这一周R语言学习，记录如下。

01

多图排列组合

patchwork包可以实现多图排列组合，并且功能强大、操作灵活。

# 多图排列组合

library(tidyverse)

library(patchwork)

theme_set(theme_light)

dat <- palmerpenguins::penguins %>% filter(!is.na(sex))

dat %>% View

# 使用patchwork包进行多图排列组合

# 第1个图

point_plot <- dat %>%

ggplot(aes(bill_length_mm, flipper_length_mm, fill = sex)) +

geom_jitter(size = 3, alpha = 0.5, shape = 21)

point_plot

# 第2个图

point_plot2 <- dat %>%

ggplot(aes(bill_length_mm, bill_depth_mm, fill = sex)) +

geom_jitter(size = 3, alpha = 0.5, shape = 21)

point_plot2

# 第3个图

# plot_plot is obviously a fun name

boxplot_plot <- dat %>%

ggplot(aes(x = body_mass_g, fill = sex)) +

geom_boxplot

boxplot_plot

# 上面3个图排列和组合

# 使用patchwork包

p <- (point_plot + point_plot2) / boxplot_plot

p

# 微调

# 图例管理

p + plot_layout(guides = "collect")

结果图

02

使用ggforce包创建Subplots

ggforce包的facet_matrix函数简易创建Subplots。

library(ggforce)

dat %>%

ggplot(aes(x = .panel_x, y = .panel_y, fill = sex)) +

geom_point(alpha = 0.5, size = 2, shape = 21) +

facet_matrix(

vars(bill_length_mm, flipper_length_mm, bill_depth_mm, body_mass_g)

)

dat %>%

ggplot +

geom_boxplot(

aes(

x = .panel_x,

y = .panel_y,

fill = island,

group= interaction(.panel_x, island)

)

) +

facet_matrix(

cols = vars(sex, species),

rows = vars(bill_depth_mm:body_mass_g)

)

结果图

03

RStudio与Github协同

RStudio与Github协同工作，进行代码管理。

1 准备工作：

1）安装R和RStudio

2）安装Git

3）注册Github账号

如下图，我的Github账号：wangluqing

我的Github：

https://github.com/wangluqing

2 配置操作

1）生成秘钥

打开git bash，参考命令

$ git config -- globaluser.name "wanglq"

$ git config -- globaluser.email wangluqing360@ 163.com

$ git config -- globaluser.name

$ git config -- globaluser.email

$ git config -- list

ssh-keygen -t rsa -C "[email protected]"

2）配置秘钥

复制上面中生成的 id_rsa.pub 中的内容，之后登陆到 GitHub，在右上角的头像上点击 Settings - SSH and
GPG keys，点击右边的 New SSH Key，然后将复制好的内容粘贴进去，标题自己随意取一个，比如 ds
key，这样就完成了远程端的配置。

3）测试连接

在 git bash，输入如下命令

ssh-Tgit@ github. com

结果

3 RStudio与Github协同

1）登录Github，创建一个项目库，例如：Tidyverse_Study_Project

2）打开项目库，复制ssh链接，如下图：

3）打开RStudio软件，选择使用版本控制创建项目，如下图：

项目创建成功后，就可以看到如下界面：

我的这个界面布局做了配置以及在项目下面创建了一些R脚本。

4）项目建设和版本管理

我们根据项目的任务和目标，在项目库下面创建一系列R脚本，每次创建完后，调试成功通过后，请同步到Github。

打开Shell，执行如下命令就可以了

git status

git add.

git commit -m 'tidyverse study'

git push

push成功后，就可以在Github对应项目库下查看相应代码了。

我创建了 R语言群，可以扫描下方二维码，备注：姓名-R语言，加我微信，进入 R语言群，一起讨论。

04

tidyverse代码学习

tidyverse包是我每天工作都要用到的R语言包。我喜欢 代码学习法，即通过阅读、编写、修改、迁移代码等多种方式来学习知识和技能。

tidyverse包学习的一份代码，总结和巩固常用函数的使用方法和实现功能。

####################

# tidyverse学习代码片段

####################

library(tidyverse)

diamonds %>%

group_by(clarity) %>%

summarise(

m = mean(price)

) %>%

ungroup

# ggplot包内置的数据集

data(package = "ggplot2")

glimpse(diamonds)

str(diamonds)

diamonds %>%

slice_head(n = 100) %>%

View

?diamonds

# dplyr包

# mutate

diamonds %>%

mutate(

JustOne = 1,

Values = "something",

Simple = TRUE

) %>%

slice_tail(n = 100) %>%

View

diamonds %>%

mutate(

price200 = price - 200

) %>%

slice_head(n = 100) %>%

View

diamonds %>%

mutate(

price200 = price - 200,

price20perc = price * 0.20,

price20percoff = price * 0.80,

pricepercarat = price / carat,

pizza = depth ^ 2

) %>%

slice_sample(n = 10) %>%

View

diamonds1 <- diamonds %>%

mutate(

price200 = price - 200,

price20perc = price * 0.20,

price20percoff = price * 0.80,

pricepercarat = price / carat,

pizza = depth ^ 2

) %>%

slice_sample(n = 10)

diamonds %>%

mutate(m = mean(price)) %>%

slice_head(n = 100) %>%

View

diamonds %>%

mutate(

m = mean(price),

sd = sd(price),

med = median(price)

) %>%

slice_head(n = 100) %>%

View

# 使用recode函数对变量取值做重编码操作

diamonds %>%

mutate(

cut_new = recode(

cut,

"Fair"= "Okay",

"Ideal"= "IDEAL"

)

) %>%

slice_head(n = 100) %>%

View

Sex <- factor(c( "male", "m", "M", "Female", "Female", "Female"))

TestScore <- c( 10, 20, 10, 25, 12, 5)

dataset <- tibble(Sex, TestScore)

str(dataset)

dataset %>%

mutate(Sex_new = recode(Sex,

"m"= "Male",

"M"= "Male",

"male"= "Male"))

# summarize函数

diamonds %>%

summarise(avg_price = mean(price))

diamonds %>%

summarise(

avg_price = mean(price),

dbl_price = 2* mean(price),

random_add = 1+ 2,

avg_carat = mean(carat),

stdev_price = sd(price)

) %>%

slice_head(n = 100) %>%

View

# group_by函数和ungroup函数

ID <- c( 1: 50)

Sex <- rep(c( "male", "female"), 25)

Age <- c( 26, 25, 39, 37, 31, 34, 34, 30, 26, 33,

39, 28, 26, 29, 33, 22, 35, 23, 26, 36,

21, 20, 31, 21, 35, 39, 36, 22, 22, 25,

27, 30, 26, 34, 38, 39, 30, 29, 26, 25,

26, 36, 23, 21, 21, 39, 26, 26, 27, 21)

Score <- c( 0.010, 0.418, 0.014, 0.090, 0.061, 0.328, 0.656, 0.002, 0.639, 0.173,

0.076, 0.152, 0.467, 0.186, 0.520, 0.493, 0.388, 0.501, 0.800, 0.482,

0.384, 0.046, 0.920, 0.865, 0.625, 0.035, 0.501, 0.851, 0.285, 0.752,

0.686, 0.339, 0.710, 0.665, 0.214, 0.560, 0.287, 0.665, 0.630, 0.567,

0.812, 0.637, 0.772, 0.905, 0.405, 0.363, 0.773, 0.410, 0.535, 0.449)

data <- tibble(ID, Sex, Age, Score)

data %>%

group_by(Sex) %>%

summarise(

m = mean(Score),

s = sd(Score),

n = n

) %>%

View

data %>%

group_by(Sex) %>%

mutate(m = mean(Score)) %>%

ungroup

# filter函数

diamonds %>%

filter(cut == "Fair") %>%

slice_head(n = 100) %>%

View

diamonds %>%

filter(cut % in% c( "Fair", "Good"), price <= 600) %>%

slice_head(n = 100) %>%

View

diamonds %>%

filter(cut == "Fair", cut == "Good", price <= 600) %>%

slice_head(n = 100) %>%

View

# select函数

diamonds %>%

select(cut, color) %>%

slice_head(n = 100) %>%

View

diamonds %>%

select(-( 1: 5)) %>%

slice_head(n = 100) %>%

View

diamonds %>%

select(x, y, z, everything) %>%

slice_head(n = 100) %>%

View

diamonds %>%

arrange(cut) %>%

slice_head(n = 100) %>%

View

# count函数

diamonds %>%

count(cut)

# 等价于

diamonds %>%

group_by(cut) %>%

count(cut)

# 等价于

diamonds %>%

group_by(cut) %>%

summarise(n = n)

# rename函数

diamonds %>%

rename(PRICE = price) %>%

slice_head(n = 100) %>%

View

# 等价于

diamonds %>%

mutate(

PRICE = price

) %>%

select(- price) %>%

slice_head(n = 100) %>%

View

# row_number函数

practice <-

tibble(Subject = rep(c( 1, 2, 3), 8),

Date= c( "2019-01-02", "2019-01-02", "2019-01-02",

"2019-01-03", "2019-01-03", "2019-01-03",

"2019-01-04", "2019-01-04", "2019-01-04",

"2019-01-05", "2019-01-05", "2019-01-05",

"2019-01-06", "2019-01-06", "2019-01-06",

"2019-01-07", "2019-01-07", "2019-01-07",

"2019-01-08", "2019-01-08", "2019-01-08",

"2019-01-01", "2019-01-01", "2019-01-01"),

DV = c(sample( 1: 10, 24, replace = TRUE)),

Inject = rep(c( "Pos", "Neg", "Neg", "Neg", "Pos", "Pos"), 4))

practice %>%

mutate(

Session = row_number

) %>% View

practice %>%

group_by(Subject, Inject) %>%

mutate(Session = row_number) %>%

View

# ifelse函数

practice %>%

mutate(Health = ifelse(Subject == 1,

"sick",

"healthy")) %>%

View

每个代码片段具体可以做什么，你可以一边审核一边做运行与写解释，遇到问题可以来R语言群讨论。

05

探索性数据分析

原始数据入手，采用数字化和可视化方式，对数据做探索性分析

助于对数据的理解和认识

基本思路：通过汇总统计和图形表示，以发现模式、异常和检验假设

# R包

library(tidyverse)

library(DataExplorer)

# 数据集

dim(gss_cat)

str(gss_cat)

# 1) 数据集概览

gss_cat %>% glimpse

# 2) 数据集简要

gss_cat %>% introduce

# 3) 数据简要信息可视化

gss_cat %>% plot_intro

# 4) 变量缺失率分析

gss_cat %>% plot_missing

gss_cat %>% profile_missing

# 5) 连续变量可视化

gss_cat %>% plot_density # 密度曲线图

gss_cat %>% plot_histogram # 直方图

# 6) 类别变量可视化

gss_cat %>% plot_bar # 条形图

# 7) 相关性可视化图

gss_cat %>% plot_correlation

gss_cat %>% plot_correlation(maxcat = 5)

# 8) 探索性分析报告

gss_cat %>%

create_report(

output_file = "gss_survey_data_profile_report",

output_dir = "./report/",

y= "rincome",

report_title = "EDA Report"

)

部分结果图

（完整结果，请运行代码自测）

06

制作优美的条形图

条形图，商业分析或者报告中常用的一种图形表示。

我们在做图的时候，要根据 变量集的类型、个数、取值以及想通过数据表达什么信息等，综合考虑选择合适图形类型。

优美的图形，就好比画画，是不断修改和打磨而成。

library(tidyverse)

election_data <- tribble(

~party, ~seats_won,

"Australian Greens", 3,

"Australian Labor Party", 55,

"Liberal", 21,

"The Nationals", 6,

"Other Candidates", 3

)

election_data_sorted <- election_data %>%

mutate(party = fct_reorder(party, seats_won, .desc = TRUE))

ggplot(election_data_sorted,

aes(x = seats_won, y = party, fill = party)) +

geom_vline(xintercept = 44, linetype = 2, colour = "grey20") +

geom_text(x = 45, y = 4, label = "majority of\nparliament",

hjust = 0, size = 11* 0.8/ .pt, colour = "grey20") +

geom_col +

scale_x_continuous(expand = expansion(mult = c( 0, 0.1))) +

scale_y_discrete(limits = rev) +

scale_fill_manual(breaks = c( "Australian Labor Party", "Liberal", "The Nationals",

"Australian Greens", "Other Candidates"),

values = c( "#DE3533", "#0047AB", "#006644",

"#10C25B", "#808080")) +

labs(x = "Number of seats won",

y = "Party",

title = "Victorian election 2018 lower house results",

caption = "Data source: Victorian Electoral Commission") +

theme_bw +

theme(panel.grid.major.y = element_blank,

legend.position = "off")

ggplot(election_data_sorted,

aes(x = seats_won,

xend = 0,

y = party,

yend = party,

colour = party)) +

geom_segment(size = 1.5) +

geom_point(size = 3) +

scale_x_continuous(expand = expansion(mult = c( 0, 0.1))) +

scale_y_discrete(limits = rev) +

scale_colour_manual(breaks = c( "Australian Labor Party", "Liberal", "The Nationals",

"Australian Greens", "Other Candidates"),

values = c( "#DE3533", "#0047AB", "#006644",

"#10C25B", "#808080")) +

labs(x = "Number of seats won",

y = "Party",

title = "Victorian election 2018 lower house results",

caption = "Data source: Victorian Electoral Commission") +

theme_bw +

theme(panel.grid.major.y = element_blank,

legend.position = "off")

结果图：

转自：网络