Esquisse Data Visualization Lab - Key
First, make sure you install and load the esquisse
package using install.packages and
library:
install.packages("esquisse")
install.packages("ggplot2")library(esquisse)
library(ggplot2)
library(dplyr)
library(dasehr)1.1
Try creating a plot in esquisse using the
calenviroscreen data from the dasehr packaged.
This dataset has a lot of variables, so first run the below code to
subset it so that you’re only working with these variables:
CES4.0Percentile, Asthma, and
ChildrenPercLess10. We will also categorize
CES4.0Percentile into three categories (high, middle, and
low) to make visualization a little easier!
ces_sub <- select(calenviroscreen, c("CES4.0Percentile", "Asthma", "ChildrenPercLess10"))
ces_sub <- ces_sub %>%
mutate(CES4.0Perc_cat =
case_when(CES4.0Percentile > 75 ~ "High",
CES4.0Percentile <= 75 & CES4.0Percentile >25 ~ "Middle",
CES4.0Percentile <= 25 ~ "Low"))- Drag and drop the
ChildrenPercLess10variable to be plotted on the x-axis. - Drag and drop the
Asthmavariable to be plotted on the y-axis. - What happens when you drag
CES4.0Perc_catto the facet region of the esquisse GUI? - What happens if you move this to color?
- Copy and paste the code from both options into the chunk below:
# esquisser(ces_sub)
ggplot(ces_sub) +
aes(x = ChildrenPercLess10, y = Asthma) +
geom_point(shape = "circle", size = 1.5, colour = "#112446") +
theme_minimal() +
facet_wrap(vars(CES4.0Perc_cat))## Warning: Removed 23 rows containing missing values or values outside the scale range
## (`geom_point()`).
ggplot(ces_sub) +
aes(x = ChildrenPercLess10, y = Asthma, colour = CES4.0Perc_cat) +
geom_point(shape = "circle", size = 1.5) +
scale_color_hue(direction = 1) +
theme_minimal()## Warning: Removed 23 rows containing missing values or values outside the scale range
## (`geom_point()`).
1.2
Click where it says “point” (may say “auto” depending on how you did the last question) on the far left side and change the plot to a different type of plot. Copy and paste the code into the chunk below. Close Esquisse and run the chunk below to generate a ggplot.
ggplot(ces_sub) +
aes(x = ChildrenPercLess10, y = Asthma, colour = CES4.0Perc_cat) +
geom_line(size = 0.5) +
scale_color_hue(direction = 1) +
theme_minimal()## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: Removed 23 rows containing missing values or values outside the scale range
## (`geom_line()`).
Practice on Your Own!
P.1
Launch Esquisse on any selection of the following datasets we have worked with before and explore!
covid_wastewater## # A tibble: 776,059 × 12
## reporting_jurisdiction sample_location key_plot_id county_names
## <chr> <chr> <chr> <chr>
## 1 Missouri Treatment plant NWSS_mo_259_Treatment pl… Barry,Lawre…
## 2 Missouri Treatment plant NWSS_mo_259_Treatment pl… Barry,Lawre…
## 3 Missouri Treatment plant NWSS_mo_259_Treatment pl… Barry,Lawre…
## 4 Missouri Treatment plant NWSS_mo_259_Treatment pl… Barry,Lawre…
## 5 Missouri Treatment plant NWSS_mo_259_Treatment pl… Barry,Lawre…
## 6 Missouri Treatment plant NWSS_mo_259_Treatment pl… Barry,Lawre…
## 7 Missouri Treatment plant NWSS_mo_259_Treatment pl… Barry,Lawre…
## 8 Missouri Treatment plant NWSS_mo_259_Treatment pl… Barry,Lawre…
## 9 Missouri Treatment plant NWSS_mo_259_Treatment pl… Barry,Lawre…
## 10 Missouri Treatment plant NWSS_mo_259_Treatment pl… Barry,Lawre…
## # ℹ 776,049 more rows
## # ℹ 8 more variables: population_served <dbl>, date_start <chr>,
## # date_end <chr>, rna_pct_change_15d <dbl>, pos_PCR_prop_15d <dbl>,
## # percentile <dbl>, sampling_prior <chr>, first_sample_date <chr>CO_heat_ER## # A tibble: 2,340 × 7
## county rate lower95cl upper95cl visits year gender
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Statewide 5.64 4.70 6.59 140 2011 Female
## 2 Statewide 7.39 6.30 8.47 183 2011 Male
## 3 Statewide 6.51 5.80 7.23 323 2011 Both genders
## 4 Statewide 5.64 4.72 6.57 146 2012 Female
## 5 Statewide 7.56 6.48 8.65 193 2012 Male
## 6 Statewide 6.58 5.88 7.29 339 2012 Both genders
## 7 Statewide 4.94 4.06 5.82 124 2013 Female
## 8 Statewide 6.72 5.72 7.72 178 2013 Male
## 9 Statewide 5.82 5.16 6.49 302 2013 Both genders
## 10 Statewide 3.52 2.80 4.25 92 2014 Female
## # ℹ 2,330 more rowsCO_heat_ER_byage## # A tibble: 216 × 7
## YEAR GENDER AGE RATE L95CL U95CL VISITS
## <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2011 Female 0-4 years old NA NA NA NA
## 2 2011 Female 15-34 years old 5.59 3.84 7.34 39
## 3 2011 Female 35-64 years old 5.51 4.08 6.94 57
## 4 2011 Female 5-14 years old 4.43 2.48 7.30 15
## 5 2011 Female 65+ years old 6.60 3.78 9.42 21
## 6 2011 Female All ages 5.48 4.57 6.39 140
## 7 2011 Male 0-4 years old NA NA NA NA
## 8 2011 Male 15-34 years old 8.99 6.84 11.1 67
## 9 2011 Male 35-64 years old 6.17 4.66 7.68 64
## 10 2011 Male 5-14 years old 5.94 3.40 8.48 21
## # ℹ 206 more rowsCO_heat_ER_bygender## # A tibble: 240 × 7
## county rate lower95cl upper95cl visits year gender
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Adams 7.60 4.38 11.7 17 2011 Female
## 2 Adams NA NA NA NA 2012 Female
## 3 Adams 6.22 3.37 9.93 14 2013 Female
## 4 Adams NA NA NA NA 2014 Female
## 5 Adams NA NA NA NA 2015 Female
## 6 Adams 6.16 3.35 9.82 14 2016 Female
## 7 Adams 4.69 2.31 7.88 11 2017 Female
## 8 Adams 6.39 3.62 9.93 16 2018 Female
## 9 Adams 6.64 3.85 10.2 17 2019 Female
## 10 Adams NA NA NA NA 2020 Female
## # ℹ 230 more rowsyearly_co2_emissions## # A tibble: 192 × 265
## country `1751` `1752` `1753` `1754` `1755` `1756` `1757` `1758` `1759` `1760`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Afghan… NA NA NA NA NA NA NA NA NA NA
## 2 Albania NA NA NA NA NA NA NA NA NA NA
## 3 Algeria NA NA NA NA NA NA NA NA NA NA
## 4 Andorra NA NA NA NA NA NA NA NA NA NA
## 5 Angola NA NA NA NA NA NA NA NA NA NA
## 6 Antigu… NA NA NA NA NA NA NA NA NA NA
## 7 Argent… NA NA NA NA NA NA NA NA NA NA
## 8 Armenia NA NA NA NA NA NA NA NA NA NA
## 9 Austra… NA NA NA NA NA NA NA NA NA NA
## 10 Austria NA NA NA NA NA NA NA NA NA NA
## # ℹ 182 more rows
## # ℹ 254 more variables: `1761` <dbl>, `1762` <dbl>, `1763` <dbl>, `1764` <dbl>,
## # `1765` <dbl>, `1766` <dbl>, `1767` <dbl>, `1768` <dbl>, `1769` <dbl>,
## # `1770` <dbl>, `1771` <dbl>, `1772` <dbl>, `1773` <dbl>, `1774` <dbl>,
## # `1775` <dbl>, `1776` <dbl>, `1777` <dbl>, `1778` <dbl>, `1779` <dbl>,
## # `1780` <dbl>, `1781` <dbl>, `1782` <dbl>, `1783` <dbl>, `1784` <dbl>,
## # `1785` <dbl>, `1786` <dbl>, `1787` <dbl>, `1788` <dbl>, `1789` <dbl>, …nitrate## # A tibble: 88 × 11
## year quarter pop_on_sampled_PWS `pop_0-3ug/L` `pop_>3-5ug/L` `pop_>5-10ug/L`
## <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1999 Q1 106720 67775 0 32
## 2 1999 Q2 85541 55476 0 212
## 3 1999 Q3 559137 319252 231186 212
## 4 1999 Q4 26995 25969 420 0
## 5 2000 Q1 34793 5904 0 92
## 6 2000 Q2 184521 157396 0 32
## 7 2000 Q3 42081 20407 345 0
## 8 2000 Q4 407219 358828 995 412
## 9 2001 Q1 90054 49552 150 0
## 10 2001 Q2 83521 43633 2536 90
## # ℹ 78 more rows
## # ℹ 5 more variables: `pop_>10-20ug/L` <dbl>, `pop_>20ug/L` <dbl>,
## # `pop_on_PWS_with_non-detect` <dbl>, pop_exposed_to_exceedances <dbl>,
## # perc_pop_exposed_to_exceedances <dbl>haa5## # A tibble: 33 × 11
## year pop_on_sampled_PWS `pop_0-15µg/L` `pop_>15-30µg/L` `pop_>30-45µg/L`
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 1999 367633 59550 149906 105097
## 2 2000 1197054 299306 122665 470362
## 3 2001 1228179 341227 551559 100539
## 4 2002 2344171 461360 1560551 68158
## 5 2003 2536748 973263 1296957 263403
## 6 2004 4335514 1716193 1714404 139075
## 7 2005 4100329 1670136 1711888 91667
## 8 2006 4401035 1576037 2192933 207336
## 9 2007 4726314 1771815 2063751 176017
## 10 2008 4678903 1958654 2006626 121242
## # ℹ 23 more rows
## # ℹ 6 more variables: `pop_>45-60µg/L` <dbl>, `pop_>60-75µg/L` <dbl>,
## # `pop_>75µg/L` <dbl>, `pop_on_PWS_with_non-detects` <dbl>,
## # pop_exposed_to_exceedances <dbl>, perc_pop_exposed_to_exceedances <dbl># esquisser(nitrate)