Programming tutorials - page 10

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Recoding data using R programming. Using the tidyverse and dplyr packages to create a new variable

Recoding data using R programming. Using the tidyverse and dplyr packages to create a new variable

Today, we're going to delve into the fascinating topic of recoding data in R. But first, let's clarify what we mean by recoding data. To illustrate this process, we'll use the Star Wars dataset. If you've already installed the tidyverse package on your computer, you'll have access to this dataset and can follow along at home.

The Star Wars dataset consists of rows representing Star Wars characters like Luke Skywalker, Princess Leia, and more, and columns representing various variables such as name, height, mass, and gender. Our goal is to transform the original dataset into a new one that contains some key differences.

In the modified dataset, which we'll create, there are a few changes to note. First, the height column is expressed in meters squared instead of centimeters as in the original dataset. Second, the gender column uses "M" and "F" to represent male and female, respectively, instead of the original values. Additionally, we have removed all missing values from the dataset. Lastly, we have created a new variable called "size" that categorizes characters as either "big" or "small" based on specific criteria—being taller than one meter and weighing more than 75 kilograms.

To begin, let's ensure we have the tidyverse package loaded, as it provides the necessary functions for data manipulation. You only need to install the package once, but you can load it for each session using the library() or require() function. Once the tidyverse package is loaded, you'll also have access to the Star Wars dataset.

Let's create a new object called SW to work with the Star Wars dataset. We'll use the assignment operator (<-) to assign the Star Wars dataset to the SW object. This way, we can make changes and perform operations without modifying the original dataset. Now, let's select the variables we want to work with. To achieve this, we'll utilize the pipe operator (%>%) to chain operations together.

First, we'll use the select() function to choose the variables we desire—name, mass, and gender. Furthermore, we'll rename the "mass" variable to "weight" using the rename() function. By executing this code, the selected variables will be retained, and the "mass" column will be renamed as "weight" in the SW dataset.

Next, we'll address missing values. Although we won't cover it in detail here, it's important to handle missing values appropriately in your data analysis. For now, we'll simply remove the missing values from the dataset. We'll cover techniques for dealing with missing values in a separate video.

Now, let's focus on transforming the "height" variable from centimeters to meters. Using the mutate() function and the pipe operator, we'll modify the "height" column by dividing each value by 100. This division ensures that the heights are expressed in meters instead of centimeters.

Moving on to the "gender" variable, we notice that it contains values other than just "male" and "female," such as "MAphrodite." To address this, we want to filter the dataset and keep only the observations with "male" and "female" values. We'll demonstrate two approaches for filtering. The first approach involves using the filter() function and specifying the conditions for retaining observations with "male" or "female" genders. The second, more elegant approach employs concatenation using the %in% operator to retain observations with "male" or "female" values. Both approaches yield the same result—only "male" and "female" observations remain in the dataset.

Once we have filtered the "gender" variable, we can proceed to recode the values in the "gender" variable. Currently, it contains "male" and "female" values, but we want to represent them as "M" and "F" respectively. To achieve this, we'll use the mutate() function and the recode() function.

Within the recode() function, we'll specify the variable we want to recode, which is "gender" in this case. Then, we'll assign the new values using the syntax old_value = new_value. In our case, we'll set "male" to be recoded as "M" and "female" as "F".

By executing this code, the "gender" variable in the SW dataset will be updated, replacing "male" and "female" with "M" and "F" respectively.

Lastly, we'll create a new variable called "size" based on certain criteria. The "size" variable will categorize characters as either "big" or "small" depending on their height and weight. We'll again use the mutate() function and the pipe operator.

Within mutate(), we'll create the "size" variable by defining its conditions. We'll use logical operators to check if the height is greater than one meter and the weight is greater than 75 kilograms. If the conditions are met, we'll assign "big" to the corresponding observation; otherwise, we'll assign "small". This is achieved using the if_else() function within mutate().

Once this code is executed, the "size" variable will be added to the SW dataset, indicating whether each character is classified as "big" or "small" based on their height and weight.

In conclusion, if you're passionate about data analysis and eager to learn R programming, you've come to the right place. Hit the subscribe button and click the notification bell to stay updated on future videos.

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10 data filtering tips using R programming. Use the tidyverse to filter and subset your data.

10 data filtering tips using R programming. Use the tidyverse to filter and subset your data.

In this video, we will explore how to filter data in R using the filter function. Filtering allows us to extract specific subsets of data based on certain criteria. To do this, we will be using the tidyverse package, which provides a powerful set of tools for data manipulation and analysis in R. Before we dive into the filter function, let's briefly discuss the basics.

Setting up the Environment:
To begin, we need to load the tidyverse package using the library function. The tidyverse package includes the tidyverse ecosystem, which expands the vocabulary and functionality of R. If you're not familiar with the tidyverse, I recommend watching my video on packages to get a better understanding.

Exploring the Data: For this demonstration, we will be working with the "msleep" dataset, which is included as a built-in dataset in the tidyverse package. The "msleep" dataset contains information about different mammals, including variables such as name, sleep total, body weight, and brain weight. This dataset will serve as our practice data for filtering.

Creating a Subset of Data: To create a subset of data, we will first make a copy of the "msleep" dataset and assign it to a new object called "my_data" using the assignment operator "=".

my_data <- msleep

Selecting Variables: Next, we will select specific variables that we want to work with. In this case, we are interested in the "name" and "sleep_total" columns. We use the select function to choose these columns and store the result back into the "my_data" object using the assignment operator.

my_data <- my_data %>% select(name, sleep_total)

Filtering Data: Now comes the main part, the filter function. We will use this function to extract rows from our dataset based on specific criteria. There are several ways we can use the filter function, and I will walk you through ten different examples.

Filtering by a Single Criterion:
To start, let's filter the data to include only mammals where the sleep total is more than 18. We use the filter function and specify the condition as "sleep_total > 18".

my_data <- my_data %>% filter(sleep_total > 18)

Filtering using the "!" Operator:
We can also use the "!" operator to filter the opposite of a given condition. In this case, we will filter out mammals with sleep totals less than 18.

my_data <- my_data %>% filter(!(sleep_total < 18))

Filtering based on Multiple Criteria using "and":
We can filter the data based on multiple criteria by combining them using the logical "and" operator (","). For example, let's extract mammals where the order is "primate" and the body weight is more than 20.

my_data <- my_data %>% filter(order == "primate", body_weight > 20)

Filtering based on Multiple Criteria using "or":
In some cases, we might want to extract rows that meet at least one of several criteria. We can achieve this using the logical "or" operator ("|"). For instance, let's extract mammals that are either cows, dogs, or goats.

my_data <- my_data %>% filter(name == "cow" | name == "dog" | name == "goat")

Filtering using a Concatenation:
Instead of specifying each criterion individually, we can create a concatenation of values and use it within the filter function. This approach provides a more elegant way of filtering multiple values. For example, we can filter by creating a vector of names and using it in the filter function as follows:

names_to_filter <- c("cow", "dog", "goat")
my_data <- my_data %>% filter(name %in% names_to_filter)

Filtering using the "between" Operator:
We can use the "between" operator to filter rows based on a range of values. Let's filter the data to include only mammals with sleep totals between 16 and 18 (inclusive).

my_data <- my_data %>% filter(between(sleep_total, 16, 18))

Filtering for Values Near a Specific Value:
If we want to filter observations that are close to a specific value within a variable, we can use the "near" function. For instance, let's filter the data to include mammals with sleep totals near 17 within a tolerance of 0.5.

my_data <- my_data %>% filter(near(sleep_total, 17, tolerance = 0.5))

Filtering for Missing Values:
To filter rows where a specific variable has missing values, we can use the "is.na" function. Let's filter the data to include only mammals with missing values in the "conservation" variable.

my_data <- my_data %>% filter(is.na(conservation))

Filtering for Non-Missing Values:
Conversely, if we want to filter out rows with missing values in a specific variable, we can use the "!" operator along with the "is.na" function. Let's filter the data to exclude mammals with missing values in the "conservation" variable.

my_data <- my_data %>% filter(!is.na(conservation))

Conclusion: By utilizing the filter function and various filtering techniques, we can extract specific subsets of data based on our criteria. Filtering allows us to focus on relevant observations and facilitate further analysis. Remember to experiment with different criteria and combinations to suit your specific data filtering needs.

If you found this video helpful and want to learn more about data analysis and R programming, make sure to subscribe to this channel and enable notifications to stay updated on future videos.

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Clean your data with R. R programming for beginners

Clean your data with R. R programming for beginners

Welcome back! Today, we're diving into the topic of data cleaning. When working with data, many people are eager to jump into statistical analysis right away. However, it's important to take a systematic approach to ensure accurate and reliable results. In this video, we will walk you through the process of cleaning your data, which is a crucial step before analysis.

First, let's start by exploring your data. I've covered this topic in a previous video, so make sure to check it out if you haven't already. Data exploration helps you get familiar with the structure and content of your dataset. Once you have a good understanding of your data, you can move on to cleaning it.

So, what do we mean by cleaning your data? Well, there are a few key tasks involved. Firstly, it's important to ensure that each variable is categorized correctly. You may need to make adjustments and change variable types as needed. I'll show you how to do this shortly.

Next, you might want to select the variables you want to work with and filter out any unwanted rows or observations. This step is particularly important when dealing with large datasets. It allows you to focus on the specific data that is relevant to your analysis.

Another important aspect of data cleaning is handling missing data. We'll discuss techniques for finding and dealing with missing values in this video. Additionally, we'll cover how to identify and handle duplicates, as well as how to recode values if necessary.

Before we proceed, let me mention that when working with R, I always use the tidyverse packages. The tidyverse is a collection of packages that extends the functionality of R and provides a wide range of useful functions. If you haven't already, make sure to install and load the tidyverse packages.

Now, let's talk about the datasets we'll be using. R comes with built-in datasets that you can use for practice and learning. In this lesson, we'll be using the Star Wars dataset, which becomes available once you've installed the tidyverse. You can access these datasets by typing "data()" and exploring the available options. For example, you can view the Star Wars dataset by typing "view(starwars)".

Now, let's focus on variable types. It's important to ensure that each variable is correctly identified and categorized. To explore the variable types in the Star Wars dataset, we can use the "glimpse(starwars)" function. This will provide a summary of the dataset, including the variable names and types.

In some cases, you may want to convert a character variable into a factor variable. Factors are categorical variables that can have predefined levels or categories. To do this, you can use the "as.factor()" function. For example, to convert the "gender" variable in the Star Wars dataset into a factor, you can use the code "starwars$gender <- as.factor(starwars$gender)". This will change the variable type and update the dataset accordingly.

If you need to specify the levels or categories of a factor variable, you can use the "levels()" function. For instance, if you want to change the order of levels in the "gender" variable, you can use the code "levels(starwars$gender) <- c('masculine', 'feminine')". This allows you to customize the order of the categories based on your specific needs.

Next, let's discuss selecting variables and filtering rows. In R, you can use the "select()" function to choose the variables you want to work with. For example, you can select variables like "name" and "height" from the Star Wars dataset by using the code "select(starwars, name, height, ends_with('color'))".

To filter rows satisfied. But that's not what we want in this case. We want to include observations that have either blonde or brown hair color. Therefore, we use the logical operator "or" (represented by |) to specify that the observation should meet either one of the conditions.

Now, let's move on to the next part of data cleaning, which is dealing with missing data. Missing data can occur in datasets for various reasons, and it's important to handle them appropriately. In the case of the Star Wars dataset, we can check for missing values by using the is.na() function.

To find and deal with missing data, we can add another step to our code:

star_wars_filtered <- star_wars %>% select(name, height, ends_with("color")) %>% filter(hair_color %in% c("blonde", "brown")) %>% filter(!is.na(height))

In this code, we first select the desired variables (name, height, and variables ending with "color"). Then we filter for hair color values that are either "blonde" or "brown". Finally, we use the !is.na(height) condition to exclude any observations where the height value is missing.

Next, let's address the issue of duplicates in the dataset. Duplicates can occur when there are multiple identical observations in the dataset. To find and deal with duplicates, we can add another step to our code:

star_wars_filtered <- star_wars_filtered %>% distinct()

In this code, we use the distinct() function to remove duplicate observations from the star_wars_filtered dataset, ensuring that each observation is unique.

Lastly, let's discuss how to recode values in the dataset. Sometimes, we may need to modify the values of certain variables to better suit our analysis. In this case, let's say we want to recode the hair color variable to have "blonde" as 1 and "brown" as 2. We can achieve this by adding another step to our code:

star_wars_filtered <- star_wars_filtered %>% mutate(hair_color = recode(hair_color, "blonde" = 1, "brown" = 2))

Here, we use the mutate() function along with the recode() function to modify the values of the hair_color variable. We specify that "blonde" should be recoded as 1 and "brown" as 2.

Now, we have completed the data cleaning process. We have selected the desired variables, filtered out unwanted observations, dealt with missing data, removed duplicates, and recoded values if necessary.

Remember, these are just some basic steps in the data cleaning process, and the specific steps may vary depending on the dataset and analysis requirements. However, following a systematic approach like this can help ensure that your data is in a clean and suitable format for further analysis.

I hope this explanation helps you understand the process of cleaning your data.

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Explore your data using R programming

Explore your data using R programming

Hello, all you programming enthusiasts! My name is Greg Martin, and I welcome you back to our Programming 101 session. Today, we're going to discuss the crucial topic of data exploration before diving into any data analysis. Understanding the data you're working with is essential. You need to grasp the dimensions, parameters, and size of your dataset or data frame. Additionally, you should be aware of the number of variables and their characteristics. This step is super important and remarkably easy, so let's do it together.

If you're here to learn about programming, you've come to the right place. On this YouTube channel, we create programming videos covering a wide range of topics.

Now, let me start by saying that I use functions and packages within the Tidyverse. If you're unfamiliar with the Tidyverse, I recommend watching one of my other videos explaining its significance. Installing the Tidyverse on your computer brings all the functions, capabilities, and expanded vocabulary that come with the packages in the Tidyverse. I'll mention some of these packages as we progress.

Importantly, the Tidyverse also includes a variety of built-in datasets that you can use to practice your data analysis. This is particularly useful, and later on, we'll be using one of these additional datasets called "star wars." The star wars dataset is a bit messy, containing missing data and other issues, making it an excellent example for exploring and cleaning data.

To begin, you can always use the question mark followed by the function or dataset name to access the documentation and obtain information about that particular dataset. For instance, by typing "?star wars" and pressing Enter, you can access information about the variables present in the star wars dataset.

Now, let's move on to some specific functions. The first function we'll learn about is "dim," which stands for dimensions. By using the command "dim(star wars)" and pressing Enter, we can determine that the dataset has 87 rows or observations and 13 variables.

Another common function used for understanding the structure of a data frame is "str" (structure). However, when we apply "str(star wars)" directly, we encounter some messy output due to the presence of lists within the dataset. Lists represent variables where each observation can be a separate list containing various data points or even an entire dataframe. To make the output more readable, we can use the "glimpse" function from the Tidyverse. So, by typing "glimpse(star wars)" and pressing Enter, we get a much neater display of the dataset's structure, including the number of observations, variables, and their types.

To view the dataset itself, you can use the "view" function by typing "view(star wars)" and pressing Enter. This will open a window displaying the dataset in a neat and organized format, with columns representing variables and rows representing observations.

Additionally, you can use the "head" and "tail" functions to quickly view the first and last few rows of the dataset, respectively. For example, "head(star wars)" will display the first six rows, and "tail(star wars)" will show the last six rows.

To access specific variables within the dataset, you can use the "$" operator. For instance, by typing "star wars$name" and pressing Enter, you can access the "name" variable directly.

Another useful function is "names," which allows you to retrieve the variable names within the dataset. By typing "names(star wars)" and pressing Enter, you will obtain a list of all the variables present. This is beneficial when referencing variables in your code, as it helps avoid typos and ensures accuracy.

Furthermore, the "length" function can be used to determine the number.

The "length" function can be used to determine the number of variables within a dataset. For example, by typing "length(names(star wars))" and pressing Enter, you can find out the total number of variables present in the star wars dataset.

Another important aspect of data exploration is understanding the data types of variables. The function "class" can be used to determine the class or data type of a variable. For instance, if you want to know the data type of the "name" variable in the star wars dataset, you can type "class(star wars$name)" and press Enter.

You can also use the "summary" function to obtain summary statistics for numeric variables in the dataset. For example, if you want to get a summary of the "height" variable, you can type "summary(star wars$height)" and press Enter.

To filter and subset the dataset based on specific conditions, you can use the "filter" function. This function allows you to specify logical conditions to select rows that meet certain criteria. For instance, if you want to filter the star wars dataset to only include characters with a height greater than 150, you can type "filter(star wars, height > 150)" and press Enter.

Additionally, you can use the "select" function to choose specific variables or columns from the dataset. This is helpful when you want to focus on a subset of variables for your analysis. For example, if you want to select only the "name" and "height" variables from the star wars dataset, you can type "select(star wars, name, height)" and press Enter.

Exploring data also involves examining the distribution of variables. The Tidyverse provides the "ggplot2" package, which offers powerful data visualization capabilities. You can use functions like "ggplot" and "geom_histogram" to create histograms to visualize the distribution of numeric variables. For example, to create a histogram of the "height" variable in the star wars dataset, you can use the following code:

library(ggplot2)
ggplot(star wars, aes(x = height)) +
  geom_histogram()

This code will generate a histogram showing the distribution of character heights in the star wars dataset.

Remember to install the required packages if you haven't done so already. You can use the "install.packages" function to install packages. For example, to install the ggplot2 package, you can type "install.packages('ggplot2')" and press Enter.

These are some of the essential functions and techniques you can use for data exploration in R. By understanding the structure, dimensions, variables, and data types of your dataset, you gain valuable insights that help guide your data analysis process.

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Manipulate your data. Data wrangling. R programmning for beginners.

Manipulate your data. Data wrangling. R programmning for beginners.

Welcome back to another exciting video on our programming series. Today, we're going to dive into the topic of manipulating your data frame, data set, or data. Data wrangling, also known as "data doctoring," can be a lot of fun. This is part three of our series, where we explore various aspects of data exploration, cleaning, manipulation, description, summarization, visualization, and analysis. These are essential steps in the data pipeline when you encounter a new data set, helping you make sense of the data you have.

In this video, we will cover a range of techniques. Some of them you may already be familiar with, while others might be new to you. We will move at a quick pace, so feel free to pause, rewind, and review the video as needed. Most of the examples and demonstrations I'll show can be easily replicated on your own computer. You don't need to download any additional data or search for it online. Built-in data frames in R will serve as our practice data sets throughout the video.

But before we proceed, let's make sure you have the tidyverse library installed. I won't go into the installation process here, but if you're unfamiliar with it, I recommend watching my video on packages. The tidyverse library consists of multiple packages that provide a range of functionalities for data manipulation and analysis. Once installed, you can load the library using the command library(tidyverse), which gives you access to all the packages and their extended vocabulary within R. Additionally, tidyverse also includes pre-loaded data sets that we can utilize for practice. To view the available data sets, you can use the command data(), which will display a list of data sets accessible on your computer.

Alright, let's dive into the content. We'll be working with the "m_sleep" data set for our demonstrations. If you're curious about the details of the data set, you can use the command ?m_sleep to get a summary and information about each variable in the data set. Alternatively, we can use the glimpse function from the tidyverse to obtain a concise overview of the data set, including variable names, types, and a few example observations.

Now, let's start with our first lesson: renaming a variable. Renaming a variable is a breeze using the rename function in the tidyverse. We typically follow a pipeline approach, starting with the data set and then applying transformations using the pipe operator %>%. To rename a variable, we specify the new name before the equal sign, followed by the existing name within the rename function. For example, we can rename the variable "conservation" to "conserve" using rename(conserve = conservation). After running the code, we can observe the updated variable name in the data set.

Moving on, let's explore how to reorder variables. As mentioned earlier, we've previously discussed the select function, which allows us to choose specific variables. However, it's worth noting that the order of variables in the select function determines their order in the resulting data set. By specifying the variable names in the desired order, separated by commas, we can rearrange the variables accordingly. For example, select(var1, var2, ..., everything()) will select "var1" and "var2" first, followed by the remaining variables in their original order.

Next, let's discuss changing variable types. We've touched on this topic before, but let's briefly review the process. Using the base R function class, we can determine the current type of a variable. For instance, class(m_sleep$var) will display the variable type as "character." To change the type of a variable into a new line for readability purposes, but you can write it all in one line if you prefer. Now, let's apply the filter to the data frame.

filtered_data <- m_sleep %>% filter(order == "Carnivora" | order == "Primates")

In this example, we filtered the data frame m_sleep to include only the observations where the order variable is either "Carnivora" or "Primates". The resulting subset of data is stored in the filtered_data object.

Moving on to arranging the data, we can use the arrange function. This function allows us to sort the rows of the data frame based on one or more variables. Let's sort the filtered_data by the vore variable in descending order.

arranged_data <- filtered_data %>% arrange(desc(vore))

Here, we used the arrange function with the argument desc(vore), which sorts the data frame in descending order based on the vore variable. The resulting arranged data is stored in the arranged_data object.

Now, let's cover recoding data. Recoding involves changing the values of a variable based on certain conditions. We can use the mutate function along with the if_else function to accomplish this.

recoded_data <- arranged_data %>% mutate(vore = if_else(vore == "carni", "Carnivorous", "Omnivorous"))

In this example, we recoded the vore variable in the arranged_data data frame. We replaced the value "carni" with "Carnivorous" and all other values with "Omnivorous". The modified data frame is stored in the recoded_data object.

Next, let's explore changing data using the mutate function. We can create new variables or modify existing ones. Here's an example:

modified_data <- recoded_data %>% mutate(new_variable = vore == "Carnivorous" & awake > 10)

In this case, we created a new variable called new_variable. Its value is based on the condition that vore is equal to "Carnivorous" and the awake variable is greater than 10. The modified data frame is stored in the modified_data object.

Lastly, let's discuss reshaping your data frame. Reshaping involves changing the structure of the data frame from wide to long or vice versa. The pivot_longer and pivot_wider functions from the tidyverse package are useful for this task. Here's an example:

reshaped_data <- modified_data %>% pivot_longer(cols = c(vore, awake, sleep_total), names_to = "variable", values_to = "value")

In this example, we transformed the data frame from wide to long format. We selected the variables vore, awake, and sleep_total to pivot. The resulting data frame has two new columns: variable and value, which store the variable names and corresponding values, respectively.

That's it for this tutorial! We covered various aspects of manipulating your data frame, including renaming variables, reordering variables, changing variable types, selecting variables, filtering and arranging data, recoding data, changing data using mutate, and reshaping the data frame. Remember, you can practice all these concepts using the built-in data frames in R. Happy data wrangling!

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Describe and Summarise your data

Describe and Summarise your data

Welcome back to R101! In this session, we will be discussing how to describe and summarize your data. Today's topic is super easy, so stick with me, and you'll learn a lot. This session is part of a series where we explore, clean, manipulate, describe, and summarize data. The next video will be about visualizing and analyzing the data. So, let's get started.

In this video, we will cover various aspects of data description and summarization. Firstly, when dealing with numeric variables, there are specific statistical parameters that we use to describe them. These include range, spread, centrality, and variance. Don't worry; we'll go through these concepts in a super easy manner, and it will only take around 30 seconds.

Next, we will learn how to summarize the entire dataset. I'll share a few tips and tricks to efficiently summarize your data. Again, this will only take around 30 seconds.

Then, we will focus on creating tables to summarize our data. Tables are an excellent way to present and summarize information effectively. We will learn how to create tables that summarize numeric variables and contingency tables that summarize categorical variables. I'll show you some examples, and you'll find it super easy to follow along.

To give you a glimpse of what we're aiming for, I've displayed an example table on the screen. This table tells a compelling story and paints a clear picture of the data. It was created using the "formattable" package in R, which allows you to create beautiful tables. However, before we dive into creating visually appealing tables, it's crucial to ensure that our data is properly structured. The key is to have your data in a format that allows you to tell a story and present a picture effectively.

Now, let's move forward and cover the main topics of this video. If you're interested in learning R programming, you're in the right place. On this YouTube channel, we create programming videos covering a wide range of topics.

First and foremost, if you haven't already, make sure to install the necessary packages. We always work with the "tidyverse" packages, which expand the vocabulary and capabilities of R. They provide useful tools like the pipe operator, which we'll be using in this video. If you're not familiar with the tidyverse and the packages within it, I recommend watching my video on packages.

In our examples, we will use publicly available data that you can access on your computer. By using this data, you can practice your analysis, coding, and data wrangling skills. R provides a variety of datasets that you can access using the "data" function. We will specifically work with the "msleep" dataset in this video. You can replicate the steps I show on your computer at home. If you run the command "view(msleep)", you can see the structure of the dataset. It contains variables such as herbivore, carnivore, omnivore, sleep time, brain weight, and more. It's a great dataset to work with.

To begin with, let's summarize the numeric variables in the dataset. We will focus on statistical parameters such as minimum, maximum, range, interquartile range, mean, median, and variance. To obtain these values, you can use the "summary" function in R. By running "summary(msleep)", you will see the summary of all the variables with the corresponding parameters. You can also use "summary" on a single variable if you want to focus on specific statistics.

Now, let's say we want to select only the variables "sleep_total" and "brain_weight" and summarize them. You can achieve this by selecting the variables using the "select" function from the tidyverse package.

Now let's introduce the second categorical variable, which is "airbags." We can use the table function again, but this time we'll include both variables within the function. Here's the code:

table(cars$origin, cars$airbags)

When we run this code, we obtain a contingency table that shows the frequency of combinations between the two categorical variables. It will display something like this:

airbags origin None Driver Driver & Passenger non-us 15 20 10 us 25 30 20

This table tells us, for example, that there are 15 cars from non-US origin without airbags, 20 cars with airbags for the driver only, and 10 cars with airbags for both the driver and passenger. Similarly, there are 25 cars from the US without airbags, 30 cars with airbags for the driver only, and 20 cars with airbags for both the driver and passenger.

Now let's see how we can achieve the same result using the tidyverse approach. We'll use the count and pivot_wider functions. Here's the code:

library(tidyverse) cars %>% count(origin, airbags) %>% pivot_wider(names_from = airbags, values_from = n)

This code follows the pipe operator %>% to perform a series of operations. First, we use count to calculate the frequencies of combinations between origin and airbags. Then, we apply pivot_wider to reshape the data, making the different types of airbags into separate columns. The resulting table will look similar to the one produced by the base R code.

These examples demonstrate how you can summarize and create tables to describe your data using both base R and the tidyverse approach. It's important to choose the method that suits your preferences and the specific requirements of your analysis.

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Chi squared test using R programming

Chi squared test using R programming

Today, we're going to dive into the topic of the chi-square test, specifically focusing on the goodness-of-fit test. This test is super duper easy, so stick with me and let's explore it together.

First things first, make sure you have the tidyverse package installed. If you're not familiar with the tidyverse, you can check out my other videos to learn more about it. The tidyverse is a collection of R packages that expands the vocabulary of R and makes data analysis more efficient. Additionally, we'll need the "forcats" package, which provides extended functionality for working with categorical variables. In this lesson, we'll be using the "GSS_cat" dataset that comes with the "forcats" package.

Once you have the packages installed, let's take a look at the "GSS_cat" dataset. It contains various variables, one of which is "marital status." We're going to focus on this variable for our analysis. To get a sense of the proportions of different marital statuses, I've created a plot on the right side of the screen, showing the categories "never married," "divorced," and "married." From the plot, we can observe that the proportions seem to differ.

Now, let's move on to the chi-square test. The purpose of this test is to determine whether there is a significant difference in the proportions of people who are never married, divorced, or married. Our null hypothesis assumes that there is no difference, and we want to examine whether the data supports this hypothesis.

Before conducting the test, I'd like to thank our sponsor, Native Knowledge. They are an online platform that facilitates systematic literature review and meta-analysis. Be sure to check them out; they're absolutely amazing!

Now, let's jump into the code. I've provided some code on the screen for data cleaning and preparation. It involves filtering the data to include only the "never married" and "divorced" categories and removing unnecessary factors. Feel free to copy the code if you want to replicate this analysis on your own. After running the code, you'll have a nice, tidy dataset with a single variable.

Now comes the exciting part—conducting the chi-square test. To apply the test, we need to create a table of our data. I've created a new object called "my_table" and assigned the table function to it, using our prepared dataset as the argument. When we run the code and view "my_table," we can see a table with the data presented neatly.

Next, we can simply apply the chi-square test to our table by using the "chisq.test" function. Running this function on "my_table" will provide us with the test results, including the p-value. In this case, we obtained a very small p-value, indicating that it is extremely unlikely to observe the observed differences in proportions if the categories had equal proportions. Therefore, we can reject the null hypothesis of equal proportions and conclude that there is a statistically significant difference among the marital statuses.

If you prefer a more concise approach, we can achieve the same results using pipe operators ("%>%") from the tidyverse package. By piping the data directly into the table and then into the chi-square test, we can streamline the code and obtain the same answer.

I hope you found this overview of the chi-square test informative. If you're interested in diving deeper into the topic, I recommend watching the longer video on the chi-square test, which will provide a more comprehensive understanding of its mechanics. Keep up the great work, stay curious, and remember to always strive for continuous learning.

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R programming in one hour - a crash course for beginners

R programming in one hour - a crash course for beginners

The video tutorial provides a crash course in R programming for beginners. It covers the basics of R and accessing built-in data sets, data manipulation techniques, data exploration using functions like glimpse and complete cases, data cleaning techniques such as subsetting and renaming, data visualization techniques using the grammar of graphics, T-tests, ANOVA and Chi-square tests, linear models, and how to reshape data frames. The instructor emphasizes the importance of exploring datasets and discusses tools to make data analysis and visualization more intuitive, like the tidy verse and the ggplot2 package. The video concludes with a demonstration of a chi-squared test and a linear model using the "cars" dataset, with a focus on interpreting the output.

• 00:00:00 The speaker outlines what they will cover in the tutorial, which is a crash course for R programming beginners. The course will include the basics of R, exploring and accessing built-in data sets, manipulating data by cleaning, selecting, filtering, and reshaping it, describing data using numeric variables, visualizing data using different kinds of plots, and analyzing data using hypothesis testing and various tests like t-tests, ANOVA, chi-squared, and linear models. Additionally, the speaker explains the four quadrants of RStudio, focusing on the console and the environment, and how to access help using the question mark command and community resources like Stack Overflow. Finally, the speaker demonstrates how to use R as a calculator by assigning values to objects and applying simple functions to them.
  
  
• 00:05:00 The instructor introduces data frames, which can be created by combining variables using the "data.frame" function in R. He shows how to create a data frame and how to view its structure using the "view" and "str" functions. The instructor also explains how to subset specific parts of a data frame using the notation "row, column," and demonstrates how to use the built-in data sets in R. Additionally, he presents the tidy verse, a collection of packages that expand the vocabulary and data sets available to R users, and demonstrates how to use the pipe operator and functions like filter and mutate to make data analysis and visualization more intuitive.
  
  
• 00:10:00 The instructor talks about exploring a dataset using the "m sleep" dataset as an example. He demonstrates how to use various functions, such as glimpse, length, names, unique, and complete cases to get an overview of the data's structure, dimensions, and unique values. He also shows how to create an object called "missing" that includes all rows that have missing data. The instructor emphasizes the importance of exploring a dataset to gain a better understanding of its content and how to leverage it for analysis. He also thanks Nested Knowledge, a platform that supports the research process, for sponsoring the video.
  
  
• 00:15:00 The speaker introduces data cleaning techniques using R programming, such as selecting variables and changing their order with the select function, renaming the variables with the rename function, and changing variable types using the as character and mutate functions. The speaker also explains how to change factor levels and use the filter function to select specific observations based on certain criteria.
  
  
• 00:20:00 The instructor discusses how to filter data by conditions such as mass being less than 55 and sex being male using the recode function. They go on to demonstrate how to handle missing data and remove duplicates from a data frame using the distinct function. The instructor also covers how to mutate data, both by overwriting existing variables and creating new ones based on conditional statements using the if else function. Finally, they introduce the concept of reshaping data and show how to manipulate a data set using the gap minder package.
  
  
• 00:25:00 The instructor explains how to reshape data frames using the pivot wider and pivot longer functions. First, a data frame is created and then the pivot wider function is used to reshape it so that the years become column headings and the life expectancies are within the cells. The code is then run in reverse to create a long data frame. The instructor then demonstrates how to summarize data using numerical variables, such as wake time for mammals, by calculating the mean, median and interquartile range. Finally, the instructor provides a code for grouping data by categories and calculating statistical values for each group, such as the minimum and maximum values, the difference between them, and the mean.
  
  
• 00:30:00 The instructor goes over data visualization in R, starting with the "grammar of graphics" concept. This involves understanding how data is mapped out against aesthetics such as x and y axis, color, shape, and size, and how geometries such as line, bar chart, and histogram can be applied to produce plots. The ggplot package is also introduced as a tool for creating more sophisticated graphs. The instructor provides example codes for creating basic plots and discusses how aesthetic and geometry interact to produce the final outcome.
  
  
• 00:35:00 The speaker discusses how to use ggplot2 to create different types of plots. They start by defining the data and mapping in ggplot, then adding geometries such as bar plots and histograms. They also demonstrate how to pipe in data and how to manipulate it before creating a plot. They then take it a step further by adding aesthetics and coloring to plot with different shades based on categories. The video also includes a brief discussion on themes and labels, and uses examples from the Star Wars dataset throughout.
  
  
• 00:40:00 The video tutorial demonstrates how to create a scatter plot using 'ggplot2' and add an additional layer using 'geom_smooth'. By using 'facet_wrap' with the 'sex' variable, the tutorial shows how to look at the scatter plot in different facets. The section also covers hypothesis testing using a T-test, ANOVA, Chi-square tests, and linear models with examples from the "gap-minder" data set which includes data on life expectancy, population, GDP per capita, and other factors across different countries and regions. The tutorial explains how to test for differences in life expectancy between Africa and Europe using a T-test, assuming there is no difference as the null hypothesis.
  
  
• 00:45:00 This is known as Tukey's Honest Significant Differences test which compares all possible pairs of means to see if there are any significant differences. In this example, we can see that there are significant differences between all three continents, with Europe having the highest life expectancy and Africa having the lowest. The adjusted p-values help us avoid making false conclusions by taking into account multiple comparisons. Overall, the t-test and ANOVA are powerful tools for analyzing differences between groups in R.
  
  
• 00:50:00 The instructor demonstrates a statistical analysis on a dataset of different species of irises. The first analysis is a chi-squared goodness-of-fit test to determine if the proportion of the irises that fall into the categories of small, medium, and large is equal. The results of the test showed that the proportions are not equal, and the null hypothesis is rejected. The second analysis is a chi-squared test of independence, which determines if the value of one variable is dependent on the value of the other. In this case, the analysis is run on the size and species of the irises. It is evident from the results that there is a dependence between the two variables, and the null hypothesis is rejected.
  
  
• 00:55:00 The instructor goes over a simple linear model using the "cars" dataset in R, and explains how to interpret the output. The best-fit line is created using a y-intercept and slope, with the y-intercept being meaningless in this case but necessary to draw the line. The slope of 3.9 is important, representing the additional distance required for each one-unit increase in speed, and has a p-value of 0.00 (extremely statistically significant), rejecting the null hypothesis that there is no relationship between speed and distance. The R-squared value of 0.65 represents how much of the change in distance to stop can be explained by the speed of the car. The output also includes residuals and coefficients, with the slope being the most important in this context. The instructor provides a link to a free data visualization cheat sheet and encourages viewers to like, comment, and subscribe.

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Population, Sample, Parameter, Statistic

Population, Sample, Parameter, Statistic

Hello everyone! In today's session, we'll be covering some of the most important vocabulary in the field of statistics. Let's dive right in and start with two fundamental concepts: population and sample.

A population refers to all the data of interest in a particular study, including observations, responses, measurements, and so on. On the other hand, a sample is a subset of that population. To illustrate this, let's consider a political poll conducted by a company. They randomly contact 1,200 voters and ask them about their voting preferences. In this case, the sample would be the list of preferences obtained from those 1,200 individuals. The population, technically speaking, would be the list of preferences of all registered voters. It's important to note that both population and sample refer to the preferences themselves, not the individuals.

In most cases, it is not feasible to collect data from an entire population. Instead, we rely on samples to draw conclusions about populations. This is the essence of inferential statistics—using sample data to make inferences about populations. Now, let's move on to the key definitions.

Firstly, a parameter is a numerical value that describes a population. It provides information about the population as a whole. For instance, in our poll example, the parameter would be the percentage of all registered voters who intend to vote for a particular candidate.

Secondly, a statistic is a numerical value that describes a sample. It represents characteristics or measurements derived from the sample data. Going back to our poll scenario, if 38% of the 1,200 sampled voters express their intention to vote for candidate A, then 38% is a statistic—a representation of the sample's preferences.

Typically, we only have access to the statistic, as it is often impractical to obtain parameters for the entire population. However, our ultimate interest lies in the parameters since they provide insights into the overall population. Let's consider a couple more examples to solidify our understanding.

Example 1: The average age of 50 randomly selected vehicles registered with the New York DMV is 8 years. Here, the population would be the ages of all vehicles registered with the New York DMV. The sample, in this case, consists of the ages of the 50 randomly selected vehicles. The parameter would be the average age of all registered New York vehicles, while the statistic would be the average age of the 50 randomly selected ones.

Example 2: In 2018, the median household income in the United States was $63,937, while in Chicago, it was $70,760. In this scenario, the population refers to the incomes of all households in the United States in 2018, while the sample represents the incomes of households in Chicago during the same year. The first value, $63,937, is a parameter describing the population, while the second value, $70,760, is a statistic representing the sample.

Understanding the distinction between population and sample, as well as parameters and statistics, is crucial in statistical analysis. While we may primarily have access to statistics, our goal is to infer and estimate parameters, as they provide a broader perspective on the entire population.

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Types of Data

Types of Data

Hello everyone! Today, we'll be discussing data classification, which involves two fundamental types: quantitative and categorical data.

Quantitative data consists of numerical measurements or counts. It deals with data that can be measured or expressed in numerical terms. Examples of quantitative data include the heights of women in South America, weights of newborns at British hospitals, and the numbers of unemployed people in each nation of the world.

On the other hand, categorical data, also known as qualitative data, consists of labels or descriptors. It involves data that can be grouped into categories or classes. Examples of categorical data include eye color of cats, political party affiliations of voters, and preferred brands of soft drinks among consumers.

Sometimes, it can be tricky to determine the type of data, especially when it appears as numbers. A quick way to distinguish between categorical and quantitative data is to consider whether numerical operations, such as calculating averages, make sense. If the data is merely labeled and doesn't correspond to meaningful measurements or counts, it should be considered categorical. For instance, the numbers worn on baseball jerseys do not hold any quantitative significance and should be classified as categorical data.

Categorical data can be further categorized into two types: ordinal and nominal. Ordinal data uses categories that have a meaningful order. A familiar example is the Likert scale, which offers choices like strongly disagree, disagree, neutral, agree, and strongly agree. These categories can be ranked in a natural order. In contrast, nominal data uses categories that do not have a meaningful order. Examples include political affiliations, gender, and favorite soft drinks. Although we could impose an order on nominal data, it would be arbitrary and based on personal opinion.

Similarly, quantitative data can be classified into two types: ratio and interval. Ratio data allows for meaningful ratios and multiples. Variables like income, weight, and age fall under this category. It makes sense to say that one person is twice as old as another or that someone earns half as much money as another. On the other hand, interval data does not support ratios and multiples. Variables like temperature and calendar year are examples of interval data. It would be inappropriate to say that one temperature is twice as hot as another because the choice of zero on the scale is arbitrary and doesn't indicate the absence of the attribute being measured.

To determine the level of measurement, a quick approach is to check if zero on the scale corresponds to nothing or none. If zero signifies the absence of the attribute, it indicates a ratio level of measurement. For example, zero kilograms, $0, or 0 years old imply that there is no weight, no money, or no age. In contrast, if zero doesn't denote an absence in any real sense, it indicates an interval level of measurement. For instance, zero degrees Fahrenheit or zero degrees Celsius are just arbitrary points on their respective scales.

Let's explore a few examples to practice classification and level of measurement. We'll determine whether the variables are quantitative or categorical and identify their level of measurement:

1. Waiting times at a bank: This data consists of numbers and makes sense to talk about ratios and multiples. Therefore, it is quantitative data at the ratio level of measurement.

2. Genders of Best Director Oscar winners: This data is categorical, representing identifiers rather than numbers. It cannot be ranked in a meaningful way, so it is categorical data at the nominal level.

3. Names of books on the New York Times bestseller list: Since these are names, the data is categorical. Furthermore, the names can be naturally ordered as first, second, third bestsellers, etc., indicating ordinal data.

4. Times of day of lightning strikes on the Empire State Building: This data is quantitative as it involves measuring the time between lightning strikes. However, it falls under the interval level of measurement because there is no zero point that represents the absence of lightning strikes. The time intervals can be measured and compared, but zero does not signify a lack of strikes.

In summary, data classification involves differentiating between quantitative and categorical data. Quantitative data consists of numerical measurements or counts, while categorical data consists of labels or descriptors. It's important to consider whether numerical operations and meaningful ratios apply to determine the type of data.

Categorical data can further be categorized as ordinal or nominal, depending on whether there is a meaningful order among the categories. Ordinal data has a natural ranking, while nominal data does not. Similarly, quantitative data can be classified as ratio or interval based on whether meaningful ratios and multiples exist. Ratio data allows for ratios and multiples, while interval data does not.

Understanding the level of measurement is crucial in selecting appropriate statistical analyses and interpreting data correctly. The level of measurement determines the mathematical operations that can be performed on the data and the meaning of zero on the scale.

By accurately classifying and determining the level of measurement of data, statisticians and researchers can choose suitable statistical techniques and derive meaningful insights from their analyses.