Differences-in-difference Graph in R-Studio - r

I have a question for my project in R-Studio.
In my research I assume that the presence of peacekeepers can positively influence the political representation of women. In this regard, I have the independent variable "pko_dummy" (0 = no peacekeeping mission, 1 = peacekeeping mission) and the two dependent variables "parl_wom.per" and "exe_wom.per", which measure the percentage of women elected to parliament and ministerial cabinets respectively.
The dataset in question includes all African countries that experienced a war between 1990 and 2018. I would like to run a differences-in-difference model. The treatment group would be the war-torn countries with a PKO, the control group the war-torn countries without a PKO. The problem is that the year of intervention of peacekeeping missions is different from country to country, so I don't know how to create a graph in R-Studio to see if the parallel trend assumption can be fulfilled.
data <- structure(list(cown = c(432, 432, 432, 432, 432, 432, 432, 432,
432, 432),
year = c(1990, 1991, 1992, 1993, 1994, 1995, 1996,
1997, 1998, 1999),
intensity_level = c(1, 1, 0, 0, 1, 0, 0, 0,
0, 0),
pa_dummy = c(0, 1, 1, 0, 0, 0, 0, 0, 0, 0),
pko_dummy = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
parl_wom.per = c(NA, NA, 0.023, 0.023, 0.023, 0.023,
0.023, 0.122449, 0.122449, 0.122449),
exe_wom.per = c(0.0588235, 0.1052632, 0.0526316,
0.0952381, 0.1111111, 0.0555556,
0.125, 0.1176471, 0.2608696, 0.2727273),
gender_mean = c(0, 0, 1.75, 0, 0, 0, 0, 0, 0, 0),
gender_art = c(0, 0, 7, 0, 0, 0, 0, 0, 0, 0),
female_pko.per = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
lf_wom.per = c(0.60855, 0.60834, 0.6082, 0.60815,
0.6082, 0.60838, 0.60806, 0.60798,
0.60804, 0.60811),
ss.per = c(0.0679799, 0.0723098, 0.0827134, 0.0837933,
0.0957365, 0.1073224, 0.1127522, 0.1228388,
0.1336761, 0.1510765),
fdi.per = c(0.0021364, 0.0004424, -0.0077276, 0.001441,
0.0083661, 0.0411724, 0.009786, 0.0275705,
0.0032724, 0.0090061),
ele.sy = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
polity = c(-7, NA, 4, 4, 4, 4, 4, 5, 5, 5),
mus.per = c(0.944, 0.944, 0.944, 0.944, 0.944,
0.944, 0.944, 0.944, 0.944, 0.944),
cons_ref = c(0, 0, 1, 1, 1, 1, 1, 1, 1, 1),
jud_ind.per = c(0.4763113, 0.5237863, 0.5575284,
0.548066, 0.548066, 0.548066, 0.548066,
0.548066, 0.548066, 0.548066)),
row.names = c(NA, -10L), class = "data.frame")

Related

Adding industry dummies to 2SLS in R

for my Master Thesis I want to regress the ESG score on the stock price drop during the pandemic. For OLS this works fine. To check for potential Endogeneity I also conduct a 2SLS regression with the industry average ESG score as instrument. This works fine as long as I leave out the industry dummies. When adding them I get the following error Message:
Warning message:
In pf(w, df[1L], df[2L], lower.tail = FALSE) : NaNs produced
Moreover, the diagnostics for weak instruments and Wu-Hausman also show NaN.
I am aware of the dummy variable trap so not all industries were included.
Does anyone know why I get this error message? Any help is appreciated. Below I will provide my results with and without dummies.
Without dummies
With dummies
I managed to replicate the warning message with the first 10 rows of my data:
structure(list(NAME = c("A-MARK PRECIOUS METALS", "AAON", "AAR", "ABBOTT LABORATORIES", "ABBVIE", "ABEONA THERAPEUTICS", "ABERCROMBIE & FITCH A", "ABIOMED", "ABM INDS.", "ABRAXAS PETROLEUM"), ESG = c(30.93, 46.31, 24.66, 70.67, 79.79, 36.58, 69.13, 25.88, 72.66, 18.88 ), LogAssets = c(13.538484837701, 13.0147959839376, 14.5473975668402, 18.0628622893116, 18.8299054426017, 11.9263455151269, 15.0139386185708, 13.9751825387466, 15.1444141253049, 11.9688365085588), Quick = c(0.38, 2.27, 1.69, 1.14, 0.6, 2.26, 1.24, 4, 1.32, 0.1), I_Agri = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), I_Cons = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), I_Fin = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), I_Man = c(0, 1, 0, 1, 1, 1, 0, 1, 0, 0), I_Mining = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1), I_Serv = c(0, 0, 0, 0, 0, 0, 0, 0, 1, 0), I_Trade = c(1, 0, 0, 0, 0, 0, 1, 0, 0, 0), I_Utility = c(0, 0, 1, 0, 0, 0, 0, 0, 0, 0), Drop = c(0.107419712070875, 0.277738886944347, 0.791427308882015, 0.297000895255148, 0.31485022307202, 0.75, 0.527002967359051, 0.222692078618225, 0.473209685729006, 0.683189655172414), Leverage = c(0.8177, 0.0178, 0.3993, 0.3623, 0.8679, 0.0169, 0.2659, 0, 0.3257, 1.5458 ), ROA = c(0.0622, 0.1926, 0.0065, 0.0726, 0.0542, -0.4324, -0.0259, 0.1888, 0.0095, -0.6467), ESG_A = c(41.9334803921569, 41.6947268673356, 42.0122772277228, 41.6947268673356, 41.6947268673356, 41.6947268673356, 41.9334803921569, 41.6947268673356, 37.5789174311926, 34.9968604651163 )), row.names = c(NA, -10L), class = c("tbl_df", "tbl", "data.frame" ))
The code I used:
library(AER)
IV2=ivreg(Drop~ESG+LogAssets+Leverage+ROA+Quick+I_Cons+I_Fin+I_Man+I_Mining+I_Serv+I_Trade+I_Utility | ESG_A+LogAssets+Leverage+ROA+Quick+I_Cons+I_Fin+I_Man+I_Mining+I_Serv+I_Trade+I_Utility, data=mwe)
summary(IV2, diagnostics = TRUE)
Rstudio Version: 2022.2.0.443 Operating system: Windows 10 pro 64 bit
Thank you!

Marginal Effect Plot with ggeffects package

The command predict <- ggpredict(fit_tw1, terms = "pko_dummy") does not work and it gives me the following error. Do you know how to solve my problem?
Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) : factor as.factor(pa_dummy) has new level 0.0906593406593407
Can you help me?
Model: (The Model has fixed effects for countries (cown) and years (year))
fit_tw1 <- lm(parl_wom.per ~ as.factor(pko_dummy)*as.factor(pa_dummy) + as.factor(cown) + as.factor(year) + female_pko.per + lf_wom.per + ss.per + fdi.per + jud_ind.per + polity + as.factor(intensity_level) + as.factor(cons_ref),
data = subset(data9, rownames!="639"))
Reproducible sample of the dataset
structure(list(cown = c(432, 432, 432, 432, 432, 432, 432, 432,
432, 432, 432, 432, 432, 432, 432, 432, 432, 432, 432, 432),
year = c(1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997,
1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
2008, 2009), intensity_level = c("1", "1", "0", "0", "1",
"0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0",
"1", "1", "1"), pa_dummy = c(0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), pko_dummy = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), parl_wom.per = c(NA,
NA, 0.023, 0.023, 0.023, 0.023, 0.023, 0.122449, 0.122449,
0.122449, 0.122449, 0.122449, 0.1020408, 0.1020408, 0.1020408,
0.1020408, 0.1020408, 0.1020408, 0.1020408, 0.1020408), exe_wom.per = c(0.0588235,
0.1052632, 0.0526316, 0.0952381, 0.1111111, 0.0555556, 0.125,
0.1176471, 0.2608696, 0.2727273, 0.45, 0.4210526, 0.15, 0.15,
0.15, 0.1923077, 0.1923077, 0.1923077, 0.1851852, 0.1785714
), gender_mean = c(0, 0, 1.75, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), gender_art = c(0, 0, 7, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), female_pko.per = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
lf_wom.per = c(0.60855, 0.60834, 0.6082, 0.60815, 0.6082,
0.60838, 0.60806, 0.60798, 0.60804, 0.60811, 0.60813, 0.60782,
0.60752, 0.60725, 0.60701, 0.60681, 0.60616, 0.60564, 0.60525,
0.60495), ss.per = c(0.0679798984527588, 0.0723097991943359,
0.0827134037017822, 0.0837932968139648, 0.0957365036010742,
0.107322397232056, 0.112752199172974, 0.122838802337646,
0.133676099777222, 0.151076498031616, 0.174537200927734,
NA, NA, 0.221253795623779, 0.239939594268799, 0.25832540512085,
0.277074604034424, 0.303055400848389, 0.33731990814209, 0.36671989440918
), fdi.per = c(0.0021364, 0.0004424, -0.0077276, 0.001441,
0.0083661, 0.0411724, 0.009786, 0.0275705, 0.0032724, 0.0090061,
0.0203215, 0.0602065, -0.0031506, 0.0153489, 0.015555, 0.0256452,
0.0214593, 0.0252638, 0.0270809, 0.0631946), ele.sy = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
polity = c(-7, NA, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5,
7, 7, 7, 7, 7), mus.per = c(0.944, 0.944, 0.944, 0.944, 0.944,
0.944, 0.944, 0.944, 0.944, 0.944, 0.944, 0.944, 0.944, 0.944,
0.944, 0.944, 0.944, 0.944, 0.944, 0.944), cons_ref = c(0,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
jud_ind.per = c(0.476311308991478, 0.523786338536123, 0.557528417528326,
0.548066004702523, 0.548066004702523, 0.548066004702523,
0.548066004702523, 0.548066004702523, 0.548066004702523,
0.548066004702523, 0.548066004702523, 0.548066004702523,
0.548066004702523, 0.548066004702523, 0.548066004702523,
0.539288342106394, 0.539288342106394, 0.548066004702523,
0.539288342106394, 0.539288342106394)), row.names = c(NA,
-20L), class = c("tbl_df", "tbl", "data.frame"))

Hanging Graphs w/ NA Values

I've been using this code
ggplot(Oak, aes(x = age, fill = factor(subject_talk))) +
geom_bar(aes(y = (..count..)/sum(..count..)),position = "stack") +
xlim(18,29) +
scale_fill_manual(breaks=c("0","1"), values = scales::hue_pal()(2))
to create graphs that look like this
Recently, some graphs end up floating where NA values should be,
which I don't want.
Here's the code for 2
ggplot(Oak, aes(x = age, fill = factor(highcho))) +
geom_bar(aes(y = (..count..)/sum(..count..)),position = "stack") +
xlim(18,29) +
scale_fill_manual(breaks=c("0","1"), values = scales::hue_pal()(2))
The output is too long to set as code, I can't post it otherwise.
dput(head(Oak,20))
structure(list(studyid = structure(c(1002, 1002, 1002, 1002,
1002, 1004, 1004, 1004, 1004, 1004, 1005, 1005, 1005, 1005, 1005,
1006, 1006, 1006, 1006, 1006), label = "Subject Study ID", format.stata = "%12.0g"),
post_flu = structure(c(1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1,
1, 1, 1, 0, 0, 0, 0, 0), label = "Receipt of Flu Vaccine - Encounter Survey", format.stata = "%10.0g"),
post_bmi = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "BMI Test Received - Encounter Survey", format.stata = "%9.0g"),
post_bp = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Blood Pressure Test Received - Encounter Survey", format.stata = "%9.0g"),
post_dia = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Diabetes Test Received - Encounter Survey", format.stata = "%9.0g"),
post_cho = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Cholesterol Test Received - Encounter Survey", format.stata = "%9.0g"),
post_flu_sl = structure(c(1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1,
1, 1, 1, 1, NA, NA, NA, NA, NA), label = "Flu Shot Received (Subsidy Received) - Encounter Survey", format.stata = "%9.0g"),
post_flu_nosl = structure(c(NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0), label = "Flu Shot Received (No Subsidy Received) - Encounter Survey", format.stata = "%9.0g"),
post_shr_invasive = structure(c(1, 1, 1, 1, 1, 0.666666686534882,
0.666666686534882, 0.666666686534882, 0.666666686534882,
0.666666686534882, 0.333333343267441, 0.333333343267441,
0.333333343267441, 0.333333343267441, 0.333333343267441,
0, 0, 0, 0, 0), label = "Post Take-Up as Share of Invasive Services", format.stata = "%9.0g"),
post_share4 = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Post Take-Up as Share of Four Services", format.stata = "%9.0g"),
pre_bmi = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Weight and Height Selected - CTO Patient Survey", format.stata = "%8.0g"),
pre_bp = structure(c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Blood Pressure Selected - CTO Patient Survey", format.stata = "%8.0g"),
pre_dia = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Diabetes Selected - CTO Patient Survey", format.stata = "%8.0g"),
pre_cho = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Cholesterol Selected - CTO Patient Survey", format.stata = "%8.0g"),
pre_flu = structure(c(1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Flu Shot Selected - CTO Patient Survey", format.stata = "%8.0g", labels = c(No = 0,
Yes = 1, Unsure = 99), class = c("haven_labelled", "vctrs_vctr",
"double")), pre_flu_sl = structure(c(1, 1, 1, 1, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, NA, NA, NA, NA, NA), label = "Flu Shot Selected (Subsidy Received) - CTO Survey", format.stata = "%9.0g"),
pre_flu_nosl = structure(c(NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 0, 0, 0, 0, 0), label = "Flu Shot Selected (No Subsidy Received) - CTO Survey", format.stata = "%9.0g"),
pre_shr_invasive = structure(c(0.333333343267441, 0.333333343267441,
0.333333343267441, 0.333333343267441, 0.333333343267441,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Pre Take-Up as Share of Invasive Services", format.stata = "%9.0g"),
pre_share4 = structure(c(0, 0, 0, 0, 0, 0.25, 0.25, 0.25,
0.25, 0.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Pre Take-Up as Share of Four Services", format.stata = "%9.0g"),
delta_bmi = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Delta BMI: Post - Pre", format.stata = "%9.0g"),
delta_bp = structure(c(1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Delta Blood Pressure: Post - Pre", format.stata = "%9.0g"),
delta_dia = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Delta Diabetes: Post - Pre", format.stata = "%9.0g"),
delta_cho = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Delta Cholesterol: Post - Pre", format.stata = "%9.0g"),
delta_flu = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
1, 1, 1, 1, 0, 0, 0, 0, 0), label = "Delta Flu: Post - Pre", format.stata = "%9.0g"),
delta_shr_invasive = structure(c(0.666666686534882, 0.666666686534882,
0.666666686534882, 0.666666686534882, 0.666666686534882,
0.666666686534882, 0.666666686534882, 0.666666686534882,
0.666666686534882, 0.666666686534882, 0.333333343267441,
0.333333343267441, 0.333333343267441, 0.333333343267441,
0.333333343267441, 0, 0, 0, 0, 0), label = "Delta Take-Up as Share of Invasive Services", format.stata = "%9.0g"),
deltaind_test = structure(c(1, 1, 1, 1, 0, 1, 0, 1, 1, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0), label = "Indicator for Delta Selection - Stacked Subject X Test", format.stata = "%9.0g"),
delta_share4 = structure(c(1, 1, 1, 1, 1, 0.75, 0.75, 0.75,
0.75, 0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Delta as Share of Four Services", format.stata = "%9.0g"),
friends_enrolled = structure(c(NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 0, 0, 0, 0, 0, NA, NA, NA, NA, NA), label = "Are Friends Enrolled in the Study - CTO Patient Survey", format.stata = "%8.0g", labels = c(No = 0,
Yes = 1, Unsure = 99), class = c("haven_labelled", "vctrs_vctr",
"double")), value_bmi = structure(c(25, 25, 25, 25, 25, 28,
28, 28, 28, 28, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), label = "Patient Test Value: bmi - Encounter Survey", format.stata = "%10.0g"),
value_dia = structure(c(5.9, 5.9, 5.9, 5.9, 5.9, 6.9, 6.9,
6.9, 6.9, 6.9, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), label = "Patient Test Value: hgb - Encounter Survey", format.stata = "%10.0g"),
value_cho = structure(c(208, 208, 208, 208, 208, 170, 170,
170, 170, 170, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), label = "Patient Test Value: cho - Encounter Survey", format.stata = "%10.0g"),
subject_talk = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0), label = "Subject Tried to Talk About Other Health Problems - Encounter Survey", format.stata = "%10.0g"),
choice_care = structure(c(3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2,
2, 2, 2, 2, 3, 3, 3, 3, 3), label = "How Much Choice Subj Has in Choice of Medical Care - Barber Survey", format.stata = "%22.0g", labels = c(`prefer not to answer` = -99,
`don't know` = -98, `a great deal of choice` = 1, `some choice` = 2,
`very little choice` = 3, `no choice` = 4), class = c("haven_labelled",
"vctrs_vctr", "double")), rating = structure(c(4, 4, 4, 4,
4, 5, 5, 5, 5, 5, 3, 3, 3, 3, 3, NA, NA, NA, NA, NA), label = "Experience Rating (1 = Bad, 5 = Excellent) - Subject Feedback", format.stata = "%10.0g"),
doctor_id = structure(c("BL6", "BL6", "BL6", "BL6", "BL6",
"NB8", "NB8", "NB8", "NB8", "NB8", "NB4", "NB4", "NB4", "NB4",
"NB4", "NB4", "NB4", "NB4", "NB4", "NB4"), label = "Doctor Mask ID", format.stata = "%9s"),
nonpreventive_agree = structure(c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Doctor Notes Relating to Personal/Other Health (Coders Agree) - Encounter Survey", format.stata = "%9.0g"),
mecherror_cho = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Mechanical Error, Cholesterol Test - Encounter Survey", format.stata = "%9.0g"),
mecherror_dia = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Mechanical Error, Diabetes Test - Encounter Survey", format.stata = "%9.0g"),
value_systolic = structure(c(165, 165, 165, 165, 165, 168,
168, 168, 168, 168, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
), label = "Blood Pressure Value: Systolic - Encounter Survey", format.stata = "%9.0g"),
hyptension = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA), label = "Subj Has Hypertension, Test Value or MD Comments - Encounter Survey", format.stata = "%9.0g"),
diabetic = structure(c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA), label = "Subj Has Diabetes, Test Value or MD Comments - Encounter Survey", format.stata = "%9.0g"),
highcho = structure(c(1, 1, 1, 1, 1, 0, 0, 0, 0, 0, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA), label = "Subj Has High Cholesterol, Test Value or MD Comments - Encounter Survey", format.stata = "%9.0g"),
obese = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA), label = "Subj Is Obese, Test Value or MD Comments - Encounter Survey", format.stata = "%9.0g"),
length_visit_dr = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "Doctor Visit Duration, Time Out of Waiting Room to Time Out - Encounter Survey", format.stata = "%9.0g"),
RO_tablet_assist = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "RO Assisted Subject with Tablet Survey - Encounter Survey", format.stata = "%9.0g"),
yes_recommend = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, NA, NA, NA, NA, NA), label = "Patient Would Recommend Doctor - Subject Feedback", format.stata = "%9.0g"),
dr_notes = structure(c(1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 1, 1), label = "Doctor Wrote Notes in 'Notable' About Subject - Encounter Survey", format.stata = "%9.0g"),
any_health_prob = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1), label = "Subject Reported Any Health Problems (A2-A11) - Barber Survey", format.stata = "%9.0g"),
hosp_visits_2years = structure(c(NA, NA, NA, NA, NA, 3, 3,
3, 3, 3, 1, 1, 1, 1, 1, 3, 3, 3, 3, 3), label = "Number of Hospital Visits in Last 2 Years - CTO Survey", format.stata = "%9.0g"),
ER_2years = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
1, 1, 1, 1, 7, 7, 7, 7, 7), label = "Number of ER Visits in Last 2 Years - Barber Survey", format.stata = "%9.0g"),
nights_hosp_2years = structure(c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0), label = "Nights in the Hospital in Last 2 Years - Barber Survey", format.stata = "%9.0g"),
has_PCP = structure(c(0, 0, 0, 0, 0, -9, -9, -9, -9, -9,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1), label = "Subject Has Primary Care Provider - Barber Survey", format.stata = "%9.0g"),
uninsured = structure(c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, -9,
-9, -9, -9, -9, 0, 0, 0, 0, 0), label = "Subject is Uninsured - Barber Survey", format.stata = "%9.0g"),
ER_visits_uninsured = structure(c(NA, NA, NA, NA, NA, 0,
0, 0, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), label = "Number of ER Visits in Last 2 Years for Uninsured - Barber Survey", format.stata = "%9.0g"),
mistrust_5levels = structure(c(3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 1, 1, 1, 1, 1, 4, 4, 4, 4, 4), label = "Doctor Mistrust (1 is Lowest, 5 is Highest) - Barber Survey", format.stata = "%9.0g"),
med_mistrust = structure(c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
1, 1, 1, 1, 1, 3, 3, 3, 3, 3), label = "Doctor Mistrust (1 is Lowest, 3 is Highest) - Barber Survey", format.stata = "%9.0g"),
age = structure(c(50, 50, 50, 50, 50, 44, 44, 44, 44, 44,
33, 33, 33, 33, 33, 35, 35, 35, 35, 35), label = "Subject Age - Barber Survey", format.stata = "%9.0g"),
age2 = structure(c(2500, 2500, 2500, 2500, 2500, 1936, 1936,
1936, 1936, 1936, 1089, 1089, 1089, 1089, 1089, 1225, 1225,
1225, 1225, 1225), label = "Subject Age Squared - Barber Survey", format.stata = "%9.0g"),
married = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Subject Is Married - Barber Survey", format.stata = "%9.0g"),
unemployed = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Subject Is Unemployed - Barber Survey", format.stata = "%9.0g"),
benefits = structure(c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Subject Receives DI/SSI/UB - Barber Survey", format.stata = "%9.0g"),
sl0 = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 1, 1), label = "Subsidy Level: $0 - CTO Survey", format.stata = "%9.0g"),
sl5 = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
1, 1, 0, 0, 0, 0, 0), label = "Subsidy Level: $5 - CTO Survey", format.stata = "%9.0g"),
sl10 = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), label = "Subsidy Level: $10 - CTO Survey", format.stata = "%9.0g"),
subsidy_level = structure(c(10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 5, 5, 5, 5, 5, 0, 0, 0, 0, 0), label = "Subsidy Level, Categorical - CTO Survey", format.stata = "%9.0g"),
black_dr = structure(c(1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Randomized to Black Doctor - CTO Survey", format.stata = "%9.0g"),
black0 = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doctor & Subsidy Level: $0 - CTO Survey", format.stata = "%9.0g"),
black5 = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doctor & Subsidy Level: $5 - CTO Survey", format.stata = "%9.0g"),
black10 = structure(c(1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doctor & Subsidy Level: $10 - CTO Survey", format.stata = "%9.0g"),
white0 = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 1, 1), label = "White Doctor & Subsidy Level: $0 - CTO Survey", format.stata = "%9.0g"),
white5 = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
1, 1, 1, 0, 0, 0, 0, 0), label = "White Doctor & Subsidy Level: $5 - CTO Survey", format.stata = "%9.0g"),
white10 = structure(c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "White Doctor & Subsidy Level: $10 - CTO Survey", format.stata = "%9.0g"),
any_subsidy = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 0, 0, 0, 0, 0), label = "Subject Received $5 or $10 Subsidy - CTO Survey", format.stata = "%9.0g"),
age5 = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), label = "Subject Age Within 5 Years of Doctor's Age - Baseline Survey", format.stata = "%9.0g"),
age10 = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), label = "Subject Age Within 10 Years of Doctor's Age - Baseline Survey", format.stata = "%9.0g"),
educ_conc = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Education Concordance: Subject Has BA or Higher - Baseline Survey", format.stata = "%9.0g"),
good_sa_health = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0), label = "Subject Rated Health as Good, Very Good, or Excellent - Barber Survey", format.stata = "%9.0g"),
no_rec_scr_interval = structure(c(1, 1, 1, 1, 1, 0, 0, 0,
0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0), label = "Subject No Recent Screenings in Recommended Interval - Barber Survey", format.stata = "%9.0g"),
millenial = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1), label = "Subject Is Less Than 40 - Barber Survey", format.stata = "%9.0g"),
HSless = structure(c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 0, 0, 0, 0, 0), label = "Subject Has a High School Degree or Less - Barber Survey", format.stata = "%9.0g"),
low_income = structure(c(0, 0, 0, 0, 0, -9, -9, -9, -9, -9,
0, 0, 0, 0, 0, 1, 1, 1, 1, 1), label = "Household Has Income Below $5k/Year - Barber Survey", format.stata = "%9.0g"),
long_wait = structure(c(NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_), label = "Subject Waited Longer Than 1 Hour to See Doctor - Barber Survey", format.stata = "%9.0g"),
high_congestion = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "More Than 8 People in Waiting Room When Subject Arrived - Congestion", format.stata = "%9.0g"),
long_driv = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Commute Via Car Above Median (18 Mins) - Barber Distance", format.stata = "%9.0g"),
atrisk_cho = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1), label = "Subject Recommended to Get Cholesterol Test - CTO Survey", format.stata = "%9.0g"),
atrisk_dia = structure(c(NA, NA, NA, NA, NA, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Subject Recommended to Get Diabetes Test - CTO Survey", format.stata = "%9.0g"),
excuses = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 1, 1, 1), label = "Subject Gave Excuse for Not Receiving Services - Suubject Feedback", format.stata = "%10.0g"),
length_dr_note = structure(c(9, 9, 9, 9, 9, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 61, 61, 61, 61, 61), label = "Length (Number of Characters) of Doctor Notes - Encounter Survey", format.stata = "%9.0g"),
mentioned_PCP = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Subject Mentioned PCP in Clinic Notes - Suubject Feedback", format.stata = "%9.0g"),
bl_ER_2years = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Number of ER Visits in Last 2 Years - Barber Survey", format.stata = "%9.0g"),
bl_med_mistrust = structure(c(2, 2, 2, 2, 2, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Doctor Mistrust (1 is Lowest, 3 is Highest) - Barber Survey", format.stata = "%9.0g"),
bl_millenial = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Subject Is Less Than 40 - Barber Survey", format.stata = "%9.0g"),
bl_HSless = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Subject Has a High School Degree or Less - Barber Survey", format.stata = "%9.0g"),
bl_low_income = structure(c(0, 0, 0, 0, 0, NA, NA, NA, NA,
NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Household Has Income Below $5k/Year - Barber Survey", format.stata = "%9.0g"),
bl_long_wait = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "Black Doc * Subject Waited Longer Than 1 Hour to See Doctor - Barber Survey", format.stata = "%9.0g"),
bl_long_driv = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Commute Via Car Above Median (18 Mins) - Barber Distance", format.stata = "%9.0g"),
bl_high_congest = structure(c(NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), label = "Black Doc * More Than 8 People in Waiting Room When Subject Arrived - Congestion", format.stata = "%9.0g"),
bl_atrisk_cho = structure(c(1, 1, 1, 1, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Subject Recommended to Get Cholesterol Test - CTO Survey", format.stata = "%9.0g"),
bl_atrisk_dia = structure(c(NA, NA, NA, NA, NA, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Subject Recommended to Get Diabetes Test - CTO Survey", format.stata = "%9.0g"),
bl_no_rec_scr_interval = structure(c(1, 1, 1, 1, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Subject No Recent Screenings in Recommended Interval - Barber Survey", format.stata = "%9.0g"),
bl_ER_visits_uninsured = structure(c(NA, NA, NA, NA, NA,
0, 0, 0, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), label = "Black Doc * Number of ER Visits in Last 2 Years for Uninsured - Barber Survey", format.stata = "%9.0g"),
bl_educ_conc = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Education Concordance: Subject Has BA or Higher - Baseline Survey", format.stata = "%9.0g"),
bl_age5 = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Subject Age Within 5 Years of Doctor's Age - Baseline Survey", format.stata = "%9.0g"),
bl_age10 = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Subject Age Within 10 Years of Doctor's Age - Baseline Survey", format.stata = "%9.0g"),
bl_sl10 = structure(c(1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Subsidy Level: $10 - CTO Survey", format.stata = "%9.0g"),
RO_id = structure(c(6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3), label = "Blind ID, Reception Officer - Encounter Survey", format.stata = "%9.0g"),
location_id = structure(c(9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9), label = "Blind ID, Recruitment Location - Barber Survey", format.stata = "%9.0g"),
date_visit_id = structure(c(2, 2, 2, 2, 2, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1), label = "Blind ID, Date of Clinic Visit - Encounter Survey", format.stata = "%9.0g"),
tag = structure(c(0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 1, 0), label = "Tag for Study ID", format.stata = "%8.0g"),
bmi_test = structure(c(1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,
0, 0, 0, 1, 0, 0, 0, 0), label = "Tag for BMI Test", format.stata = "%9.0g"),
bp_test = structure(c(0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,
0, 0, 0, 0, 1, 0, 0, 0), label = "Tag for Blood Pressure Test", format.stata = "%9.0g"),
dia_test = structure(c(0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 0, 0, 1, 0, 0), label = "Tag for Diabetes Test", format.stata = "%9.0g"),
cho_test = structure(c(0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,
0, 1, 0, 0, 0, 0, 1, 0), label = "Tag for Cholesterol Test", format.stata = "%9.0g"),
flu_test = structure(c(0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
0, 0, 1, 0, 0, 0, 0, 1), label = "Tag for Flu Shot", format.stata = "%9.0g"),
any_invasive = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0), label = "Subject Chose At Least One Invasive Screening", format.stata = "%9.0g"),
bl_any_invasive = structure(c(1, 1, 1, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Subject Chose At Least One Invasive Screening", format.stata = "%9.0g"),
preind_test = structure(c(0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Indicator for Ex pre Selection - Stacked Subject X Test", format.stata = "%9.0g"),
postind_test = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0), label = "Indicator for Ex Post Selection - Stacked Subject X Test", format.stata = "%9.0g"),
bl_bp_test = structure(c(0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Tag for Blood Pressure Test", format.stata = "%9.0g"),
bl_bmi_test = structure(c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Tag for BMI Test", format.stata = "%9.0g"),
bl_dia_test = structure(c(0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Tag for Diabetes Test", format.stata = "%9.0g"),
bl_flu_test = structure(c(0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Tag for Flu Shot", format.stata = "%9.0g"),
bl_cho_test = structure(c(0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Black Doc * Tag for Cholesterol Test", format.stata = "%9.0g"),
missing_age = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Missing Indicator for age", format.stata = "%9.0g"),
missing_HSless = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Missing Indicator for HSless", format.stata = "%9.0g"),
missing_low_income = structure(c(0, 0, 0, 0, 0, 1, 1, 1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Missing Indicator for low_income", format.stata = "%9.0g"),
missing_has_PCP = structure(c(0, 0, 0, 0, 0, 1, 1, 1, 1,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Missing Indicator for has_PCP", format.stata = "%9.0g"),
missing_uninsured = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0), label = "Missing Indicator for uninsured", format.stata = "%9.0g"),
missing_age2 = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Missing Indicator for age2", format.stata = "%9.0g"),
missing_good_sa_health = structure(c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), label = "Missing Indicator for good_sa_health", format.stata = "%9.0g")), row.names = c(NA,
-20L), class = c("tbl_df", "tbl", "data.frame"), label = "Main Oakland Clinic Analysis Dataset")

I changed geom_bar to geom_histogram and that solved the problem. It's an alternate that works.

weighted mean R

I was wondering how I can get the weighted average of my data. I have already looked on the internet, but when I try the weighted.mean function, I keep getting the same result, so I was wondering what I am doing wrong.
Below is some information of the dataset:
dput(head(new))
structure(list(comp.1 = c(0.5, 0.25, 0, 0.25, 0.31, 0.3), comp.2 = c(0.3,
0.15, 0, 0.15, 0, 0), comp.3 = c(0.2, 0.6, 1, 0.6, 0.69, 0.7),
genderMale = c(0, 1, 1, 1, 0, 0), SeniorCitizen = c(0, 0,
0, 0, 0, 0), PartnerYes = c(1, 0, 0, 0, 0, 0), DependentsYes = c(0,
0, 0, 0, 0, 0), tenure = c(-1.28015700354285, 0.064298112878097,
-1.23941593940889, 0.512449818351747, -1.23941593940889,
-0.994969554605076), MultipleLinesYes = c(0, 0, 0, 0, 0,
1), `InternetServiceFiber optic` = c(0, 0, 0, 0, 1, 1), OnlineSecurityYes = c(0,
1, 1, 1, 0, 0), OnlineBackupYes = c(1, 0, 1, 0, 0, 0), DeviceProtectionYes = c(0,
1, 0, 1, 0, 1), TechSupportYes = c(0, 0, 0, 1, 0, 0), StreamingTVYes = c(0,
0, 0, 0, 0, 1), StreamingMoviesYes = c(0, 0, 0, 0, 0, 1),
`ContractOne year` = c(0, 1, 0, 1, 0, 0), `ContractTwo year` = c(0,
0, 0, 0, 0, 0), PaperlessBillingYes = c(1, 0, 1, 0, 1, 1),
`PaymentMethodCredit card (automatic)` = c(0, 0, 0, 0, 0,
0), `PaymentMethodElectronic check` = c(1, 0, 0, 0, 1, 1),
`PaymentMethodMailed check` = c(0, 1, 1, 0, 0, 0), MonthlyCharges = c(-1.16161133177258,
-0.260859369930086, -0.363897417225722, -0.747797238601399,
0.196164226945719, 1.15840663636787), TotalCharges = c(1.47494433546539,
3.27634689625303, 2.03402652377511, 3.26499480914874, 2.18084241464668,
2.91407858538911)), row.names = c("1", "2", "3", "4", "5",
"6"), class = "data.frame")
As you can see, I have 3 components (comp.1, comp.2, comp.3). All of these components have their posterior probabilities. And I am wondering how I can get the weighted averages for all of these and the final weighted averages. I have tried:
weighted.mean(new$comp.1, new$SeniorCitizen)
weighted.mean(new$comp.2, new$SeniorCitizen)
weighted.mean(new$comp.3, new$SeniorCitizen)
It gave me the output 0.24, 0.14 and 0.61. But irrespectively which variable I put, I get the same output. What am I doing wrong?

convert a data frame into tensor in R

I have a data frame, which contain thousands of firms from year 1998 to 2007(each firm not necessarily have equal length of time duration). and I want to convert it into a tensor with index: firm, year, variables.
how to achieve this ?
I don't know how to extract a small part of this data set to put here for us to discuss the problem, any one know how to do it?
structure(list(year = c(1998, 1998, 1998, 1998, 1998, 1998, 1998,
1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998,
1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998,
1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998,
1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998,
1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998,
1998, 1998, 1998, 1998, 1998, 1998, 1998, 1998), firmid = c("QB3732337",
"113810712", "618851819", "619457768", "HU5176905", "618024813",
"617883552", "105679742", "230141773", "609442909", "HU6355534",
"617882832", "60088847X", "617881725", "618403506", "145665417",
"216582994", "14405557X", "103400293", "607369138", "617737408",
"177160683", "142418787", "245560903", "15112238X", "617880650",
"618354214", "226040099", "128955068", "61156047X", "617481385",
"226091312", "190380322", "617880255", "611567073", "GD6120293",
"617876061", "617875739", "126925703", "221461337", "614801582",
"617883931", "618129447", "101713181", "611209524", "617883974",
"706747835", "242727819", "608934944", "619723894", "139432377",
"152102399", "617866832", "614407067", "607282008", "117944574",
"617865629", "618354185", "228791275", "134789270", "113810632",
"EJ2468142", "169832427", "115319804", "602003890", "211551128",
"193929448", "105044755", "704448663", "21960081X"), provinceid = c(420000,
140000, 440000, 450000, 420000, 440000, 440000, 130000, 650000,
330000, 420000, 440000, 120000, 440000, 440000, 330000, 530000,
330000, 120000, 310000, 440000, 410000, 320000, 230000, 340000,
440000, 440000, 620000, 230000, 350000, 440000, 620000, 440000,
440000, 350000, 440000, 440000, 440000, 220000, 610000, 410000,
440000, 440000, 110000, 350000, 440000, 410000, 210000, 320000,
450000, 320000, 340000, 440000, 410000, 310000, 210000, 440000,
440000, 650000, 320000, 140000, 330000, 370000, 150000, 140000,
510000, 440000, 130000, 330000, 530000), industrycode2 = c(3400,
3500, 2900, 1900, 1500, 2200, 1400, 3600, 1500, 4000, 1500, 3000,
2400, 2100, 1800, 1300, 2900, 4000, 3600, 2300, 1900, 3700, 2200,
3400, 2600, 1800, 2400, 1300, 1800, 2400, 1900, 3100, 1400, 1700,
2400, 3400, 2600, 2600, 1400, 2600, 3100, 1800, 3100, 1400, 2600,
3300, 1300, 2200, 3000, 3100, 4100, 3000, 1500, 1400, 3500, 3500,
3700, 2600, 2300, 3200, 1700, 4000, 4200, 3600, 2500, 1300, 3500,
3600, 1700, 2600), sales = c(45860, 4050, 17034, 154721, 267,
7703, 47572, 846, 267, 5132, 1767, 8354, 5668, 75330, 8935, 1958,
154721, 13072, 10654, 40505, 20637, 1510, 12884, 10753, 45542,
5286, 27492, 267, 1557, 872, 10892, 1386, 32054, 7290, 6903,
8263, 6996, 12848, 460, 44823, 52000, 16353, 6225, 750, 10863,
35110, 10638, 154721, 18100, 16773, 2415, 8686, 14362, 19831,
46958, 1340, 79855, 61817, 1114, 154721, 7030, 9923, 599, 4060,
154721, 361, 72986, 445, 18080, 3682), cogs = c(44780, 2430,
13839, 144088, 246, 9310, 37863, 495, 52, 4170, 1582, 7416, 3964,
58090, 8639, 1667, 211569, 8066, 4960, 28399, 19831, 1280, 12564,
7540, 37058, 1855, 25519, 70, 1539, 700, 10398, 1190, 25048,
6779, 5500, 7656, 6078, 12519, 370, 39479, 26816, 16586, 6061,
534, 10064, 32783, 8519, 308403, 16000, 23833, 1282, 6918, 12097,
15663, 35182, 768, 76005, 58528, 775, 4362410, 5770, 9040, 417,
2630, 167668, 290, 64038, 306, 15898, 2511), inventory = c(2740,
280, 1950, 46914, 711, 9552, 3984, 4989, 497, 1249, 0, 4336,
1450, 3000, 284, 0, 134404, 5881, 9347, 4818, 1744, 377, 376,
12238, 11669, 835, 17355, 226, 1370, 360, 434, 1089, 12154, 4000,
2388, 7257, 1547, 808, 137, 5920, 8750, 5600, 179, 151, 1321,
3454, 5479, 135303, 7480, 5943, 565, 850, 3032, 1207, 11307,
474, 2574, 26104, 519, 604670, 400, 501, 106, 7040, 43568, 711,
6763, 558, 444, 564), fixedasset = c(8580, 460, 6750, 28874,
2878, 25901, 43081, 3065, 198, 1163, 2140, 8484, 1688, 6900,
631, 1290, 849666, 6545, 10075, 6658, 3089, 581, 114, 22299,
22499, 3967, 54033, 1106, 883, 435, 404, 1712, 29329, 7952, 3176,
10272, 533, 138, 854, 14151, 64252, 10672, 5023, 62, 213, 1068,
572, 1115119, 14090, 71451, 13017, 5390, 6657, 5840, 31943, 80,
26145, 41905, 517, 3801800, 1164, 1725, 220, 15550, 72000, 825,
4697, 1913, 735, 3415), totalasset = c(13610, 3220, 16090, 166501,
14319, 44739, 78920, 10394, 823, 4698, 3101, 25325, 4221, 14900,
3118, 1724, 1091978, 28912, 28272, 27222, 10000, 1178, 1413,
42394, 52156, 11284, 89191, 1582, 6514, 3531, 1495, 3978, 54618,
22352, 13733, 27088, 3247, 1450, 1164, 33419, 129957, 18000,
20163, 266, 3728, 15286, 17337, 1718823, 25650, 94590, 15418,
8430, 12425, 10060, 75576, 991, 46436, 75405, 1973, 5976610,
4604, 5720, 1327, 43440, 248715, 1710, 31723, 2799, 4616, 5417
), stateshare = c(0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0,
1, 0.518154501914978, 1, 1, 0, 0, 1, 1, 0.699999988079071, 0.831946730613708,
0, 0, 1, 1, 0, 0, 1, 0.200000002980232, 0, 0, 0, 0, 0, 1, 1,
0.400013834238052, 0, 0, 0.25, 0, 0.400000005960464, 0, 1, 0,
0, 1, 1, 0.823567152023315, 0, 0, 1, 0.742925107479095, 0, 1,
0.661562383174896, 1, 0, 1, 1, 0, 1, 0, 1, 0.749309420585632,
1), foreignshare = c(0.571428596973419, 0, 1, 0.385093629360199,
0, 0.5, 1, 0, 0, 0.30011722445488, 0, 0.699992954730988, 1, 1,
0.5, 0, 0, 0, 0, 1, 0.300029307603836, 0, 0, 0, 0, 0.782930612564087,
1, 0, 0, 1, 1, 0, 0.416000008583069, 1, 0.899999976158142, 1,
1, 1, 0, 0, 0.59998619556427, 0.700012564659119, 0.233907759189606,
0, 1, 0.600000023841858, 1, 0, 0.509767174720764, 0.299807518720627,
0, 0, 0, 1, 0.531239151954651, 0, 0.257074922323227, 1, 0, 0.245536029338837,
0, 0.285785287618637, 0, 0, 0.245354115962982, 0, 0.219982624053955,
0, 0.25069060921669, 0), privateshare = c(0.428571432828903,
0, 0, 0.614906370639801, 0, 0.5, 0, 0, 0, 0.699882745742798,
0, 0.300007075071335, 0, 0, 0.5, 0, 0.481845527887344, 0, 0,
0, 0.699970722198486, 0, 0, 0.300000011920929, 0.168053239583969,
0.217069372534752, 0, 0, 0, 0, 0, 0, 0.38400000333786, 0, 0.100000001490116,
0, 0, 0, 0, 0, 0, 0.299987435340881, 0.766092240810394, 0.75,
0, 0, 0, 0, 0.490232825279236, 0.700192511081696, 0, 0, 0.176432847976685,
0, 0.468760877847672, 0, 0, 0, 0, 0.0929015725851059, 0, 0.714214682579041,
0, 0, 0.754645884037018, 0, 0.780017375946045, 0, 0, 0), stateown = c(0,
1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1,
1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1,
0, 1, 0, 1, 0, 1), foreignown = c(0, 0, 1, 0, 0, 0, 1, 0, 0,
0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), privateown = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), mixown = c(1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0,
1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1,
0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0), stateonly = c(0,
1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1,
1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1,
0, 1, 0, 1, 0, 0), mixonly = c(1, 0, 0, 1, 0, 1, 0, 0, 0, 1,
0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0,
1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0), foreignonly = c(0,
0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0,
0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), privateonly = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), gs = c(0,
1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1,
1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1,
0, 1, 0, 1, 0, 1), gm = c(1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1,
0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1,
0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0,
1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0), gf = c(0, 0,
1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0,
1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), privatize = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), IR = c(0.061188030987978,
0.115226335823536, 0.140906140208244, 0.325592696666718, 2.8902440071106,
1.02599358558655, 0.105221457779408, 3.07859539985657, 3.07859539985657,
0.299520373344421, 0.0191550496965647, 0.584681749343872, 0.365792125463486,
0.0516440011560917, 0.0328741744160652, 0.0191550496965647, 0.635272681713104,
0.729109823703766, 1.8844758272171, 0.169653862714767, 0.0879431217908859,
0.294531255960464, 0.0299267750233412, 1.62307691574097, 0.314884781837463,
0.450134783983231, 0.680081486701965, 3.07859539985657, 0.890188455581665,
0.514285743236542, 0.0417387969791889, 0.915126025676727, 0.485228359699249,
0.590057551860809, 0.434181809425354, 0.947884023189545, 0.254524528980255,
0.0645418986678123, 0.370270282030106, 0.14995314180851, 0.326297730207443,
0.337634146213531, 0.0295330807566643, 0.282771527767181, 0.131259933114052,
0.105359487235546, 0.643150627613068, 0.438721418380737, 0.467500001192093,
0.249360129237175, 0.44071763753891, 0.12286788225174, 0.250640660524368,
0.0770605877041817, 0.321385949850082, 0.6171875, 0.0338661931455135,
0.446008741855621, 0.669677436351776, 0.138609156012535, 0.0693240910768509,
0.0554203540086746, 0.254196643829346, 2.67680597305298, 0.259846836328506,
2.4517240524292, 0.10560917109251, 1.82352936267853, 0.0279280412942171,
0.224611714482307), GM = c(0.0241179093718529, 0.666666686534882,
0.230869278311729, 0.25, 0, -0.0678684562444687, 0.256424486637115,
0.709090888500214, 0.0769230797886848, 0.230695441365242, 0.116940580308437,
0.126483276486397, 0.429868817329407, 0.296780854463577, 0.0342632234096527,
0.174565091729164, 0.25985848903656, 0.620629787445068, 0.807692289352417,
0.426282614469528, 0.0406434386968613, 0.1796875, 0.0254695955663919,
0.426127314567566, 0.228938415646553, 0.807692289352417, 0.0773149430751801,
-0.0678684562444687, 0.0116959065198898, 0.245714291930199, 0.0475091375410557,
0.164705887436867, 0.279702961444855, 0.0753798484802246, 0.255090922117233,
0.0792842209339142, 0.151036530733109, 0.0262800548225641, 0.243243247270584,
0.135363101959229, 0.807692289352417, -0.0140479924157262, 0.0270582418888807,
0.404494374990463, 0.0793918892741203, 0.0709819123148918, 0.24873811006546,
0.0649604573845863, 0.131249994039536, -0.0678684562444687, 0.807692289352417,
0.255565196275711, 0.187236502766609, 0.266104847192764, 0.334716618061066,
0.744791686534882, 0.0506545640528202, 0.0561953261494637, 0.437419354915619,
0.0327548310160637, 0.218370884656906, 0.0976769924163818, 0.436450839042664,
0.54372626543045, 0.595140397548676, 0.244827583432198, 0.139729529619217,
0.454248368740082, 0.137249961495399, 0.466348081827164), CI = c(0.630418837070465,
0.142857149243355, 0.41951522231102, 0.173416376113892, 0.200991690158844,
0.578935623168945, 0.545881927013397, 0.294881671667099, 0.240583226084709,
0.247552156448364, 0.690099954605103, 0.335004925727844, 0.399905234575272,
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0.351559728384018, 0.605812251567841, 0.699115037918091, 0.135554194450378,
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0.755375862121582, 0.780426323413849, 0.639383137226105, 0.535774648189545,
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0.555732369422913, 0.262037515640259, 0.636113107204437, 0.252823621034622,
0.301573425531387, 0.165787488222122, 0.357965022325516, 0.289487957954407,
0.482456147670746, 0.148062914609909, 0.683458387851715, 0.159228771924973,
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0.166220098733902, 0.0178635232150555, 0.175039649009705, 0.0304596647620201
), Salesgrowth = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_,
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"firmid", "provinceid", "industrycode2", "sales", "cogs", "inventory",
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"stateown", "foreignown", "privateown", "mixown", "stateonly",
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70L), class = "data.frame")
data added, now I know I can change a three modes array into a three modes tensor, so we could also consider how to change the current data frame to a three modes array with dimensions "firmid","year",and "all other co-variates except these two"
Or image a three dimensions reference system X-Y-Z, I want X to be firms, Y to be co-variates, Z to be years
any suggestions?

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