Total Which correlation coefficient is better to use: Spearman or Pearson? See Also X3 = sample(possible_values,size = 100, replace = TRUE), X4 = sample(possible_values,size = 100, replace = TRUE),stringsAsFactors = FALSE), # -------------------------------------------------------------------------, # --- Now, my function notFindText. You need to create some kind of coding scheme. For pointers specific to the community site, check out the reprex FAQ. How do I write such a code? I have a very large data with 286 rows and 10 columns. But you may be running into an issue with text formatting. Hello, I have a scale and I am trying to create a dummy variable from one of the socio-demographics. Should I have to use principle component analysis or there exist any index that you can recommend? The dataframe has the below mentioned columns with the Name of the country as Index. I want to know which one of the isolates grows the best in which Cu concentration.
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. I got two components from the PCA analysis. 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. My problem is trying a unique way to go about it. The question is what are the sources of your income and I let to pick multiple choices among "help from family", "part-time job", "full-time job" and "scholarship". Gold
Could you please turn this into a self-contained reprex (short for reproducible example)?
forcats.tidyverse.org
I wanted to do was that to get another column containing these categories as primary,secondary,secondaryT and etc.But it seems not to work. I don't know if you want to do this, but it may be a good idea now that you have a working product to simplify your code. Honestly, I explored the internet and there was nothing useful. Creates dummy columns from columns that have categorical variables (character or factor types).
If I break a categorical variable down into dummy variables, I get separate feature importances per class in …
It will help us help you if we can be sure we're all working with/looking at the same stuff. diff.append(df[df.columns[1][i]]- df[df.columns[6][i]]). 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 |. We can go beyond binary categorical variables such as TRUE vs FALSE.For example, suppose that \(x\) measures educational attainment, i.e. Removes the first dummy of every variable such that only n-1 dummies remain. How to iterate through a dataset while performing a specific function with the aim to get the corresponding index as answer? If one row is "cat, dog", Thanks! Also, have in mind that recoding your factor variables as integers (i.e. The other alternative is to rephrase your search criteria if you are familiar with regex. library(stringr) # --- You need this library, if(sum(str_detect(toupper(x), "AILE"))) AILE_V = 0 else AILE_V = 1. But i am getting KeyError. R and string matching by default see "H" and "h" as different characters.
Is it only capitalized letters that are affecting your unique values? All rights reserved. For example, for "55-74" to be replace with "64.5" and "35-54" to be replace with "43.5". This avoids multicollinearity issues in models. Before doing that I have to make index of climate change (with only two variables temperature and precipitation). Here, I'm providing an example, where I've recoded to integers but through the factor function. Im running a multiple regression model and therefore need to create dummy variables for a categorical predictor variable.