# r create dummy variables from categorical

Vector of column names that you want to create dummy variables from. I'm trying to do statistics in R software. I applied your function but the output was not similar to yours, [1] 1 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0, [21] 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0, [41] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0, [61] 0 0 0 1 0 1 0 0 1 1 1 0 0 0 1 0 0 0 0 1, [81] 0 0 0 0 0 0 1 0 1 0 1 0 1 1 0 1 0 1 1 1, [101] 0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 1 1 0 1, [121] 1 0 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 1, [141] 1 1 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0, [161] 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 1 1 1 1 0, [181] 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0. Can you include PCA components as both independent variable and dependent variable at the same time? 1) Check for unique responses to ensure everything is properly parsed. If TRUE (not default), removes the columns used to generate the dummy columns. dummy_rows(). The total waste collected in tonnage needs to be sent to the 3 facilities is mandatory. Can I use one of them to be an independent variable, and at the same time, use another component as a control variable in the regression analysis? If you meant something like coding c("A", "B", "A", "A", "B", "C") as c(1, 2, 1, 1, 2, 3), then you can use the as.integer function. The dataframe has the below mentioned columns with the Name of the country as Index. To my knowledge, R is creating dummy variables automatically. Który program nie wymaga najnowszego sprzętu i procesorów 4-rdzeniowych, aby szybko policzyć ekstensywne problemy numeryczne? Other dummy functions: I am also going to try your advice and let you know about the process. If NULL (default), uses all character and factor columns. Would you please help me to solve it? I have seen all this online. if ( SEX=="MALE" & SPORT=="CADET" & Bazett_formula <400) {"Primary"} else if (SEX=="MALE" & SPORT=="CADET" & Bazett_formula >400 ) {"Secondary", } else if ( SEX=="FEMALE" & SPORT=="CADET" & Bazett_formula <400) {. Using dummy variables for categorical data, Change factor levels by hand — fct_recode, http://sphweb.bumc.bu.edu/otlt/MPH-Modules/QuantCore/PH717_MultipleVariableRegression/PH717_MultipleVariableRegression4.html, Fast Creation of Dummy (Binary) Columns and Rows from Categorical Variables, FAQ: How to do a minimal reproducible example ( reprex ) for beginners. This variable is 'YSK87' and its values in the dataset correspond to the following: VALUE LABEL 1 = 1 Person 2 = 2 Persons 3 = 3 Persons 4 = 4 or more Persons. I purposely use .vars in multiple ways to show how you can approach this through slightly different means, should you need to scale your work. You have a series of answers, one of them being "Help from family." Bronze #Games See Also Gold data.df <- data.frame(X1 = sample(possible_values,size = 100, replace = TRUE). http://sphweb.bumc.bu.edu/otlt/MPH-Modules/QuantCore/PH717_MultipleVariableRegression/PH717_MultipleVariableRegression4.html. I don't know how is your database, then, I assume it is like. How do I input that into your coding? If there are other situations such as typos, you will have to do some corrections to account for them. The dataset in question is basically Olympics medal tally. You can also specify which columns to make dummies out of, or which columns to ignore. Change factor levels by hand, Also, some good info on recoding dummy variables using ifelse() here: It is a more flexible function, # allowing you to choose the columns where you search "Text" in your database, # It returns 1 if "Text" is not found, and 0 if "Text" is found, notFindText = function(x, Text, Columns) {, # --- Searching Text in Columns of x ---------------------, # Columns must be of the form c(Col1, Col2, ... , Colk), # where Col1, Col2, ... Colk are the columns in database, # Returns 1 if "Text" is not found, and 0 if "Text" is found, # ----------------------------------------------------------, if(missing(Columns)) Columns = 1:length(x), if(sum(str_detect(toupper(Stext), toupper(Text)))) notFound = 0 else notFound = 1, # -------------------------------------------------------------------, # And now, I apply my function notFindText() to calculate dummy as, # 0 if "Aile" is found, 1 if "Aile" is found, DD = cbind(data.df, notFound = apply(data.df, 1, notFindText, Text = "Aile", Columns = c(1:4))), # --- The same, but only searching in columns 3 and 4 of database, DD1 = cbind(data.df, notFound = apply(data.df, 1, notFindText, Text = "Aile", Columns = c(3, 4))), # --- You can change "Text" for any other value. # ---------Reading data (change this to read your data object): dataobject = read.table(stdin(), header = FALSE, sep = " "), data_with_dummy = cbind(dataobject, Dummy = apply(dataobject, 1, Aile_f)), V1 V2 Dummy, 1 [1] ogrenci burs veya kredisi 1, 2 [2] ogrenci burs veya kredisi, Aile destegi 0, 3 [3] ogrenci burs veya kredisi, Yari zamanli calisma 1, 4 [4] ogrenci burs veya kredisi, Yari zamanli calisma 1, 5 [5] ogrenci burs veya kredisi 1, 6 [6] Aile destegi 0, 7 [7] ogrenci burs veya kredisi, Aile destegi 0, 8 [8] Tam zamanli calisma 1, 9 [9] ogrenci burs veya kredisi, Aile destegi 0, 10 [10] ogrenci burs veya kredisi 1. Climate change index for annual temperature and precipitation? You need to create some kind of coding scheme. However, if you have several additional columns, you may have to change the financial independence classification to something that is more generalized; maybe using apply or map_lgl. then a split value of "," this row would have a value of 1 for both the cat Can you please explain what do you mean by this? Just check the type of variable in R if it is a factor, then there is no need to create dummy variable . 2) If there is recoding to do, you have some options to pursue. (i.e. I am tasked with finding the country which have the biggest difference between their summer and winter gold medal counts. Quickly create dummy (binary) columns from character and Would be helpful if I can find good insights and inputs for the problem. New replies are no longer allowed. each of these pets would become its own dummy column. It really depends on the context in which you are doing it. data$gelkay <- stringr::str_to_lower(data$gelkay). Bronze.1 #Summer If there is a tie for most frequent, will remove the first Spatial panel vector auto-regressive (VAR) model OR Spatial panel vector error correction model codes (VECM) in stata? stringr::str_detect(data$gelkay,"[Hh]elp from family"),0,1). Before doing that I have to make index of climate change (with only two variables temperature and precipitation). Using mutate_at, it will trim the white space (as you mentioned you needed), encode the variables, then create an additional column to determine financial independence based on the value of 1 being present in any of the encoded variables. Total I have a problem with solid waste management statistical modeling, my one independent variable (Cost), with three dependent variables (waste fraction to the first facility), (waste fraction to 2nd facility) and, (waste fraction to 3rd facility) can be varied. gelkay$X1 <- revalue(gelkay$X1, c("ogrenci burs veya kredisi"= 4, "Tam zamanli calisma"= 3, "Yari zamanli calisma"= 2, "Aile destegi"=1)), gelkay$X2 <- revalue(gelkay$X2, c("ogrenci burs veya kredisi"= 4, "Tam zamanli calisma"= 3, " Tam zamanli calisma"= 3, "Yari zamanli calisma"= 2, " Yari zamanli calisma"= 2, "Aile destegi"=1, " Aile destegi"=1)), gelkay$X3 <- revalue(gelkay$X3, c("ogrenci burs veya kredisi"= 4, "Tam zamanli calisma"= 3, " Tam zamanli calisma"= 3, "Yari zamanli calisma"= 2, " Yari zamanli calisma"= 2, "Aile destegi"=1, " Aile destegi"=1)), gelkay$X4 <- revalue(gelkay$X4, c("ogrenci burs veya kredisi"= 4, "Tam zamanli calisma"= 3, " Tam zamanli calisma"= 3, "Yari zamanli calisma"= 2, " Yari zamanli calisma"= 2, "Aile destegi"=1, " Aile destegi"=1)), gelkay$X1 <- as.numeric(as.character(gelkay$X1)), gelkay$X2 <- as.numeric(as.character(gelkay$X2)), gelkay$X3 <- as.numeric(as.character(gelkay$X3)), gelkay$X4 <- as.numeric(as.character(gelkay$X4)), gelkay$gelkaydummy = ifelse(gelkay\$X1 %in% 1 |.