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<h1 class="title toc-ignore">Lab 05: Addendum</h1>
<h3 class="subtitle"><em>CS631</em></h3>
<h4 class="author"><em>Alison Hill</em></h4>
</div>
<div id="packages" class="section level1">
<h1><span class="header-section-number">1</span> Packages</h1>
<pre class="r"><code>library(tidyverse)
library(babynames)
# load hd data
hot_dogs_aff <- read_csv("http://bit.ly/cs631-hotdog-affiliated",
col_types = cols(
affiliated = col_factor(levels = NULL),
gender = col_factor(levels = NULL)
)) %>%
mutate(post_ifoce = year >= 1997) %>%
filter(year >= 1981 & gender == "male") </code></pre>
</div>
<div id="plot-1-add" class="section level1">
<h1><span class="header-section-number">2</span> Plot 1: Add</h1>
<p>What is this code trying to accomplish?</p>
<pre class="r"><code>temp=hot_dogs_aff
for (i in 1:nrow(hot_dogs_aff)) {
# if the maximum num_eaten is equal to the num_eaten for the year AND it's not the same as the year before
if ((max(temp$num_eaten) == hot_dogs_aff$num_eaten[i]) && (max(temp$num_eaten) != hot_dogs_aff$num_eaten[i+1]) && (i<nrow(hot_dogs_aff))) {
hot_dogs_aff$record[i] = TRUE
temp = temp[-1,] # take the most recent year OUT of the running
} else if (i<nrow(hot_dogs_aff)) {
hot_dogs_aff$record[i] = FALSE
temp = temp[-1,]
}
}
label.df <- data.frame(year = hot_dogs_aff$year[hot_dogs_aff$record],
num_eaten = hot_dogs_aff$num_eaten[hot_dogs_aff$record]) # turn this into a label dataframe</code></pre>
</div>
<div id="tidyverse-approach" class="section level1">
<h1><span class="header-section-number">3</span> Tidyverse approach</h1>
<p>The first thing we notice is that we don’t have data about whether each year’s winner is a record or not.</p>
<pre class="r"><code>hot_dogs <- read_csv("http://bit.ly/cs631-hotdog",
col_types = cols(
gender = col_factor(levels = NULL)
))</code></pre>
<p>Since our data is nicely tidy, we can use <code>dplyr</code> window functions:</p>
<ul>
<li><p>First, we use base R’s <code>cummax</code> to create a new variable that reflects the maximum HDB eaten cumulatively, that is, compared to all earlier years. For this reason, the <code>arrange(year)</code> here is critical.</p></li>
<li><p>Next, we want to know if the <code>hdb_record</code> is actually a <em>new</em> record or not, compared to all previous years. We can use <code>case_when</code> to create a logical variable that is TRUE if the <code>hdb_record</code> for a given year is greater than the <code>hdb_record</code> from the year before (using <code>dplyr::lag</code>). If not, this variable is FALSE.</p></li>
</ul>
<pre class="r"><code>hot_dogs_records <- hot_dogs %>%
filter(year >= 1980 & gender == 'male') %>%
arrange(year) %>%
mutate(hdb_record = cummax(num_eaten),
new_record = case_when(
hdb_record > lag(hdb_record) ~ TRUE,
TRUE ~ FALSE
)) %>%
filter(year >= 1981)</code></pre>
<p>We’ll also make our x-axis ticks again…</p>
<pre class="r"><code>years_to_label <- seq(from = 1981, to = 2017, by = 4)
years_to_label</code></pre>
<pre><code> [1] 1981 1985 1989 1993 1997 2001 2005 2009 2013 2017</code></pre>
<pre class="r"><code>hd_years <- hot_dogs_records %>%
distinct(year) %>%
mutate(year_lab = ifelse(year %in% years_to_label, year, ""))</code></pre>
<pre class="r"><code>hdb_records <- ggplot(hot_dogs_records,
aes(x = year, y = num_eaten)) +
geom_col(aes(fill = new_record)) +
labs(x = "Year", y = "Hot Dogs and Buns Consumed") +
ggtitle("Nathan's Hot Dog Eating Contest Results, 1981-2017") +
scale_fill_manual(values = c('#284a29', '#629d62')) +
scale_y_continuous(expand = c(0, 0),
breaks = seq(0, 70, 10)) +
scale_x_continuous(expand = c(0, 0),
breaks = hd_years$year,
labels = hd_years$year_lab) +
coord_cartesian(xlim = c(1980, 2018), ylim = c(0, 80)) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5),
axis.text = element_text(size = 12),
panel.background = element_blank(),
axis.line.x = element_line(color = "gray92",
size = 0.5),
axis.ticks = element_line(color = "gray92",
size = 0.5),
text = element_text(family = "Lato"),
legend.position = "bottom",
panel.grid.minor = element_blank())
hdb_records</code></pre>
<p><img src="05-addendum_files/figure-html/unnamed-chunk-6-1.png" width="672" /></p>
</div>
<div id="plot-2" class="section level1">
<h1><span class="header-section-number">4</span> Plot 2</h1>
<p>Goal is to observe popular first letter trends in <code>babynames</code>. First filter/arrange data.</p>
<pre class="r"><code># add column of first letters
baby_letters <- babynames %>%
mutate(first_letter = str_sub(name, 1, 1))
# add up proportion for each letter by year + sex
letter_by_year <- baby_letters %>%
count(year, sex, first_letter, wt = prop) %>%
rename(total_prop = nn)</code></pre>
<pre><code>Error in count(., year, sex, first_letter, wt = prop): unused arguments (sex, first_letter)</code></pre>
<pre class="r"><code># label most popular for each year + sex
letter_by_year <- letter_by_year %>%
group_by(sex, year) %>%
mutate(top_yearly = case_when(
total_prop == max(total_prop) ~ first_letter)) %>%
ungroup() %>%
mutate(top_latest = case_when(
year == max(year) ~ top_yearly
))</code></pre>
<pre><code>Error in eval(lhs, parent, parent): object 'letter_by_year' not found</code></pre>
<pre class="r"><code># make a data frame with just latest top letters
top_current <- letter_by_year %>%
filter(!is.na(top_latest)) %>%
rename(color_by = top_latest) %>%
select(sex, first_letter, color_by)</code></pre>
<pre><code>Error in eval(lhs, parent, parent): object 'letter_by_year' not found</code></pre>
<pre class="r"><code># now merge in
letter_by_year <- letter_by_year %>%
left_join(top_current) %>%
mutate(color_by = replace_na(color_by, "else"))</code></pre>
<pre><code>Error in eval(lhs, parent, parent): object 'letter_by_year' not found</code></pre>
<pre class="r"><code># set up my colors for males and females
my_colors <- c("#FF1493", "gray80", "#3399ff")</code></pre>
<p>Plot it!</p>
<pre class="r"><code>ggplot(letter_by_year, aes(x = year, y = total_prop, color = color_by, group = first_letter)) +
geom_line() +
scale_color_manual(values = my_colors) +
facet_wrap(~sex)</code></pre>
<pre><code>Error in ggplot(letter_by_year, aes(x = year, y = total_prop, color = color_by, : object 'letter_by_year' not found</code></pre>
<pre class="r"><code>ColorM2 <- c(A="Gray80",B="gray80",C="gray80",D="gray80",E="gray80",F="gray80",G="gray80",H="gray80",I="gray80",J="Black",K="gray80",L="gray80",M="gray80",N="gray80",O="gray80",P="gray80",Q="gray80",R="gray80",S="gray80",T="gray80",U="gray80",V="gray80",W="gray80",X="gray80",Y="gray80",Z="gray80")
ColorF2 <- c(A="Black",B="gray80",C="gray80",D="gray80",E="gray80",F="gray80",G="gray80",H="gray80",I="gray80",J="gray80",K="gray80",L="gray80",M="gray80",N="gray80",O="gray80",P="gray80",Q="gray80",R="gray80",S="gray80",T="gray80",U="gray80",V="gray80",W="gray80",X="gray80",Y="gray80",Z="gray80")</code></pre>
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