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-10166c3e
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+466e1c70
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Lectures
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diff --git a/lectures.xml b/lectures.xml
index 3341134..b1e19cf 100644
--- a/lectures.xml
+++ b/lectures.xml
@@ -10,7 +10,7 @@
Course website for Statistical Computing (BSPH 140.776) in Fall 2023quarto-1.3.450
-Fri, 18 Aug 2023 01:10:34 GMT
+Fri, 18 Aug 2023 01:51:49 GMT01 - Welcome!Leonardo Collado Torres
@@ -20,7 +20,7 @@
-
This lecture as the rest of the course is adapted from the version Stephanie C. Hicks designed and maintained in 2021 - 2022. Check the recent changes to this file through the GitHub history.
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
This course is designed for ScM and PhD students at Johns Hopkins Bloomberg School of Public Health. I am pretty flexible about permitting outside students, but I want everyone to be aware of the goals and assumptions so no one feels like they are surprised by how the class works.
module 1week 1https://lcolladotor.github.io/jhustatcomputing2023/posts/01-welcome/index.html
- Fri, 18 Aug 2023 01:10:34 GMT
+ Fri, 18 Aug 2023 01:51:49 GMT02 - Introduction to R and RStudio!
@@ -439,6 +439,7 @@ font-style: inherit;">session_info()
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
There are only two kinds of languages: the ones people complain about and the ones nobody uses. —Bjarne Stroustrup
-
]]>
@@ -847,7 +901,7 @@ Tip
programmingRStudiohttps://lcolladotor.github.io/jhustatcomputing2023/posts/02-introduction-to-r-and-rstudio/index.html
- Fri, 18 Aug 2023 01:10:34 GMT
+ Fri, 18 Aug 2023 01:51:49 GMT
@@ -859,6 +913,7 @@ Tip
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
-
]]>
@@ -1056,7 +1164,7 @@ Tip
gitGitHubhttps://lcolladotor.github.io/jhustatcomputing2023/posts/03-introduction-to-gitgithub/index.html
- Fri, 18 Aug 2023 01:10:34 GMT
+ Fri, 18 Aug 2023 01:51:49 GMT
@@ -1068,6 +1176,7 @@ Tip
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
An article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code and data, that produced the result. —Claerbout and Karrenbach (1992)
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
-
@@ -1995,7 +2240,7 @@ Knuth, Donald E. 1984. “Literate Programming.”Comput. J.R Markdown
programminghttps://lcolladotor.github.io/jhustatcomputing2023/posts/05-literate-programming/index.html
- Fri, 18 Aug 2023 01:10:34 GMT
+ Fri, 18 Aug 2023 01:51:49 GMT
@@ -2007,6 +2252,7 @@ Knuth, Donald E. 1984. “Literate Programming.”Comput. J.This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
@@ -2422,7 +2721,7 @@ Xie, Yihui, Christophe Dervieux, and Emily Riederer. 2020. R Markdown Cookbo
R Markdownprogramminghttps://lcolladotor.github.io/jhustatcomputing2023/posts/06-reference-management/index.html
- Fri, 18 Aug 2023 01:10:34 GMT
+ Fri, 18 Aug 2023 01:51:49 GMT
@@ -2434,6 +2733,7 @@ Xie, Yihui, Christophe Dervieux, and Emily Riederer. 2020. R Markdown Cookbo
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
“When writing code, you’re always collaborating with future-you; and past-you doesn’t respond to emails”. —Hadley Wickham
The problem is, if I want to use his code, I will need to go and hand-edit every single one of those paths (C:\Users\Brian\path\only\that\Brian\has) to the path that I want to use on my computer or wherever I saved the folder on my computer (e.g. /Users/Stephanie/Documents/path/only/I/have).
+
The problem is, if I want to use his code, I will need to go and hand-edit every single one of those paths (C:\Users\Brian\path\only\that\Brian\has) to the path that I want to use on my computer or wherever I saved the folder on my computer (e.g. /Users/leocollado/Documents/path/only/I/have).
This is an unsustainable practice.
I can go in and manually edit the path, but this assumes I know how to set a working directory. Not everyone does.
Note that the col_types argument accepts a compact representation. Here "cc" indicates that the first column is character and the second column is character (there are only two columns). Using the col_types argument is useful because often it is not easy to automatically figure out the type of a column by looking at a few rows (especially if a column has many missing values).
-
]]>
@@ -3708,7 +4112,7 @@ Tip
heretidyversehttps://lcolladotor.github.io/jhustatcomputing2023/posts/07-reading-and-writing-data/index.html
- Fri, 18 Aug 2023 01:10:34 GMT
+ Fri, 18 Aug 2023 01:51:49 GMT08 - Managing data frames with the Tidyverse
@@ -3719,6 +4123,7 @@ Tip
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
-
]]>
@@ -5426,7 +5931,7 @@ Tip
tibbletidyversehttps://lcolladotor.github.io/jhustatcomputing2023/posts/08-managing-data-frames-with-tidyverse/index.html
- Fri, 18 Aug 2023 01:10:34 GMT
+ Fri, 18 Aug 2023 01:51:49 GMT09 - Tidy data and the Tidyverse
@@ -5437,6 +5942,7 @@ Tip
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
“Happy families are all alike; every unhappy family is unhappy in its own way.” —- Leo Tolstoy
-
]]>
@@ -6293,7 +6891,7 @@ Tip
heretidyversehttps://lcolladotor.github.io/jhustatcomputing2023/posts/09-tidy-data-and-the-tidyverse/index.html
- Fri, 18 Aug 2023 01:10:34 GMT
+ Fri, 18 Aug 2023 01:51:49 GMT10 - Joining data in R
@@ -6304,6 +6902,7 @@ Tip
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
# A tibble: 9 × 3
id visit outcome
<chr> <int> <dbl>
-1 a 0 1.54
-2 a 1 3.39
-3 a 2 3.03
-4 b 0 0.309
-5 b 1 2.52
-6 b 2 3.03
-7 c 0 2.13
-8 c 1 3.12
-9 c 2 3.99
+1 a 0 3.07
+2 a 1 3.25
+3 a 2 3.93
+4 b 0 2.18
+5 b 1 2.91
+6 b 2 2.83
+7 c 0 1.49
+8 c 1 2.56
+9 c 2 1.46
Note that subjects are labeled by a unique identifer in the id column.
# A tibble: 9 × 4
id visit outcome house
<chr> <int> <dbl> <chr>
-1 a 0 1.54 detached
-2 a 1 3.39 detached
-3 a 2 3.03 detached
-4 b 0 0.309 rowhouse
-5 b 1 2.52 rowhouse
-6 b 2 3.03 rowhouse
-7 c 0 2.13 rowhouse
-8 c 1 3.12 rowhouse
-9 c 2 3.99 rowhouse
+1 a 0 3.07 detached
+2 a 1 3.25 detached
+3 a 2 3.93 detached
+4 b 0 2.18 rowhouse
+5 b 1 2.91 rowhouse
+6 b 2 2.83 rowhouse
+7 c 0 1.49 rowhouse
+8 c 1 2.56 rowhouse
+9 c 2 1.46 rowhouse
# A tibble: 9 × 4
id visit outcome house
<chr> <dbl> <dbl> <chr>
-1 a 0 1.54 detached
-2 a 1 3.39 <NA>
-3 a 2 3.03 <NA>
-4 b 0 0.309 <NA>
-5 b 1 2.52 rowhouse
-6 b 2 3.03 <NA>
-7 c 0 2.13 rowhouse
-8 c 1 3.12 <NA>
-9 c 2 3.99 <NA>
+1 a 0 3.07 detached
+2 a 1 3.25 <NA>
+3 a 2 3.93 <NA>
+4 b 0 2.18 <NA>
+5 b 1 2.91 rowhouse
+6 b 2 2.83 <NA>
+7 c 0 1.49 rowhouse
+8 c 1 2.56 <NA>
+9 c 2 1.46 <NA>
# A tibble: 9 × 4
id visit outcome house
<chr> <dbl> <dbl> <chr>
-1 a 0 1.54 <NA>
-2 a 1 3.39 <NA>
-3 a 2 3.03 <NA>
-4 b 0 0.309 <NA>
-5 b 1 2.52 rowhouse
-6 b 2 3.03 <NA>
-7 c 0 2.13 rowhouse
-8 c 1 3.12 <NA>
-9 c 2 3.99 <NA>
+1 a 0 3.07 <NA>
+2 a 1 3.25 <NA>
+3 a 2 3.93 <NA>
+4 b 0 2.18 <NA>
+5 b 1 2.91 rowhouse
+6 b 2 2.83 <NA>
+7 c 0 1.49 rowhouse
+8 c 1 2.56 <NA>
+9 c 2 1.46 <NA>
-
]]>
@@ -7053,7 +7739,7 @@ Tip
heretidyversehttps://lcolladotor.github.io/jhustatcomputing2023/posts/10-joining-data-in-r/index.html
- Fri, 18 Aug 2023 01:10:34 GMT
+ Fri, 18 Aug 2023 01:51:49 GMT
11 - Plotting Systems
@@ -7064,6 +7750,7 @@ Tip
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
The data may not contain the answer. And, if you torture the data long enough, it will tell you anything. —John W. Tukey
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
“The greatest value of a picture is when it forces us to notice what we never expected to see.” —John Tukey
-
]]>
@@ -8719,7 +9591,7 @@ Tip
ggplot2data vizhttps://lcolladotor.github.io/jhustatcomputing2023/posts/12-ggplot2-plotting-system-part-1/index.html
- Fri, 18 Aug 2023 01:10:34 GMT
+ Fri, 18 Aug 2023 01:51:49 GMT13 - The ggplot2 plotting system: ggplot()
@@ -8730,6 +9602,7 @@ Tip
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
-
]]>
@@ -9922,7 +10902,7 @@ Tip
ggplot2data vizhttps://lcolladotor.github.io/jhustatcomputing2023/posts/13-ggplot2-plotting-system-part-2/index.html
- Fri, 18 Aug 2023 01:10:34 GMT
+ Fri, 18 Aug 2023 01:51:49 GMT14 - R Nuts and Bolts
@@ -9933,6 +10913,7 @@ Tip
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
-
]]>
@@ -11583,7 +12648,7 @@ Tip
Rprogramminghttps://lcolladotor.github.io/jhustatcomputing2023/posts/14-r-nuts-and-bolts/index.html
- Fri, 18 Aug 2023 01:10:34 GMT
+ Fri, 18 Aug 2023 01:51:49 GMT15 - Control Structures
@@ -11594,6 +12659,7 @@ Tip
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
# A tibble: 344 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
@@ -11928,7 +12994,7 @@ font-style: inherit;">library(palmerpenguins)
for(i for (i in:10) {
-print(i)}
@@ -11979,7 +13045,7 @@ background-color: null;
font-style: italic;">## create for loop
for(i for (i in:4) {
-## Print out each element of 'x'
-print(x[i])
+font-style: inherit;">print(x[i])}
[1] "a"
@@ -12026,7 +13092,7 @@ background-color: null;
font-style: italic;">## create for loop
for(i for (i in:4) {
-## Print out just 'i'
-print(i)}
@@ -12072,12 +13138,12 @@ background-color: null;
font-style: italic;">## Generate a sequence based on length of 'x'
for(i for (i inseq_along(x)) {
-seq_along(x)) {
+print(x[i])}
-
]]>
@@ -12732,7 +13882,7 @@ Tip
Rprogramminghttps://lcolladotor.github.io/jhustatcomputing2023/posts/15-control-structures/index.html
- Fri, 18 Aug 2023 01:10:34 GMT
+ Fri, 18 Aug 2023 01:51:49 GMT16 - Functions
@@ -12743,6 +13893,7 @@ Tip
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
Pre-lecture materials
@@ -12854,7 +14005,7 @@ background-color: null;
font-style: inherit;"><- function() {
-## This is an empty function}
@@ -12863,7 +14014,7 @@ background-color: null;
font-style: italic;">## Functions have their own classclass(f)
+font-style: inherit;">class(f)
[1] "function"
@@ -12872,7 +14023,7 @@ background-color: null;
font-style: italic;">## Execute this function
f()
+font-style: inherit;">f()
NULL
@@ -12887,10 +14038,10 @@ background-color: null;
font-style: inherit;"><- function() {
-# this is the function body
- hello hello <-\n"
-cat(hello)
+font-style: inherit;">cat(hello)}<-function(num) {
-for(i for (i inseq_len(num)) {
- hello hello <-\n"
-cat(hello)
- }
+font-style: inherit;">cat(hello)
+ }}<-function(num) {
- hello hello <-\n"
-for(i for (i inseq_len(num)) {
-cat(hello)
- }
- chars }
+ chars <-nchar(hello) * num
- chars
+ chars}meaningoflife num =1) {
- hello hello <-\n"
-for(i for (i inseq_len(num)) {
-cat(hello)
- }
- chars }
+ chars <-nchar(hello) * num
- chars
+ chars}f() f() ## Use default value for 'num'
@@ -13146,7 +14297,7 @@ font-style: italic;">## Use default value for 'num'f(2) 2) ## Use user-specified value
@@ -13241,7 +14392,7 @@ font-style: inherit;">100, 2, 1) 1) ## Generate some data
@@ -13268,9 +14419,9 @@ background-color: null;
font-style: italic;">## Positional match first argument, default for 'na.rm'
sd(mydata)
+font-style: inherit;">sd(mydata)
-
[1] 1.110707
+
[1] 1.014286
## Specify 'x' argument by name, default for 'na.rm'
background-color: null;
font-style: inherit;">sd(x = mydata)
-
]]>
@@ -13989,7 +15193,7 @@ Tip
programmingfunctionshttps://lcolladotor.github.io/jhustatcomputing2023/posts/16-functions/index.html
- Fri, 18 Aug 2023 01:10:34 GMT
+ Fri, 18 Aug 2023 01:51:49 GMT17 - Vectorization and loop functionals
@@ -14000,6 +15204,7 @@ Tip
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
Pre-lecture materials
@@ -14133,7 +15338,7 @@ font-style: inherit;">1:10
+font-style: inherit;">10
x + y
@@ -14170,9 +15375,9 @@ background-color: null;
font-style: inherit;">:10
-xx *y
+font-style: inherit;">* y
Notice that lapply() returns a list (as usual), but that each element of the list has length 1.
@@ -14635,10 +15842,10 @@ $d
sapply(x, mean)
+font-style: inherit;">sapply(x, mean)
a b c d
- 2.5000000 -0.3561419 1.0788156 5.0209365
+ 2.5000000 -0.1478465 0.8197940 4.9544836
Because the result of lapply() was a list where each element had length 1, sapply() collapsed the output into a numeric vector, which is often more useful than a list.
-
]]>
@@ -15439,7 +16700,7 @@ Tip
programmingfunctionshttps://lcolladotor.github.io/jhustatcomputing2023/posts/17-loop-functions/index.html
- Fri, 18 Aug 2023 01:10:34 GMT
+ Fri, 18 Aug 2023 01:51:49 GMT18 - Debugging R Code
@@ -15450,6 +16711,7 @@ Tip
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
Pre-lecture materials
@@ -15666,30 +16928,30 @@ background-color: null;
font-style: inherit;"><- function(x) {
-if(x if (x >0) {
-print("x is greater than zero")
- } } else {
-print("x is less than or equal to zero")
- }
- }
+invisible(x)
+font-style: inherit;">invisible(x)}
This function is simple:
@@ -15736,42 +16998,43 @@ background-color: null;
font-style: inherit;"><-function(x) {
-if(if (is.na(x))
-is.na(x)) {
+print("x is a missing value!")
- } elseif(x if (x >0)
-0) {
+print("x is greater than zero")
- } else
-else {
+print("x is less than or equal to zero")
- }
+invisible(x)
-}
+}
Error in if (is.na(x)) print("x is a missing value!") else if (x > 0) print("x is greater than zero") else print("x is less than or equal to zero"): the condition has length > 1
+
Error in if (is.na(x)) {: the condition has length > 1
Now what?? Why are we getting this warning?
@@ -15828,54 +17091,56 @@ background-color: null;
font-style: inherit;"><-function(x) {
-if(if (length(x) > 1L)
-> 1L) {
+stop("'x' has length > 1")
- }
+if(if (is.na(x))
-is.na(x)) {
+print("x is a missing value!")
- } elseif(x if (x >0)
-0) {
+print("x is greater than zero")
- } else
-else {
+print("x is less than or equal to zero")
- }
+invisible(x)
-}
+}
Now when we pass print_message3() a vector, we should get an error.
-
]]>
@@ -16502,7 +17825,7 @@ Tip
programmingdebugginghttps://lcolladotor.github.io/jhustatcomputing2023/posts/18-debugging-r-code/index.html
- Fri, 18 Aug 2023 01:10:34 GMT
+ Fri, 18 Aug 2023 01:51:49 GMT19 - Error Handling and Generation
@@ -16513,6 +17836,7 @@ Tip
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
This function takes an expression as an argument and tries to evaluate it. If the expression can be evaluated without any errors or warnings then the result of the expression is returned and the message Finally done! is printed to the R console. If an error or warning is generated, then the functions that are provided to the error or warning arguments are printed. Let’s try this function out with a few examples.
beera({
-22
beera({
-"two"
beera({
-as.numeric(<-function(n){
- n function(n) {
+ n %%"two")
-
]]>
@@ -17197,7 +18576,7 @@ Tip
programmingdebugginghttps://lcolladotor.github.io/jhustatcomputing2023/posts/19-error-handling-and-generation/index.html
- Fri, 18 Aug 2023 01:10:34 GMT
+ Fri, 18 Aug 2023 01:51:49 GMT20 - Working with dates and times
@@ -17208,6 +18587,7 @@ Tip
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
@@ -17975,7 +19357,7 @@ font-style: inherit;">"2012-01-01", tz ="") "") ## Midnighty ## this works
background-color: null;
font-style: inherit;">+ y ## what??? why does this not work?
+font-style: italic;">## what??? why does this not work?
Error in `+.POSIXt`(x, y): binary '+' is not defined for "POSIXt" objects
This lecture as the rest of the course is adapted from the version Stephanie C. Hicks designed and maintained in 2021 - 2022. Check the recent changes to this file through the GitHub history.
+
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
This course is designed for ScM and PhD students at Johns Hopkins Bloomberg School of Public Health. I am pretty flexible about permitting outside students, but I want everyone to be aware of the goals and assumptions so no one feels like they are surprised by how the class works.
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
There are only two kinds of languages: the ones people complain about and the ones nobody uses. —Bjarne Stroustrup
@@ -575,7 +577,7 @@
Installi
We will not do that now, but it is quite likely that at one point later in this course we will.
You only need to install a package once, unless you upgrade/re-install R. Once installed, you still need to load the package before you can use it. That has to happen every time you start a new R session. You do that using the library() command. For instance to load the ggplot2 package, type
-
library('ggplot2')
+
library("ggplot2")
You may or may not see a short message on the screen. Some packages show messages when you load them, and others do not.
This was a quick overview of R packages. We will use a lot of them, so you will get used to them rather quickly.
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
An article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code and data, that produced the result. —Claerbout and Karrenbach (1992)
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
Pre-lecture materials
@@ -523,7 +525,7 @@
Practice
Install the following packages:
-
install.packages(c("bibtex", "RefManageR")
+
install.packages(c("bibtex", "RefManageR"))
What do they do? How might they be helpful to you in terms of reference management?
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
“When writing code, you’re always collaborating with future-you; and past-you doesn’t respond to emails”. —Hadley Wickham
@@ -365,9 +367,9 @@
Relative versus absolute paths
function being used which explicitly tells R to change the absolute path or absolute location of which directory to move into.
For example, say I want to clone a GitHub repo from my colleague Brian, which has 100 R script files, and in every one of those files at the top is:
The problem is, if I want to use his code, I will need to go and hand-edit every single one of those paths (C:\Users\Brian\path\only\that\Brian\has) to the path that I want to use on my computer or wherever I saved the folder on my computer (e.g. /Users/Stephanie/Documents/path/only/I/have).
+
The problem is, if I want to use his code, I will need to go and hand-edit every single one of those paths (C:\Users\Brian\path\only\that\Brian\has) to the path that I want to use on my computer or wherever I saved the folder on my computer (e.g. /Users/leocollado/Documents/path/only/I/have).
This is an unsustainable practice.
I can go in and manually edit the path, but this assumes I know how to set a working directory. Not everyone does.
Here is an example of how to specify the column types explicitly:
-
teams <-read_csv(here("data", "team_standings.csv"),
-col_types ="cc")
+
teams <-read_csv(here("data", "team_standings.csv"),
+col_types ="cc"
+)
Note that the col_types argument accepts a compact representation. Here "cc" indicates that the first column is character and the second column is character (there are only two columns). Using the col_types argument is useful because often it is not easy to automatically figure out the type of a column by looking at a few rows (especially if a column has many missing values).
@@ -1044,8 +1049,9 @@
Example
The following call reads a gzip-compressed CSV file containing download logs from the RStudio CRAN mirror.
Note that the warnings indicate that read_csv() may have had some difficulty identifying the type of each column. This can be solved by using the col_types argument.
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
Pre-lecture materials
@@ -464,8 +466,8 @@
Want to see
Here, we again display the chicago data.frame as a tibble but specify that we would only like to see 5 rows. The width = Inf argument specifies that we would like to see all the possible columns. Here, there are only 8, but for larger datasets, this can be helpful to specify.
-
as_tibble(chicago) %>%
-print(n =5, width =Inf)
+
as_tibble(chicago) %>%
+print(n =5, width =Inf)
# A tibble: 6,940 × 8
city tmpd dptp date pm25tmean2 pm10tmean2 o3tmean2 no2tmean2
@@ -497,10 +499,10 @@
tibble()
In the example here, we see that the column c will contain the value ‘1’ across all rows.
tibble(
-a =1:5,
-b =6:10,
-c =1,
-z = (a + b)^2+ c
+a =1:5,
+b =6:10,
+c =1,
+z = (a + b)^2+ c)
# A tibble: 5 × 4
@@ -531,9 +533,9 @@
tibble()
Note that to refer to such columns in other tidyverse packages, you willl continue to use backticks surrounding the variable name.
df <-tibble(
-a =1:5,
-b =6:10,
-c =1,
-z = (a + b)^2+ c
+a =1:5,
+b =6:10,
+c =1,
+z = (a + b)^2+ c)# Extract by name using $ or [[]]
@@ -1043,9 +1045,10 @@
mutate()
There is also the related transmute() function, which does the same thing as mutate() but then drops all non-transformed variables.
Here, we de-trend the PM10 and ozone (O3) variables.
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
“Happy families are all alike; every unhappy family is unhappy in its own way.” —- Leo Tolstoy
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
# A tibble: 9 × 3
id visit outcome
<chr> <int> <dbl>
-1 a 0 1.54
-2 a 1 3.39
-3 a 2 3.03
-4 b 0 0.309
-5 b 1 2.52
-6 b 2 3.03
-7 c 0 2.13
-8 c 1 3.12
-9 c 2 3.99
+1 a 0 3.07
+2 a 1 3.25
+3 a 2 3.93
+4 b 0 2.18
+5 b 1 2.91
+6 b 2 2.83
+7 c 0 1.49
+8 c 1 2.56
+9 c 2 1.46
Note that subjects are labeled by a unique identifer in the id column.
@@ -429,8 +431,8 @@
A second table
Here is some code to create a second table (we will be joining the first and second tables shortly). This table contains some data about the hypothetical subjects’ housing situation by recording the type of house they live in.
# A tibble: 9 × 3
id visit outcome
<chr> <int> <dbl>
-1 a 0 1.54
-2 a 1 3.39
-3 a 2 3.03
-4 b 0 0.309
-5 b 1 2.52
-6 b 2 3.03
-7 c 0 2.13
-8 c 1 3.12
-9 c 2 3.99
+1 a 0 3.07
+2 a 1 3.25
+3 a 2 3.93
+4 b 0 2.18
+5 b 1 2.91
+6 b 2 2.83
+7 c 0 1.49
+8 c 1 2.56
+9 c 2 1.46
subjects
@@ -545,15 +547,15 @@
Left Join
# A tibble: 9 × 4
id visit outcome house
<chr> <int> <dbl> <chr>
-1 a 0 1.54 detached
-2 a 1 3.39 detached
-3 a 2 3.03 detached
-4 b 0 0.309 rowhouse
-5 b 1 2.52 rowhouse
-6 b 2 3.03 rowhouse
-7 c 0 2.13 rowhouse
-8 c 1 3.12 rowhouse
-9 c 2 3.99 rowhouse
+1 a 0 3.07 detached
+2 a 1 3.25 detached
+3 a 2 3.93 detached
+4 b 0 2.18 rowhouse
+5 b 1 2.91 rowhouse
+6 b 2 2.83 rowhouse
+7 c 0 1.49 rowhouse
+8 c 1 2.56 rowhouse
+9 c 2 1.46 rowhouse
@@ -574,9 +576,9 @@
Left Join w
In the previous examples, the subjects table didn’t have a visit column. But suppose it did? Maybe people move around during the study. We could image a table like this one.
# A tibble: 9 × 4
id visit outcome house
<chr> <dbl> <dbl> <chr>
-1 a 0 1.54 detached
-2 a 1 3.39 <NA>
-3 a 2 3.03 <NA>
-4 b 0 0.309 <NA>
-5 b 1 2.52 rowhouse
-6 b 2 3.03 <NA>
-7 c 0 2.13 rowhouse
-8 c 1 3.12 <NA>
-9 c 2 3.99 <NA>
+1 a 0 3.07 detached
+2 a 1 3.25 <NA>
+3 a 2 3.93 <NA>
+4 b 0 2.18 <NA>
+5 b 1 2.91 rowhouse
+6 b 2 2.83 <NA>
+7 c 0 1.49 rowhouse
+8 c 1 2.56 <NA>
+9 c 2 1.46 <NA>
@@ -627,9 +629,9 @@
Left Join w
We may even have a situation where we are missing housing data for a subject completely. The following table has no information about subject a.
# A tibble: 9 × 4
id visit outcome house
<chr> <dbl> <dbl> <chr>
-1 a 0 1.54 <NA>
-2 a 1 3.39 <NA>
-3 a 2 3.03 <NA>
-4 b 0 0.309 <NA>
-5 b 1 2.52 rowhouse
-6 b 2 3.03 <NA>
-7 c 0 2.13 rowhouse
-8 c 1 3.12 <NA>
-9 c 2 3.99 <NA>
+1 a 0 3.07 <NA>
+2 a 1 3.25 <NA>
+3 a 2 3.93 <NA>
+4 b 0 2.18 <NA>
+5 b 1 2.91 rowhouse
+6 b 2 2.83 <NA>
+7 c 0 1.49 rowhouse
+8 c 1 2.56 <NA>
+9 c 2 1.46 <NA>
@@ -686,8 +688,8 @@
Inner Join
# A tibble: 2 × 4
id visit outcome house
<chr> <dbl> <dbl> <chr>
-1 b 1 2.52 rowhouse
-2 c 0 2.13 rowhouse
+1 b 1 2.91 rowhouse
+2 c 0 1.49 rowhouse
@@ -700,8 +702,8 @@
Right Join
# A tibble: 2 × 4
id visit outcome house
<chr> <dbl> <dbl> <chr>
-1 b 1 2.52 rowhouse
-2 c 0 2.13 rowhouse
+1 b 1 2.91 rowhouse
+2 c 0 1.49 rowhouse
@@ -735,11 +737,15 @@
Final Questions
# Create first example data frame
-df1 <-data.frame(ID =1:3,
-X1 =c("a1", "a2", "a3"))
-# Create second example data frame
-df2 <-data.frame(ID =2:4,
-X2 =c("b1", "b2", "b3"))
+df1 <-data.frame(
+ID =1:3,
+X1 =c("a1", "a2", "a3")
+)
+# Create second example data frame
+df2 <-data.frame(
+ID =2:4,
+X2 =c("b1", "b2", "b3")
+)
Try changing the order from the above e.g. inner_join(df2, df1), semi_join(df2, df1) and anti_join(df2, df1). What changed? What did not change?
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
The data may not contain the answer. And, if you torture the data long enough, it will tell you anything. —John W. Tukey
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
“The greatest value of a picture is when it forces us to notice what we never expected to see.” —John Tukey
@@ -368,9 +370,9 @@
The ggplot2 Plotting System
Consider the following plot made using base graphics previously.
This is slightly better but the substantial overlap makes it difficult to discern any trends in the data. For this we need to add a smoother of some sort. Here we add a linear regression line (a type of smoother) to each group to see if there’s any difference.
-
qplot(log(pm25), log(eno), data = maacs, color = mopos) +
-geom_smooth(method ="lm")
+
qplot(log(pm25), log(eno), data = maacs, color = mopos) +
+geom_smooth(method ="lm")
`geom_smooth()` using formula = 'y ~ x'
@@ -1111,8 +1115,8 @@
Case Study: MAACS
Here we see quite clearly that the red group and the green group exhibit rather different relationships between PM2.5 and eNO. For the non-allergic individuals, there appears to be a slightly negative relationship between PM2.5 and eNO and for the allergic individuals, there is a positive relationship. This suggests a strong interaction between PM2.5 and allergic status, an hypothesis perhaps worth following up on in greater detail than this brief exploratory analysis.
Another, and perhaps more clear, way to visualize this interaction is to use separate panels for the non-allergic and allergic individuals using the facets argument to qplot().
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
data: logpm25, logno2_new, bmicat, NocturnalSympt [517x4]
mapping: x = ~logpm25, y = ~NocturnalSympt
@@ -510,7 +515,7 @@
Building up in layers
Now, normally if you were to print() a ggplot object a plot would appear on the plot device, however, our object g actually does not contain enough information to make a plot yet.
g <- maacs %>%
-ggplot(aes(logpm25, NocturnalSympt))
+ggplot(aes(logpm25, NocturnalSympt))print(g)
@@ -527,7 +532,7 @@
First plot wit
Here, we add the geom_point() function to create a traditional scatter plot.
g <- maacs %>%
-ggplot(aes(logpm25, NocturnalSympt))
+ggplot(aes(logpm25, NocturnalSympt))g +geom_point()
@@ -546,9 +551,9 @@
Adding more layers
smooth
Because the data appear rather noisy, it might be better if we added a smoother on top of the points to see if there is a trend in the data with PM2.5.
-
g +
-geom_point() +
-geom_smooth()
+
g +
+geom_point() +
+geom_smooth()
@@ -560,9 +565,9 @@
smooth
The default smoother is a loess smoother, which is flexible and nonparametric but might be too flexible for our purposes. Perhaps we’d prefer a simple linear regression line to highlight any first order trends. We can do this by specifying method = "lm" to geom_smooth().
-
g +
-geom_point() +
-geom_smooth(method ="lm")
+
g +
+geom_point() +
+geom_smooth(method ="lm")
@@ -588,7 +593,7 @@
smooth
# try it yourselflibrary(palmerpenguins)
-penguins
+penguins
# A tibble: 344 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
@@ -626,10 +631,10 @@
facets
We want one row and two columns, one column for each weight category. So we specify bmicat on the right hand side of the forumla passed to facet_grid().
-
g +
-geom_point() +
-geom_smooth(method ="lm") +
-facet_grid(. ~ bmicat)
+
g +
+geom_point() +
+geom_smooth(method ="lm") +
+facet_grid(. ~ bmicat)
@@ -663,7 +668,7 @@
map aesthetics
For example, here we modify the points in the scatterplot to make the color “steelblue”, the size larger, and the alpha transparency greater.
-
g +geom_point(color ="steelblue", size =4, alpha =1/2)
+
g +geom_point(color ="steelblue", size =4, alpha =1/2)
@@ -682,7 +687,7 @@
map aesthetics
For example, we can map the aesthetic color to the variable bmicat, so the points will be colored according to the levels of bmicat.
We use the aes() function to indicate this difference from the plot above.
-
g +geom_point(aes(color = bmicat), size =4, alpha =1/2)
+
g +geom_point(aes(color = bmicat), size =4, alpha =1/2)
@@ -699,14 +704,17 @@
Customizing the smo
For example, we can customize the smoother that we overlay on the points with geom_smooth().
Here we change the line type and increase the size from the default. We also remove the shaded standard error from the line.
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
Pre-lecture materials
@@ -377,45 +379,45 @@
if-else<
Here is an example of a valid if/else structure.
Let’s use the runif(n, min=0, max=1) function which draws a random value between a min and max value with the default being between 0 and 1.
-
x <-runif(n=1, min=0, max=10)
+
x <-runif(n =1, min =0, max =10)x
-
[1] 1.907048
+
[1] 3.521267
Then, we can write and if-else statement that tests whethere x is greater than 3 or not.
x >3
-
[1] FALSE
+
[1] TRUE
If x is greater than 3, then the first condition occurs. If x is not greater than 3, then the second condition occurs.
-
if(x >3) {
+
if (x >3) { y <-10
- } else {
+} else { y <-0
- }
+}
Finally, we can auto print y to see what the value is.
y
-
[1] 0
+
[1] 10
This expression can also be written a different (but equivalent!) way in R.
# A tibble: 344 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
@@ -490,8 +492,8 @@
for Loops
In R, for loops take an iterator variable and assign it successive values from a sequence or vector.
For loops are most commonly used for iterating over the elements of an object (list, vector, etc.)
-
for(i in1:10) {
-print(i)
+
for (i in1:10) {
+print(i)}
[1] 1
@@ -513,9 +515,9 @@
for Loops
x <-c("a", "b", "c", "d")## create for loop
-for(i in1:4) {
-## Print out each element of 'x'
-print(x[i])
+for (i in1:4) {
+## Print out each element of 'x'
+print(x[i])}
[1] "a"
@@ -530,9 +532,9 @@
for Loops
x <-c("a", "b", "c", "d")## create for loop
-for(i in1:4) {
-## Print out just 'i'
-print(i)
+for (i in1:4) {
+## Print out just 'i'
+print(i)}
[1] 1
@@ -558,8 +560,8 @@
seq_along()
Let’s put seq_along() and for loops together.
## Generate a sequence based on length of 'x'
-for(i inseq_along(x)) {
-print(x[i])
+for (i inseq_along(x)) {
+print(x[i])}
[1] "a"
@@ -570,8 +572,8 @@
seq_along()
It is not necessary to use an index-type variable (i.e. i).
-
for(babyshark in x) {
-print(babyshark)
+
for (babyshark in x) {
+print(babyshark)}
[1] "a"
@@ -581,8 +583,8 @@
seq_along()
-
for(candyisgreat in x) {
-print(candyisgreat)
+
for (candyisgreat in x) {
+print(candyisgreat)}
[1] "a"
@@ -592,8 +594,8 @@
seq_along()
-
for(RememberToVote in x) {
-print(RememberToVote)
+
for (RememberToVote in x) {
+print(RememberToVote)}
[1] "a"
@@ -604,18 +606,18 @@
seq_along()
You can use any character index you want (but not with symbols or numbers).
z <-5set.seed(1)
-while(z >=3&& z <=10) {
- coin <-rbinom(1, 1, 0.5)
-
-if(coin ==1) { ## random walk
- z <- z +1
- } else {
- z <- z -1
- }
+while (z >=3&& z <=10) {
+ coin <-rbinom(1, 1, 0.5)
+
+if (coin ==1) { ## random walk
+ z <- z +1
+ } else {
+ z <- z -1
+ }}print(z)
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
Pre-lecture materials
@@ -389,15 +391,15 @@
Your First FunctionHere’s a simple function that takes no arguments and does nothing.
f <-function() {
-## This is an empty function
+## This is an empty function}## Functions have their own class
-class(f)
+class(f)
[1] "function"
## Execute this function
-f()
+f()
NULL
@@ -408,9 +410,9 @@
Your First FunctionThe next thing we can do is create a function that actually has a non-trivial function body.
f <-function() {
-# this is the function body
- hello <-"Hello, world!\n"
-cat(hello)
+# this is the function body
+ hello <-"Hello, world!\n"
+cat(hello)}f()
@@ -447,10 +449,10 @@
Your First FunctionFor this basic function, we can add an argument that determines how many times “Hello, world!” is printed to the console.
Your First FunctionThis next function returns the total number of characters printed to the console.
f <-function(num) {
- hello <-"Hello, world!\n"
-for(i inseq_len(num)) {
-cat(hello)
- }
- chars <-nchar(hello) * num
- chars
+ hello <-"Hello, world!\n"
+for (i inseq_len(num)) {
+cat(hello)
+ }
+ chars <-nchar(hello) * num
+ chars}meaningoflife <-f(3)
@@ -527,23 +529,23 @@
Your First FunctionHere, for example, we could set the default value for num to be 1, so that if the function is called without the num argument being explicitly specified, then it will print “Hello, world!” to the console once.
f <-function(num =1) {
- hello <-"Hello, world!\n"
-for(i inseq_len(num)) {
-cat(hello)
- }
- chars <-nchar(hello) * num
- chars
+ hello <-"Hello, world!\n"
+for (i inseq_len(num)) {
+cat(hello)
+ }
+ chars <-nchar(hello) * num
+ chars}
-f() ## Use default value for 'num'
+f() ## Use default value for 'num'
Hello, world!
[1] 14
-
f(2) ## Use user-specified value
+
f(2) ## Use user-specified value
Hello, world!
Hello, world!
@@ -616,7 +618,7 @@
Argument matching
function (n, mean = 0, sd = 1)
-
mydata <-rnorm(100, 2, 1) ## Generate some data
+
mydata <-rnorm(100, 2, 1) ## Generate some data
100 is assigned to the n argument, 2 is assigned to the mean argument, and 1 is assigned to the sd argument, all by positional matching.
The following calls to the sd() function (which computes the empirical standard deviation of a vector of numbers) are all equivalent.
@@ -637,19 +639,19 @@
Argument matching
## Positional match first argument, default for 'na.rm'
-sd(mydata)
+sd(mydata)
-
[1] 1.110707
+
[1] 1.014286
## Specify 'x' argument by name, default for 'na.rm'
-sd(x = mydata)
+sd(x = mydata)
-
[1] 1.110707
+
[1] 1.014286
## Specify both arguments by name
-sd(x = mydata, na.rm =FALSE)
+sd(x = mydata, na.rm =FALSE)
-
[1] 1.110707
+
[1] 1.014286
@@ -658,9 +660,9 @@
Argument matching
In the example below, we specify the na.rm argument first, followed by x, even though x is the first argument defined in the function definition.
## Specify both arguments by name
-sd(na.rm =FALSE, x = mydata)
+sd(na.rm =FALSE, x = mydata)
-
[1] 1.110707
+
[1] 1.014286
You can mix positional matching with matching by name.
@@ -668,7 +670,7 @@
Argument matching
sd(na.rm =FALSE, mydata)
-
[1] 1.110707
+
[1] 1.014286
Here, the mydata object is assigned to the x argument, because it’s the only argument not yet specified.
@@ -751,8 +753,8 @@
Lazy Evaluation
In this example, the function f() has two arguments: a and b.
f <-function(a, b) {
- a^2
-}
+ a^2
+}f(2)
[1] 4
@@ -763,8 +765,8 @@
Lazy Evaluation
This example also shows lazy evaluation at work, but does eventually result in an error.
f <-function(a, b) {
-print(a)
-print(b)
+print(a)
+print(b)}f(45)
@@ -791,7 +793,7 @@
The ... Argu
function (x, ...)
UseMethod("mean")
-<bytecode: 0x138e33de8>
+<bytecode: 0x1075ea1e8>
<environment: namespace:base>
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
Pre-lecture materials
@@ -370,7 +372,7 @@
Two vectors
If we have two vectors of the same length, and we sum them in R, they will be added entry by entry as follows:
So now, instead of the random numbers being between 0 and 1 (the default), the are all between 0 and 10.
@@ -594,7 +596,7 @@
lapply()
Here I am creating a list that contains two matrices.
-
x <-list(a =matrix(1:4, 2, 2), b =matrix(1:6, 3, 2))
+
x <-list(a =matrix(1:4, 2, 2), b =matrix(1:6, 3, 2))x
$a
@@ -611,7 +613,9 @@
lapply()
Suppose I wanted to extract the first column of each matrix in the list. I could write an anonymous function for extracting the first column of each matrix.
-
lapply(x, function(elt) { elt[,1] })
+
lapply(x, function(elt) {
+ elt[, 1]
+})
$a
[1] 1 2
@@ -627,7 +631,7 @@
lapply()
For example, I could have done the following.
f <-function(elt) {
- elt[, 1]
+ elt[, 1]}lapply(x, f)
Notice that lapply() returns a list (as usual), but that each element of the list has length 1.
Here’s the result of calling sapply() on the same list.
-
sapply(x, mean)
+
sapply(x, mean)
a b c d
- 2.5000000 -0.3561419 1.0788156 5.0209365
+ 2.5000000 -0.1478465 0.8197940 4.9544836
Because the result of lapply() was a list where each element had length 1, sapply() collapsed the output into a numeric vector, which is often more useful than a list.
Unfortunately, there are NAs in the data so we cannot simply take the means of those variables. However, we can tell the colMeans function to remove the NAs before computing the mean.
Notice that I had to pass the probs = c(0.25, 0.75) argument to quantile() via the ... argument to apply().
@@ -1117,23 +1122,23 @@
Vectorizing a Funct
Here’s an example of a function that computes the sum of squares given some data, a mean parameter and a standard deviation. The formula is \(\sum_{i=1}^n(x_i-\mu)^2/\sigma^2\).
This function takes a mean mu, a standard deviation sigma, and some data in a vector x.
In many statistical applications, we want to minimize the sum of squares to find the optimal mu and sigma. Before we do that, we may want to evaluate or plot the function for many different values of mu or sigma.
-
x <-rnorm(100) ## Generate some data
-sumsq(mu=1, sigma=1, x) ## This works (returns one value)
+
x <-rnorm(100) ## Generate some data
+sumsq(mu =1, sigma =1, x) ## This works (returns one value)
-
[1] 201.5111
+
[1] 248.8765
However, passing a vector of mus or sigmas won’t work with this function because it’s not vectorized.
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
Pre-lecture materials
@@ -469,12 +471,12 @@
Something’s Wrong!
Here is another function that is designed to print a message to the console depending on the nature of its input.
print_message <-function(x) {
-if(x >0) {
-print("x is greater than zero")
- } else {
-print("x is less than or equal to zero")
- }
-invisible(x)
+if (x >0) {
+print("x is greater than zero")
+ } else {
+print("x is less than or equal to zero")
+ }
+invisible(x)}
This function is simple:
@@ -509,14 +511,15 @@
Something’s Wrong!
We can fix this problem by anticipating the possibility of NA values and checking to see if the input is NA with the is.na() function.
print_message2 <-function(x) {
-if(is.na(x))
-print("x is a missing value!")
-elseif(x >0)
-print("x is greater than zero")
-else
-print("x is less than or equal to zero")
-invisible(x)
-}
+if (is.na(x)) {
+print("x is a missing value!")
+ } elseif (x >0) {
+print("x is greater than zero")
+ } else {
+print("x is less than or equal to zero")
+ }
+invisible(x)
+}
Now we can run the following.
@@ -534,7 +537,7 @@
Something’s Wrong!
print_message2(x)
-
Error in if (is.na(x)) print("x is a missing value!") else if (x > 0) print("x is greater than zero") else print("x is less than or equal to zero"): the condition has length > 1
+
Error in if (is.na(x)) {: the condition has length > 1
Now what?? Why are we getting this warning?
@@ -551,16 +554,18 @@
Something’s Wrong!
For the first way, we simply need to check the length of the input.
print_message3 <-function(x) {
-if(length(x) > 1L)
-stop("'x' has length > 1")
-if(is.na(x))
-print("x is a missing value!")
-elseif(x >0)
-print("x is greater than zero")
-else
-print("x is less than or equal to zero")
-invisible(x)
-}
+if (length(x) > 1L) {
+stop("'x' has length > 1")
+ }
+if (is.na(x)) {
+print("x is a missing value!")
+ } elseif (x >0) {
+print("x is greater than zero")
+ } else {
+print("x is less than or equal to zero")
+ }
+invisible(x)
+}
Now when we pass print_message3() a vector, we should get an error.
@@ -787,10 +792,10 @@
Final Questions
g <-function(b) h(b)h <-function(c) i(c)i <-function(d) {
-if (!is.numeric(d)) {
-stop("`d` must be numeric", call. =FALSE)
- }
- d +10
+if (!is.numeric(d)) {
+stop("`d` must be numeric", call. =FALSE)
+ }
+ d +10}f("a")
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
Pre-lecture materials
@@ -384,8 +386,8 @@
What is an error?
Here’s a small function that will generate a message:
-
f <-function(){
-message("This is a message.")
+
f <-function() {
+message("This is a message.")}f()
@@ -419,8 +421,8 @@
Generating Errors
If an error occurs inside of a function, then the name of that function will appear in the error message:
-
name_of_function <-function(){
-stop("Something bad happened.")
+
name_of_function <-function() {
+stop("Something bad happened.")}name_of_function()
@@ -441,9 +443,9 @@
Generating Errors
Let’s take a look at an example:
-
error_if_n_is_greater_than_zero <-function(n){
-stopifnot(n <=0)
- n
+
Just like errors, a warning generated inside of a function will include the name of the function in which it was generated:
-
make_NA <-function(x){
-warning("Generating an NA.")
-NA
+
make_NA <-function(x) {
+warning("Generating an NA.")
+NA}make_NA("Sodium")
@@ -548,23 +550,24 @@
How should er
The tryCatch() function is the workhorse of handling errors and warnings in R. The first argument of this function is any R expression, followed by conditions which specify how to handle an error or a warning. The last argument, finally, specifies a function or expression that will be executed after the expression no matter what, even in the event of an error or a warning.
Let’s construct a simple function I’m going to call beera that catches errors and warnings gracefully.
This function takes an expression as an argument and tries to evaluate it. If the expression can be evaluated without any errors or warnings then the result of the expression is returned and the message Finally done! is printed to the R console. If an error or warning is generated, then the functions that are provided to the error or warning arguments are printed. Let’s try this function out with a few examples.
Notice that we’ve effectively transformed errors and warnings into messages.
Now that you know the basics of generating and catching errors you’ll need to decide when your program should generate an error. My advice to you is to limit the number of errors your program generates as much as possible. Even if you design your program so that it’s able to catch and handle errors, the error handling process slows down your program by orders of magnitude. Imagine you wanted to write a simple function that checks if an argument is an even number. You might write the following:
-
is_even <-function(n){
- n %%2==0
+
is_even <-function(n) {
+ n %%2==0}is_even(768)
@@ -609,14 +612,15 @@
How should er
You can see that providing a string causes this function to raise an error. You could imagine though that you want to use this function across a list of different data types, and you only want to know which elements of that list are even numbers. You might think to write the following:
This appears to be working the way you intended, however when applied to more data this function will be seriously slow compared to alternatives. For example I could check that n is numeric before treating n like a number:
-
is_even_check <-function(n){
-is.numeric(n) && n %%2==0
+
is_even_check <-function(n) {
+is.numeric(n) && n %%2==0}is_even_check(1876)
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
# A tibble: 336,776 × 5
year month day hour minute
@@ -681,7 +685,7 @@
3. From other types
now()
-
[1] "2023-08-17 17:32:08 EDT"
+
[1] "2023-08-17 21:47:52 EDT"
as_date(now())
@@ -732,7 +736,7 @@
POSIXct
Technically, the POSIXct class represents the number of seconds since 1 January 1970. (In case you were wondering, “POSIXct” stands for “Portable Operating System Interface”, calendar time.)
x <-ymd_hm("1970-01-01 01:00")
-class(x)
+class(x)
[1] "POSIXct" "POSIXt"
@@ -868,7 +872,7 @@
Operations on Dates and Times
Arithmetic
You can add and subtract dates and times.
-
x <-ymd("2012-01-01", tz ="") ## Midnight
+
x <-ymd("2012-01-01", tz ="") ## Midnighty <-dmy_hms("9 Jan 2011 11:34:21", tz ="")x - y ## this works
@@ -889,7 +893,7 @@
Arithmetic
[1] FALSE
-
x + y ## what??? why does this not work?
+
x + y ## what??? why does this not work?
Error in `+.POSIXt`(x, y): binary '+' is not defined for "POSIXt" objects
@@ -914,7 +918,7 @@
Arithmetic
POSIXct objects are a measure of seconds from an origin, usually the UNIX epoch (1st Jan 1970).
Just add the requisite number of seconds to the object:
-
x +3*60*60# add 3 hours
+
x +3*60*60# add 3 hours
[1] "2012-01-01 03:00:00 EST"
@@ -931,7 +935,7 @@
Arithmetic
And then add a number to the date (in this case 1 day)
-
y +1
+
y +1
[1] "2011-01-10"
@@ -989,9 +993,11 @@
Extracting Elements of Dates/Times
Date Elements
-
x <-ymd_hms(c("2012-10-25 01:13:46",
-"2015-04-23 15:11:23"), tz ="")
-year(x)
+
x <-ymd_hms(c(
+"2012-10-25 01:13:46",
+"2015-04-23 15:11:23"
+), tz ="")
+year(x)
[1] 2012 2015
@@ -1012,9 +1018,11 @@
Date Elements
Time Elements
-
x <-ymd_hms(c("2012-10-25 01:13:46",
-"2015-04-23 15:11:23"), tz ="")
-minute(x)
+
x <-ymd_hms(c(
+"2012-10-25 01:13:46",
+"2015-04-23 15:11:23"
+), tz ="")
+minute(x)
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
Pre-lecture materials
@@ -675,7 +677,7 @@
repetition
[1] FALSE
-
# Does "Mississippi" contain the pattern of an "i" followed by
+
# Does "Mississippi" contain the pattern of an "i" followed by# 2 of any character, with that pattern repeated three times adjacently?grepl("(i.{2}){3}", "Mississippi")
@@ -746,12 +748,12 @@
character sets
So for example, to include a literal single or double quote in a string you can use \ to “escape” the string and being able to include a single or double quote:
-
double_quote <-"\""
+
double_quote <-"\""double_quote
[1] "\""
-
single_quote <-'\''
+
single_quote <-"'"single_quote
[1] "'"
@@ -1109,7 +1111,7 @@
grep()
sub()
The sub(pattern, replacement, x) function takes as arguments a regex, a “replacement,” and a vector of strings. This function will replace the first instance of that regex found in each string.
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
Pre-lecture materials
@@ -382,8 +384,8 @@
Factor basics
To create a factor you must start by creating a list of the valid levels:
This behavior of the factor() function feels unexpected at best.
Another example of unexpected behavior is how the function will silently make a missing value because the values in the data and the levels do not match.
-
factor("a", levels="c")
+
factor("a", levels ="c")
[1] <NA>
Levels: c
@@ -567,10 +569,12 @@
Factors when mo
Consider a vector of character strings with three income levels:
In early versions of R, storing categorical data as a factor variable was considerably more efficient than storing the same data as strings, because factor variables only store the factor labels once.
However, R now uses a global string pool, so each unique string is only stored once, which means storage is now less of an issue.
-
format(object.size(income_level), units="Kb") # size of the character string
+
format(object.size(income_level), units ="Kb") # size of the character string
[1] "234.6 Kb"
-
format(object.size(factor(income_level)), units="Kb") # size of the factor
+
format(object.size(factor(income_level)), units ="Kb") # size of the factor
[1] "117.8 Kb"
@@ -678,8 +684,8 @@
General Social Surve
When factors are stored in a tibble, you cannot see their levels so easily. One way to view them is with count():
-
gss_cat %>%
-count(race)
+
gss_cat %>%
+count(race)
# A tibble: 3 × 2
race n
@@ -691,8 +697,8 @@
General Social Surve
Or with a bar chart using the geom_bar() geom:
-
gss_cat %>%
-ggplot(aes(x=race)) +
+
gss_cat %>%
+ggplot(aes(x = race)) +geom_bar()
@@ -722,8 +728,8 @@
Modifying factor or
It’s often useful to change the order of the factor levels in a visualization.
Let’s explore the relig (religion) factor:
-
gss_cat %>%
-count(relig)
+
gss_cat %>%
+count(relig)
# A tibble: 15 × 2
relig n
@@ -766,14 +772,16 @@
Modifying factor or
The first level is “No answer” followed by “Don’t know”, and so on.
Imagine you want to explore the average number of hours spent watching TV (tvhours) per day across religions (relig):
Another type of reordering is useful when you are coloring the lines on a plot. fct_reorder2(f, x, y) reorders the factor f by the y values associated with the largest x values.
This makes the plot easier to read because the colors of the line at the far right of the plot will line up with the legend.
Finally, for bar plots, you can use fct_infreq() to order levels in decreasing frequency: this is the simplest type of reordering because it doesn’t need any extra variables. Combine it with fct_rev() if you want them in increasing frequency so that in the bar plot largest values are on the right, not the left.
The most general and powerful tool is fct_recode(). It allows you to recode, or change, the value of each level. For example, take the gss_cat$partyid:
-
gss_cat %>%
-count(partyid)
+
gss_cat %>%
+count(partyid)
# A tibble: 10 × 2
partyid n
@@ -1007,15 +1019,16 @@
That’s the job of the fct_lump_*() family of functions.
fct_lump_lowfreq() is a simple starting point that progressively lumps the smallest groups categories into “Other”, always keeping “Other” as the smallest category.
In this case it’s not very helpful: it is true that the majority of Americans in this survey are Protestant, but we’d probably like to see some more details!
Instead, we can use the fct_lump_n() to specify that we want exactly 10 groups:
This lecture, as the rest of the course, is adapted from the version Stephanie C. Hicks designed and maintained in 2021 and 2022. Check the recent changes to this file through the GitHub history.
Pre-lecture materials
@@ -413,16 +415,18 @@
How does it work?
To make this easier, I typed this text into a vector of character strings: one string per sentence.
-
peng_preface <-
-c("I started using R in 1998 when I was a college undergraduate working on my senior thesis.",
-"The version was 0.63.",
-"I was an applied mathematics major with a statistics concentration and I was working with Dr. Nicolas Hengartner on an analysis of word frequencies in classic texts (Shakespeare, Milton, etc.).",
-"The idea was to see if we could identify the authorship of each of the texts based on how frequently they used certain words.",
-"We downloaded the data from Project Gutenberg and used some basic linear discriminant analysis for the modeling.",
-"The work was eventually published and was my first ever peer-reviewed publication.",
-"I guess you could argue it was my first real 'data science' experience.")
-
-peng_preface
+
peng_preface <-
+c(
+"I started using R in 1998 when I was a college undergraduate working on my senior thesis.",
+"The version was 0.63.",
+"I was an applied mathematics major with a statistics concentration and I was working with Dr. Nicolas Hengartner on an analysis of word frequencies in classic texts (Shakespeare, Milton, etc.).",
+"The idea was to see if we could identify the authorship of each of the texts based on how frequently they used certain words.",
+"We downloaded the data from Project Gutenberg and used some basic linear discriminant analysis for the modeling.",
+"The work was eventually published and was my first ever peer-reviewed publication.",
+"I guess you could argue it was my first real 'data science' experience."
+ )
+
+peng_preface
[1] "I started using R in 1998 when I was a college undergraduate working on my senior thesis."
[2] "The version was 0.63."
@@ -444,9 +448,11 @@
How does it work?
Then, we use the tibble() function to construct a data frame with two columns: one counting the line number and one from the character strings in peng_preface.
Next, we will use the unnest_tokens() function where we will call the output column to be created word and the input column text from the peng_preface_df.
gorman_hill_we_climb <-
-c("When day comes we ask ourselves, where can we find light in this neverending shade?",
-"The loss we carry, a sea we must wade.",
-"We’ve braved the belly of the beast, we’ve learned that quiet isn’t always peace and the norms and notions of what just is, isn’t always justice.",
-"And yet the dawn is ours before we knew it, somehow we do it, somehow we’ve weathered and witnessed a nation that isn’t broken but simply unfinished.")
-
-hill_df <-tibble(line=seq_along(gorman_hill_we_climb),
-text=gorman_hill_we_climb)
-hill_df
+
gorman_hill_we_climb <-
+c(
+"When day comes we ask ourselves, where can we find light in this neverending shade?",
+"The loss we carry, a sea we must wade.",
+"We’ve braved the belly of the beast, we’ve learned that quiet isn’t always peace and the norms and notions of what just is, isn’t always justice.",
+"And yet the dawn is ours before we knew it, somehow we do it, somehow we’ve weathered and witnessed a nation that isn’t broken but simply unfinished."
+ )
+
+hill_df <-tibble(
+line =seq_along(gorman_hill_we_climb),
+text = gorman_hill_we_climb
+)
+hill_df
# A tibble: 4 × 2
line text
@@ -623,10 +639,12 @@
Text Mining and Tok
### try it out
-hill_df %>%
-unnest_tokens(output = wordsforfun,
-input = text,
-token ="words")
# A tibble: 73,422 × 4
text book linenumber chapter
@@ -1011,8 +1045,8 @@
Example: text from works of Jane Austen
Finally, we can restructure it to a one-token-per-row format using the unnest_tokens() function and remove stop words using the anti_join() function in dplyr.
