1.syntax-Helpful conventions for wrangling
- .tbl_df
converts data to tbl class.tbls arre easier to examine than data frames.R displays only the data that fits onscreen
library(dplyr)
tbl_df(iris)
- .glimpse
Information dense summary of tbl data.
glimpse(iris)
- .View{utils}
View data set in spreadsheet-like display(note capital V)
View(iris)
- .%>%
Passes object on lefthand side as first argument(or.argument) of function on righthand side
x %>% f(y) is the same as f(x,y)
y %>% f(x,.,z) is the same as f(x,y,z)
"Piping" with %>% makes code more readable,e.g.
iris %>%
group_by(Species) %>%
summarise(avg=mean(Sepal.Width)) %>%
arrange(avg)
Tidy Data-A foundation for wrangling in R
2.Reshaping Data -Change the layout of a data set
-. Gather columns into rows
tidyr::gather(cases,"year","n",2:4)
-. Spread rows into columns
tidyr::spread(pollution,size,amount)
-. Separate one column into several.
tidyr::separate(storms,date,c("y","m","d"))
-. Unite several columns into one
tidyr::unite(data,col,...,sep)
library(dplyr)
# Combine vectors into data frame(optimized)
data_frame(a=1:3,b=4:6)
# Order rows by values od a column(low to high)
arrange(mtcars,mpg)
#Order rows by values of a column(high to low)
arrange(mtcars,desc(mpg))
#Rename the columns of a data frame
rename(tb,y=year)
3.Subset Observations(Rows)
library(dplyr)
# Extract rows that meet logical criteria
filter(iris,Sepal.Length>7)
# Remove duplicate rows
distinct(iris)
# Randomly select fraction of rows fraction(分数)
sample_frac(iris,0.5,replace = TRUE)
# Randomly select n rows
sample_n(iris,10,replace = TRUE)
#Select rows by position.
slice(iris,10:15)
#Select and order top n entries(by group if grouped data)
#top_n(storms,2,date)
Logic in R - ?Comparison, ?base::logic
4.Subset Variables(Volumns)
library(dplyr)
#Select columns by name or helper function.
select(iris,Sepal.Width,Petal.Length,Species)
## Helper functions for select - ?select
# Select columns whose name contains a character string.
select(iris,contains("."))
# Select columns whose name ends with a character string.
select(iris,ends_with("Length"))
# Select every column.
select(iris,everything())
# Select columns whose name matches a regular expression
select(iris,matches(".t."))
# Select columns whose names are in a group of names
select(iris,one_of(c("Species","Genus")))
# Select columns named x1,x2,x3,x4,x5
select(iris,num_range("x",1:5))
# Select columns whose name starts with a charactere
select(iris,starts_with("Sepal"))
# Select all columns between Sepal.Length and Petal.Width(inclusive)
select(iris,Sepal.Length:Petal.Width)
# select all columns except Species
select(iris,-Species)
5.Summarise Data
library(dplyr)
# Summarise data into single row of values
summarise(iris,avg = mean(Sepal.Length))
# Apply summary function to wach column
summarise_each(iris,funs(mean))
# Count number of rows with each unique value of variable(with or without weights)
count(iris,Species,wt = Sepal.Length)
Summarise uses summary functions,functions that take a vector of values and return a single value,such as:
...
6.Group Data
# Group data into rows with the same value of Species.
group_by(iris,Species)
# Remove grouping information from data frame
ungroup(iris)
# Compute separate summary row for each group
iris %>% group_by(Species) %>% summarise(...)
# Compute new variables by group
iris %>% group_by(Species) %>% mutate(...)
7.Make New Variables
library(dplyr)
# Compute and append one or more new columns.
mutate(iris,sepal=Sepal.Length+Sepal.Width)
#Apply window function to each column.
mutate_each(iris, funs(min_rank))
# Compute one or more new columns.Drop original columns
transmute(iris,sepal=Sepal.Length + Sepal.Width)
window function
Mutate uses window functions,functions that take a vector of values and return another vector of values,such as:
dplyr::lead Copy with values shifted by 1.
lag Copy with values lagged by 1.
dense_rank Ranks with no gaps
min_rank Ranks.Ties get min rank
percent_rank Ranks rescaled to [0,1]
row_number Ranks.Ties got to first value
ntile Bin vector into n buckets
between Are values between a and b?
cume_dist Cumulative distribution
cumall Cumulative all
cumany Cumulative any
cummean Cumulative mean
cumsun
cunmax
cummin
cumprod
pmax Element-wise max
pmin Element-wise min
8.Combine Data Set
- .Mutating Joins
a <- data.frame(x1=c('A',"B",'C'),x2=c(1,2,3))
b <- data.frame(x1=c('A',"B",'D'),x3=c('T','F','T'))
#Join matching rows from b to a
left_join(a,b,by='x1')
# Join matching rows from a to b
right_join(a,b,by='x1')
#Join data.Retain only rows in both set
inner_join(a,b,by='x1')
#Join data.Retain all values,all rows.
full_join(a,b,by='x1')
- .Filtering Joins
#Allrows in a that have a match in b
semi_join(a,b,by='x1')
# All rows in a that do not have a match in b
anti_join(a,b,by='x1')
- .Set Operations
y <- data.frame(x1=c('A',"B",'C'),x2=c(1,2,3))
z <- data.frame(x1=c("B",'C','D'),x2=c(2,3,4))
#Rows that appear in both y and z.
intersect(y, z)
#Rows that appear in either or both y and z
union(y,z)
#Rows that appear in y but not z
setdiff(y,z)
-.Binding
#append z to y as new rows
bind_rows(y,z)
# Append z to y as new columns.Caution:matches rows by position
bind_cols(y,z)
