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I have the following data structure (an "atomic vector?") output from daply in plyr, in which I had the function return three different measures for each subject, condition, and item.
x = structure(c(-0.93, 0.39, 0.88, 0.63, 0.86, -0.69, 1.02, 0.29, 0.94, 0.93, -0.01, 0.79, 0.32, 0.14, 0.13, -0.07, -0.63, 0.26, 0.07, 0.87, -0.36, 1.043, 0.33, -0.12, -0.055, 0.07, 0.67, 0.48, 0.002, 0.008, -0.19, -1.39, 0.98, 0.43, -0.02, -0.15,-0.08, 0.74, 0.96, 0.44, -0.005, 1.09, 0.36, 0.04, 0.09, 0.17, 0.68, 0.51, 0.09, 0.12, -0.05, 0.11, 0.99, 0.62, 0.13, 0.06, 0.27, 0.74, 0.96, 0.45), .Dim = c(5L, 2L, 2L, 3L), .Dimnames = structure(list(Subject = c("s1", "s2", "s3", "s4", "s5"), Cond = c("A", "B"), Item = c("1", "2"), c("Measure1", "Measure2", "Measure3")), .Names = c("Subject", "Cond", "Item", ""))) I want to change it to look like:
Subject Cond Item Measure1 Measure2 Measure3 s1 A 1 -0.93 -0.360 -0.005 s1 A 2 -0.01 -0.19 -0.05 s1 B 1 -0.69 0.070 0.17 s1 B 2 -0.07 -0.15 0.06 s2 A 1 0.39 1.043 1.090 s2 A 2 0.79 -1.39 0.11 s2 B 1 1.02 0.670 0.68 s2 B 2 -0.63 -0.08 0.27 etc.
Is there an easy way to do this?
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How do you convert an array to a DataFrame in Python? To convert an array to a dataframe with Python you need to 1) have your NumPy array (e.g., np_array), and 2) use the pd. DataFrame() constructor like this: df = pd. DataFrame(np_array, columns=['Column1', 'Column2']) .
reshape() function allows us to reshape an array in Python. Reshaping basically means, changing the shape of an array. And the shape of an array is determined by the number of elements in each dimension. Reshaping allows us to add or remove dimensions in an array.
Reshaping arrays Reshaping means changing the shape of an array. The shape of an array is the number of elements in each dimension. By reshaping we can add or remove dimensions or change number of elements in each dimension.
Use as.data.frame.table().
d0 <- as.data.frame.table(x) head(d0) # Subject Cond Item Var4 Freq # 1 s1 A 1 Measure1 -0.93 # 2 s2 A 1 Measure1 0.39 # 3 s3 A 1 Measure1 0.88 # 4 s4 A 1 Measure1 0.63 # 5 s5 A 1 Measure1 0.86 # 6 s1 B 1 Measure1 -0.69 library(tidyr) d1 <- pivot_wider(data = d0, names_from = "Var4", values_from = "Freq") head(d1) # Subject Cond Item Measure1 Measure2 Measure3 # 1 s1 A 1 -0.93 -0.360 -0.005 # 2 s1 A 2 -0.01 -0.190 -0.050 # 3 s1 B 1 -0.69 0.070 0.170 # 4 s1 B 2 -0.07 -0.150 0.060 # 5 s2 A 1 0.39 1.043 1.090 # 6 s2 A 2 0.79 -1.390 0.110 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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