Publications by Quantargo Blog

QBits Workspace: A New Online Editor to Share and Deploy R Code

26.05.2020

QBits Workspace: A New Online Editor to Share and Deploy R Code Today we are excited to announce the QBits Workspace to run and deploy R code in the browser. QBits enable you to run R in a serverless cloud environment and provide an easy and cost-effective way to develop, run, deploy and share data science projects at scale without the need to m...

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Extract or replace columns in a data frame using `$`

02.06.2020

Columns in a data frame can be easily extracted and manipulated with the $ operator. Even new columns can be added by assigning a vector. Extract columns from a data frame with the $. Replace values of existing columns in a data frame. Add new columns to a data frame. ___$___ ___$___ <- ___ Extract columns with the $ ___$___ ___$___ <- ___ Da...

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Determine the size of a data frame

09.06.2020

The size of a data frame, like the number of rows or columns, is often required and can be determined in various ways. Get number of rows of a data frame Get number of columns of a data frame Get dimensions of a data frame nrow(___) ncol(___) dim(___) length(___) Data Frame Dimensions nrow(___) ncol(___) dim(___) length(___) The number of rows ...

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Select first or last rows of a data frame

12.06.2020

We often do not need to look at all the contents of a data frame in the console. Instead, only parts of it are sufficient like the top or bottom retrieved through the head() and tail() functions. Select the top of a data frame Select the bottom of a data frame Specify the number of lines to select through the parameter n head(___, n = ___) tail...

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Introduction to dplyr

16.06.2020

Learn what dplyr does Get an overview of Select, Filter and Sort Learn what Joins, Aggregations and Pipelines are What is dplyr There’s the joke that 80 percent of data science is cleaning the data and 20 percent is complaining about cleaning the data. Anthony Goldbloom, Founder and CEO of Kaggle Having clean data in any Data Science project...

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Select columns from a data frame

19.06.2020

To select only a specific set of interesting data frame columns dplyr offers the select() function to extract columns by names, indices and ranges. You can even rename extracted columns with select(). Learn to use the select() function Select columns from a data frame by name or index Rename columns from a data frame select(my_data_frame, colum...

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Filter data frame rows

26.06.2020

We often want to operate only on a specific subset of rows of a data frame. The dplyr filter() function provides a flexible way to extract the rows of interest based on multiple conditions. Use the filter() function to sort out the rows of a data frame that fulfill a specified condition Filter a data frame by multiple conditions filter(my_data_...

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Sort data frames by columns

02.07.2020

To select areas of interest in a data frame they often need to be ordered by specific columns. The dplyr arrange() function supports data frame orderings by multiple columns in ascending and descending order. Use the arrange() function to sort data frames. Sort data frames by multiple columns using arrange(). arrange(, ) arrange(, , , ...) The ...

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Create a data transformation pipeline

06.07.2020

All data transformation functions in dplyr can be connected through the pipe %>% operator to create powerful and yet expressive data transformation pipelines. Use the pipe operator %>% to combine multiple dplyr functions into one pipeline %>% filter(___) %>% select(___) %>% arrange(___) Using the %>% operator The pipe operator %>% is a s...

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Why data visualization is important

15.07.2020

Data visualization is not only important to communicate results but also a powerful technique for exploratory data analysis. Each plot type like scatter plots, line graphs, bar charts and histograms has its own purpose and can be leveraged in a powerful way using the ggplot2 package. Understand the different roles of data visualization Understan...

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