differences for PR #290

Author: actions-user

03-basics-factors-dataframes.md

@@ -294,12 +294,13 @@ str(subset)
 
 Ok, thats a lot up unpack! Some things to notice.
 
-- the object type `data.frame` is displayed in the first row along with its
+- The object type `data.frame` is displayed in the first row along with its
   dimensions, in this case 801 observations (rows) and 4 variables (columns)
-- Each variable (column) has a name (e.g. `sample_id`). This is followed
-  by the object mode (e.g. chr, int, etc.). Notice that before each
+- Each variable (column) has a name (e.g. `sample_id`). Notice that before each
   variable name there is a `$` - this will be important later.
-
+- Each variable name is followed by the data type it contains (e.g. chr, int, etc.). 
+  The `int` type shows an integer, which is a type of numerical data, where it can only 
+  store whole numbers (i.e. no decimal points ).
 
 
   :::::::::::::::::::::::::::::::::::::::  challenge
@@ -379,11 +380,109 @@ head(alt_alleles)
 ```
 
 There are 801 alleles (one for each row). To simplify, lets look at just the
-single-nucleotide alleles (SNPs). We can use some of the vector indexing skills
-from the last episode.
+single-nucleotide alleles (SNPs). 
+
+Let's review some of the vector indexing skills from the last episode that can help:
+
+
+``` r
+# This will find all matching alleles with the single nucleotide "A" and provide a TRUE/FASE vector
+alt_alleles == "A"
+```
+
+``` output
+  [1] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
+ [13] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+ [25] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
+ [37]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
+ [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+ [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
+ [73] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+ [85] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+ [97] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+[109] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+[121] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+[133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+[145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+[157] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
+[169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+[181] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
+[193]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+[205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
+[217] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
+[229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
+[241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+[253] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE
+[265] FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+[277]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
+[289] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE
+[301] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE
+[313] FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE
+[325] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE
+[337] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
+[349] FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE
+[361] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
+[373] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE
+[385] FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE
+[397]  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE
+[409] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE
+[421] FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE
+[433]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
+[445]  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE
+[457]  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE
+[469]  TRUE  TRUE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE
+[481] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+[493]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE
+[505]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE
+[517] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
+[529] FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE
+[541]  TRUE FALSE FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
+[553] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE
+[565] FALSE  TRUE FALSE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE
+[577] FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
+[589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
+[601] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
+[613] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE
+[625]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
+[637] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
+[649]  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
+[661] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
+[673]  TRUE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+[685] FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE
+[697]  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
+[709] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
+[721] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
+[733]  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE  TRUE FALSE
+[745] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
+[757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
+[769] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE
+[781] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
+[793]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+```
 
+``` r
+# Then, we wrap them into an index to pull all the positions that match this. 
+alt_alleles[alt_alleles == "A"]
+```
+
+``` output
+  [1] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A"
+ [19] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A"
+ [37] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A"
+ [55] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A"
+ [73] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A"
+ [91] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A"
+[109] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A"
+[127] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A"
+[145] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A"
+[163] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A"
+[181] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A"
+[199] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A"
+```
 
 ``` r
+# If we repeat this for each nucleotide A, T, G, and C, and connect them using `c()`,
+# we can index all the single nucleotide changes.
 snps <- c(alt_alleles[alt_alleles == "A"],
   alt_alleles[alt_alleles=="T"],
   alt_alleles[alt_alleles=="G"],
@@ -418,7 +517,18 @@ Error in plot.window(...): need finite 'ylim' values
 ```
 
 Whoops! Though the `plot()` function will do its best to give us a quick plot,
-it is unable to do so here. One way to fix this it to tell R to treat the SNPs
+it is unable to do so here. Let's use `str()` to see why this might be:
+
+
+``` r
+str(snps)
+```
+
+``` output
+ chr [1:707] "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" "A" ...
+```
+
+R may not know how to plot a character vector! One way to fix this it to tell R to treat the SNPs
 as categories (i.e. a factor vector); we will create a new object to avoid
 confusion using the `factor()` function:
 
@@ -463,7 +573,17 @@ summary(factor_snps)
 211 139 154 203 
 ```
 
-As you can imagine, this is already useful when you want to generate a tally.
+``` r
+# Compare the character vector 
+summary(snps)
+```
+
+``` output
+   Length     Class      Mode 
+      707 character character 
+```
+
+As you can imagine, factors are already useful when you want to generate a tally.
 
 :::::::::::::::::::::::::::::::::::::::::  callout
 
@@ -489,7 +609,7 @@ possible SNP we could generate a plot:
 plot(factor_snps)
 ```
 
-<img src="fig/03-basics-factors-dataframes-rendered-unnamed-chunk-16-1.png" style="display: block; margin: auto;" />
+<img src="fig/03-basics-factors-dataframes-rendered-unnamed-chunk-17-1.png" style="display: block; margin: auto;" />
 
 This isn't a particularly pretty example of a plot but it works. We'll be
 learning much more about creating nice, publication-quality graphics later in
@@ -526,7 +646,7 @@ Now we see our plot has be reordered:
 plot(ordered_factor_snps)
 ```
 
-<img src="fig/03-basics-factors-dataframes-rendered-unnamed-chunk-18-1.png" style="display: block; margin: auto;" />
+<img src="fig/03-basics-factors-dataframes-rendered-unnamed-chunk-19-1.png" style="display: block; margin: auto;" />
 
 Factors come in handy in many places when using R. Even using more
 sophisticated plotting packages such as ggplot2 will sometimes require you
@@ -555,7 +675,7 @@ These packages will be installed into "~/work/genomics-r-intro/genomics-r-intro/
 
 # Installing packages --------------------------------------------------------
 - Installing ggplot2 ...                        OK [linked from cache]
-Successfully installed 1 package in 7.2 milliseconds.
+Successfully installed 1 package in 9.9 milliseconds.
 ```
 
 ``` r
@@ -569,7 +689,7 @@ These packages will be installed into "~/work/genomics-r-intro/genomics-r-intro/
 
 # Installing packages --------------------------------------------------------
 - Installing dplyr ...                          OK [linked from cache]
-Successfully installed 1 package in 6.4 milliseconds.
+Successfully installed 1 package in 5.5 milliseconds.
 ```
 
 These two packages are among the most popular add on packages used in R, and they are part of a large set of very useful packages called the [tidyverse](https://www.tidyverse.org). Packages in the tidyverse are designed to work well together and are made to work with tidy data (which we described earlier in this lesson).

fig/03-basics-factors-dataframes-rendered-unnamed-chunk-17-1.png

@@ -6,7 +6,7 @@
 "episodes/00-introduction.Rmd" "e1354ed92fb458179c8c00b00ee1cf55" "site/built/00-introduction.md" "2024-10-02"
 "episodes/01-r-basics.Rmd" "c9e52db6d25e0b716fce903bdf9d3ee8" "site/built/01-r-basics.md" "2024-10-02"
 "episodes/02-data-prelude.Rmd" "ab2b1fd3cdaae919f9e409f713a0a8ad" "site/built/02-data-prelude.md" "2024-10-02"
-"episodes/03-basics-factors-dataframes.Rmd" "aba49258815322842a6abc14422e68b5" "site/built/03-basics-factors-dataframes.md" "2024-10-02"
+"episodes/03-basics-factors-dataframes.Rmd" "12a92926086599da792d6ec59b2df56e" "site/built/03-basics-factors-dataframes.md" "2024-10-02"
 "episodes/04-bioconductor-vcfr.Rmd" "10eb69b4697d7ecb9695d36c0d974208" "site/built/04-bioconductor-vcfr.md" "2024-10-02"
 "episodes/05-dplyr.Rmd" "f74055bd8677338a213e0a0c6c430119" "site/built/05-dplyr.md" "2024-10-02"
 "episodes/06-data-visualization.Rmd" "0b45534421bad05f040b24c40b6da71b" "site/built/06-data-visualization.md" "2024-10-02"