Updated dtata structures part 1 section

Author: timeslerUNI

Diff for: .gitignore

@@ -6,3 +6,4 @@ _site
 pandoc
 ssh-keygen.txt.pub
 ssh-keygen.txt
+feline-data.csv

Diff for: 04-data-structures-part1.Rmd

@@ -29,13 +29,13 @@ tabby,3.2,TRUE
 cats.df <- data.frame(coat = c("calico", "black", "tabby"), 
                       weight = c(2.1, 5.0, 3.2), 
                       likes_string = c(TRUE, FALSE, TRUE))
-write.csv(cats.df, "data/feline-data.csv", row.names = FALSE)
+write.csv(cats.df, "feline-data.csv", row.names = FALSE)
 ```
 
 We can load this into R via the following:
 
 ```{r}
-cats <- read.csv(file = "data/feline-data.csv")
+cats <- read.csv(file = "feline-data.csv")
 cats
 ```
 
@@ -83,7 +83,7 @@ tabby,2.3 or 2.4,TRUE
 Reload your cats data like before, and check what type of data we find in the `weight` column:
 
 ```{r}
-cats <- read.csv(file="data/feline-data.csv")
+cats <- read.csv(file="feline-data.csv")
 typeof(cats$weight[1])
 ```
 
@@ -107,7 +107,7 @@ tabby,3.2,TRUE
 And back in RStudio:
 
 ```
-cats <- read.csv(file="data/feline-data.csv")
+cats <- read.csv(file="feline-data.csv")
 ```
 
 ## Vectors & Type Coercion
@@ -186,7 +186,7 @@ Concatenate will also append things to an existing vector:
 ```{r}
 ab_vector <- c('a', 'b')
 ab_vector
-concat_example <- c(ab_vector, 'SWC')
+concat_example <- c(ab_vector, 'RES')
 concat_example
 ```
 
@@ -220,7 +220,7 @@ names_example['b']
 > ## Challenge 1 {.challenge}
 > Start by making a vector with the numbers 11 to 20.
 > Then use the functions we just learned to extract the 3rd through 5th element in that vector into a new vector;
-> name the elements in that new vector 'S', 'W', 'C'.
+> name the elements in that new vector 'R', 'E', 'S'.
 >
 
 ## Factors
@@ -388,13 +388,13 @@ matrix_example
 > ```{r}
 > x <- c(11:20)
 > subset <- x[3:5]
-> names(subset) <- c('S', 'W', 'C')
+> names(subset) <- c('R', 'E', 'S')
 > ```
 >
 
 > ## Solution to Challenge 2 {.challenge}
 > ```{r}
-> cats <- read.csv(file="data/feline-data.csv", stringsAsFactors=FALSE)
+> cats <- read.csv(file="feline-data.csv", stringsAsFactors=FALSE)
 > str(cats$coat)
 > ```
 > Note: new students find the help files difficult to understand; make sure to let them know

Diff for: 04-data-structures-part1.html

@@ -47,7 +47,7 @@ <h2><span class="glyphicon glyphicon-certificate"></span>Learning Objectives</h2
 black,<span class="fl">5.0</span>,<span class="ot">FALSE</span>
 tabby,<span class="fl">3.2</span>,<span class="ot">TRUE</span></code></pre>
 <p>We can load this into R via the following:</p>
-<pre class="sourceCode r"><code class="sourceCode r">cats &lt;-<span class="st"> </span><span class="kw">read.csv</span>(<span class="dt">file =</span> <span class="st">&quot;data/feline-data.csv&quot;</span>)
+<pre class="sourceCode r"><code class="sourceCode r">cats &lt;-<span class="st"> </span><span class="kw">read.csv</span>(<span class="dt">file =</span> <span class="st">&quot;feline-data.csv&quot;</span>)
 cats</code></pre>
 <pre class="output"><code>    coat weight likes_string
 1 calico    2.1         TRUE
@@ -89,7 +89,7 @@ <h2 id="data-types">Data Types</h2>
 <p>Note the <code>L</code> suffix to insist that a number is an integer. No matter how complicated our analyses become, all data in R is interpreted as one of these basic data types. This strictness has some really important consequences. Go back to your text editor and add add this line to feline-data.csv:</p>
 <pre class="sourceCode r"><code class="sourceCode r">tabby,<span class="fl">2.3</span> or <span class="fl">2.4</span>,<span class="ot">TRUE</span></code></pre>
 <p>Reload your cats data like before, and check what type of data we find in the <code>weight</code> column:</p>
-<pre class="sourceCode r"><code class="sourceCode r">cats &lt;-<span class="st"> </span><span class="kw">read.csv</span>(<span class="dt">file=</span><span class="st">&quot;data/feline-data.csv&quot;</span>)
+<pre class="sourceCode r"><code class="sourceCode r">cats &lt;-<span class="st"> </span><span class="kw">read.csv</span>(<span class="dt">file=</span><span class="st">&quot;feline-data.csv&quot;</span>)
 <span class="kw">typeof</span>(cats$weight[<span class="dv">1</span>])</code></pre>
 <pre class="output"><code>[1] &quot;double&quot;
 </code></pre>
@@ -104,7 +104,7 @@ <h2 id="data-types">Data Types</h2>
 black,5.0,FALSE
 tabby,3.2,TRUE</code></pre>
 <p>And back in RStudio:</p>
-<pre><code>cats &lt;- read.csv(file=&quot;data/feline-data.csv&quot;)</code></pre>
+<pre><code>cats &lt;- read.csv(file=&quot;feline-data.csv&quot;)</code></pre>
 <h2 id="vectors-type-coercion">Vectors &amp; Type Coercion</h2>
 <p>To better understand the behavior we just saw, let’s meet another of the data structures: the <em>vector</em>.</p>
 <pre class="sourceCode r"><code class="sourceCode r">my_vector &lt;-<span class="st"> </span><span class="kw">vector</span>(<span class="dt">length=</span><span class="dv">3</span>)
@@ -168,9 +168,9 @@ <h2><span class="glyphicon glyphicon-pencil"></span>Discussion 1</h2>
 ab_vector</code></pre>
 <pre class="output"><code>[1] &quot;a&quot; &quot;b&quot;
 </code></pre>
-<pre class="sourceCode r"><code class="sourceCode r">concat_example &lt;-<span class="st"> </span><span class="kw">c</span>(ab_vector, <span class="st">&#39;SWC&#39;</span>)
+<pre class="sourceCode r"><code class="sourceCode r">concat_example &lt;-<span class="st"> </span><span class="kw">c</span>(ab_vector, <span class="st">&#39;RES&#39;</span>)
 concat_example</code></pre>
-<pre class="output"><code>[1] &quot;a&quot;   &quot;b&quot;   &quot;SWC&quot;
+<pre class="output"><code>[1] &quot;a&quot;   &quot;b&quot;   &quot;RES&quot;
 </code></pre>
 <p>You can also make series of numbers:</p>
 <pre class="sourceCode r"><code class="sourceCode r">mySeries &lt;-<span class="st"> </span><span class="dv">1</span>:<span class="dv">10</span>
@@ -216,7 +216,7 @@ <h2><span class="glyphicon glyphicon-pencil"></span>Discussion 1</h2>
 <h2><span class="glyphicon glyphicon-pencil"></span>Challenge 1</h2>
 </div>
 <div class="panel-body">
-<p>Start by making a vector with the numbers 11 to 20. Then use the functions we just learned to extract the 3rd through 5th element in that vector into a new vector; name the elements in that new vector ‘S’, ‘W’, ‘C’.</p>
+<p>Start by making a vector with the numbers 11 to 20. Then use the functions we just learned to extract the 3rd through 5th element in that vector into a new vector; name the elements in that new vector ‘R’, ‘E’, ‘S’.</p>
 </div>
 </section>
 <h2 id="factors">Factors</h2>
@@ -399,15 +399,15 @@ <h2><span class="glyphicon glyphicon-pencil"></span>Solution to Challenge 1</h2>
 <div class="panel-body">
 <pre class="sourceCode r"><code class="sourceCode r">x &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="dv">11</span>:<span class="dv">20</span>)
 subset &lt;-<span class="st"> </span>x[<span class="dv">3</span>:<span class="dv">5</span>]
-<span class="kw">names</span>(subset) &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">&#39;S&#39;</span>, <span class="st">&#39;W&#39;</span>, <span class="st">&#39;C&#39;</span>)</code></pre>
+<span class="kw">names</span>(subset) &lt;-<span class="st"> </span><span class="kw">c</span>(<span class="st">&#39;R&#39;</span>, <span class="st">&#39;E&#39;</span>, <span class="st">&#39;S&#39;</span>)</code></pre>
 </div>
 </section>
 <section class="challenge panel panel-success">
 <div class="panel-heading">
 <h2><span class="glyphicon glyphicon-pencil"></span>Solution to Challenge 2</h2>
 </div>
 <div class="panel-body">
-<pre class="sourceCode r"><code class="sourceCode r">cats &lt;-<span class="st"> </span><span class="kw">read.csv</span>(<span class="dt">file=</span><span class="st">&quot;data/feline-data.csv&quot;</span>, <span class="dt">stringsAsFactors=</span><span class="ot">FALSE</span>)
+<pre class="sourceCode r"><code class="sourceCode r">cats &lt;-<span class="st"> </span><span class="kw">read.csv</span>(<span class="dt">file=</span><span class="st">&quot;feline-data.csv&quot;</span>, <span class="dt">stringsAsFactors=</span><span class="ot">FALSE</span>)
 <span class="kw">str</span>(cats$coat)</code></pre>
 <pre class="output"><code> chr [1:3] &quot;calico&quot; &quot;black&quot; &quot;tabby&quot;
 </code></pre>

Diff for: 04-data-structures-part1.md

@@ -30,7 +30,7 @@ We can load this into R via the following:
 
 
 ~~~{.r}
-cats <- read.csv(file = "data/feline-data.csv")
+cats <- read.csv(file = "feline-data.csv")
 cats
 ~~~
 
@@ -158,7 +158,7 @@ Reload your cats data like before, and check what type of data we find in the `w
 
 
 ~~~{.r}
-cats <- read.csv(file="data/feline-data.csv")
+cats <- read.csv(file="feline-data.csv")
 typeof(cats$weight[1])
 ~~~
 
@@ -197,7 +197,7 @@ tabby,3.2,TRUE
 And back in RStudio:
 
 ```
-cats <- read.csv(file="data/feline-data.csv")
+cats <- read.csv(file="feline-data.csv")
 ```
 
 ## Vectors & Type Coercion
@@ -382,14 +382,14 @@ ab_vector
 
 
 ~~~{.r}
-concat_example <- c(ab_vector, 'SWC')
+concat_example <- c(ab_vector, 'RES')
 concat_example
 ~~~
 
 
 
 ~~~{.output}
-[1] "a"   "b"   "SWC"
+[1] "a"   "b"   "RES"
 
 ~~~
 
@@ -515,7 +515,7 @@ b
 > ## Challenge 1 {.challenge}
 > Start by making a vector with the numbers 11 to 20.
 > Then use the functions we just learned to extract the 3rd through 5th element in that vector into a new vector;
-> name the elements in that new vector 'S', 'W', 'C'.
+> name the elements in that new vector 'R', 'E', 'S'.
 >
 
 ## Factors
@@ -893,14 +893,14 @@ matrix_example
 > ~~~{.r}
 > x <- c(11:20)
 > subset <- x[3:5]
-> names(subset) <- c('S', 'W', 'C')
+> names(subset) <- c('R', 'E', 'S')
 > ~~~
 >
 
 > ## Solution to Challenge 2 {.challenge}
 > 
 > ~~~{.r}
-> cats <- read.csv(file="data/feline-data.csv", stringsAsFactors=FALSE)
+> cats <- read.csv(file="feline-data.csv", stringsAsFactors=FALSE)
 > str(cats$coat)
 > ~~~
 >