cl = read.csv("data/cl_rentals.csv")15 Data Forensics
This lesson covers a variety of ways to investigate and summarize tabular data in order to understand the data better and identify potential problems. The lesson also describes how to fix some of the most common data problems.
NoteLearning Goals
After this lesson, you should be able to:
- Explain what it means for a data set to be “tidy”
- Convert columns to appropriate data types
- Locate and count missing values in a data set
- Explain what it means for a value to be an “outlier”
- Locate and count outliers in a data set
- Explain the location statistics mean, median, and mode
- Explain the scale statistics range and standard deviation
15.1 Structural Summaries
Whenever you load a data set into R, your next step should be to investigate the data’s structure. This step is important because it can help you identify whether:
- The data was loaded correctly
- There are structural problems with the data that will make it difficult to use if they aren’t fixed
?sec-inspecting-data and ?sec-summarizing-data demonstrated several functions for getting structural summaries of data. Some of these are:
strto get a detailed structural summaryhead,tailto preview the datanrow,ncol,dim,lengthto get dimension informationnames,colnames,rownamesto get element namesclass,typeofto get classes and types
Let’s look at some examples using a data set collected from the classified advertisements website Craigslist. The data set contains information from ads for rentals in the Sacramento area. First we need to load the data set:
Now we can use the str function to check the classes of the columns:
str(cl)'data.frame': 2987 obs. of 20 variables:
$ title : chr "$1,125 / 1br - 550ft2 - 1Bedroom Prime Location -2520 S Limited Access/Gated $1125 Avail Now (2520 S St)" "$1,449 / 1br - 680ft2 - 1x1 with washer & dryer in unit! Move in ready! (The Phoenix/Sacramento/Folsom/SF)" "$1,449 / 1br - 680ft2 - 1x1 with washer & dryer in unit! Move in ready! (The Phoenix/Sacramento/Folsom/SF)" "$1,479 / 1br - 680ft2 - 1x1 with washer & dryer in unit! Move in ready! (The Phoenix/Sacramento/Folsom/SF)" ...
$ text : chr "QR Code Link to This Post\n \n \n* SEE MY OTHER MIDTOWN 1 bedroom apts-text for web site\n*An"| __truncated__ "QR Code Link to This Post\n \n \n Lease our 1x1 Apartment with Che starting at $1449+ Pric"| __truncated__ "QR Code Link to This Post\n \n \n Lease our 1x1 Apartment with Che starting at $1449+ Pric"| __truncated__ "QR Code Link to This Post\n \n \n Lease our 1x1 Apartment with Che starting at $1479+ Pric"| __truncated__ ...
$ latitude : num 38.6 38.6 38.6 38.6 38.6 ...
$ longitude : num -121 -121 -121 -121 -121 ...
$ city : chr "2520 S St" "The Phoenix/Sacramento/Folsom/SF" "The Phoenix/Sacramento/Folsom/SF" "The Phoenix/Sacramento/Folsom/SF" ...
$ date_posted : chr "2021-02-04 15:03:12" "2021-03-02 12:41:17" "2021-03-02 13:26:17" "2021-03-03 10:02:05" ...
$ date_updated: chr "2021-03-03 08:41:39" NA NA NA ...
$ price : int 1125 1449 1449 1479 1414 1441 1615 1660 1877 1611 ...
$ deleted : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ sqft : int 550 680 680 680 680 680 816 816 916 916 ...
$ bedrooms : int 1 1 1 1 1 1 2 2 2 2 ...
$ bathrooms : num 1 1 1 1 1 1 1 1 2 2 ...
$ pets : chr NA "both" "both" "both" ...
$ laundry : chr "shared" "in-unit" "in-unit" "in-unit" ...
$ parking : chr "off-street" "covered" "covered" "covered" ...
$ craigslist : chr "sacramento" "sacramento" "sacramento" "sacramento" ...
$ shp_place : chr "Sacramento" "Sacramento" "Sacramento" "Sacramento" ...
$ shp_city : chr "Sacramento" "Sacramento" "Sacramento" "Sacramento" ...
$ shp_state : chr "CA" "CA" "CA" "CA" ...
$ shp_county : chr "Sacramento" "Sacramento" "Sacramento" "Sacramento" ...Often when you load a new data set, some of the columns won’t have the correct data type (or class) for what you want to do. For instance, in the Craigslist data, the pets, laundry, and parking columns all contain categorical data, so they should be factors.
You can convert these columns to factors with the factor function from ?sec-factors:
cl$pets = factor(cl$pets)
cl$laundry = factor(cl$laundry)
cl$parking = factor(cl$parking)There’s another way we could’ve done this that uses only two lines of code, no matter how many columns there are:
cols = c("pets", "laundry", "parking")
cl[cols] = lapply(cl[cols], factor)We’ll learn more about the lapply function in Section 16.1.
You can use whichever approach is more convenient and makes more sense to you. If there were other columns to convert, we’d go through the same steps with the appropriate conversion function.
R provides as. functions to convert to the most common data types. For instance, as.character converts an object to a string:
x = 3.1
class(x)[1] "numeric"y = as.character(x)
y[1] "3.1"class(y)[1] "character"The read.csv function does a good job at identifying columns of numbers, so it’s rarely necessary to convert columns of numbers manually. However, you may have to do this for data you got some other way (rather than loading a file). For instance, it’s common to make these conversions when scraping data from the web.
It’s also a good idea to convert categorical columns into factors with the factor function, and to convert columns of dates into dates (more about this in Chapter 19).
15.2 Tidy Data
Many functions require data frames that are in tidy form. Before we see the requirements for a data set to be tidy, we need to define or review some terminology from statistics.
A feature (also called a covariate) is measurement of something, usually across multiple subjects. For example, we might decide to measure the heights of everyone in the class. Each person in the class is a subject, and the height measurement is a feature. Features don’t have to be quantitative. If we also asked each person their favorite color, then favorite color would be another feature in our data set. Features are usually, but not always, the columns in a tabular data set.
An observation is a set of features measured for a single subject or at a single time. So in the preceding example, the combined height and favorite color measurement for one student is one observation. Observations are usually, but not always, the rows in a tabular data set.
Now we can define what it means to be tidy. A tabular data set is tidy if and only if:
- Each observation has its own row.
- Each feature has its own column.
- Each value has its own cell.
These rules ensure that all of the values are visually organized and are easy to access with R’s built-in indexing operations. For instance, the $ operator gets a column, and in a tidy data set, columns are features. The rules also reflect the way statisticians traditionally arrange tabular data sets.
When you first look at a data set, think about what the observations are and what the features are. If the data set comes with documentation, it may help you figure this out. Since this data set is a tidy data set, we already know each row is an observation and each column is a feature.
Chapter 32 gives examples of tidy and untidy data, as well as explanations of how to make untidy data tidy.
15.3 Statistical Forensics
After investigating the data’s structure, it’s a good idea to check some basic statistical properties. This step is important because it can help you identify limitations of and patterns in the data.
Which statistics are appropriate for a given feature often depends on the type of the feature. Recall from Section 13.2 that the types statisticians typically think about are:
- Categorical
- Nominal - data separated into specific categories, with no order. For example, hair color (red, brown, blonde, …) is categorical.
- Ordinal - data separated into specific categories, with an order. For example, school level (elementary, middle, high, college) is ordinal.
- Numerical
- Discrete - integers, or a finite set of decimal numbers with no values in between. Sometimes discrete values can also be treated as ordinal. For example, month as a number (1, 2, …, 12) is discrete.
- Continuous - decimal numbers. There are no specific categories, but there is an order. For example, height in inches is numerical.
The table function, which was introduced in ?sec-inspecting-data, is great for summarizing categorical (and sometimes discrete) data. For example:
table(cl$pets)
both cats dogs none
2511 46 31 385 What about numerical data?
Two important questions to ask about data are:
Where is it? This is the location of the data.
How spread out is it? This is the scale of the data.
Let’s use the data
x = c(-2, -1, -1, -1, 0, 2, 6)as an example.
Location is generally summarized with a number near the middle or center of the data. A few options are:
Mode - the value that appears most frequently. The mode can be calculated for any kind of data, but doesn’t work well for continuous data.
For our example, the mode of
xis -1. You can compute the mode withtable:table(x)Median - sort the data, then find the value in the middle. The median can be calculated for ordinal or numerical data.
For our example, the median is -1. Compute this with
median:median(x)Mean - the balancing point of the data, if a waiter was trying to balance the data on a tray. The mean can only be calculated for numerical data.
For our example the mean is 0.4285. Compute this with
mean:mean(x)
Adding large values to the data affects the mean more than the median:
y = c(x, 100)
mean(y)
median(y)Because of this, we say that the median is robust.
The mean is good for getting a general idea of where the center of the data is, while comparing it with the median reveals whether there are any unusually large or small values.
Scale is generally summarized by a number that says how far the data is from the center (mean, median, etc…). Two options are:
Standard Deviation - square root of the average squared distance to the mean (the distance from a point to a mean is called a deviation). You can think of this as approximately the average distance from a data point to the mean. As a rule of thumb, most of the data will be within 3 standard deviations of the mean.
You can compute the standard deviation with
sd:sd(x)Interquartile Range (IQR) - difference between the 75th and 25th percentile. The median is the 50th percentile of the data; it’s at the middle of the sorted data. We can also consider other percentiles. For instance, the 25th percentile is the value one-quarter of the way through the sorted data.
Quantile is another word for percentile. Quartile specifically refers to the 25th, 50th, and 75th percentiles because they separate the data into four parts (hence “quart-”).
You can compute quantiles with
quantile, or compute the IQR directly withIQR:quantile(x) # IQR IQR(x)
The IQR is more robust than the standard deviation.
Many of the functions for computing statistical summaries have a parameter na.rm to ignore missing values. Setting na.rm = TRUE is often useful when you’re just trying to do an initial investigation of the data. However, in a more complete analysis, you should think carefully about what the missing values mean, whether they follow any patterns, and whether there are enough non-missing values for statistical summaries to be good representatives of the data.
Finally, the summary function computes a detailed statistical summary of an R object. For data frames, the function computes a summary of each column, guessing an appropriate statistic based on the column’s data type.
15.3.1 Missing Values
If your data contains missing values, it’s important to think about why the values are missing. Statisticians use two different terms to describe why data is missing:
- missing at random (MAR)
- missing not at random (MNAR) - causes bias!
When values are missing at random, the cause for missingness is one or more features in the data set. For example, if a soil moisture sensor overheats and doesn’t work on hot days, but air temperature is recorded in the data set, values are missing at random.
When values are missing not at random, the cause for missingness is one or more features not in the data set. Think of this as a form of censorship. For example, if people in a food survey refuse to report how much sugar they ate on days where they ate junk food, values are missing not at random. Values MNAR can bias an analysis.
Note
Technically, there’s a third kind of missing data: missing completely at random (MCAR). When values are missing completely at random, the cause for missingness is completely unrelated to the research question and features of interest. This kind of missing data is rare in practice and impossible to identify from the data alone. Values MCAR can be ignored without causing any bias.
The default way to handle missing values in R is to ignore them. This is just a default, not necessarily the best or even an appropriate way to deal with them. You can remove missing values from a data set by indexing:
cl_no_sqft_na = cl[!is.na(cl$sqft), ]
head(cl_no_sqft_na) title
1 $1,125 / 1br - 550ft2 - 1Bedroom Prime Location -2520 S Limited Access/Gated $1125 Avail Now (2520 S St)
2 $1,449 / 1br - 680ft2 - 1x1 with washer & dryer in unit! Move in ready! (The Phoenix/Sacramento/Folsom/SF)
3 $1,449 / 1br - 680ft2 - 1x1 with washer & dryer in unit! Move in ready! (The Phoenix/Sacramento/Folsom/SF)
4 $1,479 / 1br - 680ft2 - 1x1 with washer & dryer in unit! Move in ready! (The Phoenix/Sacramento/Folsom/SF)
5 $1,414 / 1br - 680ft2 - 1x1 with washer & dryer in unit! Move in ready! (The Phoenix/Sacramento/Folsom/SF)
6 $1,441 / 1br - 680ft2 - 1x1 with washer & dryer in unit! Move in ready! (The Phoenix/Sacramento/Folsom/SF)
text
1 QR Code Link to This Post\n \n \n* SEE MY OTHER MIDTOWN 1 bedroom apts-text for web site\n*An upstairs apt @ 2520 S is coming available 3/18/21\n*I have 4 apts coming avail in midtown\n*New flooring in lower apt and redone hardwood flooring in upper unit\n*1 Bedroom lower unit in 20 unit complex (2-10 unit buildings-courtyard in middle) with manager on site\n*Gated front and back\n*9 parking spots in back\n*Laundry on site with new washers and dryers (coin op)\n*Owner pays water/sewer/garbage\n*Wall heat and window air\n*New paint and new Pergo-type wood flooring \n*Updated lighting\n*Nicely maintained building and grounds\n*$500 deposit\n*Non-Smoking/vaping Complex\n*Long time Mgr on Site\n*No dogs\n*Pictures of a like unit\n*Text/call showing Wes show contact info\n to get copy of video\n*You need to make 3X rent, have good rental history and credit score of 600 or greater to qualify-no dogs.
2 QR Code Link to This Post\n \n \n Lease our 1x1 Apartment with Che starting at $1449+ Prices Subject to change daily! \n Apartment Features: \nPrivate Balconies & Patios \nLuxury Vinyl Wood Flooring \nDesigner Interior Paint \nUpgraded cabinets and countertops \nModern white doors and trim \nModern white base boards \nEnergy efficient appliances \nSpacious bathroom \nTONS of closet and storage space \nSac State Shuttle-including Sac State Shuttle App! PET FRIENDLY - MEOW! WOOF! \n\nUpgraded Fitness Centers \nTanning Booth \nHydro Massage Table \nResident Business Center \nResort-Style Pool Furniture \nSparkling Waterfall Wall \nFire pit locations \nBeach Area for lounging \nOutdoor Recreation Area \nSand Volleyball Court \nGrilling Locations Call today to schedule your tour ... show contact info\n -or- \nStop in to visit Mon - Sat 10:00a - 5:00p for more info! :D \n\n\nCommunity Amenities: \nComplete fitness center \nFour sparkling pools \nLighted tennis courts \nFull basketball court \nWhirlpool Jacuzzi \n24 hour Emergency Maintenance Hornet Shuttle Access to Sacramento State University \n Easy Access to Public Transportation (lightrail and bus) \n Easy Access to the American River Trail And so much more! \n\n Check us out online @ www.thephoenixsacramento.com TODAY! \n Connect with us: \nFacebook = The Phoenix Apartment Living \nTwitter = @thephoenixlife Prices and availability are subject to change. Offered prices are starting prices for base rent only. Other charges, conditions, fees, and terms may apply. Equal housing opportunity provider
3 QR Code Link to This Post\n \n \n Lease our 1x1 Apartment with Che starting at $1449+ Prices Subject to change daily! \n Apartment Features: \nPrivate Balconies & Patios \nLuxury Vinyl Wood Flooring \nDesigner Interior Paint \nUpgraded cabinets and countertops \nModern white doors and trim \nModern white base boards \nEnergy efficient appliances \nSpacious bathroom \nTONS of closet and storage space \nSac State Shuttle-including Sac State Shuttle App! PET FRIENDLY - MEOW! WOOF! \n\nUpgraded Fitness Centers \nTanning Booth \nHydro Massage Table \nResident Business Center \nResort-Style Pool Furniture \nSparkling Waterfall Wall \nFire pit locations \nBeach Area for lounging \nOutdoor Recreation Area \nSand Volleyball Court \nGrilling Locations Call today to schedule your tour ... show contact info\n -or- \nStop in to visit Mon - Sat 10:00a - 5:00p for more info! :D \n\n\nCommunity Amenities: \nComplete fitness center \nFour sparkling pools \nLighted tennis courts \nFull basketball court \nWhirlpool Jacuzzi \n24 hour Emergency Maintenance Hornet Shuttle Access to Sacramento State University \n Easy Access to Public Transportation (lightrail and bus) \n Easy Access to the American River Trail And so much more! \n\n Check us out online @ www.thephoenixsacramento.com TODAY! \n Connect with us: \nFacebook = The Phoenix Apartment Living \nTwitter = @thephoenixlife Prices and availability are subject to change. Offered prices are starting prices for base rent only. Other charges, conditions, fees, and terms may apply. Equal housing opportunity provider
4 QR Code Link to This Post\n \n \n Lease our 1x1 Apartment with Che starting at $1479+ Prices Subject to change daily! \n Apartment Features: \nPrivate Balconies & Patios \nLuxury Vinyl Wood Flooring \nDesigner Interior Paint \nUpgraded cabinets and countertops \nModern white doors and trim \nModern white base boards \nEnergy efficient appliances \nSpacious bathroom \nTONS of closet and storage space \nSac State Shuttle-including Sac State Shuttle App! PET FRIENDLY - MEOW! WOOF! \n\nUpgraded Fitness Centers \nTanning Booth \nHydro Massage Table \nResident Business Center \nResort-Style Pool Furniture \nSparkling Waterfall Wall \nFire pit locations \nBeach Area for lounging \nOutdoor Recreation Area \nSand Volleyball Court \nGrilling Locations Call today to schedule your tour ... show contact info\n -or- \nStop in to visit Mon - Sat 10:00a - 5:00p for more info! :D \n\n\nCommunity Amenities: \nComplete fitness center \nFour sparkling pools \nLighted tennis courts \nFull basketball court \nWhirlpool Jacuzzi \n24 hour Emergency Maintenance Hornet Shuttle Access to Sacramento State University \n Easy Access to Public Transportation (lightrail and bus) \n Easy Access to the American River Trail And so much more! \n\n Check us out online @ www.thephoenixsacramento.com TODAY! \n Connect with us: \nFacebook = The Phoenix Apartment Living \nTwitter = @thephoenixlife Prices and availability are subject to change. Offered prices are starting prices for base rent only. Other charges, conditions, fees, and terms may apply. Equal housing opportunity provider
5 QR Code Link to This Post\n \n \n Lease our 1x1 Apartment with Juliet starting at $1414+ Prices Subject to change daily! \n Apartment Features: \nPrivate Balconies & Patios \nLuxury Vinyl Wood Flooring \nDesigner Interior Paint \nUpgraded cabinets and countertops \nModern white doors and trim \nModern white base boards \nEnergy efficient appliances \nSpacious bathroom \nTONS of closet and storage space \nSac State Shuttle-including Sac State Shuttle App! PET FRIENDLY - MEOW! WOOF! \n\nUpgraded Fitness Centers \nTanning Booth \nHydro Massage Table \nResident Business Center \nResort-Style Pool Furniture \nSparkling Waterfall Wall \nFire pit locations \nBeach Area for lounging \nOutdoor Recreation Area \nSand Volleyball Court \nGrilling Locations Call today to schedule your tour ... show contact info\n -or- \nStop in to visit Mon - Sat 10:00a - 5:00p for more info! :D \n\n\nCommunity Amenities: \nComplete fitness center \nFour sparkling pools \nLighted tennis courts \nFull basketball court \nWhirlpool Jacuzzi \n24 hour Emergency Maintenance Hornet Shuttle Access to Sacramento State University \n Easy Access to Public Transportation (lightrail and bus) \n Easy Access to the American River Trail And so much more! \n\n Check us out online @ www.thephoenixsacramento.com TODAY! \n Connect with us: \nFacebook = The Phoenix Apartment Living \nTwitter = @thephoenixlife Prices and availability are subject to change. Offered prices are starting prices for base rent only. Other charges, conditions, fees, and terms may apply. Equal housing opportunity provider
6 QR Code Link to This Post\n \n \n Lease our 1x1 Apartment with Che starting at $1441+ Prices Subject to change daily! \n Apartment Features: \nPrivate Balconies & Patios \nLuxury Vinyl Wood Flooring \nDesigner Interior Paint \nUpgraded cabinets and countertops \nModern white doors and trim \nModern white base boards \nEnergy efficient appliances \nSpacious bathroom \nTONS of closet and storage space \nSac State Shuttle-including Sac State Shuttle App! PET FRIENDLY - MEOW! WOOF! \n\nUpgraded Fitness Centers \nTanning Booth \nHydro Massage Table \nResident Business Center \nResort-Style Pool Furniture \nSparkling Waterfall Wall \nFire pit locations \nBeach Area for lounging \nOutdoor Recreation Area \nSand Volleyball Court \nGrilling Locations Call today to schedule your tour ... show contact info\n -or- \nStop in to visit Mon - Sat 10:00a - 5:00p for more info! :D \n\n\nCommunity Amenities: \nComplete fitness center \nFour sparkling pools \nLighted tennis courts \nFull basketball court \nWhirlpool Jacuzzi \n24 hour Emergency Maintenance Hornet Shuttle Access to Sacramento State University \n Easy Access to Public Transportation (lightrail and bus) \n Easy Access to the American River Trail And so much more! \n\n Check us out online @ www.thephoenixsacramento.com TODAY! \n Connect with us: \nFacebook = The Phoenix Apartment Living \nTwitter = @thephoenixlife Prices and availability are subject to change. Offered prices are starting prices for base rent only. Other charges, conditions, fees, and terms may apply. Equal housing opportunity provider
latitude longitude city date_posted
1 38.5728 -121.4675 2520 S St 2021-02-04 15:03:12
2 38.5511 -121.4068 The Phoenix/Sacramento/Folsom/SF 2021-03-02 12:41:17
3 38.5511 -121.4068 The Phoenix/Sacramento/Folsom/SF 2021-03-02 13:26:17
4 38.5511 -121.4068 The Phoenix/Sacramento/Folsom/SF 2021-03-03 10:02:05
5 38.5511 -121.4068 The Phoenix/Sacramento/Folsom/SF 2021-03-04 08:47:21
6 38.5511 -121.4068 The Phoenix/Sacramento/Folsom/SF 2021-03-04 10:20:09
date_updated price deleted sqft bedrooms bathrooms pets laundry
1 2021-03-03 08:41:39 1125 FALSE 550 1 1 <NA> shared
2 <NA> 1449 FALSE 680 1 1 both in-unit
3 <NA> 1449 FALSE 680 1 1 both in-unit
4 <NA> 1479 FALSE 680 1 1 both in-unit
5 <NA> 1414 FALSE 680 1 1 both in-unit
6 <NA> 1441 FALSE 680 1 1 both in-unit
parking craigslist shp_place shp_city shp_state shp_county
1 off-street sacramento Sacramento Sacramento CA Sacramento
2 covered sacramento Sacramento Sacramento CA Sacramento
3 covered sacramento Sacramento Sacramento CA Sacramento
4 covered sacramento Sacramento Sacramento CA Sacramento
5 covered sacramento Sacramento Sacramento CA Sacramento
6 covered sacramento Sacramento Sacramento CA SacramentoThe na.omit function is less precise than indexing, because it removes rows that have a missing value in any column. This means lots of information gets lost.
Another way to handle missing values is to impute, or fill in, the values with estimates based on other data in the data set. We won’t get into the details of how to impute missing values here, since it is a fairly deep subject. Generally it is safe to impute MAR values, but not MNAR values.
15.3.2 Outliers
An outlier is an anomalous or extreme value in a data set. We can picture this as a value that’s far away from most of the other values. Sometimes outliers are a natural part of the data set. In other situations, outliers can indicate errors in how the data were measured, recorded, or cleaned.
There’s no specific definition for “extreme” or “far away”. A good starting point for detecting outliers is to make a plot that shows how the values are distributed. Box plots and density plots work especially well for this (you’ll learn about how to make plots in a later lesson):
library("ggplot2")
ggplot(cl, aes(x = sqft)) + geom_boxplot()Warning: Removed 347 rows containing non-finite outside the scale range
(`stat_boxplot()`).
Statisticians tend to use the rule of thumb that any value more than 3 standard deviations away from the mean is an outlier. You can use the scale function to compute how many standard deviations the elements in a column are from their mean:
z = scale(cl$sqft)
head(z) [,1]
[1,] -0.1910838
[2,] -0.1161393
[3,] -0.1161393
[4,] -0.1161393
[5,] -0.1161393
[6,] -0.1161393which(z <= -3 | 3 <= z)[1] 1261 2461Be careful to think about what your specific data set measures, as this definition isn’t appropriate in every situation.
How can you handle outliers? First, try inspecting other features from the row to determine whether the outlier is a valid measurement or an error. When an outlier is valid, keep it.
If the outlier interferes with a plot you want to make, you can adjust the x and y limits on plots as needed to “ignore” the outlier. Make sure to mention this in the plot’s title or caption.
When an outlier is not valid, first try to correct it. For example:
- Correct with a different covariate from the same observation.
- Estimate with a mean or median of similar observations. This is another example of imputing values.
For example, in the Craigslist data, we can use the text column to try to correct outliers:
message(cl$text[1261])QR Code Link to This Post
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- Pantry *
- Oval Roman Soaking Tub *
- Cozy Gas Fireplace with Mantel *
- Covered Parking *
- Spacious Walk-In Closet(s) *
- Energy-Saving Multi-Cycle Dishwasher
Community Amenities
- Community Garden
- Executive Business Center
- Sand Volleyball
- Close to Shopping
- Beautiful Landscaped Court Yards
- Playground
- Fitness Center
- Clubhouse
- Open Air Cabanas
- Easy Access to Freeways
- Pool and Spa
- Gated Community
- Professional Onsite Management w/ 24-Hour Emergency Maintenance
- Picnic Area with Barbecue
Office Hours
Monday - Friday 9:00 AM - 6:00 PM
Saturday 10:00 AM - 5:00 PM
Sunday 12:00 PM - 5:00 PM
Pet Policy
Maximum of 2 pets cats or dogs. No weight limit. Additional $25 rent per month and additional $500 deposit per pet. Inquire about our breed restrictions.
Equal Housing Opportunity
VJWLzl1wXGBased on the text, this apartment is 819 square feet, not 8190 square feet. So we can reassign the value:
cl$sqft[1261] = 819If other features don’t help with correction, try getting information from external sources. If you can’t correct the outlier but know it’s invalid, replace it with a missing value NA.