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LOGTIDYVERSE
The tidyverse is a set of packages that work in harmony because they share common data representations and API design. This package is designed to make it easy to install and load multiple tidyverse packages in a single step. Learn more about the tidyverse at . CREATE ELEGANT DATA VISUALISATIONS USING THE GRAMMAR OF A system for declaratively creating graphics, based on "The Grammar of Graphics". You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to A GRAMMAR OF DATA MANIPULATION • DPLYR dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges: select () picks variables based on their names. filter () picks cases based on their values. summarise () reduces multiple values down to a single summary. arrange () changes the ordering ofthe rows.
SIMPLE DATA FRAMES • TIBBLE Overview. A tibble, or tbl_df, is a modern reimagining of the data.frame, keeping what time has proven to be effective, and throwing out what is not.Tibbles are data.frames that are lazy and surly: they do less (i.e. they don’t change variable names or types, and don’t do partial matching) and complain more (e.g. when a variable does notexist).
COUNT OBSERVATIONS BY GROUP count() lets you quickly count the unique values of one or more variables: df %>% count(a, b) is roughly equivalent to df %>% group_by(a, b) %>% summarise(n = n()). count() is paired with tally(), a lower-level helper that is equivalent to df %>% summarise(n = n()). Supply wt to perform weighted counts, switching the summary from n = n() to n = sum(wt). add_count() and add_tally READ EXCEL FILES • READXL read_excel () reads both xls and xlsx files and detects the format from the extension. List the sheet names with excel_sheets (). Specify a worksheet by name or number. There are various ways to control which cells are read. You can even specify the sheet here, if providing an Excel-style cell range. BAR CHARTS — GEOM_BAR • GGPLOT2 There are two types of bar charts: geom_bar() and geom_col(). geom_bar() makes the height of the bar proportional to the number of cases in each group (or if the weight aesthetic is supplied, the sum of the weights). If you want the heights of the bars to represent values in the data, use geom_col() instead. geom_bar() uses stat_count() by default: it counts the number of cases at each x SUBSET ROWS USING COLUMN VALUES The filter() function is used to subset a data frame, retaining all rows that satisfy your conditions. To be retained, the row must produce a value of TRUE for all conditions. Note that when a condition evaluates to NA the row will be dropped, unlike base subsetting with[.
SET SCALE LIMITS
Set scale limits. Source: R/limits.r. lims.Rd. This is a shortcut for supplying the limits argument to the individual scales. By default, any values outside the limits specified are replaced with NA. Be warned that this will remove data outside the limits and this can produce unintended results. For changing x or y axis limits withoutdropping
TIDYVERSEGGPLOT2DPLYRREADXLTIDYRLEARNPACKAGES The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. Install the complete tidyverse with: install.packages ("tidyverse") EASILY INSTALL AND LOAD THE TIDYVERSE • TIDYVERSEREFERENCEARTICLESNEWSWELCOME TO THE TIDYVERSECHANGELOGTIDYVERSE
The tidyverse is a set of packages that work in harmony because they share common data representations and API design. This package is designed to make it easy to install and load multiple tidyverse packages in a single step. Learn more about the tidyverse at . CREATE ELEGANT DATA VISUALISATIONS USING THE GRAMMAR OF A system for declaratively creating graphics, based on "The Grammar of Graphics". You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to A GRAMMAR OF DATA MANIPULATION • DPLYR dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges: select () picks variables based on their names. filter () picks cases based on their values. summarise () reduces multiple values down to a single summary. arrange () changes the ordering ofthe rows.
SIMPLE DATA FRAMES • TIBBLE Overview. A tibble, or tbl_df, is a modern reimagining of the data.frame, keeping what time has proven to be effective, and throwing out what is not.Tibbles are data.frames that are lazy and surly: they do less (i.e. they don’t change variable names or types, and don’t do partial matching) and complain more (e.g. when a variable does notexist).
COUNT OBSERVATIONS BY GROUP count() lets you quickly count the unique values of one or more variables: df %>% count(a, b) is roughly equivalent to df %>% group_by(a, b) %>% summarise(n = n()). count() is paired with tally(), a lower-level helper that is equivalent to df %>% summarise(n = n()). Supply wt to perform weighted counts, switching the summary from n = n() to n = sum(wt). add_count() and add_tally READ EXCEL FILES • READXL read_excel () reads both xls and xlsx files and detects the format from the extension. List the sheet names with excel_sheets (). Specify a worksheet by name or number. There are various ways to control which cells are read. You can even specify the sheet here, if providing an Excel-style cell range. BAR CHARTS — GEOM_BAR • GGPLOT2 There are two types of bar charts: geom_bar() and geom_col(). geom_bar() makes the height of the bar proportional to the number of cases in each group (or if the weight aesthetic is supplied, the sum of the weights). If you want the heights of the bars to represent values in the data, use geom_col() instead. geom_bar() uses stat_count() by default: it counts the number of cases at each x SUBSET ROWS USING COLUMN VALUES The filter() function is used to subset a data frame, retaining all rows that satisfy your conditions. To be retained, the row must produce a value of TRUE for all conditions. Note that when a condition evaluates to NA the row will be dropped, unlike base subsetting with[.
SET SCALE LIMITS
Set scale limits. Source: R/limits.r. lims.Rd. This is a shortcut for supplying the limits argument to the individual scales. By default, any values outside the limits specified are replaced with NA. Be warned that this will remove data outside the limits and this can produce unintended results. For changing x or y axis limits withoutdropping
TIDYVERSE
The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. Install the complete tidyverse with: install.packages ("tidyverse") SIMPLE DATA FRAMES • TIBBLE Overview. A tibble, or tbl_df, is a modern reimagining of the data.frame, keeping what time has proven to be effective, and throwing out what is not.Tibbles are data.frames that are lazy and surly: they do less (i.e. they don’t change variable names or types, and don’t do partial matching) and complain more (e.g. when a variable does notexist).
EASILY HARVEST (SCRAPE) WEB PAGES • RVEST Overview. rvest helps you scrape (or harvest) data from web pages. It is designed to work with magrittr to make it easy to express common web scraping tasks, inspired by libraries like beautiful soup and RoboBrowser.. If you’re scraping multiple pages, I highly recommend using rvest in concert with polite.The polite package ensures that you’re respecting the robots.txt and not hammering SUBSET COLUMNS USING THEIR NAMES AND TYPES Select (and optionally rename) variables in a data frame, using a concise mini-language that makes it easy to refer to variables based on their name (e.g. a:f selects all columns from a on the left to f on the right). You can also use predicate functions like is.numeric to select variables based on their properties.Overview of selection features Tidyverse selections implement a dialect of R FORCATS - TOOLS FOR WORKING WITH CATEGORICAL VARIABLES Overview. R uses factors to handle categorical variables, variables that have a fixed and known set of possible values. Factors are also helpful for reordering character vectors to improve display. The goal of the forcats package is to provide a suite of tools that solve common problems with factors, including changing the order of levelsor the values.
PIVOT DATA FROM LONG TO WIDE data: A data frame to pivot. id_cols A set of columns that uniquely identifies each observation. Defaults to all columns in data except for the columns specified in names_from and values_from.Typically used when you have redundant variables, i.e. variables whose values are perfectly correlated with existing variables. TEXT — GEOM_LABEL • GGPLOT2 Text geoms are useful for labeling plots. They can be used by themselves as scatterplots or in cobination with other geoms, for example, for labeling points or for annotating the height of bars. geom_text() adds only text to the plot. geom_label() draws a rectangle behind the text, making it easier to read. CREATE, MODIFY, AND DELETE COLUMNS Create, modify, and delete columns. Source: R/mutate.R. mutate.Rd. mutate () adds new variables and preserves existing ones; transmute () adds new variables and drops existing ones. New variables overwrite existing variables of the same name. Variables can be removed by setting their value to NULL. POINTS — GEOM_POINT • GGPLOT2 The point geom is used to create scatterplots. The scatterplot is most useful for displaying the relationship between two continuous variables. It can be used to compare one continuous and one categorical variable, or two categorical variables, but a variation like geom_jitter(), geom_count(), or geom_bin2d() is usually more appropriate. A bubblechart is a scatterplot with a third variable SMOOTHED CONDITIONAL MEANS Smoothed conditional means. Source: R/geom-smooth.r, R/stat-smooth.r. geom_smooth.Rd. Aids the eye in seeing patterns in the presence of overplotting. geom_smooth () and stat_smooth () are effectively aliases: they both use the same arguments. Use stat_smooth () if you want to display the results with a non-standard geom.Tidyverse
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R packages for data science The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. Install the complete tidyverse with: install.packages("tidyverse")Learn the tidyverse
See how the tidyverse makes data science faster, easier and more fun with “R for Data Science”. Read it online , buy the book or try another resource from the community.Need help?
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