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Data frames are one of the most important data structures in R. A data frame is used for storing tabular data - numbers, strings, factors etc., organized into rows and columns. It's similar to a spreadsheet or SQL table, or a dataframe in Python or Pandas.
Data frames are the workhorses of R programming. They can store large amounts of data that can then be manipulated, summarized and visualized. Many R functions expect data frames as inputs and outputs. Learning how to create, access and manipulate data frames is an essential skill for any R user.
A key benefit of data frames is their ability to store different data types in each column. For example, you could have a column of character strings, a column of integers, and a column of logical values - all in one object. This distinguishes data frames from matrices, which can only contain a single atomic data type.
Data frames are created by loading external datasets into R or converting other R objects like vectors, matrices or lists into data frame format. The read.csv(), read.table() and read.xlsx() functions are commonly used to import tabular data as a data frame. Or the data.frame() function can convert existing objects into a dataframe.
Once created, rows and columns of a data frame can be accessed and manipulated in many ways. Columns can be referenced by name, index number or using the $ operator. Rows can be subsetted using conditions or row numbers. Entire rows or columns can also be added, updated or removed as needed.
Data frames form the foundation for many important R workflows like data cleaning, transformation, visualization and modeling. Packages like dplyr and tidyverse provide a suite of functions that make it easy to slice and dice data frames to prepare them for analysis. The ggplot2 package uses data frames as inputs for creating rich data visualizations.
Importing data into R is a critical first step in the data analysis process. While R offers many powerful analytic and visualization tools, these are useless without data to analyze. R supports importing data from a variety of sources and formats, including text files, statistical software, databases, spreadsheets, and web APIs.
A common way to import data is by reading in a CSV (comma separated values) file using the read.csv() function. CSV files contain tabular data formatted as plain text, with column values separated by commas. The read.csv() function parses the CSV file and loads it into a data frame. For example:
This will import the CSV as a data frame that can then be explored and analyzed. read.csv() has many optional arguments to control how data are imported, like specifying column data types or handling missing values.
For tab delimited data, read.table() is used instead of read.csv(). Excel spreadsheets can be imported with read.xlsx() from the openxlsx package. For statistical software like SAS, SPSS, and Stata, the haven package provides easy importing functions like read_sas(), read_spss() and read_stata().
Relational databases like MySQL, Postgres and SQLite can be connected to via R packages like RMySQL, RPostgres and RSQLite. These allow sending SQL queries and returning results as data frames. For example:
Finally, data from web APIs can be imported using packages like rvest and httr which retrieve data from API endpoints. The returned JSON or XML is parsed into a convenient R data structure.
Once a data frame is imported or created in R, an essential next step is exploring and viewing its contents. Unlike a spreadsheet, an R data frame does not have a graphical interface to simply scroll through and visually inspect. However, R provides many functions to summarize, glimpse, and extract information from data frames in order to understand their structure, variables, and observations.
Viewing data frames properly helps identify potential issues upfront - like missing values, improper data types, outliers etc. Catching these early on saves hours of frustration later when analyzing dysfunctional data. Head() and tail() are commonly used to view just the first and last rows of a dataframe respectively. This offers a quick overview without printing thousands of rows to the console. str() provides an overview of the entire dataframe including variable names, types, first observations and memory usage.
summary() can be extremely useful for numerical columns - it prints count, mean, percentiles and extremes. For character columns, it shows first entries and the number of unique levels. Applying summary() to the entire dataframe provides high level insights on all variables.
Viewing individual columns or rows is also important. Double square brackets can subset by row and column locations. For example, df[1:5,3:5] views rows 1-5 and columns 3-5 of dataframe df. Column names can also be used instead of column numbers. Specific rows and columns can be extracted with the single square bracket, like df$age for the age column.
The glimpse() function from dplyr provides another effective way to view key details on a few rows and variables in a compact tabular format. The head() and glimpse() combination delivers a rapid orientation to new data. Viewing individual columns as separate objects is also recommended, using the Vector inspection functions.
For larger data, the skimr package is quite useful. It's skim() function generates descriptive summary statistics on each variable plus histograms and information on missing data. The final output can be printed or converted to a self-contained HTML report for sharing insights. Those getting started with a new data frame would be wise to pipe it through skim() at the very beginning.
Modifying data frames by adding new columns is an essential skill for any data analyst. Real-world data is rarely perfect or complete right from the start. Enriching an existing data frame through column addition allows you to derive new variables, incorporate external data, handle missing values and wrangle your data into the exact structure needed for modeling and visualization.
Columns can be added either by creating them from scratch or importing them from other data frames. To create a new column, simply assign it as a new vector of the appropriate length. For example adding a categorical age_group column based on an existing age column:
Alternatively, columns can be imported from another external data frame using cbind(). This merges columns from two data frames together by matching rows - a powerful technique to link disparate datasets. For example, enrichment data in another data frame can be pulled in:
Now columns from enrichment_data are appended to the existing df data frame. Note that the rows must be identical in both - R will match them based on order.
Modifying data frames by adding new rows allows analysts to incorporate additional observations and expand the dataset. Real-world data collection is an ongoing process, with new information constantly accrued that can provide further insights. Expanding a data frame through row addition enables assimilation of these new cases and growth of the analytic foundation.
Rows can be appended from another external data frame using rbind(). This vertically combines observations from two separate sources into a single unified dataframe. For example, new survey results may trickle in week-over-week that need to be merged with the existing responses:
rbind() stacks the rows of new_survey underneath the rows of original_survey, integrating the new data seamlessly. The column structure must be identical in both data frames for this to work properly.
By assigning vectors of any length to the desired columns, arbitrary new rows can be synthesized as needed. This offers precise control when the source data is not already in dataframe format.
The add_row() function from tibble provides another compact way to insert a single new row into a data frame. The values for each column can be passed to add_row() and it will construct and append the row accordingly.
Reasons to add rows include expanding statistical power, incorporating new real-world data, filling in missing observations from logical groups, mocking up test cases, and balancing imbalanced classes in target variables. The resulting larger, more complete data frame supports enhanced analysis.
However, care must be taken that new rows are accurate representations drawn from the same distribution as existing data. Introducing synthetic or corrupt rows can invalidate analyses by skewing the underlying data quality. Great diligence is needed to ensure added rows mesh cleanly.
Tracking rows that have been added or modified also helps support reproducibility. Using a version control system like Git provides visibility on how scripts change the shape of data frames over time. Maintaining a record of incremental improvements provides confidence in the veracity of the final dataset.
The ability to fluidly add rows enables analysts to explore more hypotheses and enrich understanding of business problems. But growth of a data frame through row addition is not always strictly additive - statistical insights gleaned from prior versions may no longer hold true once new observations are introduced. Therefore, notebooks and analysis should be re-run periodically as major changes accrue.
The ability to merge and join data frames is an indispensable tool for any data analyst. Real-world data analysis often involves synthesizing information from multiple sources into an integrated view. Customer transaction data may need to be linked with advertising exposure and firmographic profiles. Survey responses need to be connected to user attributes and engagement metrics to paint a complete picture. Laboratory results need to be mapped to patient demographics and treatments. In all these cases, combining observations and variables from separate data frames is required to conduct proper analysis.
Three primary techniques exist for combining data frames in R: merges, joins and bindings. The merge() function allows you to merge two data frames by a common column, similar to an SQL inner join. For example, an analyst could merge separate data frames of customer transactions and customer attributes by customer ID. Any rows without a match are discarded. For outer joins to keep all observations, the full_join() function from dplyr can be used instead. Joins tend to be the most common way of linking data frames in practice.
Binding appends either columns or rows from one data frame to another. cbind() adds columns while rbind() adds rows. This provides a flexible way to expand existing data frames with new fields or observations that may not have a specific linking variable. For example, a new batch of survey results could be vertically stacked onto prior responses using rbind() to accumulate a growing dataset. Column binding is commonly used to pull in enrichment data lacking unique IDs.
Understanding these data frame combination techniques allows analysts to build comprehensive analytical datasets. But caution must be exercised to avoid inadvertently merging data with structural differences or duplicative rows. Meticulously inspecting the merged result and handling edge cases is critical.
Summarizing data is a crucial skill for any data analyst or scientist. While raw, granular data provides the complete details, it is often impossible for humans to interpret trends, patterns and insights from scanning thousands or millions of raw observations. Appropriate summarization gives analysts the big picture perspective necessary to understand key aspects and draw informed conclusions.
Data summarization techniques condense large datasets down to compact representative summaries and statistics. These synthesize the essence of the data to highlight actionable findings. Common approaches include calculating aggregates like counts, averages, spreads, quantiles and extremal values. Data can also be summarized by grouping into categories and reporting tabulations and distributions. Graphical summaries like histograms, scatterplots and heatmaps visualize data patterns. Higher level summaries describe relationships between variables and the results of statistical modeling.
Effectively summarizing data enables analysts to rapidly comprehend large datasets during exploratory analysis. This allows focusing on relevant nuances and findings faster. Data reporters are able to share key insights with stakeholders without burying them in endless detailed tables. Executives prefer digestible charts over dense spreadsheets when making strategic decisions.
However, care must be taken during summarization to avoid losing important information or drawing incorrect conclusions. Only summarizing narrow aspects risks overlooking key relationships and outliers. Over-aggregation can mask subtle data quirks and nuances that have a material impact. The analyst must determine the right level of detail and best summary statistics suited for the particular analysis objectives.
Summarization methods also need to adapt based on data types and structures. Transactional data may require different summarization like aggregating by time period or product category compared to survey responses or sensor readings. Temporal data needs to handle issues like time zones, intervals and irregular timestamps. Relational data from multiple linked tables requires thinking through useful groupings and prudent joins.
Data frames are the workhorse of data analysis in R. They enable storing, manipulating and analyzing diverse datasets with ease. While the theory is important, seeing data frames applied to solve real-world problems provides the practical motivation and skill-building needed to drive proficiency. Examining use cases across industries and applications highlights the versatility of data frames for data-driven insights.
In finance, data frames empower analysts to rapidly manipulate massive stock trade datasets. By importing CSV logs into data frames, summary statistics like daily volume and value can be calculated with functions like tapply(). Data frames allow joining separate tick data, fundamentals, and sentiment scores into unified datasets for quantitative modeling. Plotting data frames with ggplot2 visualizes trends and patterns in trading activity. Data frames are integral to building automated algorithmic trading systems where millisecond computations are required.
For data scientists, data frames facilitate the end-to-end machine learning workflow. DataFrames in Python Pandas are commonly used for data preparation - joining data, handling missing values, converting data types, adding new derived variables etc. Once ready, data frames are passed into scikit-learn models to train on features and targets. Later the trained model generates predictions on new data frames. Cross-validation and tuning procedures work by splitting data frames into separate training and test sets. Data frames are the standard tabular structure underlying the entire process.
In marketing, customer attribute and engagement data needs to be aggregated from multiple sources. SQL joins produce data frames containing customer attributes, purchase transactions, website activity, and campaign exposure history. These enable segmenting customers intoclusters to understand differences in behavior. Funnel analysis investigates how acquisition channel impacts customer lifecycle stages over time using time series data frames. Conversion rates, customer lifetime value, and predictors can be calculated from appropriately structured data frames.
For scientists, data frames help organize messy experimental data. Measurements and readings from lab instruments get compiled into data frames where outliers can be filtered and trends visually identified. Joining molecular structures and properties aids in developing predictive Quantitative Structure Activity Relationship (QSAR) models. Simulations and climate models generate multivariate time series datasets stored as data frames for statistical modeling. Genetic analysis relies on data frames linking gene variants, phenotypes and pedigrees.