R For Data Science Cheat Sheet Tidyverse for Beginners<\/p>\n
Learn More R for Data Science Interactively at www.datacamp.com<\/p>\n
Tidyverse<\/p>\n
DataCamp Learn R for Data Science Interactively<\/p>\n
The tidyverse is a powerful collection of R packages that are actually data tools for transforming and visualizing data. All packages of the tidyverse share an underlying philosophy and common APIs.<\/p>\n
The core packages are:<\/p>\n
\u2022 ggplot2, which implements the grammar of graphics. You can use it to visualize your data.<\/p>\n
\u2022 dplyr is a grammar of data manipulation. You can use it to solve the most common data manipulation challenges.<\/p>\n
\u2022 tidyr helps you to create tidy data or data where each variable is in a column, each observation is a row end each value is a cell.<\/p>\n
\u2022 readr is a fast and friendly way to read rectangular data.<\/p>\n
\u2022 purrr enhances R\u2019s functional programming (FP) toolkit by providing a complete and consistent set of tools for working with functions and vectors.<\/p>\n
\u2022 tibble is a modern re-imaginging of the data frame.<\/p>\n
\u2022 stringr provides a cohesive set of functions designed to make working with strings as easy as posssible<\/p>\n
\u2022 forcats provide a suite of useful tools that solve common problems with factors.<\/p>\n
You can install the complete tidyverse with:<\/p>\n
Then, load the core tidyverse and make it available in your current R session by running:<\/p>\n
Note: there are many other tidyverse packages with more specialised usage. They are not loaded automatically with library(tidyverse), so you\u2019ll need to load each one with its own call to library().<\/p>\n
ggplot2<\/p>\n
> install.packages(“tidyverse”)<\/p>\n
> iris %>% Select iris data of species filter(Species==”virginica”) “virginica” > iris %>% Select iris data of species filter(Species==”virginica”, “virginica” and sepal length Sepal.Length > 6) greater than 6.<\/p>\n
dplyr<\/p>\n
Filter<\/p>\n
> library(tidyverse)<\/p>\n
Useful Functions<\/p>\n
Arrange<\/p>\n
Mutate<\/p>\n
Summarize<\/p>\n
> tidyverse_conflicts() Conflicts between tidyverse and other packages > tidyverse_deps() List all tidyverse dependencies > tidyverse_logo() Get tidyverse logo, using ASCII or unicode characters > tidyverse_packages() List all tidyverse packages > tidyverse_update() Update tidyverse packages<\/p>\n
Loading in the data > library(datasets) Load the datasets package > library(gapminder) Load the gapminder package > attach(iris) Attach iris data to the R search path<\/p>\n
filter() allows you to select a subset of rows in a data frame.<\/p>\n
> iris %>% Sort in ascending order of arrange(Sepal.Length) sepal length > iris %>% Sort in descending order of arrange(desc(Sepal.Length)) sepal length<\/p>\n
arrange() sorts the observations in a dataset in ascending or descending order based on one of its variables.<\/p>\n
> iris %>% Filter for species “virginica” filter(Species==”virginica”) %>% then arrange in descending arrange(desc(Sepal.Length)) order of sepal length<\/p>\n
Combine multiple dplyr verbs in a row with the pipe operator %>%:<\/p>\n
mutate() allows you to update or create new columns of a data frame.<\/p>\n
> iris %>% Change Sepal.Length to be mutate(Sepal.Length=Sepal.Length*10) in millimeters > iris %>% Create a new column mutate(SLMm=Sepal.Length*10) called SLMm<\/p>\n
Combine the verbs filter(), arrange(), and mutate(): > iris %>% filter(Species==”Virginica”) %>% mutate(SLMm=Sepal.Length*10) %>% arrange(desc(SLMm))<\/p>\n
> iris %>% Summarize to find the summarize(medianSL=median(Sepal.Length)) median sepal length > iris %>% Filter for virginica then filter(Species==”virginica”) %>% summarize the median summarize(medianSL=median(Sepal.Length)) sepal length<\/p>\n
summarize() allows you to turn many observations into a single data point.<\/p>\n
> iris %>% filter(Species==”virginica”) %>% summarize(medianSL=median(Sepal.Length), maxSL=max(Sepal.Length))<\/p>\n
You can also summarize multiple variables at once:<\/p>\n
group_by() allows you to summarize within groups instead of summarizing the entire dataset:<\/p>\n
> iris %>% Find median and max group_by(Species) %>% sepal length of each summarize(medianSL=median(Sepal.Length), species maxSL=max(Sepal.Length)) > iris %>% Find median and max filter(Sepal.Length>6) %>% petal length of each group_by(Species) %>% species with sepal summarize(medianPL=median(Petal.Length), length > 6 maxPL=max(Petal.Length))<\/p>\n
Scatter plot<\/p>\n
> iris_small <- iris %>% filter(Sepal.Length > 5) > ggplot(iris_small, aes(x=Petal.Length, Compare petal y=Petal.Width)) + width and length geom_point()<\/p>\n
Scatter plots allow you to compare two variables within your data. To do this with ggplot2, you use geom_point()<\/p>\n
Additional Aesthetics<\/p>\n
> ggplot(iris_small, aes(x=Petal.Length, y=Petal.Width, color=Species)) + geom_point()<\/p>\n
\u2022 Color<\/p>\n
\u2022 Size > ggplot(iris_small, aes(x=Petal.Length, y=Petal.Width, color=Species, size=Sepal.Length)) + geom_point()<\/p>\n
Faceting > ggplot(iris_small, aes(x=Petal.Length, y=Petal.Width)) + geom_point()+ facet_wrap(~Species)<\/p>\n
Line Plots<\/p>\n
Bar Plots<\/p>\n
Histograms<\/p>\n
Box Plots<\/p>\n
> by_year <- gapminder %>% group_by(year) %>% summarize(medianGdpPerCap=median(gdpPercap)) > ggplot(by_year, aes(x=year, y=medianGdpPerCap))+ geom_line()+ expand_limits(y=0)<\/p>\n
> by_species <- iris %>% filter(Sepal.Length>6) %>% group_by(Species) %>% summarize(medianPL=median(Petal.Length)) > ggplot(by_species, aes(x=Species, y=medianPL)) + geom_col()<\/p>\n
> ggplot(iris_small, aes(x=Petal.Length))+ geom_histogram()<\/p>\n
> ggplot(iris_small, aes(x=Species, y=Sepal.Width))+ geom_boxplot()<\/p>\n
———-<\/p>\n
Tidyverse for Beginners R for Data Science Cheat Sheet<\/p>\n
At www.datacamp.com, you may learn more about R for Data Science in an interactive way.<\/p>\n
Tidyverse<\/p>\n
DataCamp is an interactive way to learn R for data science.<\/p>\n
The tidyverse is a powerful collection of R packages that are actually data tools for transforming and visualizing data. All packages of the tidyverse share an underlying philosophy and common APIs.<\/p>\n
The core packages are:<\/p>\n
\u2022 ggplot2, which implements the grammar of graphics. You can use it to visualize your data.<\/p>\n
\u2022 dplyr is a grammar of data manipulation. You can use it to solve the most common data manipulation challenges.<\/p>\n
\u2022 tidyr helps you to create tidy data or data where each variable is in a column, each observation is a row end each value is a cell.<\/p>\n
\u2022 readr is a<\/p>\n","protected":false},"excerpt":{"rendered":"
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