I am doing Exploratory Data Analysis on a tibble data frame. I've never used tibble so I'm experiecing some difficulties.
My tibble data frame has this structure:
spec_tbl_df [7,397 x 19] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ X1 : num [1:7397] 9617 12179 9905 5745 10067 ...
$ Administrative : num [1:7397] 5 26 4 3 7 16 4 3 2 0 ...
$ Administrative_Duration: num [1:7397] 408 1562 58 103 165 ...
$ Informational : num [1:7397] 2 9 2 0 1 3 4 5 0 0 ...
$ Informational_Duration : num [1:7397] 47.5 503.7 28.5 0 28.5 ...
$ ProductRelated : num [1:7397] 54 183 82 25 115 86 75 23 27 33 ...
$ ProductRelated_Duration: num [1:7397] 1547 9676 4729 1109 3428 ...
$ BounceRates : num [1:7397] 0 0.0111 0 0 0 ...
$ ExitRates : num [1:7397] 0.01733 0.0142 0.01454 0.00167 0.01629 ...
$ PageValues : num [1:7397] 0 19.57 9.06 61.3 4.97 ...
$ SpecialDay : num [1:7397] 0 0 0 0 0 0 0 0 0 0 ...
$ Month : Factor w/ 10 levels "Aug","Dec","Feb",..: 8 8 8 1 8 4 8 7 8 8 ...
$ OperatingSystems : Factor w/ 8 levels "1","2","3","4",..: 2 3 2 2 2 3 3 4 8 2 ...
$ Browser : Factor w/ 13 levels "1","2","3","4",..: 2 2 2 2 2 2 2 1 2 5 ...
$ Region : Factor w/ 9 levels "1","2","3","4",..: 3 2 1 6 4 8 1 1 7 3 ...
$ TrafficType : Factor w/ 19 levels "1","2","3","4",..: 2 12 2 5 10 4 2 4 2 1 ...
$ VisitorType : Factor w/ 3 levels "New_Visitor",..: 3 3 3 1 3 3 3 3 1 3 ...
$ Weekend : Factor w/ 2 levels "FALSE","TRUE": 2 1 1 1 1 1 1 1 1 1 ...
$ Revenue : Factor w/ 2 levels "FALSE","TRUE": 2 2 2 2 2 2 2 2 2 2 ...
Now if I use plot_bar to plot the cathegorical data (using DataExplorer package) I have no problem. I would like, for example, to create a boxplot for the cathegorical variable "Month" where for each month I have a boxplot showing how values are distribuited. The problem is that I can't find a way to access the frequencies. If I do the following:
boxplot(Month)
It creates a single boxplot for all the data (all the months) but it's not helpfull at all. Like this:
I would like the months on the x axis and the frequencies on the y axis and a boxplot for each month.
I've tried to "extract" the feature month, transform it to a matrix and repeat the process but it does not work.
Here is the variable montht taken alone:
> summary(x_Month)
Aug Dec Feb Jul June Mar May Nov Oct Sep
258 1034 123 259 166 1125 2014 1814 327 277
What am I missing ?
Something like this would probably work to create barplots for the frequencies of Month:
library(ggplot2)
spec_tbl_df %>%
ggplot(aes(x = Month)) +
geom_bar()
I am practicing with this dataset: http://archive.ics.uci.edu/ml/datasets/Census+Income
I loaded training & testing data.
# Downloading train and test data
trainFile = "adult.data"; testFile = "adult.test"
if (!file.exists (trainFile))
download.file (url = "http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data",
destfile = trainFile)
if (!file.exists (testFile))
download.file (url = "http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test",
destfile = testFile)
# Assigning column names
colNames = c ("age", "workclass", "fnlwgt", "education",
"educationnum", "maritalstatus", "occupation",
"relationship", "race", "sex", "capitalgain",
"capitalloss", "hoursperweek", "nativecountry",
"incomelevel")
# Reading training data
training = read.table (trainFile, header = FALSE, sep = ",",
strip.white = TRUE, col.names = colNames,
na.strings = "?", stringsAsFactors = TRUE)
# Load the testing data set
testing = read.table (testFile, header = FALSE, sep = ",",
strip.white = TRUE, col.names = colNames,
na.strings = "?", fill = TRUE, stringsAsFactors = TRUE)
I needed to combined two into one. But, there is a problem. I am seeing structure of the two data is not same.
Display structure of the training data
> str (training)
'data.frame': 32561 obs. of 15 variables:
$ age : int 39 50 38 53 28 37 49 52 31 42 ...
$ workclass : Factor w/ 8 levels "Federal-gov",..: 7 6 4 4 4 4 4 6 4 4 ...
$ fnlwgt : int 77516 83311 215646 234721 338409 284582 160187 209642 45781 159449 ...
$ education : Factor w/ 16 levels "10th","11th",..: 10 10 12 2 10 13 7 12 13 10 ...
$ educationnum : int 13 13 9 7 13 14 5 9 14 13 ...
$ maritalstatus: Factor w/ 7 levels "Divorced","Married-AF-spouse",..: 5 3 1 3 3 3 4 3 5 3 ...
$ occupation : Factor w/ 14 levels "Adm-clerical",..: 1 4 6 6 10 4 8 4 10 4 ...
$ relationship : Factor w/ 6 levels "Husband","Not-in-family",..: 2 1 2 1 6 6 2 1 2 1 ...
$ race : Factor w/ 5 levels "Amer-Indian-Eskimo",..: 5 5 5 3 3 5 3 5 5 5 ...
$ sex : Factor w/ 2 levels "Female","Male": 2 2 2 2 1 1 1 2 1 2 ...
$ capitalgain : int 2174 0 0 0 0 0 0 0 14084 5178 ...
$ capitalloss : int 0 0 0 0 0 0 0 0 0 0 ...
$ hoursperweek : int 40 13 40 40 40 40 16 45 50 40 ...
$ nativecountry: Factor w/ 41 levels "Cambodia","Canada",..: 39 39 39 39 5 39 23 39 39 39 ...
$ incomelevel : Factor w/ 2 levels "<=50K",">50K": 1 1 1 1 1 1 1 2 2 2 ...
Display structure of the testing data
> str (testing)
'data.frame': 16282 obs. of 15 variables:
$ age : Factor w/ 74 levels "|1x3 Cross validator",..: 1 10 23 13 29 3 19 14 48 9 ...
$ workclass : Factor w/ 9 levels "","Federal-gov",..: 1 5 5 3 5 NA 5 NA 7 5 ...
$ fnlwgt : int NA 226802 89814 336951 160323 103497 198693 227026 104626 369667 ...
$ education : Factor w/ 17 levels "","10th","11th",..: 1 3 13 9 17 17 2 13 16 17 ...
$ educationnum : int NA 7 9 12 10 10 6 9 15 10 ...
$ maritalstatus: Factor w/ 8 levels "","Divorced",..: 1 6 4 4 4 6 6 6 4 6 ...
$ occupation : Factor w/ 15 levels "","Adm-clerical",..: 1 8 6 12 8 NA 9 NA 11 9 ...
$ relationship : Factor w/ 7 levels "","Husband","Not-in-family",..: 1 5 2 2 2 5 3 6 2 6 ...
$ race : Factor w/ 6 levels "","Amer-Indian-Eskimo",..: 1 4 6 6 4 6 6 4 6 6 ...
$ sex : Factor w/ 3 levels "","Female","Male": 1 3 3 3 3 2 3 3 3 2 ...
$ capitalgain : int NA 0 0 0 7688 0 0 0 3103 0 ...
$ capitalloss : int NA 0 0 0 0 0 0 0 0 0 ...
$ hoursperweek : int NA 40 50 40 40 30 30 40 32 40 ...
$ nativecountry: Factor w/ 41 levels "","Cambodia",..: 1 39 39 39 39 39 39 39 39 39 ...
$ incomelevel : Factor w/ 3 levels "","<=50K.",">50K.": 1 2 2 3 3 2 2 2 3 2 ...
Problem 1:
age has become factor at testing. and all other levels of factor in testing is being increased by 1 than levels of factor in training. This is because first row is an unnecessary row in testing.
|1x3 Cross validator
I tried to get rid of this by re-assigning testing:
testing = testing[-1,]
but, after running str() command again, I don't see any change.
Problem 2:
Like I said at previous, I needed to combine those two data-frame into one data-frame. So, I run this:
combined <- rbind(training , testing)
Besides the problem-1, I can see new a problem after running str()
> str(combined)
'data.frame': 48842 obs. of 15 variables:
$ age : chr "39" "50" "38" "53" ...
$ workclass : Factor w/ 9 levels "Federal-gov",..: 7 6 4 4 4 4 4 6 4 4 ...
$ fnlwgt : int 77516 83311 215646 234721 338409 284582 160187 209642 45781 159449 ...
$ education : Factor w/ 17 levels "10th","11th",..: 10 10 12 2 10 13 7 12 13 10 ...
$ educationnum : int 13 13 9 7 13 14 5 9 14 13 ...
$ maritalstatus: Factor w/ 8 levels "Divorced","Married-AF-spouse",..: 5 3 1 3 3 3 4 3 5 3 ...
$ occupation : Factor w/ 15 levels "Adm-clerical",..: 1 4 6 6 10 4 8 4 10 4 ...
$ relationship : Factor w/ 7 levels "Husband","Not-in-family",..: 2 1 2 1 6 6 2 1 2 1 ...
$ race : Factor w/ 6 levels "Amer-Indian-Eskimo",..: 5 5 5 3 3 5 3 5 5 5 ...
$ sex : Factor w/ 3 levels "Female","Male",..: 2 2 2 2 1 1 1 2 1 2 ...
$ capitalgain : int 2174 0 0 0 0 0 0 0 14084 5178 ...
$ capitalloss : int 0 0 0 0 0 0 0 0 0 0 ...
$ hoursperweek : int 40 13 40 40 40 40 16 45 50 40 ...
$ nativecountry: Factor w/ 42 levels "Cambodia","Canada",..: 39 39 39 39 5 39 23 39 39 39 ...
$ incomelevel : Factor w/ 5 levels "<=50K",">50K",..: 1 1 1 1 1 1 1 2 2 2 ...
factor levels at target variable (incomelevel) in combined data-frame is 5 where it's 2 (which is correct) in the training data-frame and 3 (increased by 1 for problem-1) in testing data-frame. This is because there is a . (dot) after each value at incomelevel in testing data-frame (<=50K., <=50K., >50K.,......). So, I need to remove that .(dot) But, I am not getting idea how to remove it. Is there any function?
I am very in data and r. That's why, facing this type of basic issues. Can you please help me to solve the issue I am facing?
I think you can ignore the first line of test, this will solve the issue of age being a factor, because it seems like a header:
head(readLines(testFile))
[1] "|1x3 Cross validator"
[2] "25, Private, 226802, 11th, 7, Never-married, Machine-op-inspct, Own-child, Black, Male, 0, 0, 40, United-States, <=50K."
[3] "38, Private, 89814, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 50, United-States, <=50K."
We run your code, we can use read.csv, with skip=1 for test:
colNames = c ("age", "workclass", "fnlwgt", "education",
"educationnum", "maritalstatus", "occupation",
"relationship", "race", "sex", "capitalgain",
"capitalloss", "hoursperweek", "nativecountry",
"incomelevel")
# Reading training data
training = read.csv (trainFile, header = FALSE, col.names = colNames,stringsAsFactors = TRUE,na.strings = "?",strip.white = TRUE)
testing = read.csv (testFile, header = FALSE, col.names = colNames,na.strings = "?",stringsAsFactors = TRUE,skip=1,strip.white = TRUE)
Now, the income level, unfortunately we have to correct it manually, it's a good thing you check:
testing$incomelevel = factor(gsub("\\.","",as.character(testing$incomelevel)))
We check levels, only difference is native country:
all.equal(sapply(testing,levels) ,sapply(training,levels))
[1] "Component “nativecountry”: Lengths (40, 41) differ (string compare on first 40)"
[2] "Component “nativecountry”: 26 string mismatches"
And I don't think there's much you can do, maybe you have to remove it before / after joining:
setdiff(levels(training$nativecountry),levels(testing$nativecountry))
[1] "Holand-Netherlands"
I know this may be a potential duplicate question, but I found other answers didn't work in my situation.
I am using the following dataset:
> str(total_data)
'data.frame': 32260 obs. of 13 variables:
$ age : int 40 42 44 32 25 31 30 30 27 28 ...
$ workclass : Factor w/ 4 levels "Other-Unknown",..: 3 2 2 1 2 2 2 3 2 3 ...
$ education : Ord.factor w/ 7 levels "1"<"2"<"3"<"4"<..: 2 3 2 2 2 3 2 2 2 2 ...
$ marital.status : Factor w/ 5 levels "Divorced","Married",..: 2 1 2 3 3 3 3 2 2 3 ...
$ occupation : Factor w/ 6 levels "Blue-Collar",..: 5 3 6 2 1 6 6 1 1 6 ...
$ race : Factor w/ 5 levels "Amer-Indian-Eskimo",..: 1 5 1 1 5 5 5 5 5 5 ...
$ sex : Factor w/ 2 levels "Female","Male": 2 2 2 1 2 2 2 2 1 1 ...
$ hours.per.week : int 84 40 40 38 40 38 48 70 35 38 ...
$ naitive.country: Factor w/ 41 levels "?","Cambodia",..: 39 39 39 39 39 39 39 12 39 39 ...
$ classifier : chr "<=50K" "<=50K" ">50K" "<=50K" ...
$ class_num : Factor w/ 2 levels "1","2": 1 1 2 1 1 1 1 2 1 1 ...
$ age_norm : num 0.315 0.342 0.37 0.205 0.11 ...
$ hours_norm : num 0.847 0.398 0.398 0.378 0.398 ...
I'm trying to encode the factors into binary using one_hot() but receive the following error message:
encoded_data <- one_hot(total_data, dropCols = FALSE)
ERROR MESSAGE:
Error in `[.data.frame`(dt, , cols, with = FALSE) :
unused argument (with = FALSE)
I'm not sure what the "with" argument is as I don't see it in the R documentation.
I also saw that someone suggested to use model.matrix. However, when I use that, my ordered factor gets encoded as well, which is what I'm trying to avoid.
This is what happens to my ordered factor variable:
education.L education.Q education.C education^4 education^5 education^6
-3.779645e-01 9.690821e-17 4.082483e-01 -0.5640761 4.364358e-01 -0.19738551
-1.889822e-01 -3.273268e-01 4.082483e-01 0.0805823 -5.455447e-01 0.49346377
I'm also not sure why there are sometimes letters or numbers after the attribute name. i.e. education**.L** vs education**^5**
Convert the data.frame into a data.table and it should work fine.
library(data.table)
dt = data.table(total_data)
one_hot(dt)
I've used aregImpute to impute the missing values then i used impute.transcan function trying to get complete dataset using the following code.
impute_arg <- aregImpute(~ age + job + marital + education + default +
balance + housing + loan + contact + day + month + duration + campaign +
pdays + previous + poutcome + y , data = mov.miss, n.impute = 10 , nk =0)
imputed <- impute.transcan(impute_arg, imputation=1, data=mov.miss, list.out=TRUE, pr=FALSE, check=FALSE)
y <- completed[names(imputed)]
and when i used str(y) it already gives me a dataframe but with NAs as it is not imputed before, My question is how to get complete dataset without NAs after imputation?
str(y)
'data.frame': 4521 obs. of 17 variables:
$ age : int 30 NA 35 30 NA 35 36 39 41 43 ...
$ job : Factor w/ 12 levels "admin.","blue-collar",..: 11 8 5 5 2 5 7 10 3 8 ...
$ marital : Factor w/ 3 levels "divorced","married",..: 2 2 3 2 2 3 2 2 2 2 ...
$ education: Factor w/ 4 levels "primary","secondary",..: 1 2 3 3 2 3 NA 2 3 1 ...
$ default : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 NA 1 1 1 ...
$ balance : int NA 4789 1350 1476 0 747 307 147 NA -88 ...
$ housing : Factor w/ 2 levels "no","yes": NA 2 2 2 NA 1 2 2 2 2 ...
$ loan : Factor w/ 2 levels "no","yes": 1 2 1 2 NA 1 1 NA 1 2 ...
$ contact : Factor w/ 3 levels "cellular","telephone",..: 1 1 1 3 3 1 1 1 NA 1 ...
$ day : int 19 NA 16 3 5 23 14 6 14 NA ...
$ month : Factor w/ 12 levels "apr","aug","dec",..: 11 9 1 7 9 4 NA 9 9 1 ...
$ duration : int 79 220 185 199 226 141 341 151 57 313 ...
$ campaign : int 1 1 1 4 1 2 1 2 2 NA ...
$ pdays : int -1 339 330 NA -1 176 330 -1 -1 NA ...
$ previous : int 0 4 NA 0 NA 3 2 0 0 2 ...
$ poutcome : Factor w/ 4 levels "failure","other",..: 4 1 1 4 4 1 2 4 4 1 ...
$ y : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
I have tested your code myself, and it works just fine, except for the last line:
y <- completed[names(imputed)]
I believe there's a type in the above line. Plus, you do not even need the completed function.
Besides, if you want to get a data.frame from the impute.transcan function, then wrap it with as.data.frame:
imputed <- as.data.frame(impute.transcan(impute_arg, imputation=1, data=mov.miss, list.out=TRUE, pr=FALSE, check=FALSE))
Moreover, if you need to test your missing data pattern, you can also use the md.pattern function provided by the mice package.
One of the variables, 'Cabin', has a hefty amount of NAs. I am trying to use a decision tree (rpart) to predict the Cabin deck of passengers whose Cabin is not available.
Currently, this is the structure of my data table, which is a rbind of the training and test sets.
$ PassengerId: int 1 2 3 4 5 6 7 8 9 10 ...
$ Pclass : Factor w/ 3 levels "1","2","3": 3 1 3 1 3 3 1 3 3 2 ...
$ Name : chr "Braund, Mr. Owen Harris" "Cumings, Mrs. John Bradley (Florence Briggs Thayer)" "Heikkinen, Miss. Laina" "Futrelle, Mrs. Jacques Heath (Lily May Peel)" ...
$ Sex : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
$ Age : num 22 38 26 35 35 ...
$ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
$ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
$ Ticket : Factor w/ 929 levels "110152","110413",..: 524 597 670 50 473 276 86 396 345 133 ...
$ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
$ Cabin : Factor w/ 187 levels "","A10","A14",..: NA 83 NA 57 NA NA 131 NA NA NA ...
$ Embarked : Factor w/ 3 levels "C","Q","S": 3 1 3 3 3 2 3 3 3 1 ...
$ Survived : Factor w/ 2 levels "0","1": 1 2 2 2 1 1 1 1 2 2 ...
$ FamilySize : num 2 2 1 2 1 1 1 5 3 2 ...
$ FamilyID : Factor w/ 8 levels "11","3","4","5",..: 8 8 8 8 8 8 8 4 2 8 ...
$ FamilyID2 : Factor w/ 7 levels "11","4","5","6",..: 7 7 7 7 7 7 7 3 7 7 ...
$ Title : Factor w/ 11 levels "Col","Dr","Lady",..: 7 8 5 8 7 7 7 4 8 8 ...
$ Surname : chr "Braund" "Cumings" "Heikkinen" "Futrelle" ...
$ Cabin2 : Factor w/ 8 levels "A","B","C","D",..: NA 3 NA 3 NA NA 5 NA NA NA ...
Please note that I have used strsplit to create 'Cabin2' which has extracted the letter of the 'Cabin' variable, which corresponds to the deck on the Titanic to my understanding. This significantly reduced the number of levels that I was fighting with from 187 with 'Cabin' to 8 with 'Cabin2.'
I am trying to use the following code to predict the cabin deck:
cabinFit <- rpart(Cabin2 ~ Age + Sex + Fare + Embarked + SibSp + Parch + Title + FamilySize + FamilyID,
combi$Cabin2[is.na(combi$Cabin2)] <- predict(cabinFit, combi[is.na(combi$Cabin2),])
The output that I am being thrown by R is as follows:
Warning messages:
1: In `[<-.factor`(`*tmp*`, is.na(combi$Cabin2), value = c(NA, 3L, :
invalid factor level, NA generated
2: In `[<-.factor`(`*tmp*`, is.na(combi$Cabin2), value = c(NA, 3L, :
number of items to replace is not a multiple of replacement length
I am desperately trying to make sense of this as I continue fiddling with these data, however I am coming up short as to why this bit of code doesn't do the trick for me.