Missing value in my descriptive statistic

lara.man · August 26, 2023, 12:42pm

Hello, I'm a newbie in using R cause I've been introduced to this program during this semester and now it's part of my assignment to use this tool by performing a regression. I used the platform kaggle to search for an appropriate data set. After defining my dummy variables, I used the stargazer function to do the descriptive analysis. Unfortunately , not all values were included. My data set was about the loan depending on variables as gender, marriage, Education and dependents. The dependents implied numbers between zero and three plus. So is it possible that R isn’t accepting zero as a recognizable value? Because this number wasn’t included in my descriptive statistic and if not ,is there another solution?

Thanks in advance!

startz · August 26, 2023, 1:41pm

R is quite comfortable with zeros.

You might want to copy and paste your data here (text, not a picture). And similarly post a snippet of your data, or since it's from kaggle a link.

jrkrideau · August 26, 2023, 2:04pm

As a followup to startz's post

FAQ Asking Questions

A handy way to supply some sample data is the dput() function. In the case of a large dataset something like dput(head(mydata, 100)) should supply the data we need. Just do dput(mydata) where mydata is your data. Copy the output and paste it here.

lara.man · August 26, 2023, 4:55pm

I did it as you told me and this is the output of the function:

-614L), spec = structure(list(cols = list(Loan_ID = structure(list(), class = c("collector_character",
"collector")), Gender = structure(list(), class = c("collector_character",
"collector")), Married = structure(list(), class = c("collector_character",
"collector")), Dependents = structure(list(), class = c("collector_character",
"collector")), Education = structure(list(), class = c("collector_character",
"collector")), Self_Employed = structure(list(), class = c("collector_character",
"collector")), ApplicantIncome = structure(list(), class = c("collector_double",
"collector")), CoapplicantIncome = structure(list(), class = c("collector_double",
"collector")), LoanAmount = structure(list(), class = c("collector_double",
"collector")), Loan_Amount_Term = structure(list(), class = c("collector_double",
"collector")), Credit_History = structure(list(), class = c("collector_double",
"collector")), Property_Area = structure(list(), class = c("collector_character",
"collector")), Loan_Status = structure(list(), class = c("collector_character",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), delim = ","), class = "col_spec"), problems = <pointer: (nil)>, class = c("spec_tbl_df",
"tbl_df", "tbl", "data.frame"))

lara.man · August 26, 2023, 4:57pm

Here is the link for the dataset I've used. I already asked my sister cause she used R for her Master's Thesis, but she hadn't any clue either. So I'm really thankful for both contributions.

lara.man · August 26, 2023, 5:01pm

Sorry, I missed a bit of the text. Here is the complete version.

structure(list(Loan_ID = c("LP001002", "LP001003", "LP001005",
"LP001006", "LP001008", "LP001011", "LP001013", "LP001014", "LP001018",
"LP001020", "LP001024", "LP001027", "LP001028", "LP001029", "LP001030",
"LP001032", "LP001034", "LP001036", "LP001038", "LP001041", "LP001043",
"LP001046", "LP001047", "LP001050", "LP001052", "LP001066", "LP001068",
"LP001073", "LP001086", "LP001087", "LP001091", "LP001095", "LP001097",
"LP001098", "LP001100", "LP001106", "LP001109", "LP001112", "LP001114",
"LP001116", "LP001119", "LP001120", "LP001123", "LP001131", "LP001136",
"LP001137", "LP001138", "LP001144", "LP001146", "LP001151", "LP001155",
"LP001157", "LP001164", "LP001179", "LP001186", "LP001194", "LP001195",
"LP001197", "LP001198", "LP001199", "LP001205", "LP001206", "LP001207",
"LP001213", "LP001222", "LP001225", "LP001228", "LP001233", "LP001238",
"LP001241", "LP001243", "LP001245", "LP001248", "LP001250", "LP001253",
"LP001255", "LP001256", "LP001259", "LP001263", "LP001264", "LP001265",
"LP001266", "LP001267", "LP001273", "LP001275", "LP001279", "LP001280",
"LP001282", "LP001289", "LP001310", "LP001316", "LP001318", "LP001319",
"LP001322", "LP001325", "LP001326", "LP001327", "LP001333", "LP001334",
"LP001343", "LP001345", "LP001349", "LP001350", "LP001356", "LP001357",
"LP001367", "LP001369", "LP001370", "LP001379", "LP001384", "LP001385",
"LP001387", "LP001391", "LP001392", "LP001398", "LP001401", "LP001404",
"LP001405", "LP001421", "LP001422", "LP001426", "LP001430", "LP001431",
"LP001432", "LP001439", "LP001443", "LP001448", "LP001449", "LP001451",
"LP001465", "LP001469", "LP001473", "LP001478", "LP001482", "LP001487",
"LP001488", "LP001489", "LP001491", "LP001492", "LP001493", "LP001497",
"LP001498", "LP001504", "LP001507", "LP001508", "LP001514", "LP001516",
"LP001518", "LP001519", "LP001520", "LP001528", "LP001529", "LP001531",
"LP001532", "LP001535", "LP001536", "LP001541", "LP001543", "LP001546",
"LP001552", "LP001560", "LP001562", "LP001565", "LP001570", "LP001572",
"LP001574", "LP001577", "LP001578", "LP001579", "LP001580", "LP001581",
"LP001585", "LP001586", "LP001594", "LP001603", "LP001606", "LP001608",
"LP001610", "LP001616", "LP001630", "LP001633", "LP001634", "LP001636",
"LP001637", "LP001639", "LP001640", "LP001641", "LP001643", "LP001644",
"LP001647", "LP001653", "LP001656", "LP001657", "LP001658", "LP001664",
"LP001665", "LP001666", "LP001669", "LP001671", "LP001673", "LP001674",
"LP001677", "LP001682", "LP001688", "LP001691", "LP001692", "LP001693",
"LP001698", "LP001699", "LP001702", "LP001708", "LP001711", "LP001713",
"LP001715", "LP001716", "LP001720", "LP001722", "LP001726", "LP001732",
"LP001734", "LP001736", "LP001743", "LP001744", "LP001749", "LP001750",
"LP001751", "LP001754", "LP001758", "LP001760", "LP001761", "LP001765",
"LP001768", "LP001770", "LP001776", "LP001778", "LP001784", "LP001786",
"LP001788", "LP001790", "LP001792", "LP001798", "LP001800", "LP001806",
"LP001807", "LP001811", "LP001813", "LP001814", "LP001819", "LP001824",
"LP001825", "LP001835", "LP001836", "LP001841", "LP001843", "LP001844",
"LP001846", "LP001849", "LP001854", "LP001859", "LP001864", "LP001865",
"LP001868", "LP001870", "LP001871", "LP001872", "LP001875", "LP001877",
"LP001882", "LP001883", "LP001884", "LP001888", "LP001891", "LP001892",
"LP001894", "LP001896", "LP001900", "LP001903", "LP001904", "LP001907",
"LP001908", "LP001910", "LP001914", "LP001915", "LP001917", "LP001922",
"LP001924", "LP001925", "LP001926", "LP001931", "LP001935", "LP001936",
"LP001938", "LP001940", "LP001945", "LP001947", "LP001949", "LP001953",
"LP001954", "LP001955", "LP001963", "LP001964", "LP001972", "LP001974",
"LP001977", "LP001978", "LP001990", "LP001993", "LP001994", "LP001996",
"LP001998", "LP002002", "LP002004", "LP002006", "LP002008", "LP002024",
"LP002031", "LP002035", "LP002036", "LP002043", "LP002050", "LP002051",
"LP002053", "LP002054", "LP002055", "LP002065", "LP002067", "LP002068",
"LP002082", "LP002086", "LP002087", "LP002097", "LP002098", "LP002100",
"LP002101", "LP002103", "LP002106", "LP002110", "LP002112", "LP002113",
"LP002114", "LP002115", "LP002116", "LP002119", "LP002126", "LP002128",
"LP002129", "LP002130", "LP002131", "LP002137", "LP002138", "LP002139",
"LP002140", "LP002141", "LP002142", "LP002143", "LP002144", "LP002149",
"LP002151", "LP002158", "LP002160", "LP002161", "LP002170", "LP002175",
"LP002178", "LP002180", "LP002181", "LP002187", "LP002188", "LP002190",
"LP002191", "LP002194", "LP002197", "LP002201", "LP002205", "LP002209",
"LP002211", "LP002219", "LP002223", "LP002224", "LP002225", "LP002226",
"LP002229", "LP002231", "LP002234", "LP002236", "LP002237", "LP002239",
"LP002243", "LP002244", "LP002250", "LP002255", "LP002262", "LP002263",
"LP002265", "LP002266", "LP002272", "LP002277", "LP002281", "LP002284",
"LP002287", "LP002288", "LP002296", "LP002297", "LP002300", "LP002301",
"LP002305", "LP002308", "LP002314", "LP002315", "LP002317", "LP002318",
"LP002319", "LP002328", "LP002332", "LP002335", "LP002337", "LP002341",
"LP002342", "LP002345", "LP002347", "LP002348", "LP002357", "LP002361",
"LP002362", "LP002364", "LP002366", "LP002367", "LP002368", "LP002369",
"LP002370", "LP002377", "LP002379", "LP002386", "LP002387", "LP002390",
"LP002393", "LP002398", "LP002401", "LP002403", "LP002407", "LP002408",
"LP002409", "LP002418", "LP002422", "LP002424", "LP002429", "LP002434",
"LP002435", "LP002443", "LP002444", "LP002446", "LP002447", "LP002448",
"LP002449", "LP002453", "LP002455", "LP002459", "LP002467", "LP002472",
"LP002473", "LP002478", "LP002484", "LP002487", "LP002489", "LP002493",
"LP002494", "LP002500", "LP002501", "LP002502", "LP002505", "LP002515",
"LP002517", "LP002519", "LP002522", "LP002524", "LP002527", "LP002529",
"LP002530", "LP002531", "LP002533", "LP002534", "LP002536", "LP002537",
"LP002541", "LP002543", "LP002544", "LP002545", "LP002547", "LP002555",
"LP002556", "LP002560", "LP002562", "LP002571", "LP002582", "LP002585",
"LP002586", "LP002587", "LP002588", "LP002600", "LP002602", "LP002603",
"LP002606", "LP002615", "LP002618", "LP002619", "LP002622", "LP002624",
"LP002625", "LP002626", "LP002634", "LP002637", "LP002640", "LP002643",
"LP002648", "LP002652", "LP002659", "LP002670", "LP002682", "LP002683",
"LP002684", "LP002689", "LP002690", "LP002692", "LP002693", "LP002697",
"LP002699", "LP002705", "LP002706", "LP002714", "LP002716", "LP002717",
"LP002720", "LP002723", "LP002729", "LP002731", "LP002732", "LP002734",
"LP002738", "LP002739", "LP002740", "LP002741", "LP002743", "LP002753",
"LP002755", "LP002757", "LP002767", "LP002768", "LP002772", "LP002776",
"LP002777", "LP002778", "LP002784", "LP002785", "LP002788", "LP002789",
"LP002792", "LP002794", "LP002795", "LP002798", "LP002804", "LP002807",
"LP002813", "LP002820", "LP002821", "LP002832", "LP002833", "LP002836",
"LP002837", "LP002840", "LP002841", "LP002842", "LP002847", "LP002855",
"LP002862", "LP002863", "LP002868", "LP002872", "LP002874", "LP002877",
"LP002888", "LP002892", "LP002893", "LP002894", "LP002898", "LP002911",
"LP002912", "LP002916", "LP002917", "LP002925", "LP002926", "LP002928",
"LP002931", "LP002933", "LP002936", "LP002938", "LP002940", "LP002941",
"LP002943", "LP002945", "LP002948", "LP002949", "LP002950", "LP002953",
"LP002958", "LP002959", "LP002960", "LP002961", "LP002964", "LP002974",
"LP002978", "LP002979", "LP002983", "LP002984", "LP002990"),
Gender = c("1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "0", "1", "1", "1", "1",
"1", NA, "1", "1", "1", "1", "1", "0", "1", "1", "1", "1",
"1", "1", "1", "0", "1", "1", "1", "1", "1", "1", "1", "0",
"1", "1", "0", "0", "0", "0", "0", "1", "0", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "0", "1", "1", "1", "1", "0",
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "0", "1",
"0", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "0", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "0", "1", "0", "1", "1", "0", "1",
"1", "0", "1", "0", "0", "1", "1", "0", NA, "1", "1", "1",
"1", "1", "1", "1", "1", "1", "0", "1", "1", "1", "1", "1",
"1", "1", "1", "0", "0", "1", "0", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"0", "1", "1", "1", "1", NA, "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "0", "1", "1", "1", NA, "1",
"1", "1", "1", "1", "1", "1", "1", "0", "0", "1", "1", "1",
"1", "1", "1", "0", "0", "1", "1", "1", "0", "1", "1", "1",
"1", "1", "1", "1", "1", "0", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "0", "1", "1", "1", "0",
"0", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "0", "1", "1", "1", "0", "1", "1", "1", "1", "1", "1",
"0", "0", "1", "1", "1", "1", "0", "0", "0", "1", "1", "1",
"1", "1", "1", "1", "1", "0", "1", "1", "1", "0", "1", "1",
"0", "1", "0", "1", "1", "1", "1", "0", "1", "1", "1", "0",
"0", "1", "1", "1", "0", "1", "1", "1", "0", "0", "1", "1",
"0", "1", "0", "1", NA, "1", "1", "1", "0", "1", "1", "1",
"1", "0", "1", "1", "1", "1", "0", "0", "1", "1", "1", "1",
NA, "1", "1", "1", "0", "0", "1", "0", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "0", "0", "0", "1", "1",
"1", "1", "0", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"0", "1", "1", "1", "0", "1", "1", "1", "1", "1", "1", "1",
"0", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "0", "1", "1", "0", "1", "1", "1", "0", "0", "0",
"1", "0", "1", "1", "0", "1", "1", "1", "0", "0", "0", "1",
"1", "1", "1", "0", "1", "1", "1", "1", "0", "1", "1", "1",
"0", "1", "1", "1", "1", "0", "1", "1", "1", "0", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", NA, "1", "1", "0", "1", "1",
"1", NA, "0", "1", "1", "1", "1", "0", "1", "1", "1", NA,
"1", "1", "0", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "0", "1", "0", "1", "1", "1", "1", "0", "0",
"1", "1", "1", "1", "1", NA, "1", "0", "1", "1", "1", "1",
"1", "1", "0", "1", "1", "0", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "1", "0", "1", "1", "1",
"1", "1", "0", "0", "0", "1", "0", "1", "1", "1", "0", "1",
"1", "1", "1", "1", "1", "1", "0", "1", "1", "0", "1", "0",
"1", "1", "1", "1", "1", "1", "0", "1", "1", "1", "1", "1",
"1", "1", NA, "1", "1", "1", "1", "1", "0", "1", "1", "1",
"1", "0", NA, "1", "1", "1", NA, "1", "1", "1", "1", "1",
"1", "1", "0", "1", "1", "1", "0", "1", "1", "1", "1", "0",
"1", "1", "1", "0"), Married = c("0", "1", "1", "1", "0",
"1", "1", "1", "1", "1", "1", "1", "1", "0", "1", "0", "0",
"0", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "0",
"0", "1", "0", "0", "1", "0", "1", "1", "1", "0", "0", "0",
"0", "1", "1", "1", "0", "1", "1", "1", "0", "1", "0", "0",
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "0",
"1", "0", "1", "1", "0", "1", "1", "0", "1", "1", "0", "0",
"1", "1", "1", "0", "1", "1", "1", "1", "0", "1", "1", "0",
"1", "1", "1", "1", "0", "0", "0", "1", "1", "1", "1", "1",
"0", "1", "1", NA, "1", "1", "0", "1", "1", "0", "1", "1",
"0", "0", "1", "1", "1", "1", "0", "1", "0", "0", "1", "1",
"0", "1", "0", "1", "1", "0", "0", "0", "1", "0", "1", "1",
"1", "0", "1", "1", "0", "0", "1", "1", "1", "1", "1", "0",
"1", "0", "1", "0", "1", "0", "1", "1", "1", "0", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "0", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "0", "1", "0", "1", "1", "1",
"1", "1", "1", "1", "1", "0", "0", "1", "0", "0", "1", "0",
"0", "1", "0", "1", "0", "1", "1", "1", "0", "0", "0", "0",
"0", "0", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "0", "1", "1", "1", "1", "1", NA, "0", "1", "1", "0",
"0", "1", "1", "1", "0", "0", "1", "1", "1", "0", "1", "1",
"0", "1", "1", "1", "1", "1", "0", "0", "1", "0", "0", "0",
"1", "1", "1", "1", "0", "0", "0", "0", "0", "1", "1", "0",
"0", "0", "1", "0", "1", "1", "1", "1", "1", "1", "1", "0",
"1", "1", "0", "1", "0", "0", "1", "0", "0", "1", "1", "1",
"0", "1", "1", "1", "1", "0", "1", "1", "1", "0", "1", "0",
"0", "0", "0", "0", "1", "0", "0", "0", "1", "1", "1", "1",
"1", "0", "1", "1", "1", "1", "0", "1", "1", "0", "1", "1",
"0", "0", "0", "0", "1", "1", "1", "1", "1", "0", "0", "1",
"0", "1", "1", "1", "1", "1", "1", "1", "1", "1", "0", "1",
"1", "1", "0", "1", "1", "1", "1", "0", "1", "1", "1", "0",
"0", "0", "0", "1", "1", "0", "1", "1", "0", "0", "1", "1",
"1", "0", "1", "1", "0", "0", "0", "1", "0", "0", "1", "1",
"1", "0", "1", "1", "1", "1", "1", "0", "1", "0", "0", "1",
"0", "0", "0", "0", "0", "1", "0", "1", "1", "0", "1", "1",
"1", "1", "0", "0", "1", "1", "1", "1", "0", "1", "1", "1",
"1", "0", "1", "1", "0", "0", "0", "0", "1", "0", NA, "0",
"1", "0", "1", "0", "1", "0", "0", "1", "1", "1", "1", "1",
"0", "1", "1", "1", "1", "0", "1", "1", "1", "0", "1", "1",
"1", "1", "0", "0", "0", "1", "1", "1", "1", "1", "1", "1",
"0", "0", "1", "1", "1", "1", "1", "0", "1", "1", "1", "1",
"1", "0", "1", "1", "0", "0", "1", "0", "0", "1", "1", "1",
"1", "1", "0", "0", "0", "1", "1", "1", "1", "1", "0", "1",
"0", "0", "1", "1", "1", "0", "1", "1", "1", "0", "0", "1",
"0", "1", "1", "0", "1", "1", "1", "0", "0", "1", "1", "0",
"0", "0", "0", "1", "0", "1", "1", "1", "0", "0", "1", "1",
"1", "0", "0", "0", "1", "1", "1", "1", "1", "1", "1", "0",
"1", "1", "1", "1", "1", "1", "0", "1", "1", "0", "1", "0",
"1", "1", "1", "1", "1", "1", "1", "1", "0", "1", "0", "1",
"0", "1", "1", "1", "1", "1", "0", "0", "1", "1", "1", "0",
"1", "1", "0", "1", "0", "1", "1", "0", "1", "1", "0", "1",
"1", "1", "1", "1", "0", "1", "1", "1", "0"), Dependents = c("0",
"1", "0", "0", "0", "2", "0", "3+", "2", "1", "2", "2", "2",
"0", "2", "0", "1", "0", "0", "0", "0", "1", "0", "2", "1",
"0", "0", "2", "0", "2", "1", "0", "1", "0", "3+", "0", "0",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "1", "0", "0",
"0", "0", "0", "0", "2", "1", "2", "0", "0", "1", "2", "0",
"3+", "0", "1", "0", "0", "0", "1", "3+", "0", "0", "2",
"0", "3+", "3+", "0", "0", "1", "3+", "3+", "0", "1", "2",
"0", "1", "0", "2", "0", "0", "0", "0", "2", "2", "0", "0",
"0", "0", "0", "0", "0", "2", "0", NA, "0", NA, "1", "2",
"0", "2", "3+", "0", "0", "0", "1", "0", "1", "0", "1", "0",
"0", NA, "0", "0", "2", "0", "0", "3+", "0", "1", "0", "0",
"0", "0", "0", "0", "3+", "0", "2", "0", "2", "2", "0", "0",
"0", "2", "0", "2", "1", "0", "0", "0", "0", "0", "2", "0",
"3+", "1", "1", "0", "0", "0", "0", "1", "2", "0", "0", "0",
"0", "0", "2", "0", "3+", "3+", "0", "0", "0", "2", "3+",
"1", "0", "1", "0", "0", "1", "0", "0", "1", "0", "0", "0",
"0", "0", "0", "0", "0", "1", "0", "0", "0", "0", "1", "2",
"3+", "1", "2", "0", "0", "0", "0", "0", "0", "3+", "1",
"3+", "0", "3+", "0", "0", "2", "2", "0", "2", "0", "0",
"0", "0", NA, "2", NA, "0", "1", "0", "0", "0", "1", "1",
"0", "0", "1", "1", "2", "1", "0", "2", "0", "0", "2", "1",
"1", "0", "0", "2", "0", "1", "0", "3+", "0", "3+", "0",
"3+", "1", "0", "1", "0", "0", "0", "2", "3+", "0", "1",
"0", "0", "0", "0", "2", "1", "0", "0", "0", "0", "1", "0",
"2", "0", "0", "0", "0", "0", "0", "0", "0", "2", "2", NA,
"0", "3+", "1", "1", "0", "1", "0", NA, "0", "1", "0", "0",
"0", "0", "0", "2", "0", "0", "0", "2", "0", "1", "2", "0",
"1", "1", "0", "3+", "2", "0", "3+", "1", "0", "0", "0",
"0", "1", "0", NA, "0", "1", NA, "1", "2", "3+", "0", "3+",
"0", "1", "3+", "2", "0", NA, "2", "0", "0", "0", "0", "3+",
"0", "0", NA, "2", "1", "0", "3+", "1", "2", "0", "0", "0",
"0", "0", "0", "1", "0", "0", "2", "2", "1", "0", "0", "3+",
"0", "0", "2", "0", "0", "0", "0", "2", "1", "0", "0", "0",
"0", "3+", "3+", "0", "2", "2", "2", "0", "0", "0", "0",
"2", "0", "0", "0", "0", "0", "0", "0", "1", "3+", "1", "0",
"0", "0", "0", "0", "1", "2", "0", "0", "0", "0", "0", "1",
"0", "0", "1", "2", "0", "0", "1", "0", "0", "0", "0", NA,
"0", "0", "0", "0", "0", "0", "3+", "1", "0", "1", "2", "0",
"2", "1", "2", "2", "0", "0", "0", "2", "0", "0", "2", "0",
"0", "3+", "0", "1", "0", "0", "3+", "0", "2", "0", "1",
"1", "3+", "0", "2", "2", "2", "2", "1", "2", "0", "3+",
"0", "0", "2", "1", "2", "1", "2", "0", "0", "1", "0", "0",
"0", "1", "0", "0", "1", "0", "0", "0", "2", "1", "0", "2",
"0", "0", "0", "1", "0", "1", "2", "0", "0", "3+", "2", NA,
"0", "0", "2", "0", "3+", "2", "0", "2", "0", "1", "1", "0",
"0", "3+", "2", "1", "0", "0", "0", "2", "0", "3+", "1",
"0", "1", "1", "0", "0", "0", "0", "0", "0", "2", "1", "1",
"0", "0", "1", "0", "3+", "0", "0", "2", "1", "0", "0", "2",
"0", "0", "3+", "0", "0", "1", NA, "2", "2", "3+", "2", "0",
"0", "1", "0", "2", "0", "0", "1", "1", "1", "0", "0", "0",
"2", "0", "2", "3+", "0", "0", "0", "2", NA, "0", "2", "3+",
"0", "3+", "0", "1", "0", "1", "2", "0", "0", "3+", "1",
"2", "0"), Education = c("1", "1", "1", "0", "1", "1", "0",
"1", "1", "1", "1", "1", "1", "1", "1", "1", "0", "1", "0",
"1", "0", "1", "0", "0", "1", "1", "1", "0", "0", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "1", "0", "1", "1", "1",
"1", "0", "1", "1", "1", "1", "1", "0", "1", "1", "1", "1",
"1", "1", "1", "1", "0", "1", "1", "0", "1", "1", "1", "0",
"1", "0", "1", "1", "0", "1", "0", "1", "1", "1", "1", "1",
"0", "1", "1", "1", "1", "1", "1", "0", "1", "1", "1", "1",
"1", "0", "1", "0", "1", "1", "1", "0", "1", "0", "1", "1",
"1", "1", "1", "1", "0", "1", "0", "1", "1", "0", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "1", "1", "0", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"0", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "0", "1", "1", "1", "1", "1", "1", "0", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "0", "1", "0", "1", "0",
"1", "1", "1", "1", "0", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "0", "1", "0", "1", "1", "1", "1", "0", "1",
"1", "0", "1", "0", "0", "0", "0", "1", "0", "1", "1", "1",
"1", "1", "0", "1", "0", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "0", "1", "1", "1", "1", "1", "0", "1", "1",
"1", "1", "1", "1", "1", "1", "0", "1", "1", "0", "1", "1",
"0", "1", "1", "0", "1", "0", "0", "1", "1", "0", "1", "1",
"0", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "1", "0", "0", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "0", "0", "1", "1", "1", "0", "1",
"1", "1", "0", "1", "0", "1", "1", "1", "0", "1", "1", "1",
"1", "1", "1", "0", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "0", "1", "0", "1", "0",
"0", "1", "1", "0", "0", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "0", "1", "1", "1", "1", "1", "1", "0", "1",
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "0", "0", "1", "1", "1",
"1", "1", "0", "1", "1", "1", "1", "0", "1", "0", "0", "1",
"0", "1", "1", "0", "0", "1", "1", "0", "1", "0", "0", "0",
"1", "1", "1", "1", "1", "1", "0", "1", "1", "1", "1", "0",
"1", "1", "0", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"0", "1", "1", "0", "1", "1", "1", "0", "1", "1", "0", "0",
"0", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"0", "1", "1", "0", "1", "0", "1", "1", "0", "1", "1", "1",
"1", "1", "1", "1", "1", "0", "0", "1", "1", "1", "0", "1",
"1", "1", "1", "0", "0", "0", "0", "1", "1", "0", "1", "1",
"1", "1", "1", "1", "0", "0", "1", "1", "1", "1", "1", "0",
"1", "1", "1", "1", "1", "1", "0", "1", "0", "0", "1", "1",
"1", "1", "1", "1", "0", "0", "0", "1", "1", "1", "1", "0",
"0", "1", "1", "0", "1", "1", "1", "1", "0", "0", "1", "0",
"1", "1", "1", "1", "0", "1", "0", "1", "1", "1", "1", "1",
"1", "0", "1", "1", "0", "1", "0", "1", "1", "1", "1", "1",
"1", "1", "0", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "0", "1", "1", "1", "1", "1", "1", "1",
"0", "0", "1", "1", "1", "1", "0", "1", "1", "1", "0", "1",
"0", "1", "1", "1", "1", "1", "1"), Self_Employed = c("No",
"No", "Yes", "No", "No", "Yes", "No", "No", "No", "No", "No",
NA, "No", "No", "No", "No", "No", "No", "No", NA, "No", "No",
"No", "No", NA, "Yes", "No", "No", "No", NA, NA, "No", "Yes",
"No", "No", "No", "No", "No", "No", "No", "No", "No", "No",
"No", "Yes", "No", "No", "No", "No", "No", "No", "No", "No",
"No", "Yes", "No", "No", "No", "No", "No", "No", "No", "Yes",
"No", "No", "No", "No", "No", "Yes", "No", "No", "Yes", "No",
"No", "Yes", "No", "No", "Yes", "No", "Yes", "No", "Yes",
"No", "No", "No", "No", "No", "No", "No", "No", "No", "No",
"No", "No", "No", NA, "No", "No", "No", "No", "No", "No",
"No", "No", "No", "No", "No", NA, "No", "No", "No", NA, "No",
"Yes", NA, "No", "No", "No", "No", "No", "No", "No", "No",
"No", "No", "No", "No", "No", "Yes", "No", "Yes", "No", "No",
"Yes", "No", "No", "No", "Yes", "No", "No", "No", "No", "Yes",
"No", "No", "No", "No", "No", "No", "No", "No", "Yes", "No",
"No", "No", "No", "No", "No", NA, "No", "No", "No", "No",
"No", "No", "No", "No", "No", "No", "No", NA, "No", "No",
"No", "Yes", "No", "No", "No", "No", "No", "No", "No", "No",
"No", "No", "Yes", "Yes", "No", "Yes", "No", "No", "No",
"No", "No", "No", "No", "No", "No", "No", "Yes", "No", "No",
"No", "No", "No", "No", "No", "No", "No", "No", "No", "No",
"Yes", "Yes", "No", "No", "No", "No", NA, "No", "No", "No",
"No", "No", "No", "No", "Yes", "No", "No", "Yes", "No", NA,
"No", "No", "No", "No", NA, "Yes", "No", "No", "No", "No",
"No", "Yes", "No", "Yes", "No", "No", "No", "No", "No", "No",
"Yes", "No", "Yes", "No", "No", "No", "No", "No", "No", "No",
"No", "No", "Yes", "No", "No", "No", NA, "No", "No", "No",

lara.man · August 26, 2023, 5:02pm

And the other part cause the text was too much for this formular.

"No", "No", "No", "No", "No", "No", "No", "No", "Yes", "No", 
"No", "No", "No", "No", "Yes", "No", "No", "No", "No", "No", 
"No", "No", "No", NA, "No", "No", "No", "No", "No", "No", 
"No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
"No", "Yes", "No", "No", "No", "No", "No", "Yes", "No", "No", 
"No", "No", "No", "Yes", "No", "Yes", "No", "No", "No", "No", 
"No", NA, "Yes", "Yes", NA, "Yes", "No", "No", "No", "No", 
"No", "No", NA, "No", "No", "No", "No", "No", "No", "No", 
"No", "Yes", "No", "No", "No", "No", "No", "No", "No", "No", 
"No", "No", "Yes", "No", "No", "No", "No", "No", "Yes", "No", 
"Yes", "No", NA, "No", "No", "No", "No", "No", NA, "No", 
"No", "Yes", "No", NA, "No", "No", "No", "No", "No", "No", 
"No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
"No", "No", "Yes", "No", "No", "No", "No", "No", "Yes", NA, 
"No", "No", "No", "No", "No", "Yes", "No", "No", "No", "No", 
"No", "No", "No", "No", "No", "No", "No", "No", "Yes", "No", 
NA, "No", "No", "No", "No", "No", "Yes", "Yes", "No", "No", 
"No", "No", "No", "Yes", "No", NA, "No", "Yes", "No", "No", 
"No", "No", "Yes", "No", "No", "No", "No", "No", "Yes", "No", 
"No", NA, "No", "No", "No", "No", NA, "No", "Yes", "No", 
"No", "Yes", "No", "Yes", "No", "No", "Yes", "No", "No", 
"No", "No", "No", "No", "No", "No", "No", "Yes", "No", "No", 
"No", "No", "Yes", "No", "No", "No", "No", "Yes", "No", "No", 
"No", "No", "No", "No", "No", "No", "No", "Yes", "No", "No", 
"No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
"No", "Yes", "Yes", "No", "Yes", "No", "No", "No", "No", 
"No", "No", "No", "No", "Yes", NA, "No", "No", "No", "No", 
"No", "No", NA, "No", "No", "No", "No", "No", "No", "No", 
"Yes", "No", "No", "No", "No", "No", "No", "Yes", "No", "No", 
"No", "Yes", "No", "Yes", "No", "No", "No", "No", "No", "No", 
"No", "No", "No", "No", "No", "No", "No", "No", "No", NA, 
"No", "No", "No", "No", "No", "No", "No", "No", "No", "Yes", 
"No", "Yes", "Yes", "No", "Yes", "No", "Yes", "No", "Yes", 
"No", NA, NA, "No", "No", "No", "No", "No", "No", "No", "No", 
"No", "No", "No", "Yes"), ApplicantIncome = c(5849, 4583, 
3000, 2583, 6000, 5417, 2333, 3036, 4006, 12841, 3200, 2500, 
3073, 1853, 1299, 4950, 3596, 3510, 4887, 2600, 7660, 5955, 
2600, 3365, 3717, 9560, 2799, 4226, 1442, 3750, 4166, 3167, 
4692, 3500, 12500, 2275, 1828, 3667, 4166, 3748, 3600, 1800, 
2400, 3941, 4695, 3410, 5649, 5821, 2645, 4000, 1928, 3086, 
4230, 4616, 11500, 2708, 2132, 3366, 8080, 3357, 2500, 3029, 
2609, 4945, 4166, 5726, 3200, 10750, 7100, 4300, 3208, 1875, 
3500, 4755, 5266, 3750, 3750, 1000, 3167, 3333, 3846, 2395, 
1378, 6000, 3988, 2366, 3333, 2500, 8566, 5695, 2958, 6250, 
3273, 4133, 3620, 6782, 2484, 1977, 4188, 1759, 4288, 4843, 
13650, 4652, 3816, 3052, 11417, 7333, 3800, 2071, 5316, 2929, 
3572, 7451, 5050, 14583, 3167, 2214, 5568, 10408, 5667, 4166, 
2137, 2957, 4300, 3692, 23803, 3865, 10513, 6080, 20166, 
2014, 2718, 3459, 4895, 4000, 4583, 3316, 14999, 4200, 5042, 
5417, 6950, 2698, 11757, 2330, 14866, 1538, 10000, 4860, 
6277, 2577, 9166, 2281, 3254, 39999, 6000, 9538, 2980, 4583, 
1863, 7933, 3089, 4167, 9323, 3707, 4583, 2439, 2237, 8000, 
1820, 51763, 3522, 5708, 4344, 3497, 2045, 5516, 3750, 2333, 
6400, 1916, 4600, 33846, 3625, 39147, 2178, 2383, 674, 9328, 
4885, 12000, 6033, 3858, 4191, 3125, 8333, 1907, 3416, 11000, 
2600, 4923, 3992, 3500, 3917, 4408, 3244, 3975, 2479, 3418, 
10000, 3430, 7787, 5703, 3173, 3850, 150, 3727, 5000, 4283, 
2221, 4009, 2971, 7578, 6250, 3250, 4735, 6250, 4758, 6400, 
2491, 3716, 3189, 8333, 3155, 5500, 5746, 3463, 3812, 3315, 
5819, 2510, 2965, 6250, 3406, 6050, 9703, 6608, 2882, 1809, 
1668, 3427, 2583, 2661, 16250, 3083, 6045, 5250, 14683, 4931, 
6083, 2060, 3481, 7200, 5166, 4095, 4708, 4333, 3418, 2876, 
3237, 11146, 2833, 2620, 3900, 2750, 3993, 3103, 14583, 4100, 
4053, 3927, 2301, 1811, 20667, 3158, 2600, 3704, 4124, 9508, 
3075, 4400, 3153, 5417, 2383, 4416, 6875, 4666, 5000, 2014, 
1800, 2875, 5000, 1625, 4000, 2000, 3762, 2400, 20233, 7667, 
2917, 2927, 2507, 5746, 2473, 3399, 3717, 2058, 3541, 10000, 
2400, 4342, 3601, 3166, 15000, 8666, 4917, 5818, 4333, 2500, 
4384, 2935, 2833, 63337, 9833, 5503, 5250, 2500, 1830, 4160, 
2647, 2378, 4554, 3173, 2583, 2499, 3523, 3083, 6333, 2625, 
9083, 8750, 2666, 5500, 2423, 3813, 8333, 3875, 3000, 5167, 
4723, 5000, 4750, 3013, 6822, 6216, 2500, 5124, 6325, 19730, 
15759, 5185, 9323, 3062, 2764, 4817, 8750, 4310, 3069, 5391, 
3333, 5941, 6000, 7167, 4566, 3667, 2346, 3010, 2333, 5488, 
9167, 9504, 2583, 1993, 3100, 3276, 3180, 3033, 3902, 1500, 
2889, 2755, 2500, 1963, 7441, 4547, 2167, 2213, 8300, 81000, 
3867, 6256, 6096, 2253, 2149, 2995, 2600, 1600, 1025, 3246, 
5829, 2720, 1820, 7250, 14880, 2666, 4606, 5935, 2920, 2717, 
8624, 6500, 12876, 2425, 3750, 10047, 1926, 2213, 10416, 
7142, 3660, 7901, 4707, 37719, 7333, 3466, 4652, 3539, 3340, 
2769, 2309, 1958, 3948, 2483, 7085, 3859, 4301, 3708, 4354, 
8334, 2083, 7740, 3015, 5191, 4166, 6000, 2947, 16692, 210, 
4333, 3450, 2653, 4691, 2500, 5532, 16525, 6700, 2873, 16667, 
2947, 4350, 3095, 2083, 10833, 8333, 1958, 3547, 18333, 4583, 
2435, 2699, 5333, 3691, 17263, 3597, 3326, 2600, 4625, 2895, 
6283, 645, 3159, 4865, 4050, 3814, 3510, 20833, 3583, 2479, 
13262, 3598, 6065, 3283, 2130, 5815, 3466, 2031, 3074, 4683, 
3400, 2192, 2500, 5677, 7948, 4680, 17500, 3775, 5285, 2679, 
6783, 1025, 4281, 3588, 11250, 18165, 2550, 6133, 3617, 2917, 
6417, 4608, 2138, 3652, 2239, 3017, 2768, 3358, 2526, 5000, 
2785, 6633, 2492, 3333, 2454, 3593, 5468, 2667, 10139, 3887, 
4180, 3675, 19484, 5923, 5800, 8799, 4467, 3333, 3400, 2378, 
3166, 3417, 5116, 16666, 6125, 6406, 3159, 3087, 3229, 1782, 
3182, 6540, 1836, 3166, 1880, 2787, 4283, 2297, 2165, 4750, 
2726, 3000, 6000, 9357, 3859, 16120, 3833, 6383, 2987, 9963, 
5780, 416, 2894, 5703, 3676, 12000, 2400, 3400, 3987, 3232, 
2900, 4106, 8072, 7583, 4583), CoapplicantIncome = c(0, 1508, 
0, 2358, 0, 4196, 1516, 2504, 1526, 10968, 700, 1840, 8106, 
2840, 1086, 0, 0, 0, 0, 3500, 0, 5625, 1911, 1917, 2925, 
0, 2253, 1040, 0, 2083, 3369, 0, 0, 1667, 3000, 2067, 1330, 
1459, 7210, 1668, 0, 1213, 0, 2336, 0, 0, 0, 0, 3440, 2275, 
1644, 0, 0, 0, 0, 1167, 1591, 2200, 2250, 2859, 3796, 0, 
3449, 0, 0, 4595, 2254, 0, 0, 0, 3066, 1875, 0, 0, 1774, 
0, 4750, 3022, 4000, 2166, 0, 0, 1881, 2250, 0, 2531, 2000, 
2118, 0, 4167, 2900, 5654, 1820, 0, 0, 0, 2302, 997, 0, 3541, 
3263, 3806, 0, 3583, 754, 1030, 1126, 0, 3600, 754, 0, 2333, 
4114, 0, 0, 0, 2283, 1398, 2142, 0, 2667, 0, 8980, 0, 2014, 
0, 0, 1640, 3850, 2569, 0, 1929, 0, 0, 0, 7750, 0, 3500, 
0, 1430, 2083, 0, 0, 2034, 0, 4486, 0, 1425, 1666, 830, 0, 
3750, 0, 0, 0, 0, 0, 0, 2083, 5625, 1041, 0, 1280, 1447, 
0, 3166, 0, 3333, 0, 0, 1769, 0, 0, 5625, 736, 1964, 1619, 
11300, 0, 1451, 7250, 5063, 0, 0, 0, 4750, 0, 2138, 5296, 
0, 0, 0, 0, 0, 0, 2583, 3750, 2365, 2816, 0, 2500, 0, 0, 
1083, 0, 0, 0, 2531, 0, 0, 0, 1250, 0, 0, 3021, 983, 1800, 
1775, 0, 2383, 0, 1717, 2791, 1010, 0, 0, 0, 1695, 0, 0, 
2054, 0, 2598, 0, 1779, 1260, 0, 0, 0, 0, 5000, 1983, 5701, 
1300, 4417, 4333, 0, 0, 1843, 1868, 3890, 0, 2167, 7101, 
0, 0, 0, 0, 2100, 0, 4250, 2209, 0, 0, 0, 3447, 1387, 1811, 
0, 1560, 0, 0, 1857, 2223, 0, 1842, 3274, 1300, 0, 0, 2426, 
800, 985.7999878, 1666, 0, 3053, 1717, 2000, 0, 0, 2416, 
0, 1560, 0, 3334, 1250, 0, 0, 2541, 2925, 2934, 1750, 0, 
1803, 2500, 0, 1666, 1863, 0, 0, 0, 2405, 0, 0, 1843, 1640, 
0, 2134, 0, 0, 2167, 189, 1590, 2985, 0, 4983, 0, 2160, 2451, 
0, 1793, 0, 0, 0, 1833, 4490, 688, 4600, 0, 0, 1587, 0, 1229, 
0, 2330, 2458, 3230, 2168, 4583, 6250, 0, 4167, 2083, 0, 
505, 0, 3167, 0, 1666, 3167, 0, 3667, 2333, 3033, 0, 0, 0, 
0, 0, 5266, 0, 0, 7873, 1987, 1459, 923, 4996, 0, 0, 0, 2500, 
4232, 0, 0, 0, 0, 1600, 3136, 2417, 0, 0, 0, 2115, 1625, 
1400, 484, 0, 1459, 1666, 1800, 0, 0, 20000, 0, 0, 0, 2400, 
0, 0, 0, 0, 0, 0, 2033, 3237, 0, 0, 20000, 2773, 1417, 0, 
0, 1719, 1667, 0, 4300, 0, 0, 16.12000084, 0, 0, 0, 0, 2340, 
0, 0, 1851, 1125, 0, 0, 5064, 1833, 1993, 0, 8333, 1210, 
0, 1376, 1710, 1542, 1255, 1456, 1733, 2466, 0, 0, 0, 2569, 
0, 0, 4083, 0, 2188, 0, 0, 0, 1664, 0, 2917, 2451, 2079, 
1500, 0, 0, 4648, 1014, 1750, 1872, 2250, 1603, 0, 0, 3150, 
0, 0, 2436, 0, 0, 2083, 0, 2785, 1131, 0, 0, 2157, 913, 1700, 
2857, 0, 4416, 3683, 0, 5624, 5302, 1483, 4416, 6667, 0, 
3013, 0, 1287, 2004, 2035, 6666, 3666, 3428, 1632, 1800, 
1915, 0, 1742, 0, 1424, 7166, 2087, 0, 0, 1430, 1302, 0, 
5500, 0, 0, 0, 0, 2042, 3906, 0, 536, 0, 2845, 0, 0, 2524, 
663, 1950, 0, 1783, 0, 2016, 0, 2375, 3250, 2333, 4266, 1032, 
1625, 0, 2669, 2306, 242, 0, 2054, 0, 0, 0, 0, 2500, 0, 2064, 
1750, 1451, 0, 1625, 0, 461, 2210, 2739, 2232, 2917, 0, 33837, 
0, 0, 1917, 3000, 1522, 0, 0, 0, 3416, 0, 0, 3300, 0, 0, 
1000, 0, 0, 0, 41667, 2792, 0, 4301, 0, 3800, 2500, 1411, 
1950, 0, 0, 240, 0, 0), LoanAmount = c(NA, 128, 66, 120, 
141, 267, 95, 158, 168, 349, 70, 109, 200, 114, 17, 125, 
100, 76, 133, 115, 104, 315, 116, 112, 151, 191, 122, 110, 
35, 120, 201, 74, 106, 114, 320, NA, 100, 144, 184, 110, 
80, 47, 75, 134, 96, 88, 44, 144, 120, 144, 100, 120, 112, 
134, 286, 97, 96, 135, 180, 144, 120, 99, 165, NA, 116, 258, 
126, 312, 125, 136, 172, 97, 81, 95, 187, 113, 176, 110, 
180, 130, 111, NA, 167, 265, 50, 136, 99, 104, 210, 175, 
131, 188, 81, 122, 25, NA, 137, 50, 115, 131, 133, 151, NA, 
NA, 160, 100, 225, 120, 216, 94, 136, 139, 152, NA, 118, 
185, 154, 85, 175, 259, 180, 44, 137, 81, 194, 93, 370, NA, 
160, 182, 650, 74, 70, 25, 102, 290, 84, 88, 242, 129, 185, 
168, 175, 122, 187, 100, 70, 30, 225, 125, 118, 152, 244, 
113, 50, 600, 160, 187, 120, 255, 98, 275, 121, 158, 75, 
182, 112, 129, 63, 200, 95, 700, 81, 187, 87, 116, 101, 495, 
116, 102, 180, 67, 73, 260, 108, 120, 66, 58, 168, 188, 48, 
164, 160, 76, 120, 170, 187, 120, 113, 83, 90, 166, NA, 135, 
124, 120, 80, 55, 59, 127, 214, 128, 240, 130, 137, 100, 
135, 131, 72, 127, 60, 116, 144, 175, 128, 170, 138, 210, 
158, 200, 104, 42, 120, 280, 140, 170, 255, 122, 112, 96, 
120, 140, 155, 108, 123, 120, 112, 137, 123, 90, 201, 138, 
104, 279, 192, 255, 115, 94, 304, 128, 330, 134, 155, 120, 
128, 151, 150, 160, 135, 90, 30, 136, 126, 150, 90, 115, 
207, 80, 436, 124, 158, 112, 78, 54, NA, 89, 99, 120, 115, 
187, 139, 127, 134, 143, 172, 110, 200, 135, 151, 113, 93, 
105, 132, 96, 140, NA, 135, 104, 480, 185, 84, 111, 56, 144, 
159, 111, 120, 88, 112, 155, 115, 124, NA, 132, 300, 376, 
130, 184, 110, 67, 117, 98, 71, 490, 182, 70, 160, 176, NA, 
71, 173, 46, 158, 74, 125, 160, 152, 126, 259, 187, 228, 
308, 95, 105, 130, 116, 165, 67, 100, 200, 81, 236, 130, 
95, 141, 133, 96, 124, 175, 570, 55, 155, 380, 111, 110, 
120, 130, 130, 71, 130, 128, 296, 156, 128, 100, 113, 132, 
NA, 136, 125, 185, 275, 120, 113, 113, 135, 71, 95, 109, 
103, 45, 65, 103, 53, 194, 115, 115, 66, 152, 360, 62, 160, 
218, 110, 178, 60, 160, 239, 112, 138, 138, 80, 100, 110, 
96, 121, 81, 133, 87, 60, 150, 105, 405, 143, 100, NA, 50, 
NA, 187, 138, 187, 180, 148, 152, 175, 130, 110, 55, 150, 
190, 125, 60, 149, 90, 84, 96, 118, 173, 136, 160, 160, 128, 
153, 132, 98, 140, 70, 110, 98, 110, 162, 113, 100, 93, 162, 
150, 230, 132, 86, NA, 154, 113, 128, 234, 246, 131, 80, 
500, 160, 75, 96, 186, 110, 225, 119, 105, 107, 111, 95, 
209, 113, 100, 208, 138, 124, 243, 480, 96, 188, 40, 100, 
250, 148, 70, 311, 150, 113, 123, 185, 95, 45, 55, 100, 480, 
NA, 400, 110, 161, 94, 130, 216, 100, 110, 196, 125, 126, 
324, 107, 66, 157, 140, 99, 95, 128, 102, 155, 80, 145, 103, 
110, NA, NA, 158, 181, 132, 26, 84, 260, 162, 182, 108, 600, 
211, 132, 258, 120, 70, 123, 9, 104, 186, 165, 275, 187, 
150, 108, 136, 110, 107, 161, 205, 90, 36, 61, 146, 172, 
104, 70, 94, 106, 56, 205, 292, 142, 260, 110, 187, 88, 180, 
192, 350, 155, 128, 172, 496, NA, 173, 157, 108, 71, 40, 
253, 187, 133), Loan_Amount_Term = c(360, 360, 360, 360, 
360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 120, 360, 
240, 360, 360, NA, 360, 360, 360, 360, 360, 360, 360, 360, 
360, 360, 360, 360, 360, 360, 360, 360, NA, 360, 360, 360, 
360, 360, 360, 360, NA, NA, 360, 360, 360, 360, 360, 360, 
360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 180, 360, 
360, 360, 180, 360, 60, 360, 360, 360, 300, NA, 360, 480, 
360, 360, 300, 360, 360, 360, 360, 360, 240, 360, 360, 360, 
360, 360, 360, 180, 360, 360, 120, 360, 360, 360, 180, 360, 
180, 360, 360, 360, 360, 360, 360, 360, 360, 480, 360, 360, 
NA, 360, 360, 180, 360, 360, 360, 360, 360, 360, 360, 360, 
360, 360, 360, 360, 180, 360, 480, 360, 360, 120, 360, 360, 
360, 360, 360, 360, 360, 360, 180, 360, 180, 360, 360, 360, 
360, 360, 360, 360, 360, 360, 360, 180, 360, 360, 360, 360, 
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180, 360, 360, 360, 360, 360, 360, 480, 360, 360, 180, 360, 
360, 360, 300, 360, 360, 180, 360, 360, 360, 360, 360, 360, 
360, NA, 360, 360, 360, 360, 180, 360, 360, 360, 360, 360, 
360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 
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360, 180, 360, 180, 480, 360, 360, 360, 360, 180, 360, 360, 
360, 360, 360, 360, 360, 360, 36, 360, 360, 360, 360, 360, 
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360, 360, 180, 360, 360, 360, 300, 360, 360, 360, 360, 360, 
360, 480, 360, 360, 360, 360, 480, 360, 360, 360, 360, 360, 
360, 360, 360, 360, 360, 360, 360, 360, 360, 84, 360, 180, 
360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 
360, 360, 360, 360, 360, 180, 180, NA, 360, 360, 360, 360, 
360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 
360, 360, 360, 180, 360, 360, 480, 360, 360, 360, 360, 300, 
360, 360, 480, NA, 360, 360, 360, 360, 300, 180, 360, 180, 
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360, 360, 360, 360, 360, 180, 360, 360, 360, 360, 360, 360, 
180, 300, 360, 360, 360, 360, 360, 360, 300, 360, 360, 360, 
360, 360, 360, 360, 360, 360, 360, 360, 360, NA, 360, NA, 
360, 360, 360, 360, 360, 180, 360, 360, 360, 360, 360, 240, 
360, 360, 360, 360, 360, 360, 360, 360, 300, 360, 360, 360, 
360, 360, 360, 300, 360, 180, 360, 360, 360, 360, 360, 360, 
360, 180, 360, 360, 360, 360, 180, 360, 360, 360, 360, 180, 
360, 360, 360, 360, 300, 360, 360, 360, 360, 360, 360, 360, 
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360, 12, 360, 360, 480, 360, 360, 360, 300, 360, 360, 360, 
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360, 180, 360, 360, 360, 360, 360, 360, 180, 360, 360, 360, 
360, 180, 360, 360, 360, 360, 180, 360, 360, 360), Credit_History = c(1, 
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1, 1, 1, 0, 1, 1, 1, 1, 1, NA, 1, 1, 1, 0, NA, 0, NA, 0, 
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0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, NA, 1, 1, 
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"Urban", "Rural", "Rural", "Semiurban", "Rural", "Urban", 
"Urban", "Rural", "Urban", "Rural", "Semiurban", "Urban", 
"Semiurban", "Rural", "Rural", "Rural", "Rural", "Urban", 
"Urban", "Semiurban"), Loan_Status = c("Y", "N", "Y", "Y", 
"Y", "Y", "Y", "N", "Y", "N", "Y", "Y", "Y", "N", "Y", "Y", 
"Y", "N", "N", "Y", "N", "Y", "N", "N", "N", "Y", "Y", "Y", 
"N", "Y", "N", "N", "N", "Y", "N", "Y", "N", "Y", "Y", "Y", 
"N", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "N", "Y", "Y", "Y", 
"N", "N", "N", "Y", "Y", "N", "Y", "Y", "Y", "Y", "N", "N", 
"N", "N", "N", "Y", "Y", "N", "Y", "Y", "Y", "N", "Y", "N", 
"N", "N", "N", "Y", "Y", "Y", "N", "N", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "N", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "N", "N", "Y", "Y", "Y", 
"N", "Y", "Y", "Y", "Y", "Y", "N", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "N", "N", "Y", "Y", "Y", "Y", "Y", "N", 
"N", "Y", "N", "N", "N", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"N", "Y", "N", "Y", "N", "N", "Y", "Y", "Y", "Y", "Y", "Y", 
"Y", "N", "N", "Y", "Y", "Y", "N", "Y", "N", "Y", "Y", "Y", 
"N", "Y", "N", "Y", "Y", "N", "Y", "N", "N", "N", "Y", "N", 
"Y", "Y", "N", "Y", "Y", "Y", "Y", "N", "N", "Y", "Y", "N", 
"Y", "Y", "Y", "N", "Y", "Y", "N", "Y", "Y", "Y", "Y", "Y", 
"Y", "N", "N", "N", "Y", "Y", "Y", "Y", "N", "Y", "N", "Y", 
"N", "Y", "Y", "Y", "Y", "N", "N", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "Y", "Y", "N", "Y", "Y", "Y", "Y", "N", "Y", "Y", 
"Y", "N", "Y", "Y", "Y", "Y", "N", "N", "Y", "Y", "N", "Y", 
"N", "N", "N", "N", "Y", "Y", "N", "Y", "Y", "Y", "Y", "Y", 
"N", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"N", "Y", "Y", "Y", "N", "Y", "N", "Y", "Y", "Y", "Y", "N", 
"Y", "N", "Y", "Y", "Y", "Y", "N", "N", "N", "Y", "Y", "Y", 
"Y", "N", "Y", "N", "N", "Y", "Y", "Y", "Y", "Y", "N", "Y", 
"Y", "Y", "Y", "N", "Y", "Y", "Y", "Y", "Y", "N", "Y", "Y", 
"N", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "N", "Y", 
"N", "N", "Y", "Y", "Y", "Y", "N", "Y", "Y", "Y", "Y", "N", 
"Y", "N", "Y", "Y", "Y", "N", "N", "Y", "N", "Y", "Y", "Y", 
"Y", "N", "N", "N", "Y", "N", "Y", "Y", "Y", "N", "Y", "Y", 
"Y", "Y", "N", "Y", "Y", "Y", "Y", "Y", "N", "Y", "Y", "N", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "N", "Y", "Y", "N", 
"N", "N", "Y", "Y", "N", "Y", "Y", "Y", "N", "N", "N", "Y", 
"N", "Y", "N", "Y", "N", "N", "Y", "Y", "Y", "N", "Y", "N", 
"Y", "Y", "N", "Y", "Y", "Y", "Y", "N", "Y", "Y", "Y", "Y", 
"Y", "Y", "N", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "N", 
"N", "N", "N", "Y", "N", "Y", "Y", "Y", "Y", "N", "Y", "N", 
"Y", "Y", "Y", "Y", "N", "Y", "N", "Y", "Y", "N", "Y", "N", 
"Y", "Y", "Y", "Y", "Y", "N", "Y", "N", "Y", "Y", "Y", "Y", 
"Y", "Y", "N", "N", "Y", "N", "Y", "Y", "Y", "Y", "N", "Y", 
"Y", "Y", "Y", "N", "Y", "Y", "Y", "N", "Y", "Y", "Y", "N", 
"Y", "Y", "N", "Y", "Y", "N", "N", "Y", "Y", "N", "N", "N", 
"Y", "Y", "Y", "Y", "N", "Y", "Y", "Y", "Y", "Y", "Y", "Y", 
"N", "N", "Y", "Y", "Y", "Y", "N", "Y", "Y", "N", "Y", "Y", 
"Y", "Y", "N", "Y", "N", "Y", "N", "Y", "Y", "N", "N", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "N", "Y", "Y", "N", 
"N", "N", "Y", "N", "Y", "N", "N", "Y", "N", "Y", "Y", "Y", 
"Y", "N", "Y", "N", "N", "N", "Y", "Y", "Y", "N", "Y", "N", 
"Y", "Y", "Y", "Y", "N", "N", "Y", "Y", "N", "Y", "Y", "Y", 
"Y", "N", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "N")), row.names = c(NA,

-614L), spec = structure(list(cols = list(Loan_ID = structure(list(), class = c("collector_character",
"collector")), Gender = structure(list(), class = c("collector_character",
"collector")), Married = structure(list(), class = c("collector_character",
"collector")), Dependents = structure(list(), class = c("collector_character",
"collector")), Education = structure(list(), class = c("collector_character",
"collector")), Self_Employed = structure(list(), class = c("collector_character",
"collector")), ApplicantIncome = structure(list(), class = c("collector_double",
"collector")), CoapplicantIncome = structure(list(), class = c("collector_double",
"collector")), LoanAmount = structure(list(), class = c("collector_double",
"collector")), Loan_Amount_Term = structure(list(), class = c("collector_double",
"collector")), Credit_History = structure(list(), class = c("collector_double",
"collector")), Property_Area = structure(list(), class = c("collector_character",
"collector")), Loan_Status = structure(list(), class = c("collector_character",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), delim = ","), class = "col_spec"), problems = <pointer: (nil)>, class = c("spec_tbl_df",
"tbl_df", "tbl", "data.frame"))

lara.man · August 26, 2023, 5:11pm

Married <- ifelse(Loan$Married == "Yes", 1, 0)

Male <- ifelse(Loan$Gender == "Male", 1, 0)

Graduate <- ifelse(Loan$Education == "Graduate", 1, 0)

So before creating the descriptive statistic I formulated those dummy variables. Then I used the stargazer function after defining my models:

model_1 <- lm(data=Loan, ApplicantIncome ~ Married)

model_2 <- lm(data=Loan, ApplicantIncome ~ Married + Female)

model_3 <- lm(data=Loan, ApplicantIncome ~ Married + Female + Dependents)

model_4 <- lm(data=Loan, ApplicantIncome ~ Married + Female + Dependents +Graduate)

stargazer(model_1,model_2,model_3,model_4, type = "text")

And afterwards I got this here.
Dependent variable:
--------------------------------------------------------------------------------------
ApplicantIncome
(1) (2) (3) (4)

MarriedNo -1,236.615 -1,239.723 -1,489.298 -745.766
(3,552.780) (3,555.860) (3,925.014) (3,888.097)

MarriedYes -577.827 -565.121 -1,149.412 -448.653
(3,541.293) (3,547.350) (3,915.590) (3,878.224)

Male -55.164 -69.255 67.261
(651.563) (646.037) (640.162)

Dependents0 33.921 -250.041
(1,780.157) (1,762.751)

Dependents1 990.833 717.373
(1,848.867) (1,830.554)

Dependents2 -86.970 -298.523
(1,853.802) (1,834.854)

Dependents3+ 3,585.216* 3,500.115*
(1,945.336) (1,924.682)

Graduate 2,213.309***
(588.152)

Constant 6,207.000* 6,243.776* 6,253.170* 3,948.851
(3,528.022) (3,557.510) (3,521.578) (3,537.351)

Observations 614 614 614 614
R2 0.003 0.003 0.029 0.051
Adjusted R2 -0.001 -0.002 0.018 0.039
Residual Std. Error 6,110.713 (df = 611) 6,115.684 (df = 610) 6,053.763 (df = 606) 5,989.076 (df = 605)
F Statistic 0.832 (df = 2; 611) 0.556 (df = 3; 610) 2.607 (df = 7; 606) 4.100* (df = 8; 605)

Note: *p<0.1; **p<0.05; ***p<0.01

But as you can see, the dependents 0 are not pictured here.

jrkrideau · August 26, 2023, 7:37pm

I am having a problem with that dput() data. But thanks that data is what we need though a sample, dput(head(100)) would do. Anyway I just downloaded it from Kaggle. Thanks for the link.

It looks to me that the problem is you do not have all the variables that you are putting into your regressions.

I do not see where

female

is coming from. This is probably the source of the problem as Models 2:4 are not working.

In case other readers are having a problem with the dada here is a sample

structure(list(Loan_ID = c("LP001002", "LP001003", "LP001005", 
"LP001006", "LP001008", "LP001011", "LP001013", "LP001014", "LP001018", 
"LP001020", "LP001024", "LP001027", "LP001028", "LP001029", "LP001030", 
"LP001032", "LP001034", "LP001036", "LP001038", "LP001041", "LP001043", 
"LP001046", "LP001047", "LP001050", "LP001052", "LP001066", "LP001068", 
"LP001073", "LP001086", "LP001087", "LP001091", "LP001095", "LP001097", 
"LP001098", "LP001100", "LP001106", "LP001109", "LP001112", "LP001114", 
"LP001116", "LP001119", "LP001120", "LP001123", "LP001131", "LP001136", 
"LP001137", "LP001138", "LP001144", "LP001146", "LP001151", "LP001155", 
"LP001157", "LP001164", "LP001179", "LP001186", "LP001194", "LP001195", 
"LP001197", "LP001198", "LP001199", "LP001205", "LP001206", "LP001207", 
"LP001213", "LP001222", "LP001225", "LP001228", "LP001233", "LP001238", 
"LP001241", "LP001243", "LP001245", "LP001248", "LP001250", "LP001253", 
"LP001255", "LP001256", "LP001259", "LP001263", "LP001264", "LP001265", 
"LP001266", "LP001267", "LP001273", "LP001275", "LP001279", "LP001280", 
"LP001282", "LP001289", "LP001310", "LP001316", "LP001318", "LP001319", 
"LP001322", "LP001325", "LP001326", "LP001327", "LP001333", "LP001334", 
"LP001343"), Gender = c("Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Male", "Female", "Male", "Male", "Male", 
"Male", "Male", "", "Male", "Male", "Male", "Male", "Male", "Female", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Female", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Female", 
"Male", "Male", "Female", "Female", "Female", "Female", "Female", 
"Male", "Female", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Female", "Male", "Male", "Male", "Male", 
"Female", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Female", "Male", "Female", "Male", "Male", 
"Male", "Male", "Male", "Male", "Male", "Male", "Male", "Male", 
"Male", "Male", "Male", "Female", "Male", "Male", "Male"), Married = c("No", 
"Yes", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "Yes", "Yes", 
"Yes", "Yes", "Yes", "No", "Yes", "No", "No", "No", "Yes", "Yes", 
"Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "No", 
"No", "Yes", "No", "No", "Yes", "No", "Yes", "Yes", "Yes", "No", 
"No", "No", "No", "Yes", "Yes", "Yes", "No", "Yes", "Yes", "Yes", 
"No", "Yes", "No", "No", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", 
"Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes", "No", "Yes", 
"Yes", "No", "Yes", "Yes", "No", "Yes", "Yes", "No", "No", "Yes", 
"Yes", "Yes", "No", "Yes", "Yes", "Yes", "Yes", "No", "Yes", 
"Yes", "No", "Yes", "Yes", "Yes", "Yes", "No", "No", "No", "Yes", 
"Yes", "Yes", "Yes"), Dependents = c("0", "1", "0", "0", "0", 
"2", "0", "3+", "2", "1", "2", "2", "2", "0", "2", "0", "1", 
"0", "0", "0", "0", "1", "0", "2", "1", "0", "0", "2", "0", "2", 
"1", "0", "1", "0", "3+", "0", "0", "0", "0", "0", "0", "0", 
"0", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "2", "1", 
"2", "0", "0", "1", "2", "0", "3+", "0", "1", "0", "0", "0", 
"1", "3+", "0", "0", "2", "0", "3+", "3+", "0", "0", "1", "3+", 
"3+", "0", "1", "2", "0", "1", "0", "2", "0", "0", "0", "0", 
"2", "2", "0", "0", "0", "0", "0", "0", "0"), Education = c("Graduate", 
"Graduate", "Graduate", "Not Graduate", "Graduate", "Graduate", 
"Not Graduate", "Graduate", "Graduate", "Graduate", "Graduate", 
"Graduate", "Graduate", "Graduate", "Graduate", "Graduate", "Not Graduate", 
"Graduate", "Not Graduate", "Graduate", "Not Graduate", "Graduate", 
"Not Graduate", "Not Graduate", "Graduate", "Graduate", "Graduate", 
"Not Graduate", "Not Graduate", "Graduate", "Graduate", "Graduate", 
"Graduate", "Graduate", "Graduate", "Graduate", "Graduate", "Graduate", 
"Graduate", "Not Graduate", "Graduate", "Graduate", "Graduate", 
"Graduate", "Not Graduate", "Graduate", "Graduate", "Graduate", 
"Graduate", "Graduate", "Not Graduate", "Graduate", "Graduate", 
"Graduate", "Graduate", "Graduate", "Graduate", "Graduate", "Graduate", 
"Not Graduate", "Graduate", "Graduate", "Not Graduate", "Graduate", 
"Graduate", "Graduate", "Not Graduate", "Graduate", "Not Graduate", 
"Graduate", "Graduate", "Not Graduate", "Graduate", "Not Graduate", 
"Graduate", "Graduate", "Graduate", "Graduate", "Graduate", "Not Graduate", 
"Graduate", "Graduate", "Graduate", "Graduate", "Graduate", "Graduate", 
"Not Graduate", "Graduate", "Graduate", "Graduate", "Graduate", 
"Graduate", "Not Graduate", "Graduate", "Not Graduate", "Graduate", 
"Graduate", "Graduate", "Not Graduate", "Graduate"), Self_Employed = c("No", 
"No", "Yes", "No", "No", "Yes", "No", "No", "No", "No", "No", 
"", "No", "No", "No", "No", "No", "No", "No", "", "No", "No", 
"No", "No", "", "Yes", "No", "No", "No", "", "", "No", "Yes", 
"No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
"Yes", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
"Yes", "No", "No", "No", "No", "No", "No", "No", "Yes", "No", 
"No", "No", "No", "No", "Yes", "No", "No", "Yes", "No", "No", 
"Yes", "No", "No", "Yes", "No", "Yes", "No", "Yes", "No", "No", 
"No", "No", "No", "No", "No", "No", "No", "No", "No", "No", "No", 
"", "No", "No", "No", "No"), ApplicantIncome = c(5849L, 4583L, 
3000L, 2583L, 6000L, 5417L, 2333L, 3036L, 4006L, 12841L, 3200L, 
2500L, 3073L, 1853L, 1299L, 4950L, 3596L, 3510L, 4887L, 2600L, 
7660L, 5955L, 2600L, 3365L, 3717L, 9560L, 2799L, 4226L, 1442L, 
3750L, 4166L, 3167L, 4692L, 3500L, 12500L, 2275L, 1828L, 3667L, 
4166L, 3748L, 3600L, 1800L, 2400L, 3941L, 4695L, 3410L, 5649L, 
5821L, 2645L, 4000L, 1928L, 3086L, 4230L, 4616L, 11500L, 2708L, 
2132L, 3366L, 8080L, 3357L, 2500L, 3029L, 2609L, 4945L, 4166L, 
5726L, 3200L, 10750L, 7100L, 4300L, 3208L, 1875L, 3500L, 4755L, 
5266L, 3750L, 3750L, 1000L, 3167L, 3333L, 3846L, 2395L, 1378L, 
6000L, 3988L, 2366L, 3333L, 2500L, 8566L, 5695L, 2958L, 6250L, 
3273L, 4133L, 3620L, 6782L, 2484L, 1977L, 4188L, 1759L), CoapplicantIncome = c(0, 
1508, 0, 2358, 0, 4196, 1516, 2504, 1526, 10968, 700, 1840, 8106, 
2840, 1086, 0, 0, 0, 0, 3500, 0, 5625, 1911, 1917, 2925, 0, 2253, 
1040, 0, 2083, 3369, 0, 0, 1667, 3000, 2067, 1330, 1459, 7210, 
1668, 0, 1213, 0, 2336, 0, 0, 0, 0, 3440, 2275, 1644, 0, 0, 0, 
0, 1167, 1591, 2200, 2250, 2859, 3796, 0, 3449, 0, 0, 4595, 2254, 
0, 0, 0, 3066, 1875, 0, 0, 1774, 0, 4750, 3022, 4000, 2166, 0, 
0, 1881, 2250, 0, 2531, 2000, 2118, 0, 4167, 2900, 5654, 1820, 
0, 0, 0, 2302, 997, 0, 3541), LoanAmount = c(NA, 128L, 66L, 120L, 
141L, 267L, 95L, 158L, 168L, 349L, 70L, 109L, 200L, 114L, 17L, 
125L, 100L, 76L, 133L, 115L, 104L, 315L, 116L, 112L, 151L, 191L, 
122L, 110L, 35L, 120L, 201L, 74L, 106L, 114L, 320L, NA, 100L, 
144L, 184L, 110L, 80L, 47L, 75L, 134L, 96L, 88L, 44L, 144L, 120L, 
144L, 100L, 120L, 112L, 134L, 286L, 97L, 96L, 135L, 180L, 144L, 
120L, 99L, 165L, NA, 116L, 258L, 126L, 312L, 125L, 136L, 172L, 
97L, 81L, 95L, 187L, 113L, 176L, 110L, 180L, 130L, 111L, NA, 
167L, 265L, 50L, 136L, 99L, 104L, 210L, 175L, 131L, 188L, 81L, 
122L, 25L, NA, 137L, 50L, 115L, 131L), Loan_Amount_Term = c(360L, 
360L, 360L, 360L, 360L, 360L, 360L, 360L, 360L, 360L, 360L, 360L, 
360L, 360L, 120L, 360L, 240L, 360L, 360L, NA, 360L, 360L, 360L, 
360L, 360L, 360L, 360L, 360L, 360L, 360L, 360L, 360L, 360L, 360L, 
360L, 360L, NA, 360L, 360L, 360L, 360L, 360L, 360L, 360L, NA, 
NA, 360L, 360L, 360L, 360L, 360L, 360L, 360L, 360L, 360L, 360L, 
360L, 360L, 360L, 360L, 360L, 360L, 180L, 360L, 360L, 360L, 180L, 
360L, 60L, 360L, 360L, 360L, 300L, NA, 360L, 480L, 360L, 360L, 
300L, 360L, 360L, 360L, 360L, 360L, 240L, 360L, 360L, 360L, 360L, 
360L, 360L, 180L, 360L, 360L, 120L, 360L, 360L, 360L, 180L, 360L
), Credit_History = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, NA, 0L, 1L, 1L, 0L, 1L, 0L, 0L, NA, 1L, 
1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 
NA, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 
1L, 1L, 1L, 1L, 0L, NA, 1L, 1L, 1L, NA, 1L, 1L, NA, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, NA, 1L, 1L, 1L, 1L), Property_Area = c("Urban", 
"Rural", "Urban", "Urban", "Urban", "Urban", "Urban", "Semiurban", 
"Urban", "Semiurban", "Urban", "Urban", "Urban", "Rural", "Urban", 
"Urban", "Urban", "Urban", "Rural", "Urban", "Urban", "Urban", 
"Semiurban", "Rural", "Semiurban", "Semiurban", "Semiurban", 
"Urban", "Urban", "Semiurban", "Urban", "Urban", "Rural", "Semiurban", 
"Rural", "Urban", "Urban", "Semiurban", "Urban", "Semiurban", 
"Urban", "Urban", "Urban", "Semiurban", "Urban", "Urban", "Urban", 
"Urban", "Urban", "Semiurban", "Semiurban", "Semiurban", "Semiurban", 
"Urban", "Urban", "Semiurban", "Semiurban", "Rural", "Urban", 
"Urban", "Urban", "Urban", "Rural", "Rural", "Semiurban", "Semiurban", 
"Urban", "Urban", "Urban", "Semiurban", "Urban", "Semiurban", 
"Semiurban", "Semiurban", "Semiurban", "Urban", "Urban", "Urban", 
"Semiurban", "Semiurban", "Semiurban", "Semiurban", "Urban", 
"Semiurban", "Urban", "Semiurban", "Semiurban", "Semiurban", 
"Urban", "Semiurban", "Semiurban", "Semiurban", "Urban", "Semiurban", 
"Semiurban", "Urban", "Semiurban", "Semiurban", "Semiurban", 
"Semiurban"), Loan_Status = c("Y", "N", "Y", "Y", "Y", "Y", "Y", 
"N", "Y", "N", "Y", "Y", "Y", "N", "Y", "Y", "Y", "N", "N", "Y", 
"N", "Y", "N", "N", "N", "Y", "Y", "Y", "N", "Y", "N", "N", "N", 
"Y", "N", "Y", "N", "Y", "Y", "Y", "N", "Y", "Y", "Y", "Y", "Y", 
"Y", "Y", "N", "Y", "Y", "Y", "N", "N", "N", "Y", "Y", "N", "Y", 
"Y", "Y", "Y", "N", "N", "N", "N", "N", "Y", "Y", "N", "Y", "Y", 
"Y", "N", "Y", "N", "N", "N", "N", "Y", "Y", "Y", "N", "N", "Y", 
"Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "N", "Y", "Y", 
"Y", "Y")), row.names = c(NA, 100L), class = "data.frame")

lara.man · August 26, 2023, 9:17pm

I guess I've found the source. Thanks a lot for the hint.
But there is another problem, I defined those dummy variables before, so that everything is numeric, but it isn't pictured in my desciptive statistic.

jrkrideau · August 26, 2023, 9:22pm

Can you give us a reprex ? I don't use stargazer so I don't know what you are expecting.

startz · August 26, 2023, 9:28pm

What exactly are you missing in the output?

system · October 7, 2023, 9:28pm

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Missing value in my descriptive statistic

And afterwards I got this here. Dependent variable: -------------------------------------------------------------------------------------- ApplicantIncome (1) (2) (3) (4)

Observations 614 614 614 614 R2 0.003 0.003 0.029 0.051 Adjusted R2 -0.001 -0.002 0.018 0.039 Residual Std. Error 6,110.713 (df = 611) 6,115.684 (df = 610) 6,053.763 (df = 606) 5,989.076 (df = 605) F Statistic 0.832 (df = 2; 611) 0.556 (df = 3; 610) 2.607** (df = 7; 606) 4.100*** (df = 8; 605)

And afterwards I got this here.
Dependent variable:
--------------------------------------------------------------------------------------
ApplicantIncome
(1) (2) (3) (4)

Observations 614 614 614 614
R2 0.003 0.003 0.029 0.051
Adjusted R2 -0.001 -0.002 0.018 0.039
Residual Std. Error 6,110.713 (df = 611) 6,115.684 (df = 610) 6,053.763 (df = 606) 5,989.076 (df = 605)
F Statistic 0.832 (df = 2; 611) 0.556 (df = 3; 610) 2.607 (df = 7; 606) 4.100* (df = 8; 605)