa 2700 43 0.065052950
## 544 2013 1 Irving 4673 43 0.065052950
## 545 2013 1 Kerrville 9116 43 0.065052950
## 546 2013 1 Killeen-Fort Hood 18951 43 0.065052950
## 547 2013 1 Laredo 5933 43 0.065052950
## 548 2013 1 San Marcos 1440 43 0.065052950
## 549 2013 1 Lubbock 13088 43 0.065052950
## 550 2013 1 Lufkin 5126 43 0.065052950
## 551 2013 1 McAllen 26616 43 0.065052950
## 552 2013 1 Midland 5223 43 0.065052950
## 553 2013 1 Montgomery County 29255 43 0.065052950
## 554 2013 1 NE Tarrant County 19850 43 0.065052950
## 555 2013 1 Nacogdoches 3301 43 0.065052950
## 556 2013 1 Wichita Falls 10092 43 0.065052950
## 557 2013 1 Paris 4275 43 0.065052950
## 558 2013 1 Port Arthur 8032 43 0.065052950
## 559 2013 1 San Angelo 4500 43 0.065052950
## 560 2013 1 Temple-Belton 12067 43 0.065052950
## 561 2013 1 Texarkana 8804 43 0.065052950
## 562 2013 1 Sherman-Denison 9027 43 0.065052950
## 563 2013 1 South Padre Island 8162 43 0.065052950
## 564 2013 1 Waco 16177 43 0.065052950
## 565 2013 1 Tyler 33885 43 0.065052950
## 566 2013 1 Victoria 3400 43 0.065052950
## 567 2013 2 Dallas 151116 2 0.003025719
## 568 2013 2 San Antonio 109613 2 0.003025719
## 569 2013 3 Houston 247473 1 0.001512859
## 570 2014 1 Abilene 11566 43 0.065052950
## 571 2014 1 Amarillo 14820 43 0.065052950
## 572 2014 1 Arlington 9177 43 0.065052950
## 573 2014 1 Austin 75694 43 0.065052950
## 574 2014 1 Bay Area 27914 43 0.065052950
## 575 2014 1 Beaumont 19040 43 0.065052950
## 576 2014 1 Brazoria County 4299 43 0.065052950
## 577 2014 1 Brownsville 9341 43 0.065052950
## 578 2014 1 Bryan-College Station 13278 43 0.065052950
## 579 2014 1 Collin County 26520 43 0.065052950
## 580 2014 1 Corpus Christi 20787 43 0.065052950
## 581 2014 1 Longview-Marshall 22329 43 0.065052950
## 582 2014 1 Denton County 18268 43 0.065052950
## 583 2014 1 El Paso 48560 43 0.065052950
## 584 2014 1 Fort Bend 28108 43 0.065052950
## 585 2014 1 Fort Worth 30642 43 0.065052950
## 586 2014 1 Galveston 8706 43 0.065052950
## 587 2014 1 Garland 3258 43 0.065052950
## 588 2014 1 Harlingen 19261 43 0.065052950
## 589 2014 1 Odessa 3059 43 0.065052950
## 590 2014 1 Irving 4104 43 0.065052950
## 591 2014 1 Kerrville 8454 43 0.065052950
## 592 2014 1 Killeen-Fort Hood 17026 43 0.065052950
## 593 2014 1 Laredo 6281 43 0.065052950
## 594 2014 1 San Marcos 1257 43 0.065052950
## 595 2014 1 Lubbock 12348 43 0.065052950
## 596 2014 1 Lufkin 5175 43 0.065052950
## 597 2014 1 McAllen 26716 43 0.065052950
## 598 2014 1 Midland 6597 43 0.065052950
## 599 2014 1 Montgomery County 28154 43 0.065052950
## 600 2014 1 NE Tarrant County 16800 43 0.065052950
## 601 2014 1 Nacogdoches 3523 43 0.065052950
## 602 2014 1 Wichita Falls 10520 43 0.065052950
## 603 2014 1 Paris 3637 43 0.065052950
## 604 2014 1 Port Arthur 7682 43 0.065052950
## 605 2014 1 San Angelo 5859 43 0.065052950
## 606 2014 1 Temple-Belton 10645 43 0.065052950
## 607 2014 1 Texarkana 9234 43 0.065052950
## 608 2014 1 Sherman-Denison 8408 43 0.065052950
## 609 2014 1 South Padre Island 8958 43 0.065052950
## 610 2014 1 Waco 15315 43 0.065052950
## 611 2014 1 Tyler 32044 43 0.065052950
## 612 2014 1 Victoria 3323 43 0.065052950
## 613 2014 2 Dallas 136440 2 0.003025719
## 614 2014 2 San Antonio 106596 2 0.003025719
## 615 2014 3 Houston 224230 1 0.001512859
## 616 2015 1 Abilene 6150 45 0.068078669
## 617 2015 1 Amarillo 7841 45 0.068078669
## 618 2015 1 Arlington 4261 45 0.068078669
## 619 2015 1 Austin 46107 45 0.068078669
## 620 2015 1 Bay Area 13937 45 0.068078669
## 621 2015 1 Beaumont 10861 45 0.068078669
## 622 2015 1 Brazoria County 1700 45 0.068078669
## 623 2015 1 Brownsville 4372 45 0.068078669
## 624 2015 1 Bryan-College Station 6582 45 0.068078669
## 625 2015 1 Collin County 14769 45 0.068078669
## 626 2015 1 Corpus Christi 11683 45 0.068078669
## 627 2015 1 Dallas 70122 45 0.068078669
## 628 2015 1 Denton County 9578 45 0.068078669
## 629 2015 1 El Paso 28178 45 0.068078669
## 630 2015 1 Fort Bend 19728 45 0.068078669
## 631 2015 1 Fort Worth 14837 45 0.068078669
## 632 2015 1 Galveston 3759 45 0.068078669
## 633 2015 1 Garland 1322 45 0.068078669
## 634 2015 1 Harlingen 11563 45 0.068078669
## 635 2015 1 Odessa 1928 45 0.068078669
## 636 2015 1 Irving 2177 45 0.068078669
## 637 2015 1 Kerrville 4502 45 0.068078669
## 638 2015 1 Killeen-Fort Hood 8218 45 0.068078669
## 639 2015 1 Laredo 3781 45 0.068078669
## 640 2015 1 Longview-Marshall 12809 45 0.068078669
## 641 2015 1 Lubbock 7232 45 0.068078669
## 642 2015 1 Lufkin 2827 45 0.068078669
## 643 2015 1 McAllen 15297 45 0.068078669
## 644 2015 1 Midland 4290 45 0.068078669
## 645 2015 1 Montgomery County 20813 45 0.068078669
## 646 2015 1 NE Tarrant County 8992 45 0.068078669
## 647 2015 1 Nacogdoches 1951 45 0.068078669
## 648 2015 1 Wichita Falls 5559 45 0.068078669
## 649 2015 1 Paris 2083 45 0.068078669
## 650 2015 1 Port Arthur 3211 45 0.068078669
## 651 2015 1 San Angelo 3467 45 0.068078669
## 652 2015 1 San Antonio 59185 45 0.068078669
## 653 2015 1 San Marcos 578 45 0.068078669
## 654 2015 1 Sherman-Denison 3773 45 0.068078669
## 655 2015 1 South Padre Island 4992 45 0.068078669
## 656 2015 1 Temple-Belton 5905 45 0.068078669
## 657 2015 1 Texarkana 3999 45 0.068078669
## 658 2015 1 Tyler 17137 45 0.068078669
## 659 2015 1 Victoria 2066 45 0.068078669
## 660 2015 1 Waco 7141 45 0.068078669
## 661 2015 2 Houston 144132 1 0.001512859
## Probability_Class
## 1 Highest
## 2 Highest
## 3 Highest
## 4 Highest
## 5 Highest
## 6 Highest
## 7 Highest
## 8 Highest
## 9 Highest
## 10 Highest
## 11 Highest
## 12 Highest
## 13 Highest
## 14 Highest
## 15 Highest
## 16 Highest
## 17 Highest
## 18 Highest
## 19 Highest
## 20 Highest
## 21 Highest
## 22 Highest
## 23 Highest
## 24 Highest
## 25 Highest
## 26 Highest
## 27 Highest
## 28 Highest
## 29 Highest
## 30 Highest
## 31 Highest
## 32 Highest
## 33 Highest
## 34 Highest
## 35 Highest
## 36 Highest
## 37 Lowest
## 38 Lowest
## 39 Highest
## 40 Highest
## 41 Highest
## 42 Highest
## 43 Highest
## 44 Highest
## 45 Highest
## 46 Highest
## 47 Highest
## 48 Highest
## 49 Highest
## 50 Highest
## 51 Highest
## 52 Highest
## 53 Highest
## 54 Highest
## 55 Highest
## 56 Highest
## 57 Highest
## 58 Highest
## 59 Highest
## 60 Highest
## 61 Highest
## 62 Highest
## 63 Highest
## 64 Highest
## 65 Highest
## 66 Highest
## 67 Highest
## 68 Highest
## 69 Highest
## 70 Highest
## 71 Highest
## 72 Highest
## 73 Highest
## 74 Highest
## 75 Lowest
## 76 Lowest
## 77 Highest
## 78 Highest
## 79 Highest
## 80 Highest
## 81 Highest
## 82 Highest
## 83 Highest
## 84 Highest
## 85 Highest
## 86 Highest
## 87 Highest
## 88 Highest
## 89 Highest
## 90 Highest
## 91 Highest
## 92 Highest
## 93 Highest
## 94 Highest
## 95 Highest
## 96 Highest
## 97 Highest
## 98 Highest
## 99 Highest
## 100 Highest
## 101 Highest
## 102 Highest
## 103 Highest
## 104 Highest
## 105 Highest
## 106 Highest
## 107 Highest
## 108 Highest
## 109 Highest
## 110 Highest
## 111 Lowest
## 112 Other
## 113 Other
## 114 Highest
## 115 Highest
## 116 Highest
## 117 Highest
## 118 Highest
## 119 Highest
## 120 Highest
## 121 Highest
## 122 Highest
## 123 Highest
## 124 Highest
## 125 Highest
## 126 Highest
## 127 Highest
## 128 Highest
## 129 Highest
## 130 Highest
## 131 Highest
## 132 Highest
## 133 Highest
## 134 Highest
## 135 Highest
## 136 Highest
## 137 Highest
## 138 Highest
## 139 Highest
## 140 Highest
## 141 Highest
## 142 Highest
## 143 Highest
## 144 Highest
## 145 Highest
## 146 Highest
## 147 Highest
## 148 Lowest
## 149 Other
## 150 Other
## 151 Highest
## 152 Highest
## 153 Highest
## 154 Highest
## 155 Highest
## 156 Highest
## 157 Highest
## 158 Highest
## 159 Highest
## 160 Highest
## 161 Highest
## 162 Highest
## 163 Highest
## 164 Highest
## 165 Highest
## 166 Highest
## 167 Highest
## 168 Highest
## 169 Highest
## 170 Highest
## 171 Highest
## 172 Highest
## 173 Highest
## 174 Highest
## 175 Highest
## 176 Highest
## 177 Highest
## 178 Highest
## 179 Highest
## 180 Highest
## 181 Highest
## 182 Highest
## 183 Highest
## 184 Highest
## 185 Highest
## 186 Lowest
## 187 Lowest
## 188 Lowest
## 189 Highest
## 190 Highest
## 191 Highest
## 192 Highest
## 193 Highest
## 194 Highest
## 195 Highest
## 196 Highest
## 197 Highest
## 198 Highest
## 199 Highest
## 200 Highest
## 201 Highest
## 202 Highest
## 203 Highest
## 204 Highest
## 205 Highest
## 206 Highest
## 207 Highest
## 208 Highest
## 209 Highest
## 210 Highest
## 211 Highest
## 212 Highest
## 213 Highest
## 214 Highest
## 215 Highest
## 216 Highest
## 217 Highest
## 218 Highest
## 219 Highest
## 220 Highest
## 221 Highest
## 222 Other
## 223 Other
## 224 Lowest
## 225 Lowest
## 226 Highest
## 227 Highest
## 228 Highest
## 229 Highest
## 230 Highest
## 231 Highest
## 232 Highest
## 233 Highest
## 234 Highest
## 235 Highest
## 236 Highest
## 237 Highest
## 238 Highest
## 239 Highest
## 240 Highest
## 241 Highest
## 242 Highest
## 243 Highest
## 244 Highest
## 245 Highest
## 246 Highest
## 247 Highest
## 248 Highest
## 249 Highest
## 250 Highest
## 251 Highest
## 252 Highest
## 253 Highest
## 254 Highest
## 255 Highest
## 256 Highest
## 257 Highest
## 258 Other
## 259 Other
## 260 Lowest
## 261 Lowest
## 262 Highest
## 263 Highest
## 264 Highest
## 265 Highest
## 266 Highest
## 267 Highest
## 268 Highest
## 269 Highest
## 270 Highest
## 271 Highest
## 272 Highest
## 273 Highest
## 274 Highest
## 275 Highest
## 276 Highest
## 277 Highest
## 278 Highest
## 279 Highest
## 280 Highest
## 281 Highest
## 282 Highest
## 283 Highest
## 284 Highest
## 285 Highest
## 286 Highest
## 287 Highest
## 288 Highest
## 289 Highest
## 290 Highest
## 291 Highest
## 292 Highest
## 293 Highest
## 294 Highest
## 295 Highest
## 296 Highest
## 297 Other
## 298 Other
## 299 Lowest
## 300 Lowest
## 301 Highest
## 302 Highest
## 303 Highest
## 304 Highest
## 305 Highest
## 306 Highest
## 307 Highest
## 308 Highest
## 309 Highest
## 310 Highest
## 311 Highest
## 312 Highest
## 313 Highest
## 314 Highest
## 315 Highest
## 316 Highest
## 317 Highest
## 318 Highest
## 319 Highest
## 320 Highest
## 321 Highest
## 322 Highest
## 323 Highest
## 324 Highest
## 325 Highest
## 326 Highest
## 327 Highest
## 328 Highest
## 329 Highest
## 330 Highest
## 331 Highest
## 332 Highest
## 333 Highest
## 334 Highest
## 335 Highest
## 336 Highest
## 337 Highest
## 338 Highest
## 339 Other
## 340 Other
## 341 Lowest
## 342 Lowest
## 343 Highest
## 344 Highest
## 345 Highest
## 346 Highest
## 347 Highest
## 348 Highest
## 349 Highest
## 350 Highest
## 351 Highest
## 352 Highest
## 353 Highest
## 354 Highest
## 355 Highest
## 356 Highest
## 357 Highest
## 358 Highest
## 359 Highest
## 360 Highest
## 361 Highest
## 362 Highest
## 363 Highest
## 364 Highest
## 365 Highest
## 366 Highest
## 367 Highest
## 368 Highest
## 369 Highest
## 370 Highest
## 371 Highest
## 372 Highest
## 373 Highest
## 374 Highest
## 375 Highest
## 376 Highest
## 377 Highest
## 378 Highest
## 379 Highest
## 380 Highest
## 381 Highest
## 382 Other
## 383 Other
## 384 Lowest
## 385 Lowest
## 386 Highest
## 387 Highest
## 388 Highest
## 389 Highest
## 390 Highest
## 391 Highest
## 392 Highest
## 393 Highest
## 394 Highest
## 395 Highest
## 396 Highest
## 397 Highest
## 398 Highest
## 399 Highest
## 400 Highest
## 401 Highest
## 402 Highest
## 403 Highest
## 404 Highest
## 405 Highest
## 406 Highest
## 407 Highest
## 408 Highest
## 409 Highest
## 410 Highest
## 411 Highest
## 412 Highest
## 413 Highest
## 414 Highest
## 415 Highest
## 416 Highest
## 417 Highest
## 418 Highest
## 419 Highest
## 420 Highest
## 421 Highest
## 422 Highest
## 423 Highest
## 424 Highest
## 425 Highest
## 426 Highest
## 427 Highest
## 428 Other
## 429 Other
## 430 Lowest
## 431 Lowest
## 432 Highest
## 433 Highest
## 434 Highest
## 435 Highest
## 436 Highest
## 437 Highest
## 438 Highest
## 439 Highest
## 440 Highest
## 441 Highest
## 442 Highest
## 443 Highest
## 444 Highest
## 445 Highest
## 446 Highest
## 447 Highest
## 448 Highest
## 449 Highest
## 450 Highest
## 451 Highest
## 452 Highest
## 453 Highest
## 454 Highest
## 455 Highest
## 456 Highest
## 457 Highest
## 458 Highest
## 459 Highest
## 460 Highest
## 461 Highest
## 462 Highest
## 463 Highest
## 464 Highest
## 465 Highest
## 466 Highest
## 467 Highest
## 468 Highest
## 469 Highest
## 470 Highest
## 471 Highest
## 472 Highest
## 473 Highest
## 474 Other
## 475 Other
## 476 Lowest
## 477 Lowest
## 478 Highest
## 479 Highest
## 480 Highest
## 481 Highest
## 482 Highest
## 483 Highest
## 484 Highest
## 485 Highest
## 486 Highest
## 487 Highest
## 488 Highest
## 489 Highest
## 490 Highest
## 491 Highest
## 492 Highest
## 493 Highest
## 494 Highest
## 495 Highest
## 496 Highest
## 497 Highest
## 498 Highest
## 499 Highest
## 500 Highest
## 501 Highest
## 502 Highest
## 503 Highest
## 504 Highest
## 505 Highest
## 506 Highest
## 507 Highest
## 508 Highest
## 509 Highest
## 510 Highest
## 511 Highest
## 512 Highest
## 513 Highest
## 514 Highest
## 515 Highest
## 516 Highest
## 517 Highest
## 518 Highest
## 519 Highest
## 520 Highest
## 521 Lowest
## 522 Lowest
## 523 Lowest
## 524 Highest
## 525 Highest
## 526 Highest
## 527 Highest
## 528 Highest
## 529 Highest
## 530 Highest
## 531 Highest
## 532 Highest
## 533 Highest
## 534 Highest
## 535 Highest
## 536 Highest
## 537 Highest
## 538 Highest
## 539 Highest
## 540 Highest
## 541 Highest
## 542 Highest
## 543 Highest
## 544 Highest
## 545 Highest
## 546 Highest
## 547 Highest
## 548 Highest
## 549 Highest
## 550 Highest
## 551 Highest
## 552 Highest
## 553 Highest
## 554 Highest
## 555 Highest
## 556 Highest
## 557 Highest
## 558 Highest
## 559 Highest
## 560 Highest
## 561 Highest
## 562 Highest
## 563 Highest
## 564 Highest
## 565 Highest
## 566 Highest
## 567 Other
## 568 Other
## 569 Lowest
## 570 Highest
## 571 Highest
## 572 Highest
## 573 Highest
## 574 Highest
## 575 Highest
## 576 Highest
## 577 Highest
## 578 Highest
## 579 Highest
## 580 Highest
## 581 Highest
## 582 Highest
## 583 Highest
## 584 Highest
## 585 Highest
## 586 Highest
## 587 Highest
## 588 Highest
## 589 Highest
## 590 Highest
## 591 Highest
## 592 Highest
## 593 Highest
## 594 Highest
## 595 Highest
## 596 Highest
## 597 Highest
## 598 Highest
## 599 Highest
## 600 Highest
## 601 Highest
## 602 Highest
## 603 Highest
## 604 Highest
## 605 Highest
## 606 Highest
## 607 Highest
## 608 Highest
## 609 Highest
## 610 Highest
## 611 Highest
## 612 Highest
## 613 Other
## 614 Other
## 615 Lowest
## 616 Highest
## 617 Highest
## 618 Highest
## 619 Highest
## 620 Highest
## 621 Highest
## 622 Highest
## 623 Highest
## 624 Highest
## 625 Highest
## 626 Highest
## 627 Highest
## 628 Highest
## 629 Highest
## 630 Highest
## 631 Highest
## 632 Highest
## 633 Highest
## 634 Highest
## 635 Highest
## 636 Highest
## 637 Highest
## 638 Highest
## 639 Highest
## 640 Highest
## 641 Highest
## 642 Highest
## 643 Highest
## 644 Highest
## 645 Highest
## 646 Highest
## 647 Highest
## 648 Highest
## 649 Highest
## 650 Highest
## 651 Highest
## 652 Highest
## 653 Highest
## 654 Highest
## 655 Highest
## 656 Highest
## 657 Highest
## 658 Highest
## 659 Highest
## 660 Highest
## 661 LowestEvaluating our Results:
From the above boxpplot it appears as though there is a general trend where cities and years Having Lower # of listings have the highest probability of being selected, while those with a Higher number of listings have a lower probability.
However this pattern and distribution is not as extreme as in the first case, where we only grouped by year. It is possible that the inclusion of city as a second grouping parameter, allowed for the distribution to be less extreme. This might be indicative of a Conditional Probability.
Concluding Questions:
- How does the probability of selecting a given city change, given a specific year? What about month in a year?
- Is there a correlation between volume and sales? What about listings and sales?
- How do the months in a year affect sales of houses in Texas?