PySpark Split

Intro The PySpark function allows you to concatenate an array field into a single Sting field. This serves as the opposite of the function. This allows you to perform string operations on a column…

PySpark Split

Intro The PySpark method allows us to split a column that contains a string by a delimiter. For example, we have a column that combines a date string, we can split this string into an Array Column…

PySpark Fillna

Intro The PySpark and methods allow you to replace empty or null values in your dataframes. This helps when you need to run your data through algorithms or plotting that does not allow for empty…

PySpark ForEach

Intro The PySpark method allows us to take small samples from large data sets. This allows us to analyze datasets that are too large to review completely. Setting Up The quickest way to get started…

PySpark ForEach

Intro The PySpark method allows us to iterate over the rows in a DataFrame. Unlike methods like map and flatMap, the method does not transform or returna any values. In this article, we will learn…

PySpark FlatMap

Intro The PySpark method allows use to iterate over rows in an RDD and transform each item. This method is similar to method, but will produce a flat list or array of data instead of mapping to new…

PySpark Map

Intro The PySpark method allows use to iterate over rows in an RDD and transform each item. Mapping is a common functional operation and PySpark allows us to use this at scale. In this article, we…

PySpark UDF (User Defined Function)

Intro Similar to most SQL database such as Postgres, MySQL and SQL server, PySpark allows for user defined functions on its scalable platform. These functions can be run on dataframes or registers to…

PySpark UnionByName

Intro When merging two dataframes with , we sometimes have a different order of columns, or sometimes, we have one dataframe missing columns. In these cases, PySpark provides us with the method. In…

PySpark Union

Intro PySpark provides us with the function to merge two or more data frames together. There also exists a method that was deprecated since Spark 2.0, but can be used if you have an older version…

PySpark GroupBy

Intro When working we often want to group data to view distributions or aggregations. PySpark provides us with the method to group our dataframes. In this particle, we will learn how to work with…

PySpark Drop OrderBy

Intro When working with data and viewing, we often want to sort or order our data for easier review. PySpark provides the and functions to sort dataframes. In this article, we will learn how to use…

PySpark Drop Duplicates

Intro During the data cleaning process, we would like to remove duplicate rows. PySpark provides us with the and that let's us remove duplicates on large amounts of data. In this article, we will…

PySpark Filter

Intro Often when working with dataframes we want to filter our data to a subset. PySpark provides us with the and the alias to filter our data frames. In this article, we will learn how to use…

PySpark WithColumnRenamed

Intro The allows us to easily change the column names in our PySpark dataframes. In this article, we will learn how to change column names with PySpark withColumnRenamed. Setting Up The quickest way…

PySpark WithColumn

Intro The method allow us to add columns, modify their types, modify their values and more. It is one of the most commonly used methods for PySpark. In this article, we will learn how to use PySpark…

PySpark Collect

Intro The dataframe collect method is used to return the rows in a dataframe as a list of PySpark Row classes. This is used to retrieve data on small dataframes so that you can inspect and iterate…

PySpark Select

Intro Selecting columns is one of the most common operations when working with dataframes. We can select by position or name. We can also select a single or multiple columns. In this article, we will…

PySpark Row

Intro The PySpark Row class is located in the module and provides a simple way to create rows or observations in a dataframe or an RDD. In this article, we will learn how to use PySpark Row. Let's…

PySpark StructType and StructField

Intro PySpark provides two major classes, and several other minor classes, to help defined schemas. This allows us to interact with Spark's distributed environment in a type safe way. In this article…

PySpark Show

Intro The show function allows us to preview a data frame. The show method provides us with a few options to edit the output. In this article, we will learn how to use the PySpark show function. We…

PySpark Window Functions

Intro Computing operations over a window of data, or a subset, is a common task. Often we want to rank information or subsets of data. For example, we may want to see the top sales per each month. In…

PySpark Pivot (rows to columns)

Intro Often when viewing data, we have it stored in an observation format. Sometimes, we would like to turn a category feature into columns. We can use the Pivot method for this. In this article, we…

PySpark DataFrame Join

Intro Often you will have multiple datasets, tables, or dataframes that you would like to combine. For example, you may have customers and their purchases and would like to see these in a single…

PySpark DataFrame Aggregations

Intro One main feature you will use in Spark is aggregation. This will help with exploratory data analysis and building dashboards that scale. In this article, we will learn how to use pyspark…

PySpark DataFrame Select, Filter, Where

Intro Filtering and subsetting your data is a common task in Data Science. Thanks to spark, we can do similar operation to sql and pandas at scale. In this article, we will learn how to use pyspark…

PySpark Handle Null Data

Intro Often when working with data you will find null values. It is a common task to work with and know how to manage these null values. The decision to drop or to impute is important in the model…

PySpark Create Dataframe

Intro There are many ways to create a data frame in spark. You can supply the data yourself, use a pandas data frame, or read from a number of sources such as a database or even a Kafka stream. In…

Redis Sort with Node

Intro Sorting is a common programming task, and you may be tempted to pull data from Redis and sort it client side. However, using Redis's built in sort function will be more performant and general…

Redis Sort with Python

Intro Sorting is a common programming task, and you may be tempted to pull data from Redis and sort it client side. However, using Redis's built in sort function will be more performant and general…

Redis Server Sessions in Python

Intro Sessions are usually short lived data, or at least have an expiration date, used to transfer state accross RESTful applications. REST applications are stateless per their spec, yet sometimes we…

Backing Up and Restoring Redis

Intro Redis uses two methods for persistence, snapshotting and append only file. Both have different use cases and can be used separately or in conjunction. In this article, we will learn how to…

Redis Server Sessions in Node

Intro Sessions are usually short lived data, or at least have an expiration date, used to transfer state accross RESTful applications. REST applications are stateless per their spec, yet sometimes we…

Redis Transactions in Node

Intro Transactions are a common database requirement for when you need to make multiple insert or update operations together. That is, if one operation fails, you don't want to execute either…

Redis Transactions in Python

Intro Transactions are a common database requirement for when you need to make multiple insert or update operations together. That is, if one operation fails, you don't want to execute either…

Node Redis Expire

Intro Expiring keys allows you to set automatic time limits for keys in Redis. When you set a TTL (time to live), Redis will clean up and remove the key when time has run out. This can be helpful for…

Python Redis Expire

Intro Expiring keys allows you to set automatic time limits for keys in Redis. When you set a TTL (time to live), Redis will clean up and remove the key when time has run out. This can be helpful for…

Python Redis Pub Sub

Intro Redis provides a Pub/Sub api that scales well and allows for quick real time connections. Real time apps are very popular, so redis can help you solve these business problems. If you are…

Redis Pipeline in Nodejs

Intro Redis offers a feature called pipeline that allows you to bulk send commands. This can drastically improved performance if you are running queries that can be batched together. The reason for…

Redis Pipeline in Python

Intro Redis offers a feature called pipeline that allows you to bulk send commands. This can drastically improved performance if you are running queries that can be batched together. The reason for…

Node Redis Pubsub

Intro Redis provides a Pub/Sub api that scales well and allows for quick real time connections. Real time apps are very popular, so redis can help you solve these business problems. If you are…

Redis Bitmap in Python

Intro Bitmaps are a pattern in redis, not actualy a data type as they are just using string, that can help save space when using redis. For example, you can store 4 billion users subscribed to a…

Redis Bitmap in Nodejs

Intro Bitmaps are a pattern in redis, not actualy a data type as they are just using string, that can help save space when using redis. For example, you can store 4 billion users subscribed to a…

Python Redis HyperLogLog Commands

Intro Tracking unique visits to a page or user vists is a common requirement for business applications. Doing this with large volumes can be very difficult as the data requirements are high. Thus, we…

Node Redis HyperLogLog Commands

Intro Tracking unique visits to a page or user vists is a common requirement for business applications. Doing this with large volumes can be very difficult as the data requirements are high. Thus, we…

Python Redis Geo Commands

Intro Geocoding and coordinates are a common use case in modern applications. These computations can be very heavy in terms of lookups so it is often desired to cache. Thus, Redis provides us with the…

Node Redis Geo Commands

Intro Geocoding and coordinates are a common use case in modern applications. These computations can be very heavy in terms of lookups so it is often desired to cache. Thus, Redis provides us with the…

Python Redis Sorted Set Commands

Intro Sorted sets are a powerful data set used in redis. If you are familiar with binary search, you know the importance of having a presorted set to access items in log(n). This data type is often…

Node Redis Sorted Set Commands

Intro Sorted sets are a powerful data set used in redis. If you are familiar with binary search, you know the importance of having a presorted set to access items in log(n). This data type is often…

Python Redis Set Commands

Intro Sets are lists filled with unique items. The set data type is helpfuly when you want to work with unique data types, thus they help with features where you want to easily deuplicate values. In…

Node Redis Set Commands

Intro Sets are lists filled with unique items. The set data type is helpfuly when you want to work with unique data types, thus they help with features where you want to easily deuplicate values. In…

Python Redis HSET and other Hash Commands

Intro Hash data types are used in many alogrithms to increase speed. They usually take more memory but improved the processing speed. This makes them an asset in the redis database which takes the…

Node Redis HSET and other Hash Commands

Intro Hash data types are used in many alogrithms to increase speed. They usually take more memory but improved the processing speed. This makes them an asset in the redis database which takes the…

Python Redis Lists

Intro Lists are one of the fundemental data types in Redis. You will often use this data type to manage many features. In this article, we look at many of the common list commands in Redis using…

Node js Redis Lists

Intro Lists are one of the fundemental data types in Redis. You will often use this data type to manage many features. In this article, we look at many of the common list commands in Redis using Node…

Python Redis

Intro When building large scale applications, there comes a need for scaling. There are many places to start with scaling, but one place my be scaling your reads. Let's say that you have a read heavy…

Node js redis

Intro When building large scale applications, there comes a need for scaling. There are many places to start with scaling, but one place my be scaling your reads. Let's say that you have a read heavy…

Observability: Python Health Check Example

Intro Building out health checks is a common task when building a web server. You may have seen status sites, such as Github status, where we can see the update time of each service that is offered…

Observability: Node js Health Check Example

Intro Building out health checks is a common task when building a web server. You may have seen status sites, such as Github status, where we can see the update time of each service that is offered…

Observability: Python Distributed Tracing with Open Zipkin Example

Intro Eventually apps get complicated and make many requests. When building out services such as microservices or even just multi services, debugging our apps get a bit harder. The services will…

Observability: Node Distributed Tracing with Open Zipkin Example

Intro Eventually apps get complicated and make many requests. When building out services such as microservices or even just multi services, debugging our apps get a bit harder. The services will…

Observability: Python Grafana and Statsd for Performance Monitoring

Intro Monitoring performance and uptime is a common task in server development. We often want to know how fast our endpoints are performing and if they are responding at all. With the help of statsd…

Observability: Nodejs Grafana and Statsd for Performance Monitoring

Intro Monitoring performance and uptime is a common task in server development. We often want to know how fast our endpoints are performing and if they are responding at all. With the help of statsd…

Observability: Python Elasticsearch Example

Intro Logging is one of the most fundemental observability concepts needed in Python programming. Whether we have a cli app, REST api app, or graphql app, we use logs to make sure things are going…

Observability: Node js Elasticsearch Example

Intro Logging is one of the most fundemental observability concepts needed in Node.js programming. Whether we have a cli app, REST api app, or graphql app, we use logs to make sure things are going…

MA Model in Python

Intro The moving average model, or MA model, predicts a value at a particular time using previous errors. The model relies on the average of previous time serries and correlations between errors that…

AR Model in Python

Intro The auto regression model, or AR model, predicts a value at a particular time using previous lags (values at previous times). The model relies on the correlations between lags, or auto…

AR Model in R

Intro The auto regression model, or AR model, predicts a value at a particular time using previous lags (values at previous times). The model relies on the correlations between lags, or auto…

Python White Noise Simuation

Intro White noise is a base line model that appears when we have removed correlations and difference. The model is a simple list of random errors and serves as a base for many time series models. In…

R White Noise Simuation

Intro White noise is a base line model that appears when we have removed correlations and difference. The model is a simple list of random errors and serves as a base for many time series models. In…

Python Random Walk Simuation

Intro Random walks are one of the fundamental time series models. Despite this simplicity, they are able to model many real world scenarios. In this article, we will learn how to simulate a random…

R Random Walk Simuation

Intro Random walks are one of the fundamental time series models. Despite this simplicity, they are able to model many real world scenarios. In this article, we will learn how to simulate a random…

How to Check Stationarity of Time Series Data in Python

Intro Before modeling a time series data set, we often want to check if the data is stationary. Many models assume stationary time series, and if this assumption is violated, our forcast will not be…

How to Check Stationarity of Time Series data in R

Intro Before modeling a time series data set, we often want to check if the data is stationary. Many models assume stationary time series, and if this assumption is violated, our forcast will not be…

Second Order Exponential Smoothing in R

Intro Second Order Exponential Smoothing extends Simple Exponential Smoothing by adding a Trend Smoother. If SES doesn’t work well, we can see if there is a trend and add another component to our…

Second Order Exponential Smoothing in Python

Intro Second Order Exponential Smoothing extends Simple Exponential Smoothing by adding a Trend Smoother. If SES doesn't work well, we can see if there is a trend and add another component to our…

Simple Exponential Smoothing in R

Intro Simple Exponential Smoothing is a forecasting model that extends the basic moving average by adding weights to previous lags. As the lags grow, the weight, alpha, is decreased which leads to…

Simple Exponential Smoothing in Python

Intro Simple Exponential Smoothing is a forecasting model that extends the basic moving average by adding weights to previous lags. As the lags grow, the weight, alpha, is decreased which leads to…

Time Series Decomposition in R

Intro When working with time series data, we often want to decompose a time series into several components. We usually want to break out the trend, seasonality, and noise. In this article, we will…

Time Series Decomposition in Python

Intro When working with time series data, we often want to decompose a time series into several components. We usually want to break out the trend, seasonility, and noise. In this article, we will…

How to Perform a Ljung-Box Test in Python

Intro When working with time series, we deal with autocorrelation often. In our toolkit, we have a statistical test to check if a time series contains an autocorrelation. That test is Ljung-Box. In…

How to Conduct a Ljung-Box Test in R

Intro When working with time series, we deal with autocorrelation often. In our toolkit, we have a statistical test to check if a time series contains an autocorrelation. That test is Ljung-Box. In…

Detrending Time Series in Python

Intro A common task in time series analysis is taking the difference or detrending of a series. This is often used to take a non-stationary time series and make it stationary. In this article, we will…

Detrending Time Series in R

Intro A common task in time series analysis is taking the difference or detrending of a series. This is often used to take a non-stationary time series and make it stationary. In this article, we will…

Python Rolling Mean

Intro When working with time series, we often want to view the average over a certain number of days. For example, we can view a 7-day rolling average to give us an idea of change from week to week…

Moving Average in R

Intro When working with time series, we often want to view the average over a certain number of days. For example, we can view a 7-day rolling average to give us an idea of change from week to week…

Augmented Dickey-Fuller Test in Python

Intro In time series analysis, we often want to check if a time series is stationary. This is because when modeling, most of our techniques rely on stationary time series. One way to check for a…

Augmented Dickey-Fuller Test in R

Intro In time series analysis, we often want to check if a time series is stationary. This is because when modeling, most of our techniques rely on stationary time series. One way to check for a…

Plot ACF Python

Intro The autocorrelation function measures the correlations between an observation and its previous lag in a time series model. These functions are often used to determine which time series model to…

ACF Plot in R

Intro The autocorrelation function measures the correlations between an observation and its previous lag in a time series model. These functions are often used to determine which time series model to…

R Resample Time Series

Intro Resampling is a common task when working with time series dta. Resampling goes in two directions, upsampling and downsampling. Upsampling allows us to go from a lower time frame to a higher, i.e…

Pandas Resample Time Series

Intro Resampling is a common task when working with time series dta. Resampling goes in two directions, upsampling and downsampling. Upsampling allows us to go from a lower time frame to a higher, i.e…

How to Plot a Timeseries in Python

Intro When working with time series models, we would often like to plot the data to see how it changes over time. This is a simply line plot, but the x-axis is always dates. In this article, we will…

How to Plot KMeans Clusters in Python

Intro When modeling clusters with algorithms such as KMeans, it is often helpful to plot the clusters and visualize the groups. This can be done rather simply by filtered our data set and using…

How to Filter and Subset a Time Series in Python

Intro Using time series is a common task in data science with python. We often want to select specific information based on dates or a date range. In this article, we will learn how to index and…

Subsetting a Time Series in R

Intro When working with time series, we often want to access a subset of our data based on a range of dates. When using data frames, we have many ways to index and subset data. With the help of the R…

Subsetting a Time Series in R

Intro When working with time series, we often want to access a subset of our data based on a range of dates. When using data frames, we have many ways to index and subset data. With the help of the R…

Ordinal Encoding in Python

Intro Ordinal Encoding is similar to Label Encoding where we take a list of categories and convert them into integers. However, unlike Label Encoding, we preserve and order. For example, if we are…

Label Encoding in Python

Intro Label Encoding is one of many encoding techniques to convert your categorical variables into numerical variables. This is a requirement for many machine learning algorithms. Label Encoding is…

How to Create a Timeseries in Python

Intro Time series is one of the most common analysis and modeling in Data Science. In this article, we will learn how to create time series in python. Creating a Basic Time Series To create a time…

Ordinal Encoding in R

Intro Ordinal Encoding is similar to Label Encoding where we take a list of categories and convert them into integers. However, unlike Label Encoding, we preserve and order. For example, if we are…

One Hot Encoding in Python

Intro One hot encoding is a method of converting categorical variables into numerical form. It is a preprocessing needed for some machine learning algorithms to improve performance. In this article…

Label Encoding in R

Intro Label Encoding is one of many encoding techniques to convert your categorical variables into numerical variables. This is a requirement for many machine learning algorithms. Label Encoding is…

How to create time series in r

Intro Time series is one of the most common analysis and modeling in Data Science. In this article, we will learn how to create time series in R. Creating a Basic Time Series Let’s say we had a vector…

How to plot time series in R

Intro When working with time series models, we would often like to plot the data to see how it changes over time. This is a simply line plot, but the x-axis is always dates. In this article, we will…

Box Cox in Python

Intro A Box-Cox transformation is a preprocessing technique used to transform a distribution into a normally distributed one. Normal distribution is often a requirement, especially for linear…

Box Cox in R

Intro A Box-Cox transformation is a preprocessing technique used to transform a distribution into a normally distributed one. Normal distribution is often a requirement, especially for linear…

Cubist Regression in R

Intro Cubist is a rule based model that builds regression solutions based on building rules. In this article, we will learn how to use cubist model in r. Data For this tutorial, we will use the Boston…

Boosted Tree Regression in R

Intro Boosted Trees are commonly used in regression. They are an ensemble method similar to bagging, however, instead of building mutliple trees in parallel, they build tress sequentially. They used…

Random Forest in R

Intro Random Forest is a common tree model that uses the bagging technique. Many trees are built up in parallel and used to build a single tree model. In this article, we will learn how to use random…

Decision Tree Regression in R

Intro Decision Trees model regression problems by split data based on different values. This ends by creating a tree structure that you can follow to find the solution. In this article, we will learn…

KNN Regression in R

Intro The KNN model will use the K-closest samples from the training data to predict. KNN is often used in classification, but can also be used in regression. In this article, we will learn how to use…

MARS Regression in R

Intro Multivariate Adaptive Regression Splines or MARS is a regression model that extends linear models to nonlinear. It essentially creates many piecewise functions to model your data. In this…

Pivot Table in R

Intro Pivot tables allow you to summarize groups of data easily. We can simply group data by different categorize and see summaries like totals, mean, etc. In this article, we will learn how to create…

SVM Regression in R

Intro SVM models are a varied model that can work for both regression and classification. They work to find a hyperplance between points and increase the margin. We will leave the math to a different…

One Hot Encoding in R

Intro One hot encoding is a method of converting categorical variables into numerical form. It is a preprocessing needed for some machine learning algorithms to improve performance. In this article…

Partial Least Squares in R

Intro Partial Least Squares is a machine learning model that helps solbe issues with multicollinearity. It has advantages of PCA regression in the sense that it is still easily interpretable and has…

PCA Regression in R

Intro PCA or Principal component regression is the process of using PCA to preprocess the data then running a linear regression model. The PCA process will give us new variables or predictors that we…

Ridge Regression in R

Intro Ridge regression is a modified linear regression model called a penalized regression. It adds a penalty to the linear regression model when optimizing to help with multicollinearity issues. In…

Lasso Regression in R

Intro Lasso regression is a model that builds on linear regression to solve for issues of multicolinearity. The optimization functin in lasso adds a shrinkage parameter which allows for remove…

Logistic Regression in R

Intro Logistic Regression modifies a regression model to return a binary response, i.e. yes or no. This is helpful when we want to solve a classification problem to decided between two classes. In…

Linear Regression in R

Intro Linear Regression is model that predicts a response based on one or more predictors (columns). This model is one of the most fundemental model and is often used as a baseline in machine learning…

How to use dplyr group by in R

Intro The group_by method allows you to group data which allows for easy visualization and summarizing over groups. Tidyverse makes single and multiple groups easy. In this article, we will learn how…

How to use dplyr summarize in R

Intro The summarize method allows you to run summary statistics easily on your dataset. Mean and counts are easily accessed with this tidyverse method. In this article, we will learn how to use dplyr…

How to use dplyr select in R

Intro The select method let’s you easily select columns from your data set. There are many helpful operators and select helpers to get what you need. In this article, we will learn how to use the…

How to use dplyr relocate in R

Intro The relocate method allows you to reorder the columns in a data set. The method is similar to select, but has some helpful methods for moving columns around. In this article, we will learn how…

How to use dplyr transmute in R

Intro The transmute method in dplyr allows you to add new variables, especially computed ones. Unlike mutate, the transmute will remove other columns by default. A common data wrangling task is to…

How to use dplyr rename in R

Intro The rename method allows you to quickly rename columns in your data set. This is a common task when you have obscure or large names and want to rename for clarity. In this article, we will learn…

How to use dplyr mutate in R

Intro The mutate method in dplyr allows you to add new variables, especially computed ones, while preserving existing columns. A common data wrangling task is to create new columns using computations…

How to use dplyr count in R

Intro The count will display the count of unique values for a column in your data set. This helps you quickly view the count of variables in a tabular form. In this article, we will learn how to use…

How to use dplyr pull in R

Intro The pull method from dplyr allows use to simplify accessing single values from data. We can see this by comparing the base r way with the dplyr way. In this article, we will learn how to use the…

How to use dplyr filter in R

Intro Filtering and subsetting data is a common task in data wrangling. Often we have a large set and we want to either model or preview a smaller selection. In this article, we will learn how to…

How to use dplyr distinct in R

Intro Finding duplicates is simple using the verb in . There are many options that allow you to specify column combinations to find distinct rows which is essential when looking for duplicates. In…

How to Sort by Columns using dplyr Arrange in R

Intro Sorting data by columns is a common task in data wrangling. The Tidyverse includes a useful method in the package that makes sorting simple. There is support for sorting by multiple columns…

How to Create a ggplot Line Plot in R

Intro Line plots are used to show a continous varaible compared to an ordinal varaible. Most commonly line plots are used to show how some varaible changes over time. In this article, we will learn…

How to Create a ggplot Histogram Plot in R

Intro Histogram plots allow you to view distributions of continuous variables. The plot will bin a continuous variable into groups and count the number of observations in each group. This helps you…

How to Create a ggplot Density Plot in R

Intro A density plot allows for us to view the distribution of continous variables. This gives us an idea of the distribution of the variable matches one we recognize or if we want to transform the…

How to Create a ggplot BarPlot in R

Intro When analyzing a data set, you often would like to compare categorical variables to each other. For example, you may have a list of sales, and you would like to display a count per the number of…

How to Create a ggplot Jitter Plot in R

Intro Jitter plots add some variation to a scatter plot so that you can see the individual observations easier. They are commonly used when viewing overlapping points from data that is discrete. In…

How to Create a ggplot Frequency Plot in R

Intro A Frequency plot is similar to a Histogram as it bins the count of continuous data. However, instead of using bars to display, it will use a line plot. In this article, we will learn how to…

How to Create a ggplot Box Plot in R

Intro Boxplots are used to display distribution data of a continuous variable. There are five statistics included on the plot including mean, quantities and outliers. They are used to get quick visual…

How to Create a ggplot Violin Plot in R

Intro Violin plots are used to summarize continuous variables. They are similar to box plots, as they provide summary statistics like mean and quantiles, but they also display the distribution. These…

How to Conduct a Shapiro Test in R

Intro When building different models like regression and conduct statistical tests such as ANOVA, t-tests, etc, it is often required that the data be normally distributed. To check for this, you can…

How to Conduct a Normality Test in R

Intro Many tests in statics and other tasks rely on the assumption that your data is somewhat normally distributed. For example, when modeling with linear regression, normality is assumed. This is…

How to Create an Area Plot with ggplot2 in R

Intro Area plots are similar to line plots, however, they express the magnitude more clearly. They do this by coloring in the area underneath the line. In this article, we will learn how to create…

How to Conduct a Proportion Test in R

Intro During analysis, it is often required to test a sample proportion to a theoretical or known proportion to see if there is a change. For example, let’s say we conduct a survey at the end of a…

How to Create a Scatter Plot with ggplot2 in R

Intro Scatter plots allow us to view relationships between two continuous variables. For example, we may want to check if their is a linear relationship between someone’s height and how much they…

How to Create a ggplot QQ plot in R

Intro A qqplot or quantile-quantile plot helps you determine if the normality assumption of data holds. In this article, we will learn how to plot a qqplot with ggplot2. Short on Time If you are short…

Getting started with the Notion API JavaScript SDK

The public beta for the Notion API went live recently and it is going to make Notion 10x more powerful. That means it is the perfect time to jump on the bandwagon and start building integrations of…

Using the useReducer Hook in React with TypeScript

The useReducer hook is an alternative to the useState hook and is preferable when you have complex state logic or when your next state depends on your previous state. The useReducer hook accepts a…

How to Create a ggplot2 Dot Plot in R

Intro A dot plot is similar to a histogram except each plot represents a single observation. This kind of plot allows you to see individual observations and their relationships while see the summary…

How to Get Started with ggplot2 in R

Intro The library ggplot extends the normal graphics library in R greatly. At first, the syntax can seem a bit odd as it chains together function with the addition operator, +. However, you may come…