Projet 4 Txhousing

1. Create a new dataset that contains only the `city`, `year`, `month`, and `sales` variables.

select(txhousing, city:sales)

## # A tibble: 8,602 x 4
##    city     year month sales
##    <chr>   <int> <int> <dbl>
##  1 Abilene  2000     1    72
##  2 Abilene  2000     2    98
##  3 Abilene  2000     3   130
##  4 Abilene  2000     4    98
##  5 Abilene  2000     5   141
##  6 Abilene  2000     6   156
##  7 Abilene  2000     7   152
##  8 Abilene  2000     8   131
##  9 Abilene  2000     9   104
## 10 Abilene  2000    10   101
## # … with 8,592 more rows

select(txhousing, city, year, month, sales)

## # A tibble: 8,602 x 4
##    city     year month sales
##    <chr>   <int> <int> <dbl>
##  1 Abilene  2000     1    72
##  2 Abilene  2000     2    98
##  3 Abilene  2000     3   130
##  4 Abilene  2000     4    98
##  5 Abilene  2000     5   141
##  6 Abilene  2000     6   156
##  7 Abilene  2000     7   152
##  8 Abilene  2000     8   131
##  9 Abilene  2000     9   104
## 10 Abilene  2000    10   101
## # … with 8,592 more rows

2. Create a new dataset that contains all of the variables except `volume` and `date`.

select(txhousing, -volume, -date)

## # A tibble: 8,602 x 7
##    city     year month sales median listings inventory
##    <chr>   <int> <int> <dbl>  <dbl>    <dbl>     <dbl>
##  1 Abilene  2000     1    72  71400      701       6.3
##  2 Abilene  2000     2    98  58700      746       6.6
##  3 Abilene  2000     3   130  58100      784       6.8
##  4 Abilene  2000     4    98  68600      785       6.9
##  5 Abilene  2000     5   141  67300      794       6.8
##  6 Abilene  2000     6   156  66900      780       6.6
##  7 Abilene  2000     7   152  73500      742       6.2
##  8 Abilene  2000     8   131  75000      765       6.4
##  9 Abilene  2000     9   104  64500      771       6.5
## 10 Abilene  2000    10   101  59300      764       6.6
## # … with 8,592 more rows

select(txhousing, -c("volume", "date"))

## # A tibble: 8,602 x 7
##    city     year month sales median listings inventory
##    <chr>   <int> <int> <dbl>  <dbl>    <dbl>     <dbl>
##  1 Abilene  2000     1    72  71400      701       6.3
##  2 Abilene  2000     2    98  58700      746       6.6
##  3 Abilene  2000     3   130  58100      784       6.8
##  4 Abilene  2000     4    98  68600      785       6.9
##  5 Abilene  2000     5   141  67300      794       6.8
##  6 Abilene  2000     6   156  66900      780       6.6
##  7 Abilene  2000     7   152  73500      742       6.2
##  8 Abilene  2000     8   131  75000      765       6.4
##  9 Abilene  2000     9   104  64500      771       6.5
## 10 Abilene  2000    10   101  59300      764       6.6
## # … with 8,592 more rows

3. Create a new dataset that contains only the data from Dallas.

filter(txhousing, city == "Dallas")

## # A tibble: 187 x 9
##    city    year month sales    volume median listings inventory  date
##    <chr>  <int> <int> <dbl>     <dbl>  <dbl>    <dbl>     <dbl> <dbl>
##  1 Dallas  2000     1  2286 375389865 124400    13316       3.7 2000 
##  2 Dallas  2000     2  3247 555124812 127700    13495       3.7 2000.
##  3 Dallas  2000     3  4244 702148377 128500    13752       3.7 2000.
##  4 Dallas  2000     4  3977 667331427 132000    13752       3.7 2000.
##  5 Dallas  2000     5  4545 783197806 137100    14018       3.7 2000.
##  6 Dallas  2000     6  4738 846254912 138800    14392       3.8 2000.
##  7 Dallas  2000     7  4276 763793916 139600    14705       3.9 2000.
##  8 Dallas  2000     8  4373 770735708 134500    14607       3.9 2001.
##  9 Dallas  2000     9  3654 643046932 136000    15109       4   2001.
## 10 Dallas  2000    10  3601 626302634 134600    15212       4   2001.
## # … with 177 more rows

4. Create a new dataset of only the months with `sales` of at least 100 units.

filter(txhousing, sales >= 100)

## # A tibble: 5,582 x 9
##    city     year month sales   volume median listings inventory  date
##    <chr>   <int> <int> <dbl>    <dbl>  <dbl>    <dbl>     <dbl> <dbl>
##  1 Abilene  2000     3   130  9285000  58100      784       6.8 2000.
##  2 Abilene  2000     5   141 10590000  67300      794       6.8 2000.
##  3 Abilene  2000     6   156 13910000  66900      780       6.6 2000.
##  4 Abilene  2000     7   152 12635000  73500      742       6.2 2000.
##  5 Abilene  2000     8   131 10710000  75000      765       6.4 2001.
##  6 Abilene  2000     9   104  7615000  64500      771       6.5 2001.
##  7 Abilene  2000    10   101  7040000  59300      764       6.6 2001.
##  8 Abilene  2000    11   100  7890000  70900      721       6.2 2001.
##  9 Abilene  2001     2   112  8670000  68900      700       6   2001.
## 10 Abilene  2001     3   118  9550000  72300      738       6.4 2001.
## # … with 5,572 more rows

5. Create a new dataset of all the data beginning in June of 2006.

filter(txhousing, year > 2006 | (year == 2006 & month >=6))

## # A tibble: 5,060 x 9
##    city     year month sales   volume median listings inventory  date
##    <chr>   <int> <int> <dbl>    <dbl>  <dbl>    <dbl>     <dbl> <dbl>
##  1 Abilene  2006     6   225 26620000  99800      786       4.8 2006.
##  2 Abilene  2006     7   169 19160000  93200      804       4.9 2006.
##  3 Abilene  2006     8   220 25735000 104000      812       5   2007.
##  4 Abilene  2006     9   159 19850000 109500      817       5   2007.
##  5 Abilene  2006    10   156 17100000  97100      797       4.9 2007.
##  6 Abilene  2006    11   146 15930000  99400      792       4.8 2007.
##  7 Abilene  2006    12   157 17985000 103100      724       4.4 2007.
##  8 Abilene  2007     1   132 13335000  91700      746       4.4 2007 
##  9 Abilene  2007     2   140 16105000 100000      774       4.6 2007.
## 10 Abilene  2007     3   212 20410000  86500      816       4.7 2007.
## # … with 5,050 more rows

filter(txhousing, date > 2006.4)

## # A tibble: 5,060 x 9
##    city     year month sales   volume median listings inventory  date
##    <chr>   <int> <int> <dbl>    <dbl>  <dbl>    <dbl>     <dbl> <dbl>
##  1 Abilene  2006     6   225 26620000  99800      786       4.8 2006.
##  2 Abilene  2006     7   169 19160000  93200      804       4.9 2006.
##  3 Abilene  2006     8   220 25735000 104000      812       5   2007.
##  4 Abilene  2006     9   159 19850000 109500      817       5   2007.
##  5 Abilene  2006    10   156 17100000  97100      797       4.9 2007.
##  6 Abilene  2006    11   146 15930000  99400      792       4.8 2007.
##  7 Abilene  2006    12   157 17985000 103100      724       4.4 2007.
##  8 Abilene  2007     1   132 13335000  91700      746       4.4 2007 
##  9 Abilene  2007     2   140 16105000 100000      774       4.6 2007.
## 10 Abilene  2007     3   212 20410000  86500      816       4.7 2007.
## # … with 5,050 more rows

6. Create a new dataset that removes all of the months where the `median` sale price or the total number of `sales` are missing.

filter(txhousing, !is.na(median) | !is.na(sales))

## # A tibble: 8,035 x 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.
## # … with 8,025 more rows

7. Sort the entire dataset so that the fewest number of `sales` are at the top.

arrange(txhousing, sales)

## # A tibble: 8,602 x 9
##    city                year month sales  volume median listings inventory  date
##    <chr>              <int> <int> <dbl>   <dbl>  <dbl>    <dbl>     <dbl> <dbl>
##  1 San Marcos          2011    10     6 1156999 180000      163       8.3 2012.
##  2 Harlingen           2000     7     9 1110000  87500      719      30.8 2000.
##  3 South Padre Island  2011     1     9 2088500 225000     1258      55.7 2011 
##  4 San Marcos          2011     1    10 1482310 140000      165       7.5 2011 
##  5 San Marcos          2011    12    10 1561250 140000      148       8   2012.
##  6 San Marcos          2014    11    10 1506878 146700       96       4   2015.
##  7 South Padre Island  2010     1    10 2543721 200000     1290      NA   2010 
##  8 South Padre Island  2010    11    10 2098500 160000     1280      55.9 2011.
##  9 San Marcos          2001     2    11 1445000 126000      260       8.7 2001.
## 10 San Marcos          2008    10    11 1675000 143300      160       7.4 2009.
## # … with 8,592 more rows

8. Sort the entire dataset, first by `city` alphabetically, and then so that the largest `volume` is at the top for each `city`.

arrange(txhousing, city, desc(volume))

## # A tibble: 8,602 x 9
##    city     year month sales   volume median listings inventory  date
##    <chr>   <int> <int> <dbl>    <dbl>  <dbl>    <dbl>     <dbl> <dbl>
##  1 Abilene  2015     7   268 45845730 148700      986       5   2016.
##  2 Abilene  2015     6   260 41396230 141500      965       5   2015.
##  3 Abilene  2014     7   231 35861350 145800     1033       5.8 2014.
##  4 Abilene  2014     6   230 34398506 135200     1023       5.8 2014.
##  5 Abilene  2013     7   218 32547446 140000      969       5.4 2014.
##  6 Abilene  2013     5   225 31901380 130000      923       5.3 2013.
##  7 Abilene  2015     3   198 31869437 136800      821       4.4 2015.
##  8 Abilene  2012     8   220 31687638 130600     1132       7.1 2013.
##  9 Abilene  2015     5   199 31385757 144700      919       4.8 2015.
## 10 Abilene  2014     9   201 30904319 135900     1012       5.8 2015.
## # … with 8,592 more rows

9. Create a dataset that contains only the `city`, `sales`, and `date` variables for Dallas and Houston, and use it to create a line plot of `sales` versus `date` with lines colored for each city.

txhousing %>%
  select(city, sales, date) %>%
  filter(city == "Dallas" | city == "Houston") %>%
#  filter(city %in% c("Dallas", "Houston")) %>%  # equivalent filter
  ggplot() +
    geom_line(mapping = aes(x = date, y = sales, color = city))

Projet 4 Txhousing

Bernard Mutabazi

7/2/2021

1. Create a new dataset that contains only the `city`, `year`, `month`, and `sales` variables.

2. Create a new dataset that contains all of the variables except `volume` and `date`.

3. Create a new dataset that contains only the data from Dallas.

4. Create a new dataset of only the months with `sales` of at least 100 units.

5. Create a new dataset of all the data beginning in June of 2006.

6. Create a new dataset that removes all of the months where the `median` sale price or the total number of `sales` are missing.

7. Sort the entire dataset so that the fewest number of `sales` are at the top.

8. Sort the entire dataset, first by `city` alphabetically, and then so that the largest `volume` is at the top for each `city`.

9. Create a dataset that contains only the `city`, `sales`, and `date` variables for Dallas and Houston, and use it to create a line plot of `sales` versus `date` with lines colored for each city.

Projet 4 Txhousing

Bernard Mutabazi

7/2/2021

1. Create a new dataset that contains only the city, year, month, and sales variables.

2. Create a new dataset that contains all of the variables except volume and date.

3. Create a new dataset that contains only the data from Dallas.

4. Create a new dataset of only the months with sales of at least 100 units.

5. Create a new dataset of all the data beginning in June of 2006.

6. Create a new dataset that removes all of the months where the median sale price or the total number of sales are missing.

7. Sort the entire dataset so that the fewest number of sales are at the top.

8. Sort the entire dataset, first by city alphabetically, and then so that the largest volume is at the top for each city.

9. Create a dataset that contains only the city, sales, and date variables for Dallas and Houston, and use it to create a line plot of sales versus date with lines colored for each city.

1. Create a new dataset that contains only the `city`, `year`, `month`, and `sales` variables.

2. Create a new dataset that contains all of the variables except `volume` and `date`.

4. Create a new dataset of only the months with `sales` of at least 100 units.

6. Create a new dataset that removes all of the months where the `median` sale price or the total number of `sales` are missing.

7. Sort the entire dataset so that the fewest number of `sales` are at the top.

8. Sort the entire dataset, first by `city` alphabetically, and then so that the largest `volume` is at the top for each `city`.

9. Create a dataset that contains only the `city`, `sales`, and `date` variables for Dallas and Houston, and use it to create a line plot of `sales` versus `date` with lines colored for each city.