reorder chapters

_bookdown.yml

@@ -35,17 +35,17 @@ rmd_files: [
   "tidyverse_tips.Rmd",
   "dot.Rmd",
   "tidyeval.Rmd",
-
-
+  #
+  #                     #work
   "sampling.Rmd",       # 建模篇
   "tidystats.Rmd",
   "lm.Rmd",
   "lmm.Rmd",
   "glm.Rmd",
   "ordinal.Rmd",
   "tidymodels.Rmd",
-
-
+  #
+  #
   "eda_nobel.Rmd",     # 应用篇
   "eda_olympics.Rmd",
   "eda_covid2019.Rmd",

adv_dplyr.Rmd

@@ -444,6 +444,8 @@ glimpse(roster)
 ### purrr & dplyr 技巧
 ```{r message=FALSE, warning=FALSE}
 library(purrr)
+airquality <- as_tibble(airquality)
+
 airquality %>% purrr::map(~ sum(is.na(.)))
 ```
 
@@ -612,7 +614,7 @@ iris %>%
 
 ### across函数替代scope函数
 
-强大的`across()`函数，替代以上`scope`函数(_if, _at, 和 _all函数), 同时`slice_max()`, `slice_min()`, `slice_n()` 将替代 `top_n()`函数。
+强大的`across()`函数，替代以上`scope`函数(_if, _at, 和 _all函数), 同时`slice_max()`, `slice_min()`, `slice_n()` 将替代 `top_n()`函数。请参考阅读第\@ref(colwise) 章。
 
 ```{r, eval = FALSE}
 df %>% mutate_if(is.numeric, mean, na.rm = TRUE)

eda_caribou.Rmd

@@ -335,7 +335,7 @@ p
 ```{r, echo = F}
 # remove the objects
 # rm(list=ls())
-rm(df, example_animal, individuals, location_with_speed, locations, movement, p, top_animal_ids)
+rm(example_animal, individuals, location_with_speed, locations, movement, top_animal_ids)
 ```
 
 ```{r, echo = F, message = F, warning = F, results = "hide"}

eda_penguins.Rmd

@@ -22,7 +22,8 @@ d %>%
   head()
 ```
 
-```{r}
+
+```{r message = FALSE, warning = FALSE}
 library(tidyverse)
 penguins <- read_csv("./demo_data/penguins.csv") %>%
   janitor::clean_names()

ggplot2_theme.Rmd

@@ -40,7 +40,8 @@ glimpse(mpg)
 
 稍微做点数据整理
 ```{r}
-df <- mpg %>%
+df <- mpg %>% 
+  as_tibble() %>% 
   filter(class != "2seater", manufacturer %in% c("toyota", "volkswagen"))
 df
 ```

glm.Rmd

@@ -38,7 +38,7 @@ $$
 
 
 把这**四个前提**画在一张图中
-```{r}
+```{r out.width = '80%', fig.align='center', echo = FALSE}
 knitr::include_graphics(path = "images/LINE.png")
 ```
 
@@ -50,7 +50,7 @@ knitr::include_graphics(path = "images/LINE.png")
 
 > 在受污染的岛屿附近，金枪鱼出现次数
 
-```{r}
+```{r out.width = '50%', fig.align='center', echo = FALSE}
 knitr::include_graphics(path = "images/fishes.png")
 ```
 
@@ -104,6 +104,7 @@ df %>%
     title = "number of fishes (Poisson distribution)"
   )
 ```
+
 这是典型的泊松分布。
 
 ```{r}

index.Rmd

@@ -159,7 +159,7 @@ knitr::include_graphics("images/rbook1.png")
 
 ```{r, eval = FALSE}
 my_packages <- c(
-"brms", "broom", "broom.mixed", "corrr", "countrycode", "cowplot", "cranlogs", "datapasta", "devtools", "dplyr", "forcats", "gapminder", "gganimate", "ggstatsplot", "ggeffects", "ggbeeswarm", "ggforce", "gghighlight", "ggplot2", "ggpubr", "ggraph", "ggrepel", "ggridges", "ggtext", "gt", "gtsummary", "haven", "here", "janitor", "knitr", "latex2exp", "lme4", "lubridate", "maps", "margins", "MASS", "modelr", "naniar", "nycflights13", "ordinal", "pacman", "paletteer",  "patchwork", "performance", "purrr", "readr", "readxl", "remotes", "reprex", "rlang", "rmarkdown", "rstan", "rvest", "scales", "sf", "shadowtext", "showtext", "slider", "stars", "statsExpressions", "stringr", "styler", "tibble", "tibbletime", "tidybayes", "tidygraph", "tidymodels", "tidyr", "tidytext", "tidyverse", "tinytex", "viridis", "visdat", "geoshpere", "ggstatsplot", "datasauRus", "ggimage", "ggthemes", "pacman"
+"brms", "broom", "broom.mixed", "corrr", "countrycode", "cowplot", "cranlogs", "datapasta", "devtools", "dplyr", "forcats", "gapminder", "gganimate", "ggstatsplot", "ggeffects", "ggbeeswarm", "ggforce", "gghighlight", "ggplot2", "ggpubr", "ggraph", "ggrepel", "ggridges", "ggtext", "gt", "gtsummary", "haven", "here", "janitor", "knitr", "latex2exp", "lme4", "lubridate", "maps", "margins", "MASS", "modelr", "naniar", "nycflights13", "ordinal", "pacman", "paletteer",  "patchwork", "performance", "purrr", "readr", "readxl", "remotes", "reprex", "rlang", "rmarkdown", "rstan", "rvest", "scales", "sf", "shadowtext", "showtext", "slider", "stars", "statsExpressions", "stringr", "styler", "tibble", "tibbletime", "tidybayes", "tidygraph", "tidymodels", "tidyr", "tidytext", "tidyverse", "tinytex", "viridis", "visdat", "geoshpere", "ggstatsplot", "datasauRus", "ggimage", "ggthemes", "pacman", "equatiomatic"
 )
 ```
 
@@ -173,7 +173,7 @@ install.packages(my_packages, repos = "http://cran.rstudio.com", dependencies =
 可能用到的开发版本的宏包
 
 ```{r, eval = FALSE}
-remotes::install_github("datalorax/equatiomatic")
+#remotes::install_github("datalorax/equatiomatic")
 devtools::install_github("easystats/report")
 devtools::install_github("kassambara/navdata")
 devtools::install_github('cttobin/ggthemr')

lazyman.Rmd

@@ -4,7 +4,7 @@
 R社区上很多大神，贡献了很多非常优秀的工具，节省了我们的时间，也给我们的生活增添了无限乐趣。我平时逛github的时候时整理一些，现在分享出来供像我一样的懒人用，因此本文档叫“懒人系列”。欢迎大家补充。
 
 ## 列名太乱了
-```{r}
+```{r message = FALSE, warning = FALSE}
 library(tidyverse)
 library(janitor)
 ## install.packages("janitor")

rsconnect/documents/index.Rmd/bookdown.org/wangminjie/R4DS.dcf

@@ -5,6 +5,6 @@ account: wangminjie
 server: bookdown.org
 hostUrl: https://bookdown.org/__api__
 appId: 3039
-bundleId: 25015
+bundleId: 27162
 url: https://bookdown.org/wangminjie/R4DS/
-when: 1594628383.43817
+when: 1598681503.77481

rvest.Rmd

@@ -64,7 +64,8 @@ df_price <-
   mutate_at(vars(name), ~str_remove_all(., " ") ) %>% 
   mutate_at(vars(name), ~if_else( name == "黑龙江", "黑龙江省", .))
 
-df_price
+df_price %>% 
+  head()
 ```
 
 

tidygraph.Rmd

@@ -153,7 +153,7 @@ mtcars data set: R 的内置数据集，记录了32种不同品牌的轿车的
 
 ```{r}
 library(corrr)
-res.cor <- mtcars[, c(1, 3:6)] %>% # (1)
+res.cor <- datasets::mtcars[, c(1, 3:6)] %>% # (1)
   t() %>%
   correlate() %>% # (2)
   shave(upper = TRUE) %>% # (3)
@@ -222,16 +222,17 @@ Note that, to extract the current active data as a tibble, you can use the funct
 
 ```{r}
 # Car groups info
-cars.group <- data_frame(
-  name = rownames(mtcars),
-  cyl = as.factor(mtcars$cyl)
+cars.group <- tibble(
+  name = rownames(datasets::mtcars),
+  cyl = as.factor(datasets::mtcars$cyl)
 )
 cars.group
 ```
 
 
 
 
+
 ###  Modify the nodes data:
 
 ```{r}