arrange(.data, ..., .by_group = FALSE)

msleep%>%group_by(vore)%>%arrange(sleep_total)%>%write_csv("C:/Users/panda/Desktop/R-result/arrange0.csv") msleep%>%group_by(vore)%>%arrange(sleep_total,.by_group=TRUE)%>%write_csv("C:/Users/panda/Desktop/R-result/arrange1.csv")

第一次执行组存在但表中看不出

count(x, ..., wt = NULL, sort = FALSE, name = NULL) tally(x, wt = NULL, sort = FALSE, name = NULL) add_count(x, ..., wt = NULL, sort = FALSE, name = NULL, .drop = deprecated()) add_tally(x, wt = NULL, sort = FALSE, name = NULL)

msleep%>%count(vore,sort=TRUE,name='n')%>%write_csv("C:/Users/panda/Desktop/R-result/count0.csv") msleep%>%count(vore,wt=sleep_total,sort=TRUE,name='n')%>%write_csv("C:/Users/panda/Desktop/R-result/count1.csv") msleep%>%group_by(vore)%>%tally(sort=TRUE,name='n')%>%write_csv("C:/Users/panda/Desktop/R-result/tally.csv") msleep%>%add_count(vore,sort=TRUE,name='n')%>%write_csv("C:/Users/panda/Desktop/R-result/add_count.csv") msleep%>%add_tally(wt=sleep_total,sort=TRUE,name='n')%>%write_csv("C:/Users/panda/Desktop/R-result/add_tally.csv")

.drop = deprecated()
count(列名)==group_by(列名)%>%tally()
add_保留原数据并添加新列(新列重复项肯定多)
wt如果是数值型数据新列是sum的结果

distinct(.data, ..., .keep_all = FALSE)

msleep%>%distinct(vore,.keep_all=TRUE)%>%write_csv("C:/Users/panda/Desktop/R-result/distinct0.csv") msleep%>%distinct(vore)%>%write_csv("C:/Users/panda/Desktop/R-result/distinct1.csv")

.keep_all = TRUE保留所有列并重复时选择第一行

filter(.data, ..., .preserve = FALSE)

msleep%>%group_by(vore)%>%filter(sleep_total>10)%>%write_csv("C:/Users/panda/Desktop/R-result/filter0.csv")

.preserve = FALSE组的数量可能会减少，当在某组没有满足条件的行

mutate( .data, ..., .keep = c("all", "used", "unused", "none"), .before = NULL, .after = NULL ) transmute(.data, ...)

msleep%>%mutate(test=sleep_total*2,.keep='none')%>%write_csv("C:/Users/panda/Desktop/R-result/mutate.csv") msleep%>%transmute(test=sleep_total*2)%>%write_csv("C:/Users/panda/Desktop/R-result/transmute.csv")

mutate(.data,.keep = "none")==transmute(.data)
all保留所有列，used保留使用到的列，unused保留没使用的列，none只保留新列

pull(.data, var = -1, name = NULL, ...)

msleep%>%pull(name ='vore')

pull()只有数据没列名，此段代码包含两列数据
正整数计数从左边，负整数右侧计数，默认值返回最后一个列

relocate(.data, ..., .before = NULL, .after = NULL)

msleep%>%relocate(vore,.before='name')%>%write_csv("C:/Users/panda/Desktop/R-result/relocate.csv")

rename(.data, ...) rename_with(.data, .fn, .cols = everything(), ...)

msleep%>%rename(test=vore)%>%write_csv("C:/Users/panda/Desktop/R-result/rename.csv") msleep%>%rename_with(toupper)%>%write_csv("C:/Users/panda/Desktop/R-result/rename_with.csv")

.cols = everything()

select(.data, ...)

msleep%>%select(vore,sleep_total)%>%write_csv("C:/Users/panda/Desktop/R-result/select.csv")

summarise(.data, ..., .groups = NULL) summarize(.data, ..., .groups = NULL)

msleep%>%group_by(vore)%>%summarize(mean(sleep_total))%>%write_csv("C:/Users/panda/Desktop/R-result/summarise.csv")

.groups参数控制结果的分组结构，"drop_last"去掉最后一个分组，"drop"时去掉所有分组水平，此时没有组属性；"keep"时保持与.data相同的分组结构； "rowwise"每一行自成一组

slice(.data, ..., .preserve = FALSE) slice_head(.data, ..., n, prop) slice_tail(.data, ..., n, prop) slice_min(.data, order_by, ..., n, prop, with_ties = TRUE) slice_max(.data, order_by, ..., n, prop, with_ties = TRUE) slice_sample(.data, ..., n, prop, weight_by = NULL, replace = FALSE)

msleep%>%group_by(vore)%>%slice(1:5)%>%write_csv("C:/Users/panda/Desktop/R-result/slice.csv") msleep%>%slice_head(prop=0.2)%>%write_csv("C:/Users/panda/Desktop/R-result/slice_head.csv") msleep%>%slice_head(n=10)%>%write_csv("C:/Users/panda/Desktop/R-result/slice_tail.csv") msleep%>%group_by(vore)%>%slice_min(sleep_total,n=5,with_ties = TRUE)%>%write_csv("C:/Users/panda/Desktop/R-result/slice_min.csv") msleep%>%group_by(vore)%>%slice_max(sleep_total,n=5,with_ties = FALSE)%>%write_csv("C:/Users/panda/Desktop/R-result/slice_max.csv") msleep%>%slice_sample(n=30,replace=TRUE)%>%write_csv("C:/Users/panda/Desktop/R-result/slice_sample.csv")

.preserve = TRUE与上文作用相同
【dplyr】n, prop提供行数，或选择行的比例
可能会返回比您请求的更多的行，with_ties=FALSE返回第一行
weight_by增加此列值大的抽中概率

bind_rows(..., .id = NULL) bind_cols( ..., .name_repair = c("unique", "universal", "check_unique", "minimal") )

one<-msleep%>%slice_head(n=10) two<-msleep%>%slice_tail(n=10) bind_rows(one,two,.id='id')%>%write_csv("C:/Users/panda/Desktop/R-result/bind_rows.csv") bind_cols(one,two)%>%write_csv("C:/Users/panda/Desktop/R-result/bind_cols.csv")

.id将增加一列，用来区分此行数据来自哪个表
相当于pandas的concat(),横向纵向拼接
.name_repair =c("unique", "universal", "check_unique", "minimal")
检索和修复名称-vec_as_names?vctrs (r-lib.org)

union_all(x, y, ...)

one<-msleep%>%slice_head(n=10) two<-msleep%>%slice_head(n=10) union_all(one,two)%>%write_csv("C:/Users/panda/Desktop/R-result/union_all.csv")

横向拼接，不删除重复值

inner_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE, na_matches = c("na", "never") ) left_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE, na_matches = c("na", "never") ) right_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE, na_matches = c("na", "never") ) full_join( x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ..., keep = FALSE, na_matches = c("na", "never") )

one<-msleep%>%slice_head(n=5) two<-msleep%>%slice_head(n=8) left_join(one,two,by='name')%>%write_csv("C:/Users/panda/Desktop/R-result/left_join.csv") inner_join(one,two,by='name')%>%write_csv("C:/Users/panda/Desktop/R-result/inner_join.csv") right_join(one,two,by='name')%>%write_csv("C:/Users/panda/Desktop/R-result/right_join.csv") full_join(one,two,by='name')%>%write_csv("C:/Users/panda/Desktop/R-result/full_join.csv")

copy = FALSE,suffix = c(".x", ".y"), ..., keep = FALSE,na_matches = c("na", "never")

nest_join(x, y, by = NULL, copy = FALSE, keep = FALSE, name = NULL, ...)

one<-msleep%>%slice_head(n=5) two<-msleep%>%slice_head(n=8) nest_join(one,two,by='name')

nest_join()类似left_join()，返回的形式不一样two
copy = FALSE, keep = FALSE, name = NULL

Core Socialist Value prosperitydemocracycivilityharmony freedomequalityjusticerule of law patriotismdedicationintegrityfriendship

semi_join(x, y, by = NULL, copy = FALSE, ..., na_matches = c("na", "never")) anti_join(x, y, by = NULL, copy = FALSE, ..., na_matches = c("na", "never"))

one<-msleep%>%slice_head(n=5) two<-msleep%>%slice_head(n=8) semi_join(two,one,by='name')%>%write_csv("C:/Users/panda/Desktop/R-result/semi_join.csv") anti_join(two,one,by='name')%>%write_csv("C:/Users/panda/Desktop/R-result/anti_join.csv")

copy = FALSE, ..., na_matches = c("na", "never")

group_by(.data, ..., .add = FALSE, .drop = group_by_drop_default(.data))ungroup(x, ...)

msleep%>%group_by(vore)%>%ungroup()

.add = FALSE, .drop = group_by_drop_default(.data)

group_cols(vars = NULL, data = https://www.it610.com/article/NULL)

msleep%>%group_by(vore)%>%select(group_cols())%>%write_csv("C:/Users/panda/Desktop/R-result/group_cols.csv")

vars = NULL, data = https://www.it610.com/article/NULL

rowwise(data, ...)

msleep%>%rowwise(vore)

推荐阅读

• 

  乡镇电商如何加盟，乡镇电商怎么做
• 

  金铲铲之战1.23b北地拉面熊怎么玩 金铲铲之战时空裂痕1.23b阵容推荐
• 

  太阳的表面温度是多少 太阳的表面温度
• 

  dropbox搭建 Dropbox作为Git服务器的操作步骤
• 

  常见的AirPods问题有哪些,该如何解决？
• 

  Doinb比赛独创卡萨丁100%胜率玩法,前期稳定优势,战绩39-1-14,怎么评价？
• 

  生蚝怎样存放
• 

  win10系统之家官网最新推荐
• 

  python程序设置执行密码，python设置用户名和密码
• 

  冰箱|都说隔夜茶无害，为何夏天不能喝隔夜茶呢？听听老茶客的经验之谈
• 

  LeetCode|LeetCode 剑指 Offer 22. 链表中倒数第k个节点
• 

  如果现在你65岁,怎么过好每一天？
• 

  秋天到了大地回发生什么变化
• 

  爱思助手进行截图的具体使用教程视频 爱思助手进行截图的具体使用教程
• 

  安卓 开机自启热点,s10热点开机自启步骤如图
• 

  土豆炖肉做法 土豆炖肉做法大全家常
• 

  可以制作图片的软件免费的 免费图片转文字app
• 

  新诗《隐痛》（中英双语）
• 

  空调是否漏氟怎么查 空调如何自己检测是否漏氟
• 

  卧室如何摆放家具好看 卧室如何摆放家具，卧室如何摆放家具风水好

• 《学习小组Day6笔记--于多多》
• Tensorflow【branch-官网代码实践-Eager/tf.data/Keras/Graph】_8.19
• ExtJS|Ext.Data.Store中将两个字段合并为一个字段
• 嵌入式开发|ARM中的汇编指令解读（“.text“、“.data“、“.bss“，“.ascii“、“.byte“、“.short“、“.int“、“.long“、“.word“、“.quad“）
• Golang|Golang 内存管理笔记
• 来自keras.datasets的替代方法导入mnist
• Pytorch|Pytorch Torch.utils.data.Sampler
• SQLServer|System.Data.SqlClient.SqlError: 备份集中的数据库备份与现有的 'XXX' 数据库不同。
• vue|vue 前后端分离自定义的状态码如何获取res.data
• R - dplyr 包