Arquivos
#Retirando um data frame da memoria
hospay <- NULL
#Lendo o arquivo no padrão csv com separador ; e criando o data frame nf
# hospay <- read.csv(file="./dados/Hospital.csv", sep=";",stringsAsFactors = FALSE, na.strings = c(""," "))
hospay <- read.csv(file="https://raw.githubusercontent.com/flaviobrito/dataudit/master/dados/Hospital.csv", sep=";",stringsAsFactors = FALSE, na.strings = c(""," "))
#Estrutura do Arquivo
str(hospay)
## 'data.frame': 14454 obs. of 23 variables:
## $ Provider.ID : int 10005 10005 10005 10032 10032 10032 10131 10131 10131 20001 ...
## $ Hospital.name : chr "MARSHALL MEDICAL CENTER SOUTH" "MARSHALL MEDICAL CENTER SOUTH" "MARSHALL MEDICAL CENTER SOUTH" "WEDOWEE HOSPITAL" ...
## $ Address : chr "2505 U S HIGHWAY 431 NORTH" "2505 U S HIGHWAY 431 NORTH" "2505 U S HIGHWAY 431 NORTH" "209 NORTH MAIN STREET" ...
## $ City : chr "BOAZ" "BOAZ" "BOAZ" "WEDOWEE" ...
## $ State : chr "AL" "AL" "AL" "AL" ...
## $ ZIP.Code : int 35957 35957 35957 36278 36278 36278 35801 35801 35801 99508 ...
## $ County.name : chr "MARSHALL" "MARSHALL" "MARSHALL" "RANDOLPH" ...
## $ Phone.number : num 2.57e+09 2.57e+09 2.57e+09 2.56e+09 2.56e+09 ...
## $ Payment.measure.name : chr "Payment for heart attack patients" "Payment for heart failure patients" "Payment for pneumonia patients" "Payment for heart attack patients" ...
## $ Payment.measure.ID : chr "PAYM_30_AMI" "PAYM_30_HF" "PAYM_30_PN" "PAYM_30_AMI" ...
## $ Payment.category : chr "No Different than the National Average Payment" "No Different than the National Average Payment" "No Different than the National Average Payment" "Number of Cases Too Small" ...
## $ Denominator : chr "53" "347" "646" "Not Available" ...
## $ Payment : chr "$23171.00" "$16376.00" "$14384.00" NA ...
## $ Lower.estimate : chr "$20,404" "$15,237" "$13,642" "Not Available" ...
## $ Higher.estimate : chr "$26,226" "$17,547" "$15,118" "Not Available" ...
## $ Payment.footnote : chr NA NA NA "1 - The number of cases/patients is too few to report." ...
## $ Value.of.care.display.name: chr "Value of Care Heart Attack measure" "Value of Care Heart Failure measure" "Value of Care Pneumonia measure" "Value of Care Heart Attack measure" ...
## $ Value.of.care.display.ID : chr "MORT_PAYM_30_AMI" "MORT_PAYM_30_HF" "MORT_PAYM_30_PN" "MORT_PAYM_30_AMI" ...
## $ Value.of.care.category : chr "Average mortality and average payment" "Worse mortality and average payment" "Worse mortality and average payment" "Not Available" ...
## $ Value.of.care.footnote : chr NA NA NA "13 - Results cannot be calculated for this reporting period." ...
## $ Measure.start.date : chr "07/01/2012" "07/01/2012" "07/01/2012" "07/01/2012" ...
## $ Measure.End.Date : chr "06/30/2015" "06/30/2015" "06/30/2015" "06/30/2015" ...
## $ Location : chr "2505 U S HIGHWAY 431 NORTH\nBOAZ, AL 35957\n" "2505 U S HIGHWAY 431 NORTH\nBOAZ, AL 35957\n" "2505 U S HIGHWAY 431 NORTH\nBOAZ, AL 35957\n" "209 NORTH MAIN STREET\nWEDOWEE, AL 36278\n" ...
#Resumo dos Dados
summary(hospay$Measure.start.date)
## Length Class Mode
## 14454 character character
summary(hospay$Measure.End.Date)
## Length Class Mode
## 14454 character character
#Lista as categorias em um
unique(hospay$Measure.start.date) %>% head
## Error in eval(expr, envir, enclos): não foi possível encontrar a função "%>%"
levels(hospay$Measure.End.Date) %>% head
## Error in eval(expr, envir, enclos): não foi possível encontrar a função "%>%"
#Checando
hospay$Measure.start.date <- trimws(hospay$Measure.start.date, which = "both")
hospay$Measure.End.Date <- trimws(hospay$Measure.End.Date, which = "both")
#Datas com Missing
any(is.na(as.character(hospay$Measure.start.date)))
## [1] FALSE
any(is.na(as.character(hospay$Measure.End.Date)))
## [1] FALSE
#Pagamentos com Missing
any(is.na(as.numeric(hospay$Payment)))
## Warning: NAs introduzidos por coerção
## [1] TRUE
#Tratando data
hospay$Measure.start.date <- as.Date(hospay$Measure.start.date,"%m/%d/%Y" )
hospay$Measure.End.Date <- as.Date(hospay$Measure.End.Date,"%m/%d/%Y" )
class(hospay$Measure.start.date)
## [1] "Date"
class(hospay$Measure.End.Date)
## [1] "Date"
str(hospay$Measure.start.date)
## Date[1:14454], format: "2012-07-01" "2012-07-01" "2012-07-01" "2012-07-01" ...
str(hospay$Measure.End.Date)
## Date[1:14454], format: "2015-06-30" "2015-06-30" "2015-06-30" "2015-06-30" ...
# Tratamento de valores em Strings
# Remover sinal de dollar e vírgula
hospay$Payment <- as.numeric(gsub("[$,]", "", hospay$Payment))
hospay$Lower.estimate <- as.numeric(gsub("[$,]", "", hospay$Lower.estimate))
## Warning: NAs introduzidos por coerção
hospay$Higher.estimate <-as.numeric(gsub("[$,]", "", hospay$Lower.estimate))
#Limpa os brancos
hospay$Hospital.name<- gsub("[ ]", "", hospay$Hospital.name)
#Qual o total de hospitais prentes na lista
length(hospay$Hospital.name)
## [1] 14454
#Quantos hospitais
length((unique(hospay$Hospital.name)))
## [1] 4615
#Quais os hostitais
unique(hospay$Hospital.name) %>% head
## Error in eval(expr, envir, enclos): não foi possível encontrar a função "%>%"
#Pivot
# Contagem de casos por Hospital
pv1 <-data.frame(table(hospay$City, hospay$Value.of.care.category, hospay$State))
table(hospay$City, hospay$State) %>% head
## Error in eval(expr, envir, enclos): não foi possível encontrar a função "%>%"
#Frequencia
pv1 <- NULL
pv1 <- with(hospay,table(hospay$State))
pv1.freq <- table(hospay$State)
pv1.prob <- prop.table(pv1.freq)
pv1.out <- cbind(pv1.freq, pv1.prob)
#Mudando o nome das colunas
colnames(pv1.out) <-c("Freq","Perc(%)")
#Frequencia por faixa - dados numéricos
faixa1 <-cut(hospay$Payment, breaks = 10,dig.lab = 10,include.lowest = TRUE)
stack(table(faixa1)) #transpoe
## values ind
## 1 34 [9481.168,11585.2]
## 2 856 (11585.2,13668.4]
## 3 3861 (13668.4,15751.6]
## 4 2314 (15751.6,17834.8]
## 5 475 (17834.8,19918]
## 6 650 (19918,22001.2]
## 7 1130 (22001.2,24084.4]
## 8 490 (24084.4,26167.6]
## 9 64 (26167.6,28250.8]
## 10 6 (28250.8,30354.832]
faixa1 <-cut(hospay$Payment, seq(from = 1000, to = 40000, by = 5000),dig.lab = 10,include.lowest = TRUE,right = TRUE)
stack(table(faixa1)) #transpoe
## values ind
## 1 0 [1000,6000]
## 2 6 (6000,11000]
## 3 5211 (11000,16000]
## 4 2539 (16000,21000]
## 5 2043 (21000,26000]
## 6 81 (26000,31000]
## 7 0 (31000,36000]
faixa1 <-cut(hospay$Payment, seq(1000, 40000, 5000),dig.lab = 10,right = FALSE)
stack(table(faixa1)) #transpoe
## values ind
## 1 0 [1000,6000)
## 2 6 [6000,11000)
## 3 5211 [11000,16000)
## 4 2539 [16000,21000)
## 5 2043 [21000,26000)
## 6 81 [26000,31000)
## 7 0 [31000,36000)
#include.lowest = TRUE inclui o menor
faixa2 <-cut(hospay$Payment, breaks = 10, dig.lab = 10,labels=c("A","B", "C", "D", "E", "F", "G", "H", "I", "J")) # força os labels
summary(faixa2)
## A B C D E F G H I J NA's
## 34 856 3861 2314 475 650 1130 490 64 6 4574
## values ind
## 1 34 A
## 2 856 B
## 3 3861 C
## 4 2314 D
## 5 475 E
## 6 650 F
## 7 1130 G
## 8 490 H
## 9 64 I
## 10 6 J
## 11 4574 NA's
faixa2 <-cut(hospay$Payment, breaks = 10, dig.lab = 10,labels=c(1:10)) # força os labels
summary(faixa2)
## 1 2 3 4 5 6 7 8 9 10 NA's
## 34 856 3861 2314 475 650 1130 490 64 6 4574
## values ind
## 1 34 1
## 2 856 2
## 3 3861 3
## 4 2314 4
## 5 475 5
## 6 650 6
## 7 1130 7
## 8 490 8
## 9 64 9
## 10 6 10
## 11 4574 NA's
faixa <-cut(hospay$Payment, breaks = c(100,10000,20000, 30000), labels=c("medio", "maior", "avaliar"))
stack(table(faixa)) #transpoe
## values ind
## 1 1 medio
## 2 7554 maior
## 3 2324 avaliar
#Steam and Leaf - dados num?ricos
stem(hospay$Payment)
##
## The decimal point is 3 digit(s) to the right of the |
##
## 9 | 5
## 10 | 22478
## 11 | 01112222233344444444555555566666666677788888888889999999999999
## 12 | 00000000000000001111111111111111122222222222222222222333333333333333+194
## 13 | 00000000000000000000000000000000000000000000000000011111111111111111+861
## 14 | 00000000000000000000000000000000000000000000000000000000000000000000+1712
## 15 | 00000000000000000000000000000000000000000000000000000000000000000000+1972
## 16 | 00000000000000000000000000000000000000000000000000000000000000000000+1258
## 17 | 00000000000000000000000000000000000000000000000000000000000000000000+578
## 18 | 00000000000000000000000000000000000000000000011111111111111111111111+208
## 19 | 00000000000000000001111111111112222222222222333333333333344444444444+59
## 20 | 00000000011111111111111111111111111222222222222222223333333333333333+113
## 21 | 00000000000000000000000000000111111111111111111111111111111111222222+343
## 22 | 00000000000000000000000000000000000000000000000000000000111111111111+484
## 23 | 00000000000000000000000000000000000000000000000000000000111111111111+460
## 24 | 00000000000000000000000000000000000000011111111111111111111111111111+280
## 25 | 00000000000000000000000111111111111111111111111122222222222222222222+83
## 26 | 00000000001111122222222333333333344444444555566666666777788999
## 27 | 0000222345567778889
## 28 | 555
## 29 | 25
## 30 | 3
plot(density(na.omit(hospay$Payment)))
#Boxplot
bl <-boxplot(na.omit(hospay$Payment))
which(hospay$Payment %in% bl$out)
## [1] 95 133 260 300 399 494 500 506 509 515 661
## [12] 831 976 1041 1267 1348 1403 1449 1501 1572 1716 1772
## [23] 1808 1857 1991 2065 2086 2114 2154 2210 2213 2215 2259
## [34] 2295 2310 2338 2390 2478 2566 2580 2582 2875 3072 3099
## [45] 3156 3270 3282 3330 3589 3796 3983 4132 4148 4159 4166
## [56] 4336 4377 4411 4424 4603 4613 4677 4763 4937 5238 5306
## [67] 5532 5617 5938 6404 6709 6755 7092 7303 7488 7518 7553
## [78] 7603 7614 7783 7869 7988 8178 8191 8287 8521 8567 8570
## [89] 8578 8584 8625 8645 8649 8658 8661 8691 8694 8714 8756
## [100] 8791 8838 8853 8871 8950 8972 9073 9172 9207 9224 9269
## [111] 9270 9273 9318 9324 9376 9405 9541 9583 9698 9732 9811
## [122] 10069 10137 10271 10404 10636 10694 11373 12168 12285 12296 12319
## [133] 12350 12376 12378 12779 12848 12912 12913 12947 12978 12996 13048
## [144] 13085 13171 13310 13644 13699 13837 13860 13953 13990 14030
## [1] 25411
## [1] 25411 27718 25586 25429 26241 25674 25718 26316 26043 25795 27835
## [12] 25556 26159 25489 25466 25878 25941 25690 26166 25530 26186 26619
## [23] 26060 25464 30334 25656 27770 27042 25510 26674 25521 25635 26266
## [34] 27547 27152 25450 27758 27347 26395 25606 25913 27004 26731 25917
## [45] 26642 26325 25635 25408 26880 28451 26995 26044 25807 26473 25751
## [56] 26346 25455 26375 25654 26850 26278 26583 26913 28468 25579 26433
## [67] 27360 25692 25756 25985 25987 26184 27236 26014 27220 25928 26317
## [78] 26280 25856 26427 25756 26106 26200 27480 25966 25515 25582 25895
## [89] 25933 25979 25581 25529 26654 25507 26183 25428 27683 25869 26184
## [100] 25799 25527 27931 25662 25458 26999 26810 26604 25932 26459 26101
## [111] 26562 26438 26379 26110 25829 25978 26112 25720 25907 26332 25988
## [122] 25941 25624 25893 25910 25545 25704 26415 25391 26539 27743 26260
## [133] 26347 26596 26726 25932 26483 29548 25385 28454 25495 25457 25790
## [144] 26406 25701 29193 26759 25400 25625 26565 26001 26614 27616
hospay$Payment[which(hospay$Payment %in% bl$out)]
## [1] 25411 27718 25586 25429 26241 25674 25718 26316 26043 25795 27835
## [12] 25556 26159 25489 25466 25878 25941 25690 26166 25530 26186 26619
## [23] 26060 25464 30334 25656 27770 27042 25510 26674 25521 25635 26266
## [34] 27547 27152 25450 27758 27347 26395 25606 25913 27004 26731 25917
## [45] 26642 26325 25635 25408 26880 28451 26995 26044 25807 26473 25751
## [56] 26346 25455 26375 25654 26850 26278 26583 26913 28468 25579 26433
## [67] 27360 25692 25756 25985 25987 26184 27236 26014 27220 25928 26317
## [78] 26280 25856 26427 25756 26106 26200 27480 25966 25515 25582 25895
## [89] 25933 25979 25581 25529 26654 25507 26183 25428 27683 25869 26184
## [100] 25799 25527 27931 25662 25458 26999 26810 26604 25932 26459 26101
## [111] 26562 26438 26379 26110 25829 25978 26112 25720 25907 26332 25988
## [122] 25941 25624 25893 25910 25545 25704 26415 25391 26539 27743 26260
## [133] 26347 26596 26726 25932 26483 29548 25385 28454 25495 25457 25790
## [144] 26406 25701 29193 26759 25400 25625 26565 26001 26614 27616
#Agregação
with(hospay, by(hospay$Payment, list(hospay$Payment.category,hospay$State), mean))
## : Greater than the National Average Payment
## : AK
## [1] NA
## --------------------------------------------------------
## : Less than the National Average Payment
## : AK
## [1] 12421.77
## --------------------------------------------------------
## : No Different than the National Average Payment
## : AK
## [1] 18001.92
## --------------------------------------------------------
## : Not Available
## : AK
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : AK
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : AL
## [1] 17285.4
## --------------------------------------------------------
## : Less than the National Average Payment
## : AL
## [1] 14089.67
## --------------------------------------------------------
## : No Different than the National Average Payment
## : AL
## [1] 16991.26
## --------------------------------------------------------
## : Not Available
## : AL
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : AL
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : AR
## [1] 18900
## --------------------------------------------------------
## : Less than the National Average Payment
## : AR
## [1] 13911.93
## --------------------------------------------------------
## : No Different than the National Average Payment
## : AR
## [1] 16608.69
## --------------------------------------------------------
## : Not Available
## : AR
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : AR
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : AS
## [1] NA
## --------------------------------------------------------
## : Less than the National Average Payment
## : AS
## [1] NA
## --------------------------------------------------------
## : No Different than the National Average Payment
## : AS
## [1] NA
## --------------------------------------------------------
## : Not Available
## : AS
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : AS
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : AZ
## [1] 19012.15
## --------------------------------------------------------
## : Less than the National Average Payment
## : AZ
## [1] 13673.56
## --------------------------------------------------------
## : No Different than the National Average Payment
## : AZ
## [1] 17926.23
## --------------------------------------------------------
## : Not Available
## : AZ
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : AZ
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : CA
## [1] 19526.76
## --------------------------------------------------------
## : Less than the National Average Payment
## : CA
## [1] 14102.07
## --------------------------------------------------------
## : No Different than the National Average Payment
## : CA
## [1] 17927.4
## --------------------------------------------------------
## : Not Available
## : CA
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : CA
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : CO
## [1] 19856.8
## --------------------------------------------------------
## : Less than the National Average Payment
## : CO
## [1] 14245.82
## --------------------------------------------------------
## : No Different than the National Average Payment
## : CO
## [1] 16897.44
## --------------------------------------------------------
## : Not Available
## : CO
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : CO
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : CT
## [1] 18467.96
## --------------------------------------------------------
## : Less than the National Average Payment
## : CT
## [1] 13376
## --------------------------------------------------------
## : No Different than the National Average Payment
## : CT
## [1] 18478.34
## --------------------------------------------------------
## : Not Available
## : CT
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : CT
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : DC
## [1] 19980
## --------------------------------------------------------
## : Less than the National Average Payment
## : DC
## [1] 14614
## --------------------------------------------------------
## : No Different than the National Average Payment
## : DC
## [1] 17993.81
## --------------------------------------------------------
## : Not Available
## : DC
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : DC
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : DE
## [1] 19066.43
## --------------------------------------------------------
## : Less than the National Average Payment
## : DE
## [1] 14277
## --------------------------------------------------------
## : No Different than the National Average Payment
## : DE
## [1] 18641
## --------------------------------------------------------
## : Not Available
## : DE
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : DE
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : FL
## [1] 18734.56
## --------------------------------------------------------
## : Less than the National Average Payment
## : FL
## [1] 15144.15
## --------------------------------------------------------
## : No Different than the National Average Payment
## : FL
## [1] 18339.71
## --------------------------------------------------------
## : Not Available
## : FL
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : FL
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : GA
## [1] 17076
## --------------------------------------------------------
## : Less than the National Average Payment
## : GA
## [1] 14434.22
## --------------------------------------------------------
## : No Different than the National Average Payment
## : GA
## [1] 17038.91
## --------------------------------------------------------
## : Not Available
## : GA
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : GA
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : GU
## [1] NA
## --------------------------------------------------------
## : Less than the National Average Payment
## : GU
## [1] 12149.67
## --------------------------------------------------------
## : No Different than the National Average Payment
## : GU
## [1] NA
## --------------------------------------------------------
## : Not Available
## : GU
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : GU
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : HI
## [1] NA
## --------------------------------------------------------
## : Less than the National Average Payment
## : HI
## [1] 15432.78
## --------------------------------------------------------
## : No Different than the National Average Payment
## : HI
## [1] 16613.5
## --------------------------------------------------------
## : Not Available
## : HI
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : HI
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : IA
## [1] 18486.14
## --------------------------------------------------------
## : Less than the National Average Payment
## : IA
## [1] 14022.19
## --------------------------------------------------------
## : No Different than the National Average Payment
## : IA
## [1] 16551.7
## --------------------------------------------------------
## : Not Available
## : IA
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : IA
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : ID
## [1] 18422.67
## --------------------------------------------------------
## : Less than the National Average Payment
## : ID
## [1] 13592.5
## --------------------------------------------------------
## : No Different than the National Average Payment
## : ID
## [1] 16271.21
## --------------------------------------------------------
## : Not Available
## : ID
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : ID
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : IL
## [1] 19709.63
## --------------------------------------------------------
## : Less than the National Average Payment
## : IL
## [1] 13980.79
## --------------------------------------------------------
## : No Different than the National Average Payment
## : IL
## [1] 17290.41
## --------------------------------------------------------
## : Not Available
## : IL
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : IL
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : IN
## [1] 18901.9
## --------------------------------------------------------
## : Less than the National Average Payment
## : IN
## [1] 15603.47
## --------------------------------------------------------
## : No Different than the National Average Payment
## : IN
## [1] 17187.11
## --------------------------------------------------------
## : Not Available
## : IN
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : IN
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : KS
## [1] 18664.09
## --------------------------------------------------------
## : Less than the National Average Payment
## : KS
## [1] 13865.12
## --------------------------------------------------------
## : No Different than the National Average Payment
## : KS
## [1] 16484.98
## --------------------------------------------------------
## : Not Available
## : KS
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : KS
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : KY
## [1] 18545.82
## --------------------------------------------------------
## : Less than the National Average Payment
## : KY
## [1] 13187.05
## --------------------------------------------------------
## : No Different than the National Average Payment
## : KY
## [1] 17354.3
## --------------------------------------------------------
## : Not Available
## : KY
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : KY
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : LA
## [1] 17996.38
## --------------------------------------------------------
## : Less than the National Average Payment
## : LA
## [1] 15541.38
## --------------------------------------------------------
## : No Different than the National Average Payment
## : LA
## [1] 17134.23
## --------------------------------------------------------
## : Not Available
## : LA
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : LA
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : MA
## [1] 19177.19
## --------------------------------------------------------
## : Less than the National Average Payment
## : MA
## [1] 16682.38
## --------------------------------------------------------
## : No Different than the National Average Payment
## : MA
## [1] 17873.73
## --------------------------------------------------------
## : Not Available
## : MA
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : MA
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : MD
## [1] NA
## --------------------------------------------------------
## : Less than the National Average Payment
## : MD
## [1] NA
## --------------------------------------------------------
## : No Different than the National Average Payment
## : MD
## [1] NA
## --------------------------------------------------------
## : Not Available
## : MD
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : MD
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : ME
## [1] 20552
## --------------------------------------------------------
## : Less than the National Average Payment
## : ME
## [1] 15176.4
## --------------------------------------------------------
## : No Different than the National Average Payment
## : ME
## [1] 17161.68
## --------------------------------------------------------
## : Not Available
## : ME
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : ME
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : MI
## [1] 18154.17
## --------------------------------------------------------
## : Less than the National Average Payment
## : MI
## [1] 14686.7
## --------------------------------------------------------
## : No Different than the National Average Payment
## : MI
## [1] 17030.3
## --------------------------------------------------------
## : Not Available
## : MI
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : MI
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : MN
## [1] 19346.8
## --------------------------------------------------------
## : Less than the National Average Payment
## : MN
## [1] 14003.88
## --------------------------------------------------------
## : No Different than the National Average Payment
## : MN
## [1] 16028.23
## --------------------------------------------------------
## : Not Available
## : MN
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : MN
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : MO
## [1] 18579.87
## --------------------------------------------------------
## : Less than the National Average Payment
## : MO
## [1] 14045.55
## --------------------------------------------------------
## : No Different than the National Average Payment
## : MO
## [1] 17288.05
## --------------------------------------------------------
## : Not Available
## : MO
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : MO
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : MP
## [1] NA
## --------------------------------------------------------
## : Less than the National Average Payment
## : MP
## [1] 13827
## --------------------------------------------------------
## : No Different than the National Average Payment
## : MP
## [1] NA
## --------------------------------------------------------
## : Not Available
## : MP
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : MP
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : MS
## [1] 20148.77
## --------------------------------------------------------
## : Less than the National Average Payment
## : MS
## [1] 14244
## --------------------------------------------------------
## : No Different than the National Average Payment
## : MS
## [1] 16124.33
## --------------------------------------------------------
## : Not Available
## : MS
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : MS
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : MT
## [1] NA
## --------------------------------------------------------
## : Less than the National Average Payment
## : MT
## [1] 12893.35
## --------------------------------------------------------
## : No Different than the National Average Payment
## : MT
## [1] 16188.4
## --------------------------------------------------------
## : Not Available
## : MT
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : MT
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : NC
## [1] 16522.43
## --------------------------------------------------------
## : Less than the National Average Payment
## : NC
## [1] 15710.36
## --------------------------------------------------------
## : No Different than the National Average Payment
## : NC
## [1] 17070.78
## --------------------------------------------------------
## : Not Available
## : NC
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : NC
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : ND
## [1] 17798
## --------------------------------------------------------
## : Less than the National Average Payment
## : ND
## [1] 12913.4
## --------------------------------------------------------
## : No Different than the National Average Payment
## : ND
## [1] 16295.76
## --------------------------------------------------------
## : Not Available
## : ND
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : ND
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : NE
## [1] 20016.38
## --------------------------------------------------------
## : Less than the National Average Payment
## : NE
## [1] 13288.14
## --------------------------------------------------------
## : No Different than the National Average Payment
## : NE
## [1] 16508.75
## --------------------------------------------------------
## : Not Available
## : NE
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : NE
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : NH
## [1] 19609.62
## --------------------------------------------------------
## : Less than the National Average Payment
## : NH
## [1] 13108
## --------------------------------------------------------
## : No Different than the National Average Payment
## : NH
## [1] 17455.17
## --------------------------------------------------------
## : Not Available
## : NH
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : NH
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : NJ
## [1] 20405.62
## --------------------------------------------------------
## : Less than the National Average Payment
## : NJ
## [1] 14790
## --------------------------------------------------------
## : No Different than the National Average Payment
## : NJ
## [1] 18241.14
## --------------------------------------------------------
## : Not Available
## : NJ
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : NJ
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : NM
## [1] 17426.5
## --------------------------------------------------------
## : Less than the National Average Payment
## : NM
## [1] 13022.43
## --------------------------------------------------------
## : No Different than the National Average Payment
## : NM
## [1] 16578.12
## --------------------------------------------------------
## : Not Available
## : NM
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : NM
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : NV
## [1] 19818.52
## --------------------------------------------------------
## : Less than the National Average Payment
## : NV
## [1] 14664.91
## --------------------------------------------------------
## : No Different than the National Average Payment
## : NV
## [1] 18621.17
## --------------------------------------------------------
## : Not Available
## : NV
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : NV
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : NY
## [1] 19564.24
## --------------------------------------------------------
## : Less than the National Average Payment
## : NY
## [1] 14285.02
## --------------------------------------------------------
## : No Different than the National Average Payment
## : NY
## [1] 17861.65
## --------------------------------------------------------
## : Not Available
## : NY
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : NY
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : OH
## [1] 18643.94
## --------------------------------------------------------
## : Less than the National Average Payment
## : OH
## [1] 14489.12
## --------------------------------------------------------
## : No Different than the National Average Payment
## : OH
## [1] 17596.61
## --------------------------------------------------------
## : Not Available
## : OH
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : OH
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : OK
## [1] 17866.17
## --------------------------------------------------------
## : Less than the National Average Payment
## : OK
## [1] 14104.3
## --------------------------------------------------------
## : No Different than the National Average Payment
## : OK
## [1] 16377.65
## --------------------------------------------------------
## : Not Available
## : OK
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : OK
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : OR
## [1] NA
## --------------------------------------------------------
## : Less than the National Average Payment
## : OR
## [1] 13935.4
## --------------------------------------------------------
## : No Different than the National Average Payment
## : OR
## [1] 16347.31
## --------------------------------------------------------
## : Not Available
## : OR
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : OR
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : PA
## [1] 18843.46
## --------------------------------------------------------
## : Less than the National Average Payment
## : PA
## [1] 15068.27
## --------------------------------------------------------
## : No Different than the National Average Payment
## : PA
## [1] 17942.4
## --------------------------------------------------------
## : Not Available
## : PA
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : PA
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : PR
## [1] NA
## --------------------------------------------------------
## : Less than the National Average Payment
## : PR
## [1] 14970.08
## --------------------------------------------------------
## : No Different than the National Average Payment
## : PR
## [1] 15669.31
## --------------------------------------------------------
## : Not Available
## : PR
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : PR
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : RI
## [1] 21401.75
## --------------------------------------------------------
## : Less than the National Average Payment
## : RI
## [1] 17751
## --------------------------------------------------------
## : No Different than the National Average Payment
## : RI
## [1] 17813.67
## --------------------------------------------------------
## : Not Available
## : RI
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : RI
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : SC
## [1] 18163.12
## --------------------------------------------------------
## : Less than the National Average Payment
## : SC
## [1] 15075.52
## --------------------------------------------------------
## : No Different than the National Average Payment
## : SC
## [1] 17068.56
## --------------------------------------------------------
## : Not Available
## : SC
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : SC
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : SD
## [1] 20059
## --------------------------------------------------------
## : Less than the National Average Payment
## : SD
## [1] 13424.73
## --------------------------------------------------------
## : No Different than the National Average Payment
## : SD
## [1] 16392.89
## --------------------------------------------------------
## : Not Available
## : SD
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : SD
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : TN
## [1] 17335.92
## --------------------------------------------------------
## : Less than the National Average Payment
## : TN
## [1] 14710.6
## --------------------------------------------------------
## : No Different than the National Average Payment
## : TN
## [1] 16950.76
## --------------------------------------------------------
## : Not Available
## : TN
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : TN
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : TX
## [1] 18977.61
## --------------------------------------------------------
## : Less than the National Average Payment
## : TX
## [1] 14723.82
## --------------------------------------------------------
## : No Different than the National Average Payment
## : TX
## [1] 17670.5
## --------------------------------------------------------
## : Not Available
## : TX
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : TX
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : UT
## [1] 20331.2
## --------------------------------------------------------
## : Less than the National Average Payment
## : UT
## [1] 12094.86
## --------------------------------------------------------
## : No Different than the National Average Payment
## : UT
## [1] 16891.07
## --------------------------------------------------------
## : Not Available
## : UT
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : UT
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : VA
## [1] 18996.94
## --------------------------------------------------------
## : Less than the National Average Payment
## : VA
## [1] 15724.21
## --------------------------------------------------------
## : No Different than the National Average Payment
## : VA
## [1] 17360.43
## --------------------------------------------------------
## : Not Available
## : VA
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : VA
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : VI
## [1] 17791
## --------------------------------------------------------
## : Less than the National Average Payment
## : VI
## [1] NA
## --------------------------------------------------------
## : No Different than the National Average Payment
## : VI
## [1] 16387
## --------------------------------------------------------
## : Not Available
## : VI
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : VI
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : VT
## [1] 18970
## --------------------------------------------------------
## : Less than the National Average Payment
## : VT
## [1] 13326.33
## --------------------------------------------------------
## : No Different than the National Average Payment
## : VT
## [1] 17343.29
## --------------------------------------------------------
## : Not Available
## : VT
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : VT
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : WA
## [1] 21047.18
## --------------------------------------------------------
## : Less than the National Average Payment
## : WA
## [1] 15127.11
## --------------------------------------------------------
## : No Different than the National Average Payment
## : WA
## [1] 16767.32
## --------------------------------------------------------
## : Not Available
## : WA
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : WA
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : WI
## [1] 20329.8
## --------------------------------------------------------
## : Less than the National Average Payment
## : WI
## [1] 13277.52
## --------------------------------------------------------
## : No Different than the National Average Payment
## : WI
## [1] 16720.6
## --------------------------------------------------------
## : Not Available
## : WI
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : WI
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : WV
## [1] 21015.4
## --------------------------------------------------------
## : Less than the National Average Payment
## : WV
## [1] 14734.67
## --------------------------------------------------------
## : No Different than the National Average Payment
## : WV
## [1] 16638.51
## --------------------------------------------------------
## : Not Available
## : WV
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : WV
## [1] NA
## --------------------------------------------------------
## : Greater than the National Average Payment
## : WY
## [1] 18226
## --------------------------------------------------------
## : Less than the National Average Payment
## : WY
## [1] 12828.78
## --------------------------------------------------------
## : No Different than the National Average Payment
## : WY
## [1] 15584.72
## --------------------------------------------------------
## : Not Available
## : WY
## [1] NA
## --------------------------------------------------------
## : Number of Cases Too Small
## : WY
## [1] NA
with(hospay, table(hospay$Payment.category,hospay$State))
##
## AK AL AR AS AZ CA
## Greater than the National Average Payment 0 5 18 0 26 176
## Less than the National Average Payment 13 48 15 0 16 59
## No Different than the National Average Payment 12 144 127 0 117 496
## Not Available 6 12 15 3 42 101
## Number of Cases Too Small 32 58 50 0 42 203
##
## CO CT DC DE FL GA
## Greater than the National Average Payment 5 24 3 7 135 5
## Less than the National Average Payment 11 1 2 1 13 58
## No Different than the National Average Payment 126 59 16 10 356 230
## Not Available 20 7 3 3 26 21
## Number of Cases Too Small 72 2 0 0 31 88
##
## GU HI IA ID IL IN
## Greater than the National Average Payment 0 0 21 3 67 29
## Less than the National Average Payment 3 9 32 10 39 17
## No Different than the National Average Payment 0 26 161 47 339 233
## Not Available 3 24 24 12 13 27
## Number of Cases Too Small 0 10 110 51 79 54
##
## KS KY LA MA MD ME
## Greater than the National Average Payment 34 17 32 32 0 3
## Less than the National Average Payment 17 37 29 8 0 20
## No Different than the National Average Payment 147 166 158 124 0 68
## Not Available 61 7 82 17 147 0
## Number of Cases Too Small 152 55 68 11 0 8
##
## MI MN MO MP MS MT
## Greater than the National Average Payment 30 5 31 0 22 0
## Less than the National Average Payment 33 16 29 3 28 17
## No Different than the National Average Payment 236 166 184 0 129 48
## Not Available 27 31 41 0 40 38
## Number of Cases Too Small 67 175 57 0 69 80
##
## NC ND NE NH NJ NM
## Greater than the National Average Payment 7 5 21 8 111 2
## Less than the National Average Payment 61 5 7 1 1 30
## No Different than the National Average Payment 199 51 102 59 78 48
## Not Available 16 8 32 0 6 5
## Number of Cases Too Small 35 63 108 10 2 38
##
## NV NY OH OK OR PA
## Greater than the National Average Payment 25 62 71 12 0 68
## Less than the National Average Payment 11 88 8 44 40 30
## No Different than the National Average Payment 36 301 302 154 89 317
## Not Available 10 31 61 54 5 57
## Number of Cases Too Small 20 40 62 111 46 38
##
## PR RI SC SD TN TX
## Greater than the National Average Payment 0 4 8 1 13 182
## Less than the National Average Payment 24 2 27 11 25 50
## No Different than the National Average Payment 36 24 105 55 211 477
## Not Available 18 1 14 53 27 281
## Number of Cases Too Small 78 2 26 60 57 231
##
## UT VA VI VT WA WI
## Greater than the National Average Payment 5 17 1 1 11 10
## Less than the National Average Payment 14 28 0 9 28 31
## No Different than the National Average Payment 56 175 4 28 141 227
## Not Available 21 21 0 0 23 22
## Number of Cases Too Small 42 17 1 4 70 88
##
## WV WY
## Greater than the National Average Payment 5 1
## Less than the National Average Payment 15 9
## No Different than the National Average Payment 87 29
## Not Available 3 9
## Number of Cases Too Small 37 33
aggregate(hospay$Payment ~ hospay$State, FUN=mean, hospay)
## hospay$State hospay$Payment
## 1 AK 15100.24
## 2 AL 16291.74
## 3 AR 16613.64
## 4 AZ 17675.86
## 5 CA 18003.73
## 6 CO 16796.23
## 7 CT 18414.63
## 8 DC 17955.67
## 9 DE 18564.00
## 10 FL 18363.05
## 11 GA 16523.94
## 12 GU 12149.67
## 13 HI 16309.89
## 14 IA 16363.29
## 15 ID 15932.33
## 16 IL 17364.60
## 17 IN 17268.85
## 18 KS 16634.23
## 19 KY 16745.51
## 20 LA 17049.28
## 21 MA 18069.95
## 22 ME 16837.12
## 23 MI 16884.40
## 24 MN 15943.76
## 25 MO 17066.80
## 26 MP 13827.00
## 27 MS 16324.82
## 28 MT 15326.62
## 29 NC 16745.60
## 30 ND 16141.66
## 31 NE 16901.95
## 32 NH 17644.71
## 33 NJ 19487.49
## 34 NM 15265.95
## 35 NV 18432.49
## 36 NY 17397.83
## 37 OH 17726.53
## 38 OK 15986.39
## 39 OR 15599.43
## 40 PA 17882.27
## 41 PR 15389.62
## 42 RI 18287.90
## 43 SC 16746.74
## 44 SD 15960.30
## 45 TN 16745.95
## 46 TX 17798.23
## 47 UT 16225.12
## 48 VA 17278.64
## 49 VI 16667.80
## 50 VT 16434.71
## 51 WA 16773.72
## 52 WI 16457.01
## 53 WV 16576.14
## 54 WY 15016.46
# Removendo hospitais que apresentam MISSING
hospay<-hospay[hospay$Payment.category !="Not Available" & hospay$Payment.category !="Number of Cases Too Small",]
summary(hospay)
## Provider.ID Hospital.name Address City
## Min. : 10001 Length:9880 Length:9880 Length:9880
## 1st Qu.:140034 Class :character Class :character Class :character
## Median :260011 Mode :character Mode :character Mode :character
## Mean :263324
## 3rd Qu.:390080
## Max. :670098
## State ZIP.Code County.name Phone.number
## Length:9880 Min. : 603 Length:9880 Min. :9.369e+08
## Class :character 1st Qu.:30342 Class :character 1st Qu.:3.867e+09
## Mode :character Median :49783 Mode :character Median :6.073e+09
## Mean :51324 Mean :5.906e+09
## 3rd Qu.:74631 3rd Qu.:8.025e+09
## Max. :99901 Max. :9.899e+09
## Payment.measure.name Payment.measure.ID Payment.category
## Length:9880 Length:9880 Length:9880
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## Denominator Payment Lower.estimate Higher.estimate
## Length:9880 Min. : 9502 Min. : 7661 Min. : 7661
## Class :character 1st Qu.:14650 1st Qu.:13152 1st Qu.:13152
## Mode :character Median :15845 Median :14420 Median :14420
## Mean :17167 Mean :15521 Mean :15521
## 3rd Qu.:18940 3rd Qu.:17415 3rd Qu.:17415
## Max. :30334 Max. :28411 Max. :28411
## Payment.footnote Value.of.care.display.name Value.of.care.display.ID
## Length:9880 Length:9880 Length:9880
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## Value.of.care.category Value.of.care.footnote Measure.start.date
## Length:9880 Length:9880 Min. :2012-07-01
## Class :character Class :character 1st Qu.:2012-07-01
## Mode :character Mode :character Median :2012-07-01
## Mean :2012-07-01
## 3rd Qu.:2012-07-01
## Max. :2012-07-01
## Measure.End.Date Location
## Min. :2015-06-30 Length:9880
## 1st Qu.:2015-06-30 Class :character
## Median :2015-06-30 Mode :character
## Mean :2015-06-30
## 3rd Qu.:2015-06-30
## Max. :2015-06-30
hospay_pneumo <- NULL
hospay_pneumo <- subset(hospay, hospay$State == "CA" & grepl("pneumonia",hospay$Payment.measure.name) )
# Contagem de casos por Hospital
library(lubridate)
table(hospay_pneumo$City, hospay_pneumo$Value.of.care.category) %>% head
## Error in eval(expr, envir, enclos): não foi possível encontrar a função "%>%"
with(hospay_pneumo,
table(hospay_pneumo$Payment.category=="Greater than the National Average Payment",
year(hospay_pneumo$Measure.start.date)==2012,
months(hospay_pneumo$Measure.start.date),
City,
useNA = "ifany"))
## , , = Julho, City = ALAMEDA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = ALHAMBRA
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = ALTURAS
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = ANAHEIM
##
##
## TRUE
## FALSE 1
## TRUE 2
##
## , , = Julho, City = ANTIOCH
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = APPLE VALLEY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = ARCADIA
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = ARCATA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = AUBURN
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = BAKERSFIELD
##
##
## TRUE
## FALSE 3
## TRUE 2
##
## , , = Julho, City = BANNING
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = BARSTOW
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = BERKELEY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = BISHOP
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = BLYTHE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = BRAWLEY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = BURBANK
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = BURLINGAME
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = CAMARILLO
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = CARMICHAEL
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = CASTRO VALLEY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = CHESTER
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = CHICO
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = CHULA VISTA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = CLEARLAKE
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = CLOVIS
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = COLTON
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = COLUSA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = CONCORD
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = CORONA
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = COVINA
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = CRESCENT CITY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = DALY CITY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = DAVIS
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = DELANO
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = DOWNEY
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = EL CENTRO
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = ENCINITAS
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = ENCINO
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = ESCONDIDO
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = EUREKA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = FAIRFIELD
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = FALL RIVER MILLS
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = FOLSOM
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = FORT BRAGG
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = FORTUNA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = FOUNTAIN VALLEY
##
##
## TRUE
## FALSE 0
## TRUE 2
##
## , , = Julho, City = FREMONT
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = FRENCH CAMP
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = FRESNO
##
##
## TRUE
## FALSE 2
## TRUE 0
##
## , , = Julho, City = FULLERTON
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = GARDEN GROVE
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = GARDENA
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = GILROY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = GLENDALE
##
##
## TRUE
## FALSE 1
## TRUE 2
##
## , , = Julho, City = GLENDORA
##
##
## TRUE
## FALSE 1
## TRUE 1
##
## , , = Julho, City = GRASS VALLEY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = GREENBRAE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = GRIDLEY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = HANFORD
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = HAWAIIAN GARDENS
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = HAYWARD
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = HEALDSBURG
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = HEMET
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = HOLLISTER
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = HOLLYWOOD
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = HUNTINGTON BEACH
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = HUNTINGTON PARK
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = INDIO
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = INGLEWOOD
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = JACKSON
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = JOSHUA TREE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = KING CITY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = LA JOLLA
##
##
## TRUE
## FALSE 1
## TRUE 1
##
## , , = Julho, City = LA MESA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = LA PALMA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = LAGUNA HILLS
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = LAKE ARROWHEAD
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = LAKE ISABELLA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = LAKEPORT
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = LAKEWOOD
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = LANCASTER
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = LODI
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = LOMA LINDA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = LOMPOC
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = LONG BEACH
##
##
## TRUE
## FALSE 0
## TRUE 3
##
## , , = Julho, City = LOS ALAMITOS
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = LOS ANGELES
##
##
## TRUE
## FALSE 7
## TRUE 7
##
## , , = Julho, City = LOS BANOS
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = LYNWOOD
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = MADERA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = MANTECA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = MARINA DEL REY
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = MARIPOSA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = MARTINEZ
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = MARYSVILLE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = MERCED
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = MISSION HILLS
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = MISSION VIEJO
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = MODESTO
##
##
## TRUE
## FALSE 2
## TRUE 0
##
## , , = Julho, City = MONTCLAIR
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = MONTEBELLO
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = MONTEREY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = MONTEREY PARK
##
##
## TRUE
## FALSE 1
## TRUE 1
##
## , , = Julho, City = MORENO VALLEY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = MOUNT SHASTA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = MOUNTAIN VIEW
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = MURRIETA
##
##
## TRUE
## FALSE 2
## TRUE 0
##
## , , = Julho, City = NAPA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = NATIONAL CITY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = NEWPORT BEACH
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = NORTHRIDGE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = NORWALK
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = NOVATO
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = OAKDALE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = OAKLAND
##
##
## TRUE
## FALSE 2
## TRUE 0
##
## , , = Julho, City = OCEANSIDE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = OJAI
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = ORANGE
##
##
## TRUE
## FALSE 3
## TRUE 0
##
## , , = Julho, City = OROVILLE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = OXNARD
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = PALM SPRINGS
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = PALMDALE
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = PANORAMA CITY
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = PARADISE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = PASADENA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = PETALUMA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = PLACENTIA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = PLACERVILLE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = PLEASANTON
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = POMONA
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = PORTERVILLE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = PORTOLA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = POWAY
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = QUINCY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = RANCHO MIRAGE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = RED BLUFF
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = REDDING
##
##
## TRUE
## FALSE 0
## TRUE 2
##
## , , = Julho, City = REDLANDS
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = REDWOOD CITY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = REEDLEY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = RIVERSIDE
##
##
## TRUE
## FALSE 0
## TRUE 2
##
## , , = Julho, City = ROSEVILLE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = SACRAMENTO
##
##
## TRUE
## FALSE 4
## TRUE 0
##
## , , = Julho, City = SAINT HELENA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = SALINAS
##
##
## TRUE
## FALSE 2
## TRUE 0
##
## , , = Julho, City = SAN ANDREAS
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = SAN BERNARDINO
##
##
## TRUE
## FALSE 1
## TRUE 1
##
## , , = Julho, City = SAN DIEGO
##
##
## TRUE
## FALSE 3
## TRUE 1
##
## , , = Julho, City = SAN DIMAS
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = SAN FRANCISCO
##
##
## TRUE
## FALSE 9
## TRUE 0
##
## , , = Julho, City = SAN GABRIEL
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = SAN JOSE
##
##
## TRUE
## FALSE 4
## TRUE 0
##
## , , = Julho, City = SAN LEANDRO
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = SAN LUIS OBISPO
##
##
## TRUE
## FALSE 2
## TRUE 0
##
## , , = Julho, City = SAN PEDRO
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = SAN RAMON
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = SANTA ANA
##
##
## TRUE
## FALSE 1
## TRUE 1
##
## , , = Julho, City = SANTA BARBARA
##
##
## TRUE
## FALSE 2
## TRUE 0
##
## , , = Julho, City = SANTA CRUZ
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = SANTA MARIA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = SANTA MONICA
##
##
## TRUE
## FALSE 2
## TRUE 0
##
## , , = Julho, City = SANTA ROSA
##
##
## TRUE
## FALSE 2
## TRUE 0
##
## , , = Julho, City = SHERMAN OAKS
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = SIMI VALLEY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = SOLVANG
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = SONOMA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = SONORA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = SOUTH EL MONTE
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = SOUTH LAKE TAHOE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = STANFORD
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = STOCKTON
##
##
## TRUE
## FALSE 1
## TRUE 1
##
## , , = Julho, City = SUN CITY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = SUN VALLEY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = SUSANVILLE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = TARZANA
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = TEHACHAPI
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = TEMECULA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = TEMPLETON
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = THOUSAND OAKS
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = TORRANCE
##
##
## TRUE
## FALSE 2
## TRUE 0
##
## , , = Julho, City = TRACY
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = TRUCKEE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = TULARE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = TURLOCK
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = UKIAH
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = UPLAND
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = VALENCIA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = VALLEJO
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = VAN NUYS
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = VENTURA
##
##
## TRUE
## FALSE 2
## TRUE 0
##
## , , = Julho, City = VICTORVILLE
##
##
## TRUE
## FALSE 2
## TRUE 0
##
## , , = Julho, City = VISALIA
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = WALNUT CREEK
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = WATSONVILLE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = WEAVERVILLE
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = WEST HILLS
##
##
## TRUE
## FALSE 0
## TRUE 1
##
## , , = Julho, City = WHITTIER
##
##
## TRUE
## FALSE 1
## TRUE 1
##
## , , = Julho, City = WILLITS
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = WILLOWS
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = WOODLAND
##
##
## TRUE
## FALSE 1
## TRUE 0
##
## , , = Julho, City = YREKA
##
##
## TRUE
## FALSE 1
## TRUE 0
## [1] 23171 16376 14384 16649 13168 20007
library(reshape2)
hosp_melt<-melt(data=hospay,id=c(2,5,9,11), measure=as.numeric(c(13)), value.name='Estimate')
head(hosp_melt)
## Hospital.name State Payment.measure.name
## 1 MARSHALLMEDICALCENTERSOUTH AL Payment for heart attack patients
## 2 MARSHALLMEDICALCENTERSOUTH AL Payment for heart failure patients
## 3 MARSHALLMEDICALCENTERSOUTH AL Payment for pneumonia patients
## 4 WEDOWEEHOSPITAL AL Payment for heart failure patients
## 5 WEDOWEEHOSPITAL AL Payment for pneumonia patients
## 6 CRESTWOODMEDICALCENTER AL Payment for heart attack patients
## Payment.category variable Estimate
## 1 No Different than the National Average Payment Payment 23171
## 2 No Different than the National Average Payment Payment 16376
## 3 No Different than the National Average Payment Payment 14384
## 4 No Different than the National Average Payment Payment 16649
## 5 No Different than the National Average Payment Payment 13168
## 6 Less than the National Average Payment Payment 20007
## [1] "Hospital.name" "State" "Payment.measure.name"
## [4] "Payment.category" "variable" "Estimate"
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
names(hosp_melt) [3] <- "PaymentMeasureName"
hosp_est <- sqldf("SELECT State, AVG(Estimate) AS Estimate
FROM hosp_melt
WHERE paymentmeasurename = 'Payment for heart attack patients'
GROUP BY State")
## Loading required package: tcltk
## State Estimate
## 1 AK 22678.80
## 2 AL 22540.78
## 3 AR 22806.00
## 4 AZ 23389.11
## 5 CA 23437.82
## 6 CO 22827.00