Dairy Work
Valeria Alvarado
2025-09-04
Introduction
In this analysis, we will investigate the mental and physical risks of being a non-citizen in the dairy workforce, focusing on wage disparities. The analysis will include descriptive statistics, regression models, and contingency tables.
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## chisq.test, fisher.testDairy_worker_data <- read_sav("/Users/valeriaalvarado/Documents/Dairy Worker Data/Dairy_works.sav")
View(Dairy_worker_data)
names (Dairy_worker_data)## [1] "RT" "SERIALNO" "DIVISION" "SPORDER" "PUMA" "REGION"
## [7] "STATE" "ADJINC" "PWGTP" "AGEP" "CIT" "CITWP"
## [13] "COW" "DDRS" "DEAR" "DEYE" "DOUT" "DPHY"
## [19] "DRAT" "DRATX" "DREM" "ENG" "FER" "GCL"
## [25] "GCM" "GCR" "HIMRKS" "HINS1" "HINS2" "HINS3"
## [31] "HINS4" "HINS5" "HINS6" "HINS7" "INTP" "JWMNP"
## [37] "JWRIP" "JWTRNS" "LANX" "MAR" "MARHD" "MARHM"
## [43] "MARHT" "MARHW" "MARHYP" "MIG" "MIL" "MLPA"
## [49] "MLPB" "MLPCD" "MLPE" "MLPFG" "MLPH" "MLPIK"
## [55] "MLPJ" "NWAB" "NWAV" "NWLA" "NWLK" "NWRE"
## [61] "OIP" "PAP" "RELSHIPP" "RETP" "SCH" "SCHG"
## [67] "SCHL" "SEMP" "SEX" "SSIP" "SSP" "WAGP"
## [73] "WKHP" "WKL" "WKWN" "WRK" "YOEP" "ANC"
## [79] "ANC1P" "ANC2P" "DECADE" "DIS" "DRIVESP" "ESP"
## [85] "ESR" "FOD1P" "FOD2P" "HICOV" "HISP" "INDP"
## [91] "JWAP" "JWDP" "LANP" "MIGPUMA" "MIGSP" "MSP"
## [97] "NAICSP" "NATIVITY" "NOP" "OC" "OCCP" "PAOC"
## [103] "PERNP" "PINCP" "POBP" "POVPIP" "POWPUMA" "POWSP"
## [109] "PRIVCOV" "PUBCOV" "QTRBIR" "RAC1P" "RAC2P" "RAC3P"
## [115] "RACAIAN" "RACASN" "RACBLK" "RACNH" "RACNUM" "RACPI"
## [121] "RACSOR" "RACWHT" "RC" "SCIENGP" "SCIENGRLP" "SFN"
## [127] "SFR" "SOCP" "VPS" "WAOB" "FAGEP" "FANCP"
## [133] "FCITP" "FCITWP" "FCOWP" "FDDRSP" "FDEARP" "FDEYEP"
## [139] "FDISP" "FDOUTP" "FDPHYP" "FDRATP" "FDRATXP" "FDREMP"
## [145] "FENGP" "FESRP" "FFERP" "FFODP" "FGCLP" "FGCMP"
## [151] "FGCRP" "FHICOVP" "FHIMRKSP" "FHINS1P" "FHINS2P" "FHINS3C"
## [157] "FHINS3P" "FHINS4C" "FHINS4P" "FHINS5C" "FHINS5P" "FHINS6P"
## [163] "FHINS7P" "FHISP" "FINDP" "FINTP" "FJWDP" "FJWMNP"
## [169] "FJWRIP" "FJWTRNSP" "FLANP" "FLANXP" "FMARP" "FMARHDP"
## [175] "FMARHMP" "FMARHTP" "FMARHWP" "FMARHYP" "FMIGP" "FMIGSP"
## [181] "FMILPP" "FMILSP" "FOCCP" "FOIP" "FPAP" "FPERNP"
## [187] "FPINCP" "FPOBP" "FPOWSP" "FPRIVCOVP" "FPUBCOVP" "FRACP"
## [193] "FRELSHIPP" "FRETP" "FSCHGP" "FSCHLP" "FSCHP" "FSEMP"
## [199] "FSEXP" "FSSIP" "FSSP" "FWAGP" "FWKHP" "FWKLP"
## [205] "FWKWNP" "FWRKP" "FYOEP" "PWGTP1" "PWGTP2" "PWGTP3"
## [211] "PWGTP4" "PWGTP5" "PWGTP6" "PWGTP7" "PWGTP8" "PWGTP9"
## [217] "PWGTP10" "PWGTP11" "PWGTP12" "PWGTP13" "PWGTP14" "PWGTP15"
## [223] "PWGTP16" "PWGTP17" "PWGTP18" "PWGTP19" "PWGTP20" "PWGTP21"
## [229] "PWGTP22" "PWGTP23" "PWGTP24" "PWGTP25" "PWGTP26" "PWGTP27"
## [235] "PWGTP28" "PWGTP29" "PWGTP30" "PWGTP31" "PWGTP32" "PWGTP33"
## [241] "PWGTP34" "PWGTP35" "PWGTP36" "PWGTP37" "PWGTP38" "PWGTP39"
## [247] "PWGTP40" "PWGTP41" "PWGTP42" "PWGTP43" "PWGTP44" "PWGTP45"
## [253] "PWGTP46" "PWGTP47" "PWGTP48" "PWGTP49" "PWGTP50" "PWGTP51"
## [259] "PWGTP52" "PWGTP53" "PWGTP54" "PWGTP55" "PWGTP56" "PWGTP57"
## [265] "PWGTP58" "PWGTP59" "PWGTP60" "PWGTP61" "PWGTP62" "PWGTP63"
## [271] "PWGTP64" "PWGTP65" "PWGTP66" "PWGTP67" "PWGTP68" "PWGTP69"
## [277] "PWGTP70" "PWGTP71" "PWGTP72" "PWGTP73" "PWGTP74" "PWGTP75"
## [283] "PWGTP76" "PWGTP77" "PWGTP78" "PWGTP79" "PWGTP80" "filter_$"Dairy_worker_data_2<-Dairy_worker_data %>% select("DEAR", "DEYE", "CIT", "LANX", "DOUT", "NATIVITY", "AGEP", "SEX", "WAGP", "DIS")
View(Dairy_worker_data_2)
library(readxl)
Dairy_worker_data_2_1_ <- read_excel("~/Downloads/Dairy_worker_data_2 (1).xlsx")
View(Dairy_worker_data_2_1_)
Dairy_worker_data_2<-Dairy_worker_data %>% mutate(CIT_2=ifelse(CIT>4, 2, 1))
table(Dairy_worker_data_2$CIT_2)##
## 1 2
## 1176 67Plot
You can create code snippet by using plot()
plot(Dairy_worker_data_2$WAGP ~ Dairy_worker_data_2$CIT_2)
plot(Dairy_worker_data_2$WAGP)
class(Dairy_worker_data_2$CIT_2)## [1] "numeric"glimpse(Dairy_worker_data_2$CIT_2)## num [1:1243] 1 1 1 1 1 1 1 1 1 1 ...unique(Dairy_worker_data_2$CIT_2) ## [1] 1 2 # male is a dummy variable; and there is no missing value in male
# Dependent variable: WAGE
# check data type: WAGE
class(Dairy_worker_data_2$WAGP)## [1] "numeric"glimpse(Dairy_worker_data_2$WAGP)## num [1:1243] 32000 31700 100000 110000 50000 105000 28000 40000 48000 86000 ...
## - attr(*, "format.spss")= chr "F6.0"unique(Dairy_worker_data_2$WAGP)## [1] 32000 31700 100000 110000 50000 105000 28000 40000 48000 86000
## [11] 60000 55000 24000 150000 52000 45500 30400 90000 0 497000
## [21] 65000 70000 10000 75000 120000 88000 57000 58000 7000 37000
## [31] 64000 36000 45000 3300 48200 49000 35000 30000 80000 18800
## [41] 112000 25000 42000 41600 9400 21800 22800 84000 59000 46000
## [51] 79000 29200 140000 14000 38000 19200 72000 9000 81000 85000
## [61] 4000 34000 130000 31200 32200 20000 5000 160000 118000 99000
## [71] 49300 106000 500 37400 47200 63000 9200 127000 47000 347000
## [81] 36200 56000 22000 33000 21000 114000 53000 92000 48700 38500
## [91] 101000 770000 7300 61000 190000 31000 37500 18500 200000 170000
## [101] 54000 82000 98000 69000 17000 66000 8000 62000 43000 39500
## [111] 10400 12000 5400 109000 400 23000 26000 93000 74000 22500
## [121] 6500 240000 68000 51000 7200 13200 11200 800 46600 39400
## [131] 27300 44000 3200 87000 11000 15000 46900 506000 27000 12100
## [141] 6000 48500 34500 37200 73000 565000 42800 566000 71000 108000
## [151] 123000 67000 2400 131000 6700 77000 28300 19000 250000 76000
## [161] 83000 95000 260000 900 78000 2000 135000 189000 28500 18000
## [171] 2200 2500 16000 1000 89000 8900 36600 117000 28600 300000
## [181] 220000 12500 241000 43300 49100 113000 116000 37800 103000 156000
## [191] 155000 27600 9600 10500 46800 41500 15200 153000 31800 29500
## [201] 42100 38300 172000 39000 41000 3000 4400 265000 47800 180000
## [211] 26400 12800 30900 499000 1200 32400 1600 210000 115000 30800
## [221] 13000 31500 48600 42900 42500 47400 42700 32300 22400 4500
## [231] 23800 44600 8500# salary is a numeric variable; and there is no missing value in salary
## Step 1 & 2: Construct a summary table of variables in the sample
# See all the variables and their class in the dataset
str(Dairy_worker_data_2)## tibble [1,243 × 289] (S3: tbl_df/tbl/data.frame)
## $ RT : chr [1:1243] "P" "P" "P" "P" ...
## ..- attr(*, "format.spss")= chr "A1"
## ..- attr(*, "display_width")= int 1
## $ SERIALNO : chr [1:1243] "2023HU0023418" "2023HU0045887" "2023HU0059978" "2023HU0085718" ...
## ..- attr(*, "format.spss")= chr "A13"
## ..- attr(*, "display_width")= int 13
## $ DIVISION : num [1:1243] 4 4 4 4 4 4 4 4 4 4 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ SPORDER : num [1:1243] 1 2 2 2 1 1 2 1 2 2 ...
## ..- attr(*, "format.spss")= chr "F2.0"
## $ PUMA : num [1:1243] 2701 2900 2900 800 2702 ...
## ..- attr(*, "format.spss")= chr "F5.0"
## $ REGION : num [1:1243] 2 2 2 2 2 2 2 2 2 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ STATE : num [1:1243] 29 29 29 29 29 29 29 29 29 29 ...
## ..- attr(*, "format.spss")= chr "F2.0"
## $ ADJINC : num [1:1243] 1019518 1019518 1019518 1019518 1019518 ...
## ..- attr(*, "format.spss")= chr "F7.0"
## $ PWGTP : num [1:1243] 41 862 30 28 98 72 67 44 59 54 ...
## ..- attr(*, "format.spss")= chr "F4.0"
## $ AGEP : num [1:1243] 32 55 42 43 66 53 65 34 61 62 ...
## ..- attr(*, "format.spss")= chr "F2.0"
## $ CIT : num [1:1243] 1 1 1 1 1 1 1 4 1 1 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ CITWP : num [1:1243] NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F4.0"
## $ COW : num [1:1243] 1 1 1 1 1 1 1 5 1 1 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ DDRS : num [1:1243] 2 2 2 2 2 2 2 2 2 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ DEAR : num [1:1243] 2 2 2 2 2 2 2 2 2 1 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ DEYE : num [1:1243] 2 2 2 2 2 2 2 2 2 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ DOUT : num [1:1243] 2 2 2 2 2 2 2 2 2 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ DPHY : num [1:1243] 2 2 2 2 2 2 2 2 2 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ DRAT : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ DRATX : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ DREM : num [1:1243] 2 2 2 2 2 2 2 2 2 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ ENG : num [1:1243] NA NA NA NA NA NA NA 1 NA NA ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ FER : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ GCL : num [1:1243] 2 2 2 2 2 2 2 2 2 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ GCM : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ GCR : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ HIMRKS : num [1:1243] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ HINS1 : num [1:1243] 1 1 1 1 1 1 2 1 1 1 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ HINS2 : num [1:1243] 2 2 2 2 2 2 2 2 2 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ HINS3 : num [1:1243] 2 2 2 2 2 2 1 2 2 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ HINS4 : num [1:1243] 2 2 2 2 2 2 2 2 2 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ HINS5 : num [1:1243] 2 2 2 2 2 2 2 2 2 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ HINS6 : num [1:1243] 2 2 2 2 2 2 2 2 2 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ HINS7 : num [1:1243] 2 2 2 2 2 2 2 2 2 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ INTP : num [1:1243] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "format.spss")= chr "F6.0"
## $ JWMNP : num [1:1243] 30 30 20 55 15 25 25 40 20 10 ...
## ..- attr(*, "format.spss")= chr "F3.0"
## $ JWRIP : num [1:1243] 1 1 1 2 1 1 1 1 2 1 ...
## ..- attr(*, "format.spss")= chr "F2.0"
## $ JWTRNS : num [1:1243] 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "format.spss")= chr "F2.0"
## $ LANX : num [1:1243] 2 2 2 2 2 2 2 1 2 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ MAR : num [1:1243] 3 5 1 1 5 1 1 3 1 1 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ MARHD : num [1:1243] 1 NA 2 2 NA 2 2 2 2 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ MARHM : num [1:1243] 2 NA 2 2 NA 1 2 2 2 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ MARHT : num [1:1243] 1 NA 2 1 NA 3 1 1 1 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ MARHW : num [1:1243] 2 NA 2 2 NA 2 2 2 2 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ MARHYP : num [1:1243] 2013 NA 2020 2017 NA ...
## ..- attr(*, "format.spss")= chr "F4.0"
## $ MIG : num [1:1243] 3 1 1 1 3 3 1 1 1 1 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ MIL : num [1:1243] 4 4 4 4 4 4 4 4 4 4 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ MLPA : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ MLPB : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ MLPCD : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ MLPE : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ MLPFG : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ MLPH : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ MLPIK : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ MLPJ : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ NWAB : num [1:1243] 3 3 3 3 2 3 2 3 3 3 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ NWAV : num [1:1243] 5 5 5 5 5 5 5 5 5 5 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ NWLA : num [1:1243] 3 3 3 3 2 3 2 3 3 3 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ NWLK : num [1:1243] 3 3 3 3 2 3 2 3 3 3 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ NWRE : num [1:1243] 3 3 3 3 3 3 3 3 3 3 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ OIP : num [1:1243] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "format.spss")= chr "F5.0"
## $ PAP : num [1:1243] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "format.spss")= chr "F5.0"
## $ RELSHIPP : num [1:1243] 20 25 21 21 20 20 21 20 21 21 ...
## ..- attr(*, "format.spss")= chr "F2.0"
## $ RETP : num [1:1243] 11000 0 0 0 0 0 0 18000 0 0 ...
## ..- attr(*, "format.spss")= chr "F6.0"
## $ SCH : num [1:1243] 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ SCHG : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F2.0"
## $ SCHL : num [1:1243] 16 18 17 17 18 19 19 14 18 16 ...
## ..- attr(*, "format.spss")= chr "F2.0"
## $ SEMP : num [1:1243] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "format.spss")= chr "F6.0"
## $ SEX : num [1:1243] 1 2 1 1 1 1 2 1 1 1 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ SSIP : num [1:1243] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "format.spss")= chr "F5.0"
## $ SSP : num [1:1243] 0 0 0 0 0 0 0 0 0 0 ...
## ..- attr(*, "format.spss")= chr "F5.0"
## $ WAGP : num [1:1243] 32000 31700 100000 110000 50000 105000 28000 40000 48000 86000 ...
## ..- attr(*, "format.spss")= chr "F6.0"
## $ WKHP : num [1:1243] 40 84 60 45 40 45 40 40 40 48 ...
## ..- attr(*, "format.spss")= chr "F2.0"
## $ WKL : num [1:1243] 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ WKWN : num [1:1243] 52 52 52 52 52 52 52 52 52 52 ...
## ..- attr(*, "format.spss")= chr "F2.0"
## $ WRK : num [1:1243] 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ YOEP : num [1:1243] NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F4.0"
## $ ANC : num [1:1243] 4 4 1 4 1 1 2 1 1 2 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ ANC1P : num [1:1243] 999 999 939 999 51 32 50 226 32 22 ...
## ..- attr(*, "format.spss")= chr "F3.0"
## $ ANC2P : num [1:1243] 999 999 999 999 999 999 21 999 999 26 ...
## ..- attr(*, "format.spss")= chr "F3.0"
## $ DECADE : num [1:1243] NA NA NA NA NA NA NA 7 NA NA ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ DIS : num [1:1243] 2 2 2 2 2 2 2 2 2 1 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ DRIVESP : num [1:1243] 1 1 1 2 1 1 1 1 2 1 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ ESP : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ ESR : num [1:1243] 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ FOD1P : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F4.0"
## $ FOD2P : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F4.0"
## $ HICOV : num [1:1243] 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ HISP : num [1:1243] 1 1 1 1 1 1 1 11 1 1 ...
## ..- attr(*, "format.spss")= chr "F2.0"
## $ INDP : num [1:1243] 1170 1170 1170 1170 1170 1170 1170 1170 1170 1170 ...
## ..- attr(*, "format.spss")= chr "F4.0"
## $ JWAP : num [1:1243] 67 58 200 87 94 90 93 66 67 75 ...
## ..- attr(*, "format.spss")= chr "F3.0"
## $ JWDP : num [1:1243] 22 16 118 37 52 46 49 19 24 34 ...
## ..- attr(*, "format.spss")= chr "F3.0"
## $ LANP : num [1:1243] NA NA NA NA NA NA NA 1200 NA NA ...
## ..- attr(*, "format.spss")= chr "F4.0"
## $ MIGPUMA : num [1:1243] 2704 NA NA NA 2704 ...
## ..- attr(*, "format.spss")= chr "F5.0"
## $ MIGSP : num [1:1243] 29 NA NA NA 29 29 NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F3.0"
## $ MSP : num [1:1243] 4 6 1 1 6 1 1 4 1 1 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ NAICSP : chr [1:1243] "3115" "3115" "3115" "3115" ...
## ..- attr(*, "format.spss")= chr "A7"
## ..- attr(*, "display_width")= int 7
## $ NATIVITY : num [1:1243] 1 1 1 1 1 1 1 2 1 1 ...
## ..- attr(*, "format.spss")= chr "F1.0"
## $ NOP : num [1:1243] NA NA NA NA NA NA NA NA NA NA ...
## ..- attr(*, "format.spss")= chr "F1.0"
## [list output truncated]table## function (..., exclude = if (useNA == "no") c(NA, NaN), useNA = c("no",
## "ifany", "always"), dnn = list.names(...), deparse.level = 1)
## {
## list.names <- function(...) {
## l <- as.list(substitute(list(...)))[-1L]
## if (length(l) == 1L && is.list(..1) && !is.null(nm <- names(..1)))
## return(nm)
## nm <- names(l)
## fixup <- if (is.null(nm))
## seq_along(l)
## else nm == ""
## dep <- vapply(l[fixup], function(x) switch(deparse.level +
## 1, "", if (is.symbol(x)) as.character(x) else "",
## deparse(x, nlines = 1)[1L]), "")
## if (is.null(nm))
## dep
## else {
## nm[fixup] <- dep
## nm
## }
## }
## miss.use <- missing(useNA)
## miss.exc <- missing(exclude)
## useNA <- if (miss.use && !miss.exc && !match(NA, exclude,
## nomatch = 0L))
## "ifany"
## else match.arg(useNA)
## doNA <- useNA != "no"
## if (!miss.use && !miss.exc && doNA && match(NA, exclude,
## nomatch = 0L))
## warning("'exclude' containing NA and 'useNA' != \"no\"' are a bit contradicting")
## args <- list(...)
## if (length(args) == 1L && is.list(args[[1L]])) {
## args <- args[[1L]]
## if (length(dnn) != length(args))
## dnn <- paste(dnn[1L], seq_along(args), sep = ".")
## }
## if (!length(args))
## stop("nothing to tabulate")
## bin <- 0L
## lens <- NULL
## dims <- integer()
## pd <- 1L
## dn <- NULL
## for (a in args) {
## if (is.null(lens))
## lens <- length(a)
## else if (length(a) != lens)
## stop("all arguments must have the same length")
## fact.a <- is.factor(a)
## if (doNA)
## aNA <- anyNA(a)
## if (!fact.a) {
## a0 <- a
## op <- options(warn = 2)
## on.exit(options(op))
## a <- factor(a, exclude = exclude)
## options(op)
## }
## add.na <- doNA
## if (add.na) {
## ifany <- (useNA == "ifany")
## anNAc <- anyNA(a)
## add.na <- if (!ifany || anNAc) {
## ll <- levels(a)
## if (add.ll <- !anyNA(ll)) {
## ll <- c(ll, NA)
## TRUE
## }
## else if (!ifany && !anNAc)
## FALSE
## else TRUE
## }
## else FALSE
## }
## if (add.na)
## a <- factor(a, levels = ll, exclude = NULL)
## else ll <- levels(a)
## a <- as.integer(a)
## if (fact.a && !miss.exc) {
## ll <- ll[keep <- which(match(ll, exclude, nomatch = 0L) ==
## 0L)]
## a <- match(a, keep)
## }
## else if (!fact.a && add.na) {
## if (ifany && !aNA && add.ll) {
## ll <- ll[!is.na(ll)]
## is.na(a) <- match(a0, c(exclude, NA), nomatch = 0L) >
## 0L
## }
## else {
## is.na(a) <- match(a0, exclude, nomatch = 0L) >
## 0L
## }
## }
## nl <- length(ll)
## dims <- c(dims, nl)
## if (prod(dims) > .Machine$integer.max)
## stop("attempt to make a table with >= 2^31 elements")
## dn <- c(dn, list(ll))
## bin <- bin + pd * (a - 1L)
## pd <- pd * nl
## }
## names(dn) <- dnn
## bin <- bin[!is.na(bin)]
## if (length(bin))
## bin <- bin + 1L
## y <- array(tabulate(bin, pd), dims, dimnames = dn)
## class(y) <- "table"
## y
## }
## <bytecode: 0x1478bf748>
## <environment: namespace:base>range(Dairy_worker_data_2$WAGP)## [1] 0 770000# Mean
mean(Dairy_worker_data_2$WAGP)## [1] 51758# SD
sd(Dairy_worker_data_2$WAGP)## [1] 54936.59# Create summary table for wage
WAGP_summary <- Dairy_worker_data_2 %>%
summarise(
mean_WAGP = mean(WAGP, na.rm = TRUE), # Mean salary
sd_WAGP = sd(WAGP, na.rm = TRUE), # Standard deviation
WAGP_min = min(WAGP, na.rm = TRUE), # Min salary
p25_WAGP = quantile(WAGP, 0.25, na.rm = TRUE), # 25th percentile
median_WAGP = quantile(WAGP, 0.50, na.rm = TRUE), # 50th percentile (median)
p75_WAGP = quantile(WAGP, 0.75, na.rm = TRUE), # 75th percentile
WAGP_max = max(WAGP, na.rm = TRUE), # Max salary
WAGP_range = max(WAGP, na.rm = TRUE) - min(WAGP, na.rm = TRUE) # Range
)
# Print the summary table
print(WAGP_summary)## # A tibble: 1 × 8
## mean_WAGP sd_WAGP WAGP_min p25_WAGP median_WAGP p75_WAGP WAGP_max WAGP_range
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 51758. 54937. 0 25000 47800 64000 770000 770000num_vars <- c("WAGP", "AGEP")
# Create descriptive table
# Note: In the following codes, I removed obs with missing values
descriptive_table_num <- Dairy_worker_data_2 %>%
summarise(
across(all_of(num_vars), list( # ensures that only columns listed in vars are selected for transformation
mean = ~mean(.x, na.rm = TRUE), # the .x is a placeholder for each variable being processed inside the across() function
sd = ~sd(.x, na.rm = TRUE),
min = ~min(.x, na.rm = TRUE),
p25 = ~quantile(.x, 0.25, na.rm = TRUE),
median = ~median(.x, na.rm = TRUE),
p75 = ~quantile(.x, 0.75, na.rm = TRUE),
max = ~max(.x, na.rm = TRUE),
range = ~max(.x, na.rm = TRUE) - min(.x, na.rm = TRUE)
),
.names = "{.col}_{.fn}") # ensures that the column names are structured as "variable_stat"
) %>%
pivot_longer(cols = everything(), # converts the wide format (one row for all variables) into a long format
names_to = c("Variable", ".value"), # assigns the statistic type (e.g., "mean", "sd", "min", etc.) as new column names
names_sep = "_") %>% # specifies that the column names are separated by an underscore (_)
select(Variable, mean, sd, min, p25, median, p75, max, range) # reorder the variables
# Print the formatted table
print(descriptive_table_num)## # A tibble: 2 × 9
## Variable mean sd min p25 median p75 max range
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WAGP 51758. 54937. 0 25000 47800 64000 770000 770000
## 2 AGEP 45.7 15.1 16 33 47 59 95 79# Round all numeric columns to two decimal places
descriptive_table_num <- descriptive_table_num %>%
mutate(across(where(is.numeric), ~round(.x, 2)))
# Print the rounded table
print(descriptive_table_num)## # A tibble: 2 × 9
## Variable mean sd min p25 median p75 max range
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 WAGP 51758 54937. 0 25000 47800 64000 770000 770000
## 2 AGEP 45.7 15.1 16 33 47 59 95 79cat_vars <- c("DEAR", "DEYE", "CIT_2", "LANX", "DOUT", "NATIVITY", "SEX", "DIS")
get_mode <- function(x) {
uniq_vals <- unique(x[!is.na(x)]) # Remove NA values
uniq_vals[which.max(tabulate(match(x, uniq_vals)))] # Get the most frequent value
}
# Create descriptive table
descriptive_table_cat <- Dairy_worker_data_2 %>%
select(all_of(cat_vars)) %>%
pivot_longer(everything(), names_to = "Variable", values_to = "Category") %>%
group_by(Variable, Category) %>%
summarise(
Count = n(), # Count occurrences
.groups = "drop" # Remove grouping after summarization
) %>%
group_by(Variable) %>%
mutate(
Total = sum(Count), # Get total count for each Variable
Percentage = round((Count / Total) * 100, 2) # Correct percentage calculation
) %>%
ungroup() %>% # Remove grouping for proper mode calculation
mutate(
Mode = sapply(Variable, function(var) get_mode(Dairy_worker_data_2[[var]])) # Correct mode calculation for each variable
) %>%
select(-Total) %>% # Remove unnecessary column
arrange(Variable, desc(Count)) # Sort by variable and count
# Print the summary table
print(descriptive_table_cat)## # A tibble: 16 × 5
## Variable Category Count Percentage Mode
## <chr> <dbl> <int> <dbl> <dbl>
## 1 CIT_2 1 1176 94.6 1
## 2 CIT_2 2 67 5.39 1
## 3 DEAR 2 1199 96.5 2
## 4 DEAR 1 44 3.54 2
## 5 DEYE 2 1223 98.4 2
## 6 DEYE 1 20 1.61 2
## 7 DIS 2 1131 91.0 2
## 8 DIS 1 112 9.01 2
## 9 DOUT 2 1224 98.5 2
## 10 DOUT 1 19 1.53 2
## 11 LANX 2 1104 88.8 2
## 12 LANX 1 139 11.2 2
## 13 NATIVITY 1 1138 91.6 1
## 14 NATIVITY 2 105 8.45 1
## 15 SEX 1 839 67.5 1
## 16 SEX 2 404 32.5 1summary_table <- Dairy_worker_data_2 %>%
summarise(
# Descriptive statistics for male (count and percentage)
DEYE_count_0 = sum(DEYE == 0, na.rm = TRUE),
DEYE_count_1 = sum(DEYE == 1, na.rm = TRUE),
DEYE_percent_0 = round((DEYE_count_0 / n()) * 100, 2),
DEYE_percent_1 = round((DEYE_count_1 / n()) * 100, 2),
# Descriptive statistics for salary (mean, median, sd, min, max)
WAGP_mean = mean(WAGP, na.rm = TRUE),
WAGP_median = median(WAGP, na.rm = TRUE),
WAGP_sd = sd(WAGP, na.rm = TRUE),
WAGP_min = min(WAGP, na.rm = TRUE),
WAGP_max = max(WAGP, na.rm = TRUE)
)
print(summary_table)## # A tibble: 1 × 9
## DEYE_count_0 DEYE_count_1 DEYE_percent_0 DEYE_percent_1 WAGP_mean WAGP_median
## <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 0 20 0 1.61 51758. 47800
## # ℹ 3 more variables: WAGP_sd <dbl>, WAGP_min <dbl>, WAGP_max <dbl>contingency_table <- Dairy_worker_data_2 %>%
group_by(CIT_2) %>% # Group by male (0 and 1)
summarise(
Count = n(), # Count of observations per male group
Mean_CIT_2 = round(mean(CIT_2, na.rm = TRUE), 2), # Mean salary (rounded to 2 decimals)
SD_dis = round(sd(CIT_2, na.rm = TRUE), 2), # Standard deviation of salary (rounded to 2 decimals)
Min_Salary = min(CIT_2, na.rm = TRUE), # Minimum salary
Max_Salary = max(CIT_2, na.rm = TRUE), # Maximum salary
Median_Salary = median(CIT_2, na.rm = TRUE) # Median salary
)
# Print the summary table
print(contingency_table)## # A tibble: 2 × 7
## CIT_2 Count Mean_CIT_2 SD_dis Min_Salary Max_Salary Median_Salary
## <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1176 1 0 1 1 1
## 2 2 67 2 0 2 2 2Change Themes for the HTML Output
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sessionInfo()## R version 4.4.2 (2024-10-31)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sequoia 15.3
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] readxl_1.4.5 janitor_2.2.1 labelled_2.14.0 magrittr_2.0.3
## [5] haven_2.5.4 arsenal_3.6.3 lubridate_1.9.4 forcats_1.0.0
## [9] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.4 readr_2.1.5
## [13] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.2 tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 jsonlite_1.9.0 compiler_4.4.2 tidyselect_1.2.1
## [5] snakecase_0.11.1 jquerylib_0.1.4 scales_1.3.0 yaml_2.3.10
## [9] fastmap_1.2.0 R6_2.6.1 generics_0.1.3 knitr_1.49
## [13] munsell_0.5.1 bslib_0.9.0 pillar_1.10.1 tzdb_0.4.0
## [17] rlang_1.1.5 utf8_1.2.4 cachem_1.1.0 stringi_1.8.4
## [21] xfun_0.51 sass_0.4.9 timechange_0.3.0 cli_3.6.4
## [25] withr_3.0.2 digest_0.6.37 grid_4.4.2 rstudioapi_0.17.1
## [29] hms_1.1.3 lifecycle_1.0.4 vctrs_0.6.5 evaluate_1.0.3
## [33] glue_1.8.0 cellranger_1.1.0 colorspace_2.1-1 rmarkdown_2.29
## [37] tools_4.4.2 pkgconfig_2.0.3 htmltools_0.5.8.1