Here we are creating a new object from an existing one:
new_rivers <- sample(rivers, 5)
new_rivers## [1] 1243 1100 268 1270 332## [1] 1054 135 265 246 890Using just this will only print the result and not actually change new_rivers:
new_rivers + 1## [1] 1244 1101 269 1271 333## [1] 1055 136 266 247 891If 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] 1244 1101 269 1271 333## [1] 1055 136 266 247 891If 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] 1245 1102 270 1272 334## [1] 1056 137 267 248 892Testimonials
Find an Error!?
Feel free to submit typos/errors/etc via the GitHub repository associated with the class: https://github.com/jhudsl/intro_to_r
-This page was last updated on 2025-01-13.
+This page was last updated on 2025-01-14.
Note that levels are, by default, in alphanumerical order.
-Factors
Factors - get the levels
Extract the levels of a factor vector using levels():
## [1] "blue" "red" "yellow"-
forcats package
A package called forcats is really helpful for working with factors.
factor() vs as_factor()
factor() is from base R and as_factor() is from forcats
Both can change a variable to be of class factor.
- --
-
factor()will order alphabetically unless told otherwise.
-as_factor()will order by first appearance unless told otherwise.
-
If you are assigning your levels manually either function is fine!
- -as_factor() function
x <- c("yellow", "red", "red", "blue", "yellow", "blue")
-x_fact_2 <- as_factor(x)
-x_fact_2
-
-## [1] yellow red red blue yellow blue -## Levels: yellow red blue- -
# Compare to factor() method: -x_fact- -
## [1] yellow red red blue yellow blue -## Levels: blue red yellow- -
A Factor Example
A Factor Example
We will use data on student dropouts from the State of California during the 2016-2017 school year. More on this data can be found here: https://www.cde.ca.gov/ds/ad/filesdropouts.asp
@@ -284,7 +250,7 @@Preparing the data
Aggregate (sum) across ethnicity and gender:
+Group by each school and aggregate (sum):
dropouts <- dropouts %>% @@ -362,23 +328,35 @@
For the next steps, let’s take a subset of data.
Use set.seed() to take the same random sample each time.
set.seed(123) # same random sample each time +dropouts_subset <- slice_sample(dropouts, n = 32) +dropouts_subset-
set.seed(123) -dropouts_subset <- slice_sample(dropouts, n = 32)+
## # A tibble: 32 × 3 +## CDS_CODE grade n_dropouts +## <chr> <chr> <dbl> +## 1 45699716050231 Junior 0 +## 2 45700036050330 Junior 0 +## 3 12630401230069 Sophomore 0 +## 4 09100900930131 Sophomore 1 +## 5 15633216009179 Junior 0 +## 6 33670330113647 Sophomore 0 +## 7 19643941931823 Freshman 1 +## 8 19647331933381 Sophomore 7 +## 9 38684786062053 Senior 0 +## 10 11626616007611 Freshman 0 +## # ℹ 22 more rows
Plot the data
Let’s make a plot first.
+Let’s make a plot first. We might not like the ordering on the x-axis, though.
dropouts_subset %>% - ggplot(mapping = aes(x = grade, y = n_dropouts)) + + ggplot(aes(x = grade, y = n_dropouts)) + geom_boxplot() + theme_bw(base_size = 16) # make all labels size 16-
OK this is very useful, but it is a bit difficult to read. We expect the values to be plotted by the order that we know, not by alphabetical order.
+
Change to factor
Now let’s make our plot again:
dropouts_fct %>% - ggplot(mapping = aes(x = grade, y = n_dropouts)) + + ggplot(aes(x = grade, y = n_dropouts)) + geom_boxplot() + theme_bw(base_size = 16)-
Now that’s more like it! Notice how the data is automatically plotted in the order we would like.
+The factor data is automatically plotted in the order we would like.
-What about if we arrange() the data by grade ?
What about if we arrange() the data by grade ?
Character data is arranged alphabetically.
@@ -447,7 +425,7 @@Notice that the order is not what we would hope for!
-Arranging Factors
Arranging Factors
Factor data is arranged by level.
@@ -503,67 +481,67 @@ ## 3 Junior 2 ## 4 Senior 13 -forcats for ordering
GUT CHECK: Why use factors?
A. Meaningful ordering of text data
+ +B. Automatic ordering or numeric data
+ +C. More precise values
+ +forcats package
A package called forcats is really helpful for working with factors.
forcats for ordering
What if we wanted to order grade by increasing n_dropouts?
library(forcats) dropouts_fct %>% - ggplot(mapping = aes(x = grade, y = n_dropouts)) + + ggplot(aes(x = grade, y = n_dropouts)) + geom_boxplot() + theme_bw(base_size = 16)-
This would be useful for identifying easily which grade to focus on.
-forcats for ordering
forcats for ordering
We can order a factor by another variable by using the fct_reorder() function of the forcats package.
fct_reorder({column getting changed}, {guiding column}, {summarizing function})forcats for ordering
forcats for ordering
We can order a factor by another variable by using the fct_reorder() function of the forcats package.
library(forcats) dropouts_fct %>% - ggplot(mapping = aes(x = fct_reorder(grade, n_dropouts, mean), y = n_dropouts)) + + ggplot(aes(x = fct_reorder(grade, n_dropouts, mean), y = n_dropouts)) + geom_boxplot() + labs(x = "Student Grade") + theme_bw(base_size = 16)-
forcats for ordering.. with .desc = argument
forcats for ordering.. with .desc = argument
library(forcats) dropouts_fct %>% - ggplot(mapping = aes(x = fct_reorder(grade, n_dropouts, mean, .desc = TRUE), y = n_dropouts)) + + ggplot(aes(x = fct_reorder(grade, n_dropouts, mean, .desc = TRUE), y = n_dropouts)) + geom_boxplot() + labs(x = "Student Grade") + theme_bw(base_size = 16)-
forcats for ordering.. can be used to sort datasets
dropouts_fct %>% pull(grade) %>% levels() # By year order- -
## [1] "Freshman" "Sophomore" "Junior" "Senior"- -
dropouts_fct <- dropouts_fct %>% - mutate( - grade = fct_reorder(grade, n_dropouts, mean) - ) -dropouts_fct %>% pull(grade) %>% levels() # by increasing mean dropouts- -
## [1] "Junior" "Freshman" "Sophomore" "Senior"+
Checking Proportions with fct_count()
## # A tibble: 4 × 3 ## f n p ## <fct> <int> <dbl> -## 1 Junior 12 0.375 -## 2 Freshman 7 0.219 -## 3 Sophomore 7 0.219 +## 1 Freshman 7 0.219 +## 2 Sophomore 7 0.219 +## 3 Junior 12 0.375 ## 4 Senior 6 0.188
Summary
- the factor class allows us to have a different order from alphanumeric for categorical data -
- we can change data to be a factor variable using
mutateand a factor creating function likefactor()oras_factor
- - the
as_factor()is from theforcatspackage (first appearance order by default)
- - the
factor()base R function (alphabetical order by default)
+ - we can change data to be a factor variable using
mutateand a factor creating function likefactor()(alphabetical order by default) - with
factor()we can specify the levels with thelevelsargument if we want a specific order - the
fct_reorder({variable_to_reorder}, {variable_to_order_by}, {summary function})helps us reorder a variable by the values of another variable - arranging, tabulating, and plotting the data will reflect the new order diff --git a/modules/Factors/lab/Factors_Lab.Rmd b/modules/Factors/lab/Factors_Lab.Rmd index c31aaa1c9..bdfb43d65 100644 --- a/modules/Factors/lab/Factors_Lab.Rmd +++ b/modules/Factors/lab/Factors_Lab.Rmd @@ -8,8 +8,7 @@ editor_options: Load all the libraries we will use in this lab. ```{r message=FALSE} -library(dplyr) -library(ggplot2) +library(tidyverse) ``` ### 1.0 diff --git a/modules/Factors/lab/Factors_Lab_Key.html b/modules/Factors/lab/Factors_Lab_Key.html index e0d60530e..17425fe46 100644 --- a/modules/Factors/lab/Factors_Lab_Key.html +++ b/modules/Factors/lab/Factors_Lab_Key.html @@ -166,8 +166,7 @@
Factors Lab - Key
Load all the libraries we will use in this lab.
-library(dplyr)
-library(ggplot2)library(tidyverse)1.0
Load the Youth Tobacco Survey data from http://jhudatascience.org/intro_to_r/data/Youth_Tobacco_Survey_YTS_Data.csv. select “Sample_Size”, “Education”, and “LocationAbbr”. Name this data “yts”.
1.0
1.1
Create a boxplot showing the difference in “Sample_Size” between Middle School and High School “Education”. Hint: Use aes(x = Education, y = Sample_Size) and geom_boxplot().
yts %>%
- ggplot(mapping = aes(x = Education, y = Sample_Size)) +
+ ggplot(aes(x = Education, y = Sample_Size)) +
geom_boxplot()## Warning: Removed 425 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
1.2
@@ -215,11 +214,11 @@1.3
1.4
Repeat question 1.1 and 1.2 using the “yts_fct” data. You should see different ordering in the plot and count table.
yts_fct %>%
- ggplot(mapping = aes(x = Education, y = Sample_Size)) +
+ ggplot(aes(x = Education, y = Sample_Size)) +
geom_boxplot()## Warning: Removed 425 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
yts_fct %>%
count(Education)## # A tibble: 2 × 2
@@ -263,7 +262,7 @@ P.3
yts_fct_plot <- yts_fct %>%
drop_na() %>%
- ggplot(mapping = aes(
+ ggplot(aes(
x = fct_reorder(
LocationAbbr, med_sample_size
),
diff --git a/modules/Factors/lab/yts_fct.png b/modules/Factors/lab/yts_fct.png
index 56c20c7aa..068447de7 100644
Binary files a/modules/Factors/lab/yts_fct.png and b/modules/Factors/lab/yts_fct.png differ