dummy-variable

I have a simple table with 2 columns: UserID and Category, and each UserID can repeat with a few categories, like so: UserID Category ------ -------- 1 A 1 B 2 C 3 A 3 C 3 B I want to "dummify" this table: i.e. to create an output table that has a unique column for each Category consisting of dummy variables (0/1 depending on whether the UserID belongs to that particular Category): UserID A B C ------ -- -- -- 1 1 1 0 2 0 0 1 3 1 1 1 My problem is that I have THOUSANDS of categories (not just 3 as in this example) and so this cannot be efficiently accomplished using CASE WHEN statement. So my

I am working with an extremely large dataset in R and have been operating with data frames and have decided to switch to data.tables to help speed up with operations. I am having trouble understanding the J operations, in particular I'm trying to generate dummy variables but I can't figure out how to code conditional operations within data.tables[]. MWE: test <- data.table("index"=rep(letters[1:10],100),"var1"=rnorm(1000,0,1)) What I would like to do is to add columns a through j as dummy variables such that column a would have a value 1 when the index == "a" and 0 otherwise. In the data.frame

I have the following dataframe: amount catcode cid cycle date di feccandid type 0 1000 E1600 N00029285 2014 2014-05-15 D H8TX22107 24K 1 5000 G4600 N00026722 2014 2013-10-22 D H4TX28046 24K 2 4 C2100 N00030676 2014 2014-03-26 D H0MO07113 24Z I want to make dummy variables for the values in column type . There about 15. I have tried this: pd.get_dummies(df['type']) And it returns this: 24A 24C 24E 24F 24K 24N 24P 24R 24Z date 2014-05-15 0 0 0 0 1 0 0 0 0 2013-10-22 0 0 0 0 1 0 0 0 0 2014-03-26 0 0 0 0 0 0 0 0 1 What I would like is to have a dummy variable column for each unique value in Type

I have a set of dataframes where one of the columns contains a categorical variable. I'd like to convert it to several dummy variables, in which case I'd normally use get_dummies . What happens is that get_dummies looks at the data available in each dataframe to find out how many categories there are, and thus create the appropriate number of dummy variables. However, in the problem I'm working right now, I actually know in advance what the possible categories are. But when looking at each dataframe individually, not all categories necessarily appear. My question is: is there a way to pass to

How can I tell R to use a certain level as reference if I use binary explanatory variables in a regression? It's just using some level by default. lm(x ~ y + as.factor(b)) with b {0, 1, 2, 3, 4} . Let's say I want to use 3 instead of the zero that is used by R. See the relevel() function. Here is an example: set.seed(123) x <- rnorm(100) DF <- data.frame(x = x, y = 4 + (1.5*x) + rnorm(100, sd = 2), b = gl(5, 20)) head(DF) str(DF) m1 <- lm(y ~ x + b, data = DF) summary(m1) Now alter the factor b in DF by use of the relevel() function: DF <- within(DF, b <- relevel(b, ref = 3)) m2 <- lm(y ~ x +