Correction at the end.
On Sun, 16 Jul 2017, Jeff Newmiller wrote:
On Sat, 15 Jul 2017, Michael Reed via R-help wrote:
Dear All,
I need some help arranging data that was imported.
It would be helpful if you were to use dput to give us the sample data since
you say you have already imported it.
The imported data frame looks something like this (the actual file is huge,
so this is example data)
DF:
IDKey X1 Y1 X2 Y2 X3 Y3 X4 Y4
Name1 21 15 25 10
Name2 15 18 35 24 27 45
Name3 17 21 30 22 15 40 32 55
That data is missing in X3 etc, but would be NA in an actual data frame, so I
don't know if my workaround was the same as your workaround. Dput
would have clarified the starting point.
I would like to create a new data frame with the following
NewDF:
IDKey X Y
Name1 21 15
Name1 25 10
Name2 15 18
Name2 35 24
Name2 27 45
Name3 17 21
Name3 30 22
Name3 15 40
Name3 32 55
With the data like this I think I can do the following
ggplot(NewDF, aes(x=X, y=Y, color=IDKey) + geom_line
You are missing parentheses. If you use the reprex library to test your
examples before posting them, you can be sure your simple errors don't send
us off on wild goose chases.
and get 3 lines with the various number of points.
The point is that each of the XY pairs is a data point tied to NameX. I
would like to rearrange the data so I can plot the points/lines by the
IDKey. There will be at least 2 points, but the number of points for each
IDKey can be as many as 4.
I have tried using the gather() function from the tidyverse package, but
The tidyverse package is a virtual package that pulls in many packages.
I can't make it work. The issue is that I believe I need two separate
gather statements (one for X, another for Y) to consolidate the data. This
causes the pairs to not stay together and the data becomes jumbled.
No, what you need is a gather-spread.
######
library(dplyr)
library(tidyr)
DF <- read.table( text=
"IDKey X1 Y1 X2 Y2 X3 Y3 X4 Y4
Name1 21 15 25 10 NA NA NA NA
Name2 15 18 35 24 27 45 NA NA
Name3 17 21 30 22 15 40 32 55
", header=TRUE, as.is=TRUE )
NewDF <- ( dta
%>% gather( XY, value, -IDKey )
%>% separate( XY, c( "Coord", "Num" ), 1 )
%>% spread( Coord, value )
%>% filter( !is.na( X ) & !is.na( Y ) )
)
######
Sorry, should have practiced what I preached...
##########
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tidyr)
DF <- structure(list(IDKey = c("Name1", "Name2", "Name3"), X1 = c(21L,
15L, 17L), Y1 = c(15L, 18L, 21L), X2 = c(25L, 35L, 30L), Y2 = c(10L, 24L,
22L), X3 = c(NA, 27L, 15L), Y3 = c(NA, 45L, 40L), X4 = c(NA, NA, 32L), Y4
= c(NA, NA, 55L)), .Names = c("IDKey", "X1", "Y1", "X2", "Y2", "X3", "Y3",
"X4", "Y4"), class = "data.frame", row.names = c(NA, -3L))
NewDF <- ( DF
%>% gather( XY, value, -IDKey )
%>% separate( XY, c( "Coord", "Num" ), 1 )
%>% spread( Coord, value )
%>% filter( !is.na( X ) & !is.na( Y ) )
)
NewDF
#> IDKey Num X Y
#> 1 Name1 1 21 15
#> 2 Name1 2 25 10
#> 3 Name2 1 15 18
#> 4 Name2 2 35 24
#> 5 Name2 3 27 45
#> 6 Name3 1 17 21
#> 7 Name3 2 30 22
#> 8 Name3 3 15 40
#> 9 Name3 4 32 55
##########
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