update slide design

assignments/HW2.Rmd

@@ -0,0 +1,50 @@
+---
+title: "Homework 2"
+---
+
+```{r global_options, include=FALSE}
+library(knitr)
+library(tidyverse)
+opts_chunk$set(fig.align="center", fig.height=4, fig.width=5.5)
+
+# data prep:
+txhouse <- txhousing %>%
+  filter(city %in% c('Austin', 'Houston', 'San Antonio', 'Dallas')) %>%
+  filter(year %in% c('2000', '2005', '2010', '2015')) %>%
+  group_by(city, year) %>%
+  summarize(total_sales = sum(sales))
+```
+
+**This homework is due on Feb. 1, 2024 at 11:00pm. Please submit as a pdf file on Canvas.**
+
+**Problem 1: (6 pts)**  For this Problem you will be working with the `iris` dataset built into R. This data set contains measurements of flowers (sepal length, sepal width, petal length, petal width) for three different *Iris* species (*I. setosa*, *I. versicolor*, *I. virginica*).
+
+```{r}
+head(iris)
+```
+
+Use ggplot to make a histogram of the `Sepal.Length` column. Manually choose appropriate values for `binwidth` and `center`. Explain your choice of values in 2-3 sentences.
+
+```{r}
+# Your code goes here.
+```
+
+*Your explanation goes here.*
+
+**Problem 2: (6 pts)** For this problem you will work with the dataset `txhouse` that has been derived from the `txhousing` dataset provided by **ggplot2**. See here for details of the original dataset: https://ggplot2.tidyverse.org/reference/txhousing.html. `txhouse` contains three columns: `city` (listing four Texas cities), `year` (containing four years between 2000 and 2015) and `total_sales` indicating the total number of sales for the specified year and city.
+
+```{r}
+txhouse
+```
+
+Use ggplot to make a bar plot of the total housing sales (column `total_sales`) for each `year`, color the bar borders "gray34", and fill the bars by `city`. 
+
+```{r}
+# Your code goes here.
+```
+
+**Problem 3: (8 pts)** Modify the plot from Problem 2 by placing `city` bars side-by-side, rather than stacked. See Slide 35 from the lecture on visualizing amounts. Next, reorder the bars for each `year` by `total_sales` in descending order. See Slide 25 from the lecture on visualizing amounts.
+
+```{r}
+# Your code goes here.
+```

schedule.Rmd

@@ -57,7 +57,8 @@ All homeworks are due by 11:00pm on the day they are due. Homeworks need to be s
 
 <p class="nospace">Materials:</p>
 
-- coming soon
+- [R Markdown template](assignments/HW2.Rmd)
+- [HTML](assignments/HW2.html)
 
 ### Homework 3 (due Feb 8, 2024)
 

slides/coordinate-systems-axes.Rmd

@@ -33,7 +33,7 @@ tempnormals <- read_csv(here("datasets", "tempnormals.csv"))
 ## Most data visualizations use Cartesian coordinates
 
 .center[
-```{r cartesian-coord, echo = FALSE, fig.width = 5.5, fig.asp = 0.8, dev = "svg"}
+```{r cartesian-coord, echo = FALSE, fig.width = 7.5, fig.asp = 0.8, dev = "svg"}
 df_points <- data.frame(x = c(-1, 0, 2),
                         y = c(-1, 0, 1),
                         label = c("(–1, –1)", "(0, 0)", "(2, 1)"),
@@ -55,9 +55,9 @@ ggplot(df_points, aes(x, y)) +
                linetype = 2) +
   geom_point(size = 3, color = "#0072B2") +
   geom_text(aes(label = label, vjust = vjust, hjust = hjust),
-            size = 14/.pt) +
+            size = 16/.pt) +
   geom_text(data = df_labels, aes(label = label, hjust = hjust, vjust = vjust),
-            size = 14/.pt) +
+            size = 16/.pt) +
   coord_fixed(xlim = c(-2.2, 3.2), ylim = c(-2.2, 2.2), expand = FALSE) +
   scale_x_continuous(
     name = "x axis",
@@ -69,7 +69,7 @@ ggplot(df_points, aes(x, y)) +
     breaks = -2:2,
     labels = c("–2", "–1", "0", "1", "2")
   ) +
-  theme_minimal_grid(16) +
+  theme_minimal_grid(18) +
   theme(
     axis.ticks.length = grid::unit(0, "pt")
   )
@@ -85,7 +85,7 @@ Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reill
 ## Changing units does not change the plot
 
 .pull-left[
-```{r temperature-normals-Houston-San-Diego, echo = FALSE, fig.width = 5, fig.asp = 1, dev = "svg"}
+```{r temperature-normals-Houston-San-Diego, echo = FALSE, fig.width = 6, fig.asp = 1, dev = "svg"}
 temps_wide <- tempnormals %>%
   pivot_wider(
     id_cols = c("month_name", "month", "day", "date", "day_of_year"),
@@ -137,7 +137,7 @@ Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reill
 --
 
 .pull-right[
-```{r temperature-normals-Houston-San-Diego-Celsius, echo = FALSE, fig.width = 5, fig.asp = 1, dev = "svg"}
+```{r temperature-normals-Houston-San-Diego-Celsius, echo = FALSE, fig.width = 6, fig.asp = 1, dev = "svg"}
 
 # Fahrenheit to Celsius conversion
 F2C <- function(t) {(t-32)*5/9}
@@ -166,7 +166,7 @@ ggplot(temps_wide_label, aes(x = F2C(`San Diego`), y = F2C(Houston))) +
 ## If scale units are unrelated, aspect ratio is arbitrary
 
 .center[
-```{r temperature-normals-Houston, echo = FALSE, fig.width = 5*6/4.2, fig.asp = 3/4, dev = "svg"}
+```{r temperature-normals-Houston, echo = FALSE, fig.width = 9, fig.asp = 3/4, dev = "svg"}
 temp_plot <- ggplot(temps_wide_label, aes(x = date, y = Houston)) +
   geom_line(linewidth = 1, color = "#0072B2") +
   scale_x_date(
@@ -183,7 +183,7 @@ temp_plot <- ggplot(temps_wide_label, aes(x = date, y = Houston)) +
     limits = c(50, 90),
     name = "temperature (°F)"
   ) +
-  theme_minimal_grid(14)
+  theme_minimal_grid(16)
 
 plot_grid(
   plot_grid(
@@ -218,13 +218,13 @@ Visualize these five values: 1, &nbsp; 3.16, &nbsp; 10, &nbsp; 31.6, &nbsp; 100
 --
 
 .center.nogap[
-```{r linear-log-scales, echo = FALSE, fig.width = 8, fig.asp = (1/3)*0.5, dev = "svg"}
+```{r linear-log-scales, echo = FALSE, fig.width = 10, fig.asp = (1/3)*0.5, dev = "svg"}
 df <- tibble(x = c(1, 3.16, 10, 31.6, 100))
 
 xaxis_lin <- ggplot(df, aes(x, y = 1)) + 
   geom_point(size = 3, color = "#0072B2") + 
   scale_y_continuous(limits = c(0.8, 1.2), expand = c(0, 0), breaks = 1) +
-  theme_minimal_grid(16, rel_large = 1) +
+  theme_minimal_grid(18, rel_large = 1) +
   theme(
     axis.ticks.length = grid::unit(0, "pt"),
     axis.text.y = element_blank(),
@@ -236,7 +236,7 @@ xaxis_lin <- ggplot(df, aes(x, y = 1)) +
 xaxis_log <- ggplot(df, aes(log10(x), y = 1)) + 
   geom_point(size = 3, color = "#0072B2") + 
   scale_y_continuous(limits = c(0.8, 1.2), expand = c(0, 0), breaks = 1) +
-  theme_minimal_grid(16, rel_large = 1) +
+  theme_minimal_grid(18, rel_large = 1) +
   theme(
     axis.ticks.length = grid::unit(0, "pt"),
     axis.text.y = element_blank(),
@@ -273,7 +273,7 @@ ggdraw(plotlist[[1]])
 --
 
 .center.nogap[
-```{r linear-log-scales2, echo = FALSE, fig.width = 8, fig.asp = (1/3)*0.5, dev = "svg"}
+```{r linear-log-scales2, echo = FALSE, fig.width = 10, fig.asp = (1/3)*0.5, dev = "svg"}
 ggdraw(plotlist[[2]])
 ```
 ]
@@ -282,7 +282,7 @@ ggdraw(plotlist[[2]])
 --
 
 .center.nogap[
-```{r linear-log-scales3, echo = FALSE, fig.width = 8, fig.asp = (1/3)*0.5, dev = "svg"}
+```{r linear-log-scales3, echo = FALSE, fig.width = 10, fig.asp = (1/3)*0.5, dev = "svg"}
 ggdraw(plotlist[[3]])
 ```
 ]
@@ -294,7 +294,7 @@ ggdraw(plotlist[[3]])
 A linear scale emphasizes large counties
 
 .center.nogap[
-```{r texas-counties-linear, echo = FALSE, message = FALSE, fig.width = 7, fig.asp = 0.6, dev = "svg"}
+```{r texas-counties-linear, echo = FALSE, message = FALSE, fig.width = 9, fig.asp = 0.6, dev = "svg"}
 US_census <- read_csv(here("datasets", "US_census.csv"))
 
 library(ggrepel)
@@ -316,7 +316,7 @@ ggplot(tx_counties, aes(x = index, y = popratio)) +
   geom_point(size = 0.5, color = "#0072B2") +
   geom_text_repel(
     aes(label = label_large),
-    size = 10/.pt,
+    size = 14/.pt,
     point.padding = .4, color = "black",
     min.segment.length = 0,
     max.overlaps = 1000
@@ -327,7 +327,7 @@ ggplot(tx_counties, aes(x = index, y = popratio)) +
     breaks = NULL, #c(1, 50*(1:5)),
     name = "Texas counties, from most to least populous"
   ) +
-  theme_minimal_hgrid(14) +
+  theme_minimal_hgrid(16) +
   theme(axis.line = element_blank())
 ```
 ]
@@ -345,13 +345,13 @@ Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reill
 A log scale shows symmetry around the median
 
 .center.nogap[
-```{r texas-counties-log, echo = FALSE, fig.width = 7, fig.asp = 0.6, dev = "svg"}
+```{r texas-counties-log, echo = FALSE, fig.width = 9, fig.asp = 0.6, dev = "svg"}
 ggplot(tx_counties, aes(x = index, y = popratio)) +
   geom_hline(yintercept = 1, linetype = 2, color = "grey40") +
   geom_point(size = 0.5, color = "#0072B2") +
   geom_text_repel(
     aes(label = label),
-    size = 10/.pt,
+    size = 14/.pt,
     point.padding = .4, color = "black",
     min.segment.length = 0,
     max.overlaps = 1000
@@ -366,7 +366,7 @@ ggplot(tx_counties, aes(x = index, y = popratio)) +
     breaks = NULL, #c(1, 50*(1:5)),
     name = "Texas counties, from most to least populous"
   ) +
-  theme_minimal_hgrid(14) +
+  theme_minimal_hgrid(16) +
   theme(axis.line = element_blank())
 ```
 ]
@@ -381,7 +381,7 @@ Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reill
 ## Nonlinear coordinate systems: Polar coordinates
 
 .pull-left[
-```{r cartesian-polar-left, echo = FALSE, fig.width = 5, fig.asp = 1, dev = "svg"}
+```{r cartesian-polar-left, echo = FALSE, fig.width = 6, fig.asp = 1, dev = "svg"}
 df_points <- tibble(
   x = c(1, 3.5, 0),
   y = c(3, 4, 0),
@@ -430,7 +430,7 @@ Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reill
 --
 
 .pull-right[
-```{r cartesian-polar-right, echo = FALSE, fig.width = 5, fig.asp = 1, dev = "svg"}
+```{r cartesian-polar-right, echo = FALSE, fig.width = 6, fig.asp = 1, dev = "svg"}
 ggplot(df_points, aes(x, y)) +
   geom_segment(
     data = df_segments,
@@ -467,7 +467,7 @@ ggplot(df_points, aes(x, y)) +
 ## Cartesian vs polar example
 
 .pull-left[
-```{r temp-normals-vs-time-cartesian, echo=FALSE, message=FALSE, fig.width = 5, fig.asp = 0.618, dev="svg"}
+```{r temp-normals-vs-time-cartesian, echo=FALSE, message=FALSE, fig.width = 6.5, fig.asp = 0.618, dev="svg"}
 temps_long <- read_csv(here("datasets", "tempnormals.csv")) %>%
   mutate(
     location = factor(
@@ -495,7 +495,7 @@ ggplot(temps_long, aes(x = date, y = temperature, color = location)) +
     name = NULL,
     values = c("#E69F00", "#56B4E9", "#009E73", "#CC79A7")
   ) +
-  theme_minimal_grid(14)
+  theme_minimal_grid(16)
 ```
 ]
 
@@ -506,7 +506,7 @@ Figure redrawn from [Claus O. Wilke. Fundamentals of Data Visualization. O'Reill
 --
 
 .pull-right[
-```{r temp-normals-vs-time-polar, echo=FALSE, message=FALSE, fig.width = 5, fig.asp = 0.618, dev="svg"}
+```{r temp-normals-vs-time-polar, echo=FALSE, message=FALSE, fig.width = 6.5, fig.asp = 0.618, dev="svg"}
 ggplot(temps_long, aes(x = date, y = temperature, color = location)) +
   geom_line(linewidth = 1) +
   scale_x_date(name = "date", expand = c(0, 0)) + 
@@ -521,7 +521,7 @@ ggplot(temps_long, aes(x = date, y = temperature, color = location)) +
     values = c("#E69F00", "#56B4E9", "#009E73", "#CC79A7")
   ) +
   coord_polar(theta = "x") +
-  theme_minimal_grid(14)
+  theme_minimal_grid(16)
 ```
 ]
 
@@ -844,7 +844,7 @@ ggplot(temperatures, aes(day_of_year, temperature, color = location)) +
 ```
 
 .center[
-```{r temperatures-cartesian-out, echo = FALSE, ref.label="temperatures-cartesian", fig.width = 5.5, fig.asp = 0.618, dev = "svg"}
+```{r temperatures-cartesian-out, echo = FALSE, ref.label="temperatures-cartesian", fig.width = 6.5, fig.asp = 0.618, dev = "svg"}
 
 ```
 ]]
@@ -862,7 +862,7 @@ ggplot(temperatures, aes(day_of_year, temperature, color = location)) +
 ```
 
 .center[
-```{r temperatures-polar-out, echo = FALSE, ref.label="temperatures-polar", fig.width = 5.5, fig.asp = 0.618, dev = "svg"}
+```{r temperatures-polar-out, echo = FALSE, ref.label="temperatures-polar", fig.width = 6.5, fig.asp = 0.618, dev = "svg"}
 
 ```
 ]]
@@ -881,7 +881,7 @@ ggplot(temperatures, aes(day_of_year, temperature, color = location)) +
 ```
 
 .center[
-```{r temperatures-polar2-out, echo = FALSE, ref.label="temperatures-polar2", fig.width = 5.5, fig.asp = 0.618, dev = "svg"}
+```{r temperatures-polar2-out, echo = FALSE, ref.label="temperatures-polar2", fig.width = 6.5, fig.asp = 0.618, dev = "svg"}
 
 ```
 ]]

slides/coordinate-systems-axes.html

@@ -19,7 +19,7 @@
 ### Claus O. Wilke
 ]
 .date[
-### last updated: 2023-01-19
+### last updated: 2024-01-22
 ]
 
 ---