Here we are creating a new object from an existing one:
new_rivers <- sample(rivers, 5)
new_rivers## [1] 310 1100 2348 380 500## [1] 360 255 600 377 720Using just this will only print the result and not actually change new_rivers:
new_rivers + 1## [1] 311 1101 2349 381 501## [1] 361 256 601 378 721If we want to modify new_rivers and save that modified version, then we need to reassign new_rivers like so:
new_rivers <- new_rivers + 1
new_rivers## [1] 311 1101 2349 381 501## [1] 361 256 601 378 721If we forget to reassign this can cause subsequent steps to not work as expected because we will not be working with the data that has been modified.
@@ -403,7 +403,7 @@
Error: object ‘X’ not found
Make sure you run something like this, with the <- operator:
rivers2 <- new_rivers + 1
rivers2## [1] 312 1102 2350 382 502## [1] 362 257 602 379 722
diff --git a/modules/Esquisse_Data_Visualization/Esquisse_Data_Visualization.html b/modules/Esquisse_Data_Visualization/Esquisse_Data_Visualization.html
index 6c4799b3e..0d5bdf2cd 100644
--- a/modules/Esquisse_Data_Visualization/Esquisse_Data_Visualization.html
+++ b/modules/Esquisse_Data_Visualization/Esquisse_Data_Visualization.html
@@ -184,9 +184,9 @@
+
-
-
+
@@ -291,7 +291,7 @@
5 Alaska May_vacc_rate 0.626
6 Alaska April_vacc_rate 0.623
- Pivoting using
+ Pivoting using
+
+
+
-
@@ -539,7 +556,7 @@
It’s super nifty! 
First, get some data..
First, get some data..
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.
+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 Emergency rooms in Colorado from 2011-2022, adjusted for age.
er <-
read_csv("https://jhudatascience.org/intro_to_r/data/CO_ER_heat_visits.csv")
@@ -299,7 +299,7 @@
Wide Data
As a comparison, let’s also load a wide version of this dataset.
+As a comparison, let’s also load a wide version of this dataset. {.codesmall}
wide_er <- read_csv(file =
"https://jhudatascience.org/intro_to_r/data/CO_heat_er_visits_DenverBoulder_wide.csv")
@@ -313,7 +313,7 @@
## 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.
-Wide vs Long Data
Wide vs Long Data: Which is better for plotting?
head(long_er)@@ -362,7 +362,12 @@
Summary
-
-
- Use the
esquisser()function on a dataset
+ - Use Esquisse:
+
+
-
+
library(esquisse)
+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! @@ -376,7 +381,7 @@ -
Image by Gerd Altmann from Pixabay
Esquisse Data Visualization Lab - Key
install.packages("esquisse")
install.packages("ggplot2")library(esquisse)
-library(ggplot2)FALSE ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
+FALSE ✔ dplyr 1.1.4 ✔ readr 2.1.5
+FALSE ✔ forcats 1.0.0 ✔ stringr 1.5.1
+FALSE ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
+FALSE ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
+FALSE ✔ purrr 1.0.2
+FALSE ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
+FALSE ✖ dplyr::filter() masks stats::filter()
+FALSE ✖ dplyr::lag() masks stats::lag()
+FALSE ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors1.1
Try creating a plot using the Orange data that automatically comes with R using the esquisse package.
1.1
geom_point(shape = "circle", size = 1.5, colour = "#112446") + theme_minimal() + facet_wrap(vars(Tree)) -
ggplot(Orange) +
aes(x = age, y = circumference, colour = Tree) +
geom_point(shape = "circle", size = 1.5) +
scale_color_hue(direction = 1) +
theme_minimal()
1.2
@@ -207,7 +217,7 @@1.2
## This warning is displayed once every 8 hours. ## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was ## generated. -
Practice on Your Own!
diff --git a/modules/Manipulating_Data_in_R/Manipulating_Data_in_R.html b/modules/Manipulating_Data_in_R/Manipulating_Data_in_R.html index 898298076..de58eb618 100644 --- a/modules/Manipulating_Data_in_R/Manipulating_Data_in_R.html +++ b/modules/Manipulating_Data_in_R/Manipulating_Data_in_R.html @@ -200,7 +200,7 @@ - +Manipulating Data
Pivoting using tidyr package
Pivoting using tidyr package (part of tidyverse)
tidyr allows you to “tidy” your data. We will be talking about:
C. Reshape data
-Reshaping wide to long: Better column names
Reshaping wide to long: Better column names
pivot_longer() - puts column data into rows (tidyr package)
Reshaping wide to long: Better column names
Reshaping wide to long: Better column names
Newly created column names (“Month” and “Rate”) are enclosed in quotation marks. It helps us be more specific than “name” and “value”.
@@ -373,9 +373,10 @@ 5 Alaska May_vacc_rate 0.626 6 Alaska April_vacc_rate 0.623 -Data used: Charm City Circulator
Data used: Charm City Circulator
circ <- read_csv("http://jhudatascience.org/intro_to_r/data/Charm_City_Circulator_Ridership.csv")
+circ <-
+ read_csv("http://jhudatascience.org/intro_to_r/data/Charm_City_Circulator_Ridership.csv")
head(circ, 5)
# A tibble: 5 × 15
@@ -428,7 +429,23 @@
Filter by Boardings only..
-long <- long %>% filter(str_detect(name, "Boardings"))
+long <- long %>% filter(str_detect(name, "Boardings"))
+long
+
+# A tibble: 4,584 × 5
+ day date daily name value
+ <chr> <chr> <dbl> <chr> <dbl>
+ 1 Monday 01/11/2010 952 orangeBoardings 877
+ 2 Monday 01/11/2010 952 purpleBoardings NA
+ 3 Monday 01/11/2010 952 greenBoardings NA
+ 4 Monday 01/11/2010 952 bannerBoardings NA
+ 5 Tuesday 01/12/2010 796 orangeBoardings 777
+ 6 Tuesday 01/12/2010 796 purpleBoardings NA
+ 7 Tuesday 01/12/2010 796 greenBoardings NA
+ 8 Tuesday 01/12/2010 796 bannerBoardings NA
+ 9 Wednesday 01/13/2010 1212. orangeBoardings 1203
+10 Wednesday 01/13/2010 1212. purpleBoardings NA
+# ℹ 4,574 more rows
Mission: Taking the average boardings by line
Summary
-
-
tidyrpackage helps us convert between wide and long data
+tidyrpackage (part oftidyverse) helps us convert between wide and long datapivot_longer()goes from wide -> long-
@@ -897,13 +914,17 @@
💻 Lab
+~
+ -
+ + +Image by Gerd Altmann from Pixabay
diff --git a/modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab.Rmd b/modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab.Rmd index 77b28a003..6c349a110 100644 --- a/modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab.Rmd +++ b/modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab.Rmd @@ -12,9 +12,7 @@ knitr::opts_chunk$set(echo = TRUE) Data in this lab comes from the CDC (https://covid.cdc.gov/covid-data-tracker/#vaccinations_vacc-total-admin-rate-total - snapshot from January 12, 2022) and the Bureau of Economic Analysis (https://www.bea.gov/data/income-saving/personal-income-by-state). ```{r message=FALSE} -library(readr) -library(dplyr) -library(tidyr) +library(tidyverse) ``` # Part 1 diff --git a/modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab_Key.html b/modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab_Key.html index 6d58a081c..01513f1cc 100644 --- a/modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab_Key.html +++ b/modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab_Key.html @@ -166,9 +166,7 @@Manipulating Data in R Lab - Key
Data in this lab comes from the CDC (https://covid.cdc.gov/covid-data-tracker/#vaccinations_vacc-total-admin-rate-total - snapshot from January 12, 2022) and the Bureau of Economic Analysis (https://www.bea.gov/data/income-saving/personal-income-by-state).
-
+library(readr) -library(dplyr) -library(tidyr)library(tidyverse)Part 1