I have a data frame in R that generally takes this form:
ID Year Amount
3 2000 45
3 2000 55
3 2002 10
3 2002 10
3 2004 30
4 2000 25
4 2002 40
4 2002 15
4 2004 45
4 2004 50
I want to sum the Amount by ID for each year, and get a new data frame with this output.
ID Year Amount
3 2000 100
3 2002 20
3 2004 30
4 2000 25
4 2002 55
4 2004 95
This is an example of what I need to do, in reality the data is much larger. Please help, thank you!
With data.table
library("data.table")
D <- fread(
"ID Year Amount
3 2000 45
3 2000 55
3 2002 10
3 2002 10
3 2004 30
4 2000 25
4 2002 40
4 2002 15
4 2004 45
4 2004 50"
)
D[, .(Amount=sum(Amount)), by=.(ID, Year)]
and with base R
:
aggregate(Amount ~ ID + Year, data=D, FUN=sum)
(as commented by @markus)
You can group_by
ID
and Year
then use sum
within summarise
library(dplyr)
txt <- "ID Year Amount
3 2000 45
3 2000 55
3 2002 10
3 2002 10
3 2004 30
4 2000 25
4 2002 40
4 2002 15
4 2004 45
4 2004 50"
df <- read.table(text = txt, header = TRUE)
df %>%
group_by(ID, Year) %>%
summarise(Total = sum(Amount, na.rm = TRUE))
#> # A tibble: 6 x 3
#> # Groups: ID [?]
#> ID Year Total
#> <int> <int> <int>
#> 1 3 2000 100
#> 2 3 2002 20
#> 3 3 2004 30
#> 4 4 2000 25
#> 5 4 2002 55
#> 6 4 2004 95
If you have more than one Amount
column & want to apply more than one function, you can use either summarise_if
or summarise_all
df %>%
group_by(ID, Year) %>%
summarise_if(is.numeric, funs(sum, mean))
#> # A tibble: 6 x 4
#> # Groups: ID [?]
#> ID Year sum mean
#> <int> <int> <int> <dbl>
#> 1 3 2000 100 50
#> 2 3 2002 20 10
#> 3 3 2004 30 30
#> 4 4 2000 25 25
#> 5 4 2002 55 27.5
#> 6 4 2004 95 47.5
df %>%
group_by(ID, Year) %>%
summarise_all(funs(sum, mean, max, min))
#> # A tibble: 6 x 6
#> # Groups: ID [?]
#> ID Year sum mean max min
#> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1 3 2000 100 50 55 45
#> 2 3 2002 20 10 10 10
#> 3 3 2004 30 30 30 30
#> 4 4 2000 25 25 25 25
#> 5 4 2002 55 27.5 40 15
#> 6 4 2004 95 47.5 50 45
Created on 2018-09-19 by the reprex package (v0.2.1.9000)
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