# install.packages("esquisse")
library(esquisse)

The esquisse package is helpful for getting used to creating plots in R.

It is an interactive tool to help you in RStudio.

It’s super nifty!

We can use the CO heat-related ER visits dataset. This dataset contains information about the number and rate of visits for heat-related illness to ERs in Colorado from 2011-2022, adjusted for age.

er <-
  read_csv("https://daseh.org/data/CO_ER_heat_visits.csv")

head(er)

## # A tibble: 6 × 6
##   county  rate lower95cl upper95cl visits  year
##   <chr>  <dbl>     <dbl>     <dbl>  <dbl> <dbl>
## 1 Adams   6.73     NA         9.24     29  2011
## 2 Adams   4.84      2.85     NA        23  2012
## 3 Adams   6.84      4.36      9.31     31  2013
## 4 Adams   3.08      1.71      4.85     15  2014
## 5 Adams   3.36      1.89      5.23     16  2015
## 6 Adams   8.85      6.12     11.6      42  2016

Using the esquisser() function you can start creating a plot for a data.frame or tibble. That’s it!

esquisser(er)

esquisse::esquisser(er, viewer = "browser")

To select variables you can drag and drop variables to the respective axis that you would like the variable to be plotted on.

To select variables you can drag and drop variables to the respective axis that you would like the variable to be plotted on.

esquisse automatically assumes a plot type, but you might want to change this.

Facets create multiple plots based on the different values of a variable.

Sometimes it is useful to change the way points are plotted so that size represents a variable. This can especially be helpful if you need your plot to be black and white.

For plots with points use the color region to change coloring according to a variable. (use “fill” for bar plots)

You can change the overall appearance with “Geometries” and “Theme”.

To change titles on your plot, use the “Labels & Titles” tab.

You can also easily view data

You’ll need to “interrupt” Esquisse to launch it with a new dataset.

Use the stop button or press ctrl+c to stop the Esquisse app.

If you don’t see the stop button, you need to resize your window.

As a comparison, let’s also load a wide version of this dataset.

wide_er <- read_csv(file =
    "https://daseh.org/data/CO_heat_er_visits_DenverBoulder_wide.csv")

## Rows: 2 Columns: 13
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (1): county
## dbl (12): 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, ...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(long_er)

## # A tibble: 6 × 3
##   county   year visits
##   <chr>   <dbl>  <dbl>
## 1 Boulder  2012     13
## 2 Boulder  2014     19
## 3 Boulder  2016     18
## 4 Boulder  2018     18
## 5 Boulder  2020     12
## 6 Boulder  2022     19

head(wide_er)

## # A tibble: 2 × 13
##   county  `2011` `2012` `2013` `2014` `2015` `2016` `2017` `2018` `2019` `2020`
##   <chr>    <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
## 1 Boulder   4.03   4.08   3.79   6.29   4.76   5.68   3.51   5.07   3.71   3.64
## 2 Denver    7.11   6.79   2.95   3.56   3.84   6.18   3.32   5.81   4.54   4.42
## # ℹ 2 more variables: `2021` <dbl>, `2022` <dbl>

esquisser(wide_er) # county as x...? Tricky!
esquisser(long_er) #county as x, visit rate as y, year as fill

Why use Esquisse?

A. Explore your data

B. Get a “head start” on your code

C. Both of these!

• ggquickeda: https://smouksassi.github.io/ggquickeda/
• ggraptR: https://github.com/cargomoose/ggraptR/
• autoplot can be helpful for some packages (see this blog post)

• Use the esquisser() function on a dataset
• Use the viewer = "browser" argument to launch in your browser.
• Code from Esquisse can copied into code chunks to be generated in the “Plots” pane
• It’s easier if your code is in “long” form!

🏠 Class Website

💻 Lab

📃 Day 6 Cheatsheet

Image by Gerd Altmann from Pixabay