Reducing multiple rows to 1 by index in R [duplicate] - r

Question

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How to reshape data from long to wide format
(14 answers)
Closed 6 years ago.
I am relatively new to R. I am working with a dataset that has multiple datapoints per timestamp, but they are in multiple rows. I am trying to make a single row for each timestamp with a columns for each variable.
Example dataset
Time Variable Value
10 Speed 10
10 Acc -2
10 Energy 10
15 Speed 9
15 Acc -1
20 Speed 9
20 Acc 0
20 Energy 2
I'd like to convert this to
Time Speed Acc Energy
10 10 -2 10
15 9 -1 (blank or N/A)
20 8 0 2
These are measured values so they are not always complete.
I have tried ddply to extract each individual value into an array and recombine, but the columns are different lengths. I have tried aggregate, but I can't figure out how to keep the variable and value linked. I know I could do this with a for loop type solution, but that seems a poor way to do this in R. Any advice or direction would help. Thanks!

Answer 1

I assume data.frame's name is df
library(tidyr)
spread(df,Variable,Value)

Answer 2

Typically a job for dcast in reshape2.First, we make your example reproducible:
df <- structure(list(Time = c(10L, 10L, 10L, 15L, 15L, 20L, 20L, 20L),
Variable = structure(c(3L, 1L, 2L, 3L, 1L, 3L, 1L, 2L), .Label = c("Acc",
"Energy", "Speed"), class = "factor"), Value = c(10L, -2L, 10L,
9L, -1L, 9L, 0L, 2L)), .Names = c("Time", "Variable", "Value"),
class = "data.frame", row.names = c(NA, -8L))
Then:
library(reshape2)
dcast(df, Time ~ ...)
Time Acc Energy Speed
10 -2 10 10
15 -1 NA 9
20 0 2 9
With dplyr you can (cosmetics) reorder the columns with:
library(dplyr)
dcast(df, Time ~ ...) %>% select(Time, Speed, Acc, Energy)
Time Speed Acc Energy
10 10 -2 10
15 9 -1 NA
20 9 0 2