Minimal. No dependencies.
Data manipulation in one package and in base R.
‘dplyr’ and ‘tidyr’-like in one place.
‘pipe’ effect replaced by object ‘m61r’.
Nothing else than base R to build the package.
R-package ‘m61r’ is at the core of m61r-cli, data manipulation in bash command line.
install.packages("m61r")setwd("~")
download.file("https://github.com/pv71u98h1/m61r/archive/0.0.3.zip",destfile="m61r-0.0.3.zip")
unzip("m61r-0.0.3.zip")
# install
install.packages(file.path("~","m61r-0.0.3"), repos=NULL, type='source')
# build vignettes
vign <- list.files(file.path("~","m61r-0.0.3","vignettes"))
dir.create(file.path("~","m61r-0.0.3","inst","doc"),recursive=TRUE)
lapply(vign,function(x){
tools::buildVignette(file = file.path("~","m61r-0.0.3","vignettes",x),
dir = file.path("~","m61r-0.0.3","inst","doc"))
})
# install the vignettes
install.packages(file.path("~","m61r-0.0.3"), repos=NULL, type='source')
# clean
unlink(file.path("~","m61r-0.0.3"),recursive=TRUE)
file.remove(file.path("~","m61r-0.0.3.zip"))library(m61r)
co2 <- m61r(CO2)
co2$filter(~Plant %in% c("Qn1","Qc3"))
co2$mutate(z1=~uptake/conc,y=~conc/100)
co2$group_by(~c(Type,Treatment))
co2$summarise(foo=~mean(z1),bar=~sd(y))
co2 # print results
head(co2) # back to normal
# init
co2 <- m61r(df=CO2)
# filter
co2$filter(~Plant=="Qn1")
co2
co2$filter(~Type=="Quebec")
co2
# select
co2$select(~Type)
co2
co2$select(~c(Plant,Type))
co2
co2$select(~-Type)
co2
co2$select(variable=~-(Plant:Treatment))
co2
# mutate/transmutate
co2$mutate(z=~conc/uptake)
co2
co2$mutate(mean=~mean(uptake))
co2
co2$mutate(z1=~uptake/conc,y=~conc/100)
co2
co2$transmutate(z2=~uptake/conc,y2=~conc/100)
co2
# summarise
co2$summarise(mean=~mean(uptake),sd=~sd(uptake))
co2
co2$group_by(~c(Type,Treatment))
co2$summarise(mean=~mean(uptake),sd=~sd(uptake))
co2
# arrange/dessange
co2$arrange(~c(conc))
co2
co2$arrange(~c(Treatment,conc,uptake))
co2
co2$desange(~c(Treatment,conc,uptake))
co2
# join
authors <- data.frame(
surname = I(c("Tukey", "Venables", "Tierney", "Ripley", "McNeil")),
nationality = c("US", "Australia", "US", "UK", "Australia"),
deceased = c("yes", rep("no", 4)))
books <- data.frame(
name = I(c("Tukey", "Venables", "Tierney","Ripley",
"Ripley", "McNeil", "R Core")),
title = c("Exploratory Data Analysis",
"Modern Applied Statistics ...",
"LISP-STAT",
"Spatial Statistics", "Stochastic Simulation",
"Interactive Data Analysis",
"An Introduction to R"),
other.author = c(NA, "Ripley", NA, NA, NA, NA,"Venables & Smith"))
## inner join
tmp <- m61r(df=authors)
tmp$inner_join(books, by.x = "surname", by.y = "name")
tmp
## left join
tmp$left_join(books, by.x = "surname", by.y = "name")
tmp
## right join
tmp$right_join(books, by.x = "surname", by.y = "name")
tmp
## full join
tmp$full_join(books, by.x = "surname", by.y = "name")
tmp
## semi join
tmp$semi_join(books, by.x = "surname", by.y = "name")
tmp
## anti join #1
tmp$anti_join(books, by.x = "surname", by.y = "name")
tmp
## anti join #2
tmp2 <- m61r(df=books)
tmp2$anti_join(authors, by.x = "name", by.y = "surname")
tmp2
## with two m61r objects
tmp1 <- m61r(books)
tmp2 <- m61r(authors)
tmp3 <- anti_join(tmp1,tmp2, by.x = "name", by.y = "surname")
tmp3
# Reshape
## gather
df3 <- data.frame(id = 1:4,
age = c(40,50,60,50),
dose.a1 = c(1,2,1,2),
dose.a2 = c(2,1,2,1),
dose.a14 = c(3,3,3,3))
df4 <- m61r::m61r(df3)
df4$gather(pivot = c("id","age"))
df4
## spread
df3 <- data.frame(id = 1:4,
age = c(40,50,60,50),
dose.a1 = c(1,2,1,2),
dose.a2 = c(2,1,2,1),
dose.a14 = c(3,3,3,3))
df4 <- m61r::gather_(df3,pivot = c("id","age"))
df4 <- rbind(df4,
data.frame(id=5, age=20,parameters="dose.a14",values=8),
data.frame(id=6, age=10,parameters="dose.a1",values=5))
tmp <- m61r::m61r(df4)
tmp$spread(col_name="parameters",col_values="values",pivot=c("id","age"))
tmp
# equivalence
co2 # is not equivalent to co2[]
co2[] # is equivalent to co2$values()
co2[1,] # is equivalent to co2$values(1,)
co2[,2:3] # is equivalent to co2$values(,2:3)
co2[1:10,1:3] # is equivalent to co2$values(1:10,2:3)
co2[1,"Plant"]# is equivalent to co2$values(1,"Plant")
# modification on m61r object only stay for one step
co2[1,"conc"] <- 100
co2[1,] # temporary result
co2[1,] # back to normal
# WARNING:
# Keep the brackets to manipulate the intern data.frame
co2[] <- co2[-1,]
co2[1:3,] # temporary result
co2[1:3,] # back to normal
# ... OR you will destroy co2, and only keep the data.frame
# co2 <- co2[-1,]
# class(co2) # data.frame
# descriptive manipulation
names(co2)
dim(co2)
str(co2)
## cloning
# The following will only create a second variable that point on
# the same object (!= cloning)
foo <- co2
str(co2)
str(foo)
# Instead, cloning into a new environemnt
foo <- co2$clone()
str(co2)
str(foo)