Training: Datasets in R

Data Visualisatie

10 uur

Engels (US)

Training: Datasets in R

Data Visualisatie

10 uur

Engels (US)

Datasets in R

Productinformatie

Het transformeren en manipuleren van enorme hoeveelheden data is cruciaal voor alle organisaties. De programmeertaal R biedt een overvloed aan pakketten om gegevens te laden, verkennen, manipuleren en transformeren. R is ideaal voor data-analyse, mutatie en opschoning, waardoor het een veel gekozen taal is voor statistici en datawetenschappers. In deze training leer je hoe je R-scriptbestanden schrijft met behulp van de RStudio-omgeving. Je leert om verbinding te maken met relationele databases met behulp van de API's die worden geleverd in het Database Interface-pakket (DBI) in R. Je maakt verbinding met SQLite-gegevens en voert, maakt, leest, updatet en verwijdert (CRUD)-bewerkingen op uw database tabellen. Vervolgens onderzoek je hoe je complexe transformaties kunt oplossen met behulp van pipe-operator- en ketenbewerkingen. Ten slotte oefen je met methoden om gegevens samen te voegen om je voor te bereiden op statistische en voorspellende modellering en om relaties in je gegevens te identificeren met behulp van grafieken en diagrammen.

Inhoud van de training

Datasets in R

10 uur

Datasets in R: Loading & Saving Data

Transforming and manipulating massive amounts of data is crucial
for all organizations. The R programming language offers a plethora
of packages to load, explore, manipulate, and transform data. R is
ideal for data analysis, mutation, and cleaning, making it a choice
language for statisticians and data scientists. In this course,
you'll learn how to write R script files using the RStudio
environment. You'll use different panes to debug and evaluate your
R program, import data in various file formats, and access files
embedded in an R package and stored on your machine. Additionally,
you'll learn how to export data to different file formats. Once
you've completed this course, you'll know how to work R using
RStudio, import and export data in R, and perform simple data
transformation and exploration operations.

Datasets in R: Transforming Data

Organizations store data in various ways. The R programming language offers a straightforward interface to work with data in relational databases and transform it to the format you need for analysis.
In this course, you'll learn how to connect to relational databases using the APIs provided in the Database Interface package (DBI) in R. You'll connect to SQLite data and perform create, read, update, and delete (CRUD) operations on your database tables.
You'll also use R functions to mutate and transform data. You'll practice renaming columns, changing variable types, and creating new columns from derived data.
You'll examine the tidyverse universe of data science packages and work with data in the wide and long formats.
Once you've completed this course, you'll have a strong foundation in basic data manipulation and transformation using the R programming language.

Datasets in R: Selecting, Filtering, Ordering, & Grouping Data

Data analysis often requires performing a series of complex transformations. R makes this hassle-free via the forward pipe operator for chaining operations, data selection and filtering based on conditional operations, and grouping and aggregating options to compute summaries. Learn how to carry out all these operations in this course.
Task you'll carry out include using logical and relational operators to perform conditional filtering, sampling records at random, and computing the top N records based on values in a variable.
You'll also learn to use the forward pipe operator in the magrittr package and tibbles, the next-generation data frame, to store and transform your data.
You'll round this course off by performing ordering, grouping, and aggregations on your data.
When you're finished, you'll have a solid grasp of complex operations on data frames and be able to apply these concepts using the R programming language.

Datasets in R: Joining & Visualizing Data

Data for the same entity is often stored in multiple locations. Your analysis may require bringing this data together in a single location. Doing this forms a core part of data preprocessing. Another core task is recognizing the relationships in your data.
In this course, you'll practice methods to merge data to prepare for statistical and predictive modeling and identify relationships in your data using charts and graphs.
You'll combine data in different data frames (or tibbles) based on the values in common columns. You'll use the merge() function to perform join operations and implement joins using functions from the tidyverse.
You'll also examine the plotting systems available in R and use the plot() functionality and the ggplot2 package to visualize and explore your data.
Upon completion of this course, you'll be able to combine your data in a meaningful way and uncover data relationships.

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Docent inbegrepen

Bereidt voor op officieel examen

Engels (US)

10 uur

Data Visualisatie

90 dagen online toegang

HBO

Meer informatie

Doelgroep	Databasebeheerders, Data-analist
Voorkennis	Geen voorkennis vereist. Basis kennis van R is aangeraden.
Resultaat	Na het afronden van deze training heb je een meer gevorderde kennis van het gebruik van datasets in R.

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Training: Datasets in R

Productinformatie

Inhoud van de training

Datasets in R

Datasets in R: Loading & Saving Data

Datasets in R: Transforming Data

Datasets in R: Selecting, Filtering, Ordering, & Grouping Data

Datasets in R: Joining & Visualizing Data

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Training: Datasets in R

Productinformatie

Inhoud van de training

Datasets in R

Datasets in R: Loading & Saving Data

Datasets in R: Transforming Data

Datasets in R: Selecting, Filtering, Ordering, & Grouping Data

Datasets in R: Joining & Visualizing Data

Kenmerken

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