You can accomplish this by using the MERGE statement in a DATA step:
They will each be converted to the other format below.In this example, the source columns that are gathered are specified with Optional: A few things to make the data look nicer.The order of factor levels determines the order of the columns.
melt function in pandas is one of the efficient function to transform the data from wide to long format. #> 5 1 M cond1 12.3 There is a time when people need to convert data in the long format (you call it "the long-form" and what it means will be clear later) to the wide format and the vice versa. For example, a Product for SavBal would be Savings; similarly a Product for CheckBal would be Chequing).Sorry, but I don't understand your question. we also create a numeric variable year based on the SAS automatic variableIn the following data set we have three groups #> 7 3 F cond1 13.1 #> 12 4 M cond2 12.9 The first variable uniquely identifies each subject and is used in the BY statement of the TRANSPOSE procedure. #> 8 3 F cond1 13.1 # - value: Name of column containing values
#> subject sex condition measurement #> 3 3 F control 9.5
#> 4 4 M 13.4 12.9 11.5 use below, we can reshape it into long format using In the following data set we have two groups #> 1 1 M control 7.9 #> 10 2 F cond2 11.1
births.long1-reshape(births.wide, varying=c(“b2_01″,”b2_02″,”b2_03”, “b4_01”, “b4_02”, “b4_03″), direction=”long”, idvar=”caseid”, sep=”_”) stubnames str or list-like. #> 5 1 M cond1 12.3 #> 1 1 M control 7.9
#> subject sex cond1 cond2 control We specify a long direction, that our id variable is caseid, and, importantly, that the separating symbol between the name of the variable and its order number is a “_”. For a data set in wide format such as the one Clear enough?
#> 2 1 M cond1 12.3 #> 9 1 M second 10.7 For example, variables for a customer in a bank might include the customer's name, checking account balance, savings account balance, mortgage amount, and whether the customer is part of a loyalty program. These examples take #> 1 1 M 7.9 12.3 10.7
The level order can be This site is powered by knitr and Jekyll. 1 M cond2 10.7 When going from long to wide, you need to consider whether there are an equal number of observations for each group. You want to do convert data from a wide format to a long format.Many functions in R expect data to be in a long format rather than a wide format. #> 7 3 F control 9.5 #> subject sex condition measurement subject sex condition measurement '#> subject sex control cond1 cond2
Is there an equivalent method to transpose long to wide format?Yes. 1 M 7.9 12.3 10.7
#> 8 4 M cond1 13.4 This is a simple and working formula. #> 7 3 F cond1 13.1
The Wide Format. ' Learn why you would transform your data from a long to a wide format and vice versa and explore how to do this in R with melt() and dcast()!
The critical questions
Lets reshape this data into a long format. Consider the file containing the kids and their heights at 1 year of age (ht1) and at 2 years of age (ht2). I don't have to think about DATA step arrays. #> 5 1 M first 12.3 We will create a new variable called year, which will be set equal to each year for which we have data. # Specify id.vars: the variables to keep but not split apart on #> 2 2 F control 6.3 #> 2 2 F 6.3 10.6 11.1 questions, the reshape command will look like this.It also is possible to reshape a wide data file to be long when there are character suffixes.
#> 11 3 F cond2 13.8
#> subject sex control first second
#> subject sex condition measurement For example, to reshape all three variables over time in gapminder dataframe in wide form, … #> subject sex condition measurement Let’s create a simple data frame to demonstrate our reshape example in python pandas. This module illustrates the power (and simplicity) of Stata in its ability to reshape data files. #> 9 3 F cond2 13.8 #> 7 3 F cond1 13.1 3 F 9.5 13.1 13.8 # Name of the destination column that will identify the original #> 1 1 M 7.9 12.3 10.7 #> 4 4 M 11.5 13.4 12.9 2 F cond1 10.6 1 M cond1 12.3 #> 3 3 F 9.5 13.1 13.8
In your example, you showed how to make a new column called VALUES by choosing the variables:I think the best way to get your question answered is to post sample data and the desired end result to the /** 2. #> 7 3 F first 13.1 #> 2 2 F control 6.3
For example, you might want to retain the Customer and Loyalty variables.
However, I have found that for long to wide, you need to provide data = your data.frame, idvar = the variable that identifies your groups, v.names = the variables that will become multiple columns in wide format, timevar = the variable containing the values that will be appended to v.names in wide format, direction = wide, and sep = "_".
#> 4 4 M 11.5 13.4 12.9 Column(s) to use as id variable(s). Reshaping a data from wide to long in pandas python is done with melt() function. melt() Function in python pandas depicted with an example. #> 7 3 F control 9.5
#> 2 2 F control 6.3
#> 8 4 M first 13.4 Create dataframe:
Cat uses a DATA step to convert the data from wide to long format. #> 4 4 M 13.4 12.9 11.5 All I have to do is to identify the three kinds of variables mentioned in this blog post. 1 M control 7.9 Reshaping a data from wide to long in pandas python is done with melt() function.
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