Introduction

The R-package m61r gathers functions similar to the ones present in dplyr and tidyr, but written entirely in Base R without any external dependencies. All functions are designed to work directly with standard data.frames.

filter

The filter_ function subsets a data frame, retaining all rows that satisfy your specific conditions.

tmp <- filter_(CO2, ~Plant == "Qn1")
head(tmp)

##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4
## 3   Qn1 Quebec nonchilled  250   34.8
## 4   Qn1 Quebec nonchilled  350   37.2
## 5   Qn1 Quebec nonchilled  500   35.3
## 6   Qn1 Quebec nonchilled  675   39.2

select

The select_ function allows you to zoom in on specific columns of interest.

tmp <- select_(CO2, ~c(Plant, Type))
head(tmp, 2)

##   Plant   Type
## 1   Qn1 Quebec
## 2   Qn1 Quebec

Transformative selections of a data.frame

mutate_ adds new variables while preserving existing ones, whereas transmutate_ keeps only the newly created variables.

tmp <- mutate_(CO2, z = ~conc / uptake)
head(tmp, 2)

##   Plant   Type  Treatment conc uptake        z
## 1   Qn1 Quebec nonchilled   95   16.0 5.937500
## 2   Qn1 Quebec nonchilled  175   30.4 5.756579

summarise

summarise_ creates a new data frame with aggregated statistics. It can be used on the whole data frame or on specific groups.

# Global summary
summarise_(CO2, mean = ~mean(uptake), sd = ~sd(uptake))

##      mean       sd
## 1 27.2131 10.81441

# Grouped summary
g_info <- get_group_indices_(CO2, ~c(Type, Treatment))
summarise_(CO2, group_info = g_info, mean = ~mean(uptake))

##          Type  Treatment     mean
## 1      Quebec nonchilled 35.33333
## 2 Mississippi nonchilled 25.95238
## 3      Quebec    chilled 31.75238
## 4 Mississippi    chilled 15.81429

join

m61r provides a full suite of join functions. Here is an example of an inner join between two datasets.

inner_join_(authors, books, by.x = "surname", by.y = "name")

##    surname nationality       title
## 1    Tukey          US         EDA
## 2 Venables   Australia        MASS
## 3  Tierney          US   LISP-STAT
## 4   Ripley          UK     Spatial
## 5   McNeil   Australia Interactive

reshape

The gather_ function transforms data from a “wide” format to a “long” format, making it easier to analyse certain types of data.

df3 <- data.frame(id = 1:2, age = c(40, 50), dose.a1 = c(1, 2), dose.a2 = c(2, 1))
df4 <- gather_(df3, pivot = c("id", "age"))
df4

##   id age parameters values
## 1  1  40    dose.a1      1
## 2  2  50    dose.a1      2
## 3  1  40    dose.a2      2
## 4  2  50    dose.a2      1