Software Development
Working with Datasets in R
Datasets in R: Joining & Visualizing Data
it_dawdardj_04_enus
Datasets in R: Loading & Saving Data
it_dawdardj_01_enus
Datasets in R: Selecting, Filtering, Ordering, & Grouping Data
it_dawdardj_03_enus
Datasets in R: Transforming Data
it_dawdardj_02_enus
Final Exam: Working with Datasets in R
it_fedawr_03_enus
Datasets in R: Joining & Visualizing Data
Lesson Objectives
Datasets in R: Joining & Visualizing Data
- discover the key concepts covered in this course
- perform joins on data frames using the merge() function
- use the dplyr inner_join() function and perform filtering joins
- create histograms and KDE curves using plot() and ggplot2
- visualize data using scatter plots, box plots, and line charts
- summarize the key concepts covered in this course
Overview/Description
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.
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.
Target
Prerequisites: none
Datasets in R: Loading & Saving Data
Lesson Objectives
Datasets in R: Loading & Saving Data
- discover the key concepts covered in this course
- install and set up the R programming language on macOS
- download and set up the RStudio IDE on macOS
- install and set up the R programming language on Windows
- download and set up the RStudio IDE on Windows
- run commands on the RStudio console
- recall the use of the different panes available in RStudio
- create a new project and a new R script file and execute code
- demonstrate and visualize built-in R datasets
- use vignettes for help on packages
- read in csv files from the file system and built-in packages
- import data from XML, Excel, and JSON files
- export data to various text, CSV, JSON, and Excel files
- summarize the key concepts covered in this course
Overview/Description
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.
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.
Target
Prerequisites: none
Datasets in R: Selecting, Filtering, Ordering, & Grouping Data
Lesson Objectives
Datasets in R: Selecting, Filtering, Ordering, & Grouping Data
- discover the key concepts covered in this course
- edit data frame columns to be of the right data type
- select variables from data frames
- filter data using relational operators
- use the select() function and chaining to filter data in tibbles
- use the %>% operator and the filter() function to filter tibbles
- sample rows using sample() and select top N rows using top_n()
- change columns to be of their logically correct data type
- use the order() and arrange() functions to sort data frames
- create crosstabs and view the aggregate statistics of data frames
- view aggregate statistics of tibbles with summarize() and group_by()
- summarize the key concepts covered in this course
Overview/Description
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.
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.
Target
Prerequisites: none
Datasets in R: Transforming Data
Lesson Objectives
Datasets in R: Transforming Data
- discover the key concepts covered in this course
- connect to an in-memory SQLite database and create tables
- query database tables with dbGetQuery() and dbSendQuery()
- perform create, read, update, and delete operations on tables
- delete and rename columns in an R data frame
- change data types for variables in a data frame
- use the transform() function to transform data in data frames
- use apply() to iterate over and perform operations on data frames
- apply transformations on dataframes using mutate() and if_else()
- use the stack() and unstack() functions to reformat data frames
- use the melt() and dcast() functions to reformat data frames
- reformat a real-world dataset
- use spread() and gather() to reformat data frames
- summarize the key concepts covered in this course
Overview/Description
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.
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.
Target
Prerequisites: none
Final Exam: Working with Datasets in R
Lesson Objectives
Final Exam: Working with Datasets in R
- change data types for variables in a data frame
- connect to an in-memory SQLite database and create tables
- create histograms and KDE curves using plot() and ggplot2
- delete and rename columns in an R data frame
- download and set up the RStudio IDE on macOS
- download and set up the RStudio IDE on Windows
- edit data frame columns to be of the right data type
- filter data using relational operators
- install and set up the R programming language on macOS
- install and set up the R programming language on Windows
- perform create, read, update, and delete operations on tables
- perform joins on data frames using the merge() function
- query database tables with dbGetQuery() and dbSendQuery()
- recall the use of the different panes available in RStudio
- run commands on the RStudio console
- select variables from data frames
- use apply() to iterate over and perform operations on data frames
- use the arrange() function to sort data frames
- use the order() function to sort data frames
- use the transform() function to transform data in data frames
Overview/Description
Final Exam: Working with Datasets in R will test your knowledge and application of the topics presented throughout the Working with Datasets in R track of the Skillsoft Aspire Data Analysis with R Journey.
Target
Prerequisites: none
