tidyr is a package by Hadley Wickham that makes it easy to tidy your data. It is often used in conjunction with dplyr. Data is tidy when each column represents a variable and each row represents an observation.
tidyr is part of the tidyverse. tidyverse is a set of packages that work in harmony because they share common data representations and API design. tidyverse is designed to make it easy to install and load core packages from the tidyverse in a single command. I recommend you become intimately familiar with these tools.
Explore tidyrfunctions:
melt in reshape2.cast in reshape2.reshape2 is another popular R package. Like many function in R, there are many ways to accomplish any given task. With all due respect to reshape2, I prefer to use the tools in
tidyverse.
Use the mtcars dataset:
head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1Include the names of the cars in a column called car.
mtcars$car <- rownames(mtcars)
mtcars <- mtcars[, c(12, 1:11)]
head(mtcars)## car mpg cyl disp hp drat wt qsec vs
## Mazda RX4 Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0
## Mazda RX4 Wag Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0
## Datsun 710 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1
## Hornet 4 Drive Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1
## Hornet Sportabout Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0
## Valiant Valiant 18.1 6 225 105 2.76 3.460 20.22 1
## am gear carb
## Mazda RX4 1 4 4
## Mazda RX4 Wag 1 4 4
## Datsun 710 1 4 1
## Hornet 4 Drive 0 3 1
## Hornet Sportabout 0 3 2
## Valiant 0 3 1gather takes the form: gather(data, key, value, ..., na.rm = FALSE, convert = FALSE)
where … is the specification of the columns to gather.
mtcarsNew <- mtcars %>% gather(attribute, value, -car)
head(mtcarsNew)## car attribute value
## 1 Mazda RX4 mpg 21.0
## 2 Mazda RX4 Wag mpg 21.0
## 3 Datsun 710 mpg 22.8
## 4 Hornet 4 Drive mpg 21.4
## 5 Hornet Sportabout mpg 18.7
## 6 Valiant mpg 18.1It gathers all the columns except car and places their name and value into the attritube and value column.
The great thing about tidyr is that you can gather only certain columns and leave the others alone. If we want to gather all the columns from mpg to gear and leave the carb and car columns as they are we do this:
mtcarsNew <- mtcars %>% gather(attribute, value, mpg:gear)
head(mtcarsNew)## car carb attribute value
## 1 Mazda RX4 4 mpg 21.0
## 2 Mazda RX4 Wag 4 mpg 21.0
## 3 Datsun 710 1 mpg 22.8
## 4 Hornet 4 Drive 1 mpg 21.4
## 5 Hornet Sportabout 2 mpg 18.7
## 6 Valiant 1 mpg 18.1spread takes the form: spread(data, key, value, fill = NA, convert = FALSE, drop = TRUE)
Here is an example:
mtcarsSpread <- mtcarsNew %>% spread(attribute, value)
head(mtcarsSpread)## car carb am cyl disp drat gear hp mpg qsec vs wt
## 1 AMC Javelin 2 0 8 304 3.15 3 150 15.2 17.30 0 3.435
## 2 Cadillac Fleetwood 4 0 8 472 2.93 3 205 10.4 17.98 0 5.250
## 3 Camaro Z28 4 0 8 350 3.73 3 245 13.3 15.41 0 3.840
## 4 Chrysler Imperial 4 0 8 440 3.23 3 230 14.7 17.42 0 5.345
## 5 Datsun 710 1 1 4 108 3.85 4 93 22.8 18.61 1 2.320
## 6 Dodge Challenger 2 0 8 318 2.76 3 150 15.5 16.87 0 3.520unite takes the form: unite(data, col, ..., sep = "_", remove = TRUE)
where … represents the columns to unite and col represents the column to add.
We need new data to demo this::
date <- as.Date('2017-07-01') + 0:14
hour <- sample(1:24, 15)
min <- sample(1:60, 15)
second <- sample(1:60, 15)
event <- sample(letters, 15)
data <- data.frame(date, hour, min, second, event)
head(data,5)## date hour min second event
## 1 2017-07-01 1 42 22 y
## 2 2017-07-02 7 43 29 f
## 3 2017-07-03 2 53 48 q
## 4 2017-07-04 8 31 53 e
## 5 2017-07-05 18 56 17 mCombine the date, hour, min, and second columns into a new column called datetime. Datetime in R is of the form Year-Month-Day Hour:Min:Second.
dataNew <- data %>% unite(datehour, date, hour, sep = ' ') %>% unite(datetime, datehour, min, second, sep = ':')
head(dataNew, 5)## datetime event
## 1 2017-07-01 1:42:22 y
## 2 2017-07-02 7:43:29 f
## 3 2017-07-03 2:53:48 q
## 4 2017-07-04 8:31:53 e
## 5 2017-07-05 18:56:17 mseparate takes the form: separate(data, col, into, sep = "[^[:alnum:]]+", remove = TRUE, convert = FALSE, extra = "warn", fill = "warn", ...)
Revert back to the original data using separate:
data1 <- dataNew %>% separate(datetime, c('date', 'time'), sep = ' ') %>% separate(time, c('hour', 'min', 'second'), sep = ':')
head(data1,5)## date hour min second event
## 1 2017-07-01 1 42 22 y
## 2 2017-07-02 7 43 29 f
## 3 2017-07-03 2 53 48 q
## 4 2017-07-04 8 31 53 e
## 5 2017-07-05 18 56 17 mIt first splits the datetime column into date and time, and then splits time into hour, min, and second.