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R Basics | Spread
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How to Use Spread Function in R (With Examples)
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Spread a key-value pair across multiple columns — spread • tidyr
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Development on spread() is complete, and for new code we recommend
switching to pivot_wider(), which is easier to use, more featureful, and
still under active development.
df %>% spread(key, value) is equivalent to
df %>% pivot_wider(names_from = key, values_from = value)
See more details in vignette(“pivot”). - Table of Contents:
Usage
Arguments
Examples
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What is the tidyr spread() function in R?
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Tổ chức dữ liệu với spread và gather trong tidyverse
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## 1 … bằng việc sử dụng hàm gather() và spread() trong package tyverse . devows, hugo, goSite template made by devcows using hugo - Table of Contents:
Tổ chức dữ liệu với spread và gather trong tidyverse
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A Survey of Spread-F. – F N Glover – Google Sách
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R Basics
The spread() Function
The first tidyr function we will look into is the spread() function. With spread() it does similar to what you would expect. We have a data frame where some of the rows contain information that is really a variable name. This means the columns are a combination of variable names as well as some data. The picture below displays this:
We can consider the following data which is table 2:
## # A tibble: 12 × 4 ## country year key value ##
## 1 Afghanistan 1999 cases 745 ## 2 Afghanistan 1999 population 19987071 ## 3 Afghanistan 2000 cases 2666 ## 4 Afghanistan 2000 population 20595360 ## 5 Brazil 1999 cases 37737 ## 6 Brazil 1999 population 172006362 ## 7 Brazil 2000 cases 80488 ## 8 Brazil 2000 population 174504898 ## 9 China 1999 cases 212258 ## 10 China 1999 population 1272915272 ## 11 China 2000 cases 213766 ## 12 China 2000 population 1280428583 Notice that in the column of key , instead of there being values we see the following variable names:
cases
population
In order to use this data we need to have it so the data frame looks like this instead:
## # A tibble: 6 × 4 ## country year cases population ## *
## 1 Afghanistan 1999 745 19987071 ## 2 Afghanistan 2000 2666 20595360 ## 3 Brazil 1999 37737 172006362 ## 4 Brazil 2000 80488 174504898 ## 5 China 1999 212258 1272915272 ## 6 China 2000 213766 1280428583 Now we can see that we have all the columns representing the variables we are interested in and each of the rows is now a complete observation.
In order to do this we need to learn about the spread() function:
spread(data, key, value)
Where
data is your dataframe of interest.
is your dataframe of interest. key is the column whose values will become variable names.
is the column whose values will become variable names. value is the column where values will fill in under the new variables created from key .
If we consider piping, we can write this as:
data %>% spread(key, value)
How to Use Spread Function in R (With Examples)
The spread() function from the tidyr package can be used to “spread” a key-value pair across multiple columns.
This function uses the following basic syntax:
spread(data, key value)
where:
data : Name of the data frame
key : Column whose values will become variable names
value: Column where values will fill under new variables created from key
The following examples show how to use this function in practice.
Example 1: Spread Values Across Two Columns
Suppose we have the following data frame in R:
#create data frame df <- data. frame (player= rep (c(' A ', ' B '), each= 4 ), year= rep (c(1, 1, 2, 2), times= 2 ), stat= rep (c(' points ', ' assists '), times= 4 ), amount=c(14, 6, 18, 7, 22, 9, 38, 4)) #view data frame df player year stat amount 1 A 1 points 14 2 A 1 assists 6 3 A 2 points 18 4 A 2 assists 7 5 B 1 points 22 6 B 1 assists 9 7 B 2 points 38 8 B 2 assists 4 We can use the spread() function to turn the values in the stat column into their own columns: library (tidyr) #spread stat column across multiple columns spread(df, key=stat, value=amount) player year assists points 1 A 1 6 14 2 A 2 7 18 3 B 1 9 22 4 B 2 4 38 Example 2: Spread Values Across More Than Two Columns Suppose we have the following data frame in R: #create data frame df2 <- data. frame (player= rep (c(' A '), times= 8 ), year= rep (c(1, 2), each= 4 ), stat= rep (c(' points ', ' assists ', ' steals ', ' blocks '), times= 2 ), amount=c(14, 6, 2, 1, 29, 9, 3, 4)) #view data frame df2 player year stat amount 1 A 1 points 14 2 A 1 assists 6 3 A 1 steals 2 4 A 1 blocks 1 5 A 2 points 29 6 A 2 assists 9 7 A 2 steals 3 8 A 2 blocks 4 We can use the spread() function to turn the four unique values in the stat column into four new columns: library (tidyr) #spread stat column across multiple columns spread(df2, key=stat, value=amount) player year assists blocks points steals 1 A 1 6 1 14 2 2 A 2 9 4 29 3 Additional Resources The goal of the tidyr package is to create “tidy” data, which has the following characteristics: Every column is a variable. Every row is an observation. Every cell is a single value. The tidyr package uses four core functions to create tidy data: 1. The spread() function. 2. The gather() function. 3. The separate() function. 4. The unite() function. If you can master these four functions, you will be able to create “tidy” data from any data frame.
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