Spread a key-value pair across multiple columns.

spread(data, key, value, fill = NA, convert = FALSE, drop = TRUE,
  sep = NULL)

Arguments

data

A data frame.

key, value

Column names or positions. This is passed to tidyselect::vars_pull().

These arguments are passed by expression and support quasiquotation (you can unquote column names or column positions).

fill

If set, missing values will be replaced with this value. Note that there are two types of missingness in the input: explicit missing values (i.e. NA), and implicit missings, rows that simply aren't present. Both types of missing value will be replaced by fill.

convert

If TRUE, type.convert() with asis = TRUE will be run on each of the new columns. This is useful if the value column was a mix of variables that was coerced to a string. If the class of the value column was factor or date, note that will not be true of the new columns that are produced, which are coerced to character before type conversion.

drop

If FALSE, will keep factor levels that don't appear in the data, filling in missing combinations with fill.

sep

If NULL, the column names will be taken from the values of key variable. If non-NULL, the column names will be given by "<key_name><sep><key_value>".

Examples

library(dplyr) stocks <- data.frame( time = as.Date('2009-01-01') + 0:9, X = rnorm(10, 0, 1), Y = rnorm(10, 0, 2), Z = rnorm(10, 0, 4) ) stocksm <- stocks %>% gather(stock, price, -time) stocksm %>% spread(stock, price)
#> time X Y Z #> 1 2009-01-01 -0.25148344 0.03835518 -0.9835856 #> 2 2009-01-02 0.44479712 0.05912151 -4.7102532 #> 3 2009-01-03 2.75541758 1.09965508 -3.9034025 #> 4 2009-01-04 0.04653138 -4.54822971 4.2602293 #> 5 2009-01-05 0.57770907 5.36511437 0.5266825 #> 6 2009-01-06 0.11819487 -0.72244251 1.9545152 #> 7 2009-01-07 -1.91172049 0.42671150 -6.7978023 #> 8 2009-01-08 0.86208648 2.14869176 -5.8829452 #> 9 2009-01-09 -0.24323674 -1.33017650 1.1366014 #> 10 2009-01-10 -0.20608719 2.22790484 5.3492817
stocksm %>% spread(time, price)
#> stock 2009-01-01 2009-01-02 2009-01-03 2009-01-04 2009-01-05 2009-01-06 #> 1 X -0.25148344 0.44479712 2.755418 0.04653138 0.5777091 0.1181949 #> 2 Y 0.03835518 0.05912151 1.099655 -4.54822971 5.3651144 -0.7224425 #> 3 Z -0.98358565 -4.71025324 -3.903402 4.26022928 0.5266825 1.9545152 #> 2009-01-07 2009-01-08 2009-01-09 2009-01-10 #> 1 -1.9117205 0.8620865 -0.2432367 -0.2060872 #> 2 0.4267115 2.1486918 -1.3301765 2.2279048 #> 3 -6.7978023 -5.8829452 1.1366014 5.3492817
# Spread and gather are complements df <- data.frame(x = c("a", "b"), y = c(3, 4), z = c(5, 6)) df %>% spread(x, y) %>% gather(x, y, a:b, na.rm = TRUE)
#> z x y #> 1 5 a 3 #> 4 6 b 4
# Use 'convert = TRUE' to produce variables of mixed type df <- data.frame(row = rep(c(1, 51), each = 3), var = c("Sepal.Length", "Species", "Species_num"), value = c(5.1, "setosa", 1, 7.0, "versicolor", 2)) df %>% spread(var, value) %>% str
#> 'data.frame': 2 obs. of 4 variables: #> $ row : num 1 51 #> $ Sepal.Length: Factor w/ 6 levels "1","2","5.1",..: 3 4 #> $ Species : Factor w/ 6 levels "1","2","5.1",..: 5 6 #> $ Species_num : Factor w/ 6 levels "1","2","5.1",..: 1 2
df %>% spread(var, value, convert = TRUE) %>% str
#> 'data.frame': 2 obs. of 4 variables: #> $ row : num 1 51 #> $ Sepal.Length: num 5.1 7 #> $ Species : chr "setosa" "versicolor" #> $ Species_num : int 1 2