1/7/2020
#check1 <- BDdata %>% group_by(Measure_Num)%>% count(Measure_Num)
## # A tibble: 26 x 2 ## # Groups: Measure_Num [26] ## Measure_Num n ## <fct> <int> ## 1 M00237 158146 ## 2 M00238 300167 ## 3 M00239 158146 ## 4 M00240 300167 ## 5 M00241 151996 ## 6 M00242 288242 ## 7 M00243 158146 ## 8 M00244 300167 ## 9 M00245 153226 ## 10 M00246 290627 ## # ... with 16 more rows
#View(check1)
#check2 <- BDdata %>% group_by(Measure_Type)%>% count(Measure_Type)
## # A tibble: 2 x 2 ## # Groups: Measure_Type [2] ## Measure_Type n ## <fct> <int> ## 1 BASIC 3748980 ## 2 SMOOTH 1702840
#View(check2)
#check3 <- BDdata %>% group_by(Cal_Level)%>% count(Cal_Level)
## # A tibble: 12 x 2 ## # Groups: Cal_Level [12] ## Cal_Level n ## <fct> <int> ## 1 S_C_Y 499422 ## 2 S_C_Y_A 1687824 ## 3 S_C_Y_RE 1997688 ## 4 S_C_Y_S 923586 ## 5 S_Y 5528 ## 6 S_Y_A 18672 ## 7 S_Y_A_RE 74688 ## 8 S_Y_A_RE_S 136768 ## 9 S_Y_A_S 34192 ## 10 S_Y_RE 22112 ## 11 S_Y_RE_S 41072 ## 12 S_Y_S 10268
#View(check3)
#check4 <- BDdata %>% group_by(Measure_Num, Cal_Level)%>% count(Cal_Level)
## # A tibble: 282 x 3 ## # Groups: Measure_Num, Cal_Level [282] ## Measure_Num Cal_Level n ## <fct> <fct> <int> ## 1 M00237 S_C_Y 12911 ## 2 M00237 S_C_Y_A 51644 ## 3 M00237 S_C_Y_RE 51644 ## 4 M00237 S_C_Y_S 25822 ## 5 M00237 S_Y 215 ## 6 M00237 S_Y_A 860 ## 7 M00237 S_Y_A_RE 3440 ## 8 M00237 S_Y_A_RE_S 6880 ## 9 M00237 S_Y_A_S 1720 ## 10 M00237 S_Y_RE 860 ## # ... with 272 more rows
#View(check4)
#check5 <- BDdata %>% group_by(Cal_Level, Age_band_cd)%>% count(Age_band_cd)
## # A tibble: 27 x 3 ## # Groups: Cal_Level, Age_band_cd [27] ## Cal_Level Age_band_cd n ## <fct> <int> <int> ## 1 S_C_Y NA 499422 ## 2 S_C_Y_A 1 421956 ## 3 S_C_Y_A 2 421956 ## 4 S_C_Y_A 3 421956 ## 5 S_C_Y_A 4 421956 ## 6 S_C_Y_RE NA 1997688 ## 7 S_C_Y_S NA 923586 ## 8 S_Y NA 5528 ## 9 S_Y_A 1 4668 ## 10 S_Y_A 2 4668 ## # ... with 17 more rows
#View(check5)
check6 <- BDdata %>% group_by(Cal_Level, Raceethnicity_cd)%>% count(Raceethnicity_cd)
## Warning: Factor `Raceethnicity_cd` contains implicit NA, consider using ## `forcats::fct_explicit_na` ## Warning: Factor `Raceethnicity_cd` contains implicit NA, consider using ## `forcats::fct_explicit_na` ## Warning: Factor `Raceethnicity_cd` contains implicit NA, consider using ## `forcats::fct_explicit_na`
#View(check6) head(check6)
## Warning: Factor `Raceethnicity_cd` contains implicit NA, consider using ## `forcats::fct_explicit_na` ## Warning: Factor `Raceethnicity_cd` contains implicit NA, consider using ## `forcats::fct_explicit_na`
## # A tibble: 6 x 3 ## # Groups: Cal_Level, Raceethnicity_cd [6] ## Cal_Level Raceethnicity_cd n ## <fct> <fct> <int> ## 1 S_C_Y <NA> 499422 ## 2 S_C_Y_A <NA> 1687824 ## 3 S_C_Y_RE B 499422 ## 4 S_C_Y_RE H 499422 ## 5 S_C_Y_RE O 499422 ## 6 S_C_Y_RE W 499422
#check7 <- BDdata %>% group_by(Cal_Level, PS_Flag)%>% count(PS_Flag)
## # A tibble: 16 x 3 ## # Groups: Cal_Level, PS_Flag [16] ## Cal_Level PS_Flag n ## <fct> <int> <int> ## 1 S_C_Y 0 332662 ## 2 S_C_Y 1 166760 ## 3 S_C_Y_A 0 1283500 ## 4 S_C_Y_A 1 404324 ## 5 S_C_Y_RE 0 1598096 ## 6 S_C_Y_RE 1 399592 ## 7 S_C_Y_S 0 624494 ## 8 S_C_Y_S 1 299092 ## 9 S_Y 0 5528 ## 10 S_Y_A 0 18672 ## 11 S_Y_A_RE 0 74688 ## 12 S_Y_A_RE_S 0 136768 ## 13 S_Y_A_S 0 34192 ## 14 S_Y_RE 0 22112 ## 15 S_Y_RE_S 0 41072 ## 16 S_Y_S 0 10268
#View(check7)
length(which(is.na(BDdata$County_cd)))
## [1] 343300
#343,300 #check8 <- BDdata %>% filter(is.na(County_cd))%>% group_by(State_CD, County_cd)%>% count(County_cd)
## # A tibble: 22 x 3 ## # Groups: State_CD, County_cd [22] ## State_CD County_cd n ## <int> <int> <int> ## 1 4 NA 15600 ## 2 8 NA 20930 ## 3 9 NA 1610 ## 4 12 NA 24150 ## 5 19 NA 17710 ## 6 20 NA 11530 ## 7 21 NA 14490 ## 8 23 NA 17460 ## 9 24 NA 3220 ## 10 25 NA 20930 ## # ... with 12 more rows
#View(check8)
#check9 <- BDdata %>% group_by(State_CD, Min_rpt_yr, Max_rpt_yr)%>% count((Max_rpt_yr-Min_rpt_yr))
## # A tibble: 242 x 5 ## # Groups: State_CD, Min_rpt_yr, Max_rpt_yr [242] ## State_CD Min_rpt_yr Max_rpt_yr `(Max_rpt_yr - Min_rpt_yr)` n ## <int> <int> <int> <int> <int> ## 1 4 2003 2007 4 7140 ## 2 4 2004 2008 4 7140 ## 3 4 2005 2009 4 7140 ## 4 4 2006 2010 4 7140 ## 5 4 2007 2011 4 7140 ## 6 4 2008 2012 4 7140 ## 7 4 2009 2013 4 7140 ## 8 4 2010 2014 4 7140 ## 9 4 2011 2015 4 7140 ## 10 4 2012 2016 4 7140 ## # ... with 232 more rows
#View(check9)
list_of_states <- c("6", "22", "27")
check12 <- filter(BDdata, State_CD %in% list_of_states)
unique(check12$State_CD)
## [1] 22 27 6
#check12_actual <- check12 %>% group_by(State_CD, Cal_Level)%>% count(Cal_Level)
## # A tibble: 12 x 3 ## # Groups: State_CD, Cal_Level [12] ## State_CD Cal_Level n ## <int> <fct> <int> ## 1 6 S_C_Y 29406 ## 2 6 S_C_Y_A 99528 ## 3 6 S_C_Y_RE 117624 ## 4 6 S_C_Y_S 54288 ## 5 22 S_C_Y 8151 ## 6 22 S_C_Y_A 27588 ## 7 22 S_C_Y_RE 32604 ## 8 22 S_C_Y_S 15048 ## 9 27 S_C_Y 23751 ## 10 27 S_C_Y_A 80388 ## 11 27 S_C_Y_RE 95004 ## 12 27 S_C_Y_S 43848
#View(check12_actual)
length(which(BDdata$ROLLING_YEAR_COUNT != 5))
## [1] 0
#Conditions for -1: birth defect count > 0 AND live birth count > 0 check14_1 <- subset(BDdata, TOTAL_LIVE_BIRTHS_WITH_BD_AMT >0 & TOTAL_LIVE_BIRTHS_AMT>0) length(which(check14_1$no_data_id != -1))
## [1] 301106
#Incorrect: n=301106 #Conditions for 0: Birth defect count = 0 AND live birth count > 0 check14_0 <- subset(BDdata, TOTAL_LIVE_BIRTHS_WITH_BD_AMT ==0 & TOTAL_LIVE_BIRTHS_AMT>0) length(which(check14_0$no_data_id != 0))
## [1] 0
#Correct #Conditions for 2: NS race code where ethnicity = hispanic check14_2 <- subset(BDdata, Raceethnicity_cd=="H") NS <- subset(check14_2, no_data_id ==2) length(which(check14_2$no_data_id == 2))
## [1] 45045
#Impossible to check in tier 4. No race variable to check NS with Raceethnicitiy code. #Conditions for 3: birth defect count > 0 and live birth count = 0 check14_3 <- subset(BDdata, TOTAL_LIVE_BIRTHS_WITH_BD_AMT >0 & TOTAL_LIVE_BIRTHS_AMT == 0) length(which(check14_3$no_data_id != 3))
## [1] 0
#Correct
suppression <- subset(BDdata, EVENTS_AMT <6) length(which(suppression$PS_Flag != 1))
## [1] 1475327
VTnuance <- subset(BDdata, State_CD == 50 & Min_rpt_yr <2010) length(which(VTnuance$Indicator_num== "I00010"))
## [1] 0
length(which(VTnuance$Indicator_num== "I00011"))
## [1] 0
MEnuance <- subset(BDdata, State_CD == 23 & Min_rpt_yr <2008) length(which(MEnuance$Indicator_num== "I00010"))
## [1] 0
length(which(MEnuance$Indicator_num== "I00011"))
## [1] 0
KSnuance <- subset(BDdata, State_CD == 20 & Min_rpt_yr == 2012) length(which(KSnuance$Birth_def_CD== "26"))
## [1] 0
unique(KSnuance$Birth_def_CD)
## [1] 29 21 22 28 24 23 25 30 32 27
#31 is also missing KSnuance2 <- subset(BDdata, State_CD == 20 & Min_rpt_yr == 2013) length(which(KSnuance2$Birth_def_CD== "31"))
## [1] 0
#No data
#Expanded variable not in T4. names(BDdata)
## [1] "State_CD" "County_cd" ## [3] "Birth_def_CD" "Indicator_num" ## [5] "Measure_Num" "Measure_Type" ## [7] "RPT_YR" "ROLLING_YEAR_COUNT" ## [9] "INFANT_GNDR_CD" "Min_rpt_yr" ## [11] "Max_rpt_yr" "Cal_Level" ## [13] "Age_band_cd" "Raceethnicity_cd" ## [15] "PS_Flag" "STABILITY" ## [17] "PREVALENCE" "EVENTS_AMT" ## [19] "Is_Smoothed" "no_data_id" ## [21] "TOTAL_LIVE_BIRTHS_AMT" "TOTAL_LIVE_BIRTHS_WITH_BD_AMT"