Handling data is a key skill for a data analyst. In the video below examples are provided of how to transform variables using functions in the dplyr package.

# Tag Archives: R programming

# Data Aggregation with dplyr

In this post, we will learn about data aggregation with the dplyr package. Data aggregation is primarily a tool for summarizing the information you have collected. Let’s start by loading our packages.

**library**(dplyr)
**library**(gapminder)

dplyr is for the data manipulation while gapminder provides us with the data. We will learn the following functions from the dplyr package

- count()
- summarize
- group_by()
- top_n()

# count

The count function allows you to count the number of observations in your dataset as shown below.

```
gapminder %>%
count()
```

```
## # A tibble: 1 x 1
## n
## <int>
## 1 1704
```

The output tells us that there are over 1700 rows of data. However, the count function can do much more. For example, we can also count values in a specific column. Below, we calculated how many rows of data we have by continent.

```
gapminder %>%
count(continent)
```

```
## # A tibble: 5 x 2
## continent n
## <fct> <int>
## 1 Africa 624
## 2 Americas 300
## 3 Asia 396
## 4 Europe 360
## 5 Oceania 24
```

The output speaks for its self. There are two columns the left is continent and the right is how many times that particular continent appears in the dataset. You can also sort this data by adding the argument called sort as shown below.

```
gapminder %>%
count(continent, sort =TRUE)
```

```
## # A tibble: 5 x 2
## continent n
## <fct> <int>
## 1 Africa 624
## 2 Asia 396
## 3 Europe 360
## 4 Americas 300
## 5 Oceania 24
```

There is another argument we can add and this is called the weight or wt argument. The wt argument adds up the values of the population in our example and we can now see how many respondents there were from each continent. Below is the code an example

```
gapminder %>%
count(continent, wt=pop, sort=TRUE)
```

```
## # A tibble: 5 x 2
## continent n
## <fct> <dbl>
## 1 Asia 30507333901
## 2 Americas 7351438499
## 3 Africa 6187585961
## 4 Europe 6181115304
## 5 Oceania 212992136
```

You can see that we now know how many people from each continent were in the dataset.

# summarize

The summarize function takes many rows of data and reduce it to a single output. For example, if we want to know the total number of people in the dataset we could run the code below.

```
gapminder %>%
summarize(total_pop=sum(pop))
```

```
## # A tibble: 1 x 1
## total_pop
## <dbl>
## 1 50440465801
```

You can also continue to add more and more things you want to know be separating them with a comma. In the code below, we add to it the average GDP.

```
gapminder %>%
summarize(total_pop=sum(pop), average_gdp=mean(gdpPercap))
```

```
## # A tibble: 1 x 2
## total_pop average_gdp
## <dbl> <dbl>
## 1 50440465801 7215.
```

# group_by

The group by function allows you to aggregate data by groups. For example, if we want to know the total population and the average gdp by continent the code below would help to learn this.

```
gapminder %>%
group_by(continent) %>%
summarize(total_pop=sum(pop), mean_gdp=mean(gdpPercap)) %>%
arrange(desc(total_pop))
```

```
## # A tibble: 5 x 3
## continent total_pop mean_gdp
## <fct> <dbl> <dbl>
## 1 Asia 30507333901 7902.
## 2 Americas 7351438499 7136.
## 3 Africa 6187585961 2194.
## 4 Europe 6181115304 14469.
## 5 Oceania 212992136 18622.
```

It is also possible to group by more than one column. However, to do this we need to create another categorical variable. We are going to use mutate to create a categorical variable that breaks the data into two parts. Before 1980 and after 1980. Then we will group by country and whether the mean of the gdp was collected before or after 1980. Below is the code

```
gapminder %>%
mutate(before_1980=if_else(year < 1980, "yes","no")) %>%
group_by(country, before_1980) %>%
summarize(mean_gdp=mean(gdpPercap))
```

```
## # A tibble: 284 x 3
## # Groups: country [142]
## country before_1980 mean_gdp
## <fct> <chr> <dbl>
## 1 Afghanistan no 803.
## 2 Afghanistan yes 803.
## 3 Albania no 3934.
## 4 Albania yes 2577.
## 5 Algeria no 5460.
## 6 Algeria yes 3392.
## 7 Angola no 2944.
## 8 Angola yes 4270.
## 9 Argentina no 9998.
## 10 Argentina yes 7913.
## # … with 274 more rows
```

# top_n

The top_n function allows you to find the most extreme values when looking at groups. For example, we could find which countries has the highest life expectancy by continent. The answer is below

```
gapminder %>%
group_by(continent) %>%
top_n(1, lifeExp)
```

```
## # A tibble: 5 x 6
## # Groups: continent [5]
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Australia Oceania 2007 81.2 20434176 34435.
## 2 Canada Americas 2007 80.7 33390141 36319.
## 3 Iceland Europe 2007 81.8 301931 36181.
## 4 Japan Asia 2007 82.6 127467972 31656.
## 5 Reunion Africa 2007 76.4 798094 7670.
```

As an example, Japan has the highest life expectancy in Asia. Canada has the highest life expectancy in the Americas. Naturally you are not limited to the top 1. This number can be changed to whatever you want. For example, below we change the number to 3.

```
gapminder %>%
group_by(continent) %>%
top_n(3, lifeExp)
```

```
## # A tibble: 15 x 6
## # Groups: continent [5]
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Australia Oceania 2002 80.4 19546792 30688.
## 2 Australia Oceania 2007 81.2 20434176 34435.
## 3 Canada Americas 2002 79.8 31902268 33329.
## 4 Canada Americas 2007 80.7 33390141 36319.
## 5 Costa Rica Americas 2007 78.8 4133884 9645.
## 6 Hong Kong, China Asia 2007 82.2 6980412 39725.
## 7 Iceland Europe 2007 81.8 301931 36181.
## 8 Japan Asia 2002 82 127065841 28605.
## 9 Japan Asia 2007 82.6 127467972 31656.
## 10 New Zealand Oceania 2007 80.2 4115771 25185.
## 11 Reunion Africa 1997 74.8 684810 6072.
## 12 Reunion Africa 2002 75.7 743981 6316.
## 13 Reunion Africa 2007 76.4 798094 7670.
## 14 Spain Europe 2007 80.9 40448191 28821.
## 15 Switzerland Europe 2007 81.7 7554661 37506.
```

# Transform Data with dplyr

In this post, we will be exposed to tools for wrangling and manipulating data in R.

Let’s begin by loading the libraries we will be using. We will use the dplyr package and the gapminder package. dplyr is for manipulating the data and gapminder provides the dataset.

**library**(dplyr)
**library**(gapminder)

You can look at the data briefly by using a function called “glimpse” as shown below.

`glimpse(gapminder)`

```
## Rows: 1,704
## Columns: 6
## $ country <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", …
## $ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, …
## $ year <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, …
## $ lifeExp <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.8…
## $ pop <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 12…
## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, …
```

You can see that we have six columns or variables and over 1700 rows of data. This data provides information about countries and various demographic statistics.

# select()

The select function allows you to grab only the variables you want for analysis. This becomes exceptionally important when you have a large number of variables. In our next example, we will select 4 variables from the gapminder dataset. Below is the code to achieve this.

```
gapminder %>%
select(country,continent, pop, lifeExp)
```

```
## # A tibble: 1,704 x 4
## country continent pop lifeExp
## <fct> <fct> <int> <dbl>
## 1 Afghanistan Asia 8425333 28.8
## 2 Afghanistan Asia 9240934 30.3
## 3 Afghanistan Asia 10267083 32.0
## 4 Afghanistan Asia 11537966 34.0
## 5 Afghanistan Asia 13079460 36.1
## 6 Afghanistan Asia 14880372 38.4
## 7 Afghanistan Asia 12881816 39.9
## 8 Afghanistan Asia 13867957 40.8
## 9 Afghanistan Asia 16317921 41.7
## 10 Afghanistan Asia 22227415 41.8
## # … with 1,694 more rows
```

The strange symbol %>% is called a “pipe” and allows you to continuously build your code. You can also save this information by assigning a name to an object like any other variable in r.

```
country_data<-gapminder %>%
select(country,continent, pop, lifeExp)
```

# arrange

The arrange verb sorts your data based on one or more variables. For example, let’s say we want to know which country has the highest population. The code below provides the answer.

```
country_data %>%
arrange(pop)
```

```
## # A tibble: 1,704 x 4
## country continent pop lifeExp
## <fct> <fct> <int> <dbl>
## 1 Sao Tome and Principe Africa 60011 46.5
## 2 Sao Tome and Principe Africa 61325 48.9
## 3 Djibouti Africa 63149 34.8
## 4 Sao Tome and Principe Africa 65345 51.9
## 5 Sao Tome and Principe Africa 70787 54.4
## 6 Djibouti Africa 71851 37.3
## 7 Sao Tome and Principe Africa 76595 56.5
## 8 Sao Tome and Principe Africa 86796 58.6
## 9 Djibouti Africa 89898 39.7
## 10 Sao Tome and Principe Africa 98593 60.4
## # … with 1,694 more rows
```

To complete this task we had to use the arrange function and place the name of the variable we want to sort by inside the parentheses. However, this is not exactly what we want. What we have found is the countries with the smallest population. To sort from largest to smallest you must use the desc function as well and this is shown below.

```
country_data %>%
arrange(desc(pop))
```

```
## # A tibble: 1,704 x 4
## country continent pop lifeExp
## <fct> <fct> <int> <dbl>
## 1 China Asia 1318683096 73.0
## 2 China Asia 1280400000 72.0
## 3 China Asia 1230075000 70.4
## 4 China Asia 1164970000 68.7
## 5 India Asia 1110396331 64.7
## 6 China Asia 1084035000 67.3
## 7 India Asia 1034172547 62.9
## 8 China Asia 1000281000 65.5
## 9 India Asia 959000000 61.8
## 10 China Asia 943455000 64.0
## # … with 1,694 more rows
```

Now, this is what we want. China claims several of the top spots. The reason a country is on the list more than once is that the data was collected several different years.

# filter

The filter function is used to obtain only specific values that meet the criteria. For example, what if we want to know the population of only India in descending order. Below is the code for how to do this.

```
country_data %>%
arrange(desc(pop)) %>%
filter(country=='India')
```

```
## # A tibble: 12 x 4
## country continent pop lifeExp
## <fct> <fct> <int> <dbl>
## 1 India Asia 1110396331 64.7
## 2 India Asia 1034172547 62.9
## 3 India Asia 959000000 61.8
## 4 India Asia 872000000 60.2
## 5 India Asia 788000000 58.6
## 6 India Asia 708000000 56.6
## 7 India Asia 634000000 54.2
## 8 India Asia 567000000 50.7
## 9 India Asia 506000000 47.2
## 10 India Asia 454000000 43.6
## 11 India Asia 409000000 40.2
## 12 India Asia 372000000 37.4
```

Now we have only data that relates to India. All we did was include one more pipe and the filter function. We had to tell R which country by placing the information above in the parentheses.

filter is not limited to text searches. You can also search based on numerical values. For example, what if we only want countries with a life expectancy of 81 or higher

```
country_data %>%
arrange(desc(pop)) %>%
filter(lifeExp >= 81)
```

```
## # A tibble: 7 x 4
## country continent pop lifeExp
## <fct> <fct> <int> <dbl>
## 1 Japan Asia 127467972 82.6
## 2 Japan Asia 127065841 82
## 3 Australia Oceania 20434176 81.2
## 4 Switzerland Europe 7554661 81.7
## 5 Hong Kong, China Asia 6980412 82.2
## 6 Hong Kong, China Asia 6762476 81.5
## 7 Iceland Europe 301931 81.8
```

You can see the results for yourself. It is also possible to combine multiply conditions for whatever functions are involved. For example, if I want to arrange my data by population and country while also filtering it by a population greater than 100,000,000,000 and with a life expectancy of less than 45. This is shown below

```
country_data %>%
arrange(desc(pop, country)) %>%
filter(pop>100000000, lifeExp<45)
```

```
## # A tibble: 5 x 4
## country continent pop lifeExp
## <fct> <fct> <int> <dbl>
## 1 China Asia 665770000 44.5
## 2 China Asia 556263527 44
## 3 India Asia 454000000 43.6
## 4 India Asia 409000000 40.2
## 5 India Asia 372000000 37.4
```

# mutate

The mutate function is for manipulating variables and creating new ones. For example, the gdpPercap variable is highly skewed. We can create a variable of gdpercap that is the log of this variable. Using the log will help the data to assume the characteristics of a normal distribution. Below is the code for this.

```
gapminder %>%
select(country,continent, pop, gdpPercap) %>%
mutate(log_gdp=log(gdpPercap))
```

```
## # A tibble: 1,704 x 5
## country continent pop gdpPercap log_gdp
## <fct> <fct> <int> <dbl> <dbl>
## 1 Afghanistan Asia 8425333 779. 6.66
## 2 Afghanistan Asia 9240934 821. 6.71
## 3 Afghanistan Asia 10267083 853. 6.75
## 4 Afghanistan Asia 11537966 836. 6.73
## 5 Afghanistan Asia 13079460 740. 6.61
## 6 Afghanistan Asia 14880372 786. 6.67
## 7 Afghanistan Asia 12881816 978. 6.89
## 8 Afghanistan Asia 13867957 852. 6.75
## 9 Afghanistan Asia 16317921 649. 6.48
## 10 Afghanistan Asia 22227415 635. 6.45
## # … with 1,694 more rows
```

In the code above we had to select our variables again and then we create the new variable “log_gdp”. This new variable appears all the way to the right in the dataset. Naturally, we can extend our code by using our new variable in other functions as shown below.

**Conclusion**

This post was longer than normal but several practical things were learned. You now know some basic techniques for wrangling data using the dplyr package in R.

# T-SNE Visualization and R

It is common in research to want to visualize data in order to search for patterns. When the number of features increases, this can often become even more important. Common tools for visualizing numerous features include principal component analysis and linear discriminant analysis. Not only do these tools work for visualization they can also be beneficial in dimension reduction.

However, the available tools for us are not limited to these two options. Another option for achieving either of these goals is t-Distributed Stochastic Embedding. This relative young algorithm (2008) is the focus of the post. We will explain what it is and provide an example using a simple dataset from the Ecdat package in R.

## t-sne Defined

t-sne is a nonlinear dimension reduction visualization tool. Essentially what it does is identify observed clusters. However, it is not a clustering algorithm because it reduces the dimensions (normally to 2) for visualizing. This means that the input features are not longer present in their original form and this limits the ability to make inference. Therefore, t-sne is often used for exploratory purposes.

T-sne non-linear characteristic is what makes it often appear to be superior to PCA, which is linear. Without getting too technical t-sne takes simultaneously a global and local approach to mapping points while PCA can only use a global approach.

The downside to t-sne approach is that it requires a large amount of calculations. The calculations are often pairwise comparisons which can grow exponential in large datasets.

## Initial Packages

We will use the “Rtsne” package for the analysis, and we will use the “Fair” dataset from the “Ecdat” package. The “Fair” dataset is data collected from people who had cheated on their spouse. We want to see if we can find patterns among the unfaithful people based on their occupation. Below is some initial code.

```
library(Rtsne)
library(Ecdat)
```

## Dataset Preparation

To prepare the data, we first remove in rows with missing data using the “na.omit” function. This is saved in a new object called “train”. Next, we change or outcome variable into a factor variable. The categories range from 1 to 9

- Farm laborer, day laborer,
- Unskilled worker, service worker,
- Machine operator, semiskilled worker,
- Skilled manual worker, craftsman, police,
- Clerical/sales, small farm owner,
- Technician, semiprofessional, supervisor,
- Small business owner, farm owner, teacher,
- Mid-level manager or professional,
- Senior manager or professional.

Below is the code.

```
train<-na.omit(Fair)
train$occupation<-as.factor(train$occupation)
```

## Visualization Preparation

Before we do the analysis we need to set the colors for the different categories. This is done with the code below.

```
colors<-rainbow(length(unique(train$occupation)))
names(colors)<-unique(train$occupation)
```

We can now do are analysis. We will use the “Rtsne” function. When you input the dataset you must exclude the dependent variable as well as any other factor variables. You also set the dimensions and the perplexity. Perplexity determines how many neighbors are used to determine the location of the datapoint after the calculations. Verbose just provides information during the calculation. This is useful if you want to know what progress is being made. max_iter is the number of iterations to take to complete the analysis and check_duplicates checks for duplicates which could be a problem in the analysis. Below is the code.

`tsne<-Rtsne(train[,-c(1,4,7)],dims=2,perplexity=30,verbose=T,max_iter=1500,check_duplicates=F)`

```
## Performing PCA
## Read the 601 x 6 data matrix successfully!
## OpenMP is working. 1 threads.
## Using no_dims = 2, perplexity = 30.000000, and theta = 0.500000
## Computing input similarities...
## Building tree...
## Done in 0.05 seconds (sparsity = 0.190597)!
## Learning embedding...
## Iteration 1450: error is 0.280471 (50 iterations in 0.07 seconds)
## Iteration 1500: error is 0.279962 (50 iterations in 0.07 seconds)
## Fitting performed in 2.21 seconds.
```

Below is the code for making the visual.

```
plot(tsne$Y,t='n',main='tsne',xlim=c(-30,30),ylim=c(-30,30))
text(tsne$Y,labels=train$occupation,col = colors[train$occupation])
legend(25,5,legend=unique(train$occupation),col = colors,,pch=c(1))
```

You can see that there are clusters however, the clusters are all mixed with the different occupations. What this indicates is that the features we used to make the two dimensions do not discriminant between the different occupations.

**Conclusion**

T-SNE is an improved way to visualize data. This is not to say that there is no place for PCA anymore. Rather, this newer approach provides a different way of quickly visualizing complex data without the limitations of PCA.

# Web Scraping with R

In this post we are going to learn how to do web scrapping with R.Web scraping is a process for extracting data from a website. We have all done web scraping before. For example, whenever you copy and paste something from a website into another document such as Word this is an example of web scraping. Technically, this is an example of manual web scraping. The main problem with manual web scraping is that it is labor intensive and takes a great deal of time.

Another problem with web scraping is that the data can come in an unstructured manner. This means that you have to organize it in some way in order to conduct a meaningful analysis. This also means that you must have a clear purpose for what you are scraping along with answerable questions. Otherwise, it is easy to become confused quickly when web scraping

Therefore, we will learn how to automate this process using R. We will need the help of the “rest” and “xml2” packages to do this. Below is some initial code

`library(rvest);library(xml2)`

For our example, we are going to scrape the titles and prices of books from a webpage on Amazon. When simply want to make an organized data frame. The first thing we need to do is load the URL into R and have R read the website using the “read_html” function. The code is below.

```
url<-'https://www.amazon.com/s/ref=nb_sb_noss?url=search-alias%3Daps&field-keywords=books'
webpage<-read_html(url)
```

We now need to specifically harvest the titles from the webpage as this is one of our goals. There are at least two ways to do this. If you are an expert in HTML you can find the information by inspecting the page’s HTML. Another way is to the selectorGadget extension available in Chrome. When using this extension you simply click on the information you want to inspect and it gives you the CSS selector for that particular element. This is shown below

The green highlight is the CSS selector that you clicked on. The yellow represents all other elements that have the same CSS selector. The red represents what you do not want to be included. In this picture, I do not want the price because I want to scrape this separately.

Once you find your information you want to copy the CSS element information in the bar at the bottom of the picture. This information is then pasted into R and use the “html_nodes” function to pull this specific information from the webpage.

`bookTitle<- html_nodes(webpage,'.a-link-normal .a-size-base')`

We now need to convert this information to text and we are done.

`title <- html_text(bookTitle, trim = TRUE) `

Next, we repeat this process for the price.

```
bookPrice<- html_nodes(webpage,'.acs_product-price__buying')
price <- html_text(bookPrice, trim = TRUE)
```

Lastly, we make our data frame with all of our information.

```
books<-as.data.frame(title)
books$price<-price
```

With this done we can do basic statistical analysis such as the mean, standard deviation, histogram, etc. This was not a complex example but the basics of pulling data was provided. Below is what the first few entries of the data frame look like.

```
head(books)
## title price
## 1 Silent Child $17.95
## 2 Say You're Sorry (Morgan Dane Book 1) $4.99
## 3 A Wrinkle in Time $19.95
## 4 The Whiskey Sea $3.99
## 5 Speaking in Bones: A Novel $2.99
## 6 Harry Potter and the Sorcerer's Stone $8.99
```

**Conclusion**

Web scraping using automated tools saves time and increases the possibilities of data analysis. The most important thing to remember is to understand what exactly it is you want to know. Otherwise, you will quickly become lost due to the overwhelming amounts of available information.

# aggregate Function in R VIDEO

Using the aggregate function in R.

# Creating Subgroups of Data in R VIDEO

Create subgroups in R

# Subsetting Data in R VIDEO

Subsetting data in R

# Getting Data Out of R Video

Getting data out of R

# Importing Data into R VIDEO

Importing data into R

# for loops in R VIDEO

for loops in R

# APA Tables in R

Anybody who has ever had to do any writing for academic purposes or in industry has had to deal with APA formatting. The rules and expectations seem to be endless and always changing. If you are able to maneuver the endless list of rules you still have to determine what to report and how when writing an article.

There is a package in R that can at least take away the mystery of how to report ANOVA, correlation, and regression tables. This package is called “apaTables”. In this post, we will look at how to use this package for making tables that are formatted according to APA.

We are going to create examples of ANOVA, correlation, and regression tables using the ‘mtcars’ dataset. Below is the initial code that we need to begin.

```
library(apaTables)
data("mtcars")
```

**ANOVA**

We will begin with the results of ANOVA. In order for this to be successful, you have to use the “lm” function to create the model. If you are familiar with ANOVA and regression this should not be surprising as they both find the same answer using different approaches. After the “lm” function you must use the “filename” argument and give the output a name in quotations. This file will be saved in your R working directory. You can also provide other information such as the table number and confidence level if you desire.

There will be two outputs in our code. The output to the console is in R. A second output will be in a word doc. Below is the code.

`apa.aov.table(lm(mpg~cyl,mtcars),filename = "Example1.doc",table.number = 1)`

```
##
##
## Table 1
##
## ANOVA results using mpg as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2
## (Intercept) 3429.84 1 3429.84 333.71 .000
## cyl 817.71 1 817.71 79.56 .000 .73
## Error 308.33 30 10.28
## CI_90_partial_eta2
##
## [.56, .80]
##
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
```

Here is the word doc output

Perhaps you are beginning to see the beauty of using this package and its functions. The “apa.aov.table”” function provides a nice table that requires no formatting by the researcher.

You can even make a table of the means and standard deviations of ANOVA. This is similar to what you would get if you used the “aggregate” function. Below is the code.

`apa.1way.table(cyl, mpg,mtcars,filename = "Example2.doc",table.number = 2)`

```
##
##
## Table 2
##
## Descriptive statistics for mpg as a function of cyl.
##
## cyl M SD
## 4 26.66 4.51
## 6 19.74 1.45
## 8 15.10 2.56
##
## Note. M and SD represent mean and standard deviation, respectively.
##
```

Here is what it looks like in word

**Correlation **

We will now look at an example of a correlation table. The function for this is “apa.cor.table”. This function works best with only a few variables. Otherwise, the table becomes bigger than a single sheet of paper. In addition, you probably will want to suppress the confidence intervals to save space. There are other arguments that you can explore on your own. Below is the code

`apa.cor.table(mtcars,filename = "Example3.doc",table.number = 3,show.conf.interval = F)`

```
##
##
## Table 3
##
## Means, standard deviations, and correlations
##
##
## Variable M SD 1 2 3 4 5 6 7
## 1. mpg 20.09 6.03
##
## 2. cyl 6.19 1.79 -.85**
##
## 3. disp 230.72 123.94 -.85** .90**
##
## 4. hp 146.69 68.56 -.78** .83** .79**
##
## 5. drat 3.60 0.53 .68** -.70** -.71** -.45**
##
## 6. wt 3.22 0.98 -.87** .78** .89** .66** -.71**
##
## 7. qsec 17.85 1.79 .42* -.59** -.43* -.71** .09 -.17
##
## 8. vs 0.44 0.50 .66** -.81** -.71** -.72** .44* -.55** .74**
##
## 9. am 0.41 0.50 .60** -.52** -.59** -.24 .71** -.69** -.23
##
## 10. gear 3.69 0.74 .48** -.49** -.56** -.13 .70** -.58** -.21
##
## 11. carb 2.81 1.62 -.55** .53** .39* .75** -.09 .43* -.66**
##
## 8 9 10
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## .17
##
## .21 .79**
##
## -.57** .06 .27
##
##
## Note. * indicates p < .05; ** indicates p < .01.
## M and SD are used to represent mean and standard deviation, respectively.
##
```

Here is the word doc results

If you run this code at home and open the word doc in Word you will not see variables 9 and 10 because the table is too big by itself for a single page. I hade to resize it manually. One way to get around this is to delate the M and SD column and place those as rows below the table.

**Regression**

Our final example will be a regression table. The code is as follows

`apa.reg.table(lm(mpg~disp,mtcars),filename = "Example4",table.number = 4)`

```
##
##
## Table 4
##
## Regression results using mpg as the criterion
##
##
## Predictor b b_95%_CI beta beta_95%_CI sr2 sr2_95%_CI
## (Intercept) 29.60** [27.09, 32.11]
## disp -0.04** [-0.05, -0.03] -0.85 [-1.05, -0.65] .72 [.51, .81]
##
##
##
## r Fit
##
## -.85**
## R2 = .718**
## 95% CI[.51,.81]
##
##
## Note. * indicates p < .05; ** indicates p < .01.
## A significant b-weight indicates the beta-weight and semi-partial correlation are also significant.
## b represents unstandardized regression weights; beta indicates the standardized regression weights;
## sr2 represents the semi-partial correlation squared; r represents the zero-order correlation.
## Square brackets are used to enclose the lower and upper limits of a confidence interval.
##
```

Here are the results in word

You can also make regression tables that have multiple blocks or models. Below is an example

`apa.reg.table(lm(mpg~disp,mtcars),lm(mpg~disp+hp,mtcars),filename = "Example5",table.number = 5)`

```
##
##
## Table 5
##
## Regression results using mpg as the criterion
##
##
## Predictor b b_95%_CI beta beta_95%_CI sr2 sr2_95%_CI
## (Intercept) 29.60** [27.09, 32.11]
## disp -0.04** [-0.05, -0.03] -0.85 [-1.05, -0.65] .72 [.51, .81]
##
##
##
## (Intercept) 30.74** [28.01, 33.46]
## disp -0.03** [-0.05, -0.02] -0.62 [-0.94, -0.31] .15 [.00, .29]
## hp -0.02 [-0.05, 0.00] -0.28 [-0.59, 0.03] .03 [-.03, .09]
##
##
##
## r Fit Difference
##
## -.85**
## R2 = .718**
## 95% CI[.51,.81]
##
##
## -.85**
## -.78**
## R2 = .748** Delta R2 = .03
## 95% CI[.54,.83] 95% CI[-.03, .09]
##
##
## Note. * indicates p < .05; ** indicates p < .01.
## A significant b-weight indicates the beta-weight and semi-partial correlation are also significant.
## b represents unstandardized regression weights; beta indicates the standardized regression weights;
## sr2 represents the semi-partial correlation squared; r represents the zero-order correlation.
## Square brackets are used to enclose the lower and upper limits of a confidence interval.
##
```

Here is the word doc version

**Conculsion **

This is a real time saver for those of us who need to write and share statistical information.

# Vectorization of function in R VIDEO

Vectorization of function in R

# If Else Statements in Functions R VIDEO

If else statements in functions in R

# If Statements with Functions in R VIDEO

If Statements in R

# Arguments and Functions in R VIDEO

Arguments and functions in R

# Intro to Functions in R VIDEOS

R functions intro

# Intro to List VIDEO

Introduction to using List

# Manipulating Dataframes in R VIDEO

Manipulating Dataframes in R

# Intro to Dataframes in R VIDEO

Intro to dataframes in R

# Local Regression in R

Local regression uses something similar to nearest neighbor classification to generate a regression line. In local regression, nearby observations are used to fit the line rather than all observations. It is necessary to indicate the percentage of the observations you want R to use for fitting the local line. The name for this hyperparameter is the span. The higher the span the smoother the line becomes.

Local regression is great one there are only a handful of independent variables in the model. When the total number of variables becomes too numerous the model will struggle. As such, we will only fit a bivariate model. This will allow us to process the model and to visualize it.

In this post, we will use the “Clothing” dataset from the “Ecdat” package and we will examine innovation (inv2) relationship with total sales (tsales). Below is some initial code.

`library(Ecdat)`

```
data(Clothing)
str(Clothing)
```

```
## 'data.frame': 400 obs. of 13 variables:
## $ tsales : int 750000 1926395 1250000 694227 750000 400000 1300000 495340 1200000 495340 ...
## $ sales : num 4412 4281 4167 2670 15000 ...
## $ margin : num 41 39 40 40 44 41 39 28 41 37 ...
## $ nown : num 1 2 1 1 2 ...
## $ nfull : num 1 2 2 1 1.96 ...
## $ npart : num 1 3 2.22 1.28 1.28 ...
## $ naux : num 1.54 1.54 1.41 1.37 1.37 ...
## $ hoursw : int 76 192 114 100 104 72 161 80 158 87 ...
## $ hourspw: num 16.8 22.5 17.2 21.5 15.7 ...
## $ inv1 : num 17167 17167 292857 22207 22207 ...
## $ inv2 : num 27177 27177 71571 15000 10000 ...
## $ ssize : int 170 450 300 260 50 90 400 100 450 75 ...
## $ start : num 41 39 40 40 44 41 39 28 41 37 ...
```

There is no data preparation in this example. The first thing we will do is fit two different models that have different values for the span hyperparameter. “fit” will have a span of .41 which means it will use 41% of the nearest examples. “fit2” will use .82. Below is the code.

```
fit<-loess(tsales~inv2,span = .41,data = Clothing)
fit2<-loess(tsales~inv2,span = .82,data = Clothing)
```

In the code above, we used the “loess” function to fit the model. The “span” argument was set to .41 and .82.

We now need to prepare for the visualization. We begin by using the “range” function to find the distance from the lowest to the highest value. Then use the “seq” function to create a grid. Below is the code.

```
inv2lims<-range(Clothing$inv2)
inv2.grid<-seq(from=inv2lims[1],to=inv2lims[2])
```

The information in the code above is for setting our x-axis in the plot. We are now ready to fit our model. We will fit the models and draw each regression line.

```
plot(Clothing$inv2,Clothing$tsales,xlim=inv2lims)
lines(inv2.grid,predict(fit,data.frame(inv2=inv2.grid)),col='blue',lwd=3)
lines(inv2.grid,predict(fit2,data.frame(inv2=inv2.grid)),col='red',lwd=3)
```

Not much difference in the two models. For our final task, we will predict with our “fit” model using all possible values of “inv2” and also fit the confidence interval lines.

```
pred<-predict(fit,newdata=inv2.grid,se=T)
plot(Clothing$inv2,Clothing$tsales)
lines(inv2.grid,pred$fit,col='red',lwd=3)
lines(inv2.grid,pred$fit+2*pred$se.fit,lty="dashed",lwd=2,col='blue')
lines(inv2.grid,pred$fit-2*pred$se.fit,lty="dashed",lwd=2,col='blue')
```

**Conclusion**

Local regression provides another way to model complex non-linear relationships in low dimensions. The example here provides just the basics of how this is done is much more complicated than described here.

# Smoothing Splines in R

This post will provide information on smoothing splines. Smoothing splines are used in regression when we want to reduce the residual sum of squares by adding more flexibility to the regression line without allowing too much overfitting.

In order to do this, we must tune the parameter called the smoothing spline. The smoothing spline is essentially a natural cubic spline with a knot at every unique value of x in the model. Having this many knots can lead to severe overfitting. This is corrected for by controlling the degrees of freedom through the parameter called lambda. You can manually set this value or select it through cross-validation.

We will now look at an example of the use of smoothing splines with the “Clothing” dataset from the “Ecdat” package. We want to predict “tsales” based on the use of innovation in the stores. Below is some initial code.

`library(Ecdat)`

```
data(Clothing)
str(Clothing)
```

```
## 'data.frame': 400 obs. of 13 variables:
## $ tsales : int 750000 1926395 1250000 694227 750000 400000 1300000 495340 1200000 495340 ...
## $ sales : num 4412 4281 4167 2670 15000 ...
## $ margin : num 41 39 40 40 44 41 39 28 41 37 ...
## $ nown : num 1 2 1 1 2 ...
## $ nfull : num 1 2 2 1 1.96 ...
## $ npart : num 1 3 2.22 1.28 1.28 ...
## $ naux : num 1.54 1.54 1.41 1.37 1.37 ...
## $ hoursw : int 76 192 114 100 104 72 161 80 158 87 ...
## $ hourspw: num 16.8 22.5 17.2 21.5 15.7 ...
## $ inv1 : num 17167 17167 292857 22207 22207 ...
## $ inv2 : num 27177 27177 71571 15000 10000 ...
## $ ssize : int 170 450 300 260 50 90 400 100 450 75 ...
## $ start : num 41 39 40 40 44 41 39 28 41 37 ...
```

We are going to create three models. Model one will have 70 degrees of freedom, model two will have 7, and model three will have the number of degrees of freedom are determined through cross-validation. Below is the code.

```
fit1<-smooth.spline(Clothing$inv2,Clothing$tsales,df=57)
fit2<-smooth.spline(Clothing$inv2,Clothing$tsales,df=7)
fit3<-smooth.spline(Clothing$inv2,Clothing$tsales,cv=T)
```

```
## Warning in smooth.spline(Clothing$inv2, Clothing$tsales, cv = T): cross-
## validation with non-unique 'x' values seems doubtful
```

`(data.frame(fit1$df,fit2$df,fit3$df))`

```
## fit1.df fit2.df fit3.df
## 1 57 7.000957 2.791762
```

In the code above we used the “smooth.spline” function which comes with base r.Notice that we did not use the same coding syntax as the “lm” function calls for. The code above also indicates the degrees of freedom for each model. You can see that for “fit3” the cross-validation determine that 2.79 was the most appropriate degrees of freedom. In addition, if you type in the following code.

`sapply(data.frame(fit1$x,fit2$x,fit3$x),length)`

```
## fit1.x fit2.x fit3.x
## 73 73 73
```

You will see that there are only 73 data points in each model. The “Clothing” dataset has 400 examples in it. The reason for this reduction is that the “smooth.spline” function only takes unique values from the original dataset. As such, though there are 400 examples in the dataset only 73 of them are unique.

Next, we plot our data and add regression lines

```
plot(Clothing$inv2,Clothing$tsales)
lines(fit1,col='red',lwd=3)
lines(fit2,col='green',lwd=3)
lines(fit3,col='blue',lwd=3)
legend('topright',lty=1,col=c('red','green','blue'),c("df = 57",'df=7','df=CV 2.8'))
```

You can see that as the degrees of freedom increase so does the flexibility in the line. The advantage of smoothing splines is to have a more flexible way to assess the characteristics of a dataset.

# Polynomial Spline Regression in R

Normally, when least squares regression is used, you fit one line to the model. However, sometimes you may want enough flexibility that you fit different lines over different regions of your independent variable. This process of fitting different lines over different regions of X is known as Regression Splines.

How this works is that there are different coefficient values based on the regions of X. As the researcher, you can set the cutoff points for each region. The cutoff point is called a “knot.” The more knots you use the more flexible the model becomes because there are fewer data points with each range allowing for more variability.

We will now go through an example of polynomial regression splines. Remeber that polynomial means that we will have a curved line as we are using higher order polynomials. Our goal will be to predict total sales based on the amount of innovation a store employs. We will use the “Ecdat” package and the “Clothing” dataset. In addition, we will need the “splines” package. The code is as follows.

`library(splines);library(Ecdat)`

`data(Clothing)`

We will now fit our model. We must indicate the number and placement of the knots. This is commonly down at the 25th 50th and 75th percentile. Below is the code

`fit<-lm(tsales~bs(inv2,knots = c(12000,60000,150000)),data = Clothing)`

In the code above we used the traditional “lm” function to set the model. However, we also used the “bs” function which allows us to create our spline regression model. The argument “knots” was set to have three different values. Lastly, the dataset was indicated.

Remember that the default spline model in R is a third-degree polynomial. This is because it is hard for the eye to detect the discontinuity at the knots.

We now need X values that we can use for prediction purposes. In the code below we first find the range of the “inv2” variable. We then create a grid that includes all the possible values of “inv2” in increments of 1. Lastly, we use the “predict” function to develop the prediction model. We set the “se” argument to true as we will need this information. The code is below.

```
inv2lims<-range(Clothing$inv2)
inv2.grid<-seq(from=inv2lims[1],to=inv2lims[2])
pred<-predict(fit,newdata=list(inv2=inv2.grid),se=T)
```

We are now ready to plot our model. The code below graphs the model and includes the regression line (red), confidence interval (green), as well as the location of each knot (blue)

```
plot(Clothing$inv2,Clothing$tsales,main="Regression Spline Plot")
lines(inv2.grid,pred$fit,col='red',lwd=3)
lines(inv2.grid,pred$fit+2*pred$se.fit,lty="dashed",lwd=2,col='green')
lines(inv2.grid,pred$fit-2*pred$se.fit,lty="dashed",lwd=2,col='green')
segments(12000,0,x1=12000,y1=5000000,col='blue' )
segments(60000,0,x1=60000,y1=5000000,col='blue' )
segments(150000,0,x1=150000,y1=5000000,col='blue' )
```

When this model was created it was essentially three models connected. Model on goes from the first blue line to the second. Model 2 goes form the second blue line to the third and model three was from the third blue line until the end. This kind of flexibility is valuable in understanding nonlinear relationship

# Factors in R VIDEO

Factors in R

# Logistic Polynomial Regression in R

Polynomial regression is used when you want to develop a regression model that is not linear. It is common to use this method when performing traditional least squares regression. However, it is also possible to use polynomial regression when the dependent variable is categorical. As such, in this post, we will go through an example of logistic polynomial regression.

Specifically, we will use the “Clothing” dataset from the “Ecdat” package. We will divide the “tsales” dependent variable into two categories to run the analysis. Below is the code to get started.

`library(Ecdat)`

`data(Clothing)`

There is little preparation for this example. Below is the code for the model

`fitglm<-glm(I(tsales>900000)~poly(inv2,4),data=Clothing,family = binomial)`

Here is what we did

1. We created an object called “fitglm” to save our results

2. We used the “glm” function to process the model

3. We used the “I” function. This told R to process the information inside the parentheses as is. As such, we did not have to make a new variable in which we split the “tsales” variable. Simply, if sales were greater than 900000 it was code 1 and 0 if less than this amount.

4. Next, we set the information for the independent variable. We used the “poly” function. Inside this function, we placed the “inv2” variable and the highest order polynomial we want to explore.

5. We set the data to “Clothing”

6. Lastly, we set the “family” argument to “binomial” which is needed for logistic regression

Below is the results

`summary(fitglm)`

```
##
## Call:
## glm(formula = I(tsales > 9e+05) ~ poly(inv2, 4), family = binomial,
## data = Clothing)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5025 -0.8778 -0.8458 1.4534 1.5681
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.074 2.685 1.145 0.2523
## poly(inv2, 4)1 641.710 459.327 1.397 0.1624
## poly(inv2, 4)2 585.975 421.723 1.389 0.1647
## poly(inv2, 4)3 259.700 178.081 1.458 0.1448
## poly(inv2, 4)4 73.425 44.206 1.661 0.0967 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 521.57 on 399 degrees of freedom
## Residual deviance: 493.51 on 395 degrees of freedom
## AIC: 503.51
##
## Number of Fisher Scoring iterations: 13
```

It appears that only the 4th-degree polynomial is significant and barely at that. We will now find the range of our independent variable “inv2” and make a grid from this information. Doing this will allow us to run our model using the full range of possible values for our independent variable.

```
inv2lims<-range(Clothing$inv2)
inv2.grid<-seq(from=inv2lims[1],to=inv2lims[2])
```

The “inv2lims” object has two values. The lowest value in “inv2” and the highest value. These values serve as the highest and lowest values in our “inv2.grid” object. This means that we have values started at 350 and going to 400000 by 1 in a grid to be used as values for “inv2” in our prediction model. Below is our prediction model.

`predsglm<-predict(fitglm,newdata=list(inv2=inv2.grid),se=T,type="response")`

Next, we need to calculate the probabilities that a given value of “inv2” predicts a store has “tsales” greater than 900000. The equation is as follows.

`pfit<-exp(predsglm$fit)/(1+exp(predsglm$fit))`

Graphing this leads to interesting insights. Below is the code

`plot(pfit)`

You can see the curves in the line from the polynomial expression. As it appears. As inv2 increase the probability increase until the values fall between 125000 and 200000. This is interesting, to say the least.

We now need to plot the actual model. First, we need to calculate the confidence intervals. This is done with the code below.

```
se.bandsglm.logit<-cbind(predsglm$fit+2*predsglm$se.fit,predsglm$fit-2*predsglm$se.fit)
se.bandsglm<-exp(se.bandsglm.logit)/(1+exp(se.bandsglm.logit))
```

The ’se.bandsglm” object contains the log odds of each example and the “se.bandsglm” has the probabilities. Now we plot the results

```
plot(Clothing$inv2,I(Clothing$tsales>900000),xlim=inv2lims,type='n')
points(jitter(Clothing$inv2),I((Clothing$tsales>900000)),cex=2,pch='|',col='darkgrey')
lines(inv2.grid,pfit,lwd=4)
matlines(inv2.grid,se.bandsglm,col="green",lty=6,lwd=6)
```

In the code above we did the following.

1. We plotted our dependent and independent variables. However, we set the argument “type” to n which means nothing. This was done so we can add the information step-by-step.

2. We added the points. This was done using the “points” function. The “jitter” function just helps to spread the information out. The other arguments (cex, pch, col) our for aesthetics and our optional.

3. We add our logistic polynomial line based on our independent variable grid and the “pfit” object which has all of the predicted probabilities.

4. Last, we add the confidence intervals using the “matlines” function. Which includes the grid object as well as the “se.bandsglm” information.

You can see that these results are similar to when we only graphed the “pfit” information. However, we also add the confidence intervals. You can see the same dip around 125000-200000 were there is also a larger confidence interval. if you look at the plot you can see that there are fewer data points in this range which may be what is making the intervals wider.

**Conclusion**

Logistic polynomial regression allows the regression line to have more curves to it if it is necessary. This is useful for fitting data that is non-linear in nature.

# Polynomial Regression in R

Polynomial regression is one of the easiest ways to fit a non-linear line to a data set. This is done through the use of higher order polynomials such as cubic, quadratic, etc to one or more predictor variables in a model.

Generally, polynomial regression is used for one predictor and one outcome variable. When there are several predictor variables it is more common to use generalized additive modeling/ In this post, we will use the “Clothing” dataset from the “Ecdat” package to predict total sales with the use of polynomial regression. Below is some initial code.

`library(Ecdat)`

`data(Clothing) str(Clothing)`

```
## 'data.frame': 400 obs. of 13 variables:
## $ tsales : int 750000 1926395 1250000 694227 750000 400000 1300000 495340 1200000 495340 ...
## $ sales : num 4412 4281 4167 2670 15000 ...
## $ margin : num 41 39 40 40 44 41 39 28 41 37 ...
## $ nown : num 1 2 1 1 2 ...
## $ nfull : num 1 2 2 1 1.96 ...
## $ npart : num 1 3 2.22 1.28 1.28 ...
## $ naux : num 1.54 1.54 1.41 1.37 1.37 ...
## $ hoursw : int 76 192 114 100 104 72 161 80 158 87 ...
## $ hourspw: num 16.8 22.5 17.2 21.5 15.7 ...
## $ inv1 : num 17167 17167 292857 22207 22207 ...
## $ inv2 : num 27177 27177 71571 15000 10000 ...
## $ ssize : int 170 450 300 260 50 90 400 100 450 75 ...
## $ start : num 41 39 40 40 44 41 39 28 41 37 ...
```

We are going to use the “inv2” variable as our predictor. This variable measures the investment in automation by a particular store. We will now run our polynomial regression model.

```
fit<-lm(tsales~poly(inv2,5),data = Clothing)
summary(fit)
```

```
##
## Call:
## lm(formula = tsales ~ poly(inv2, 5), data = Clothing)
##
## Residuals:
## Min 1Q Median 3Q Max
## -946668 -336447 -96763 184927 3599267
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 833584 28489 29.259 < 2e-16 ***
## poly(inv2, 5)1 2391309 569789 4.197 3.35e-05 ***
## poly(inv2, 5)2 -665063 569789 -1.167 0.2438
## poly(inv2, 5)3 49793 569789 0.087 0.9304
## poly(inv2, 5)4 1279190 569789 2.245 0.0253 *
## poly(inv2, 5)5 -341189 569789 -0.599 0.5497
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 569800 on 394 degrees of freedom
## Multiple R-squared: 0.05828, Adjusted R-squared: 0.04633
## F-statistic: 4.876 on 5 and 394 DF, p-value: 0.0002428
```

The code above should be mostly familiar. We use the “lm” function as normal for regression. However, we then used the “poly” function on the “inv2” variable. What this does is runs our model 5 times (5 is the number next to “inv2”). Each time a different polynomial is used from 1 (no polynomial) to 5 (5th order polynomial). The results indicate that the 4th-degree polynomial is significant.

We now will prepare a visual of the results but first, there are several things we need to prepare. First, we want to find what the range of our predictor variable “inv2” is and we will save this information in a grade. The code is below.

`inv2lims<-range(Clothing$inv2)`

Second, we need to create a grid that has all the possible values of “inv2” from the lowest to the highest broken up in intervals of one. Below is the code.

`inv2.grid<-seq(from=inv2lims[1],to=inv2lims[2])`

We now have a dataset with almost 400000 data points in the “inv2.grid” object through this approach. We will now use these values to predict “tsales.” We also want the standard errors so we se “se” to TRUE

`preds<-predict(fit,newdata=list(inv2=inv2.grid),se=TRUE)`

We now need to find the confidence interval for our regression line. This is done by making a dataframe that takes the predicted fit adds or subtracts 2 and multiples this number by the standard error as shown below.

`se.bands<-cbind(preds$fit+2*preds$se.fit,preds$fit-2*preds$se.fit)`

With these steps completed, we are ready to create our civilization.

To make our visual, we use the “plot” function on the predictor and outcome. Doing this gives us a plot without a regression line. We then use the “lines” function to add the polynomial regression line, however, this line is based on the “inv2.grid” object (40,000 observations) and our predictions. Lastly, we use the “matlines” function to add the confidence intervals we found and stored in the “se.bands” object.

```
plot(Clothing$inv2,Clothing$tsales)
lines(inv2.grid,preds$fit,lwd=4,col='blue')
matlines(inv2.grid,se.bands,lwd = 4,col = "yellow",lty=4)
```

**Conclusion**

You can clearly see the curvature of the line. Which helped to improve model fit. Now any of you can tell that we are fitting this line to mostly outliers. This is one reason we the standard error gets wider and wider it is because there are fewer and fewer observations on which to base it. However, for demonstration purposes, this is a clear example of the power of polynomial regression.

# Search Text within Vector in R VIDEO

Searching text within an object in R

# Partial Least Squares Regression in R

Partial least squares regression is a form of regression that involves the development of components of the original variables in a supervised way. What this means is that the dependent variable is used to help create the new components form the original variables. This means that when pls is used the linear combination of the new features helps to explain both the independent and dependent variables in the model.

In this post, we will use predict “income” in the “Mroz” dataset using pls. Below is some initial code.

`library(pls);library(Ecdat)`

```
data("Mroz")
str(Mroz)
```

```
## 'data.frame': 753 obs. of 18 variables:
## $ work : Factor w/ 2 levels "yes","no": 2 2 2 2 2 2 2 2 2 2 ...
## $ hoursw : int 1610 1656 1980 456 1568 2032 1440 1020 1458 1600 ...
## $ child6 : int 1 0 1 0 1 0 0 0 0 0 ...
## $ child618 : int 0 2 3 3 2 0 2 0 2 2 ...
## $ agew : int 32 30 35 34 31 54 37 54 48 39 ...
## $ educw : int 12 12 12 12 14 12 16 12 12 12 ...
## $ hearnw : num 3.35 1.39 4.55 1.1 4.59 ...
## $ wagew : num 2.65 2.65 4.04 3.25 3.6 4.7 5.95 9.98 0 4.15 ...
## $ hoursh : int 2708 2310 3072 1920 2000 1040 2670 4120 1995 2100 ...
## $ ageh : int 34 30 40 53 32 57 37 53 52 43 ...
## $ educh : int 12 9 12 10 12 11 12 8 4 12 ...
## $ wageh : num 4.03 8.44 3.58 3.54 10 ...
## $ income : int 16310 21800 21040 7300 27300 19495 21152 18900 20405 20425 ...
## $ educwm : int 12 7 12 7 12 14 14 3 7 7 ...
## $ educwf : int 7 7 7 7 14 7 7 3 7 7 ...
## $ unemprate : num 5 11 5 5 9.5 7.5 5 5 3 5 ...
## $ city : Factor w/ 2 levels "no","yes": 1 2 1 1 2 2 1 1 1 1 ...
## $ experience: int 14 5 15 6 7 33 11 35 24 21 ...
```

First, we must prepare our data by dividing it into a training and test set. We will do this by doing a 50/50 split of the data.

```
set.seed(777)
train<-sample(c(T,F),nrow(Mroz),rep=T) #50/50 train/test split
test<-(!train)
```

In the code above we set the “set.seed function in order to assure reduplication. Then we created the “train” object and used the “sample” function to make a vector with ‘T’ and ‘F’ based on the number of rows in “Mroz”. Lastly, we created the “test”” object base don everything that is not in the “train” object as that is what the exclamation point is for.

Now we create our model using the “plsr” function from the “pls” package and we will examine the results using the “summary” function. We will also scale the data since this the scale affects the development of the components and use cross-validation. Below is the code.

```
set.seed(777)
pls.fit<-plsr(income~.,data=Mroz,subset=train,scale=T,validation="CV")
summary(pls.fit)
```

```
## Data: X dimension: 392 17
## Y dimension: 392 1
## Fit method: kernelpls
## Number of components considered: 17
##
## VALIDATION: RMSEP
## Cross-validated using 10 random segments.
## (Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
## CV 11218 8121 6701 6127 5952 5886 5857
## adjCV 11218 8114 6683 6108 5941 5872 5842
## 7 comps 8 comps 9 comps 10 comps 11 comps 12 comps 13 comps
## CV 5853 5849 5854 5853 5853 5852 5852
## adjCV 5837 5833 5837 5836 5836 5835 5835
## 14 comps 15 comps 16 comps 17 comps
## CV 5852 5852 5852 5852
## adjCV 5835 5835 5835 5835
##
## TRAINING: % variance explained
## 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps 7 comps
## X 17.04 26.64 37.18 49.16 59.63 64.63 69.13
## income 49.26 66.63 72.75 74.16 74.87 75.25 75.44
## 8 comps 9 comps 10 comps 11 comps 12 comps 13 comps 14 comps
## X 72.82 76.06 78.59 81.79 85.52 89.55 92.14
## income 75.49 75.51 75.51 75.52 75.52 75.52 75.52
## 15 comps 16 comps 17 comps
## X 94.88 97.62 100.00
## income 75.52 75.52 75.52
```

The printout includes the root mean squared error for each of the components in the VALIDATION section as well as the variance explained in the TRAINING section. There are 17 components because there are 17 independent variables. You can see that after component 3 or 4 there is little improvement in the variance explained in the dependent variable. Below is the code for the plot of these results. It requires the use of the “validationplot” function with the “val.type” argument set to “MSEP” Below is the code

`validationplot(pls.fit,val.type = "MSEP")`

We will do the predictions with our model. We use the “predict” function, use our “Mroz” dataset but only those index in the “test” vector and set the components to three based on our previous plot. Below is the code.

```
set.seed(777)
pls.pred<-predict(pls.fit,Mroz[test,],ncomp=3)
```

After this, we will calculate the mean squared error. This is done by subtracting the results of our predicted model from the dependent variable of the test set. We then square this information and calculate the mean. Below is the code

`mean((pls.pred-Mroz$income[test])^2)`

`## [1] 63386682`

As you know, this information is only useful when compared to something else. Therefore, we will run the data with a tradition least squares regression model and compare the results.

```
set.seed(777)
lm.fit<-lm(income~.,data=Mroz,subset=train)
lm.pred<-predict(lm.fit,Mroz[test,])
mean((lm.pred-Mroz$income[test])^2)
```

`## [1] 59432814`

The least squares model is slightly better then our partial least squares model but if we look at the model we see several variables that are not significant. We will remove these see what the results are

`summary(lm.fit)`

```
##
## Call:
## lm(formula = income ~ ., data = Mroz, subset = train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20131 -2923 -1065 1670 36246
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.946e+04 3.224e+03 -6.036 3.81e-09 ***
## workno -4.823e+03 1.037e+03 -4.651 4.59e-06 ***
## hoursw 4.255e+00 5.517e-01 7.712 1.14e-13 ***
## child6 -6.313e+02 6.694e+02 -0.943 0.346258
## child618 4.847e+02 2.362e+02 2.052 0.040841 *
## agew 2.782e+02 8.124e+01 3.424 0.000686 ***
## educw 1.268e+02 1.889e+02 0.671 0.502513
## hearnw 6.401e+02 1.420e+02 4.507 8.79e-06 ***
## wagew 1.945e+02 1.818e+02 1.070 0.285187
## hoursh 6.030e+00 5.342e-01 11.288 < 2e-16 ***
## ageh -9.433e+01 7.720e+01 -1.222 0.222488
## educh 1.784e+02 1.369e+02 1.303 0.193437
## wageh 2.202e+03 8.714e+01 25.264 < 2e-16 ***
## educwm -4.394e+01 1.128e+02 -0.390 0.697024
## educwf 1.392e+02 1.053e+02 1.322 0.186873
## unemprate -1.657e+02 9.780e+01 -1.694 0.091055 .
## cityyes -3.475e+02 6.686e+02 -0.520 0.603496
## experience -1.229e+02 4.490e+01 -2.737 0.006488 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5668 on 374 degrees of freedom
## Multiple R-squared: 0.7552, Adjusted R-squared: 0.744
## F-statistic: 67.85 on 17 and 374 DF, p-value: < 2.2e-16
```

```
set.seed(777)
lm.fit<-lm(income~work+hoursw+child618+agew+hearnw+hoursh+wageh+experience,data=Mroz,subset=train)
lm.pred<-predict(lm.fit,Mroz[test,])
mean((lm.pred-Mroz$income[test])^2)
```

`## [1] 57839715`

As you can see the error decreased even more which indicates that the least squares regression model is superior to the partial least squares model. In addition, the partial least squares model is much more difficult to explain because of the use of components. As such, the least squares model is the favored one.

# Principal Component Regression in R

This post will explain and provide an example of principal component regression (PCR). Principal component regression involves having the model construct components from the independent variables that are a linear combination of the independent variables. This is similar to principal component analysis but the components are designed in a way to best explain the dependent variable. Doing this often allows you to use fewer variables in your model and usually improves the fit of your model as well.

Since PCR is based on principal component analysis it is an unsupervised method, which means the dependent variable has no influence on the development of the components. As such, there are times when the components that are developed may not be beneficial for explaining the dependent variable.

Our example will use the “Mroz” dataset from the “Ecdat” package. Our goal will be to predict “income” based on the variables in the dataset. Below is the initial code

`library(pls);library(Ecdat)`

```
data(Mroz)
str(Mroz)
```

```
## 'data.frame': 753 obs. of 18 variables:
## $ work : Factor w/ 2 levels "yes","no": 2 2 2 2 2 2 2 2 2 2 ...
## $ hoursw : int 1610 1656 1980 456 1568 2032 1440 1020 1458 1600 ...
## $ child6 : int 1 0 1 0 1 0 0 0 0 0 ...
## $ child618 : int 0 2 3 3 2 0 2 0 2 2 ...
## $ agew : int 32 30 35 34 31 54 37 54 48 39 ...
## $ educw : int 12 12 12 12 14 12 16 12 12 12 ...
## $ hearnw : num 3.35 1.39 4.55 1.1 4.59 ...
## $ wagew : num 2.65 2.65 4.04 3.25 3.6 4.7 5.95 9.98 0 4.15 ...
## $ hoursh : int 2708 2310 3072 1920 2000 1040 2670 4120 1995 2100 ...
## $ ageh : int 34 30 40 53 32 57 37 53 52 43 ...
## $ educh : int 12 9 12 10 12 11 12 8 4 12 ...
## $ wageh : num 4.03 8.44 3.58 3.54 10 ...
## $ income : int 16310 21800 21040 7300 27300 19495 21152 18900 20405 20425 ...
## $ educwm : int 12 7 12 7 12 14 14 3 7 7 ...
## $ educwf : int 7 7 7 7 14 7 7 3 7 7 ...
## $ unemprate : num 5 11 5 5 9.5 7.5 5 5 3 5 ...
## $ city : Factor w/ 2 levels "no","yes": 1 2 1 1 2 2 1 1 1 1 ...
## $ experience: int 14 5 15 6 7 33 11 35 24 21 ...
```

Our first step is to divide our dataset into a train and test set. We will do a simple 50/50 split for this demonstration.

```
train<-sample(c(T,F),nrow(Mroz),rep=T) #50/50 train/test split
test<-(!train)
```

In the code above we use the “sample” function to create a “train” index based on the number of rows in the “Mroz” dataset. Basically, R is making a vector that randomly assigns different rows in the “Mroz” dataset to be marked as True or False. Next, we use the “train” vector and we assign everything or every number that is not in the “train” vector to the test vector by using the exclamation mark.

We are now ready to develop our model. Below is the code

```
set.seed(777)
pcr.fit<-pcr(income~.,data=Mroz,subset=train,scale=T,validation="CV")
```

To make our model we use the “pcr” function from the “pls” package. The “subset” argument tells r to use the “train” vector to select examples from the “Mroz” dataset. The “scale” argument makes sure everything is measured the same way. This is important when using a component analysis tool as variables with different scale have a different influence on the components. Lastly, the “validation” argument enables cross-validation. This will help us to determine the number of components to use for prediction. Below is the results of the model using the “summary” function.

`summary(pcr.fit)`

```
## Data: X dimension: 381 17
## Y dimension: 381 1
## Fit method: svdpc
## Number of components considered: 17
##
## VALIDATION: RMSEP
## Cross-validated using 10 random segments.
## (Intercept) 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps
## CV 12102 11533 11017 9863 9884 9524 9563
## adjCV 12102 11534 11011 9855 9878 9502 9596
## 7 comps 8 comps 9 comps 10 comps 11 comps 12 comps 13 comps
## CV 9149 9133 8811 8527 7265 7234 7120
## adjCV 9126 9123 8798 8877 7199 7172 7100
## 14 comps 15 comps 16 comps 17 comps
## CV 7118 7141 6972 6992
## adjCV 7100 7123 6951 6969
##
## TRAINING: % variance explained
## 1 comps 2 comps 3 comps 4 comps 5 comps 6 comps 7 comps
## X 21.359 38.71 51.99 59.67 65.66 71.20 76.28
## income 9.927 19.50 35.41 35.63 41.28 41.28 46.75
## 8 comps 9 comps 10 comps 11 comps 12 comps 13 comps 14 comps
## X 80.70 84.39 87.32 90.15 92.65 95.02 96.95
## income 47.08 50.98 51.73 68.17 68.29 68.31 68.34
## 15 comps 16 comps 17 comps
## X 98.47 99.38 100.00
## income 68.48 70.29 70.39
```

There is a lot of information here.The VALIDATION: RMSEP section gives you the root mean squared error of the model broken down by component. The TRAINING section is similar the printout of any PCA but it shows the amount of cumulative variance of the components, as well as the variance, explained for the dependent variable “income.” In this model, we are able to explain up to 70% of the variance if we use all 17 components.

We can graph the MSE using the “validationplot” function with the argument “val.type” set to “MSEP”. The code is below.

`validationplot(pcr.fit,val.type = "MSEP")`

How many components to pick is subjective, however, there is almost no improvement beyond 13 so we will use 13 components in our prediction model and we will calculate the means squared error.

```
set.seed(777)
pcr.pred<-predict(pcr.fit,Mroz[test,],ncomp=13)
mean((pcr.pred-Mroz$income[test])^2)
```

`## [1] 48958982`

MSE is what you would use to compare this model to other models that you developed. Below is the performance of a least squares regression model

```
set.seed(777)
lm.fit<-lm(income~.,data=Mroz,subset=train)
lm.pred<-predict(lm.fit,Mroz[test,])
mean((lm.pred-Mroz$income[test])^2)
```

`## [1] 47794472`

If you compare the MSE the least squares model performs slightly better than the PCR one. However, there are a lot of non-significant features in the model as shown below.

`summary(lm.fit)`

```
##
## Call:
## lm(formula = income ~ ., data = Mroz, subset = train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27646 -3337 -1387 1860 48371
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.215e+04 3.987e+03 -5.556 5.35e-08 ***
## workno -3.828e+03 1.316e+03 -2.909 0.00385 **
## hoursw 3.955e+00 7.085e-01 5.582 4.65e-08 ***
## child6 5.370e+02 8.241e+02 0.652 0.51512
## child618 4.250e+02 2.850e+02 1.491 0.13673
## agew 1.962e+02 9.849e+01 1.992 0.04709 *
## educw 1.097e+02 2.276e+02 0.482 0.63013
## hearnw 9.835e+02 2.303e+02 4.270 2.50e-05 ***
## wagew 2.292e+02 2.423e+02 0.946 0.34484
## hoursh 6.386e+00 6.144e-01 10.394 < 2e-16 ***
## ageh -1.284e+01 9.762e+01 -0.132 0.89542
## educh 1.460e+02 1.592e+02 0.917 0.35982
## wageh 2.083e+03 9.930e+01 20.978 < 2e-16 ***
## educwm 1.354e+02 1.335e+02 1.014 0.31115
## educwf 1.653e+02 1.257e+02 1.315 0.18920
## unemprate -1.213e+02 1.148e+02 -1.057 0.29140
## cityyes -2.064e+02 7.905e+02 -0.261 0.79421
## experience -1.165e+02 5.393e+01 -2.159 0.03147 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6729 on 363 degrees of freedom
## Multiple R-squared: 0.7039, Adjusted R-squared: 0.69
## F-statistic: 50.76 on 17 and 363 DF, p-value: < 2.2e-16
```

Removing these and the MSE is almost the same for the PCR and least square models

```
set.seed(777)
lm.fit2<-lm(income~work+hoursw+hearnw+hoursh+wageh,data=Mroz,subset=train)
lm.pred2<-predict(lm.fit2,Mroz[test,])
mean((lm.pred2-Mroz$income[test])^2)
```

`## [1] 47968996`

**Conclusion**

Since the least squares model is simpler it is probably the superior model. PCR is strongest when there are a lot of variables involve and if there are issues with multicollinearity.

# Combining and Splitting Strings in R VIDEO

Combining and Splitting Strings in R

# Example of Best Subset Regression in R

This post will provide an example of best subset regression. This is a topic that has been covered before in this blog. However, in the current post, we will approach this using a slightly different coding and a different dataset. We will be using the “HI” dataset from the “Ecdat” package. Our goal will be to predict the number of hours a women works based on the other variables in the dataset. Below is some initial code.

`library(leaps);library(Ecdat)`

```
data(HI)
str(HI)
```

```
## 'data.frame': 22272 obs. of 13 variables:
## $ whrswk : int 0 50 40 40 0 40 40 25 45 30 ...
## $ hhi : Factor w/ 2 levels "no","yes": 1 1 2 1 2 2 2 1 1 1 ...
## $ whi : Factor w/ 2 levels "no","yes": 1 2 1 2 1 2 1 1 2 1 ...
## $ hhi2 : Factor w/ 2 levels "no","yes": 1 1 2 2 2 2 2 1 1 2 ...
## $ education : Ord.factor w/ 6 levels "<9years"<"9-11years"<..: 4 4 3 4 2 3 5 3 5 4 ...
## $ race : Factor w/ 3 levels "white","black",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ hispanic : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ experience: num 13 24 43 17 44.5 32 14 1 4 7 ...
## $ kidslt6 : int 2 0 0 0 0 0 0 1 0 1 ...
## $ kids618 : int 1 1 0 1 0 0 0 0 0 0 ...
## $ husby : num 12 1.2 31.3 9 0 ...
## $ region : Factor w/ 4 levels "other","northcentral",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ wght : int 214986 210119 219955 210317 219955 208148 213615 181960 214874 214874 ...
```

To develop a model we use the “regsubset” function from the “leap” package. Most of the coding is the same as linear regression. The only difference is the “nvmax” argument which is set to 13. The default setting for “nvmax” is 8. This is good if you only have 8 variables. However, the results from the “str” function indicate that we have 13 functions. Therefore, we need to set the “nvmax” argument to 13 instead of the default value of 8 in order to be sure to include all variables. Below is the code

`regfit.full<-regsubsets(whrswk~.,HI, nvmax = 13)`

We can look at the results with the “summary” function. For space reasons, the code is shown but the results will not be shown here.

`summary(regfit.full)`

If you run the code above in your computer you will 13 columns that are named after the variables created. A star in a column means that that variable is included in the model. To the left is the numbers 1-13 which. One means one variable in the model two means two variables in the model etc.

Our next step is to determine which of these models is the best. First, we need to decide what our criteria for inclusion will be. Below is a list of available fit indices.

`names(summary(regfit.full))`

`## [1] "which" "rsq" "rss" "adjr2" "cp" "bic" "outmat" "obj"`

For our purposes, we will use “rsq” (r-square) and “bic” “Bayesian Information Criteria.” In the code below we are going to save the values for these two fit indices in their own objects.

```
rsq<-summary(regfit.full)$rsq
bic<-summary(regfit.full)$bic
```

Now let’s plot them

`plot(rsq,type='l',main="R-Square",xlab="Number of Variables")`

`plot(bic,type='l',main="BIC",xlab="Number of Variables")`

You can see that for r-square the values increase and for BIC the values decrease. We will now make both of these plots again but we will have r tell the optimal number of variables when considering each model index. For we use the “which” function to determine the max r-square and the minimum BIC

`which.max(rsq)`

`## [1] 13`

`which.min(bic)`

`## [1] 12`

The model with the best r-square is the one with 13 variables. This makes sense as r-square always improves as you add variables. Since this is a demonstration we will not correct for this. For BIC the lowest values was for 12 variables. We will now plot this information and highlight the best model in the plot using the “points” function, which allows you to emphasis one point in a graph

```
plot(rsq,type='l',main="R-Square with Best Model Highlighted",xlab="Number of Variables")
points(13,(rsq[13]),col="blue",cex=7,pch=20)
```

```
plot(bic,type='l',main="BIC with Best Model Highlighted",xlab="Number of Variables")
points(12,(bic[12]),col="blue",cex=7,pch=20)
```

Since BIC calls for only 12 variables it is simpler than the r-square recommendation of 13. Therefore, we will fit our final model using the BIC recommendation of 12. Below is the code.

`coef(regfit.full,12)`

```
## (Intercept) hhiyes whiyes
## 30.31321796 1.16940604 18.25380263
## education.L education^4 education^5
## 6.63847641 1.54324869 -0.77783663
## raceblack hispanicyes experience
## 3.06580207 -1.33731802 -0.41883100
## kidslt6 kids618 husby
## -6.02251640 -0.82955827 -0.02129349
## regionnorthcentral
## 0.94042820
```

So here is our final model. This is what we would use for our test set.

**Conclusion**

Best subset regression provides the researcher with insights into every possible model as well as clues as to which model is at least statistically superior. This knowledge can be used for developing models for data science applications.

# Characters Vectors in R VIDEO

Understanding character vectors in R

# High Dimensionality Regression

There are times when least squares regression is not able to provide accurate predictions or explanation in an object. One example in which least scares regression struggles with a small sample size. By small, we mean when the total number of variables is greater than the sample size. Another term for this is high dimensions which means more variables than examples in the dataset

This post will explain the consequences of what happens when high dimensions is a problem and also how to address the problem.

**Inaccurate measurements**

One problem with high dimensions in regression is that the results for the various metrics are overfitted to the data. Below is an example using the “attitude” dataset. There are 2 variables and 3 examples for developing a model. This is not strictly high dimensions but it is an example of a small sample size.

```
data("attitude")
reg1 <- lm(complaints[1:3]~rating[1:3],data=attitude[1:3])
summary(reg1)
```

```
##
## Call:
## lm(formula = complaints[1:3] ~ rating[1:3], data = attitude[1:3])
##
## Residuals:
## 1 2 3
## 0.1026 -0.3590 0.2564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 21.95513 1.33598 16.43 0.0387 *
## rating[1:3] 0.67308 0.02221 30.31 0.0210 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4529 on 1 degrees of freedom
## Multiple R-squared: 0.9989, Adjusted R-squared: 0.9978
## F-statistic: 918.7 on 1 and 1 DF, p-value: 0.021
```

With only 3 data points the fit is perfect. You can also examine the mean squared error of the model. Below is a function for this followed by the results

```
mse <- function(sm){
mean(sm$residuals^2)}
mse(reg1)
```

`## [1] 0.06837607`

Almost no error. Lastly, let’s look at a visual of the model

```
with(attitude[1:3],plot(complaints[1:3]~ rating[1:3]))
title(main = "Sample Size 3")
abline(lm(complaints[1:3]~rating[1:3],data = attitude))
```

You can see that the regression line goes almost perfectly through each data point. If we tried to use this model on the test set in a real data science problem there would be a huge amount of bias. Now we will rerun the analysis this time with the full sample.

```
reg2<- lm(complaints~rating,data=attitude)
summary(reg2)
```

```
##
## Call:
## lm(formula = complaints ~ rating, data = attitude)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.3880 -6.4553 -0.2997 6.1462 13.3603
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.2445 7.6706 1.075 0.292
## rating 0.9029 0.1167 7.737 1.99e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.65 on 28 degrees of freedom
## Multiple R-squared: 0.6813, Adjusted R-squared: 0.6699
## F-statistic: 59.86 on 1 and 28 DF, p-value: 1.988e-08
```

You can clearly see a huge reduction in the r-square from .99 to .68. Next, is the mean-square error

`mse(reg2)`

`## [1] 54.61425`

The error has increased a great deal. Lastly, we fit the regression line

```
with(attitude,plot(complaints~ rating))
title(main = "Full Sample Size")
abline(lm(complaints~rating,data = attitude))
```

Naturally, the second model is more likely to perform better with a test set. The problem is that least squares regression is too flexible when the number of features is greater than or equal to the number of examples in a dataset.

**What to Do?**

If least squares regression must be used. One solution to overcoming high dimensionality is to use some form of regularization regression such as ridge, lasso, or elastic net. Any of these regularization approaches will help to reduce the number of variables or dimensions in the final model through the use of shrinkage.

However, keep in mind that no matter what you do as the number of dimensions increases so does the r-square even if the variable is useless. This is known as the curse of dimensionality. Again, regularization can help with this.

Remember that with a large number of dimensions there are normally several equally acceptable models. To determine which is most useful depends on understanding the problem and context of the study.

**Conclusion**

With the ability to collect huge amounts of data has led to the growing problem of high dimensionality. One there are more features than examples it can lead to statistical errors. However, regularization is one tool for dealing with this problem.

# Logical Vectors in R VIDEO

Logical vectors in r studio

# Leave One Out Cross Validation in R

Leave one out cross validation. (LOOCV) is a variation of the validation approach in that instead of splitting the dataset in half, LOOCV uses one example as the validation set and all the rest as the training set. This helps to reduce bias and randomness in the results but unfortunately, can increase variance. Remember that the goal is always to reduce the error rate which is often calculated as the mean-squared error.

In this post, we will use the “Hedonic” dataset from the “Ecdat” package to assess several different models that predict the taxes of homes In order to do this, we will also need to use the “boot” package. Below is the code.

`library(Ecdat);library(boot)`

`data(Hedonic) str(Hedonic)`

```
## 'data.frame': 506 obs. of 15 variables:
## $ mv : num 10.09 9.98 10.45 10.42 10.5 ...
## $ crim : num 0.00632 0.02731 0.0273 0.03237 0.06905 ...
## $ zn : num 18 0 0 0 0 0 12.5 12.5 12.5 12.5 ...
## $ indus : num 2.31 7.07 7.07 2.18 2.18 ...
## $ chas : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ nox : num 28.9 22 22 21 21 ...
## $ rm : num 43.2 41.2 51.6 49 51.1 ...
## $ age : num 65.2 78.9 61.1 45.8 54.2 ...
## $ dis : num 1.41 1.6 1.6 1.8 1.8 ...
## $ rad : num 0 0.693 0.693 1.099 1.099 ...
## $ tax : int 296 242 242 222 222 222 311 311 311 311 ...
## $ ptratio: num 15.3 17.8 17.8 18.7 18.7 ...
## $ blacks : num 0.397 0.397 0.393 0.395 0.397 ...
## $ lstat : num -3 -2.39 -3.21 -3.53 -2.93 ...
## $ townid : int 1 2 2 3 3 3 4 4 4 4 ...
```

First, we need to develop our basic least squares regression model. We will do this with the “glm” function. This is because the “cv.glm” function (more on this later) only works when models are developed with the “glm” function. Below is the code.

`tax.glm<-glm(tax ~ mv+crim+zn+indus+chas+nox+rm+age+dis+rad+ptratio+blacks+lstat, data = Hedonic)`

We now need to calculate the MSE. To do this we will use the “cv.glm” function. Below is the code.

```
cv.error<-cv.glm(Hedonic,tax.glm)
cv.error$delta
```

`## [1] 4536.345 4536.075`

cv.error$delta contains two numbers. The first is the MSE for the training set and the second is the error for the LOOCV. As you can see the numbers are almost identical.

We will now repeat this process but with the inclusion of different polynomial models. The code for this is a little more complicated and is below.

```
cv.error=rep(0,5)
for (i in 1:5){
tax.loocv<-glm(tax ~ mv+poly(crim,i)+zn+indus+chas+nox+rm+poly(age,i)+dis+rad+ptratio+blacks+lstat, data = Hedonic)
cv.error[i]=cv.glm(Hedonic,tax.loocv)$delta[1]
}
cv.error
```

`## [1] 4536.345 4515.464 4710.878 7047.097 9814.748`

Here is what happen.

- First, we created an empty object called “cv.error” with five empty spots, which we will use to store information later.
- Next, we created a for loop that repeats 5 times
- Inside the for loop, we create the same regression model except we added the “poly” function in front of “age”” and also “crim”. These are the variables we want to try polynomials 1-5 one to see if it reduces the error.
- The results of the polynomial models are stored in the “cv.error” object and we specifically request the results of “delta” Finally, we printed “cv.error” to the console.

From the results, you can see that the error decreases at a second order polynomial but then increases after that. This means that high order polynomials are not beneficial generally.

**Conclusion**

LOOCV is another option in assessing different models and determining which is most appropriate. As such, this is a tool that is used by many data scientist.

# Validation Set for Regression in R

Estimating error and looking for ways to reduce it is a key component of machine learning. In this post, we will look at a simple way of addressing this problem through the use of the validation set method.

The validation set method is a standard approach in model development. To put it simply, you divide your dataset into a training and a hold-out set. The model is developed on the training set and then the hold-out set is used for prediction purposes. The error rate of the hold-out set is assumed to be reflective of the test error rate.

In the example below, we will use the “Carseats” dataset from the “ISLR” package. Our goal is to predict the competitors’ price for a carseat based on the other available variables. Below is some initial code

```
library(ISLR)
data("Carseats")
str(Carseats)
```

```
## 'data.frame': 400 obs. of 11 variables:
## $ Sales : num 9.5 11.22 10.06 7.4 4.15 ...
## $ CompPrice : num 138 111 113 117 141 124 115 136 132 132 ...
## $ Income : num 73 48 35 100 64 113 105 81 110 113 ...
## $ Advertising: num 11 16 10 4 3 13 0 15 0 0 ...
## $ Population : num 276 260 269 466 340 501 45 425 108 131 ...
## $ Price : num 120 83 80 97 128 72 108 120 124 124 ...
## $ ShelveLoc : Factor w/ 3 levels "Bad","Good","Medium": 1 2 3 3 1 1 3 2 3 3 ...
## $ Age : num 42 65 59 55 38 78 71 67 76 76 ...
## $ Education : num 17 10 12 14 13 16 15 10 10 17 ...
## $ Urban : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 1 2 2 1 1 ...
## $ US : Factor w/ 2 levels "No","Yes": 2 2 2 2 1 2 1 2 1 2 ...
```

We need to divide our dataset into two part. One will be the training set and the other the hold-out set. Below is the code.

```
set.seed(7)
train<-sample(x=400,size=200)
```

Now, for those who are familiar with R you know that we haven’t actually made our training set. We are going to use the “train” object to index items from the “Carseat” dataset. What we did was set the seed so that the results can be replicated. Then we used the “sample” function using two arguments “x” and “size”. X represents the number of examples in the “Carseat” dataset. Size represents how big we want the sample to be. In other words, we want a sample size of 200 of the 400 examples to be in the training set. Therefore, R will randomly select 200 numbers from 400.

We will now fit our initial model

`car.lm<-lm(CompPrice ~ Income+Sales+Advertising+Population+Price+ShelveLoc+Age+Education+Urban, data = Carseats,subset = train)`

The code above should not be new. However, one unique twist is the use of the “subset” argument. What this argument does is tell R to only use rows that are in the “train” index. Next, we calculate the mean squared error.

`mean((Carseats$CompPrice-predict(car.lm,Carseats))[-train]^2)`

`## [1] 77.13932`

Here is what the code above means

- We took the “CompPrice” results and subtracted them from the prediction made by the “car.lm” model we developed.
- Used the test set which here is identified as “-train” minus means everything that is not in the “train”” index
- the results were squared.

The results here are the baseline comparison. We will now make two more models each with a polynomial in one of the variables. First, we will square the “income” variable

```
car.lm2<-lm(CompPrice ~ Income+Sales+Advertising+Population+I(Income^2)+Price+ShelveLoc+Age+Education+Urban, data = Carseats,subset = train)
mean((Carseats$CompPrice-predict(car.lm2,Carseats))[-train]^2)
```

`## [1] 75.68999`

You can see that there is a small decrease in the MSE. Also, notice the use of the “I” function which allows us to square “income”. Now, let’s try a cubic model

```
car.lm3<-lm(CompPrice ~ Income+Sales+Advertising+Population+I(Income^3)+Price+ShelveLoc+Age+Education+Urban, data = Carseats,subset = train)
mean((Carseats$CompPrice-predict(car.lm3,Carseats))[-train]^2)
```

`## [1] 75.84575`

This time there was an increase when compared to the second model. As such, higher order polynomials will probably not improve the model.

**Conclusion**

This post provided a simple example of assessing several different models use the validation approach. However, in practice, this approach is not used as frequently as there are so many more ways to do this now. Yet, it is still good to be familiar with a standard approach such as this.

# Boolean Logic in Rstudio VIDEO

Understanding boolean logic in r programming

# Working with vectors in R VIDEO

Working with vectors in R

# Additive Assumption and Multiple Regression

In regression, one of the assumptions is the additive assumption. This assumption states that the influence of a predictor variable on the dependent variable is independent of any other influence. However, in practice, it is common that this assumption does not hold.

In this post, we will look at how to address violations of the additive assumption through the use of interactions in a regression model.

An interaction effect is when you have two predictor variables whose effect on the dependent variable is not the same. As such, their effect must be considered simultaneously rather than separately. This is done through the use of an interaction term. An interaction term is the product of the two predictor variables.

Let’s begin by making a regular regression model with an interaction. To do this we will use the “Carseats” data from the “ISLR” package to predict “Sales”. Below is the code.

```
library(ISLR);library(ggplot2)
data(Carseats)
saleslm<-lm(Sales~.,Carseats)
summary(saleslm)
```

```
##
## Call:
## lm(formula = Sales ~ ., data = Carseats)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8692 -0.6908 0.0211 0.6636 3.4115
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.6606231 0.6034487 9.380 < 2e-16 ***
## CompPrice 0.0928153 0.0041477 22.378 < 2e-16 ***
## Income 0.0158028 0.0018451 8.565 2.58e-16 ***
## Advertising 0.1230951 0.0111237 11.066 < 2e-16 ***
## Population 0.0002079 0.0003705 0.561 0.575
## Price -0.0953579 0.0026711 -35.700 < 2e-16 ***
## ShelveLocGood 4.8501827 0.1531100 31.678 < 2e-16 ***
## ShelveLocMedium 1.9567148 0.1261056 15.516 < 2e-16 ***
## Age -0.0460452 0.0031817 -14.472 < 2e-16 ***
## Education -0.0211018 0.0197205 -1.070 0.285
## UrbanYes 0.1228864 0.1129761 1.088 0.277
## USYes -0.1840928 0.1498423 -1.229 0.220
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.019 on 388 degrees of freedom
## Multiple R-squared: 0.8734, Adjusted R-squared: 0.8698
## F-statistic: 243.4 on 11 and 388 DF, p-value: < 2.2e-16
```

The results are rather excellent for the social sciences. The model explains 87.3% of the variance in “Sales”. The current results that we have are known as main effects. These are effects that directly influence the dependent variable. Most regression models only include main effects.

We will now examine an interaction effect between two continuous variables. Let’s see if there is an interaction between “Population” and “Income”.

```
saleslm1<-lm(Sales~CompPrice+Income+Advertising+Population+Price+Age+Education+US+
Urban+ShelveLoc+Population*Income, Carseats)
summary(saleslm1)
```

```
##
## Call:
## lm(formula = Sales ~ CompPrice + Income + Advertising + Population +
## Price + Age + Education + US + Urban + ShelveLoc + Population *
## Income, data = Carseats)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8699 -0.7624 0.0139 0.6763 3.4344
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.195e+00 6.436e-01 9.625 <2e-16 ***
## CompPrice 9.262e-02 4.126e-03 22.449 <2e-16 ***
## Income 7.973e-03 3.869e-03 2.061 0.0400 *
## Advertising 1.237e-01 1.107e-02 11.181 <2e-16 ***
## Population -1.811e-03 9.524e-04 -1.901 0.0580 .
## Price -9.511e-02 2.659e-03 -35.773 <2e-16 ***
## Age -4.566e-02 3.169e-03 -14.409 <2e-16 ***
## Education -2.157e-02 1.961e-02 -1.100 0.2722
## USYes -2.160e-01 1.497e-01 -1.443 0.1498
## UrbanYes 1.330e-01 1.124e-01 1.183 0.2375
## ShelveLocGood 4.859e+00 1.523e-01 31.901 <2e-16 ***
## ShelveLocMedium 1.964e+00 1.255e-01 15.654 <2e-16 ***
## Income:Population 2.879e-05 1.253e-05 2.298 0.0221 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.013 on 387 degrees of freedom
## Multiple R-squared: 0.8751, Adjusted R-squared: 0.8712
## F-statistic: 226 on 12 and 387 DF, p-value: < 2.2e-16
```

The new contribution is at the bottom of the coefficient table and is the “Income:Population” coefficient. What this means is “the increase of Sales given a one unit increase in Income and Population simultaneously” In other words the “Income:Population” coefficient looks at their combined simultaneous effect on Sales rather than just their independent effect on Sales.

This makes practical sense as well. The larger the population the more available income and vice versa. However, for our current model, the improvement in the r-squared is relatively small. The actual effect is a small increase in sales. Below is a graph of income and population by sales. Notice how the lines cross. This is a visual of what an interaction looks like. The lines are not parallel by any means.

```
ggplot(data=Carseats, aes(x=Income, y=Sales, group=1)) +geom_smooth(method=lm,se=F)+
geom_smooth(aes(Population,Sales), method=lm, se=F,color="black")+xlab("Income and Population")+labs(
title="Income in Blue Population in Black")
```

We will now repeat this process but this time using a categorical variable and a continuous variable. We will look at the interaction between “US” location (categorical) and “Advertising” (continuous).

```
saleslm2<-lm(Sales~CompPrice+Income+Advertising+Population+Price+Age+Education+US+
Urban+ShelveLoc+US*Advertising, Carseats)
summary(saleslm2)
```

```
##
## Call:
## lm(formula = Sales ~ CompPrice + Income + Advertising + Population +
## Price + Age + Education + US + Urban + ShelveLoc + US * Advertising,
## data = Carseats)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8531 -0.7140 0.0266 0.6735 3.3773
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.6995305 0.6023074 9.463 < 2e-16 ***
## CompPrice 0.0926214 0.0041384 22.381 < 2e-16 ***
## Income 0.0159111 0.0018414 8.641 < 2e-16 ***
## Advertising 0.2130932 0.0530297 4.018 7.04e-05 ***
## Population 0.0001540 0.0003708 0.415 0.6782
## Price -0.0954623 0.0026649 -35.823 < 2e-16 ***
## Age -0.0463674 0.0031789 -14.586 < 2e-16 ***
## Education -0.0233500 0.0197122 -1.185 0.2369
## USYes -0.1057320 0.1561265 -0.677 0.4987
## UrbanYes 0.1191653 0.1127047 1.057 0.2910
## ShelveLocGood 4.8726025 0.1532599 31.793 < 2e-16 ***
## ShelveLocMedium 1.9665296 0.1259070 15.619 < 2e-16 ***
## Advertising:USYes -0.0933384 0.0537807 -1.736 0.0834 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.016 on 387 degrees of freedom
## Multiple R-squared: 0.8744, Adjusted R-squared: 0.8705
## F-statistic: 224.5 on 12 and 387 DF, p-value: < 2.2e-16
```

Again, you can see that when the store is in the US you have to also consider the advertising budget as well. When these two variables are considered there is a slight decline in sales. What this means in practice is that advertising in the US is not as beneficial as advertising outside the US.

Below you can again see a visual of the interaction effect when the lines for US yes and no cross each other in the plot below.

```
ggplot(data=Carseats, aes(x=Advertising, y=Sales, group = US, colour = US)) +
geom_smooth(method=lm,se=F)+scale_x_continuous(limits = c(0, 25))+scale_y_continuous(limits = c(0, 25))
```

Lastly, we will look at an interaction effect for two categorical variables.

```
saleslm3<-lm(Sales~CompPrice+Income+Advertising+Population+Price+Age+Education+US+
Urban+ShelveLoc+ShelveLoc*US, Carseats)
summary(saleslm3)
```

```
##
## Call:
## lm(formula = Sales ~ CompPrice + Income + Advertising + Population +
## Price + Age + Education + US + Urban + ShelveLoc + ShelveLoc *
## US, data = Carseats)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.8271 -0.6839 0.0213 0.6407 3.4537
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.8120748 0.6089695 9.544 <2e-16 ***
## CompPrice 0.0929370 0.0041283 22.512 <2e-16 ***
## Income 0.0158793 0.0018378 8.640 <2e-16 ***
## Advertising 0.1223281 0.0111143 11.006 <2e-16 ***
## Population 0.0001899 0.0003721 0.510 0.6100
## Price -0.0952439 0.0026585 -35.826 <2e-16 ***
## Age -0.0459380 0.0031830 -14.433 <2e-16 ***
## Education -0.0267021 0.0197807 -1.350 0.1778
## USYes -0.3683074 0.2379400 -1.548 0.1225
## UrbanYes 0.1438775 0.1128171 1.275 0.2030
## ShelveLocGood 4.3491643 0.2734344 15.906 <2e-16 ***
## ShelveLocMedium 1.8967193 0.2084496 9.099 <2e-16 ***
## USYes:ShelveLocGood 0.7184116 0.3320759 2.163 0.0311 *
## USYes:ShelveLocMedium 0.0907743 0.2631490 0.345 0.7303
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.014 on 386 degrees of freedom
## Multiple R-squared: 0.8753, Adjusted R-squared: 0.8711
## F-statistic: 208.4 on 13 and 386 DF, p-value: < 2.2e-16
```

In this case, we can see that when the store is in the US and the shelf location is good it has an effect on Sales when compared to a bad location. The plot below is a visual of this. However, it is harder to see this because the x-axis has only two categories

```
ggplot(data=Carseats, aes(x=US, y=Sales, group = ShelveLoc, colour = ShelveLoc)) +
geom_smooth(method=lm,se=F)
```

**Conclusion**

Interactions effects are a great way to fine-tune a model, especially for explanatory purposes. Often, the change in r-square is not strong enough for prediction but can be used for nuanced understanding of the relationships among the variables.

# Vectors and Functions in R VIDEO

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