使用 RMarkdown 的 child 参数，进行文档拼接。
这样拼接以后的笔记方便复习。
相关问题提交到 Issue

包含 r-base 和 GitHub 上 r-lib 相关的包

1 unload package

detach(package:fortunes, unload=TRUE)

2 remove object

R: Remove Objects from a Specified Environment

rm(...)

3 文本排序

mixedsort {gtools}  R Documentation
Order or Sort strings with embedded numbers so that the numbers are in the correct order

library(gtools)
a <- c("f9","f10","f1")
a

## [1] "f9"  "f10" "f1"

a %>% sort()

## [1] "f1"  "f10" "f9"

a %>% mixedsort()

## [1] "f1"  "f9"  "f10"

4 相关性矩阵处理方式

具体参考因子分析 Factor Analysis

library(psych)
corr.test(mtcars, use = "pairwise.complete.obs")$ci

4.1 `cor`和`as.numeric()`的一种处理方式

mtcars[1:5] %>% 
  cor() %>% 
  as.numeric() %>% 
  # 这样的处理，就让所有的cor变成一个vestor，方便统计。
  as.data.frame()

5 $\hat y$的各种统计指标

mtcars %>%
  lm(mpg ~ disp, data = .) %>%
  predict(.,newdata = mtcars, se.fit = TRUE)

## $fit
##           Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive 
##            23.00544            23.00544            25.14862            18.96635 
##   Hornet Sportabout             Valiant          Duster 360           Merc 240D 
##            14.76241            20.32645            14.76241            23.55360 
##            Merc 230            Merc 280           Merc 280C          Merc 450SE 
##            23.79677            22.69220            22.69220            18.23272 
##          Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental 
##            18.23272            18.23272            10.14632            10.64090 
##   Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla 
##            11.46520            26.35622            26.47987            26.66946 
##       Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28 
##            24.64992            16.49345            17.07046            15.17456 
##    Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa 
##            13.11381            26.34386            24.64168            25.68030 
##      Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E 
##            15.13335            23.62366            17.19410            24.61283 
## 
## $se.fit
##           Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive 
##           0.6643912           0.6643912           0.8153165           0.5889767 
##   Hornet Sportabout             Valiant          Duster 360           Merc 240D 
##           0.8375091           0.5754133           0.8375091           0.6979313 
##            Merc 230            Merc 280           Merc 280C          Merc 450SE 
##           0.7140677           0.6471724           0.6471724           0.6127705 
##          Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental 
##           0.6127705           0.6127705           1.2739033           1.2237098 
##   Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla 
##           1.1413740           0.9184018           0.9294688           0.9465968 
##       Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28 
##           0.7759228           0.7067474           0.6705132           0.8038897 
##    Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa 
##           0.9831357           0.9172997           0.7752901           0.8594940 
##      Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E 
##           0.8071908           0.7025060           0.6633450           0.7730805 
## 
## $df
## [1] 30
## 
## $residual.scale
## [1] 3.251454

se.fit: standard error of predicted means。

6 赋值符号箭头(<-)和等号（=）的区别

参考: 你知道R中的赋值符号箭头(<-)和等号（=）的区别吗？

总结下。

<-相当于定义个一个全局变量， =相当于定义一个局部变量。

mean(x <- 1:10)

## [1] 5.5

##  [1]  1  2  3  4  5  6  7  8  9 10

rm(x)
mean(x = 1:10)

## [1] 5.5

# x # Error: object 'x' not found

因为=相当于定义一个局部变量，没有定义一个全局变量，因此在全局我们调用x的时候，x是不存在的。

matrix(c(1:12),ncol=3)

##      [,1] [,2] [,3]
## [1,]    1    5    9
## [2,]    2    6   10
## [3,]    3    7   11
## [4,]    4    8   12

matrix(c(1:12),ncol <- 3)

##      [,1] [,2] [,3] [,4]
## [1,]    1    4    7   10
## [2,]    2    5    8   11
## [3,]    3    6    9   12

是因为

matrix(data = NA, nrow = 1, ncol = 1, byrow = FALSE,
       dimnames = NULL)

这里ncol <- 3相当于nrow = ncol <- 3，定义一个全局变量，ncol，等于3，然后是的参数nrow等于ncol，在函数中。

因此总结一下，follow大神的code，在定义变量的时候，用<-，在定义参数，局部变量的时候，用=。

7 插入时间

{snippet} ts: # Thu Apr 12 23:03:49 2018 ------------------------------

8 try & install packages

try(require("tidyquant") || install.packages("tidyquant"))可以配合批量列表，完成包的预装。

try(require("OneR") || install.packages("OneR"))

## [1] TRUE

8.1 建立一个专门安装包的`Rproj`

建立一个专门安装包的Rproj，因为有时候安装R包的时候，耽误时间，并且可能中断命令，R还要强制退出。

8.2 R包的结构

参考 Salmon and Csárdi (2020)

path.package("ggplot2")

## [1] "D:/install/R-3.6.0/library/ggplot2"

find.package("ggplot2")

## [1] "D:/install/R-3.6.0/library/ggplot2"

.libPaths()

## [1] "D:/install/R-3.6.0/library"

find.package("ggplot2") %>% 
    fs::dir_tree()

## D:/install/R-3.6.0/library/ggplot2
## +-- CITATION
## +-- data
## |   +-- Rdata.rdb
## |   +-- Rdata.rds
## |   \-- Rdata.rdx
## +-- DESCRIPTION
## +-- doc
## |   +-- extending-ggplot2.html
## |   +-- extending-ggplot2.R
## |   +-- extending-ggplot2.Rmd
## |   +-- ggplot2-in-packages.html
## |   +-- ggplot2-in-packages.R
## |   +-- ggplot2-in-packages.Rmd
## |   +-- ggplot2-specs.html
## |   +-- ggplot2-specs.R
## |   +-- ggplot2-specs.Rmd
## |   \-- index.html
## +-- help
## |   +-- aliases.rds
## |   +-- AnIndex
## |   +-- figures
## |   |   +-- logo.png
## |   |   \-- README-example-1.png
## |   +-- ggplot2.rdb
## |   +-- ggplot2.rdx
## |   \-- paths.rds
## +-- html
## |   +-- 00Index.html
## |   \-- R.css
## +-- INDEX
## +-- LICENSE
## +-- MD5
## +-- Meta
## |   +-- data.rds
## |   +-- features.rds
## |   +-- hsearch.rds
## |   +-- links.rds
## |   +-- nsInfo.rds
## |   +-- package.rds
## |   +-- Rd.rds
## |   \-- vignette.rds
## +-- NAMESPACE
## +-- NEWS.md
## \-- R
##     +-- ggplot2
##     +-- ggplot2.rdb
##     \-- ggplot2.rdx

8.2.1 升级R，不需要重新下载包

如果你是在windows环境下，可以安装一个包：installr。不仅能帮你更新R，还能将原来安装的扩展包配置到新版本下，不用再重新安装。 (张杰 EasyCharts)

library(installr)
updateR()

8.2.2 安装包报错的解决办法

.libPaths()查看使用的R版本装包的路径，删除老的yaml包，再重装。
1. 在RStudio Community 上，有人很多提问yaml包安装报错的办法。
2. .libPaths()列出R包的安装路径，退出R和RStudio，删除yaml文件夹，然后重新安装。 (Hallgrímsson 2018)
3. 另一种办法是查询包安装路径的方式(Dervieux 2018)
  1. 如find.package("ggplot2")
查看自己的R版本是不是用错了，Tools$\to$ Global Option，查看。

file.path(.libPaths(),"ambient") %>% 
    # dir.exists()
    # fs::dir_delete()
    # 没有权限
    clipr::write_clip()

rm -R "C:/Program Files/R/R-3.6.0/library/ambient"

9 参考包的版本

installed.packages() %>% 
  as.data.frame() %>% 
  select(Package,Version) %>% 
  filter(Package == "xgboost")

10 查看特定包有哪些dataset

10.1 安装`vcdExtra`包

# library(vcdExtra)

# datasets("plyr")

10.2 `data()`

但是对于tidyverse和caret这种集成包就不适用了。因此直接使用原始函数。 (Scriven 2014)

names(data())

data(package = "caret")

然后我们发现存在

oilType (oil)                 Fatty acid composition of commercial oils

因此，我们可以调用oilType这个dataset。

11 `NA`相关问题

data.table::data.table(a = c(1,2,NA)) %>% 
  mutate(b = case_when(
    a %in% NA ~ 3,
    TRUE ~ 1
  ))

NA不能用于比较(UCLA: Statistical Consulting Group 2012)，因此用a == NA、a = NA、a -> NA时，都是在做比较，因此系统是报错的。这里的修改的方法是a %in% NA(Mahto 2014)，类似于子集的概念。

12 `eval`和`parse`

"1+1"

## [1] "1+1"

parse(text = "1+1")

## expression(1 + 1)

eval(parse(text = "1+1"))

## [1] 2

parse: 把string表达式化
eval: 实现表达式
别忘记了text参数，这不是第一个参数，因此会报错。

13 中文单位替换函数

data_frame(price = c("300", "2.5k", "3.1万", "4.1k", "3.8万")) %>%
  mutate(number = str_extract(price,"\\d+\\.\\d+|\\d+"),
         number = as.double(number),
         unit = str_extract(price,"k|万"),
         prod = case_when(
           unit == "k" ~ 10^3,
           unit == "万" ~ 10^4,
           is.na(unit) ~ 1
         ),
         final = number*prod,
         final = formattable::accounting(final)
  ) %>%
  select(price, final)

money <- data.frame(price = c("300", "2.5k", "3.1万", "4.1k", "3.8万"))

final <- money$price %>%
  str_replace_all("k", "*1000") %>%
  str_replace_all("万", "*10000") %>%
  sapply(function(x) eval(parse(text = x))) %>%
  data.frame() %>%
  rename(final = ".")

rownames(final) <- NULL

final

14 处理json格式数据进dataframe(Rodriguez 2018)

your_list <- list(
    name = "name",
    age = 20,
    sex = "M",
    classification = list(
        category = list(
            id = 1001,
            loc = c("A", "B", "C")
        ),
        subcategory = list(
            id = 2001,
            loc = c("A", "B", "C")
        ),
        type = list(
            id = 3001,
            loc = c("A", "B", "C")
        )
    )
)
library(tidyverse)
library(jsonlite)
your_list %>%

    # make json, then make list
    toJSON() %>%
    fromJSON() %>%

    # remove classification level
    purrr::flatten() %>%

    # turn nested lists into dataframes
    map_if(is_list, as_tibble) %>%

    # bind_cols needs tibbles to be in lists
    map_if(is_tibble, list) %>%

    # creates nested dataframe
    bind_cols()

15 分离路径函数

这个函数可以分离一个路径的不同层级，并且方便复制粘贴、set函数分析路径。函数构建主要借鉴(Stack Overflow 2015)。

split_path2 <- function(path){
split_path <- function(path) {
  if (dirname(path) %in% c(".", path)) return(basename(path))
  return(c(basename(path), split_path(dirname(path))))
}
data_frame(path = split_path(path)) %>% 
mutate(index = 1:length(path)) %>% 
arrange(desc(index)) %>% 
filter(!path == "") %>% 
select(path)
}

假设一条路径a

a <- "/Users/JiaxiangLi/Downloads/me/trans/imp_rmd"
split_path2(a)

这样方便复制粘贴。

或者可以进行set函数分析路径。

b <- "/Users/JiaxiangLi/Downloads/me/trans"
setdiff(
split_path2(a)$path,
split_path2(b)$path
)

## [1] "imp_rmd"

可以查看路径b少了imp_rmd这一环节。

basename("C:/some_dir/a.ext")
# [1] "a.ext"
dirname("C:/some_dir/a.ext")
# [1] "C:/some_dir"

其实就是这两个函数的思想(Stack Overflow 2010)。

并且这两个函数还可以进行批量操作。

basename(file.path("","p1","p2","p3", c("file1", "file2")))

## [1] "file1" "file2"

dirname(file.path("","p1","p2","p3","filename"))

## [1] "/p1/p2/p3"

16 `file.exists`和`download.file`搭配使用

if(!file.exists("2008.csv.bz2"))
  {download.file("http://stat-computing.org/dataexpo/2009/2008.csv.bz2", "2008.csv.bz2")}
if(!file.exists("2007.csv.bz2"))
  {download.file("http://stat-computing.org/dataexpo/2009/2007.csv.bz2", "2007.csv.bz2")}

17 会计格式的string保持计算功能

"152,821" %>% factor %>% str_replace(',', '') %>% as.numeric

## [1] 152821

"152,821" %>% formattable::accounting()

## [1] 152,821

"152,821" %>% formattable::accounting() %>% as.numeric

## [1] 152821

18 letters

letters

##  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
## [20] "t" "u" "v" "w" "x" "y" "z"

LETTERS

##  [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S"
## [20] "T" "U" "V" "W" "X" "Y" "Z"

LETTERS[1:2]

## [1] "A" "B"

19 当前`Rmd`文件和`Rproj`的绝对路径

我在RStudio Community上进行了回答。

rstudioapi::getActiveProject() # project path
rstudioapi::getActiveDocumentContext()$path # file path

同时也有可视化的选项进行设定，非常方便，如图。

20 提取R对象 (R语言中文社区 2018)

20.1 `drop`参数保持矩阵特性

x <- matrix(1:6,2,3)
x

##      [,1] [,2] [,3]
## [1,]    1    3    5
## [2,]    2    4    6

x[1,2]

## [1] 3

x[1,2,drop=F]

##      [,1]
## [1,]    3

20.2 `[`, `[[`, `$`在list中

x <- list(foo = 1:4, bar = 0.6)
x

## $foo
## [1] 1 2 3 4
## 
## $bar
## [1] 0.6

x[1]

## $foo
## [1] 1 2 3 4

x[[1]]

## [1] 1 2 3 4

x$foo

## [1] 1 2 3 4

x[['foo']]

## [1] 1 2 3 4

x[c(1,2)]

## $foo
## [1] 1 2 3 4
## 
## $bar
## [1] 0.6

20.3 `[[]]`可以后续再次赋值变量

name <- 'foo'
x[[name]]

## [1] 1 2 3 4

x[['foo']]

## [1] 1 2 3 4

x$foo

## [1] 1 2 3 4

x$name

## NULL

20.4 `[[]]`可以有传导功能

x <- list(a = list(10, 12, 14), b = c(3.14, 2.81))
x[[1]][[1]]

## [1] 10

x[[c(1,1)]]

## [1] 10

20.5 模糊匹配

x <- list(aardvark = 1:5)
x$a

## [1] 1 2 3 4 5

x[['a', exact = F]]

## [1] 1 2 3 4 5

20.6 drop = FALSE 保证输出为 data.frame

参考 Developed (2019)

mtcars[, 'cyl', drop = FALSE]

mtcars[, 'cyl']

##  [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4

21 R查找某一元素位置 (常玉俊 2017)

需求驱动，简单最好。(index = 1)

a <- 1:10
a

##  [1]  1  2  3  4  5  6  7  8  9 10

which(a == 5)

## [1] 5

22 字符串中的引号处理 (Abel 2015)

a <- paste("modelCheck(var\"",i,"_d.bug\")",sep="")
a
str_length(a)
b <- paste('modelCheck(var"',i,'_d.bug")',sep="")

所以这么操作以后，虽然\也在print中，但是是不存入string内的，str_length(a)验证了字符串的长度为24，因此不包含\。

23 字符串中有会计符号的处理方法

'8,013.11' %>% typeof()

## [1] "character"

'8,013.11' %>% accounting() %>% typeof()

## [1] "double"

24 cache文件报错

如果R某个cache文件报错了，那么把整个文件夹删除，重新all run。

25 error reading from connection

报错

Error in install.packages : error reading from connection

每次更新RStudio，需要更新下下载包的路径。

\[\text{Global Options}\to\text{Packages}\to\text{CRAN mirror}\]

可以选择上海同济大学。

26 查询文件夹内文件数量

library(tidyverse)
list.files('travel_user_id') %>% length()

27 `read.table`用来读`.txt`和`.sql` (Shamma 2009)

read.table("datacamp.txt", sep="\t")

28 本地安装R包

首先查看是否有Github版本，有的话，打开Shell

git clone ...

下载后，格式为文件夹，

如果Github上没有，去R CRAN上下载，本地解压。

在R console，输入

install.packages("文件路径", repos=NULL, type="source")

例如，

install.packages(here('../','projIN','add2md_0.1.5.tar.gz'), repos=NULL, type="source")

29 rproj 解读

Version: 1.0

RestoreWorkspace: Default
SaveWorkspace: Default
AlwaysSaveHistory: Default

EnableCodeIndexing: Yes
UseSpacesForTab: Yes
NumSpacesForTab: 4
Encoding: UTF-8

RnwWeave: Sweave
LaTeX: pdfLaTeX

NumSpacesForTab: 4限定了缩进选择几个。

30 批量修改文件名

list.files() %>% 
    str_subset('a\\d+.Rmd') %>% 
    file.rename(.,glue('note{1:4}.Rmd'))

去除

list.files('poker',full.names = T) %>% 
    str_subset('.pdf') %>% 
    file.rename(.,str_remove_all(.,'\\(\\d\\)'))

31 展示所有 sub directory 的文件

参考 www.masterdataanalysis.com

#list of subfolders and files within the subfolders
list.files(recursive = TRUE)

应用见github的 README 文件。

invisible 什么数据。

32 R 查询文件编码

read_utf8("a20181115.Rmd") %>% head(1) %>% Encoding()

33 Fatal Error: cannot open the base package RStudio

有一个包更新报错，不能删除旧版本，查询

> .libPaths()
[1] "C:/Program Files/R/R-3.6.0/library"

就不小心删除了。

rm "C:/Program Files/R/R-3.6.0/library"

相当于把 base 包删除了。

如果还记得当时打开的默认 R 版本，就重新安装，相当于下载了 base 包，就可以打开了。

34 层层递进学习函数的结构

参考 https://github.com/r-lib/lobstr

This package is to visualize R object maded by Hadley Wickham.

library(lobstr)
f <- function(x) g(x)
g <- function(x) h(x)
h <- function(x) x
f(cst())

##     x
##  1. +-global::f(cst())
##  2. | \-global::g(x)
##  3. |   \-global::h(x)
##  4. \-lobstr::cst()

add2text::add_wechat_portfolio(cst())

## Error in loadNamespace(name): there is no package called 'add2text'

35 查看两个df是否相等

参考 https://sharla.party/post/comparing-two-dfs/

dplyr::all_equal(mtcars, mtcars[,-1])

## - different number of columns: 11 vs 10

diffdf::diffdf(mtcars, mtcars[,-1])

## Differences found between the objects!
## 
## A summary is given below.
## 
## There are columns in BASE that are not in COMPARE !!
## All rows are shown in table below
## 
##   =========
##    COLUMNS 
##   ---------
##      mpg   
##   ---------

36 If 条件安装包

if (!"pacman" %in% dir(.libPaths())) devtools::install_github("trinker/pacman") 这个命令直观，学习。

37 zip

library(zip)

zip_list("../emotion_detection_2019_ncov/data/train_ dataset.zip")

可以在不解压的情况下，先查看其中文件的信息。
并且也可以知道 csv 文件在压缩前后的大小。
也可以知道文件最后被压缩的时间

38 函数中覆盖 global 变量

参考 http://adv-r.had.co.nz/Environments.html

To make assignments to global variables, superassignment operator, <<-, is used. https://www.datamentor.io/r-programming/environment-scope/

方便另外一个函数调用。

39 file name conversion problem

解决 Github Issue 486 analysis/漏斗图代码.Rmd尽量不要用中文命名，会产生这样的报错。

It looks like you have a problem because the path name has non-English characters in it. (Artem 2018)

file name conversion problem -- name too long? 这是一个误导信息，不是文件名太长，而是有文件名中文字符，或者文档中调用的文件路径有中文导致的。

40 查看R和RStudio版本

R.Version()$version.string
rstudioapi::getVersion()
[1] "R version 3.6.0 (2019-04-26)"
[1] ‘1.3.923’

41 argmax

参考 https://r.789695.n4.nabble.com/argmax-td822125.html argmax in R 为 which.max

42 fs

library(fs)

fs::path_ext_remove("analysis/a20191126101202.Rmd")

## [1] "analysis/a20191126101202"

fs::path_ext("analysis/a20191126101202.Rmd")

## [1] "Rmd"

is_file_empty("analysis/a20191126101202.Rmd")

## analysis/a20191126101202.Rmd 
##                        FALSE

is_file("analysis/a20191126101202.Rmd")

## analysis/a20191126101202.Rmd 
##                        FALSE

is_dir("analysis/a20191126101202.Rmd")

## analysis/a20191126101202.Rmd 
##                        FALSE

path_ext_set("analysis/a20191126101202.Rmd", ext = "aaa") # 去除原有的 Rmd 换为 aaa https://github.com/r-lib/fs/issues/205

## analysis/a20191126101202.aaa

43 drop = FALSE

参考 Object of type ‘closure’ is not subsettable - Jenny Bryan 在 38:00

mtcars[, "mpg", drop = FALSE]

# 修改了 dim
mtcars[, "mpg"] %>% dim

## NULL

44 安装 Rtools

例如，roxygen2 等底层包是需要的。
rtools 就是集合了 make 等命令行工具

参考 https://cran.r-project.org/bin/windows/Rtools/ 和 https://github.com/r-lib/roxygen2/issues/1128

配置环境变量，名字不要写错了，中间有一个 usr，C:\\rtools40\\usr\\bin\

45 element operation data.frames

参考 https://stackoverflow.com/questions/6408016/paste-together-two-data-frames-element-by-element-in-r

library(tidyverse)

value_df <- mtcars %>% head() %>% select(1:4)
sig_df <-
    matrix(
        "**",
        nrow = nrow(value_df),
        ncol = ncol(value_df),
        dimnames = dimnames(value_df)
    )

matrix(
    paste0(as.matrix(sig_df), as.matrix(value_df), as.matrix(sig_df)),
    nrow = nrow(value_df),
    ncol = ncol(value_df),
    dimnames = dimnames(value_df)
)

##                   mpg        cyl     disp      hp       
## Mazda RX4         "**21**"   "**6**" "**160**" "**110**"
## Mazda RX4 Wag     "**21**"   "**6**" "**160**" "**110**"
## Datsun 710        "**22.8**" "**4**" "**108**" "**93**" 
## Hornet 4 Drive    "**21.4**" "**6**" "**258**" "**110**"
## Hornet Sportabout "**18.7**" "**8**" "**360**" "**175**"
## Valiant           "**18.1**" "**6**" "**225**" "**105**"

# as.data.frame() # 如果要 df 就加上

46 waldo 查询对象差异

参考 r-lib/waldo: Find differences between R objects

# devtools::install_github("r-lib/waldo")
library(waldo)
df1 <- data.frame(x = 1:3, y = 3:1)
df2 <- tibble::tibble(y = 3:1, x = 1:3)
compare(df1, df2)

## `class(x)`:                "data.frame"
## `class(y)`: "tbl_df" "tbl" "data.frame"
## 
## `names(x)`: "x" "y"    
## `names(y)`:     "y" "x"

library(waldo)
df1 <- data.frame(x = 1:3, y = 3:1) %>% matrix()
df2 <- tibble::tibble(y = 3:1, x = 1:3) %>% matrix()
compare(df1, df2)

## `x[[1]]`: 1 2 3    
## `y[[1]]`:     3 2 1
## 
## `x[[2]]`: 3 2 1    
## `y[[2]]`:     1 2 3

47 drop column

参考 https://www.listendata.com/2015/06/r-keep-drop-columns-from-data-frame.html

mtcars %>% names

##  [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear"
## [11] "carb"

mtcars[!names(mtcars) %in% 'cyl'] %>% names

##  [1] "mpg"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear" "carb"

48 extract list from data.frame

参考 https://stackoverflow.com/questions/4605206/drop-data-frame-columns-by-name

mtcars[, 'cyl', drop = TRUE]

##  [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4

包含了一些数据集的 input 条件，最好还是别 run ipynb，改成 py 调用吧。

49 安装包的老版本

参考 https://community.rstudio.com/t/trouble-installing-package-rodbc/66107

要么 R 更新到 4.0，要么用老版本。

packageurl <- "http://cran.r-project.org/src/contrib/Archive/ggplot2/ggplot2_0.9.1.tar.gz"
install.packages(packageurl, repos=NULL, type="source")

50 设置 Python

当不在默认路径里面安装 Python 时，如果重新安装 Python 会导致平时用的 Python 和当前 Python 的库和配置不能共享，很不方便，因此建议如下设置。

library(reticulate)
# reticulate::miniconda_path()
# Sys.setenv(RETICULATE_MINICONDA_PATH = "D:/install/miniconda/")
Sys.getenv("RETICULATE_MINICONDA_PATH")
# reticulate::use_miniconda("name") # work

51 rlang

!! rlang::parse_expr(filter)

library(tidyverse)

mtcars %>% 
    filter(mpg<mean(mpg))

mtcars %>% 
    filter(!! rlang::parse_expr("mpg<mean(mpg)"))

52 Rtools

加速下载链接（使用清华镜像站）： https://mirrors.tuna.tsinghua.edu.cn/CRAN/bin/windows/Rtools/Rtools35.exe 下载镜像。

附录

参考文献

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Rodriguez, Tony. 2018. “How to Read Multilevel Json Data and Convert to Data Frame in R.” 2018. https://community.rstudio.com/t/how-to-read-multilevel-json-data-and-convert-to-data-frame-in-r/7571/11.

R语言中文社区. 2018. “R语言笔记3：提取R对象的子集.” 2018. https://mp.weixin.qq.com/s/_ePkjK3cuoPgvCjnQP0o2A.

Salmon, Maëlle, and Gábor Csárdi. 2020. “State of R Packages in Your Library.” The R-hub blog. 2020. https://blog.r-hub.io/2020/09/03/keep.source/.

Scriven, Rich. 2014. “Get a List of the Data Sets in a Particular Package.” 2014. https://stackoverflow.com/questions/27709936/get-a-list-of-the-data-sets-in-a-particular-package.

Shamma, David Ayman. 2009. “How to Call the Output of a Function in Another Function?” Stack Overflow. 2009. https://stackoverflow.com/questions/1407647/reading-text-files-using-read-table.

Stack Overflow. 2010. “Find File Name from Full File Path.” 2010. https://stackoverflow.com/questions/2548815/find-file-name-from-full-file-path.

———. 2015. “Split a File Path into Folder Names Vector.” 2015. https://stackoverflow.com/questions/29214932/split-a-file-path-into-folder-names-vector.

UCLA: Statistical Consulting Group. 2012. “How Does R Handle Missing Values? | R Faq.” 2012. https://stats.idre.ucla.edu/r/faq/how-does-r-handle-missing-values/.

常玉俊. 2017. “R查找某一元素位置.” 2017. https://blog.csdn.net/chang349276/article/details/77299104.

R Base Notes