Tutorial to show how to train ARIMA models

The goal of this tutorial is to set the working directory in the folder where the script is saved.

The goal of this tutorial is to unset the seed after it has been set in order to have random results again.

In this tutorial you'll learn how to measure the time of a process within R. You would want to do this in order to benchmark different process or if you simply want to know how how much time training that random forest with a data set of 500k rows took.

The goal of this tutorial is to use a “mode” function in order to find the most common value within a row or column.

The goal of this tutorial is to create our first interactive plot using the plotly library.

The goal of this tutorial is to group the missing values to learn which missing values are isolated and which belongs to largue groups. This could lead to different treatment of the missing values according to different criteria.

The goal of this tutorial is to create a line plot having categorical data in the x axis.

The goal of this tutorial is to learn how to create Density Ridgeline plots combining the ggplot and ggridges libraries. These plots partially overlap line plots to create the visual impression of a mountain range, so they can be quite useful for visualizing changes in distributions over time or space.

The goal of this tutorial is to remove a certain item from rules in a basket analysis. This way we could find underlying relationships.

The goal of this tutorial is to change the legent title in ggplot2.

The goal of this tutorial is to split a string in different characters and keep only the first element

The goal of this tutorial is to rename the variables of a dataframe.

This tutorial shows you how to select/ manipulate/ delete individual rows using conditions.

The goal of this tutorial is to learn how to do polynomial models of any degree.

The goal of this tutorial is to learn how to use the two parameters from caret package: TuneGrid and TuneLength.

In this tutorial you will learn to quickly identify & remove outliers from your data set.

The goal of this tutorial is to clean the environment to check that there is no clash between variables. This instruction can be written at the beginning of a script for safety.

The goal of this tutorial is to write footnotes in a markdown document. This could be really useful if we want to add extra information to our markdown in a nice way.

The goal of this tutorial is to generate an html notebook. You can download the code for this tutorial on the top right corner of the webpage: code -> Download Rmd.

The goal of this tutorial is to save a model in a file to be read afterwards. This process will allow us to save all the training time if the model is trained over a really big table.

The goal of this tutorial is to learn how to define the breaks in a geom point plot in ggplot.

The goal of this tutorial is to plot time series and forecast objects as ggplot plots that can be later customized using the ggplot grammar.

The goal of this tutorial is to learn the basics and how to use the pipe operator.

The goal of this tutorial is to learn how to use the time series linear model.

The goal of this tutorial is to reorder the levels of a factor. By default the levels of a factor are ordered alphabetically which can be convenient in some cases but we may want to define a different order.

The goal of this tutorial is to change the time language in R for MAC OS.

The goal of this tutorial is to read dates in the POSIXct format. In addition we will see the power of defining time zones.

The goal of this tutorial is to separate one column into several columns. It can be done using the separate function from tidyr.

The goal of this tutorial is to read and plot topological data from .bil files. There are many free sites to get this kind of data and could be useful to learn how to deal with this filetype.

The goal of this tutorial is to learn how to define default values for parameters in a handmade function.

The goal of this tutorial is to learn how to melt a table. The process of melting consists of transforming a table with different variables in a table where the columns are melted into variable and value. We can define which columns to use as ids and the rest will be melted.

The goal of this tutorial is to remove empty tickets from a transaction file. Sometimes we could load empty lines at the end of a file and we may need to get rid of them.

The goal of this tutorial is to be able to visualize a decision tree in order to get information and insights from it.

The goal of this tutorial is to load several libraries in one single action. This could be very useful for example if we can define a common list of libraries for a project. We could even load the list of libraries from a source file.

The goal of this tutorial is to find missing values in a factor variable.

The goal of this tutorial is to center the title of a plot in ggplot.

The goal of this tutorial is to learn how to capture output that is sent to the console so it can be stored in a variable or stored in a text file. This could be useful if a void function like str prints to the console but returns no object to be stored.

The goal of this tutorial is to learn how to assign values to variables

The goal of this tutorial is to learn how to remove rows from our dataset.

The goal of this tutorial is to list all the installed packages and list all the loaded packages. This could be very useful if need to check if a library has been installed.

The goal of this tutorial is to show how to store our plots made in R in different formats. Available formats cover some of this extensions: pdf, png, jpeg. The logic will always be the same: open a canvas in the format we want, plot inside the canvas and then close the canvas in order to save the file.

The goal of this tutorial is to know the different types of vectors we can build in R.

The goal of this tutorial is to learn how to use the grep function. A function very useful to find patterns in texts.

The goal of this tutorial is to learn different ways to avoid the factor variable trap (FVT). The unfactor function will change a vector to character or numeric from factor avoiding the FVT.

The goal of this tutorial is to learn how to split a dataframe into several dataframes by the value of one column. This could be useful if we want to perform analysis in a certain subset of a dataset.

The goal of this tutorial is to handle month abbreviations in Datasets. We will learn how to transform numerical months into abbreviations and the other way around

The goal of this tutorial is to learn the percentage of NA that each variable has. In addition we can understand the distribution of these NA. This means that we can identify if two variables are missing at the same time in a systematic way.

The goal of this tutorial is to learn how to count absolute and relative frequencies of the entries of a vector in a fast way.

The goal of this tutorial is to append text to the beginning of each line of a document. This could be useful if we want to modify urls.

The goal of this tutorial is to learn fast functions to read datasets in case we need to make code faster. For example if we want to run the code in rented servers.

The goal of this tutorial is to build our first neural network predictive algorithm. We have used the nnet library for this exercise.

This tutorial shows how different ways of code leads to the same result but those methods have big differences in terms of efficiency.

The goal of this tutorial is to understand some basic use of the try function. Sometimes we need to be able to handle errors in functions because the error is just information that something went wrong.

The goal of this tutorial is to learn how to match functions using the name as a string.

The goal of this tutorial is to learn the basics of normalization.

The goal of this tutorial is to learn how to add empty levels to a factor.

The goal of this tutorial is to create a list of lists. Being able to append a list inside of another list.

The goal of this tutorial is to drop empty levels of a factor that are inherited from a previous dataset.

The goal of this tutorial is to list all the functions defined inside a package. In order to do this, the package should be first installed and loaded. This could be useful if we want to search for methods inside a package.

The goal of this tutorial is to learn how to name variables inside loops or using arguments. This could be useful if we don't know how many variables we want to create.

The goal of this tutorial is to reduce the dimension of a dataframe into a vector. This could be very useful if we want to create a histogram of all the dataset.

The goal of this tutorial is to learn how to add transparency to lines in ggplot in order to best see lines that are on top of each other.

The goal of this tutorial is to learn the basics about the print function

The goal of this tutorial is to avoid writing by hand a path if we want to use data from different folders in the same script. In this example we will recreate the case of reading 3 tables from three different folders.

The goal of this tutorial is to learn how to draw manually additional lines in ggplot.

The goal of this tutorial is to get familiar with the apply family of functions using typical examples.

The goal of this tutorial is to learn how to find which columns of our dataset with zero variance and remove them from the dataset in order to perform certain analysis.

The goal of this tutorial is to learn how to use the facet grid function on ggplot. The idea is to create a different plot for different values of a categorical variable.

The goal of this tutorial is to learn how to draw a linear model on top of a scatter plot using stat smooth in ggplot. This can be very useful when we want to see quickly the linear trends in our data.

The goal of this tutorial is to show a sample of complete themes contained in the ggthemes library. For default themes check the default themes tutorial.

The goal of this tutorial is to learn different ways to introduce a third variable in a scatter plot in ggplot using shape, colour and size in geom_point.

The goal of this tutorial is to show a sample of complete themes that can be used to customize plots in order to use them in presentations or reports. We can change the colours of the plot by defining a theme rather than changing everything by hand.

The goal of this tutorial is to learn the different configurations of column plot in ggplot.

The goal of this tutorial is to show the different ways a time series can be forecast from the simplest method to HoltWinters method.

The goal of this tutorial is to learn how to properly define a table in order to draw a different line per year when plotting a variable per month.

The goal of this tutorial is to learn how to remove all elements from a plot with ggplot. This could be useful if we want to produce a plot with certain characteristics.

The goal of this tutorial is to learn how to build properly time series not based on years but on different time periods.

The goal of this tutorial is to read the transaction rules containing one specific product on the right or left side of the rule.

The goal of this tutorial is to learn the use of the scale_colour_manual function of ggplot. We will draw different scandinavian flags for this purpose. We will define a different colour palette for each country.

The goal of this tutorial is to see how different algorithms work on a 2D canvas. For this example we are going to use the Japanese flag because it is symmetrical. We will try to predict a missing part of the flag as well as doing the standard train-test separation to learn how different algorithms see the flag.

The goal of this tutorial is to keep only numerical columns of a dataframe. This is useful when using sum function on aggregate or group_by functions because it returns errors when non-numerical columns are present.

The goal of this tutorial is to learn how to build properly a time series in order to prepare data to do forecasting and all sort of time related predictions.

The goal of this tutorial is to learn how to transpose a dataframe. We will show two different ways to do it.

The goal of this tutorial is to understand the use of the SetSeed function and key concepts like random number generation with R.

The goal of this tutorial is to use the group_by function to create datasets grouped by a defined variable.

The goal of this tutorial is to read dates in the POSIXct format. In addition we will learn how to create labels for different uses.

The goal of this tutorial is to understand the different parameters that can help us when we read a csv file.

The goal of this tutorial is to remove all white spaces from a dataframe. This could be useful if we want to compare strings that could have white spaces in different positions.

The goal of this tutorial is to learn how to introduce numbers in a string, filling with zeroes up to a given number. This is useful when creating filenames.

The goal of this tutorial is to learn how to create strings pasting information from different variables. This process can be useful when we need to create a filename from variables or using the index of a loop.

The goal of this tutorial is to learn how to change the order of the columns of a dataframe. This can be useful when we create a new variable that we want to put first in the dataframe like the date or a name.

The goal of this tutorial is to learn how to create new variables using existing ones to do so. This can be useful to create total results from partial variables, calculate percentages or calculating total benefits from different sources.

The goal of this tutorial is to learn how to change the name of the levels of a factor.

The aim of this tutorial is to order a factor variable by the frequency of its levels in order to visualize it on plots in a smarter way.

The goal of this tutorial is to learn how to read a csv without typing the name into R. We will ask which files ara available in the working directory and open the file we want to use.

The goal of this tutorial is to learn how to change specific names to more general categories read from a different table. This process can be useful when we want to make analysis by category instead of by individual products.

The goal of this tutorial is to order a dataframe by one column in particular. This process is interesting if we want for example to sort products by volume of sales or by profit made.

The goal of this tutorial is to remove non-UTF8 characters from text. This process is very useful and a function can be created to automatize this procedure.

The goal of this tutorial is to remove an item from a transaction file if the item is not interesting for our analysis. This is important because removing the item does affect the probabilities and the numbers of our rules.

The goal of this tutorial is to create a dataframe containing the name of the products from transactions and the number of products sold.

The goal of this tutorial is to access the dataframe containing the parameters of the rules created from transactions. This dataframe can be used later to plot, write csv files or be analysed.

The goal of this tutorial is to save a dataframe into a csv without the column names. By definition a dataframe contains column names as every column is a variable. However for certain analysis this is not true and we may want to modify a csv file using a dataframe without inheriting the name of the columns.

The goal of this tutorial is to change all column types to factor in a single command.

The goal of this tutorial is to properly use the loop for in R. Even when apply family functions are often recommended in R, the use of the for loop can be useful in several moments of the analysis.

The goal of this tutorial is to filter 1 item purchases in a basket analysis using the libraries arules and arulesViz. This procedure can be used to study 1 item transactions or n items transactions once we understand the logic of the procedure.

The goal of this tutorial is to understand the use of the which function. This function is widely used to filter and find specific values in a dataframe, filter ranges and create indices to subset dataframes.

The goal of this tutorial is to learn methods to find, identify and handle missing values. We will use different methods to solve the existence of missing values in our dataset.

The goal of this exercise is to find which variables have a high correlation inside of the correlation matrix. The way to do so is to make pairs of variables with correlation higher than 0.85. We will use the iris database for this example. This method will be useful when the pool of variables is too big to look at the correlation matrix.

The goal of this tutorial is to understand the use of the seq_len function. In addition we will see different ways to declare sequences in R.

The goal of this tutorial is to avoid one common mistake related to the use of factors. When trying to transform a factor containing numbers to numerical value we obtain as a result the position of the levels instead of the content of the variable. We will see how to find this problem and check that everything went fine.

The goal of this tutorial is to be comfortable with the use of columns in a dataset. We will learn how to create new variables and how to remove entire columns with very simple commands.

The goal of this tutorial is to understand how to use the aggregate function. Through clear examples we will show the correct use of this function and some useful configurations. We will use the Iris dataset and aggregate the values obtained using the different species.