Part of the tidyverse, it provides practitioners with a host of tools and functions to manipulate data, transform columns and rows, calculate aggregations, and join different datasets together. In this cheat sheet, you'll find a handy list of functions covering dplyr functions —all collected ...
2)使用bind_rows函数,对跨周期的多个数据表做横向拼接,做队列研究 3)使用连接函数(full_join/left_join/inner_join),对同一周期的多个数据表做纵向延伸,做横断面研究 4)使用filter函数,做基于条件的纳排处理 5)使用recode_factor函数,对变量取值做映射编码并为因子类型 6)使用mutate函数,做变量设计和衍生 7)使用...
dplyr now has full support for all two-table verbs provided by SQL: Mutating joins, which add new variables to one table from matching rows in another:inner_join(),left_join(),right_join(),full_join(). (Support for non-equi joins is planned for dplyr 0.5.0.) Filtering joins, which ...
Cheat Sheet Usage library(dplyr)starwars%>% filter(species=="Droid")#> # A tibble: 6 × 14#> name height mass hair_color skin_color eye_color birth_year sex gender#> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>#> 1 C-3PO 167 75 <NA> gold yellow 112 none ma...
inner_join(table_1, table_2, by = "ID") # Full join joins and retains all values full_join(table_1, table_2, by = "ID" That wraps up a brief demonstration of some of dplyr’s excellent functions. For additional information on the functions and their arguments, check out the help ...
This is the first post in a new series featuring translations between R and Python code for common data science and machine learning tasks. A Rosetta Stone, if you will. I’m writing this mainly as a documented cheat sheet for myself, as I’m frequently
3)使用连接函数(full_join/left_join/inner_join),对同一周期的多个数据表做纵向延伸,做横断面研究 4)使用filter函数,做基于条件的纳排处理 5)使用recode_factor函数,对变量取值做映射编码并为因子类型 6)使用mutate函数,做变量设计和衍生 7)使用rename函数,对变量名做重命名 ...
3)使用连接函数(full_join/left_join/inner_join),对同一周期的多个数据表做纵向延伸,做横断面研究 4)使用filter函数,做基于条件的纳排处理 5)使用recode_factor函数,对变量取值做映射编码并为因子类型 6)使用mutate函数,做变量设计和衍生 7)使用rename函数,对变量名做重命名 ...
Cheat SheetUsagelibrary(dplyr) starwars %>% filter(species == "Droid") #> # A tibble: 6 × 14 #> name height mass hair_color skin_color eye_color birth_year sex gender #> <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> #> 1 C-3PO 167 75 <NA> gold yellow ...
Join multiple data frames Example originally from http://stat545.com/bit001_dplyr-cheatsheet.html superheroes <- c("name, alignment, gender, publisher", "Magneto, bad, male, Marvel", "Storm, good, female, Marvel", "Mystique, bad, female, Marvel", "Batman, good, male, DC", "Joker, ...