You could use:
library(dplyr)
library(tidyr)
x.df %>% group_by(Year, Group, Eye_Color) %>% summarize(n=n()) %>%
spread(Eye_Color,n, fill=0)
Source: local data frame [6 x 5]
Year Group blue brown green
1 2000 1 2 1 0
2 2000 2 0 0 2
3 2001 1 1 0 0
4 2001 2 1 1 0
5 2001 3 1 0 0
6 2002 1 1 0 0
Or
library(reshape2)
dcast(x.df, Year+Group~Eye_Color, value.var="Eye_Color")
A.K.
On Friday, August 1, 2014 7:06 PM, Kathy Haapala <ka...@haapi.mn.org> wrote:
If I have a dataframe x.df as follows:
> x.df <- data.frame(Year = c(2000, 2000, 2000, 2000, 2000, 2001, 2001,
2001, 2001, 2002), Group = c(1, 1, 1, 2, 2, 1, 2, 2, 3, 1), Eye_Color =
c("blue", "blue", "brown", "green", "green", "blue", "brown", "blue",
"blue", "blue"))
> x.df
Year Group Eye_Color
1 2000 1 blue
2 2000 1 blue
3 2000 1 brown
4 2000 2 green
5 2000 2 green
6 2001 1 blue
7 2001 2 brown
8 2001 2 blue
9 2001 3 blue
10 2002 1 blue
how can I turn it into a new dataframe that would take the data from
multiple rows of Year/Group combinations and output the data into one row
for each combination, like this:
> x_new.df
Year Group No_blue No_brown No_green
1 2000 1 2 1 0
2 2000 2 0 0 2
3 2001 1 1 0 0
4 2001 2 1 1 0
5 2001 3 1 0 0
6 2002 1 1 0 0
I've been trying to use for loops, but I'm wondering if anyone has a better
or more simple suggestion.
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