Posted: September 20th, 2022

Name at least 3 barriers for our homeless population when it comes to Diabetes prevention/treatment? Give a solution for each barrier.

 Name at least 3 barriers for our homeless population when it comes to Diabetes prevention/treatment? Give a solution for each barrier. 

R For Data Science Cheat Sheet
Tidyverse for Beginners

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Learn R for Data Science Interactively

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.

The core packages are:

• ggplot2, which implements the grammar of graphics. You can use it
to visualize your data.

• dplyr is a grammar of data manipulation. You can use it to solve the
most common data manipulation challenges.

• 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.

• readr is a fast and friendly way to read rectangular data.

• purrr enhances R’s functional programming (FP) toolkit by providing a
complete and consistent set of tools for working with functions and

• tibble is a modern re-imaginging of the data frame.

• stringr provides a cohesive set of functions designed to make
working with strings as easy as posssible

• forcats provide a suite of useful tools that solve common problems
with factors.

You can install the complete tidyverse with:

Then, load the core tidyverse and make it available in your current R
session by running:

Note: there are many other tidyverse packages with more specialised usage. They are not
loaded automatically with library(tidyverse), so you’ll need to load each one with its own call
to library().


> install.packages(“tidyverse”)

> 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.



> library(tidyverse)

Useful Functions




> tidyverse_conflicts() Conflicts between tidyverse and other
> tidyverse_deps() List all tidyverse dependencies
> tidyverse_logo() Get tidyverse logo, using ASCII or unicode
> tidyverse_packages() List all tidyverse packages
> tidyverse_update() Update tidyverse packages

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

filter() allows you to select a subset of rows in a data frame.

> iris %>% Sort in ascending order of
arrange(Sepal.Length) sepal length
> iris %>% Sort in descending order of
arrange(desc(Sepal.Length)) sepal length

arrange() sorts the observations in a dataset in ascending or descending order
based on one of its variables.

> iris %>% Filter for species “virginica”
filter(Species==”virginica”) %>% then arrange in descending
arrange(desc(Sepal.Length)) order of sepal length

Combine multiple dplyr verbs in a row with the pipe operator %>%:

mutate() allows you to update or create new columns of a data frame.

> 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

Combine the verbs filter(), arrange(), and mutate():
> iris %>%
filter(Species==”Virginica”) %>%
mutate(SLMm=Sepal.Length*10) %>%

> 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

summarize() allows you to turn many observations into a single data point.

> iris %>%
filter(Species==”virginica”) %>%

You can also summarize multiple variables at once:

group_by() allows you to summarize within groups instead of summarizing the
entire dataset:

> iris %>% Find median and max
group_by(Species) %>% sepal length of each
summarize(medianSL=median(Sepal.Length), species
> 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

Scatter plot

> iris_small <- iris %>%
filter(Sepal.Length > 5)
> ggplot(iris_small, aes(x=Petal.Length, Compare petal
y=Petal.Width)) + width and length

Scatter plots allow you to compare two variables within your data. To do this with
ggplot2, you use geom_point()

Additional Aesthetics

> ggplot(iris_small, aes(x=Petal.Length,
color=Species)) +

• Color

• Size
> ggplot(iris_small, aes(x=Petal.Length,
size=Sepal.Length)) +

> ggplot(iris_small, aes(x=Petal.Length,
y=Petal.Width)) +

Line Plots

Bar Plots


Box Plots

> by_year <- gapminder %>%
group_by(year) %>%
> ggplot(by_year, aes(x=year,

> by_species <- iris %>%
filter(Sepal.Length>6) %>%
group_by(Species) %>%
> ggplot(by_species, aes(x=Species,
y=medianPL)) +

> ggplot(iris_small, aes(x=Petal.Length))+

> ggplot(iris_small, aes(x=Species,

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