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POMelting/casting my data into shape
primarykey
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6159498
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6159610
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2
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0
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CreationDate
2011-05-28T03:36:02.513
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2015-12-03T21:48:54.480
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2015-12-03T21:48:54.480
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4370109
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176995
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0
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1
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2
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933
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text
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I have a table that looks like this: <img src="https://i.stack.imgur.com/Lf92A.png" alt="enter image description here"> And I need it to look like this, where net=gross-tare: <img src="https://i.stack.imgur.com/19u4V.png" alt="enter image description here"> How do I do this? <hr> I started by melting the data, then casting as columns, and then creating new columns for the net readings. <pre><code> df_m <- melt(df, id = 1:3) df_c <- cast(df_m, ... ~ variable + type) df_c$wr_net <- df_c$wr_gross - df_c$wr_tare df_c$wc_net <- df_c$wc_gross - df_c$wc_tare df_c$tsa_net <- df_c$tsa_gross - df_c$tsa_tare </code></pre> Which gives <img src="https://i.stack.imgur.com/vYsXA.png" alt="enter image description here"> But now I can't figure out how to melt this table to get the dataframe to look the way I need with a column for 'type' with values 'gross' and 'tare' and 'net'. Is there an easier way? Am I barking up the wrong tree with melt/cast? <hr> You can reproduce a small sample of my data using this... <pre><code> df <- structure(list(train = structure(c(1L, 1L, 1L, 1L, 1L, 1L), .Label = "AC0485n", class = "factor"), position = c(1L, 1L, 2L, 2L, 3L, 3L), type = structure(c(2L, 1L, 2L, 1L, 2L, 1L), .Label = c("gross", "tare"), class = "factor"), wids_raw = c(24.85, 146.2, 26.16, 135, 24.7, 135.1), wids_corr = c(26.15, 145.43, 27.44, 134.43, 26, 134.52), tsa = c(24.1, 139.2, 25, 133.6, 24, 131.1)), .Names = c("train", "position", "type", "wr", "wc", "tsa"), class = "data.frame", row.names = c(NA, -6L)) </code></pre>
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Title
Melting/casting my data into shape
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PostAcceptedAnswerId
1. PO
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1. PTQuestion
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1. This table or related slice is empty.
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1. USTommy O'Dell
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1. PO
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2. PO
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1. VO
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2. VO
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