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Merging disparate data frames
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Merging disparate data frames
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So this issue involves merging two data frames of disparate length..
example code:
library(ggplot2)
p_i = data.frame(date=sample(1:40,20,replace=F),index_1 = runif(20,0,1),index_2 = runif(20,0,2))
p_i<-p_i[with(p_i,order(date)),]
w_d = data.frame(date=c(1:40),C=runif(40,2,38))
y_w <- seq(0, 40, length = 9)
x_w <- c(unique(p_i$date))
p_w <- ggplot(w_d, aes(x = date, y = C))+
geom_bin2d(breaks = list(x = x_w, y = y_w))+
scale_fill_continuous(low = "#99CCFF", high = "#CC0000", space = "Lab", na.value = "grey50", guide = "colourbar")
w_dat <- ggplot_build(p_w)$data[[1]]
What i'm trying to do is get the counts from "w_dat" merged into the "p_i" data frame, organized by date so I can regress either of the indices in p_i against the count in a given range, each range being a unique column.
Sorry, I confused w_d
with w_dat
You can use dplyr
for that.
library(dplyr)
mydf <- full_join(p_i, w_d, by = "date")
If you also want to remove the rows with missings, use filter(mydf, !is.na(index_1))
.
Tags
ggplot2
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