dplyr.Rmd

---
title: "dplyr - An accompanian to dplyr Cheat Sheet"
author: "Thu Nguyen"
output: pdf_document #beamer_presentation
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```

## R Cheat Sheet

- RStudio >>> Help >>> Cheatsheet
- data()

Data sets in package 'datasets':

AirPassengers                       Monthly Airline Passenger Numbers 1949-1960

BJsales                             Sales Data with Leading Indicator

...

- data(package = "MASS")

Data sets in package 'MASS':

Aids2                               Australian AIDS Survival Data

Animals                             Brain and Body Weights for 28 Species

....

## Summarise cases
```{r, echo = TRUE}
library(dplyr)
summarise(mtcars,avg=mean(mpg))
count(iris,Species)
```
- n_distinct()
```{r, echo = TRUE}
x = 1:5
x = c(x,x)
x
n_distinct(x)
```
## Group_by and pipes
```{r, echo = TRUE}
iris %>%
  group_by(Species) %>%
  summarise_all(mean)
# g_iris = group_by(iris,Species)
# upgroup(g_iris)
starwars %>% summarise_at(c("height", "mass"), mean, na.rm = TRUE)
starwars %>% summarise_if(is.numeric, mean, na.rm = TRUE)
```

## Manipulate cases
### Filter
```{r, echo = TRUE}
filter(iris, Sepal.Length > 7)[1:5,]
# Multiple criteria
filter(starwars, hair_color == "none" & eye_color == "black")[1:5,]
filter(starwars, hair_color == "none" | eye_color == "black")[1:5,]
# Multiple arguments are equivalent to and
filter(starwars, hair_color == "none", eye_color == "black")[1:5,]
```
### Distinct
```{r, echo = TRUE}
df <- tibble(
  g = c(1, 1, 2, 2),
  x = c(1, 1, 2, 1)
) 
df
df %>% distinct()
df %>% distinct(x)
```
### Sample
```{r, echo = TRUE}
sample_frac(mtcars, 0.1)
sample_frac(mtcars, 1.5, replace = TRUE)[1:5,]
sample_frac(mtcars, 0.1, weight = 1 / mpg)
```
weight: Sampling weights. This must evaluate to a vector of non-negative numbers the same length as the input. Weights are automatically standardised to sum to 1.
```{r, echo=TRUE}
by_cyl <- mtcars %>% group_by(cyl)
sample_n(by_cyl, 3)
sample_n(by_cyl, 7, replace = TRUE)
sample_n(by_cyl, 3, weight = mpg / mean(mpg))
```

### Arrange
Use desc() to sort a variable in descending order.
```{r, echo = TRUE}
arrange(mtcars, cyl, disp)[1:5,]
arrange(mtcars, desc(disp))[1:5,]
```

## Manipulate variables
- Select/rename variables by name:
```{r, echo = TRUE}
select(iris, starts_with("Petal"))[1:5,]
select(iris, ends_with("Width"))[1:5,]
# Drop variables with -
select(iris, -starts_with("Petal"))[1:5,]
```
Some useful helpers to use with select(): contains(), matches(), num_range(prefix,range), one_of, everything(), starts_with, ends_with()

- mutate() adds new variables and preserves existing; transmute(): transform existing variables
```{r, echo = TRUE}
mtcars[1:5,] %>%
  transmute(displ_l = disp / 61.0237)

mtcars %>%
 group_by(cyl) %>%
 mutate(rank = min_rank(desc(mpg)))

iris %>% 
  group_by(Species)%>% 
  mutate_all(funs(. / 2.54))

mutate_if(iris, is.numeric,funs(log(.)))[1:5,]

mutate_at(iris,vars(-Species), funs(log(.),log2(.)))[1:5,]

```

## Vetorized function: misc: 
- between()
```{r, echo = TRUE}
x = 2*(1:10)
x
between(x,6,13)
x[between(x,6,13)]
```

- case_when()
```{r, echo = TRUE}
mtcars$carb
mtcars %>%  
     mutate(cg = case_when(.$carb <= 2 ~ "low",  .$carb > 2 ~ "high")) %>%
    .$cg %>%
    table()
```

- na_if()
```{r, echo = TRUE}
y <- c("abc", "def", "", "ghi")
na_if(y, "")
```

## Names: library(tibble)

```{r, echo=TRUE}
names(iris)
rename(iris, Length = Sepal.Length)[1:3,]
library(tibble)
# ? rownames_to_column
mtcars[1:3,]
rownames_to_column(mtcars)[1:3,]
```

```{r, echo = TRUE}
x= rownames_to_column(mtcars)
column_to_rownames(x,"rowname")[1:3,]
```

## Combine 
- cbind(): column bind
- rbind(): row bind
```{r, echo = TRUE}
x = 1:5
u = 2*x
rbind(x,u)
cbind(x,u)
```

- inner_join(): return all rows from x where there are matching values in y, and all columns from x and y. If there are multiple matches between x and y, all combination of the matches are returned.

- left_join(): return all rows from x, and all columns from x and y. Rows in x with no match in y will have NA values in the new columns. If there are multiple matches between x and y, all combinations of the matches are returned.

- right_join(): return all rows from y, and all columns from x and y. Rows in y with no match in x will have NA values in the new columns. If there are multiple matches between x and y, all combinations of the matches are returned.

- full_join(): return all rows and all columns from both x and y. Where there are not matching values, returns NA for the one missing.

- semi_join(): return all rows from x where there are matching values in y, keeping just columns from x.
A semi join differs from an inner join because an inner join will return one row of x for each matching row of y, where a semi join will never duplicate rows of x.

- anti_join(): return all rows from x where there are not matching values in y, keeping just columns from x.

```{r, echo = TRUE}
head(band_members)
band_members %>% inner_join(band_instruments)
band_members %>% left_join(band_instruments)
band_members %>% right_join(band_instruments)
band_members %>% full_join(band_instruments)
band_members %>% semi_join(band_instruments)
band_members %>% anti_join(band_instruments)
#  Use a named `by` if the join variables have different names
band_members %>% full_join(band_instruments2, by = c("name" = "artist"))
```

- intersect(x, y, ...)

- union(x, y, ...)

- union_all(x, y, ...)

- setdiff(x, y, ...)

- setequal(x, y, ...)
```{r, echo = TRUE}
mtcars$model <- rownames(mtcars)
first <- mtcars[1:20, ]
second <- mtcars[10:32, ]

intersect(first, second)[1:4,]
union(first, second)[1:4,]
setdiff(first, second)[1:4,]
setdiff(second, first)[1:4,]

union_all(first, second)[1:4,]
setequal(mtcars, mtcars[12:1, ])

```