add practice chapter

_bookdown.yml

@@ -34,6 +34,7 @@ rmd_files: [
   "ggplot2_guides.Rmd",
   "ggplot2_customize.Rmd",
   "ggplot2_stat_layer.Rmd",
+  "ggplot2_colors.Rmd",
   "tidyverse_workflow.Rmd",
   "tidyverse_tips.Rmd",
   "adv_dplyr.Rmd",
@@ -76,10 +77,11 @@ rmd_files: [
   "stars.Rmd",
   "rowwise.Rmd",
   "ggplot2_academic.Rmd",
-  "ggplot2_colors.Rmd",
+
   "ggplot2_gganimate.Rmd",
   "lazyman.Rmd",
-
+ # "pandas.Rmd",
+  "practice.Rmd",
 
   "Appendix.Rmd",
   "references.Rmd"

dplyr.Rmd

@@ -198,7 +198,8 @@ df %>% select(!type)
 
 ## `filter() `
 
-`select`是列方向的选择，我们还可以对数据行方向的选择和筛选，选出符合我们条件的某些行
+`select`是列方向的选择，我们还可以对数据行方向的选择和筛选，选出符合我们条件的某些行^[
+注意，这里filter()函数不是字面上“过滤掉”的意思，而是保留符合条件的行，也就说keep，不是drop的意思。 第一次会有一点点迷惑，我相信习惯就好了]
 
 ```{r dplyr-24, out.width = '65%', echo = FALSE}
 knitr::include_graphics("images/dplyr-filter.png")

forcats.Rmd

@@ -14,6 +14,7 @@
 ## 创建因子
 ```{r forcats-1}
 library(tidyverse)
+library(palmerpenguins)
 ```
 
 ```{r forcats-2}
@@ -167,13 +168,154 @@ d %>%
 ```
 
 
-```{r forcats-20, echo = F}
+
+## 可视化中应用 
+可能没说明白，那就看企鹅柱状图吧
+```{r forcats-20}
+ggplot(penguins, aes(y = species)) +
+  geom_bar()
+```
+
+
+```{r forcats-21}
+ggplot(penguins, aes(y = fct_rev(species))) +
+  geom_bar()
+```
+
+
+
+
+
+```{r forcats-22a, eval=FALSE}
+penguins %>% 
+  count(species) %>% 
+  pull(species)
+
+penguins %>% 
+  count(species) %>% 
+  mutate(species = fct_relevel(species, "Chinstrap", "Gentoo", "Adelie")) %>% 
+  pull(species)
+```
+
+
+
+```{r forcats-22}
+# Move "Chinstrap" in front, rest alphabetic
+ggplot(penguins, aes(y = fct_relevel(species, "Chinstrap"))) +
+  geom_bar()
+```
+
+
+
+```{r forcats-23}
+# Use order "Chinstrap", "Gentoo", "Adelie"
+ggplot(penguins, aes(y = fct_relevel(species, "Chinstrap", "Gentoo", "Adelie"))) +
+  geom_bar()
+```
+
+
+```{r forcats-24}
+penguins %>%
+  mutate(species = fct_relevel(species, "Chinstrap", "Gentoo", "Adelie")) %>%
+  ggplot(aes(y = species)) +
+  geom_bar()
+```
+
+
+```{r forcats-25}
+ggplot(penguins, aes(y = fct_relevel(species, "Adelie", after = Inf))) +
+  geom_bar()
+```
+
+
+```{r forcats-26}
+# Use the order defined by the number of penguins of different species
+# The order is descending, from most frequent to least frequent
+
+penguins %>%
+  mutate(species = fct_infreq(species)) %>%
+  ggplot(aes(y = species)) +
+  geom_bar()
+```
+
+
+```{r forcats-27}
+penguins %>%
+  mutate(species = fct_rev(fct_infreq(species))) %>%
+  ggplot(aes(y = species)) + 
+  geom_bar()
+```
+
+
+```{r forcats-28}
+# Reorder based on numeric values
+penguins %>%
+  count(species) %>%
+  mutate(species = fct_reorder(species, n)) %>%
+  ggplot(aes(n, species)) + 
+  geom_col()
+```
+
+## 作业
+
+- 画出的2007年美洲人口寿命的柱状图，要求从高到低排序
+```{r forcats-29}
+library(gapminder)
+gapminder %>%
+   filter(
+     year == 2007,
+     continent == "Americas"
+)
+```
+
+
+```{r forcats-30, eval=FALSE, echo = FALSE}
+gapminder %>%
+  filter( year == 2007, continent == "Americas") %>%
+  mutate( country = fct_reorder(country, lifeExp)) %>%
+  ggplot(aes(lifeExp, country)) +
+  geom_point()
+```
+
+- 这是四个国家人口寿命的变化图
+```{r forcats-31}
+gapminder %>%
+  filter(country %in% c("Norway", "Portugal", "Spain", "Austria")) %>%
+  ggplot(aes(year, lifeExp)) + geom_line() +
+  facet_wrap(vars(country), nrow = 1)
+```
+
+- 要求给四个分面排序，按每个国家寿命的中位数
+```{r forcats-32, eval=FALSE, echo = FALSE}
+gapminder %>%
+  filter(country %in% c("Norway", "Portugal", "Spain", "Austria")) %>%
+  mutate(country = fct_reorder(country, lifeExp)) %>% # default: order by median
+  ggplot(aes(year, lifeExp)) + geom_line() +
+  facet_wrap(vars(country), nrow = 1)
+```
+
+
+
+- 要求给四个分面排序，按每个国家寿命差（最大值减去最小值）
+
+```{r forcats-33, eval=FALSE, echo = FALSE}
+gapminder %>%
+  filter(country %in% c("Norway", "Portugal", "Spain", "Austria")) %>%
+  # order by custom function: here, difference between max and min
+  mutate(country = fct_reorder(country, lifeExp, function(x) { max(x) - min(x) })) %>%
+  ggplot(aes(year, lifeExp)) + geom_line() +
+  facet_wrap(vars(country), nrow = 1)
+```
+
+
+
+```{r forcats-34, echo = F}
 # remove the objects
 # rm(list=ls())
 rm(d, income, x)
 ```
 
 
-```{r forcats-21, echo = F, message = F, warning = F, results = "hide"}
+```{r forcats-35, echo = F, message = F, warning = F, results = "hide"}
 pacman::p_unload(pacman::p_loaded(), character.only = TRUE)
 ```

ggplot2_colors.Rmd

@@ -31,13 +31,28 @@ knitr::include_graphics("images/hcl-palettes-principles.png")
 三种类型对应着三个函数 `qualitative_hcl()`, `sequential_hcl()`, 和 `diverging_hcl()`. 
 
 
+
 ## 配色模板
 
 ```{r ggplot2-colors-4, out.width = '100%'}
 hcl_palettes(plot = TRUE)
 ```
 
-## 使用案例
+**colorspace** 模板使用起来很方便，有统一格式`scale_<aesthetic>_<datatype>_<colorscale>()`, 
+
+- 这里 `<aesthetic>` 是指定映射 (`fill`, `color`, `colour`),
+- 这里 `<datatype>` 是表明数据变量的类型 (`discrete`, `continuous`, `binned`), 
+- 这里 `colorscale` 是声明颜色标度类型 (`qualitative`, `sequential`, `diverging`, `divergingx`).
+
+
+|Scale function                       | Aesthetic | Data type  | Palette type |
+|:-----------                         | :--------  | :--------- | :------------|
+|`scale_color_discrete_qualitative()` | `color`    | discrete   | qualitative |
+|`scale_fill_continuous_sequential()` | `fill`     | continuous | sequential |
+|`scale_colour_continous_divergingx()` | `colour`   | continuous | diverging |
+
+
+## 使用案例1
 
 ggplot2默认
 ```{r ggplot2-colors-5}
@@ -67,6 +82,126 @@ penguins %>%
 ```
 
 
+## 使用案例2
+
+
+```{r ggplot2-colors-8}
+temps_months <- read_csv("./demo_data/tempnormals.csv") %>%
+  group_by(location, month_name) %>%
+  summarize(mean = mean(temperature)) %>%
+  mutate(
+    month = factor(
+      month_name,
+      levels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")
+    ),
+    location = factor(
+      location, levels = c("Death Valley", "Houston", "San Diego", "Chicago")
+    )
+  ) %>%
+  select(-month_name)
+
+temps_months
+```
+
+
+
+
+
+```{r ggplot2-colors-9}
+temps_months %>% 
+  ggplot(aes(x = month, y = location, fill = mean)) +
+  geom_tile(width = 0.95, height = 0.95) +
+  coord_fixed(expand = FALSE) +
+  theme_classic()
+```
+
+
+```{r ggplot2-colors-10}
+temps_months %>% 
+  ggplot(aes(x = month, y = location, fill = mean)) +
+  geom_tile(width = 0.95, height = 0.95) +
+  coord_fixed(expand = FALSE) +
+  theme_classic() +
+  scale_fill_gradient()
+```
+
+
+```{r ggplot2-colors-11}
+temps_months %>% 
+  ggplot(aes(x = month, y = location, fill = mean)) +
+  geom_tile(width = 0.95, height = 0.95) +
+  coord_fixed(expand = FALSE) +
+  theme_classic() +
+  scale_fill_viridis_c()
+```
+
+
+```{r ggplot2-colors-12}
+temps_months %>% 
+  ggplot(aes(x = month, y = location, fill = mean)) +
+  geom_tile(width = 0.95, height = 0.95) +
+  coord_fixed(expand = FALSE) +
+  theme_classic() +
+  scale_fill_viridis_c(option = "B", begin = 0.15)
+```
+
+
+```{r ggplot2-colors-13}
+temps_months %>% 
+  ggplot(aes(x = month, y = location, fill = mean)) +
+  geom_tile(width = 0.95, height = 0.95) +
+  coord_fixed(expand = FALSE) +
+  theme_classic() +
+  scale_fill_continuous_sequential(palette = "YlGnBu", rev = FALSE)
+```
+
+
+```{r ggplot2-colors-14}
+temps_months %>% 
+  ggplot(aes(x = month, y = location, fill = mean)) +
+  geom_tile(width = 0.95, height = 0.95) +
+  coord_fixed(expand = FALSE) +
+  theme_classic() +
+  scale_fill_continuous_sequential(palette = "Viridis", rev = FALSE)
+```
+
+
+```{r ggplot2-colors-15}
+temps_months %>% 
+  ggplot( aes(x = month, y = location, fill = mean)) +
+  geom_tile(width = 0.95, height = 0.95) +
+  coord_fixed(expand = FALSE) +
+  theme_classic() +
+  scale_fill_continuous_sequential(palette = "Inferno", begin = 0.15, rev = FALSE)
+```
+
+
+```{r ggplot2-colors-16}
+temps_months %>% 
+  ggplot(aes(x = month, y = location, fill = mean)) +
+  geom_tile(width = 0.95, height = 0.95) +
+  coord_fixed(expand = FALSE) +
+  theme_classic() +
+  scale_fill_continuous_sequential(palette = "Plasma", begin = 0.35, rev = FALSE)
+```
+
+## 配色
+
+```{block ggplot2-colors-17, type="danger"}
+这里有个小小的提示：
+
+- 尽可能不避免使用`"red"`, `"green"`, `"blue"`, `"cyan"`, `"magenta"`, `"yellow"`颜色
+- 使用相对柔和的颜色`"firebrick"`, `"springgreen4"`, `"blue3"`, `"turquoise3"`, `"darkorchid2"`, `"gold2"`，会让人觉得舒服
+
+```
+
+
+可以对比下
+```{r ggplot2-colors-18}
+colorspace::swatchplot(c("red", "green", "blue", "cyan", "magenta", "yellow"))
+colorspace::swatchplot(c("firebrick", "springgreen4", "blue3", "turquoise3", "darkorchid2", "gold2"))
+```
+
 
 ## color-wheel
 

ggplot2_gganimate.Rmd

@@ -466,7 +466,7 @@ transition_reveal(along = ) +
 ### Renderer options
 
 ```{r renderer-tbl, echo=FALSE}
-tribble(
+tibble::tribble(
   ~Function, ~Description,
   "gifski_renderer", "Default, super fast gif renderer.",
   "magick_renderer", "Somewhat slower gif renderer.",

ggplot2_scales.Rmd

@@ -1,5 +1,10 @@
 # ggplot2之标度 {#ggplot2-scales}
 
+> 用 ggplot2 画图，有种恋爱的感觉:
+> “你懂我的图谋不轨，我懂你的故作矜持”
+
+
+
 这一章我们一起学习ggplot2中的scales语法，推荐大家阅读Hadley Wickham最新版的[《ggplot2: Elegant Graphics for Data Analysis》](https://ggplot2-book.org/)，但如果需要详细了解**标度**参数体系，还是要看[ggplot2官方文档](https://cran.r-project.org/web/packages/ggplot2/index.html)