Define Your Research Question

Define your research question below. What about the data interests you? What is a specific question you want to find out about the data?

I choose data from the 2013-2014 National Health and Nutrition Examination Survey (NHANES) from the Centers for Disease Control (CDC). It contains data on almost 10,000 persons with information about diet, laboratory tests, demographics, physical examinations, medications, and more.

I chose this data because I’m interested in using complete blood count (CBC) data as a marker of inflammation. CBCs are a routine blood test performed at any annual check up and any time a person has an urgent or emergent medical situation. They provide information about anemia, infection, inflammation, and much more. Recently, interest has grown in looking at the ratios of certain blood cells, ie, neutrophils to lymphocytes, as a way of assessing inflammation.

My research question is: does neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) differ in people with various health concerns? I am interested in whether NLR and PLR are different in people with and without history of the following: stroke, asthma, chronic bronchitis, and mental health concerns. I will divide people into age groups, as some of these blood parameters can change with age.

Given your question, what is your expectation about the data?

I imagine that because the dataset is sufficiently large (~10,000 observations) I will be able to answer my question with these data. I think I will find that NLR and PLR are higher in people with a history of physical and mental health challenges. I imagine I will have a lot of NA’s to deal with.

Loading the Data

Load the data below and use dplyr::glimpse() or skimr::skim() on the data. You should upload the data file into the data directory.

# load the labs data
cbc_data <- read_csv(file=here("data","nhanes_labs.csv"), na="NA")
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
## Rows: 9813 Columns: 424
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (424): SEQN, URXUMA, URXUMS, URXUCR.x, URXCRS, URDACT, WTSAF2YR.x, LBXAP...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# get a look at the data
cbc_data %>% skimr::skim() # 424 variables, 9813 observations
Data summary
Name Piped data
Number of rows 9813
Number of columns 424
_______________________
Column type frequency:
numeric 424
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
SEQN 0 1.00 78644.56 2938.59 73557.00 76092.00 78643.00 81191.00 83731.00 ▇▇▇▇▇
URXUMA 1761 0.82 41.22 238.91 0.21 4.50 8.40 17.62 9600.00 ▇▁▁▁▁
URXUMS 1761 0.82 41.22 238.91 0.21 4.50 8.40 17.62 9600.00 ▇▁▁▁▁
URXUCR.x 1761 0.82 121.07 78.57 5.00 60.00 106.00 163.00 659.00 ▇▃▁▁▁
URXCRS 1761 0.82 10702.81 6946.02 442.00 5304.00 9370.40 14409.20 58255.60 ▇▃▁▁▁
URDACT 1761 0.82 41.91 276.26 0.21 5.02 7.78 15.30 9000.00 ▇▁▁▁▁
WTSAF2YR.x 6484 0.34 78917.20 71088.02 0.00 33217.41 56397.70 99356.56 395978.47 ▇▂▁▁▁
LBXAPB 6668 0.32 85.90 25.60 20.00 68.00 84.00 101.00 234.00 ▂▇▂▁▁
LBDAPBSI 6668 0.32 0.86 0.26 0.20 0.68 0.84 1.01 2.34 ▂▇▂▁▁
LBXSAL 3260 0.67 4.28 0.34 2.40 4.10 4.30 4.50 5.60 ▁▁▇▆▁
LBDSALSI 3260 0.67 42.82 3.44 24.00 41.00 43.00 45.00 56.00 ▁▁▇▆▁
LBXSAPSI 3261 0.67 78.58 51.61 9.00 53.00 66.00 83.00 907.00 ▇▁▁▁▁
LBXSASSI 3262 0.67 25.12 17.74 9.00 19.00 22.00 27.00 882.00 ▇▁▁▁▁
LBXSATSI 3262 0.67 24.07 18.48 6.00 15.00 20.00 27.00 536.00 ▇▁▁▁▁
LBXSBU 3260 0.67 12.86 5.99 1.00 9.00 12.00 15.00 95.00 ▇▁▁▁▁
LBDSBUSI 3260 0.67 4.59 2.14 0.36 3.21 4.28 5.36 33.92 ▇▁▁▁▁
LBXSC3SI 3260 0.67 25.17 2.27 16.00 24.00 25.00 27.00 43.00 ▁▇▃▁▁
LBXSCA 3302 0.66 9.49 0.37 7.60 9.20 9.50 9.70 14.80 ▁▇▁▁▁
LBDSCASI 3302 0.66 2.37 0.09 1.90 2.30 2.38 2.42 3.70 ▁▇▁▁▁
LBXSCH 3262 0.67 184.51 41.98 72.00 155.00 181.00 210.00 639.00 ▇▆▁▁▁
LBDSCHSI 3262 0.67 4.77 1.09 1.86 4.01 4.68 5.43 16.52 ▇▆▁▁▁
LBXSCK 3271 0.67 153.99 185.26 6.00 75.00 108.00 173.00 3966.00 ▇▁▁▁▁
LBXSCLSI 3260 0.67 104.31 2.75 83.00 103.00 104.00 106.00 119.00 ▁▁▇▇▁
LBXSCR 3260 0.67 0.88 0.49 0.29 0.69 0.82 0.98 17.41 ▇▁▁▁▁
LBDSCRSI 3260 0.67 77.81 43.07 25.64 61.00 72.49 86.63 1539.04 ▇▁▁▁▁
LBXSGB 3269 0.67 2.83 0.44 1.40 2.50 2.80 3.10 6.50 ▂▇▁▁▁
LBDSGBSI 3269 0.67 28.26 4.41 14.00 25.00 28.00 31.00 65.00 ▂▇▁▁▁
LBXSGL 3260 0.67 102.29 38.72 49.00 86.00 93.00 104.00 577.00 ▇▁▁▁▁
LBDSGLSI 3260 0.67 5.68 2.15 2.72 4.77 5.16 5.77 32.03 ▇▁▁▁▁
LBXSGTSI 3261 0.67 26.08 42.90 4.00 13.00 17.00 26.00 1510.00 ▇▁▁▁▁
LBXSIR 3286 0.67 83.45 36.40 5.00 59.00 79.00 103.00 557.00 ▇▂▁▁▁
LBDSIRSI 3286 0.67 14.95 6.52 0.90 10.60 14.10 18.40 99.80 ▇▂▁▁▁
LBXSKSI 3261 0.67 4.03 0.35 2.80 3.80 4.00 4.20 5.80 ▁▇▆▁▁
LBXSLDSI 3262 0.67 126.80 32.06 38.00 109.00 123.00 140.00 1274.00 ▇▁▁▁▁
LBXSNASI 3260 0.67 139.78 2.22 119.00 139.00 140.00 141.00 154.00 ▁▁▇▅▁
LBXSOSSI 3260 0.67 279.27 5.02 237.00 276.00 279.00 282.00 323.00 ▁▁▇▁▁
LBXSPH 3261 0.67 3.93 0.65 1.80 3.50 3.90 4.30 10.90 ▅▇▁▁▁
LBDSPHSI 3261 0.67 1.27 0.21 0.58 1.13 1.26 1.39 3.52 ▅▇▁▁▁
LBXSTB 3264 0.67 0.64 0.31 0.10 0.40 0.60 0.80 7.10 ▇▁▁▁▁
LBDSTBSI 3264 0.67 10.93 5.28 1.71 6.84 10.26 13.68 121.41 ▇▁▁▁▁
LBXSTP 3269 0.67 7.11 0.47 4.70 6.80 7.10 7.40 10.20 ▁▅▇▁▁
LBDSTPSI 3269 0.67 71.08 4.68 47.00 68.00 71.00 74.00 102.00 ▁▅▇▁▁
LBXSTR 3264 0.67 143.08 134.54 19.00 72.00 111.00 175.00 6057.00 ▇▁▁▁▁
LBDSTRSI 3264 0.67 1.62 1.52 0.22 0.81 1.25 1.98 68.38 ▇▁▁▁▁
LBXSUA 3262 0.67 5.35 1.40 0.70 4.30 5.20 6.20 13.30 ▁▇▅▁▁
LBDSUASI 3262 0.67 318.21 83.50 41.60 255.80 309.30 368.80 791.10 ▁▇▅▁▁
LBXWBCSI 1269 0.87 7.38 2.30 2.30 5.80 7.10 8.60 55.70 ▇▁▁▁▁
LBXLYPCT 1294 0.87 33.67 10.58 2.60 26.30 32.60 39.70 88.00 ▁▇▅▁▁
LBXMOPCT 1294 0.87 8.19 2.25 1.30 6.70 7.90 9.40 38.90 ▇▃▁▁▁
LBXNEPCT 1294 0.87 54.49 11.39 8.40 47.60 55.50 62.30 92.50 ▁▂▇▆▁
LBXEOPCT 1294 0.87 2.99 2.44 0.00 1.50 2.30 3.70 36.60 ▇▁▁▁▁
LBXBAPCT 1294 0.87 0.72 0.34 0.00 0.50 0.60 0.90 5.80 ▇▁▁▁▁
LBDLYMNO 1294 0.87 2.43 1.11 0.20 1.80 2.20 2.80 49.00 ▇▁▁▁▁
LBDMONO 1294 0.87 0.59 0.20 0.10 0.50 0.60 0.70 3.40 ▇▂▁▁▁
LBDNENO 1294 0.87 4.09 1.78 0.40 2.90 3.80 4.90 25.60 ▇▂▁▁▁
LBDEONO 1294 0.87 0.22 0.20 0.00 0.10 0.20 0.30 4.30 ▇▁▁▁▁
LBDBANO 1294 0.87 0.04 0.05 0.00 0.00 0.00 0.10 0.80 ▇▁▁▁▁
LBXRBCSI 1269 0.87 4.66 0.46 1.67 4.36 4.64 4.95 8.30 ▁▂▇▁▁
LBXHGB 1269 0.87 13.68 1.49 6.40 12.70 13.60 14.70 19.50 ▁▁▇▅▁
LBXHCT 1269 0.87 40.44 4.11 17.90 37.70 40.20 43.20 56.50 ▁▁▇▆▁
LBXMCVSI 1269 0.87 87.02 6.47 55.70 83.20 87.50 91.30 115.30 ▁▁▇▃▁
LBXMCHSI 1269 0.87 29.44 2.56 16.80 28.10 29.70 31.10 74.50 ▃▇▁▁▁
LBXMC 1269 0.87 33.81 1.04 28.00 33.30 33.80 34.40 69.60 ▇▁▁▁▁
LBXRDW 1269 0.87 13.63 1.20 11.30 12.90 13.40 14.00 30.60 ▇▁▁▁▁
LBXPLTSI 1269 0.87 251.20 66.05 18.00 206.00 244.00 288.00 723.00 ▁▇▂▁▁
LBXMPSI 1269 0.87 8.30 0.95 5.50 7.60 8.20 8.90 13.60 ▂▇▃▁▁
URXUCL 7639 0.22 1.97 0.16 1.00 2.00 2.00 2.00 2.00 ▁▁▁▁▇
WTSA2YR.x 7058 0.28 104301.88 87271.68 0.00 44409.69 71416.93 131754.23 530325.35 ▇▂▁▁▁
LBXSCU 7293 0.26 119.18 29.23 24.70 99.60 115.10 133.33 297.50 ▁▇▂▁▁
LBDSCUSI 7293 0.26 18.71 4.59 3.88 15.64 18.07 20.93 46.71 ▁▇▂▁▁
LBXSSE 7294 0.26 128.35 18.29 74.90 116.10 126.80 138.80 297.90 ▅▇▁▁▁
LBDSSESI 7294 0.26 1.63 0.23 0.95 1.47 1.61 1.76 3.78 ▃▇▁▁▁
LBXSZN 7294 0.26 81.55 15.49 38.90 71.30 80.30 90.20 160.90 ▂▇▃▁▁
LBDSZNSI 7294 0.26 12.48 2.37 5.95 10.91 12.29 13.80 24.62 ▂▇▃▁▁
URXUCR.y 7132 0.27 115.00 77.01 5.00 55.00 100.00 156.00 546.00 ▇▅▁▁▁
WTSB2YR.x 7036 0.28 103475.58 89962.67 0.00 42660.60 68840.37 126354.41 482310.79 ▇▂▁▁▁
URXBP3 7127 0.27 253.00 1324.75 0.28 5.60 16.60 63.30 28259.00 ▇▁▁▁▁
URDBP3LC 7127 0.27 0.03 0.18 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXBPH 7127 0.27 2.76 16.16 0.14 0.70 1.30 2.60 792.00 ▇▁▁▁▁
URDBPHLC 7127 0.27 0.04 0.20 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXBPF 7131 0.27 2.81 14.47 0.14 0.14 0.40 1.00 298.70 ▇▁▁▁▁
URDBPFLC 7131 0.27 0.35 0.48 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▅
URXBPS 7131 0.27 1.44 6.73 0.07 0.20 0.40 0.98 211.90 ▇▁▁▁▁
URDBPSLC 7131 0.27 0.10 0.30 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXTLC 7127 0.27 3.29 20.94 0.07 0.07 0.07 0.20 588.00 ▇▁▁▁▁
URDTLCLC 7127 0.27 0.61 0.49 0.00 0.00 1.00 1.00 1.00 ▅▁▁▁▇
URXTRS 7127 0.27 71.33 274.50 1.20 1.80 5.75 27.80 9572.00 ▇▁▁▁▁
URDTRSLC 7127 0.27 0.24 0.43 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
URXBUP 7127 0.27 1.63 11.55 0.07 0.07 0.07 0.20 352.60 ▇▁▁▁▁
URDBUPLC 7127 0.27 0.69 0.46 0.00 0.00 1.00 1.00 1.00 ▃▁▁▁▇
URXEPB 7127 0.27 18.86 87.75 0.71 0.71 0.71 4.60 1980.60 ▇▁▁▁▁
URDEPBLC 7127 0.27 0.53 0.50 0.00 0.00 1.00 1.00 1.00 ▇▁▁▁▇
URXMPB 7127 0.27 209.83 502.79 0.71 15.60 49.30 196.28 7369.00 ▇▁▁▁▁
URDMPBLC 7127 0.27 0.01 0.07 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXPPB 7127 0.27 50.13 137.16 0.07 1.10 5.50 37.60 2188.20 ▇▁▁▁▁
URDPPBLC 7127 0.27 0.01 0.11 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URX14D 7127 0.27 124.10 1002.74 0.07 0.80 2.50 10.30 30426.30 ▇▁▁▁▁
URD14DLC 7127 0.27 0.02 0.12 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXDCB 7127 0.27 3.81 21.53 0.07 0.30 0.60 1.40 430.10 ▇▁▁▁▁
URDDCBLC 7127 0.27 0.05 0.21 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXUCR 7123 0.27 127.58 81.98 8.00 65.00 112.00 171.00 659.00 ▇▅▁▁▁
PHQ020 631 0.94 1.99 0.12 1.00 2.00 2.00 2.00 2.00 ▁▁▁▁▇
PHACOFHR 9684 0.01 3.04 3.59 0.00 1.00 2.00 4.00 18.00 ▇▂▁▁▁
PHACOFMN 9684 0.01 29.08 17.90 0.00 14.00 30.00 46.00 59.00 ▇▆▇▆▇
PHQ030 631 0.94 2.00 0.07 1.00 2.00 2.00 2.00 2.00 ▁▁▁▁▇
PHAALCHR 9771 0.00 7.50 5.30 0.00 2.25 9.00 11.00 21.00 ▇▂▇▃▁
PHAALCMN 9771 0.00 31.60 18.02 0.00 19.50 31.50 47.50 59.00 ▆▆▅▆▇
PHQ040 631 0.94 1.96 0.20 1.00 2.00 2.00 2.00 2.00 ▁▁▁▁▇
PHAGUMHR 9442 0.04 1.47 2.85 0.00 0.00 0.00 2.00 18.00 ▇▁▁▁▁
PHAGUMMN 9442 0.04 22.76 18.36 0.00 6.00 18.00 39.00 59.00 ▇▃▃▃▂
PHQ050 631 0.94 2.00 0.06 1.00 2.00 2.00 2.00 2.00 ▁▁▁▁▇
PHAANTHR 9776 0.00 6.11 7.24 0.00 1.00 3.00 10.00 38.00 ▇▅▁▁▁
PHAANTMN 9776 0.00 31.51 14.47 2.00 21.00 32.00 40.00 57.00 ▃▅▇▆▅
PHQ060 631 0.94 1.99 0.10 1.00 2.00 2.00 2.00 2.00 ▁▁▁▁▇
PHASUPHR 9727 0.01 2.91 3.21 0.00 1.00 2.00 3.00 13.00 ▇▃▁▁▁
PHASUPMN 9727 0.01 30.56 15.32 0.00 21.25 32.00 42.00 58.00 ▃▅▇▃▅
PHAFSTHR.x 631 0.94 6.25 5.43 0.00 2.00 4.00 11.00 39.00 ▇▅▁▁▁
PHAFSTMN.x 631 0.94 29.38 17.45 0.00 14.00 30.00 44.00 59.00 ▇▇▇▇▇
PHDSESN 391 0.96 0.71 0.74 0.00 0.00 1.00 1.00 2.00 ▇▁▆▁▃
LBDPFL 7488 0.24 0.45 0.44 0.13 0.29 0.38 0.50 16.67 ▇▁▁▁▁
LBDWFL 5713 0.42 0.56 0.36 0.01 0.27 0.61 0.78 7.32 ▇▁▁▁▁
LBDHDD 2189 0.78 53.11 15.23 10.00 42.00 51.00 61.00 173.00 ▃▇▁▁▁
LBDHDDSI 2189 0.78 1.37 0.39 0.26 1.09 1.32 1.58 4.47 ▃▇▁▁▁
LBXHA 1549 0.84 1.42 0.49 1.00 1.00 1.00 2.00 2.00 ▇▁▁▁▆
LBXHBS 1552 0.84 1.68 0.47 1.00 1.00 2.00 2.00 2.00 ▃▁▁▁▇
LBXHBC 2157 0.78 1.94 0.23 1.00 2.00 2.00 2.00 2.00 ▁▁▁▁▇
LBDHBG 2161 0.78 2.00 0.07 1.00 2.00 2.00 2.00 2.00 ▁▁▁▁▇
LBDHD 2161 0.78 2.00 0.05 1.00 2.00 2.00 2.00 2.00 ▁▁▁▁▇
LBXHCR 9676 0.01 1.51 0.50 1.00 1.00 2.00 2.00 2.00 ▇▁▁▁▇
LBXHCG 9746 0.01 1.75 1.52 1.00 1.00 1.00 2.00 9.00 ▇▁▁▁▁
LBDHEG 2157 0.78 1.96 0.21 1.00 2.00 2.00 2.00 2.00 ▁▁▁▁▇
LBDHEM 2157 0.78 1.99 0.08 1.00 2.00 2.00 2.00 2.00 ▁▁▁▁▇
LBXHE1 6144 0.37 1.47 0.50 1.00 1.00 1.00 2.00 3.00 ▇▁▇▁▁
LBXHE2 6768 0.31 1.84 0.38 1.00 2.00 2.00 2.00 3.00 ▂▁▇▁▁
LBXGH 3170 0.68 5.64 1.00 3.50 5.20 5.40 5.80 17.50 ▇▁▁▁▁
LBDHI 5901 0.40 2.00 0.08 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXGH 4756 0.52 1.13 0.50 1.00 1.00 1.00 1.00 3.00 ▇▁▁▁▁
ORXGL 4756 0.52 1.13 0.50 1.00 1.00 1.00 1.00 3.00 ▇▁▁▁▁
ORXH06 4756 0.52 2.06 0.26 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH11 4756 0.52 2.07 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH16 4756 0.52 2.06 0.27 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH18 4756 0.52 2.06 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH26 4756 0.52 2.07 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH31 4756 0.52 2.07 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH33 4756 0.52 2.06 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH35 4756 0.52 2.06 0.26 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH39 4756 0.52 2.06 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH40 4756 0.52 2.07 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH42 4756 0.52 2.06 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH45 4756 0.52 2.07 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH51 4756 0.52 2.06 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH52 4756 0.52 2.06 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH53 4756 0.52 2.06 0.26 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH54 4756 0.52 2.07 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH55 4756 0.52 2.06 0.26 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH56 4756 0.52 2.06 0.26 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH58 4756 0.52 2.06 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH59 4756 0.52 2.06 0.26 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH61 4756 0.52 2.06 0.26 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH62 4756 0.52 2.06 0.26 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH64 4756 0.52 2.07 0.25 2.00 2.00 2.00 2.00 3.00 ▇▁▁▁▁
ORXH66 4756 0.52 2.06 0.26 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH67 4756 0.52 2.07 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH68 4756 0.52 2.06 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH69 4756 0.52 2.06 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH70 4756 0.52 2.07 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH71 4756 0.52 2.06 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH72 4756 0.52 2.06 0.26 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH73 4756 0.52 2.06 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH81 4756 0.52 2.06 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH82 4756 0.52 2.07 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH83 4756 0.52 2.06 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXH84 4756 0.52 2.06 0.26 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXHPC 4756 0.52 2.06 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXHPI 4756 0.52 2.07 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
ORXHPV 4756 0.52 1.99 0.37 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDRPCR.x 7945 0.19 1.63 0.59 1.00 1.00 2.00 2.00 3.00 ▆▁▇▁▁
LBDRHP.x 8056 0.18 1.03 0.16 1.00 1.00 1.00 1.00 2.00 ▇▁▁▁▁
LBDRLP.x 8056 0.18 1.26 0.44 1.00 1.00 1.00 2.00 2.00 ▇▁▁▁▃
LBDR06.x 7945 0.19 2.03 0.29 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR11.x 7945 0.19 2.06 0.24 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR16.x 7945 0.19 2.02 0.31 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR18.x 7945 0.19 2.04 0.28 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR26.x 7945 0.19 2.06 0.24 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR31.x 7945 0.19 2.04 0.27 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR33.x 7945 0.19 2.05 0.26 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR35.x 7945 0.19 2.04 0.28 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR39.x 7945 0.19 2.03 0.30 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR40.x 7945 0.19 2.05 0.26 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR42.x 7945 0.19 2.03 0.29 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR45.x 7945 0.19 2.04 0.28 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR51.x 7945 0.19 2.01 0.33 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR52.x 7945 0.19 2.03 0.30 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR53.x 7945 0.19 2.01 0.33 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR54.x 7945 0.19 2.02 0.31 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR55.x 7945 0.19 2.03 0.29 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR56.x 7945 0.19 2.05 0.26 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR58.x 7945 0.19 2.05 0.26 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR59.x 7945 0.19 2.02 0.31 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR61.x 7945 0.19 2.01 0.33 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR62.x 7945 0.19 1.99 0.36 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR64.x 7945 0.19 2.06 0.24 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR66.x 7945 0.19 2.01 0.32 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR67.x 7945 0.19 2.05 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR68.x 7945 0.19 2.05 0.27 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR69.x 7945 0.19 2.06 0.24 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR70.x 7945 0.19 2.04 0.28 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR71.x 7945 0.19 2.05 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR72.x 7945 0.19 2.04 0.27 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR73.x 7945 0.19 2.04 0.27 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR81.x 7945 0.19 2.04 0.29 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR82.x 7945 0.19 2.05 0.26 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR83.x 7945 0.19 2.02 0.31 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR84.x 7945 0.19 1.99 0.36 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR89.x 7945 0.19 2.01 0.33 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDRPI.x 8056 0.18 1.99 0.08 1.00 2.00 2.00 2.00 2.00 ▁▁▁▁▇
LBXHP2C 7818 0.20 1.82 0.45 1.00 2.00 2.00 2.00 3.00 ▂▁▇▁▁
LBDRPCR.y 7818 0.20 1.58 0.50 1.00 1.00 2.00 2.00 3.00 ▆▁▇▁▁
LBDRHP.y 7828 0.20 1.00 0.06 1.00 1.00 1.00 1.00 2.00 ▇▁▁▁▁
LBDRLP.y 7828 0.20 1.03 0.16 1.00 1.00 1.00 1.00 2.00 ▇▁▁▁▁
LBDR06.y 7818 0.20 1.99 0.12 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR11.y 7818 0.20 2.00 0.09 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR16.y 7818 0.20 1.97 0.19 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR18.y 7818 0.20 1.99 0.15 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR26.y 7818 0.20 2.00 0.08 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR31.y 7818 0.20 1.99 0.14 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR33.y 7818 0.20 2.00 0.11 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR35.y 7818 0.20 1.99 0.14 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR39.y 7818 0.20 1.98 0.18 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR40.y 7818 0.20 2.00 0.11 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR42.y 7818 0.20 1.99 0.16 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR45.y 7818 0.20 1.98 0.17 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR51.y 7818 0.20 1.97 0.20 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR52.y 7818 0.20 1.97 0.19 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR53.y 7818 0.20 1.94 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR54.y 7818 0.20 1.95 0.23 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR55.y 7818 0.20 1.98 0.18 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR56.y 7818 0.20 1.98 0.16 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR58.y 7818 0.20 1.99 0.14 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR59.y 7818 0.20 1.98 0.17 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR61.y 7818 0.20 1.95 0.24 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR62.y 7818 0.20 1.94 0.26 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR64.y 7818 0.20 2.01 0.07 2.00 2.00 2.00 2.00 3.00 ▇▁▁▁▁
LBDR66.y 7818 0.20 1.98 0.18 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR67.y 7818 0.20 1.99 0.13 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR68.y 7818 0.20 1.99 0.15 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR69.y 7818 0.20 2.00 0.08 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR70.y 7818 0.20 1.99 0.16 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR71.y 7818 0.20 2.00 0.11 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR72.y 7818 0.20 1.99 0.14 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR73.y 7818 0.20 1.98 0.17 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR81.y 7818 0.20 1.97 0.19 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR82.y 7818 0.20 1.99 0.12 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR83.y 7818 0.20 1.97 0.21 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR84.y 7818 0.20 1.95 0.24 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDR89.y 7818 0.20 1.96 0.22 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBDRPI.y 7818 0.20 2.00 0.10 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
WTSAF2YR.y 6484 0.34 78917.20 71088.02 0.00 33217.41 56397.70 99356.56 395978.47 ▇▂▁▁▁
LBXIN 6720 0.32 13.53 18.64 0.14 6.08 9.47 15.35 682.48 ▇▁▁▁▁
LBDINSI 6720 0.32 81.16 111.83 0.84 36.48 56.82 92.10 4094.88 ▇▁▁▁▁
PHAFSTHR.y 6522 0.34 10.87 3.51 0.00 10.00 11.00 13.00 34.00 ▁▇▂▁▁
PHAFSTMN.y 6522 0.34 29.98 17.15 0.00 15.00 31.00 44.00 59.00 ▇▆▇▇▇
URXUIO 7147 0.27 240.74 1095.49 7.40 72.62 130.75 231.57 33046.00 ▇▁▁▁▁
WTSAF2YR 6484 0.34 78917.20 71088.02 0.00 33217.41 56397.70 99356.56 395978.47 ▇▂▁▁▁
LBXTR 6667 0.32 112.31 115.61 13.00 60.00 88.00 133.00 4233.00 ▇▁▁▁▁
LBDTRSI 6667 0.32 1.27 1.31 0.15 0.68 0.99 1.50 47.79 ▇▁▁▁▁
LBDLDL 6708 0.32 106.22 34.99 14.00 81.00 103.00 127.00 375.00 ▅▇▁▁▁
LBDLDLSI 6708 0.32 2.75 0.90 0.36 2.10 2.66 3.28 9.70 ▅▇▁▁▁
WTSH2YR.x 3881 0.60 51839.77 55772.29 0.00 14018.72 34086.06 61047.56 322113.59 ▇▁▁▁▁
LBXIHG 4638 0.53 0.25 0.28 0.19 0.19 0.19 0.19 13.00 ▇▁▁▁▁
LBDIHGSI 4638 0.53 1.24 1.39 0.95 0.95 0.95 0.95 64.87 ▇▁▁▁▁
LBDIHGLC 4638 0.53 0.83 0.38 0.00 1.00 1.00 1.00 1.00 ▂▁▁▁▇
LBXBGE 4638 0.53 0.11 0.02 0.11 0.11 0.11 0.11 0.66 ▇▁▁▁▁
LBDBGELC 4638 0.53 0.99 0.09 0.00 1.00 1.00 1.00 1.00 ▁▁▁▁▇
LBXBGM 4638 0.53 0.94 2.14 0.08 0.08 0.31 0.87 47.15 ▇▁▁▁▁
LBDBGMLC 4638 0.53 0.26 0.44 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▃
WTSOG2YR 6904 0.30 90311.22 78338.02 0.00 39307.75 64226.69 116273.63 415382.80 ▇▃▂▁▁
LBXGLT 7468 0.24 115.28 47.22 40.00 87.00 105.00 131.00 604.00 ▇▁▁▁▁
LBDGLTSI 7468 0.24 6.40 2.62 2.22 4.83 5.83 7.27 33.53 ▇▁▁▁▁
GTDSCMMN 7404 0.25 2.17 2.18 0.00 1.00 2.00 3.00 53.00 ▇▁▁▁▁
GTDDR1MN 7404 0.25 6.02 3.47 1.00 4.00 5.00 7.00 74.00 ▇▁▁▁▁
GTDBL2MN 7467 0.24 118.44 6.43 42.00 114.00 117.00 121.00 183.00 ▁▁▇▁▁
GTDDR2MN 7467 0.24 112.49 5.43 65.00 108.00 111.00 115.00 135.00 ▁▁▁▇▁
GTXDRANK 7299 0.26 1.10 0.44 1.00 1.00 1.00 1.00 3.00 ▇▁▁▁▁
PHAFSTHR 6904 0.30 11.63 1.91 8.00 10.00 11.00 13.00 23.00 ▇▆▁▁▁
PHAFSTMN 6904 0.30 30.05 17.09 0.00 15.00 31.00 44.00 59.00 ▇▆▇▇▇
GTDCODE 6904 0.30 4.97 10.27 0.00 0.00 0.00 0.00 30.00 ▇▁▁▁▁
WTSA2YR.y 7058 0.28 104301.88 87271.68 0.00 44409.69 71416.93 131754.23 530325.35 ▇▂▁▁▁
URXP01 7173 0.27 27874.13 724051.50 42.40 558.00 1145.50 3241.25 35920000.00 ▇▁▁▁▁
URDP01LC 7173 0.27 0.00 0.04 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXP02 7172 0.27 8737.88 13875.15 130.00 2051.00 4599.00 10380.00 368500.00 ▇▁▁▁▁
URDP02LC 7172 0.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ▁▁▇▁▁
URXP03 7163 0.27 218.96 462.62 5.70 35.00 72.00 166.00 7889.00 ▇▁▁▁▁
URDP03LC 7163 0.27 0.02 0.15 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXP04 7163 0.27 390.08 713.54 5.70 84.25 167.00 363.00 14850.00 ▇▁▁▁▁
URDP04LC 7163 0.27 0.00 0.03 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXP06 7163 0.27 142.18 223.26 6.40 49.00 91.00 168.00 7019.00 ▇▁▁▁▁
URDP06LC 7163 0.27 0.01 0.11 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXP10 7163 0.27 212.27 353.12 49.50 72.00 124.50 232.00 7560.00 ▇▁▁▁▁
URDP10LC 7163 0.27 0.24 0.42 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
URXP25 7163 0.27 188.78 293.77 7.10 64.00 117.00 215.00 6515.00 ▇▁▁▁▁
URDP25LC 7163 0.27 0.00 0.07 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
WTSA2YR 7058 0.28 104301.88 87271.68 0.00 44409.69 71416.93 131754.23 530325.35 ▇▂▁▁▁
URXUP8 7169 0.27 4.64 14.31 0.04 1.50 2.76 4.86 583.00 ▇▁▁▁▁
URDUP8LC 7169 0.27 0.00 0.02 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXNO3 7169 0.27 57085.70 49863.53 495.00 25475.00 44800.00 73125.00 584000.00 ▇▁▁▁▁
URDNO3LC 7169 0.27 0.00 0.05 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXSCN 7170 0.27 2051.29 4174.96 14.14 483.50 994.00 1995.00 131000.00 ▇▁▁▁▁
URDSCNLC 7170 0.27 0.00 0.03 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
WTSB2YR.y 7474 0.24 112319.51 93293.88 0.00 48299.47 75783.46 141848.61 482310.79 ▇▂▂▁▁
LBXPFDE 7645 0.22 0.31 1.21 0.07 0.10 0.20 0.30 51.30 ▇▁▁▁▁
LBDPFDEL 7645 0.22 0.21 0.41 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
LBXPFHS 7645 0.22 1.94 2.21 0.07 0.70 1.30 2.40 33.90 ▇▁▁▁▁
LBDPFHSL 7645 0.22 0.01 0.11 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
LBXMPAH 7645 0.22 0.18 0.29 0.07 0.07 0.07 0.20 6.30 ▇▁▁▁▁
LBDMPAHL 7645 0.22 0.56 0.50 0.00 0.00 1.00 1.00 1.00 ▆▁▁▁▇
LBXPFBS 7645 0.22 0.07 0.01 0.07 0.07 0.07 0.07 0.30 ▇▁▁▁▁
LBDPFBSL 7645 0.22 0.99 0.08 0.00 1.00 1.00 1.00 1.00 ▁▁▁▁▇
LBXPFHP 7645 0.22 0.08 0.06 0.07 0.07 0.07 0.07 1.30 ▇▁▁▁▁
LBDPFHPL 7645 0.22 0.88 0.33 0.00 1.00 1.00 1.00 1.00 ▁▁▁▁▇
LBXPFNA 7645 0.22 0.87 0.83 0.07 0.40 0.70 1.00 16.30 ▇▁▁▁▁
LBDPFNAL 7645 0.22 0.01 0.11 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
LBXPFUA 7645 0.22 0.23 1.93 0.07 0.07 0.07 0.20 77.40 ▇▁▁▁▁
LBDPFUAL 7645 0.22 0.57 0.50 0.00 0.00 1.00 1.00 1.00 ▆▁▁▁▇
LBXPFDO 7645 0.22 0.10 0.16 0.07 0.07 0.07 0.07 6.90 ▇▁▁▁▁
LBDPFDOL 7645 0.22 0.83 0.38 0.00 1.00 1.00 1.00 1.00 ▂▁▁▁▇
WTSB2YR 7036 0.28 103475.58 89962.67 0.00 42660.60 68840.37 126354.41 482310.79 ▇▂▁▁▁
URXCNP 7128 0.27 5.99 25.40 0.14 1.40 2.60 5.20 876.40 ▇▁▁▁▁
URDCNPLC 7128 0.27 0.01 0.11 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXCOP 7128 0.27 54.09 115.83 0.21 7.80 18.50 49.30 1813.10 ▇▁▁▁▁
URDCOPLC 7128 0.27 0.00 0.03 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXECP 7128 0.27 20.92 44.24 0.28 6.30 12.30 22.40 1421.90 ▇▁▁▁▁
URDECPLC 7128 0.27 0.00 0.05 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXMBP 7128 0.27 17.93 25.84 0.28 5.30 11.30 21.60 489.60 ▇▁▁▁▁
URDMBPLC 7128 0.27 0.02 0.13 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXMC1 7128 0.27 5.31 15.45 0.28 0.90 2.00 4.50 449.50 ▇▁▁▁▁
URDMC1LC 7128 0.27 0.10 0.30 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXMEP 7128 0.27 200.85 1067.64 0.85 14.20 34.20 97.30 29083.00 ▇▁▁▁▁
URDMEPLC 7128 0.27 0.00 0.05 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXMHH 7128 0.27 13.64 32.82 0.28 3.60 7.50 14.70 1029.70 ▇▁▁▁▁
URDMHHLC 7128 0.27 0.01 0.08 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXMHNC 7128 0.27 0.72 3.40 0.28 0.28 0.28 0.50 151.70 ▇▁▁▁▁
URDMCHLC 7128 0.27 0.70 0.46 0.00 0.00 1.00 1.00 1.00 ▃▁▁▁▇
URXMHP 7128 0.27 2.45 5.33 0.57 0.57 1.20 2.60 152.30 ▇▁▁▁▁
URDMHPLC 7128 0.27 0.38 0.48 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▅
URXMIB 7128 0.27 14.77 23.48 0.57 4.00 8.70 16.70 393.10 ▇▁▁▁▁
URDMIBLC 7128 0.27 0.03 0.16 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXMNP 7128 0.27 3.13 10.60 0.64 0.64 0.64 1.70 286.00 ▇▁▁▁▁
URDMNPLC 7128 0.27 0.60 0.49 0.00 0.00 1.00 1.00 1.00 ▆▁▁▁▇
URXMOH 7128 0.27 8.58 18.72 0.14 2.40 5.00 9.50 585.20 ▇▁▁▁▁
URDMOHLC 7128 0.27 0.00 0.07 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXMZP 7128 0.27 11.24 22.46 0.21 2.00 4.80 12.10 387.40 ▇▁▁▁▁
URDMZPLC 7128 0.27 0.02 0.15 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
LBXTC 2189 0.78 179.53 40.95 69.00 151.00 175.00 204.00 813.00 ▇▂▁▁▁
LBDTCSI 2189 0.78 4.64 1.06 1.78 3.90 4.53 5.28 21.02 ▇▂▁▁▁
LBXTTG 2236 0.77 2.00 0.07 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
LBXEMA 9779 0.00 1.41 0.61 1.00 1.00 1.00 2.00 3.00 ▇▁▃▁▁
WTSH2YR.y 3881 0.60 51839.77 55772.29 0.00 14018.72 34086.06 61047.56 322113.59 ▇▁▁▁▁
LBXBPB 4598 0.53 1.11 1.28 0.07 0.50 0.78 1.28 34.11 ▇▁▁▁▁
LBDBPBSI 4598 0.53 0.05 0.06 0.00 0.02 0.04 0.06 1.65 ▇▁▁▁▁
LBDBPBLC 4598 0.53 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ▁▁▇▁▁
LBXBCD 4598 0.53 0.31 0.44 0.07 0.07 0.15 0.32 7.23 ▇▁▁▁▁
LBDBCDSI 4598 0.53 2.73 3.89 0.62 0.62 1.33 2.85 64.33 ▇▁▁▁▁
LBDBCDLC 4598 0.53 0.29 0.45 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▃
LBXTHG 4598 0.53 1.09 2.09 0.20 0.20 0.50 1.06 46.39 ▇▁▁▁▁
LBDTHGSI 4598 0.53 5.45 10.42 1.00 1.00 2.50 5.30 231.50 ▇▁▁▁▁
LBDTHGLC 4598 0.53 0.26 0.44 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▃
LBXBSE 4598 0.53 188.61 26.08 86.09 171.74 186.50 202.16 734.80 ▇▁▁▁▁
LBDBSESI 4598 0.53 2.40 0.33 1.09 2.18 2.37 2.57 9.33 ▇▁▁▁▁
LBDBSELC 4598 0.53 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ▁▁▇▁▁
LBXBMN 4598 0.53 10.51 3.72 1.75 7.97 9.89 12.39 54.92 ▇▂▁▁▁
LBDBMNSI 4598 0.53 191.22 67.67 31.85 145.05 180.00 225.59 999.54 ▇▂▁▁▁
LBDBMNLC 4598 0.53 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ▁▁▇▁▁
URXUTRI 5756 0.41 1.98 0.13 1.00 2.00 2.00 2.00 2.00 ▁▁▁▁▇
URXUAS3 7159 0.27 0.54 0.48 0.08 0.08 0.52 0.78 6.71 ▇▁▁▁▁
URDUA3LC 7159 0.27 0.28 0.45 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▃
URXUAS5 7159 0.27 0.57 0.22 0.56 0.56 0.56 0.56 8.02 ▇▁▁▁▁
URDUA5LC 7159 0.27 0.98 0.13 0.00 1.00 1.00 1.00 1.00 ▁▁▁▁▇
URXUAB 7159 0.27 9.22 44.06 0.82 0.82 0.82 3.92 997.00 ▇▁▁▁▁
URDUABLC 7159 0.27 0.56 0.50 0.00 0.00 1.00 1.00 1.00 ▆▁▁▁▇
URXUAC 7159 0.27 0.16 0.92 0.08 0.08 0.08 0.08 31.80 ▇▁▁▁▁
URDUACLC 7159 0.27 0.84 0.37 0.00 1.00 1.00 1.00 1.00 ▂▁▁▁▇
URXUDMA 7159 0.27 4.96 5.83 1.35 1.96 3.41 5.66 84.70 ▇▁▁▁▁
URDUDALC 7159 0.27 0.24 0.43 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
URXUMMA 7159 0.27 0.62 0.69 0.14 0.14 0.48 0.84 17.00 ▇▁▁▁▁
URDUMMAL 7159 0.27 0.30 0.46 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▃
URXVOL1 1756 0.82 109.11 82.49 0.00 46.00 86.00 150.00 552.00 ▇▃▁▁▁
URDFLOW1 2663 0.73 0.91 0.95 0.00 0.38 0.65 1.11 26.00 ▇▁▁▁▁
URXVOL2 7957 0.19 118.48 93.15 0.00 47.00 87.00 170.00 452.00 ▇▃▂▁▁
URDFLOW2 7958 0.19 1.26 2.14 0.00 0.48 0.82 1.63 76.67 ▇▁▁▁▁
URXVOL3 9714 0.01 96.34 83.38 4.00 28.00 73.00 138.50 383.00 ▇▅▂▁▁
URDFLOW3 9714 0.01 1.78 1.77 0.04 0.54 0.98 2.66 10.92 ▇▃▁▁▁
URXUHG 7147 0.27 0.51 2.04 0.09 0.09 0.20 0.47 83.03 ▇▁▁▁▁
URDUHGLC 7147 0.27 0.34 0.47 0.00 0.00 0.00 1.00 1.00 ▇▁▁▁▅
URXUBA 7149 0.27 1.77 3.00 0.04 0.51 1.02 2.04 60.11 ▇▁▁▁▁
URDUBALC 7149 0.27 0.00 0.05 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXUCD 7149 0.27 0.24 0.36 0.03 0.05 0.12 0.28 5.06 ▇▁▁▁▁
URDUCDLC 7149 0.27 0.18 0.39 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
URXUCO 7149 0.27 0.62 1.78 0.02 0.24 0.43 0.71 75.80 ▇▁▁▁▁
URDUCOLC 7149 0.27 0.00 0.03 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXUCS 7149 0.27 4.97 3.14 0.39 2.54 4.32 6.66 29.00 ▇▃▁▁▁
URDUCSLC 7149 0.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ▁▁▇▁▁
URXUMO 7149 0.27 56.10 56.12 1.43 20.43 41.31 73.90 1200.00 ▇▁▁▁▁
URDUMOLC 7149 0.27 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ▁▁▇▁▁
URXUMN 7149 0.27 0.16 0.49 0.09 0.09 0.09 0.14 18.95 ▇▁▁▁▁
URDUMNLC 7149 0.27 0.70 0.46 0.00 0.00 1.00 1.00 1.00 ▃▁▁▁▇
URXUPB 7149 0.27 0.46 0.77 0.02 0.16 0.30 0.52 23.40 ▇▁▁▁▁
URDUPBLC 7149 0.27 0.01 0.10 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXUSB 7149 0.27 0.08 0.14 0.02 0.02 0.05 0.08 4.23 ▇▁▁▁▁
URDUSBLC 7149 0.27 0.22 0.42 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
URXUSN 7149 0.27 1.44 4.71 0.06 0.20 0.46 1.14 90.97 ▇▁▁▁▁
URDUSNLC 7149 0.27 0.09 0.29 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXUSR 7149 0.27 118.07 182.79 1.66 46.44 87.57 150.83 7565.49 ▇▁▁▁▁
URDUSRLC 7149 0.27 0.00 0.02 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXUTL 7149 0.27 0.19 0.13 0.01 0.09 0.16 0.25 1.29 ▇▂▁▁▁
URDUTLLC 7149 0.27 0.01 0.09 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▁
URXUTU 7149 0.27 0.15 0.58 0.01 0.03 0.07 0.15 25.76 ▇▁▁▁▁
URDUTULC 7149 0.27 0.16 0.37 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
URXUUR 7149 0.27 0.01 0.03 0.00 0.00 0.01 0.01 0.84 ▇▁▁▁▁
URDUURLC 7149 0.27 0.18 0.38 0.00 0.00 0.00 0.00 1.00 ▇▁▁▁▂
URXPREG 8552 0.13 1.97 0.25 1.00 2.00 2.00 2.00 3.00 ▁▁▇▁▁
URXUAS 7151 0.27 15.67 46.74 0.28 3.21 6.24 12.44 1071.30 ▇▁▁▁▁
LBDB12 4497 0.54 640.10 751.97 18.00 381.00 514.00 711.00 26801.00 ▇▁▁▁▁
LBDB12SI 4497 0.54 472.39 554.95 13.30 281.20 379.30 524.70 19779.10 ▇▁▁▁▁ 
head(cbc_data) # variable names are annoying, but I have the data dictionary
## # A tibble: 6 × 424
##    SEQN URXUMA URXUMS URXUCR.x URXCRS URDACT WTSAF2YR.x LBXAPB LBDAPBSI LBXSAL
##   <dbl>  <dbl>  <dbl>    <dbl>  <dbl>  <dbl>      <dbl>  <dbl>    <dbl>  <dbl>
## 1 73557    4.3    4.3       39  3448.   11.0        NA      NA    NA       4.1
## 2 73558  153    153         50  4420   306          NA      NA    NA       4.7
## 3 73559   11.9   11.9      113  9989.   10.5    142197.     57     0.57    3.7
## 4 73560   16     16         76  6718.   21.0        NA      NA    NA      NA  
## 5 73561  255    255        147 12995.  173.     142266.     92     0.92    4.3
## 6 73562  123    123         74  6542.  166.         NA      NA    NA       4.3
## # … with 414 more variables: LBDSALSI <dbl>, LBXSAPSI <dbl>, LBXSASSI <dbl>,
## #   LBXSATSI <dbl>, LBXSBU <dbl>, LBDSBUSI <dbl>, LBXSC3SI <dbl>, LBXSCA <dbl>,
## #   LBDSCASI <dbl>, LBXSCH <dbl>, LBDSCHSI <dbl>, LBXSCK <dbl>, LBXSCLSI <dbl>,
## #   LBXSCR <dbl>, LBDSCRSI <dbl>, LBXSGB <dbl>, LBDSGBSI <dbl>, LBXSGL <dbl>,
## #   LBDSGLSI <dbl>, LBXSGTSI <dbl>, LBXSIR <dbl>, LBDSIRSI <dbl>,
## #   LBXSKSI <dbl>, LBXSLDSI <dbl>, LBXSNASI <dbl>, LBXSOSSI <dbl>,
## #   LBXSPH <dbl>, LBDSPHSI <dbl>, LBXSTB <dbl>, LBDSTBSI <dbl>, LBXSTP <dbl>, …
tail(cbc_data) # no blank rows at the end
## # A tibble: 6 × 424
##    SEQN URXUMA URXUMS URXUCR.x URXCRS URDACT WTSAF2YR.x LBXAPB LBDAPBSI LBXSAL
##   <dbl>  <dbl>  <dbl>    <dbl>  <dbl>  <dbl>      <dbl>  <dbl>    <dbl>  <dbl>
## 1 83726    5.2    5.2      114 10078.   4.56        NA      NA    NA      NA  
## 2 83727    1.9    1.9       47  4155.   4.04     67775.     88     0.88    4.9
## 3 83728   NA     NA         NA    NA   NA           NA      NA    NA      NA  
## 4 83729    6      6        117 10343.   5.13        NA      NA    NA       4.1
## 5 83730    4.5    4.5       86  7602.   5.23        NA      NA    NA      NA  
## 6 83731    5.3    5.3      114 10078.   4.65        NA      NA    NA      NA  
## # … with 414 more variables: LBDSALSI <dbl>, LBXSAPSI <dbl>, LBXSASSI <dbl>,
## #   LBXSATSI <dbl>, LBXSBU <dbl>, LBDSBUSI <dbl>, LBXSC3SI <dbl>, LBXSCA <dbl>,
## #   LBDSCASI <dbl>, LBXSCH <dbl>, LBDSCHSI <dbl>, LBXSCK <dbl>, LBXSCLSI <dbl>,
## #   LBXSCR <dbl>, LBDSCRSI <dbl>, LBXSGB <dbl>, LBDSGBSI <dbl>, LBXSGL <dbl>,
## #   LBDSGLSI <dbl>, LBXSGTSI <dbl>, LBXSIR <dbl>, LBDSIRSI <dbl>,
## #   LBXSKSI <dbl>, LBXSLDSI <dbl>, LBXSNASI <dbl>, LBXSOSSI <dbl>,
## #   LBXSPH <dbl>, LBDSPHSI <dbl>, LBXSTB <dbl>, LBDSTBSI <dbl>, LBXSTP <dbl>, …
# check for na's
cbc_data %>% janitor::clean_names() # they're still all upper case
## # A tibble: 9,813 × 424
##     seqn urxuma urxums urxucr_x urxcrs urdact wtsaf2yr_x lbxapb lbdapbsi lbxsal
##    <dbl>  <dbl>  <dbl>    <dbl>  <dbl>  <dbl>      <dbl>  <dbl>    <dbl>  <dbl>
##  1 73557    4.3    4.3       39  3448.  11.0         NA      NA    NA       4.1
##  2 73558  153    153         50  4420  306           NA      NA    NA       4.7
##  3 73559   11.9   11.9      113  9989.  10.5     142197.     57     0.57    3.7
##  4 73560   16     16         76  6718.  21.0         NA      NA    NA      NA  
##  5 73561  255    255        147 12995. 173.      142266.     92     0.92    4.3
##  6 73562  123    123         74  6542. 166.          NA      NA    NA       4.3
##  7 73563   NA     NA         NA    NA   NA           NA      NA    NA      NA  
##  8 73564   19     19        242 21393.   7.85    134054.     77     0.77    3.9
##  9 73566    1.3    1.3       18  1591.   7.22        NA      NA    NA       4.1
## 10 73567   35     35        215 19006   16.3         NA      NA    NA       4  
## # … with 9,803 more rows, and 414 more variables: lbdsalsi <dbl>,
## #   lbxsapsi <dbl>, lbxsassi <dbl>, lbxsatsi <dbl>, lbxsbu <dbl>,
## #   lbdsbusi <dbl>, lbxsc3si <dbl>, lbxsca <dbl>, lbdscasi <dbl>, lbxsch <dbl>,
## #   lbdschsi <dbl>, lbxsck <dbl>, lbxsclsi <dbl>, lbxscr <dbl>, lbdscrsi <dbl>,
## #   lbxsgb <dbl>, lbdsgbsi <dbl>, lbxsgl <dbl>, lbdsglsi <dbl>, lbxsgtsi <dbl>,
## #   lbxsir <dbl>, lbdsirsi <dbl>, lbxsksi <dbl>, lbxsldsi <dbl>,
## #   lbxsnasi <dbl>, lbxsossi <dbl>, lbxsph <dbl>, lbdsphsi <dbl>, …
names(cbc_data) <-tolower(names(cbc_data)) #change to lower case

# check some individual variables for sense
cbc_data %>% summarize(lbdlymno)
## # A tibble: 9,813 × 1
##    lbdlymno
##       <dbl>
##  1      2  
##  2      3.4
##  3      1  
##  4      2.3
##  5      1.4
##  6      1.6
##  7     NA  
##  8      1.6
##  9      3  
## 10      1.4
## # … with 9,803 more rows

If there are any quirks that you have to deal with NA coded as something else, or it is multiple tables, please make some notes here about what you need to do before you start transforming the data in the next section.

I will make sure NAs are in the data as “NA.” I don’t see any other isues with the data, like NA being coded as “-888” or anything. I used the “na” argument in read_csv to code missing data as NA. I did see that for binary variables: * 1=yes * 2=no * 7= refused * 9= don’t know.

I will have to relabel or drop those. I will most likely also have to turn these binary variables into factors.

Transforming the data

Bonus points (5 points) for datasets that require merging of tables, but only if you reason through whether you should use left_join, inner_join, or right_join on these tables. No credit will be provided if you don’t.

NHANES provided the data in several different csv files: diet, lab values, physical exam values, questionnaire data, and demographics. I have the lab values loaded, and I want to join with the demographics and the questionnaire data.

I only want to keep the observations where I have all variables in all datasets, just for the same of this analysis (and because the dataset is so large), so I will do an inner join.

This would be different if I wanted to keep incomplete observations (where some values were missing for some variables) but I don’t.

# load the demographics data
demographics <- read_csv(file=here("data","nhanes_demographics.csv"), na="NA")
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
## Rows: 10175 Columns: 47
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (47): SEQN, SDDSRVYR, RIDSTATR, RIAGENDR, RIDAGEYR, RIDAGEMN, RIDRETH1, ...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# get a look at the data
demographics %>% skimr::skim() # 424 variables, 9813 observations
Data summary
Name Piped data
Number of rows 10175
Number of columns 47
_______________________
Column type frequency:
numeric 47
________________________
Group variables None

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
SEQN 0 1.00 78644.00 2937.41 73557.00 76100.50 78644.00 81187.50 83731.0 ▇▇▇▇▇
SDDSRVYR 0 1.00 8.00 0.00 8.00 8.00 8.00 8.00 8.0 ▁▁▇▁▁
RIDSTATR 0 1.00 1.96 0.19 1.00 2.00 2.00 2.00 2.0 ▁▁▁▁▇
RIAGENDR 0 1.00 1.51 0.50 1.00 1.00 2.00 2.00 2.0 ▇▁▁▁▇
RIDAGEYR 0 1.00 31.48 24.42 0.00 10.00 26.00 52.00 80.0 ▇▃▃▃▃
RIDAGEMN 9502 0.07 10.34 6.89 0.00 5.00 9.00 16.00 24.0 ▆▇▅▅▃
RIDRETH1 0 1.00 3.09 1.26 1.00 2.00 3.00 4.00 5.0 ▃▂▇▅▃
RIDRETH3 0 1.00 3.29 1.61 1.00 2.00 3.00 4.00 7.0 ▆▇▅▁▃
RIDEXMON 362 0.96 1.51 0.50 1.00 1.00 2.00 2.00 2.0 ▇▁▁▁▇
RIDEXAGM 5962 0.41 107.48 69.77 0.00 45.00 103.00 166.00 239.0 ▇▆▆▅▅
DMQMILIZ 3914 0.62 1.91 0.29 1.00 2.00 2.00 2.00 7.0 ▇▁▁▁▁
DMQADFC 9632 0.05 1.49 0.59 1.00 1.00 1.00 2.00 9.0 ▇▁▁▁▁
DMDBORN4 0 1.00 1.23 1.83 1.00 1.00 1.00 1.00 99.0 ▇▁▁▁▁
DMDCITZN 4 1.00 1.10 0.34 1.00 1.00 1.00 1.00 9.0 ▇▁▁▁▁
DMDYRSUS 8267 0.19 8.84 17.59 1.00 4.00 5.00 7.00 99.0 ▇▁▁▁▁
DMDEDUC3 7372 0.28 6.16 5.86 0.00 2.00 5.00 9.00 99.0 ▇▁▁▁▁
DMDEDUC2 4406 0.57 3.52 1.24 1.00 3.00 4.00 5.00 9.0 ▃▇▃▁▁
DMDMARTL 4406 0.57 2.57 2.63 1.00 1.00 1.00 5.00 99.0 ▇▁▁▁▁
RIDEXPRG 8866 0.13 2.02 0.35 1.00 2.00 2.00 2.00 3.0 ▁▁▇▁▁
SIALANG 0 1.00 1.11 0.31 1.00 1.00 1.00 1.00 2.0 ▇▁▁▁▁
SIAPROXY 1 1.00 1.63 0.48 1.00 1.00 2.00 2.00 2.0 ▅▁▁▁▇
SIAINTRP 0 1.00 1.97 0.18 1.00 2.00 2.00 2.00 2.0 ▁▁▁▁▇
FIALANG 121 0.99 1.06 0.24 1.00 1.00 1.00 1.00 2.0 ▇▁▁▁▁
FIAPROXY 121 0.99 2.00 0.04 1.00 2.00 2.00 2.00 2.0 ▁▁▁▁▇
FIAINTRP 121 0.99 1.97 0.17 1.00 2.00 2.00 2.00 2.0 ▁▁▁▁▇
MIALANG 2864 0.72 1.06 0.23 1.00 1.00 1.00 1.00 2.0 ▇▁▁▁▁
MIAPROXY 2863 0.72 1.99 0.08 1.00 2.00 2.00 2.00 2.0 ▁▁▁▁▇
MIAINTRP 2862 0.72 1.97 0.18 1.00 2.00 2.00 2.00 2.0 ▁▁▁▁▇
AIALANGA 3858 0.62 1.10 0.34 1.00 1.00 1.00 1.00 3.0 ▇▁▁▁▁
DMDHHSIZ 0 1.00 3.87 1.72 1.00 2.00 4.00 5.00 7.0 ▇▆▆▆▆
DMDFMSIZ 0 1.00 3.73 1.78 1.00 2.00 4.00 5.00 7.0 ▇▅▆▅▅
DMDHHSZA 0 1.00 0.54 0.81 0.00 0.00 0.00 1.00 3.0 ▇▃▁▁▁
DMDHHSZB 0 1.00 1.01 1.19 0.00 0.00 1.00 2.00 4.0 ▇▃▃▂▁
DMDHHSZE 0 1.00 0.40 0.70 0.00 0.00 0.00 1.00 3.0 ▇▂▁▁▁
DMDHRGND 0 1.00 1.50 0.50 1.00 1.00 1.00 2.00 2.0 ▇▁▁▁▇
DMDHRAGE 0 1.00 45.96 15.64 18.00 34.00 43.00 56.00 80.0 ▅▇▇▃▃
DMDHRBR4 297 0.97 1.39 2.68 1.00 1.00 1.00 2.00 77.0 ▇▁▁▁▁
DMDHREDU 294 0.97 3.50 1.25 1.00 3.00 4.00 4.00 9.0 ▃▇▃▁▁
DMDHRMAR 123 0.99 2.74 5.92 1.00 1.00 1.00 4.00 99.0 ▇▁▁▁▁
DMDHSEDU 4833 0.53 3.61 1.32 1.00 3.00 4.00 5.00 9.0 ▃▇▅▁▁
WTINT2YR 0 1.00 30585.18 26948.43 3697.77 12754.49 20233.00 36280.43 167884.5 ▇▂▁▁▁
WTMEC2YR 0 1.00 30585.18 27941.01 0.00 12561.60 20174.57 36748.22 171395.3 ▇▂▁▁▁
SDMVPSU 0 1.00 1.48 0.50 1.00 1.00 1.00 2.00 2.0 ▇▁▁▁▇
SDMVSTRA 0 1.00 110.93 4.26 104.00 107.00 111.00 115.00 118.0 ▇▇▇▇▇
INDHHIN2 133 0.99 10.88 13.88 1.00 5.00 8.00 14.00 99.0 ▇▁▁▁▁
INDFMIN2 123 0.99 10.51 13.64 1.00 5.00 7.00 14.00 99.0 ▇▁▁▁▁
INDFMPIR 785 0.92 2.25 1.63 0.00 0.87 1.71 3.61 5.0 ▇▇▃▃▆ 
head(demographics) # variable names are annoying, but I have the data dictionary
## # A tibble: 6 × 47
##    SEQN SDDSRVYR RIDST…¹ RIAGE…² RIDAG…³ RIDAG…⁴ RIDRE…⁵ RIDRE…⁶ RIDEX…⁷ RIDEX…⁸
##   <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
## 1 73557        8       2       1      69      NA       4       4       1      NA
## 2 73558        8       2       1      54      NA       3       3       1      NA
## 3 73559        8       2       1      72      NA       3       3       2      NA
## 4 73560        8       2       1       9      NA       3       3       1     119
## 5 73561        8       2       2      73      NA       3       3       1      NA
## 6 73562        8       2       1      56      NA       1       1       1      NA
## # … with 37 more variables: DMQMILIZ <dbl>, DMQADFC <dbl>, DMDBORN4 <dbl>,
## #   DMDCITZN <dbl>, DMDYRSUS <dbl>, DMDEDUC3 <dbl>, DMDEDUC2 <dbl>,
## #   DMDMARTL <dbl>, RIDEXPRG <dbl>, SIALANG <dbl>, SIAPROXY <dbl>,
## #   SIAINTRP <dbl>, FIALANG <dbl>, FIAPROXY <dbl>, FIAINTRP <dbl>,
## #   MIALANG <dbl>, MIAPROXY <dbl>, MIAINTRP <dbl>, AIALANGA <dbl>,
## #   DMDHHSIZ <dbl>, DMDFMSIZ <dbl>, DMDHHSZA <dbl>, DMDHHSZB <dbl>,
## #   DMDHHSZE <dbl>, DMDHRGND <dbl>, DMDHRAGE <dbl>, DMDHRBR4 <dbl>, …
tail(demographics) # no blank rows at the end
## # A tibble: 6 × 47
##    SEQN SDDSRVYR RIDST…¹ RIAGE…² RIDAG…³ RIDAG…⁴ RIDRE…⁵ RIDRE…⁶ RIDEX…⁷ RIDEX…⁸
##   <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
## 1 83726        8       2       1      40      NA       1       1       1      NA
## 2 83727        8       2       1      26      NA       2       2       2      NA
## 3 83728        8       2       2       2      24       1       1       2      24
## 4 83729        8       2       2      42      NA       4       4       2      NA
## 5 83730        8       2       1       7      NA       2       2       1      84
## 6 83731        8       2       1      11      NA       5       6       1     140
## # … with 37 more variables: DMQMILIZ <dbl>, DMQADFC <dbl>, DMDBORN4 <dbl>,
## #   DMDCITZN <dbl>, DMDYRSUS <dbl>, DMDEDUC3 <dbl>, DMDEDUC2 <dbl>,
## #   DMDMARTL <dbl>, RIDEXPRG <dbl>, SIALANG <dbl>, SIAPROXY <dbl>,
## #   SIAINTRP <dbl>, FIALANG <dbl>, FIAPROXY <dbl>, FIAINTRP <dbl>,
## #   MIALANG <dbl>, MIAPROXY <dbl>, MIAINTRP <dbl>, AIALANGA <dbl>,
## #   DMDHHSIZ <dbl>, DMDFMSIZ <dbl>, DMDHHSZA <dbl>, DMDHHSZB <dbl>,
## #   DMDHHSZE <dbl>, DMDHRGND <dbl>, DMDHRAGE <dbl>, DMDHRBR4 <dbl>, …
# check for na's
demographics %>% janitor::clean_names() # they're still all upper case
## # A tibble: 10,175 × 47
##     seqn sddsr…¹ ridst…² riage…³ ridag…⁴ ridag…⁵ ridre…⁶ ridre…⁷ ridex…⁸ ridex…⁹
##    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
##  1 73557       8       2       1      69      NA       4       4       1      NA
##  2 73558       8       2       1      54      NA       3       3       1      NA
##  3 73559       8       2       1      72      NA       3       3       2      NA
##  4 73560       8       2       1       9      NA       3       3       1     119
##  5 73561       8       2       2      73      NA       3       3       1      NA
##  6 73562       8       2       1      56      NA       1       1       1      NA
##  7 73563       8       2       1       0       5       3       3       2       6
##  8 73564       8       2       2      61      NA       3       3       2      NA
##  9 73565       8       1       1      42      NA       2       2      NA      NA
## 10 73566       8       2       2      56      NA       3       3       1      NA
## # … with 10,165 more rows, 37 more variables: dmqmiliz <dbl>, dmqadfc <dbl>,
## #   dmdborn4 <dbl>, dmdcitzn <dbl>, dmdyrsus <dbl>, dmdeduc3 <dbl>,
## #   dmdeduc2 <dbl>, dmdmartl <dbl>, ridexprg <dbl>, sialang <dbl>,
## #   siaproxy <dbl>, siaintrp <dbl>, fialang <dbl>, fiaproxy <dbl>,
## #   fiaintrp <dbl>, mialang <dbl>, miaproxy <dbl>, miaintrp <dbl>,
## #   aialanga <dbl>, dmdhhsiz <dbl>, dmdfmsiz <dbl>, dmdhhsza <dbl>,
## #   dmdhhszb <dbl>, dmdhhsze <dbl>, dmdhrgnd <dbl>, dmdhrage <dbl>, …
names(demographics) <-tolower(names(demographics)) #change to lower case

#load the questionnaire data
questions <- read_csv(file=here("data","nhanes_questionnaire.csv"), na="NA")
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
## Rows: 10175 Columns: 953
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (2): SMDUPCA, SMD100BR
## dbl (878): SEQN, ACD011A, ACD011C, ACD040, ACD110, ALQ101, ALQ110, ALQ120Q, ...
## lgl  (73): ACD011B, CSQ120A, CDQ009A, CDQ009G, CDQ009H, DIQ175V, DIQ175W, DU...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
questions %>% skimr::skim() # 424 variables, 9813 observations
Data summary
Name Piped data
Number of rows 10175
Number of columns 953
_______________________
Column type frequency:
character 2
logical 73
numeric 878
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
SMDUPCA 0 1 0 12 9359 209 0
SMD100BR 0 1 0 30 9083 131 0

Variable type: logical

skim_variable n_missing complete_rate mean count
ACD011B 10175 0 NaN :
CSQ120A 10165 0 1 TRU: 10
CDQ009A 10165 0 1 TRU: 10
CDQ009G 10175 0 NaN :
CDQ009H 10175 0 NaN :
DIQ175V 10175 0 NaN :
DIQ175W 10175 0 NaN :
DUQ320 10174 0 1 TRU: 1
DUQ380D 10175 0 NaN :
MCQ240B 10175 0 NaN :
MCQ240BB 10175 0 NaN :
MCQ240C 10175 0 NaN :
MCQ240D 10175 0 NaN :
MCQ240H 10175 0 NaN :
MCQ240I 10175 0 NaN :
MCQ240J 10175 0 NaN :
MCQ240K 10175 0 NaN :
MCQ240M 10175 0 NaN :
MCQ240Q 10175 0 NaN :
MCQ240R 10175 0 NaN :
MCQ240T 10175 0 NaN :
MCQ240V 10175 0 NaN :
MCQ240Z 10175 0 NaN :
OHQ780D 10175 0 NaN :
OHQ780F 10175 0 NaN :
OSD030AB 10175 0 NaN :
OSQ040AB 10174 0 1 TRU: 1
OSD050AB 10171 0 1 TRU: 4
OSD030AC 10175 0 NaN :
OSQ040AC 10175 0 NaN :
OSD050AC 10175 0 NaN :
OSD050BB 10171 0 1 TRU: 4
OSD050BC 10175 0 NaN :
OSD050BD 10174 0 1 TRU: 1
OSD030BG 10175 0 NaN :
OSQ040BG 10174 0 1 TRU: 1
OSD030BH 10175 0 NaN :
OSQ040BH 10174 0 1 TRU: 1
OSD030BI 10175 0 NaN :
OSQ040BI 10174 0 1 TRU: 1
OSD030BJ 10175 0 NaN :
OSQ040BJ 10174 0 1 TRU: 1
OSD030CB 10175 0 NaN :
OSQ040CB 10168 0 1 TRU: 7
OSD050CB 10175 0 NaN :
OSD030CC 10175 0 NaN :
OSQ040CC 10173 0 1 TRU: 2
OSQ090E 10166 0 1 TRU: 9
OSQ100E 10175 0 NaN :
OSD110E 10175 0 NaN :
OSQ120E 10170 0 1 TRU: 5
OSQ090F 10170 0 1 TRU: 5
OSQ120F 10171 0 1 TRU: 4
OSQ090G 10173 0 1 TRU: 2
OSQ100G 10175 0 NaN :
OSD110G 10175 0 NaN :
OSQ120G 10173 0 1 TRU: 2
OSQ090H 10173 0 1 TRU: 2
OSQ120H 10175 0 NaN :
OSQ190 10172 0 1 TRU: 3
PAQ724L 10175 0 NaN :
PAQ759C 10175 0 NaN :
PAQ759F 10175 0 NaN :
PAQ759H 10175 0 NaN :
PAQ759O 10175 0 NaN :
PAQ759R 10175 0 NaN :
PAQ759T 10175 0 NaN :
SMQ665B 10175 0 NaN :
SMQ690I 10175 0 NaN :
SMQ857 10173 0 1 TRU: 2
SMQ690J 10175 0 NaN :
SMQ861 10175 0 NaN :
WHD080P 10175 0 NaN :

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
SEQN 0 1.00 78644.00 2937.41 73557.0 76100.50 78644.00 81187.50 83731 ▇▇▇▇▇
ACD011A 4416 0.57 1.00 0.00 1.0 1.00 1.00 1.00 1 ▁▁▇▁▁
ACD011C 10004 0.02 9.00 0.00 9.0 9.00 9.00 9.00 9 ▁▁▇▁▁
ACD040 7801 0.23 3.10 1.51 1.0 2.00 3.00 4.00 9 ▇▇▅▁▁
ACD110 9168 0.10 2.96 1.73 1.0 1.00 3.00 5.00 5 ▇▂▂▂▇
ALQ101 4754 0.53 1.31 0.55 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
ALQ110 8544 0.16 1.59 0.62 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
ALQ120Q 5696 0.44 4.71 34.43 0.0 1.00 2.00 4.00 999 ▇▁▁▁▁
ALQ120U 6582 0.35 1.92 0.85 1.0 1.00 2.00 3.00 3 ▇▁▅▁▆
ALQ130 6579 0.35 3.51 28.87 1.0 1.00 2.00 3.00 999 ▇▁▁▁▁
ALQ141Q 6580 0.35 4.07 45.41 0.0 0.00 0.00 2.00 999 ▇▁▁▁▁
ALQ141U 8711 0.14 2.33 0.84 1.0 2.00 3.00 3.00 3 ▃▁▂▁▇
ALQ151 5698 0.44 1.84 0.39 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
ALQ160 8309 0.18 1.22 23.24 0.0 0.00 0.00 0.00 999 ▇▁▁▁▁
BPQ020 3711 0.64 1.67 0.51 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
BPQ030 8001 0.21 1.23 0.62 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
BPD035 8011 0.21 58.85 109.92 10.0 35.00 47.00 58.00 999 ▇▁▁▁▁
BPQ040A 8001 0.21 1.17 0.44 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
BPQ050A 8360 0.18 1.13 0.38 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
BPQ056 3711 0.64 1.76 0.45 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
BPD058 8596 0.16 150.07 599.22 1.0 4.00 24.00 104.00 9999 ▇▁▁▁▁
BPQ059 3711 0.64 1.87 0.35 1.0 2.00 2.00 2.00 7 ▇▁▁▁▁
BPQ080 3711 0.64 1.72 0.72 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
BPQ060 5748 0.44 1.66 1.47 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
BPQ070 5555 0.45 1.62 1.07 1.0 1.00 1.00 2.00 9 ▇▂▁▁▁
BPQ090D 5555 0.45 1.70 0.54 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
BPQ100D 8725 0.14 1.24 0.58 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
CBD070 123 0.99 20255.04 138337.30 0.0 257.00 410.50 642.00 999999 ▇▁▁▁▁
CBD090 132 0.99 22629.87 147786.44 0.0 0.00 0.00 50.00 999999 ▇▁▁▁▁
CBD110 123 0.99 7130.04 82577.74 0.0 0.00 0.00 100.00 999999 ▇▁▁▁▁
CBD120 123 0.99 19586.70 137063.76 0.0 30.00 100.00 200.00 999999 ▇▁▁▁▁
CBD130 123 0.99 7043.60 82346.64 0.0 0.00 0.00 30.00 999999 ▇▁▁▁▁
HSD010 3708 0.64 2.77 0.97 1.0 2.00 3.00 3.00 9 ▅▇▁▁▁
HSQ500 1700 0.83 1.83 0.49 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
HSQ510 1700 0.83 1.94 0.39 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
HSQ520 1700 0.83 1.98 0.42 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
HSQ571 4406 0.57 1.96 0.28 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
HSQ580 9915 0.03 6.80 12.08 1.0 2.00 5.00 8.00 99 ▇▁▁▁▁
HSQ590 4407 0.57 2.02 1.66 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
HSAQUEX 753 0.93 1.74 0.44 1.0 1.00 2.00 2.00 2 ▃▁▁▁▇
CSQ010 6360 0.37 1.93 0.37 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
CSQ020 6360 0.37 2.75 0.65 1.0 3.00 3.00 3.00 9 ▂▇▁▁▁
CSQ030 6360 0.37 1.76 0.66 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
CSQ040 6360 0.37 1.94 0.40 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
CSQ060 9405 0.08 3.77 1.66 1.0 3.00 4.00 5.00 9 ▃▇▆▁▁
CSQ070 9405 0.08 1.79 1.14 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
CSQ080 6360 0.37 1.95 0.27 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
CSQ090A 6360 0.37 2.81 0.68 1.0 3.00 3.00 3.00 9 ▁▇▁▁▁
CSQ090B 6360 0.37 2.91 0.60 1.0 3.00 3.00 3.00 9 ▁▇▁▁▁
CSQ090C 6360 0.37 2.87 0.57 1.0 3.00 3.00 3.00 9 ▁▇▁▁▁
CSQ090D 6360 0.37 2.93 0.66 1.0 3.00 3.00 3.00 9 ▁▇▁▁▁
CSQ100 6360 0.37 1.12 0.65 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
CSQ110 6360 0.37 1.94 0.29 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
CSQ120B 10152 0.00 2.00 0.00 2.0 2.00 2.00 2.00 2 ▁▁▇▁▁
CSQ120C 10167 0.00 3.00 0.00 3.0 3.00 3.00 3.00 3 ▁▁▇▁▁
CSQ120D 10093 0.01 4.00 0.00 4.0 4.00 4.00 4.00 4 ▁▁▇▁▁
CSQ120E 10114 0.01 5.00 0.00 5.0 5.00 5.00 5.00 5 ▁▁▇▁▁
CSQ120F 10156 0.00 6.00 0.00 6.0 6.00 6.00 6.00 6 ▁▁▇▁▁
CSQ120G 10119 0.01 7.00 0.00 7.0 7.00 7.00 7.00 7 ▁▁▇▁▁
CSQ120H 10128 0.00 8.00 0.00 8.0 8.00 8.00 8.00 8 ▁▁▇▁▁
CSQ140 9515 0.06 3.63 2.11 1.0 2.00 3.00 5.00 9 ▆▇▃▁▂
CSQ160 8489 0.17 1.92 0.33 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
CSQ170 10030 0.01 1.90 1.31 1.0 1.00 1.00 2.00 9 ▇▂▁▁▁
CSQ180 8489 0.17 1.97 0.17 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
CSQ190 8489 0.17 1.98 0.23 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
CSQ200 6360 0.37 1.94 0.30 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
CSQ202 6360 0.37 1.85 0.40 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
CSQ204 6360 0.37 1.72 0.55 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
CSQ210 6360 0.37 1.37 0.96 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
CSQ220 6360 0.37 1.79 0.79 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
CSQ240 6360 0.37 1.88 0.50 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
CSQ250 6360 0.37 1.87 0.45 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
CSQ260 6360 0.37 1.69 0.64 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
AUQ136 6360 0.37 1.94 1.03 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
AUQ138 6360 0.37 2.01 0.57 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
CDQ001 6360 0.37 1.77 0.45 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
CDQ002 9296 0.09 1.81 0.75 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
CDQ003 9892 0.03 1.63 0.66 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
CDQ004 9922 0.02 1.12 0.57 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
CDQ005 9946 0.02 1.21 0.81 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
CDQ006 9979 0.02 1.18 0.38 1.0 1.00 1.00 1.00 2 ▇▁▁▁▂
CDQ009B 10149 0.00 2.00 0.00 2.0 2.00 2.00 2.00 2 ▁▁▇▁▁
CDQ009C 10146 0.00 3.00 0.00 3.0 3.00 3.00 3.00 3 ▁▁▇▁▁
CDQ009D 10095 0.01 4.00 0.00 4.0 4.00 4.00 4.00 4 ▁▁▇▁▁
CDQ009E 10139 0.00 5.00 0.00 5.0 5.00 5.00 5.00 5 ▁▁▇▁▁
CDQ009F 10123 0.01 6.00 0.00 6.0 6.00 6.00 6.00 6 ▁▁▇▁▁
CDQ008 9296 0.09 1.75 0.56 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
CDQ010 6360 0.37 1.72 0.56 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
DIQ010 406 0.96 1.95 0.35 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
DID040 9438 0.07 60.58 104.53 1.0 40.00 50.00 60.00 999 ▇▁▁▁▁
DIQ160 3888 0.62 1.96 0.32 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
DIQ170 3706 0.64 1.89 0.52 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
DIQ172 3706 0.64 1.81 0.84 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
DIQ175A 8837 0.13 10.07 2.43 10.0 10.00 10.00 10.00 99 ▇▁▁▁▁
DIQ175B 9629 0.05 11.00 0.00 11.0 11.00 11.00 11.00 11 ▁▁▇▁▁
DIQ175C 10063 0.01 12.00 0.00 12.0 12.00 12.00 12.00 12 ▁▁▇▁▁
DIQ175D 9805 0.04 13.00 0.00 13.0 13.00 13.00 13.00 13 ▁▁▇▁▁
DIQ175E 10083 0.01 14.00 0.00 14.0 14.00 14.00 14.00 14 ▁▁▇▁▁
DIQ175F 10154 0.00 15.00 0.00 15.0 15.00 15.00 15.00 15 ▁▁▇▁▁
DIQ175G 10024 0.01 16.00 0.00 16.0 16.00 16.00 16.00 16 ▁▁▇▁▁
DIQ175H 10024 0.01 17.00 0.00 17.0 17.00 17.00 17.00 17 ▁▁▇▁▁
DIQ175I 10105 0.01 18.00 0.00 18.0 18.00 18.00 18.00 18 ▁▁▇▁▁
DIQ175J 10068 0.01 19.00 0.00 19.0 19.00 19.00 19.00 19 ▁▁▇▁▁
DIQ175K 10136 0.00 20.00 0.00 20.0 20.00 20.00 20.00 20 ▁▁▇▁▁
DIQ175L 10135 0.00 21.00 0.00 21.0 21.00 21.00 21.00 21 ▁▁▇▁▁
DIQ175M 10084 0.01 22.00 0.00 22.0 22.00 22.00 22.00 22 ▁▁▇▁▁
DIQ175N 10124 0.01 23.00 0.00 23.0 23.00 23.00 23.00 23 ▁▁▇▁▁
DIQ175O 10090 0.01 24.00 0.00 24.0 24.00 24.00 24.00 24 ▁▁▇▁▁
DIQ175P 10106 0.01 25.00 0.00 25.0 25.00 25.00 25.00 25 ▁▁▇▁▁
DIQ175Q 10073 0.01 26.00 0.00 26.0 26.00 26.00 26.00 26 ▁▁▇▁▁
DIQ175R 10155 0.00 27.00 0.00 27.0 27.00 27.00 27.00 27 ▁▁▇▁▁
DIQ175S 10145 0.00 28.00 0.00 28.0 28.00 28.00 28.00 28 ▁▁▇▁▁
DIQ175T 10119 0.01 29.00 0.00 29.0 29.00 29.00 29.00 29 ▁▁▇▁▁
DIQ175U 10102 0.01 30.00 0.00 30.0 30.00 30.00 30.00 30 ▁▁▇▁▁
DIQ175X 10169 0.00 33.00 0.00 33.0 33.00 33.00 33.00 33 ▁▁▇▁▁
DIQ180 3706 0.64 1.73 1.25 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
DIQ050 407 0.96 1.98 0.19 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
DID060 9955 0.02 20.59 91.44 1.0 3.00 8.00 15.00 999 ▇▁▁▁▁
DIQ060U 9958 0.02 1.91 0.29 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
DIQ070 8975 0.12 1.56 0.58 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
DIQ230 9438 0.07 2.94 1.77 1.0 1.00 3.00 5.00 9 ▇▅▅▁▁
DIQ240 9438 0.07 1.23 0.42 1.0 1.00 1.00 1.00 2 ▇▁▁▁▂
DID250 9610 0.06 57.11 727.03 0.0 2.00 3.00 4.00 9999 ▇▁▁▁▁
DID260 9440 0.07 10.18 90.11 0.0 1.00 1.00 2.00 999 ▇▁▁▁▁
DIQ260U 9584 0.06 1.50 0.79 1.0 1.00 1.00 2.00 4 ▇▃▁▁▁
DIQ275 9438 0.07 1.95 2.29 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
DIQ280 9655 0.05 368.85 477.04 4.1 6.88 8.20 999.00 999 ▇▁▁▁▅
DIQ291 9655 0.05 18.09 35.34 1.0 1.00 2.00 6.00 99 ▇▁▁▁▂
DIQ300S 9443 0.07 3841.49 4777.05 88.0 123.00 140.00 9999.00 9999 ▇▁▁▁▅
DIQ300D 9443 0.07 4038.83 4856.04 5.0 72.00 85.00 9999.00 9999 ▇▁▁▁▅
DID310S 9443 0.07 5400.20 3556.11 70.0 130.00 6666.00 6666.00 9999 ▅▁▁▇▃
DID310D 9443 0.07 5441.19 3570.06 5.0 80.00 6666.00 6666.00 9999 ▅▁▁▇▃
DID320 9443 0.07 8107.36 3413.77 5.0 6666.00 9999.00 9999.00 9999 ▂▁▁▁▇
DID330 9549 0.06 7483.40 2911.12 2.0 6666.00 6666.00 9999.00 9999 ▂▁▁▇▇
DID341 9445 0.07 112.43 1041.43 0.0 0.00 2.00 4.00 9999 ▇▁▁▁▁
DID350 9445 0.07 69.77 825.15 0.0 1.00 1.00 1.00 9999 ▇▁▁▁▁
DIQ350U 9578 0.06 1.41 0.73 1.0 1.00 1.00 2.00 4 ▇▂▁▁▁
DIQ360 9443 0.07 2.66 1.27 1.0 2.00 2.00 3.25 9 ▇▃▁▁▁
DIQ080 9443 0.07 1.85 0.81 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
DBQ010 8310 0.18 1.28 0.54 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
DBD030 8817 0.13 3123.57 54203.17 0.0 30.00 91.00 243.00 999999 ▇▁▁▁▁
DBD041 8310 0.18 3800.89 61163.00 0.0 1.00 1.00 60.00 999999 ▇▁▁▁▁
DBD050 8522 0.16 8759.96 91640.07 0.0 121.00 365.00 365.00 999999 ▇▁▁▁▁
DBD055 8310 0.18 6076.18 76579.30 0.0 121.00 182.00 182.00 999999 ▇▁▁▁▁
DBD061 8456 0.17 4389.70 63681.67 0.0 304.00 365.00 365.00 999999 ▇▁▁▁▁
DBQ073A 9085 0.11 10.00 0.00 10.0 10.00 10.00 10.00 10 ▁▁▇▁▁
DBQ073B 9913 0.03 11.00 0.00 11.0 11.00 11.00 11.00 11 ▁▁▇▁▁
DBQ073C 10144 0.00 12.00 0.00 12.0 12.00 12.00 12.00 12 ▁▁▇▁▁
DBQ073D 10164 0.00 13.00 0.00 13.0 13.00 13.00 13.00 13 ▁▁▇▁▁
DBQ073E 10146 0.00 14.00 0.00 14.0 14.00 14.00 14.00 14 ▁▁▇▁▁
DBQ073U 10144 0.00 30.00 0.00 30.0 30.00 30.00 30.00 30 ▁▁▇▁▁
DBQ700 3711 0.64 2.96 1.00 1.0 2.00 3.00 4.00 9 ▃▇▁▁▁
DBQ197 406 0.96 2.11 1.08 0.0 1.00 3.00 3.00 7 ▅▃▇▁▁
DBQ223A 7379 0.27 10.41 5.95 10.0 10.00 10.00 10.00 99 ▇▁▁▁▁
DBQ223B 6384 0.37 11.00 0.00 11.0 11.00 11.00 11.00 11 ▁▁▇▁▁
DBQ223C 9301 0.09 12.00 0.00 12.0 12.00 12.00 12.00 12 ▁▁▇▁▁
DBQ223D 9429 0.07 13.00 0.00 13.0 13.00 13.00 13.00 13 ▁▁▇▁▁
DBQ223E 9940 0.02 14.00 0.00 14.0 14.00 14.00 14.00 14 ▁▁▇▁▁
DBQ223U 9648 0.05 30.00 0.00 30.0 30.00 30.00 30.00 30 ▁▁▇▁▁
DBQ229 4406 0.57 1.96 0.88 1.0 1.00 2.00 3.00 9 ▇▅▁▁▁
DBQ235A 5895 0.42 2.86 0.82 0.0 3.00 3.00 3.00 9 ▁▇▁▁▁
DBQ235B 5895 0.42 2.66 0.83 0.0 2.00 3.00 3.00 9 ▁▇▁▁▁
DBQ235C 5895 0.42 2.33 0.90 0.0 2.00 3.00 3.00 9 ▂▇▁▁▁
DBQ301 8335 0.18 1.96 0.19 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
DBQ330 8335 0.18 1.93 0.26 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
DBQ360 6934 0.32 1.16 0.37 1.0 1.00 1.00 1.00 2 ▇▁▁▁▂
DBQ370 7467 0.27 1.05 0.30 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
DBD381 7587 0.25 22.10 421.30 0.0 2.00 5.00 5.00 9999 ▇▁▁▁▁
DBQ390 8063 0.21 1.61 0.91 1.0 1.00 1.00 3.00 9 ▇▃▁▁▁
DBQ400 7467 0.27 1.21 0.85 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
DBD411 7840 0.23 31.28 531.00 0.0 0.00 1.00 5.00 9999 ▇▁▁▁▁
DBQ421 8959 0.12 1.20 0.54 1.0 1.00 1.00 1.00 3 ▇▁▁▁▁
DBQ424 8540 0.16 2.27 0.76 1.0 2.00 2.00 3.00 9 ▇▆▁▁▁
DBD895 472 0.95 21.95 419.82 0.0 1.00 2.00 4.00 9999 ▇▁▁▁▁
DBD900 2851 0.72 9.59 269.07 0.0 0.00 1.00 3.00 9999 ▇▁▁▁▁
DBD905 494 0.95 15.05 360.61 0.0 0.00 0.00 1.00 9999 ▇▁▁▁▁
DBD910 500 0.95 8.67 240.70 0.0 0.00 0.00 2.00 9999 ▇▁▁▁▁
CBQ596 3711 0.64 1.84 0.42 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
CBQ606 9121 0.10 1.67 0.47 1.0 1.00 2.00 2.00 2 ▃▁▁▁▇
CBQ611 9121 0.10 1.66 0.53 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
CBQ505 3711 0.64 1.16 0.37 1.0 1.00 1.00 1.00 2 ▇▁▁▁▂
CBQ535 4772 0.53 1.66 0.77 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
CBQ540 8073 0.21 1.60 0.52 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
CBQ545 4772 0.53 2.41 1.16 1.0 1.00 2.00 4.00 9 ▇▆▁▁▁
CBQ550 3711 0.64 1.18 0.39 1.0 1.00 1.00 1.00 2 ▇▁▁▁▂
CBQ552 4890 0.52 1.39 0.66 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
CBQ580 4890 0.52 1.74 0.78 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
CBQ585 8547 0.16 1.58 0.56 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
CBQ590 4890 0.52 2.39 1.17 1.0 1.00 2.00 4.00 9 ▇▆▁▁▁
DED031 6248 0.39 4.10 3.74 1.0 3.00 4.00 5.00 99 ▇▁▁▁▁
DEQ034A 6248 0.39 2.94 1.90 1.0 2.00 3.00 4.00 99 ▇▁▁▁▁
DEQ034C 6294 0.38 3.99 1.18 1.0 3.00 4.00 5.00 5 ▁▁▃▃▇
DEQ034D 6294 0.38 3.60 1.44 1.0 3.00 4.00 5.00 9 ▅▆▇▁▁
DEQ038G 6248 0.39 1.66 0.61 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
DEQ038Q 8767 0.14 2.05 2.18 1.0 1.00 1.00 2.00 20 ▇▁▁▁▁
DED120 8071 0.21 152.34 145.07 14.0 45.00 120.00 240.00 480 ▇▅▁▁▂
DED125 6906 0.32 171.76 210.59 14.0 60.00 120.00 240.00 9999 ▇▁▁▁▁
DLQ010 406 0.96 1.96 0.23 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
DLQ020 406 0.96 1.96 0.25 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
DLQ040 1395 0.86 1.90 0.31 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
DLQ050 1395 0.86 1.91 0.30 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
DLQ060 1395 0.86 1.96 0.20 1.0 2.00 2.00 2.00 7 ▇▁▁▁▁
DLQ080 3535 0.65 1.93 0.29 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
DPQ010 4777 0.53 0.42 0.83 0.0 0.00 0.00 1.00 9 ▇▁▁▁▁
DPQ020 4779 0.53 0.37 0.77 0.0 0.00 0.00 0.00 9 ▇▁▁▁▁
DPQ030 4780 0.53 0.62 0.96 0.0 0.00 0.00 1.00 7 ▇▁▁▁▁
DPQ040 4780 0.53 0.77 0.94 0.0 0.00 1.00 1.00 7 ▇▁▁▁▁
DPQ050 4780 0.53 0.40 0.80 0.0 0.00 0.00 1.00 9 ▇▁▁▁▁
DPQ060 4781 0.53 0.27 0.70 0.0 0.00 0.00 0.00 9 ▇▁▁▁▁
DPQ070 4781 0.53 0.29 0.72 0.0 0.00 0.00 0.00 9 ▇▁▁▁▁
DPQ080 4781 0.53 0.18 0.59 0.0 0.00 0.00 0.00 9 ▇▁▁▁▁
DPQ090 4782 0.53 0.06 0.37 0.0 0.00 0.00 0.00 9 ▇▁▁▁▁
DPQ100 6501 0.36 0.35 0.68 0.0 0.00 0.00 1.00 9 ▇▁▁▁▁
DUQ200 6474 0.36 1.48 0.61 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
DUQ210 8184 0.20 18.00 28.17 0.0 15.00 16.00 18.00 999 ▇▁▁▁▁
DUQ211 8185 0.20 1.53 0.55 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
DUQ213 9222 0.09 18.33 24.99 8.0 15.00 17.00 19.00 777 ▇▁▁▁▁
DUQ215Q 9230 0.09 28.18 411.65 0.0 1.00 4.00 15.00 9999 ▇▁▁▁▁
DUQ215U 9232 0.09 2.90 1.35 1.0 1.00 4.00 4.00 4 ▅▁▁▁▇
DUQ217 9222 0.09 3.68 1.23 1.0 3.00 4.00 5.00 9 ▃▇▅▁▁
DUQ219 9222 0.09 2.03 1.10 1.0 1.00 2.00 3.00 9 ▇▃▁▁▁
DUQ220Q 8188 0.20 33.59 459.43 0.0 2.00 6.00 18.00 9999 ▇▁▁▁▁
DUQ220U 8193 0.19 3.11 1.24 1.0 2.00 4.00 4.00 4 ▃▁▁▂▇
DUQ230 9605 0.06 12.60 11.46 1.0 2.00 7.00 25.00 30 ▇▂▂▁▅
DUQ240 5635 0.45 1.86 0.52 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
DUQ250 9452 0.07 1.08 0.40 1.0 1.00 1.00 1.00 7 ▇▁▁▁▁
DUQ260 9601 0.06 21.52 5.39 12.0 18.00 20.00 24.00 47 ▇▇▂▁▁
DUQ270Q 9601 0.06 39.87 458.01 1.0 5.00 10.00 20.00 7777 ▇▁▁▁▁
DUQ270U 9603 0.06 3.70 0.77 1.0 4.00 4.00 4.00 4 ▁▁▁▁▇
DUQ272 9601 0.06 3.65 3.47 1.0 2.00 3.00 5.00 77 ▇▁▁▁▁
DUQ280 10130 0.00 3.71 4.63 1.0 1.00 2.00 5.00 25 ▇▂▁▁▁
DUQ290 9452 0.07 1.87 0.52 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
DUQ300 10095 0.01 35.05 84.35 15.0 20.00 24.50 30.00 777 ▇▁▁▁▁
DUQ310Q 10095 0.01 12.01 10.35 0.0 3.00 8.00 19.00 35 ▇▂▃▂▂
DUQ310U 10095 0.01 3.59 0.88 1.0 4.00 4.00 4.00 4 ▁▁▁▁▇
DUQ330 9452 0.07 1.61 0.49 1.0 1.00 2.00 2.00 2 ▅▁▁▁▇
DUQ340 9929 0.02 22.68 6.74 10.0 18.00 21.00 26.00 50 ▆▇▃▁▁
DUQ350Q 9929 0.02 43.46 495.17 0.0 4.00 10.00 18.00 7777 ▇▁▁▁▁
DUQ350U 9930 0.02 3.55 0.94 1.0 4.00 4.00 4.00 4 ▁▁▁▁▇
DUQ352 9929 0.02 3.95 1.72 1.0 2.00 4.00 6.00 6 ▇▃▅▃▇
DUQ360 10145 0.00 9.67 9.77 1.0 2.00 5.50 15.00 30 ▇▂▂▂▂
DUQ370 5636 0.45 1.98 0.22 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
DUQ380A 10119 0.01 1.14 1.07 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
DUQ380B 10120 0.01 2.00 0.00 2.0 2.00 2.00 2.00 2 ▁▁▇▁▁
DUQ380C 10128 0.00 3.00 0.00 3.0 3.00 3.00 3.00 3 ▁▁▇▁▁
DUQ380E 10163 0.00 5.00 0.00 5.0 5.00 5.00 5.00 5 ▁▁▇▁▁
DUQ390 10070 0.01 24.49 6.53 12.0 20.00 24.00 28.00 42 ▅▇▇▃▁
DUQ400Q 10070 0.01 15.37 12.47 0.0 5.00 10.00 25.00 48 ▇▅▃▂▂
DUQ400U 10070 0.01 3.65 0.80 1.0 4.00 4.00 4.00 4 ▁▁▁▁▇
DUQ410 10070 0.01 4.78 9.45 1.0 2.00 4.00 6.00 99 ▇▁▁▁▁
DUQ420 10080 0.01 2.62 1.57 1.0 1.00 2.00 4.00 7 ▇▃▁▃▁
DUQ430 8161 0.20 1.90 0.32 1.0 2.00 2.00 2.00 7 ▇▁▁▁▁
ECD010 6465 0.36 138.84 1045.15 14.0 22.00 27.00 32.00 9999 ▇▁▁▁▁
ECQ020 6465 0.36 1.96 0.70 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
ECD070A 6465 0.36 243.79 1520.77 1.0 6.00 7.00 8.00 9999 ▇▁▁▁▁
ECD070B 6465 0.36 243.47 1520.82 0.0 2.00 7.00 11.00 9999 ▇▁▁▁▁
ECQ080 10087 0.01 2.42 2.90 1.0 1.00 1.00 2.00 9 ▇▁▁▁▂
ECQ090 10114 0.01 2.16 1.29 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
WHQ030E 7133 0.30 2.67 0.70 1.0 3.00 3.00 3.00 9 ▂▇▁▁▁
MCQ080E 7133 0.30 1.88 0.35 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
ECQ150 9800 0.04 1.20 0.40 1.0 1.00 1.00 1.00 2 ▇▁▁▁▂
FSD032A 116 0.99 2.61 0.70 1.0 2.00 3.00 3.00 9 ▃▇▁▁▁
FSD032B 116 0.99 2.69 0.60 1.0 3.00 3.00 3.00 7 ▂▇▁▁▁
FSD032C 116 0.99 2.74 0.59 1.0 3.00 3.00 3.00 9 ▂▇▁▁▁
FSD041 6847 0.33 1.65 0.51 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
FSD052 8986 0.12 1.93 0.76 1.0 1.00 2.00 3.00 3 ▆▁▇▁▅
FSD061 6847 0.33 1.63 0.50 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
FSD071 6847 0.33 1.79 0.41 1.0 2.00 2.00 2.00 2 ▂▁▁▁▇
FSD081 6847 0.33 1.91 0.43 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
FSD092 8594 0.16 1.82 0.38 1.0 2.00 2.00 2.00 2 ▂▁▁▁▇
FSD102 9892 0.03 2.04 0.81 1.0 1.00 2.00 3.00 3 ▇▁▇▁▇
FSD032D 3542 0.65 2.82 0.47 1.0 3.00 3.00 3.00 7 ▂▇▁▁▁
FSD032E 3542 0.65 2.88 0.38 1.0 3.00 3.00 3.00 7 ▁▇▁▁▁
FSD032F 3542 0.65 2.94 0.28 1.0 3.00 3.00 3.00 7 ▁▇▁▁▁
FSD111 9096 0.11 1.81 0.39 1.0 2.00 2.00 2.00 2 ▂▁▁▁▇
FSD122 9096 0.11 1.94 0.23 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
FSD132 10114 0.01 2.16 0.80 1.0 2.00 2.00 3.00 3 ▅▁▇▁▇
FSD141 9096 0.11 1.90 0.30 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
FSD146 9096 0.11 1.99 0.12 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
FSDHH 122 0.99 1.63 0.98 1.0 1.00 1.00 2.00 4 ▇▂▁▂▁
FSDAD 122 0.99 1.62 0.99 1.0 1.00 1.00 2.00 4 ▇▂▁▂▁
FSDCH 3543 0.65 1.27 0.67 1.0 1.00 1.00 1.00 4 ▇▁▁▁▁
FSD151 116 0.99 1.89 0.33 1.0 2.00 2.00 2.00 7 ▇▁▁▁▁
FSQ165 116 0.99 1.64 0.54 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
FSQ012 6428 0.37 1.24 0.49 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
FSD012N 7298 0.28 3.42 7.64 1.0 1.00 2.00 4.00 99 ▇▁▁▁▁
FSD230 7298 0.28 1.13 0.55 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
FSD225 7299 0.28 1991.59 11585.30 0.0 8.00 15.00 25.00 99999 ▇▁▁▁▁
FSQ235 7298 0.28 3312.79 16406.60 10.0 200.00 350.00 525.00 99999 ▇▁▁▁▁
FSQ162 1909 0.81 1.79 0.49 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
FSD650ZC 8572 0.16 1.49 0.57 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
FSD660ZC 9339 0.08 1.25 0.44 1.0 1.00 1.00 2.00 2 ▇▁▁▁▃
FSD675 7203 0.29 1.41 0.68 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
FSD680 7608 0.25 1.46 0.73 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
FSD670ZC 7203 0.29 29.19 97.13 0.0 0.00 12.00 36.00 999 ▇▁▁▁▁
FSQ690 7202 0.29 1.54 0.99 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
FSQ695 8532 0.16 8.08 21.32 1.0 2.00 3.00 5.00 99 ▇▁▁▁▁
FSD650ZW 9934 0.02 1.49 0.50 1.0 1.00 1.00 2.00 2 ▇▁▁▁▇
FSD660ZW 10103 0.01 1.19 0.40 1.0 1.00 1.00 1.00 2 ▇▁▁▁▂
FSD670ZW 10103 0.01 18.11 77.82 1.0 4.00 8.00 12.00 666 ▇▁▁▁▁
HEQ010 1603 0.84 2.01 0.40 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
HEQ020 10109 0.01 3.24 2.89 1.0 2.00 2.00 2.00 9 ▇▁▁▁▂
HEQ030 1603 0.84 2.01 0.41 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
HEQ040 10099 0.01 2.14 1.89 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
HIQ011 0 1.00 1.16 0.43 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
HIQ031A 5354 0.47 14.42 5.93 14.0 14.00 14.00 14.00 99 ▇▁▁▁▁
HIQ031B 8949 0.12 15.00 0.00 15.0 15.00 15.00 15.00 15 ▁▁▇▁▁
HIQ031C 10164 0.00 16.00 0.00 16.0 16.00 16.00 16.00 16 ▁▁▇▁▁
HIQ031D 7773 0.24 17.00 0.00 17.0 17.00 17.00 17.00 17 ▁▁▇▁▁
HIQ031E 10122 0.01 18.00 0.00 18.0 18.00 18.00 18.00 18 ▁▁▇▁▁
HIQ031F 9935 0.02 19.00 0.00 19.0 19.00 19.00 19.00 19 ▁▁▇▁▁
HIQ031G 10165 0.00 20.00 0.00 20.0 20.00 20.00 20.00 20 ▁▁▇▁▁
HIQ031H 9612 0.06 21.00 0.00 21.0 21.00 21.00 21.00 21 ▁▁▇▁▁
HIQ031I 9969 0.02 22.00 0.00 22.0 22.00 22.00 22.00 22 ▁▁▇▁▁
HIQ031J 9942 0.02 23.00 0.00 23.0 23.00 23.00 23.00 23 ▁▁▇▁▁
HIQ031AA 10167 0.00 40.00 0.00 40.0 40.00 40.00 40.00 40 ▁▁▇▁▁
HIQ260 9915 0.03 1.63 1.24 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
HIQ105 9241 0.09 1.09 0.29 1.0 1.00 1.00 1.00 2 ▇▁▁▁▁
HIQ270 1572 0.85 1.09 0.66 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
HIQ210 1572 0.85 1.95 0.29 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
HOD050 121 0.99 7.69 37.93 1.0 4.00 6.00 7.00 999 ▇▁▁▁▁
HOQ065 121 0.99 1.48 0.58 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
HUQ010 0 1.00 2.41 1.09 1.0 1.00 2.00 3.00 9 ▇▇▁▁▁
HUQ020 406 0.96 2.54 0.80 1.0 2.00 3.00 3.00 9 ▃▇▁▁▁
HUQ030 0 1.00 1.12 0.34 1.0 1.00 1.00 1.00 3 ▇▁▁▁▁
HUQ041 1194 0.88 1.89 1.01 1.0 2.00 2.00 2.00 77 ▇▁▁▁▁
HUQ051 0 1.00 2.49 3.71 0.0 1.00 2.00 3.00 99 ▇▁▁▁▁
HUQ061 8888 0.13 3.93 6.60 1.0 3.00 3.00 4.00 99 ▇▁▁▁▁
HUQ071 0 1.00 1.91 0.30 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
HUD080 9248 0.09 1.43 0.96 1.0 1.00 1.00 1.00 6 ▇▁▁▁▁
HUQ090 1165 0.89 1.92 0.32 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
IMQ011 668 0.93 2.80 2.46 1.0 1.00 3.00 3.00 9 ▇▇▁▁▂
IMQ020 0 1.00 2.50 2.30 1.0 1.00 1.00 3.00 9 ▇▅▁▁▂
IMQ040 7087 0.30 2.09 1.47 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
IMQ070 7249 0.29 2.40 1.84 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
IMQ080 9575 0.06 4.07 3.21 1.0 2.00 2.00 9.00 9 ▇▁▁▁▃
IMQ090 9298 0.09 106.56 284.14 5.0 12.00 15.00 19.00 999 ▇▁▁▁▁
IMQ045 9298 0.09 2.77 1.78 1.0 2.00 3.00 3.00 9 ▆▇▁▁▁
INQ020 123 0.99 1.22 0.59 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
INQ012 123 0.99 1.86 0.44 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
INQ030 123 0.99 1.80 0.52 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
INQ060 123 0.99 1.94 0.39 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
INQ080 123 0.99 1.91 0.39 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
INQ090 123 0.99 1.94 0.40 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
INQ132 123 0.99 1.95 0.39 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
INQ140 123 0.99 1.82 0.54 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
INQ150 123 0.99 1.86 0.43 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
IND235 369 0.96 12.87 22.18 1.0 4.00 7.00 11.00 99 ▇▁▁▁▁
INDFMMPI 1066 0.90 2.10 1.57 0.0 0.83 1.54 3.09 5 ▇▇▃▂▅
INDFMMPC 369 0.96 2.12 1.26 1.0 1.00 2.00 3.00 9 ▇▆▁▁▁
INQ244 4661 0.54 2.14 1.19 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
IND247 5366 0.47 2.55 10.42 1.0 1.00 1.00 1.00 99 ▇▁▁▁▁
MCQ010 406 0.96 1.85 0.42 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ025 8637 0.15 1639.67 12647.59 1.0 2.00 7.00 20.00 99999 ▇▁▁▁▁
MCQ035 8637 0.15 1.47 0.93 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
MCQ040 9236 0.09 1.52 0.61 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
MCQ050 9236 0.09 1.80 0.47 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
AGQ030 9236 0.09 1.74 0.61 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
MCQ053 406 0.96 1.97 0.25 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ070 3711 0.64 1.98 0.26 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ075 10013 0.02 1.80 1.21 1.0 1.00 1.00 2.00 9 ▇▂▁▁▁
MCQ080 3711 0.64 1.67 0.49 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
MCQ082 1603 0.84 2.00 0.18 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ084 8335 0.18 1.83 0.41 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ086 1603 0.84 1.99 0.18 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ092 1603 0.84 1.96 0.64 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCD093 9443 0.07 2.63 1.49 1.0 2.00 3.00 3.00 9 ▆▇▁▁▁
MCQ149 9743 0.04 1.91 0.28 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
MCQ151 10137 0.00 10.32 0.77 8.0 10.00 10.00 11.00 11 ▁▂▁▇▇
MCQ160A 4406 0.57 1.75 0.56 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
MCQ180A 8667 0.15 2037.52 13961.21 1.0 39.00 50.00 62.00 99999 ▇▁▁▁▁
MCQ195 8667 0.15 3.60 3.36 1.0 1.00 2.00 9.00 9 ▇▂▁▁▃
MCQ160N 4406 0.57 1.96 0.27 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ180N 9941 0.02 1330.18 11268.47 8.0 36.00 50.00 60.00 99999 ▇▁▁▁▁
MCQ160B 4406 0.57 1.98 0.31 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ180B 9993 0.02 605.44 7408.26 0.0 46.00 58.00 68.00 99999 ▇▁▁▁▁
MCQ160C 4406 0.57 1.98 0.44 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ180C 9943 0.02 488.35 6561.49 0.0 50.00 59.00 67.00 99999 ▇▁▁▁▁
MCQ160D 4406 0.57 1.99 0.30 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ180D 10039 0.01 789.11 8570.21 9.0 43.75 55.00 68.00 99999 ▇▁▁▁▁
MCQ160E 4406 0.57 1.96 0.25 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ180E 9946 0.02 491.49 6604.50 16.0 45.00 57.00 65.00 99999 ▇▁▁▁▁
MCQ160F 4406 0.57 1.97 0.28 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ180F 9973 0.02 1048.01 9919.69 7.0 50.00 60.50 69.75 99999 ▇▁▁▁▁
MCQ160G 4406 0.57 1.99 0.20 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ180G 10080 0.01 2156.54 14424.44 10.0 41.00 51.00 65.00 99999 ▇▁▁▁▁
MCQ160M 4406 0.57 1.91 0.44 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ170M 9574 0.06 1.51 1.45 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
MCQ180M 9574 0.06 1706.17 12796.47 1.0 30.00 43.00 56.00 99999 ▇▁▁▁▁
MCQ160K 4406 0.57 1.95 0.35 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ170K 9855 0.03 1.71 1.27 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
MCQ180K 9855 0.03 3156.55 17420.67 0.0 13.75 30.50 52.00 99999 ▇▁▁▁▁
MCQ160L 4406 0.57 1.97 0.35 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ170L 9941 0.02 2.00 2.14 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
MCQ180L 9941 0.02 1751.18 12984.32 5.0 32.00 42.50 54.00 99999 ▇▁▁▁▁
MCQ160O 4406 0.57 1.97 0.26 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ203 1703 0.83 1.99 0.29 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ206 10018 0.02 34.56 111.30 0.0 9.00 19.00 34.00 999 ▇▁▁▁▁
MCQ220 4406 0.57 1.91 0.29 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
MCQ230A 9628 0.05 25.97 10.65 10.0 15.00 30.00 32.00 99 ▇▇▁▁▁
MCQ230B 10114 0.01 26.39 9.14 10.0 17.00 28.00 33.00 39 ▅▂▃▇▃
MCQ230C 10171 0.00 20.75 7.27 14.0 15.50 19.50 24.75 30 ▇▁▃▁▃
MCQ230D 10173 0.00 66.00 0.00 66.0 66.00 66.00 66.00 66 ▁▁▇▁▁
MCQ240A 10164 0.00 66.73 11.91 50.0 57.50 69.00 77.50 80 ▆▁▃▃▇
MCQ240AA 10172 0.00 27.33 5.03 22.0 25.00 28.00 30.00 32 ▇▁▇▁▇
MCQ240CC 10157 0.00 42.50 16.90 14.0 30.50 43.50 59.25 67 ▃▇▃▁▇
MCQ240DD 10142 0.00 51.55 22.77 4.0 30.00 57.00 70.00 80 ▁▅▃▃▇
MCQ240DK 10171 0.00 39.50 8.10 28.0 37.75 41.50 43.25 47 ▃▁▁▇▃
MCQ240E 10074 0.01 2033.86 13993.61 14.0 48.00 55.00 65.00 99999 ▇▁▁▁▁
MCQ240F 10138 0.00 31.59 12.07 15.0 23.00 28.00 38.00 65 ▇▅▃▁▁
MCQ240G 10147 0.00 57.89 14.75 25.0 48.75 60.00 69.25 80 ▂▃▇▇▇
MCQ240L 10170 0.00 48.00 25.50 9.0 38.00 57.00 61.00 75 ▃▁▃▇▃
MCQ240N 10154 0.00 64.14 12.18 29.0 60.00 65.00 71.00 80 ▁▁▃▇▇
MCQ240O 10164 0.00 54.82 18.34 25.0 41.00 62.00 65.50 79 ▃▃▁▇▃
MCQ240P 10133 0.00 49.76 15.93 18.0 39.00 46.50 61.50 80 ▃▇▇▇▅
MCQ240S 10165 0.00 45.70 20.50 16.0 32.25 43.50 66.25 70 ▃▃▃▁▇
MCQ240U 10107 0.01 63.49 7.75 49.0 58.00 61.50 70.00 80 ▃▇▅▃▃
MCQ240W 10070 0.01 56.20 15.13 7.0 48.00 58.00 67.00 80 ▁▂▆▇▇
MCQ240X 10118 0.01 58.93 15.09 18.0 50.00 60.00 70.00 80 ▁▂▅▆▇
MCQ240Y 10172 0.00 39.67 17.90 20.0 32.00 44.00 49.50 55 ▇▁▁▇▇
MCQ300A 4406 0.57 2.07 1.15 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ300B 1603 0.84 1.89 1.07 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ300C 4406 0.57 1.74 1.15 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
MCQ365A 3711 0.64 1.74 0.45 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
MCQ365B 3711 0.64 1.65 0.48 1.0 1.00 2.00 2.00 2 ▅▁▁▁▇
MCQ365C 3711 0.64 1.77 0.50 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
MCQ365D 3711 0.64 1.74 0.49 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
MCQ370A 3711 0.64 1.40 0.52 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
MCQ370B 3711 0.64 1.41 0.50 1.0 1.00 1.00 2.00 7 ▇▁▁▁▁
MCQ370C 3711 0.64 1.51 0.51 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
MCQ370D 3711 0.64 1.47 0.53 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
MCQ380 8335 0.18 0.80 1.09 0.0 0.00 0.00 1.00 9 ▇▂▁▁▁
OCD150 3716 0.63 2.39 1.46 1.0 1.00 1.00 4.00 4 ▇▁▁▁▇
OCQ180 6830 0.33 205.42 3941.96 1.0 32.00 40.00 47.00 99999 ▇▁▁▁▁
OCQ210 9164 0.10 1.71 0.61 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
OCQ260 6732 0.34 1.60 2.83 1.0 1.00 1.00 1.00 77 ▇▁▁▁▁
OCD270 6732 0.34 226.95 3242.53 0.0 12.00 48.00 120.00 77777 ▇▁▁▁▁
OCQ380 7359 0.28 3.24 2.20 1.0 2.00 3.00 4.00 77 ▇▁▁▁▁
OCD390G 3716 0.63 1.67 0.91 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
OCD395 6732 0.34 314.29 3651.42 0.0 48.00 132.00 276.00 99999 ▇▁▁▁▁
OHQ030 407 0.96 2.71 3.71 1.0 1.00 2.00 4.00 99 ▇▁▁▁▁
OHQ033 1057 0.90 1.88 1.26 1.0 1.00 1.00 3.00 9 ▇▃▁▁▁
OHQ770 1057 0.90 1.85 0.36 1.0 2.00 2.00 2.00 7 ▇▁▁▁▁
OHQ780A 9085 0.11 10.08 2.70 10.0 10.00 10.00 10.00 99 ▇▁▁▁▁
OHQ780B 10110 0.01 11.00 0.00 11.0 11.00 11.00 11.00 11 ▁▁▇▁▁
OHQ780C 9967 0.02 12.00 0.00 12.0 12.00 12.00 12.00 12 ▁▁▇▁▁
OHQ780E 10154 0.00 14.00 0.00 14.0 14.00 14.00 14.00 14 ▁▁▇▁▁
OHQ780G 10099 0.01 16.00 0.00 16.0 16.00 16.00 16.00 16 ▁▁▇▁▁
OHQ780H 10130 0.00 17.00 0.00 17.0 17.00 17.00 17.00 17 ▁▁▇▁▁
OHQ780I 10128 0.00 18.00 0.00 18.0 18.00 18.00 18.00 18 ▁▁▇▁▁
OHQ780J 10140 0.00 19.00 0.00 19.0 19.00 19.00 19.00 19 ▁▁▇▁▁
OHQ780K 10088 0.01 20.00 0.00 20.0 20.00 20.00 20.00 20 ▁▁▇▁▁
OHQ555G 7410 0.27 1.08 0.75 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
OHQ555Q 7452 0.27 25.86 468.77 1.0 1.00 2.00 4.00 9999 ▇▁▁▁▁
OHQ555U 7458 0.27 1.76 0.42 1.0 2.00 2.00 2.00 2 ▂▁▁▁▇
OHQ560G 7458 0.27 1.03 0.42 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
OHQ560Q 7489 0.26 7.42 192.92 1.0 1.00 2.00 3.00 9999 ▇▁▁▁▁
OHQ560U 7490 0.26 1.84 0.37 1.0 2.00 2.00 2.00 2 ▂▁▁▁▇
OHQ565 7410 0.27 1.96 0.63 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OHQ570Q 9946 0.02 440.84 2046.93 1.0 2.00 3.00 6.00 9999 ▇▁▁▁▁
OHQ570U 9956 0.02 1.70 0.46 1.0 1.00 2.00 2.00 2 ▃▁▁▁▇
OHQ575G 9956 0.02 1.42 1.52 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
OHQ575Q 9992 0.02 114.31 1041.91 1.0 3.00 4.00 6.00 9999 ▇▁▁▁▁
OHQ575U 9994 0.02 1.92 0.28 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
OHQ580 7410 0.27 2.01 0.66 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OHQ585Q 10052 0.01 492.74 2161.56 1.0 3.00 4.00 6.00 9999 ▇▁▁▁▁
OHQ585U 10058 0.01 1.93 0.25 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
OHQ590G 10058 0.01 1.38 0.85 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
OHQ590Q 10096 0.01 7.23 4.13 3.0 5.00 6.00 8.00 30 ▇▁▁▁▁
OHQ590U 10096 0.01 1.96 0.19 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
OHQ610 6494 0.36 1.91 0.34 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OHQ612 6494 0.36 1.94 0.33 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OHQ614 6494 0.36 1.84 0.52 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OHQ620 5362 0.47 4.26 1.07 1.0 4.00 5.00 5.00 9 ▁▅▇▁▁
OHQ640 5362 0.47 4.84 0.60 1.0 5.00 5.00 5.00 9 ▁▁▇▁▁
OHQ680 5362 0.47 4.54 1.07 1.0 5.00 5.00 5.00 9 ▁▁▇▁▁
OHQ835 5362 0.47 1.93 0.96 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OHQ845 407 0.96 2.71 1.16 1.0 2.00 3.00 3.00 9 ▆▇▁▁▁
OHQ848G 6714 0.34 1.04 0.31 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
OHQ848Q 6803 0.33 1.75 0.61 0.0 1.00 2.00 2.00 6 ▅▇▁▁▁
OHQ849 6761 0.34 2.00 1.04 1.0 1.00 2.00 3.00 9 ▇▃▁▁▁
OHQ850 5362 0.47 1.81 0.64 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OHQ855 5362 0.47 1.85 0.51 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OHQ860 5362 0.47 1.92 0.70 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OHQ865 5362 0.47 1.85 0.49 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OHQ870 5362 0.47 3.21 4.06 0.0 0.00 2.00 7.00 99 ▇▁▁▁▁
OHQ875 5362 0.47 3.17 3.96 0.0 0.00 2.00 7.00 99 ▇▁▁▁▁
OHQ880 5362 0.47 1.84 0.69 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OHQ885 5362 0.47 1.88 0.86 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OHQ895 8886 0.13 1.54 0.97 1.0 1.00 1.00 2.00 9 ▇▂▁▁▁
OHQ900 9091 0.11 2.76 0.94 1.0 3.00 3.00 3.00 9 ▂▇▁▁▁
OSQ010A 6360 0.37 1.98 0.18 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OSQ010B 6360 0.37 1.92 0.34 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OSQ010C 6360 0.37 1.99 0.27 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OSQ020A 10092 0.01 121.61 1097.41 1.0 1.00 1.00 1.00 9999 ▇▁▁▁▁
OSQ020B 9859 0.03 1.24 0.77 1.0 1.00 1.00 1.00 10 ▇▁▁▁▁
OSQ020C 10098 0.01 1.16 0.43 1.0 1.00 1.00 1.00 3 ▇▁▁▁▁
OSD030AA 10093 0.01 2491.59 15512.17 2.0 40.75 58.00 75.00 99999 ▇▁▁▁▁
OSQ040AA 10093 0.01 1.73 0.94 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
OSD050AA 10123 0.01 2.08 2.12 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
OSD030BA 9859 0.03 1296.81 11193.57 1.0 13.00 24.50 49.00 99999 ▇▁▁▁▁
OSQ040BA 9859 0.03 1.23 0.42 1.0 1.00 1.00 1.00 2 ▇▁▁▁▂
OSD050BA 10101 0.01 1.39 0.54 1.0 1.00 1.00 2.00 3 ▇▁▅▁▁
OSD030BB 10126 0.00 31.45 19.21 6.0 16.00 26.00 45.00 74 ▇▅▃▃▂
OSQ040BB 10126 0.00 1.18 0.39 1.0 1.00 1.00 1.00 2 ▇▁▁▁▂
OSD030BC 10161 0.00 34.50 21.55 7.0 21.25 31.00 41.50 80 ▆▇▅▁▃
OSQ040BC 10161 0.00 1.14 0.36 1.0 1.00 1.00 1.00 2 ▇▁▁▁▁
OSD030BD 10170 0.00 35.40 29.45 9.0 15.00 23.00 50.00 80 ▇▁▂▁▂
OSQ040BD 10170 0.00 1.40 0.55 1.0 1.00 1.00 2.00 2 ▇▁▁▁▅
OSD030BE 10173 0.00 14.00 2.83 12.0 13.00 14.00 15.00 16 ▇▁▁▁▇
OSQ040BE 10173 0.00 1.00 0.00 1.0 1.00 1.00 1.00 1 ▁▁▇▁▁
OSD030BF 10173 0.00 19.50 4.95 16.0 17.75 19.50 21.25 23 ▇▁▁▁▇
OSQ040BF 10173 0.00 1.00 0.00 1.0 1.00 1.00 1.00 1 ▁▁▇▁▁
OSD030CA 10099 0.01 5298.36 22469.48 2.0 23.00 38.00 51.25 99999 ▇▁▁▁▁
OSQ040CA 10099 0.01 1.24 0.43 1.0 1.00 1.00 1.00 2 ▇▁▁▁▂
OSD050CA 10156 0.00 3.58 2.99 1.0 1.50 3.00 3.00 9 ▇▇▁▁▃
OSQ080 6360 0.37 1.76 0.47 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OSQ090A 9253 0.09 1.26 0.51 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
OSQ100A 9939 0.02 20.80 4.85 10.0 15.00 23.00 25.00 27 ▁▆▂▆▇
OSD110A 9939 0.02 58.86 124.76 20.0 30.00 40.00 55.00 999 ▇▁▁▁▁
OSQ120A 9253 0.09 1.74 0.44 1.0 1.00 2.00 2.00 2 ▃▁▁▁▇
OSQ090B 9933 0.02 1.33 0.67 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
OSQ100B 10104 0.01 20.87 5.26 10.0 15.00 23.00 25.50 27 ▂▅▁▅▇
OSD110B 10104 0.01 56.35 114.46 20.0 29.50 44.00 54.00 999 ▇▁▁▁▁
OSQ120B 9933 0.02 1.67 0.47 1.0 1.00 2.00 2.00 2 ▃▁▁▁▇
OSQ090C 10095 0.01 1.25 0.44 1.0 1.00 1.00 1.25 2 ▇▁▁▁▂
OSQ100C 10155 0.00 21.10 4.69 12.0 16.75 23.00 25.00 26 ▁▅▁▂▇
OSD110C 10155 0.00 43.65 17.78 20.0 29.00 44.00 53.25 80 ▇▃▇▁▂
OSQ120C 10095 0.01 1.55 0.50 1.0 1.00 2.00 2.00 2 ▆▁▁▁▇
OSQ090D 10139 0.00 1.22 0.42 1.0 1.00 1.00 1.00 2 ▇▁▁▁▂
OSQ100D 10167 0.00 20.00 4.99 15.0 16.50 18.00 24.00 27 ▇▅▁▂▅
OSD110D 10167 0.00 153.50 341.88 21.0 22.75 29.00 51.25 999 ▇▁▁▁▁
OSQ120D 10139 0.00 1.67 0.48 1.0 1.00 2.00 2.00 2 ▃▁▁▁▇
OSQ060 6360 0.37 1.94 0.48 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OSQ072 9853 0.03 1.56 1.06 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
OSQ130 6360 0.37 1.98 0.58 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OSQ140Q 9955 0.02 63.42 235.07 1.0 1.00 3.00 6.00 999 ▇▁▁▁▁
OSQ140U 9968 0.02 1.36 0.48 1.0 1.00 1.00 2.00 2 ▇▁▁▁▅
OSQ150 6360 0.37 2.28 1.68 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OSQ160A 9739 0.04 1.00 0.00 1.0 1.00 1.00 1.00 1 ▁▁▇▁▁
OSQ160B 10145 0.00 2.00 0.00 2.0 2.00 2.00 2.00 2 ▁▁▇▁▁
OSQ170 6360 0.37 2.20 1.37 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OSQ180 9919 0.03 123.47 211.48 19.0 65.00 76.00 85.00 999 ▇▁▁▁▁
OSQ200 6360 0.37 2.32 1.51 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
OSQ210 10094 0.01 115.25 203.54 19.0 60.00 75.00 84.00 999 ▇▁▁▁▁
OSQ220 10171 0.00 1.50 0.58 1.0 1.00 1.50 2.00 2 ▇▁▁▁▇
PFQ020 6714 0.34 1.97 0.22 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
PFQ030 10047 0.01 1.21 0.78 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
PFQ033 10070 0.01 1.78 0.42 1.0 2.00 2.00 2.00 2 ▂▁▁▁▇
PFQ041 7058 0.31 1.91 0.40 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
PFQ049 4406 0.57 1.85 0.39 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
PFQ051 4406 0.57 1.79 0.45 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
PFQ054 4406 0.57 1.90 0.30 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
PFQ057 4406 0.57 1.92 0.32 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
PFQ059 5905 0.42 1.97 0.20 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
PFQ061A 7580 0.26 1.39 1.02 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
PFQ061B 8172 0.20 1.36 0.76 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
PFQ061C 8172 0.20 1.25 0.67 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
PFQ061D 7580 0.26 1.86 1.04 1.0 1.00 2.00 2.00 5 ▇▅▂▂▁
PFQ061E 7580 0.26 1.57 1.02 1.0 1.00 1.00 2.00 5 ▇▂▁▁▁
PFQ061F 7580 0.26 1.56 1.03 1.0 1.00 1.00 2.00 5 ▇▂▁▁▁
PFQ061G 7580 0.26 1.40 1.03 1.0 1.00 1.00 1.00 5 ▇▁▁▁▁
PFQ061H 7580 0.26 1.20 0.59 1.0 1.00 1.00 1.00 5 ▇▁▁▁▁
PFQ061I 7580 0.26 1.38 0.73 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
PFQ061J 7580 0.26 1.27 0.60 1.0 1.00 1.00 1.00 5 ▇▂▁▁▁
PFQ061K 7580 0.26 1.09 0.34 1.0 1.00 1.00 1.00 4 ▇▁▁▁▁
PFQ061L 7580 0.26 1.21 0.52 1.0 1.00 1.00 1.00 5 ▇▁▁▁▁
PFQ061M 7580 0.26 1.92 1.17 1.0 1.00 1.00 3.00 9 ▇▂▁▁▁
PFQ061N 7580 0.26 1.38 0.72 1.0 1.00 1.00 2.00 5 ▇▂▁▁▁
PFQ061O 7580 0.26 1.30 0.67 1.0 1.00 1.00 1.00 5 ▇▂▁▁▁
PFQ061P 7580 0.26 1.24 0.56 1.0 1.00 1.00 1.00 5 ▇▂▁▁▁
PFQ061Q 7580 0.26 1.50 1.01 1.0 1.00 1.00 2.00 7 ▇▁▁▁▁
PFQ061R 7580 0.26 1.48 1.05 1.0 1.00 1.00 1.00 7 ▇▁▁▁▁
PFQ061S 7580 0.26 1.13 0.46 1.0 1.00 1.00 1.00 7 ▇▁▁▁▁
PFQ061T 7580 0.26 1.90 1.26 1.0 1.00 1.00 3.00 7 ▇▁▁▁▁
PFQ063A 8351 0.18 15.54 8.82 10.0 10.00 11.00 19.00 99 ▇▁▁▁▁
PFQ063B 9060 0.11 17.01 6.10 10.0 11.00 16.00 20.00 28 ▇▅▃▁▅
PFQ063C 9477 0.07 19.04 5.05 10.0 16.00 18.00 21.75 28 ▂▇▆▂▅
PFQ063D 9726 0.04 21.20 4.43 10.0 18.00 20.00 26.00 28 ▁▃▇▂▇
PFQ063E 9913 0.03 22.26 4.62 10.0 19.00 21.00 27.00 28 ▁▂▅▂▇
PFQ090 4406 0.57 1.90 0.30 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
PAQ605 3027 0.70 1.84 0.38 1.0 2.00 2.00 2.00 7 ▇▁▁▁▁
PAQ610 9003 0.12 4.08 3.30 1.0 2.00 4.00 5.00 99 ▇▁▁▁▁
PAD615 9007 0.11 187.51 433.82 10.0 60.00 120.00 240.00 9999 ▇▁▁▁▁
PAQ620 3027 0.70 1.68 0.49 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
PAQ625 7868 0.23 4.36 4.14 1.0 3.00 5.00 5.00 99 ▇▁▁▁▁
PAD630 7876 0.23 152.82 379.69 10.0 45.00 120.00 180.00 9999 ▇▁▁▁▁
PAQ635 3028 0.70 1.71 0.46 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
PAQ640 8128 0.20 4.80 1.91 1.0 3.00 5.00 7.00 7 ▃▃▂▆▇
PAD645 8132 0.20 71.96 387.57 10.0 20.00 30.00 60.00 9999 ▇▁▁▁▁
PAQ650 3028 0.70 1.71 0.46 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
PAQ655 8117 0.20 3.66 3.41 1.0 2.00 3.00 5.00 99 ▇▁▁▁▁
PAD660 8120 0.20 91.97 383.01 10.0 40.00 60.00 120.00 9999 ▇▁▁▁▁
PAQ665 3030 0.70 1.57 0.51 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
PAQ670 7115 0.30 3.64 3.51 1.0 2.00 3.00 5.00 99 ▇▁▁▁▁
PAD675 7118 0.30 63.19 59.70 0.0 30.00 60.00 60.00 900 ▇▁▁▁▁
PAD680 3036 0.70 478.55 644.31 0.0 300.00 480.00 600.00 9999 ▇▁▁▁▁
PAQ706 7186 0.29 5.80 5.28 0.0 4.00 7.00 7.00 99 ▇▁▁▁▁
PAQ710 727 0.93 2.52 2.59 0.0 1.00 2.00 4.00 99 ▇▁▁▁▁
PAQ715 727 0.93 3.16 3.16 0.0 0.00 2.00 8.00 8 ▇▃▁▁▅
PAQ722 7468 0.27 1.20 0.42 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
PAQ724A 10049 0.01 1.00 0.00 1.0 1.00 1.00 1.00 1 ▁▁▇▁▁
PAQ724B 9955 0.02 2.00 0.00 2.0 2.00 2.00 2.00 2 ▁▁▇▁▁
PAQ724C 9732 0.04 3.00 0.00 3.0 3.00 3.00 3.00 3 ▁▁▇▁▁
PAQ724D 9560 0.06 4.00 0.00 4.0 4.00 4.00 4.00 4 ▁▁▇▁▁
PAQ724E 10133 0.00 5.00 0.00 5.0 5.00 5.00 5.00 5 ▁▁▇▁▁
PAQ724F 9822 0.03 6.00 0.00 6.0 6.00 6.00 6.00 6 ▁▁▇▁▁
PAQ724G 10165 0.00 7.00 0.00 7.0 7.00 7.00 7.00 7 ▁▁▇▁▁
PAQ724H 9923 0.02 8.00 0.00 8.0 8.00 8.00 8.00 8 ▁▁▇▁▁
PAQ724I 10160 0.00 9.00 0.00 9.0 9.00 9.00 9.00 9 ▁▁▇▁▁
PAQ724J 10021 0.02 10.00 0.00 10.0 10.00 10.00 10.00 10 ▁▁▇▁▁
PAQ724K 10126 0.00 11.00 0.00 11.0 11.00 11.00 11.00 11 ▁▁▇▁▁
PAQ724M 10164 0.00 13.00 0.00 13.0 13.00 13.00 13.00 13 ▁▁▇▁▁
PAQ724N 10034 0.01 14.00 0.00 14.0 14.00 14.00 14.00 14 ▁▁▇▁▁
PAQ724O 10168 0.00 15.00 0.00 15.0 15.00 15.00 15.00 15 ▁▁▇▁▁
PAQ724P 10098 0.01 16.00 0.00 16.0 16.00 16.00 16.00 16 ▁▁▇▁▁
PAQ724Q 9784 0.04 17.00 0.00 17.0 17.00 17.00 17.00 17 ▁▁▇▁▁
PAQ724R 10120 0.01 18.00 0.00 18.0 18.00 18.00 18.00 18 ▁▁▇▁▁
PAQ724S 9249 0.09 19.00 0.00 19.0 19.00 19.00 19.00 19 ▁▁▇▁▁
PAQ724T 10001 0.02 20.00 0.00 20.0 20.00 20.00 20.00 20 ▁▁▇▁▁
PAQ724U 10096 0.01 21.00 0.00 21.0 21.00 21.00 21.00 21 ▁▁▇▁▁
PAQ724V 9786 0.04 22.00 0.00 22.0 22.00 22.00 22.00 22 ▁▁▇▁▁
PAQ724W 9903 0.03 23.00 0.00 23.0 23.00 23.00 23.00 23 ▁▁▇▁▁
PAQ724X 10139 0.00 24.00 0.00 24.0 24.00 24.00 24.00 24 ▁▁▇▁▁
PAQ724Y 10150 0.00 25.00 0.00 25.0 25.00 25.00 25.00 25 ▁▁▇▁▁
PAQ724Z 10129 0.00 26.00 0.00 26.0 26.00 26.00 26.00 26 ▁▁▇▁▁
PAQ724AA 9703 0.05 27.00 0.00 27.0 27.00 27.00 27.00 27 ▁▁▇▁▁
PAQ724AB 10068 0.01 28.00 0.00 28.0 28.00 28.00 28.00 28 ▁▁▇▁▁
PAQ724AC 10129 0.00 29.00 0.00 29.0 29.00 29.00 29.00 29 ▁▁▇▁▁
PAQ724AD 9294 0.09 30.00 0.00 30.0 30.00 30.00 30.00 30 ▁▁▇▁▁
PAQ724AE 10010 0.02 31.00 0.00 31.0 31.00 31.00 31.00 31 ▁▁▇▁▁
PAQ724AF 10161 0.00 32.00 0.00 32.0 32.00 32.00 32.00 32 ▁▁▇▁▁
PAQ724CM 10152 0.00 91.00 0.00 91.0 91.00 91.00 91.00 91 ▁▁▇▁▁
PAQ731 7918 0.22 1.07 2.76 0.0 0.00 0.00 1.00 99 ▇▁▁▁▁
PAD733 9377 0.08 106.12 499.88 5.0 45.00 60.00 120.00 9999 ▇▁▁▁▁
PAQ677 9496 0.07 3.39 4.27 0.0 1.00 3.00 5.00 99 ▇▁▁▁▁
PAQ678 9496 0.07 2.17 2.06 0.0 0.00 2.00 3.00 7 ▇▃▃▂▁
PAQ740 9496 0.07 1.59 0.86 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
PAQ742 9855 0.03 1.37 0.48 1.0 1.00 1.00 2.00 2 ▇▁▁▁▅
PAQ744 9496 0.07 1.18 0.39 1.0 1.00 1.00 1.00 2 ▇▁▁▁▂
PAQ746 9619 0.05 3.88 1.35 1.0 3.00 5.00 5.00 5 ▁▂▃▁▇
PAQ748 9619 0.05 2.61 0.72 1.0 2.00 3.00 3.00 9 ▅▇▁▁▁
PAQ755 7918 0.22 1.64 0.48 1.0 1.00 2.00 2.00 2 ▅▁▁▁▇
PAQ759A 10055 0.01 2.63 12.60 1.0 1.00 1.00 1.00 99 ▇▁▁▁▁
PAQ759B 9922 0.02 2.00 0.00 2.0 2.00 2.00 2.00 2 ▁▁▇▁▁
PAQ759D 10123 0.01 4.00 0.00 4.0 4.00 4.00 4.00 4 ▁▁▇▁▁
PAQ759E 10041 0.01 5.00 0.00 5.0 5.00 5.00 5.00 5 ▁▁▇▁▁
PAQ759G 10135 0.00 7.00 0.00 7.0 7.00 7.00 7.00 7 ▁▁▇▁▁
PAQ759I 10166 0.00 9.00 0.00 9.0 9.00 9.00 9.00 9 ▁▁▇▁▁
PAQ759J 9956 0.02 10.00 0.00 10.0 10.00 10.00 10.00 10 ▁▁▇▁▁
PAQ759K 10133 0.00 11.00 0.00 11.0 11.00 11.00 11.00 11 ▁▁▇▁▁
PAQ759L 10150 0.00 12.00 0.00 12.0 12.00 12.00 12.00 12 ▁▁▇▁▁
PAQ759M 10098 0.01 13.00 0.00 13.0 13.00 13.00 13.00 13 ▁▁▇▁▁
PAQ759N 10126 0.00 14.00 0.00 14.0 14.00 14.00 14.00 14 ▁▁▇▁▁
PAQ759P 10119 0.01 16.00 0.00 16.0 16.00 16.00 16.00 16 ▁▁▇▁▁
PAQ759Q 10088 0.01 17.00 0.00 17.0 17.00 17.00 17.00 17 ▁▁▇▁▁
PAQ759S 10060 0.01 19.00 0.00 19.0 19.00 19.00 19.00 19 ▁▁▇▁▁
PAQ759U 10160 0.00 21.00 0.00 21.0 21.00 21.00 21.00 21 ▁▁▇▁▁
PAQ759V 10172 0.00 22.00 0.00 22.0 22.00 22.00 22.00 22 ▁▁▇▁▁
PAQ762 8597 0.16 1.09 0.47 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
PAQ764 8708 0.14 4.85 0.83 1.0 5.00 5.00 5.00 9 ▁▁▇▁▁
PAQ766 8723 0.14 3.14 1.10 1.0 3.00 3.00 3.00 9 ▂▇▁▁▁
PAQ679 9503 0.07 4.56 1.23 1.0 3.00 5.00 5.00 9 ▁▅▇▃▁
PAQ750 7925 0.22 1.44 0.95 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
PAQ770 7918 0.22 1.95 0.47 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
PAQ772A 10126 0.00 2.47 3.13 1.0 1.00 1.00 1.00 9 ▇▁▁▁▂
PAQ772B 10130 0.00 2.00 0.00 2.0 2.00 2.00 2.00 2 ▁▁▇▁▁
PAQ772C 10100 0.01 3.00 0.00 3.0 3.00 3.00 3.00 3 ▁▁▇▁▁
PAAQUEX 691 0.93 1.08 0.26 1.0 1.00 1.00 1.00 2 ▇▁▁▁▁
PUQ100 2364 0.77 1.93 0.54 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
PUQ110 2365 0.77 2.06 0.92 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
RHQ010 6869 0.32 19.83 85.52 0.0 11.00 12.00 13.00 999 ▇▁▁▁▁
RHQ020 10150 0.00 4.12 2.86 1.0 2.00 3.00 4.00 9 ▆▇▁▁▅
RHQ031 6919 0.32 1.44 0.51 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
RHD043 8752 0.14 5.89 5.38 1.0 3.00 7.00 7.00 99 ▇▁▁▁▁
RHQ060 8752 0.14 79.70 181.10 15.0 40.00 46.00 51.00 999 ▇▁▁▁▁
RHQ070 10122 0.01 18.89 34.13 1.0 4.00 5.00 7.00 99 ▇▁▁▁▂
RHQ074 8246 0.19 1.88 0.33 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
RHQ076 8246 0.19 1.91 0.29 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
RHQ078 8246 0.19 1.99 0.62 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
RHQ131 7543 0.26 1.17 0.43 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
RHD143 9236 0.09 2.04 0.82 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
RHQ160 7982 0.22 3.46 2.67 1.0 2.00 3.00 4.00 77 ▇▁▁▁▁
RHQ162 7982 0.22 1.94 0.50 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
RHQ163 9979 0.02 33.36 69.63 16.0 23.00 29.00 34.00 999 ▇▁▁▁▁
RHQ166 7999 0.21 2.25 1.84 0.0 1.00 2.00 3.00 13 ▇▅▁▁▁
RHQ169 8934 0.12 0.69 1.01 0.0 0.00 0.00 1.00 6 ▇▁▁▁▁
RHQ172 8094 0.20 1.88 0.67 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
RHD173 9840 0.03 31.84 75.32 14.0 21.00 25.00 30.00 999 ▇▁▁▁▁
RHQ171 8094 0.20 2.71 1.59 0.0 2.00 2.00 3.00 12 ▇▅▁▁▁
RHD180 8523 0.16 23.89 41.87 14.0 19.00 21.00 25.00 999 ▇▁▁▁▁
RHD190 8121 0.20 35.33 77.16 14.0 25.00 29.00 34.00 999 ▇▁▁▁▁
RHQ197 10025 0.01 10.31 5.96 1.0 5.00 10.00 15.00 27 ▇▇▇▃▁
RHQ200 10025 0.01 1.73 0.45 1.0 1.00 2.00 2.00 2 ▃▁▁▁▇
RHD280 7555 0.26 1.79 0.46 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
RHQ291 9610 0.06 56.75 120.41 20.0 35.00 41.00 48.00 999 ▇▁▁▁▁
RHQ305 7554 0.26 1.94 0.70 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
RHQ332 9871 0.03 53.10 95.22 20.0 36.00 43.00 50.00 999 ▇▁▁▁▁
RHQ420 6922 0.32 1.43 0.56 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
RHQ540 7546 0.26 1.83 0.56 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
RHQ542A 9767 0.04 10.22 4.41 10.0 10.00 10.00 10.00 99 ▇▁▁▁▁
RHQ542B 10112 0.01 11.00 0.00 11.0 11.00 11.00 11.00 11 ▁▁▇▁▁
RHQ542C 10063 0.01 12.00 0.00 12.0 12.00 12.00 12.00 12 ▁▁▇▁▁
RHQ542D 10160 0.00 13.00 0.00 13.0 13.00 13.00 13.00 13 ▁▁▇▁▁
RHQ554 9768 0.04 1.88 2.22 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
RHQ560Q 9880 0.03 10.59 15.53 1.0 3.00 6.00 11.50 99 ▇▁▁▁▁
RHQ560U 9886 0.03 1.82 0.38 1.0 2.00 2.00 2.00 2 ▂▁▁▁▇
RHQ570 9768 0.04 2.47 2.16 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
RHQ576Q 10095 0.01 8.20 15.28 1.0 2.00 5.00 10.00 99 ▇▁▁▁▁
RHQ576U 10097 0.01 1.74 0.44 1.0 1.25 2.00 2.00 2 ▃▁▁▁▇
RHQ580 10112 0.01 2.60 2.99 1.0 1.00 1.00 2.00 9 ▇▁▁▁▂
RHQ586Q 10136 0.00 8.31 21.51 1.0 1.50 3.00 5.00 99 ▇▁▁▁▁
RHQ586U 10138 0.00 1.59 0.50 1.0 1.00 2.00 2.00 2 ▆▁▁▁▇
RHQ596 10112 0.01 2.81 2.58 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
RHQ602Q 10163 0.00 5.33 4.40 1.0 2.00 3.00 8.50 15 ▇▁▁▂▁
RHQ602U 10163 0.00 1.58 0.51 1.0 1.00 2.00 2.00 2 ▆▁▁▁▇
RXQ510 6360 0.37 1.67 0.55 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
RXQ515 8891 0.13 1.39 0.75 1.0 1.00 1.00 2.00 4 ▇▂▁▁▁
RXQ520 7644 0.25 1.96 0.37 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
RXQ525G 9042 0.11 1.24 0.63 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
RXQ525Q 10079 0.01 12.24 101.78 1.0 1.00 2.00 2.00 999 ▇▁▁▁▁
RXQ525U 10080 0.01 1.87 0.33 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
RXD530 9042 0.11 1172.29 10229.85 20.0 81.00 81.00 81.00 99999 ▇▁▁▁▁
SLD010H 3714 0.63 7.05 3.35 2.0 6.00 7.00 8.00 99 ▇▁▁▁▁
SLQ050 3711 0.64 1.76 0.45 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
SLQ060 3711 0.64 1.92 0.43 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
SMQ020 4062 0.60 1.58 0.51 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
SMD030 7596 0.25 21.33 64.32 0.0 15.00 17.00 20.00 999 ▇▁▁▁▁
SMQ040 7596 0.25 2.14 0.94 1.0 1.00 3.00 3.00 3 ▆▁▂▁▇
SMQ050Q 8828 0.13 3109.90 14964.33 1.0 6.00 15.00 30.00 99999 ▇▁▁▁▁
SMQ050U 8885 0.13 3.86 0.46 1.0 4.00 4.00 4.00 4 ▁▁▁▁▇
SMD055 8960 0.12 46.97 92.16 14.0 26.00 36.00 49.00 999 ▇▁▁▁▁
SMD057 8828 0.13 25.65 104.35 1.0 4.00 10.00 20.00 999 ▇▁▁▁▁
SMQ078 9176 0.10 2.59 1.54 1.0 1.00 2.00 3.00 7 ▇▃▂▁▁
SMD641 8859 0.13 24.33 10.10 0.0 21.75 30.00 30.00 30 ▂▁▁▁▇
SMD650 8927 0.12 11.99 40.45 1.0 4.00 10.00 15.00 999 ▇▁▁▁▁
SMD093 8943 0.12 1.47 0.79 1.0 1.00 1.00 2.00 4 ▇▃▁▁▁
SMD100FL 9084 0.11 0.99 0.10 0.0 1.00 1.00 1.00 1 ▁▁▁▁▇
SMD100MN 9083 0.11 0.39 0.49 0.0 0.00 0.00 1.00 1 ▇▁▁▁▅
SMD100LN 9083 0.11 2.36 0.58 1.0 2.00 2.00 3.00 4 ▁▇▁▅▁
SMD100TR 9347 0.08 12.98 2.23 2.0 12.00 13.00 15.00 24 ▁▁▇▁▁
SMD100NI 9347 0.08 1.00 0.20 0.1 0.90 1.00 1.10 2 ▁▃▇▂▁
SMD100CO 9347 0.08 13.58 2.24 3.0 12.00 13.00 15.00 19 ▁▁▅▇▂
SMQ621 9168 0.10 1.60 4.08 1.0 1.00 1.00 1.00 99 ▇▁▁▁▁
SMD630 10092 0.01 13.78 2.15 6.0 13.00 14.00 15.00 17 ▁▁▂▆▇
SMQ661 10146 0.00 2.79 2.60 1.0 1.00 2.00 3.00 10 ▇▅▁▁▁
SMQ665A 10162 0.00 4.38 2.99 1.0 1.00 3.00 7.00 8 ▆▅▁▂▇
SMQ665C 10166 0.00 1.00 0.00 1.0 1.00 1.00 1.00 1 ▁▁▇▁▁
SMQ665D 10173 0.00 8.00 1.41 7.0 7.50 8.00 8.50 9 ▇▁▁▁▇
SMQ670 8914 0.12 1.52 0.50 1.0 1.00 2.00 2.00 2 ▇▁▁▁▇
SMQ848 9592 0.06 11.85 91.96 1.0 1.00 2.00 4.00 999 ▇▁▁▁▁
SMQ852Q 9595 0.06 52.51 668.82 0.0 1.00 2.00 4.00 9999 ▇▁▁▁▁
SMQ852U 9598 0.06 1.79 0.85 1.0 1.00 2.00 3.00 3 ▇▁▃▁▅
SMAQUEX2 3007 0.70 1.15 0.35 1.0 1.00 1.00 1.00 2 ▇▁▁▁▂
SMD460 116 0.99 0.85 19.98 0.0 0.00 0.00 0.00 999 ▇▁▁▁▁
SMD470 7723 0.24 0.80 0.87 0.0 0.00 1.00 1.00 3 ▇▅▁▃▁
SMD480 8868 0.13 4.71 5.30 0.0 2.00 7.00 7.00 99 ▇▁▁▁▁
SMQ856 4062 0.60 1.48 0.51 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
SMQ858 6994 0.31 1.87 0.34 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
SMQ860 70 0.99 1.53 0.51 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
SMQ862 5378 0.47 1.97 0.25 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
SMQ866 4062 0.60 1.89 0.31 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
SMQ868 9495 0.07 1.70 0.46 1.0 1.00 2.00 2.00 2 ▃▁▁▁▇
SMQ870 70 0.99 1.17 0.37 1.0 1.00 1.00 1.00 2 ▇▁▁▁▂
SMQ872 1744 0.83 1.84 0.38 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
SMQ874 70 0.99 1.49 0.50 1.0 1.00 1.00 2.00 2 ▇▁▁▁▇
SMQ876 5057 0.50 1.86 0.56 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
SMQ878 70 0.99 1.44 0.50 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
SMQ880 4497 0.56 1.93 0.31 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
SMAQUEX.x 33 1.00 2.19 0.60 1.0 2.00 2.00 3.00 3 ▂▁▇▁▃
SMQ681 3745 0.63 1.80 0.41 1.0 2.00 2.00 2.00 7 ▇▁▁▁▁
SMQ690A 9056 0.11 1.00 0.00 1.0 1.00 1.00 1.00 1 ▁▁▇▁▁
SMQ710 9056 0.11 4.34 1.21 1.0 4.00 5.00 5.00 5 ▁▁▁▁▇
SMQ720 9056 0.11 10.88 30.59 1.0 4.00 8.00 15.00 999 ▇▁▁▁▁
SMQ725 9056 0.11 1.31 0.58 1.0 1.00 1.00 2.00 3 ▇▁▂▁▁
SMQ690B 10153 0.00 2.00 0.00 2.0 2.00 2.00 2.00 2 ▁▁▇▁▁
SMQ740 10153 0.00 2.68 1.46 1.0 1.00 3.00 4.00 5 ▇▃▆▅▃
SMQ690C 10010 0.02 3.00 0.00 3.0 3.00 3.00 3.00 3 ▁▁▇▁▁
SMQ770 10010 0.02 3.25 1.65 1.0 2.00 3.00 5.00 5 ▅▂▃▂▇
SMQ690G 10116 0.01 7.00 0.00 7.0 7.00 7.00 7.00 7 ▁▁▇▁▁
SMQ845 10116 0.01 1.59 1.00 1.0 1.00 1.00 2.00 5 ▇▃▁▁▁
SMQ690H 10050 0.01 8.00 0.00 8.0 8.00 8.00 8.00 8 ▁▁▇▁▁
SMQ849 10050 0.01 3.14 1.74 1.0 1.00 3.00 5.00 5 ▆▂▂▂▇
SMQ851 3745 0.63 1.98 0.15 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
SMQ690D 10123 0.01 4.00 0.00 4.0 4.00 4.00 4.00 4 ▁▁▇▁▁
SMQ800 10123 0.01 3.83 1.53 1.0 3.00 5.00 5.00 5 ▂▁▂▂▇
SMQ690E 10121 0.01 5.00 0.00 5.0 5.00 5.00 5.00 5 ▁▁▇▁▁
SMQ817 10121 0.01 4.44 1.02 1.0 4.00 5.00 5.00 5 ▁▁▁▂▇
SMQ863 4752 0.53 2.00 0.07 1.0 2.00 2.00 2.00 2 ▁▁▁▁▇
SMQ690F 10151 0.00 6.00 0.00 6.0 6.00 6.00 6.00 6 ▁▁▇▁▁
SMQ830 10151 0.00 3.17 1.63 1.0 1.75 4.00 5.00 5 ▇▅▁▇▇
SMQ840 10151 0.00 1.79 0.78 1.0 1.00 2.00 2.00 3 ▇▁▇▁▃
SMDANY 4702 0.54 1.74 0.44 1.0 1.00 2.00 2.00 2 ▃▁▁▁▇
SMAQUEX.y 3196 0.69 1.85 0.36 1.0 2.00 2.00 2.00 2 ▂▁▁▁▇
SXD021 5649 0.44 1.06 0.27 1.0 1.00 1.00 1.00 7 ▇▁▁▁▁
SXQ800 7977 0.22 1.10 0.38 1.0 1.00 1.00 1.00 7 ▇▁▁▁▁
SXQ803 7977 0.22 1.24 0.62 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
SXQ806 7977 0.22 1.64 0.65 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
SXQ809 7977 0.22 1.96 0.36 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
SXQ700 7836 0.23 1.09 0.48 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
SXQ703 7837 0.23 1.28 0.61 1.0 1.00 1.00 1.75 7 ▇▁▁▁▁
SXQ706 7837 0.23 1.70 0.67 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
SXQ709 7837 0.23 1.93 0.41 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
SXD031 5882 0.42 17.78 5.96 9.0 15.00 17.00 19.00 99 ▇▁▁▁▁
SXD171 7978 0.22 970.31 9119.43 0.0 2.00 6.00 19.00 99999 ▇▁▁▁▁
SXD510 8380 0.18 299.05 5171.90 0.0 1.00 1.00 1.00 99999 ▇▁▁▁▁
SXQ824 8552 0.16 976.84 9122.96 1.0 3.00 6.00 16.00 99999 ▇▁▁▁▁
SXQ827 8553 0.16 563.72 7184.90 0.0 1.00 1.00 1.00 99999 ▇▁▁▁▁
SXD633 8587 0.16 20.32 7.27 9.0 17.00 18.00 22.00 99 ▇▁▁▁▁
SXQ636 8587 0.16 553.77 6911.47 1.0 2.00 3.00 7.00 99999 ▇▁▁▁▁
SXQ639 8587 0.16 162.04 3728.05 0.0 0.00 1.00 1.00 99999 ▇▁▁▁▁
SXD642 8981 0.12 3084.35 10941.76 0.0 5.50 365.00 2557.00 99999 ▇▁▁▁▁
SXQ410 10060 0.01 18.25 56.68 1.0 1.00 3.00 11.50 500 ▇▁▁▁▁
SXQ550 10079 0.01 2.25 6.33 0.0 0.00 1.00 2.00 50 ▇▁▁▁▁
SXQ836 10060 0.01 11.57 41.56 0.0 0.00 2.00 5.50 400 ▇▁▁▁▁
SXQ841 10101 0.01 2.50 6.86 0.0 0.00 1.00 1.00 48 ▇▁▁▁▁
SXQ853 10060 0.01 1.19 0.40 1.0 1.00 1.00 1.00 2 ▇▁▁▁▂
SXD621 8593 0.16 20.52 7.05 9.0 17.00 19.00 22.00 99 ▇▁▁▁▁
SXQ624 8593 0.16 475.73 6261.58 1.0 1.00 2.00 5.00 99999 ▇▁▁▁▁
SXQ627 8606 0.15 199.25 3923.27 0.0 0.00 1.00 1.00 77777 ▇▁▁▁▁
SXD630 9078 0.11 3029.99 10422.30 0.0 0.00 608.00 2922.00 99999 ▇▁▁▁▁
SXQ645 7922 0.22 1.45 1.03 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
SXQ648 7142 0.30 1.81 0.41 1.0 2.00 2.00 2.00 7 ▇▁▁▁▁
SXQ610 7154 0.30 3.46 5.79 0.0 2.00 3.00 4.00 99 ▇▁▁▁▁
SXQ251 7249 0.29 3.37 1.73 1.0 1.00 4.00 5.00 9 ▇▂▇▁▁
SXQ590 9245 0.09 299.14 5286.79 0.0 0.00 0.00 1.00 99999 ▇▁▁▁▁
SXQ600 9245 0.09 107.77 3279.09 0.0 0.00 0.00 0.00 99999 ▇▁▁▁▁
SXD101 7836 0.23 643.42 7274.44 0.0 2.00 4.00 8.00 99999 ▇▁▁▁▁
SXD450 8273 0.19 287.32 4710.88 0.0 1.00 1.00 1.00 77777 ▇▁▁▁▁
SXQ724 8396 0.17 457.02 6003.00 1.0 2.00 4.00 8.00 99999 ▇▁▁▁▁
SXQ727 8396 0.17 450.83 6003.45 0.0 1.00 1.00 1.00 99999 ▇▁▁▁▁
SXQ130 9983 0.02 3.91 8.80 1.0 1.00 2.00 3.00 100 ▇▁▁▁▁
SXQ490 9983 0.02 405.61 5613.03 0.0 0.00 0.00 1.00 77777 ▇▁▁▁▁
SXQ741 9983 0.02 1.19 0.39 1.0 1.00 1.00 1.00 2 ▇▁▁▁▂
SXQ753 8377 0.18 1.92 0.39 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
SXQ260 6695 0.34 1.97 0.28 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
SXQ265 6695 0.34 1.97 0.25 1.0 2.00 2.00 2.00 7 ▇▁▁▁▁
SXQ267 10048 0.01 25.00 7.96 0.0 20.00 23.00 29.00 50 ▁▅▇▂▁
SXQ270 6695 0.34 2.00 0.18 1.0 2.00 2.00 2.00 7 ▇▁▁▁▁
SXQ272 6695 0.34 1.99 0.24 1.0 2.00 2.00 2.00 9 ▇▁▁▁▁
SXQ280 8493 0.17 1.32 0.57 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
SXQ292 8385 0.18 1.16 0.85 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
SXQ294 8276 0.19 1.27 0.95 1.0 1.00 1.00 1.00 9 ▇▁▁▁▁
WHD010 3736 0.63 163.17 974.67 48.0 63.00 66.00 69.00 9999 ▇▁▁▁▁
WHD020 3745 0.63 254.22 865.67 75.0 143.00 170.00 200.00 9999 ▇▁▁▁▁
WHQ030 3711 0.64 1.96 1.02 1.0 1.00 2.00 3.00 9 ▇▆▁▁▁
WHQ040 3711 0.64 2.24 0.63 1.0 2.00 2.00 3.00 9 ▇▃▁▁▁
WHD050 3753 0.63 312.52 1142.81 75.0 140.00 170.00 200.00 9999 ▇▁▁▁▁
WHQ060 8936 0.12 1.37 0.53 1.0 1.00 1.00 2.00 9 ▇▁▁▁▁
WHQ070 4501 0.56 1.64 0.50 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁
WHD080A 8482 0.17 10.00 0.00 10.0 10.00 10.00 10.00 10 ▁▁▇▁▁
WHD080B 9292 0.09 11.00 0.00 11.0 11.00 11.00 11.00 11 ▁▁▇▁▁
WHD080C 9373 0.08 12.00 0.00 12.0 12.00 12.00 12.00 12 ▁▁▇▁▁
WHD080D 8345 0.18 13.00 0.00 13.0 13.00 13.00 13.00 13 ▁▁▇▁▁
WHD080E 9776 0.04 14.00 0.00 14.0 14.00 14.00 14.00 14 ▁▁▇▁▁
WHD080F 9942 0.02 15.00 0.00 15.0 15.00 15.00 15.00 15 ▁▁▇▁▁
WHD080G 10060 0.01 16.00 0.00 16.0 16.00 16.00 16.00 16 ▁▁▇▁▁
WHD080H 10061 0.01 17.00 0.00 17.0 17.00 17.00 17.00 17 ▁▁▇▁▁
WHD080I 10091 0.01 31.00 0.00 31.0 31.00 31.00 31.00 31 ▁▁▇▁▁
WHD080J 9975 0.02 32.00 0.00 32.0 32.00 32.00 32.00 32 ▁▁▇▁▁
WHD080K 10149 0.00 33.00 0.00 33.0 33.00 33.00 33.00 33 ▁▁▇▁▁
WHD080M 9142 0.10 34.00 0.00 34.0 34.00 34.00 34.00 34 ▁▁▇▁▁
WHD080N 10000 0.02 30.00 0.00 30.0 30.00 30.00 30.00 30 ▁▁▇▁▁
WHD080O 9486 0.07 41.00 0.00 41.0 41.00 41.00 41.00 41 ▁▁▇▁▁
WHD080Q 9054 0.11 43.00 0.00 43.0 43.00 43.00 43.00 43 ▁▁▇▁▁
WHD080R 9153 0.10 44.00 0.00 44.0 44.00 44.00 44.00 44 ▁▁▇▁▁
WHD080S 9232 0.09 45.00 0.00 45.0 45.00 45.00 45.00 45 ▁▁▇▁▁
WHD080T 9179 0.10 46.00 0.00 46.0 46.00 46.00 46.00 46 ▁▁▇▁▁
WHD080U 10160 0.00 35.00 0.00 35.0 35.00 35.00 35.00 35 ▁▁▇▁▁
WHD080L 10146 0.00 40.00 0.00 40.0 40.00 40.00 40.00 40 ▁▁▇▁▁
WHD110 5996 0.41 412.16 1508.01 75.0 140.00 165.00 198.00 9999 ▇▁▁▁▁
WHD120 5151 0.49 568.53 1977.09 55.0 125.00 150.00 180.00 9999 ▇▁▁▁▁
WHD130 7410 0.27 377.32 1726.34 50.0 63.00 66.00 70.00 9999 ▇▁▁▁▁
WHD140 4072 0.60 315.62 1077.07 85.0 155.00 185.00 224.00 9999 ▇▁▁▁▁
WHQ150 4155 0.59 571.31 7269.07 10.0 25.00 38.00 53.00 99999 ▇▁▁▁▁
WHQ030M 8697 0.15 2.58 0.78 1.0 3.00 3.00 3.00 9 ▂▇▁▁▁
WHQ500 8697 0.15 2.30 1.21 1.0 1.00 2.00 3.00 9 ▇▇▁▁▁
WHQ520 8697 0.15 1.75 0.71 1.0 1.00 2.00 2.00 9 ▇▁▁▁▁ 
head(questions) # variable names are annoying, but I have the data dictionary
## # A tibble: 6 × 953
##    SEQN ACD011A ACD011B ACD011C ACD040 ACD110 ALQ101 ALQ110 ALQ120Q ALQ120U
##   <dbl>   <dbl> <lgl>     <dbl>  <dbl>  <dbl>  <dbl>  <dbl>   <dbl>   <dbl>
## 1 73557       1 NA           NA     NA     NA      1     NA       1       3
## 2 73558       1 NA           NA     NA     NA      1     NA       7       1
## 3 73559       1 NA           NA     NA     NA      1     NA       0      NA
## 4 73560       1 NA           NA     NA     NA     NA     NA      NA      NA
## 5 73561       1 NA           NA     NA     NA      1     NA       0      NA
## 6 73562      NA NA           NA      4     NA      1     NA       5       3
## # … with 943 more variables: ALQ130 <dbl>, ALQ141Q <dbl>, ALQ141U <dbl>,
## #   ALQ151 <dbl>, ALQ160 <dbl>, BPQ020 <dbl>, BPQ030 <dbl>, BPD035 <dbl>,
## #   BPQ040A <dbl>, BPQ050A <dbl>, BPQ056 <dbl>, BPD058 <dbl>, BPQ059 <dbl>,
## #   BPQ080 <dbl>, BPQ060 <dbl>, BPQ070 <dbl>, BPQ090D <dbl>, BPQ100D <dbl>,
## #   CBD070 <dbl>, CBD090 <dbl>, CBD110 <dbl>, CBD120 <dbl>, CBD130 <dbl>,
## #   HSD010 <dbl>, HSQ500 <dbl>, HSQ510 <dbl>, HSQ520 <dbl>, HSQ571 <dbl>,
## #   HSQ580 <dbl>, HSQ590 <dbl>, HSAQUEX <dbl>, CSQ010 <dbl>, CSQ020 <dbl>, …
tail(questions) # no blank rows at the end
## # A tibble: 6 × 953
##    SEQN ACD011A ACD011B ACD011C ACD040 ACD110 ALQ101 ALQ110 ALQ120Q ALQ120U
##   <dbl>   <dbl> <lgl>     <dbl>  <dbl>  <dbl>  <dbl>  <dbl>   <dbl>   <dbl>
## 1 83726      NA NA           NA      1     NA     NA     NA      NA      NA
## 2 83727      NA NA           NA      3     NA      1     NA       1       2
## 3 83728      NA NA           NA     NA     NA     NA     NA      NA      NA
## 4 83729       1 NA           NA     NA     NA     NA     NA      NA      NA
## 5 83730      NA NA           NA      4     NA     NA     NA      NA      NA
## 6 83731      NA NA           NA     NA      5     NA     NA      NA      NA
## # … with 943 more variables: ALQ130 <dbl>, ALQ141Q <dbl>, ALQ141U <dbl>,
## #   ALQ151 <dbl>, ALQ160 <dbl>, BPQ020 <dbl>, BPQ030 <dbl>, BPD035 <dbl>,
## #   BPQ040A <dbl>, BPQ050A <dbl>, BPQ056 <dbl>, BPD058 <dbl>, BPQ059 <dbl>,
## #   BPQ080 <dbl>, BPQ060 <dbl>, BPQ070 <dbl>, BPQ090D <dbl>, BPQ100D <dbl>,
## #   CBD070 <dbl>, CBD090 <dbl>, CBD110 <dbl>, CBD120 <dbl>, CBD130 <dbl>,
## #   HSD010 <dbl>, HSQ500 <dbl>, HSQ510 <dbl>, HSQ520 <dbl>, HSQ571 <dbl>,
## #   HSQ580 <dbl>, HSQ590 <dbl>, HSAQUEX <dbl>, CSQ010 <dbl>, CSQ020 <dbl>, …
# check for na's
questions %>% janitor::clean_names() # they're still all upper case
## # A tibble: 10,175 × 953
##     seqn acd011a acd011b acd011c acd040 acd110 alq101 alq110 alq120q alq120u
##    <dbl>   <dbl> <lgl>     <dbl>  <dbl>  <dbl>  <dbl>  <dbl>   <dbl>   <dbl>
##  1 73557       1 NA           NA     NA     NA      1     NA       1       3
##  2 73558       1 NA           NA     NA     NA      1     NA       7       1
##  3 73559       1 NA           NA     NA     NA      1     NA       0      NA
##  4 73560       1 NA           NA     NA     NA     NA     NA      NA      NA
##  5 73561       1 NA           NA     NA     NA      1     NA       0      NA
##  6 73562      NA NA           NA      4     NA      1     NA       5       3
##  7 73563      NA NA           NA     NA     NA     NA     NA      NA      NA
##  8 73564       1 NA           NA     NA     NA      2      1       2       3
##  9 73565      NA NA           NA      5     NA     NA     NA      NA      NA
## 10 73566       1 NA           NA     NA     NA      1     NA       1       1
## # … with 10,165 more rows, and 943 more variables: alq130 <dbl>, alq141q <dbl>,
## #   alq141u <dbl>, alq151 <dbl>, alq160 <dbl>, bpq020 <dbl>, bpq030 <dbl>,
## #   bpd035 <dbl>, bpq040a <dbl>, bpq050a <dbl>, bpq056 <dbl>, bpd058 <dbl>,
## #   bpq059 <dbl>, bpq080 <dbl>, bpq060 <dbl>, bpq070 <dbl>, bpq090d <dbl>,
## #   bpq100d <dbl>, cbd070 <dbl>, cbd090 <dbl>, cbd110 <dbl>, cbd120 <dbl>,
## #   cbd130 <dbl>, hsd010 <dbl>, hsq500 <dbl>, hsq510 <dbl>, hsq520 <dbl>,
## #   hsq571 <dbl>, hsq580 <dbl>, hsq590 <dbl>, hsaquex <dbl>, csq010 <dbl>, …
names(questions) <-tolower(names(questions)) #change to lower case
# subset the data

# labs
cbc_small <- cbc_data %>% select(
  seqn, lbdbano, lbdeono, lbdlymno, lbdmono, lbdneno, lbxhct, lbxhgb, lbxmc, lbxmpsi, lbxpltsi, lbxmcvsi)

cbc_small <- cbc_small %>% rename(basophils=lbdbano, eosinophils=lbdeono, lymphocytes=lbdlymno, monocytes=lbdmono, neutrophils_seg=lbdneno, hematocrit=lbxhct, hemaglobin=lbxhgb, mchc=lbxmc, mpv=lbxmpsi, platelets=lbxpltsi, mcv=lbxmcvsi)
glimpse(cbc_small)  
## Rows: 9,813
## Columns: 12
## $ seqn            <dbl> 73557, 73558, 73559, 73560, 73561, 73562, 73563, 73564…
## $ basophils       <dbl> 0.1, 0.1, 0.1, 0.0, 0.1, 0.1, NA, 0.0, 0.0, 0.1, 0.0, …
## $ eosinophils     <dbl> 0.2, 0.8, 0.4, 0.1, 0.2, 0.6, NA, 0.3, 0.3, 0.2, 0.0, …
## $ lymphocytes     <dbl> 2.0, 3.4, 1.0, 2.3, 1.4, 1.6, NA, 1.6, 3.0, 1.4, 1.3, …
## $ monocytes       <dbl> 0.5, 1.0, 0.8, 0.7, 0.5, 0.6, NA, 0.3, 0.7, 0.6, 0.5, …
## $ neutrophils_seg <dbl> 2.0, 7.4, 4.9, 4.6, 4.5, 6.5, NA, 3.0, 5.5, 4.0, 4.2, …
## $ hematocrit      <dbl> 45.4, 36.7, 49.9, 37.8, 43.8, 41.5, NA, 39.8, 41.4, 43…
## $ hemaglobin      <dbl> 15.2, 11.9, 17.2, 12.9, 14.5, 14.2, NA, 13.5, 14.3, 15…
## $ mchc            <dbl> 33.4, 32.5, 34.3, 34.0, 33.0, 34.2, NA, 33.8, 34.6, 35…
## $ mpv             <dbl> 9.0, 8.4, 9.3, 8.0, 8.6, 8.1, NA, 9.0, 7.2, 7.7, 9.3, …
## $ platelets       <dbl> 204, 314, 237, 240, 300, 249, NA, 180, 296, 187, 231, …
## $ mcv             <dbl> 89.3, 95.4, 90.5, 82.1, 92.8, 84.1, NA, 85.1, 93.5, 99…
# demographics
demo_small <- demographics %>% select(seqn, dmdeduc3, dmdhrgnd, ridagemn, ridageyr, dmdhrmar, dmqmiliz, indfmin2)

demo_small <- demo_small %>% rename(
  education=dmdeduc3, gender=dmdhrgnd, age_yr=ridageyr, age_month=ridagemn, martial=dmdhrmar, military=dmqmiliz, income=indfmin2)
demo_small %>% tabyl(gender) 
##  gender    n   percent
##       1 5088 0.5000491
##       2 5087 0.4999509
glimpse(demo_small)
## Rows: 10,175
## Columns: 8
## $ seqn      <dbl> 73557, 73558, 73559, 73560, 73561, 73562, 73563, 73564, 7356…
## $ education <dbl> NA, NA, NA, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA, 3,…
## $ gender    <dbl> 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 2, …
## $ age_month <dbl> NA, NA, NA, NA, NA, NA, 5, NA, NA, NA, NA, NA, 10, NA, NA, N…
## $ age_yr    <dbl> 69, 54, 72, 9, 73, 56, 0, 61, 42, 56, 65, 26, 0, 9, 76, 10, …
## $ martial   <dbl> 4, 1, 1, 1, 1, 3, 1, 2, 1, 3, 2, 5, 1, 3, 1, 5, 1, 1, NA, 77…
## $ military  <dbl> 1, 2, 1, NA, 2, 1, NA, 2, 2, 2, 2, 2, NA, NA, 2, NA, NA, 2, …
## $ income    <dbl> 4, 7, 10, 9, 15, 9, 15, 10, 15, 4, 3, 15, 77, 5, 14, 2, 8, 8…
# questionnaires
# note that 1=yes, 2=no, 7= refused, 9= don't know
questions_small <- questions %>% select(seqn, ded120, ded125, mcq010, mcq040, mcq050, mcq160k, mcq160f, hiq011, hiq210, huq090)
questions_small <- questions_small %>% rename(outside_wkdy=ded120, outside_wknd=ded125, asthma_ever=mcq010, asthma_year=mcq040, asthma_er=mcq050, bronchitis_chronic=mcq160k, stroke_ever=mcq160f, health_ins_now=hiq011, health_ins_past_year=hiq210, talked_psychiatrist=huq090)
questions_small %>% tabyl(talked_psychiatrist)
##  talked_psychiatrist    n      percent valid_percent
##                    1  734 0.0721375921  0.0814650388
##                    2 8271 0.8128746929  0.9179800222
##                    7    1 0.0000982801  0.0001109878
##                    9    4 0.0003931204  0.0004439512
##                   NA 1165 0.1144963145            NA
# merging 
cbc_full <- cbc_small %>% inner_join(y=demo_small, 
            by=c("seqn"="seqn")) # labs and demo

cbc_full <- cbc_full %>% inner_join(y=questions_small, 
          by=c("seqn"="seqn")) # add in questions

Show your transformed table here. Use tools such as glimpse(), skim() or head() to illustrate your point.

glimpse(cbc_full) #great! it's all there
## Rows: 9,813
## Columns: 29
## $ seqn                 <dbl> 73557, 73558, 73559, 73560, 73561, 73562, 73563, …
## $ basophils            <dbl> 0.1, 0.1, 0.1, 0.0, 0.1, 0.1, NA, 0.0, 0.0, 0.1, …
## $ eosinophils          <dbl> 0.2, 0.8, 0.4, 0.1, 0.2, 0.6, NA, 0.3, 0.3, 0.2, …
## $ lymphocytes          <dbl> 2.0, 3.4, 1.0, 2.3, 1.4, 1.6, NA, 1.6, 3.0, 1.4, …
## $ monocytes            <dbl> 0.5, 1.0, 0.8, 0.7, 0.5, 0.6, NA, 0.3, 0.7, 0.6, …
## $ neutrophils_seg      <dbl> 2.0, 7.4, 4.9, 4.6, 4.5, 6.5, NA, 3.0, 5.5, 4.0, …
## $ hematocrit           <dbl> 45.4, 36.7, 49.9, 37.8, 43.8, 41.5, NA, 39.8, 41.…
## $ hemaglobin           <dbl> 15.2, 11.9, 17.2, 12.9, 14.5, 14.2, NA, 13.5, 14.…
## $ mchc                 <dbl> 33.4, 32.5, 34.3, 34.0, 33.0, 34.2, NA, 33.8, 34.…
## $ mpv                  <dbl> 9.0, 8.4, 9.3, 8.0, 8.6, 8.1, NA, 9.0, 7.2, 7.7, …
## $ platelets            <dbl> 204, 314, 237, 240, 300, 249, NA, 180, 296, 187, …
## $ mcv                  <dbl> 89.3, 95.4, 90.5, 82.1, 92.8, 84.1, NA, 85.1, 93.…
## $ education            <dbl> NA, NA, NA, 3, NA, NA, NA, NA, NA, NA, NA, 2, NA,…
## $ gender               <dbl> 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2…
## $ age_month            <dbl> NA, NA, NA, NA, NA, NA, 5, NA, NA, NA, NA, NA, NA…
## $ age_yr               <dbl> 69, 54, 72, 9, 73, 56, 0, 61, 56, 65, 26, 9, 76, …
## $ martial              <dbl> 4, 1, 1, 1, 1, 3, 1, 2, 3, 2, 5, 3, 1, 5, 1, 1, N…
## $ military             <dbl> 1, 2, 1, NA, 2, 1, NA, 2, 2, 2, 2, NA, 2, NA, NA,…
## $ income               <dbl> 4, 7, 10, 9, 15, 9, 15, 10, 4, 3, 15, 5, 14, 2, 8…
## $ outside_wkdy         <dbl> NA, NA, NA, NA, NA, 14, NA, NA, 30, NA, NA, NA, N…
## $ outside_wknd         <dbl> NA, 120, NA, NA, NA, 60, NA, NA, 30, NA, 120, NA,…
## $ asthma_ever          <dbl> 2, 1, 2, 2, 2, 2, NA, 1, 2, 2, 2, 2, 2, 1, 1, 2, …
## $ asthma_year          <dbl> NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ asthma_er            <dbl> NA, 2, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ bronchitis_chronic   <dbl> 2, 2, 2, NA, 1, 2, NA, 2, 2, 2, 2, NA, 2, NA, NA,…
## $ stroke_ever          <dbl> 1, 2, 2, NA, 2, 2, NA, 2, 2, 2, 2, NA, 2, NA, NA,…
## $ health_ins_now       <dbl> 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1…
## $ health_ins_past_year <dbl> 2, NA, 2, 2, 2, 2, 2, 2, NA, 1, 2, 2, 2, 2, 2, 2,…
## $ talked_psychiatrist  <dbl> 2, 2, 2, 2, 2, 2, NA, 2, 2, 2, 2, 2, 2, 1, 2, 2, …
head(cbc_full)
## # A tibble: 6 × 29
##    seqn basophils eosinoph…¹ lymph…² monoc…³ neutr…⁴ hemat…⁵ hemag…⁶  mchc   mpv
##   <dbl>     <dbl>      <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl> <dbl> <dbl>
## 1 73557       0.1        0.2     2       0.5     2      45.4    15.2  33.4   9  
## 2 73558       0.1        0.8     3.4     1       7.4    36.7    11.9  32.5   8.4
## 3 73559       0.1        0.4     1       0.8     4.9    49.9    17.2  34.3   9.3
## 4 73560       0          0.1     2.3     0.7     4.6    37.8    12.9  34     8  
## 5 73561       0.1        0.2     1.4     0.5     4.5    43.8    14.5  33     8.6
## 6 73562       0.1        0.6     1.6     0.6     6.5    41.5    14.2  34.2   8.1
## # … with 19 more variables: platelets <dbl>, mcv <dbl>, education <dbl>,
## #   gender <dbl>, age_month <dbl>, age_yr <dbl>, martial <dbl>, military <dbl>,
## #   income <dbl>, outside_wkdy <dbl>, outside_wknd <dbl>, asthma_ever <dbl>,
## #   asthma_year <dbl>, asthma_er <dbl>, bronchitis_chronic <dbl>,
## #   stroke_ever <dbl>, health_ins_now <dbl>, health_ins_past_year <dbl>,
## #   talked_psychiatrist <dbl>, and abbreviated variable names ¹​eosinophils,
## #   ²​lymphocytes, ³​monocytes, ⁴​neutrophils_seg, ⁵​hematocrit, ⁶​hemaglobin

If the data needs to be transformed in any way (values recoded, pivoted, etc), do it here. Examples include transforming a continuous variable into a categorical using case_when(), etc.

# create the variables you're interested in
cbc_full <- cbc_full %>% mutate(nlr= neutrophils_seg/lymphocytes)
cbc_full <- cbc_full %>% mutate(plr= platelets/lymphocytes)

#move columns of interest to the front
cbc_full <- cbc_full %>% select(age_yr, gender, nlr, plr, everything())

# make categories for age
cbc_full <- cbc_full %>% mutate(
    age_group=case_when(
      age_yr <=5 ~ "5 & under", 
      age_yr >6 & age_yr <=10 ~ "6-10 years old", 
      age_yr>10 & age_yr <=14 ~ "11-14 years old", 
      age_yr >14 & age_yr <=18 ~ "15-18 years old", 
      age_yr >18 & age_yr <=29 ~ "19-29 years old",
      age_yr >29 & age_yr <=39 ~ "30-39 years old",
      age_yr >39 & age_yr <=49 ~ "40-49 years old", 
      age_yr >49 & age_yr <=59 ~ "50-59 years old", 
      age_yr >59 & age_yr <=69~ "60-69 years old",
      age_yr >69 ~ "70 and older"))

cbc_full %>% tabyl(age_group) # looks great but not in order
##        age_group    n    percent valid_percent
##  11-14 years old  761 0.07755019    0.07956922
##  15-18 years old  698 0.07113013    0.07298202
##  19-29 years old 1067 0.10873331    0.11156420
##  30-39 years old  961 0.09793132    0.10048097
##  40-49 years old 1007 0.10261897    0.10529067
##        5 & under 1522 0.15510038    0.15913844
##  50-59 years old  916 0.09334556    0.09577583
##   6-10 years old  847 0.08631407    0.08856127
##  60-69 years old  918 0.09354937    0.09598494
##     70 and older  867 0.08835219    0.09065245
##             <NA>  249 0.02537450            NA
# create a factor with ordered levels
cbc_full <- cbc_full %>% mutate(age_group=factor(age_group, levels=c("5 & under", "6-10 years old", "11-14 years old", "15-18 years old", "19-29 years old", "30-39 years old", "40-49 years old", "50-59 years old", "60-69 years old", "70 and older")))

cbc_full %>% tabyl(age_group) # perfect
##        age_group    n    percent valid_percent
##        5 & under 1522 0.15510038    0.15913844
##   6-10 years old  847 0.08631407    0.08856127
##  11-14 years old  761 0.07755019    0.07956922
##  15-18 years old  698 0.07113013    0.07298202
##  19-29 years old 1067 0.10873331    0.11156420
##  30-39 years old  961 0.09793132    0.10048097
##  40-49 years old 1007 0.10261897    0.10529067
##  50-59 years old  916 0.09334556    0.09577583
##  60-69 years old  918 0.09354937    0.09598494
##     70 and older  867 0.08835219    0.09065245
##             <NA>  249 0.02537450            NA
# stroke
cbc_full %>% tabyl(stroke_ever)
##  stroke_ever    n      percent valid_percent
##            1  194 0.0197696933  0.0347172513
##            2 5389 0.5491694691  0.9643879742
##            9    5 0.0005095282  0.0008947745
##           NA 4225 0.4305513095            NA
#asthma
cbc_full %>% tabyl(asthma_ever)
##  asthma_ever    n      percent valid_percent
##            1 1492 0.1520432080  0.1583695998
##            2 7920 0.8070926322  0.8406750876
##            7    1 0.0001019056  0.0001061458
##            9    8 0.0008152451  0.0008491668
##           NA  392 0.0399470091            NA
cbc_full %>% tabyl(asthma_er)
##  asthma_er    n      percent valid_percent
##          1  187 0.0190563538   0.205043860
##          2  724 0.0737796800   0.793859649
##          9    1 0.0001019056   0.001096491
##         NA 8901 0.9070620605            NA
#bronchitis
cbc_full %>% tabyl(bronchitis_chronic)
##  bronchitis_chronic    n      percent valid_percent
##                   1  312 0.0317945582   0.055833930
##                   2 5268 0.5368388872   0.942734431
##                   9    8 0.0008152451   0.001431639
##                  NA 4225 0.4305513095            NA

Are the values what you expected for the variables? Why or Why not?

# look at nlr

# mean, SD
cbc_full %>% group_by(age_group, gender) %>%
  summarize(mean_nlr=mean(nlr, na.rm=TRUE), # mean NLR <3 
            sd_nlr=sd(nlr, na.rm = TRUE)) # looks right
## `summarise()` has grouped output by 'age_group'. You can override using the
## `.groups` argument.
## # A tibble: 22 × 4
## # Groups:   age_group [11]
##    age_group       gender mean_nlr sd_nlr
##    <fct>            <dbl>    <dbl>  <dbl>
##  1 5 & under            1    0.925  0.571
##  2 5 & under            2    0.975  0.876
##  3 6-10 years old       1    1.40   0.582
##  4 6-10 years old       2    1.42   0.973
##  5 11-14 years old      1    1.55   0.688
##  6 11-14 years old      2    1.58   1.17 
##  7 15-18 years old      1    1.86   0.910
##  8 15-18 years old      2    1.98   1.03 
##  9 19-29 years old      1    2.08   0.989
## 10 19-29 years old      2    2.02   0.927
## # … with 12 more rows
# range
cbc_full %>% group_by(age_group, gender) %>%
  summarize(min_nlr=min(nlr, na.rm=TRUE),
            max_nlr=max(nlr, na.rm = TRUE))
## `summarise()` has grouped output by 'age_group'. You can override using the
## `.groups` argument.
## # A tibble: 22 × 4
## # Groups:   age_group [11]
##    age_group       gender min_nlr max_nlr
##    <fct>            <dbl>   <dbl>   <dbl>
##  1 5 & under            1   0.123    3.37
##  2 5 & under            2   0.111   12.2 
##  3 6-10 years old       1   0.446    4   
##  4 6-10 years old       2   0.344   14.3 
##  5 11-14 years old      1   0.429    4.82
##  6 11-14 years old      2   0.407   17.4 
##  7 15-18 years old      1   0.417    7.71
##  8 15-18 years old      2   0.538    9.17
##  9 19-29 years old      1   0.571    9.12
## 10 19-29 years old      2   0.436    6.64
## # … with 12 more rows
# look at plr

# mean, SD
cbc_full %>% group_by(age_group, gender) %>%
  summarize(mean_plr=mean(plr, na.rm=TRUE), # mean PLR is around 100
            sd_plr=sd(plr, na.rm = TRUE)) 
## `summarise()` has grouped output by 'age_group'. You can override using the
## `.groups` argument.
## # A tibble: 22 × 4
## # Groups:   age_group [11]
##    age_group       gender mean_plr sd_plr
##    <fct>            <dbl>    <dbl>  <dbl>
##  1 5 & under            1     86.0   36.5
##  2 5 & under            2     91.2   47.1
##  3 6-10 years old       1    112.    35.9
##  4 6-10 years old       2    114.    36.3
##  5 11-14 years old      1    114.    36.3
##  6 11-14 years old      2    118.    43.8
##  7 15-18 years old      1    115.    37.1
##  8 15-18 years old      2    117.    39.5
##  9 19-29 years old      1    111.    36.5
## 10 19-29 years old      2    112.    38.5
## # … with 12 more rows
# range
cbc_full %>% group_by(age_group, gender) %>%
  summarize(min_plr=min(plr, na.rm=TRUE), # looks good. note that PLR values go up to several hundred
            max_plr=max(plr, na.rm = TRUE))
## `summarise()` has grouped output by 'age_group'. You can override using the
## `.groups` argument.
## # A tibble: 22 × 4
## # Groups:   age_group [11]
##    age_group       gender min_plr max_plr
##    <fct>            <dbl>   <dbl>   <dbl>
##  1 5 & under            1    30.5    311.
##  2 5 & under            2    32.0    748 
##  3 6-10 years old       1    45.5    290 
##  4 6-10 years old       2    45.1    275.
##  5 11-14 years old      1    40.2    308.
##  6 11-14 years old      2    35.6    415.
##  7 15-18 years old      1    43.7    305 
##  8 15-18 years old      2    46.7    498.
##  9 19-29 years old      1    36.4    341.
## 10 19-29 years old      2    45.3    359.
## # … with 12 more rows

Yes, the mean and standard deviations for NLR and PLR are about what I expected. Note they do vary by age group though! I’m glad I separated the data into the age groups I did.

Visualizing and Summarizing the Data

Use group_by() and summarize() to make a summary of the data here. The summary should be relevant to your research question

cbc_full %>% group_by(age_group, talked_psychiatrist) %>%
  summarize(mean_nlr=mean(nlr, na.rm=TRUE),
            sd_nlr=sd(nlr, na.rm = TRUE)) # note that for "have you talked to a psychiatrist in the past 12 months about your mental health", answers are: 1=yes, 2=no, 7= refused, 9= don't know
## `summarise()` has grouped output by 'age_group'. You can override using the
## `.groups` argument.
## # A tibble: 28 × 4
## # Groups:   age_group [11]
##    age_group       talked_psychiatrist mean_nlr sd_nlr
##    <fct>                         <dbl>    <dbl>  <dbl>
##  1 5 & under                         1    1.21   0.617
##  2 5 & under                         2    1.25   1.02 
##  3 5 & under                        NA    0.777  0.474
##  4 6-10 years old                    1    1.44   0.677
##  5 6-10 years old                    2    1.41   0.832
##  6 11-14 years old                   1    1.69   0.791
##  7 11-14 years old                   2    1.56   0.985
##  8 11-14 years old                   7    2.64  NA    
##  9 15-18 years old                   1    1.83   0.820
## 10 15-18 years old                   2    1.93   0.991
## # … with 18 more rows
# interesting, for all the age groups, nlr is higher for those who report recent conversations with psychiatrist or psychologist about their mental health

# stroke
cbc_full %>% group_by(age_group, stroke_ever) %>%
  filter(age_group!="5 & under" & age_group!="6-10 years old" & age_group!="11-14 years old" & age_group!="15-18 years old") %>%
  summarize(mean_plr=mean(plr, na.rm=TRUE),
            sd_plr=sd(plr, na.rm = TRUE)) # plr is higher in those with history of stroke
## `summarise()` has grouped output by 'age_group'. You can override using the
## `.groups` argument.
## # A tibble: 15 × 4
## # Groups:   age_group [6]
##    age_group       stroke_ever mean_plr sd_plr
##    <fct>                 <dbl>    <dbl>  <dbl>
##  1 19-29 years old           1    122     11.3
##  2 19-29 years old           2    111.    37.1
##  3 19-29 years old          NA    115.    40.2
##  4 30-39 years old           1    119.    17.7
##  5 30-39 years old           2    114.    37.1
##  6 40-49 years old           1    132.    56.4
##  7 40-49 years old           2    120.    44.8
##  8 50-59 years old           1    126.    50.7
##  9 50-59 years old           2    121.    46.6
## 10 50-59 years old           9     76.2   NA  
## 11 60-69 years old           1    111.    37.8
## 12 60-69 years old           2    120.    47.6
## 13 70 and older              1    143.    77.5
## 14 70 and older              2    127.    58.0
## 15 70 and older              9     73.5   33.5
# asthma
cbc_full %>% group_by(age_group, asthma_ever) %>%
  filter(asthma_ever<3) %>% # remove NAs, not sure
  summarize(mean_nlr=mean(nlr, na.rm=TRUE), # asthma ever
            sd_nlr=sd(nlr, na.rm = TRUE)) 
## `summarise()` has grouped output by 'age_group'. You can override using the
## `.groups` argument.
## # A tibble: 22 × 4
## # Groups:   age_group [11]
##    age_group       asthma_ever mean_nlr sd_nlr
##    <fct>                 <dbl>    <dbl>  <dbl>
##  1 5 & under                 1    0.983  0.609
##  2 5 & under                 2    0.951  0.769
##  3 6-10 years old            1    1.36   0.578
##  4 6-10 years old            2    1.42   0.863
##  5 11-14 years old           1    1.60   0.878
##  6 11-14 years old           2    1.56   0.994
##  7 15-18 years old           1    1.95   1.05 
##  8 15-18 years old           2    1.92   0.957
##  9 19-29 years old           1    2.00   0.952
## 10 19-29 years old           2    2.06   0.962
## # … with 12 more rows
  # for some age groups only, NLR is higher in those with history of asthma

cbc_full %>% group_by(age_group, asthma_er) %>%
  filter(asthma_er<3) %>% # remove NAs, not sure
  summarize(mean_nlr=mean(nlr, na.rm=TRUE), # asthma ER visit
            sd_nlr=sd(nlr, na.rm = TRUE)) 
## `summarise()` has grouped output by 'age_group'. You can override using the
## `.groups` argument.
## # A tibble: 22 × 4
## # Groups:   age_group [11]
##    age_group       asthma_er mean_nlr sd_nlr
##    <fct>               <dbl>    <dbl>  <dbl>
##  1 5 & under               1    0.968  0.709
##  2 5 & under               2    0.979  0.389
##  3 6-10 years old          1    1.11   0.424
##  4 6-10 years old          2    1.41   0.608
##  5 11-14 years old         1    1.41   0.672
##  6 11-14 years old         2    1.66   1.06 
##  7 15-18 years old         1    2.20   1.03 
##  8 15-18 years old         2    1.75   0.708
##  9 19-29 years old         1    1.95   0.551
## 10 19-29 years old         2    2.14   0.976
## # … with 12 more rows
# NLR isn't higher in those with history of ER visit for asthma in the past 12 months

# chronic bronchitis
cbc_full %>% group_by(age_group, bronchitis_chronic) %>%
  filter(bronchitis_chronic<3) %>% # remove NAs, not sure
  summarize(mean_nlr=mean(nlr, na.rm=TRUE),
            sd_nlr=sd(nlr, na.rm = TRUE)) 
## `summarise()` has grouped output by 'age_group'. You can override using the
## `.groups` argument.
## # A tibble: 12 × 4
## # Groups:   age_group [6]
##    age_group       bronchitis_chronic mean_nlr sd_nlr
##    <fct>                        <dbl>    <dbl>  <dbl>
##  1 19-29 years old                  1     1.87  0.772
##  2 19-29 years old                  2     2.06  0.990
##  3 30-39 years old                  1     2.36  0.916
##  4 30-39 years old                  2     2.06  1.04 
##  5 40-49 years old                  1     2.33  1.54 
##  6 40-49 years old                  2     2.13  1.03 
##  7 50-59 years old                  1     2.43  1.25 
##  8 50-59 years old                  2     2.10  0.992
##  9 60-69 years old                  1     2.50  2.17 
## 10 60-69 years old                  2     2.16  1.34 
## 11 70 and older                     1     2.97  1.76 
## 12 70 and older                     2     2.67  1.70
# NLR higher in those with history of chronic bronchitis

The variable I am most interested in is talked_psychiatrist. Interviewees were asked Have you talked to a psychiatrist, psychologist, or other mental health professional in the past 12 months about your mental health? Possible answers were yes, no, refused, or I don’t know.

I looked at values of NLR and PLR by age group and answers to talked_psychiatrist. I noticed that NLR and PLR were higher in those who had talked to a mental health professional compared to those who had not, in all age groups except kids under 5 years old and adolescents 15-18 years old.

I also looked at stroke history and PLR, and history of asthma, asthma ER visits, and chronic bronchitis and NLR. For stroke, asthma, and bronchitis, it seemed that mean NLR was higher in people with a history of that disorder compared to those without.

What are your findings about the summary? Are they what you expected?

I found that NLR and PLR, as indicators of inflammation, were higher in most age groups in those who had recently talked to a mental health professional about their mental health. This suggests that inflammation is higher in people experiencing mental health concerns. However, since this is cross-sectional, I can’t say what causes what, or if there’s even a causal relationship.

It could be that the process of trying to find a mental health provider is stressful in and of itself and that causes inflammation, not the other way around.

PLR was also higher in those with a history of stroke, and in those with history of asthma and chronic bronchitis. However, PLR was not higher in people with a history of an ER visit in the past 12 months for asthma.

Make at least two plots that help you answer your question on the transformed or summarized data. Use scales and/or labels to make each plot informative.

# first I want to just select the data with answers "yes" or "no" to the question about mental health
cbc_filter <- cbc_full %>% filter(
        talked_psychiatrist <3) #gets rid of 7 and 9
cbc_filter %>% tabyl(talked_psychiatrist) # great, it worked
##  talked_psychiatrist    n    percent
##                    1  707 0.08127371
##                    2 7992 0.91872629
# make talked_psychiatrist a factor so you can plot
cbc_filter <- cbc_filter %>%mutate(
        talked_psychiatrist = factor(talked_psychiatrist, 
                           levels=c(1, 2)))
  cbc_filter %>% tabyl(talked_psychiatrist)
##  talked_psychiatrist    n    percent
##                    1  707 0.08127371
##                    2 7992 0.91872629
  is.factor(cbc_filter$talked_psychiatrist) # great, it worked
## [1] TRUE
  cbc_filter%>%tabyl(gender, talked_psychiatrist)
##  gender   1    2
##       1 302 4123
##       2 405 3869
# now remove all answers that aren't "yes" or "no" from the stroke, asthma, and bronchitis variables
  
# stroke
cbc_stroke <- cbc_full %>% filter(stroke_ever==1 | stroke_ever==2) 
cbc_stroke %>% tabyl(stroke_ever)
##  stroke_ever    n    percent
##            1  194 0.03474834
##            2 5389 0.96525166
cbc_stroke <- cbc_stroke %>% mutate(stroke_ever= factor(stroke_ever, levels=c("1", "2"))) 
is.factor(cbc_stroke$stroke_ever) # great, it worked
## [1] TRUE
# asthma
cbc_asthma <- cbc_full %>% filter(asthma_ever==1 | asthma_ever==2) 
cbc_asthma %>% tabyl(asthma_ever)
##  asthma_ever    n  percent
##            1 1492 0.158521
##            2 7920 0.841479
cbc_asthma <- cbc_asthma %>% mutate(asthma_ever= factor(asthma_ever, levels=c("1", "2"))) 
is.factor(cbc_asthma$asthma_ever) # great, it worked
## [1] TRUE
# bronchitis
cbc_asthma <- cbc_asthma %>% filter(bronchitis_chronic==1 | bronchitis_chronic==2) 
cbc_asthma %>% tabyl(bronchitis_chronic)
##  bronchitis_chronic    n    percent
##                   1  310 0.05560538
##                   2 5265 0.94439462
cbc_asthma <- cbc_asthma %>% mutate(bronchitis_chronic= factor(bronchitis_chronic, levels=c("1", "2"))) 
is.factor(cbc_asthma$bronchitis_chronic) # great, it worked
## [1] TRUE
cbc_asthma %>% tabyl(asthma_ever, bronchitis_chronic)
##  asthma_ever   1    2
##            1 152  700
##            2 158 4565
# boxplot
# outliers; any value >6 would be >3 SDs above the mean, so I'm going to drop those values

nlr_box <-ggplot(cbc_filter) +
  aes(x=talked_psychiatrist, y=nlr,
      color=talked_psychiatrist)+
  geom_boxplot()+
  geom_violin(alpha=0.5, width=0.5)+
  ylim(0,6)+
  facet_wrap(vars(age_group))+
  labs(title="NLR & mental health concerns, by age category", x="Mental Health Concerns", y="NLR")
nlr_box +  scale_color_manual(
      values = ghibli_palette("PonyoLight"), name="Mental health concerns", labels =c("Yes", "No")) + 
  scale_x_discrete(labels=c('Yes','No'))
## Warning: Removed 781 rows containing non-finite values (`stat_boxplot()`).
## Warning: Removed 781 rows containing non-finite values (`stat_ydensity()`).

# looks good

# bar plot 
cbc_filter <- cbc_filter %>% filter(nlr!="NA")

nlr_bar <- cbc_filter %>%
  group_by(gender, talked_psychiatrist, age_group) %>%
  summarize(mean_nlr=mean(nlr)) %>%
  ggplot(aes(x=age_group, y=mean_nlr, fill = talked_psychiatrist)) + geom_bar(position = "dodge", stat="identity")
## `summarise()` has grouped output by 'gender', 'talked_psychiatrist'. You can
## override using the `.groups` argument.
nlr_bar +
   labs(title="NLR and mental health, by age category", x="Mental Health Concerns", y="NLR") + 
  scale_fill_discrete(name = "Mental health concerns", labels = c("Yes", "No")) + theme_classic() # don't like this as much; and the labels are crunched together

  # it's not as informative as a box plot
# now plot plr
cbc_filter <- cbc_filter %>% mutate(across(
  c(stroke_ever, asthma_ever, bronchitis_chronic), .fns = factor))
class(cbc_filter$stroke_ever) # great, it worked
## [1] "factor"
# plr and stroke

# will remove upper outliers in PLR and limit to 300 at the top end

# remove children (they don't usually have strokes)
# remove people who answered "not sure"

cbc_stroke <- cbc_stroke %>% filter(age_group=="30-39 years old"| age_group=="40-49 years old"| age_group=="50-59 years old"| age_group=="60-69 years old"| age_group=="70 and older") 

plr_stroke <- ggplot(cbc_stroke) +
  aes(x=stroke_ever, y=plr, 
      color=age_group)+ 
  geom_boxplot()+
  ylim(0, 300)+
  facet_wrap(vars(age_group))+
  labs(title="PLR and history of stroke", x="History of stroke", y="PLR")
plr_stroke +     
  scale_color_manual(
      values = ghibli_palette("LaputaMedium"), name="Age category") + 
  scale_x_discrete(labels=c('Yes','No')) # looks good
## Warning: Removed 205 rows containing non-finite values (`stat_boxplot()`).

# plr and asthma

plr_asthma <-ggplot(cbc_asthma) +
  aes(x=asthma_ever, y=plr, 
      color=age_group)+ 
  geom_boxplot()+
  ylim(0, 300)+
  facet_wrap(vars(age_group))+
  labs(title="PLR and asthma by age category", x="History of asthma", y="PLR")
plr_asthma +
    scale_color_manual(
      values = ghibli_palette("TotoroMedium"), name="Age category") + 
  scale_x_discrete(labels=c('Yes','No'))
## Warning: Removed 241 rows containing non-finite values (`stat_boxplot()`).

# plr and bronchitis

plr_bronchitis <-ggplot(cbc_asthma) +
  aes(x=bronchitis_chronic, y=plr, 
      color=age_group)+ 
  geom_boxplot()+
  ylim(0, 300)+
  facet_wrap(vars(age_group))+
  labs(title="PLR and chronic bronchitis", x="History of chronic bronchitis", y="PLR")

plr_bronchitis+ 
    scale_color_manual(
      values = ghibli_palette("PonyoMedium"), name="Age category") + 
  scale_x_discrete(labels=c('Yes','No'))
## Warning: Removed 241 rows containing non-finite values (`stat_boxplot()`).

Final Summary (10 points)

Summarize your research question and findings below.

My research question was whether two inflammatory markers, neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) were higher in individuals who had a history of physical and mental health issues than those without. Specifically, I looked at NLR in people who endorsed a history of mental health concerns over the past year. I also looked at PLR in people who did or did not endorse a history of stroke, asthma, asthma-related ER visits, and chronic bronchitis.

There was a pattern of higher NLR in individuals who endorsed a history of mental health concerns.For those with a history of stroke, asthma diagnosis, and bronchitis, PLR was higher than individuals without those conditions. I didn’t notice a pattern in asthma-related ER visits.

Are your findings what you expected? Why or Why not?

For the most part, I found what I expected. The only surprising thing was that asthma-related ER visits were not related to PLR. This could be because there are many variables that inform the decision to make an ER visit; parental involvement or anxiety (in the case of a child); rural or metro location; access to transportation; access to emergency rooms; access to health insurance; access to translators; and many more things.