HAQ figures and data
Datasets
haq_files %>%
map(., skim)
[[1]]
Skim summary statistics
n obs: 41
n variables: 2
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
cause 0 41 41 6 67 0 41
cause1 2 39 41 0 4 7 6
[[2]]
Skim summary statistics
n obs: 150
n variables: 7
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
GeoName 0 150 150 4 28 0 150
i.GeoName 0 150 150 4 28 0 150
UTLA13CD 0 150 150 9 9 0 150
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
Period 0 150 150 2016 0 2016 2016 2016 2016 2016 ▁▁▁▇▁▁▁▁
sum 0 150 150 368389.93 274974.04 38949 2e+05 276704 413393.25 1540438 ▇▇▂▂▁▁▁▁
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
per_capita_beds 0 150 150 175.92 36.16 10.89 153.22 172.59 194.7 271.23 ▁▁▁▃▇▇▂▂
total_beds 0 150 150 643.4 481.5 48.09 354.5 492.16 757.14 2360.04 ▃▇▂▁▁▁▁▁
[[3]]
Skim summary statistics
n obs: 152
n variables: 3
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
OfficialCode 0 152 152 9 9 0 152
RESLADST 0 152 152 2 4 0 152
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
N 0 152 152 323 0 323 323 323 323 323 ▁▁▁▇▁▁▁▁
[[4]]
Skim summary statistics
n obs: 148
n variables: 6
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
estimate 0 148 148 89.77 3.12 79.71 87.6 90.15 92.1 96.6 ▁▁▃▅▆▇▃▂
IMDscore 4 144 148 23.11 7.96 5.65 17.22 23.23 28.57 42 ▂▅▇▆▇▅▃▂
per_capita_beds 1 147 148 176.82 33.78 114.45 153.26 172.59 194.46 271.23 ▂▆▇▇▅▁▂▁
per_capita_gp 4 144 148 47.96 6.45 31.34 43.29 47.84 51.8 65.55 ▁▂▇▇▇▅▂▁
per_capita_nurses 4 144 148 24.58 7.29 10.95 18.73 24.71 29.27 48.23 ▃▇▆▇▅▂▁▁
value 4 144 148 39.59 8.88 20.37 33.52 37.75 44.87 67.23 ▁▃▇▆▃▂▂▁
[[5]]
Skim summary statistics
n obs: 1208
n variables: 5
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
AreaName 0 1208 1208 4 28 0 151
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
FYEAR 0 1208 1208 1364.5 231.52 1011 1187.75 1364.5 1541.25 1718 ▇▇▇▇▇▇▇▇
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
lowercl 0 1208 1208 4431.47 1498.52 1990.38 3389.27 3950.35 5007.75 11122.24 ▂▇▃▂▁▁▁▁
uppercl 0 1208 1208 4604.02 1522.37 2433.46 3539.21 4104.58 5197.69 11473.72 ▅▇▃▂▁▁▁▁
value 0 1208 1208 4516.96 1510.01 2351.24 3464 4034.18 5105.26 11296.97 ▅▇▃▂▁▁▁▁
[[6]]
Skim summary statistics
n obs: 1359
n variables: 5
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
RESLADST 0 1359 1359 2 4 0 151
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
Year 0 1359 1359 2012 2.58 2008 2010 2012 2014 2016 ▇▃▃▃▃▃▃▃
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
lowercl 0 1359 1359 22592.39 6377.37 546.39 19287.52 22611.2 26071.47 61491.27 ▁▁▇▆▁▁▁▁
uppercl 0 1359 1359 22978.42 6463.28 605.72 19634.69 23001.21 26508.11 62438 ▁▁▇▆▁▁▁▁
value 0 1359 1359 22784.63 6419.32 575.5 19456.64 22794.51 26290.44 61963.28 ▁▁▇▆▁▁▁▁
[[7]]
Skim summary statistics
n obs: 1359
n variables: 6
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
OfficialCode 0 1359 1359 9 9 0 151
RESLADST 0 1359 1359 2 4 0 151
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
Year 0 1359 1359 2012 2.58 2008 2010 2012 2014 2016 ▇▃▃▃▃▃▃▃
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
lowercl 0 1359 1359 22592.39 6377.37 546.39 19287.52 22611.2 26071.47 61491.27 ▁▁▇▆▁▁▁▁
uppercl 0 1359 1359 22978.42 6463.28 605.72 19634.69 23001.21 26508.11 62438 ▁▁▇▆▁▁▁▁
value 0 1359 1359 22784.63 6419.32 575.5 19456.64 22794.51 26290.44 61963.28 ▁▁▇▆▁▁▁▁
[[8]]
Skim summary statistics
n obs: 2416
n variables: 5
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
RESLADST 0 2416 2416 2 4 0 151
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
Year 0 2416 2416 2008.5 4.61 2001 2004.75 2008.5 2012.25 2016 ▇▇▇▇▇▇▇▇
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
lowercl 0 2416 2416 8087.71 1584.93 2855.04 6889.26 7923.32 9187.08 14018.74 ▁▁▆▇▆▃▁▁
uppercl 0 2416 2416 8331.79 1606.49 3045.9 7105.99 8152.68 9447.93 14426.77 ▁▁▇▇▆▃▁▁
value 0 2416 2416 8208.91 1594.89 2949.37 6990.91 8041.27 9311.03 14221.68 ▁▁▆▇▆▃▁▁
[[9]]
Skim summary statistics
n obs: 45904
n variables: 9
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
GeoName 0 45904 45904 4 28 0 151
OfficialCode 0 45904 45904 9 9 0 151
RESLADST 0 45904 45904 2 4 0 151
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
pop 0 45904 45904 18160.9 16336.18 28 8165 13575 21809.75 112802 ▇▅▂▁▁▁▁▁
sum 0 45904 45904 1379.06 1228.02 1 639 995 1647 15549 ▇▂▁▁▁▁▁▁
Year 0 45904 45904 2008.5 4.61 2001 2004.75 2008.5 2012.25 2016 ▇▇▇▇▇▇▇▇
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
lowercl 0 45904 45904 8087.71 1584.62 2855.04 6889.26 7923.32 9187.08 14018.74 ▁▁▆▇▆▃▁▁
uppercl 0 45904 45904 8331.79 1606.17 3045.9 7105.99 8152.68 9447.93 14426.77 ▁▁▇▇▆▃▁▁
value 0 45904 45904 8208.91 1594.57 2949.37 6990.91 8041.27 9311.03 14221.68 ▁▁▆▇▆▃▁▁
[[10]]
Skim summary statistics
n obs: 5920
n variables: 12
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
age_group_name 0 5920 5920 8 8 0 1
covariate_name_short 0 5920 5920 4 4 0 1
location_name 0 5920 5920 4 28 0 160
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
age_group_id 0 5920 5920 22 0 22 22 22 22 22 ▁▁▁▇▁▁▁▁
covariate_id 0 5920 5920 1099 0 1099 1099 1099 1099 1099 ▁▁▁▇▁▁▁▁
location_id 0 5920 5920 42212.32 9703.42 4618 44672.75 44712.5 44752.25 44792 ▁▁▁▁▁▁▁▇
model_version_id 0 5920 5920 11180 0 11180 11180 11180 11180 11180 ▁▁▁▇▁▁▁▁
sex_id 0 5920 5920 3 0 3 3 3 3 3 ▁▁▁▇▁▁▁▁
year_id 0 5920 5920 1998 10.68 1980 1989 1998 2007 2016 ▇▇▆▇▆▇▆▇
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
lower_value 0 5920 5920 76.75 4.95 65.83 72.84 76.81 80.69 88.28 ▂▆▇▇▇▇▅▁
mean_value 0 5920 5920 77.68 5 66.64 73.76 77.72 81.66 89.35 ▂▆▇▇▇▇▅▁
upper_value 0 5920 5920 78.62 5.06 67.46 74.64 78.67 82.64 90.48 ▂▆▇▇▇▇▅▁
[[11]]
Skim summary statistics
n obs: 4650
n variables: 42
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
ihme_loc_id 0 4650 4650 1 9 0 775
location_name 0 4650 4650 3 48 0 771
national_iso3 0 4650 4650 1 3 0 224
region_name 0 4650 4650 0 28 48 22
sdi_quintile 174 4476 4650 7 15 0 5
super_region_name 0 4650 4650 0 48 6 8
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
level 0 4650 4650 4.14 1.14 0 3 4 5 6 ▁▁▁▅▁▇▂▃
location_id 0 4650 4650 18216.26 20068.08 1 197 4735 43910 44792 ▇▁▁▁▁▁▂▃
year_id 0 4650 4650 2002.67 8.79 1990 1995 2002.5 2010 2016 ▇▇▁▇▇▁▇▇
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
i_adversemed 0 4650 4650 57.08 26.53 0 37.21 57.27 79.84 100 ▂▅▅▇▆▆▇▇
i_appendicitis 0 4650 4650 68.4 31.4 0.46 40.64 73.69 99.92 100 ▁▂▂▂▂▁▁▇
i_breastcancer 0 4650 4650 56.73 24.68 0 36.24 56.22 77.68 100 ▁▃▇▇▇▇▇▆
i_cervicalcancer 0 4650 4650 56.94 19.51 0 43.74 54.6 68.89 99.99 ▁▁▂▇▇▅▂▃
i_chronicresp 0 4650 4650 61.54 21.58 0.03 46.56 65.32 76.76 100 ▁▁▃▃▅▇▆▃
i_ckd 0 4650 4650 56.27 28.43 0.14 32.2 53.58 80.99 100 ▂▃▇▆▅▅▃▇
i_coloncancer 0 4650 4650 55.26 25.11 0.04 33.93 53.44 77.98 100 ▁▂▇▅▅▅▆▅
i_congheart 0 4650 4650 51.87 22.08 0.016 35.04 51.38 66.58 99.99 ▁▃▆▇▇▆▃▃
i_diabetes 0 4650 4650 66.27 21.38 0 52.07 66 84.82 100 ▁▁▂▅▇▇▆▇
i_diarrhea 0 4650 4650 59.01 31.79 0 27.62 62.02 89.98 100 ▂▃▃▂▂▂▃▇
i_diptheria 0 4650 4650 97.68 10.86 0.098 100 100 100 100 ▁▁▁▁▁▁▁▇
i_epilepsy 0 4650 4650 62.97 22.83 0 48.83 63.8 77.21 100 ▁▁▂▅▇▇▃▆
i_gallbladder 0 4650 4650 63.14 23.77 0 43.69 65.92 83.63 100 ▁▂▃▇▅▅▇▇
i_haq 0 4650 4650 60.96 23.25 10.59 39.28 61.51 83.71 97.13 ▁▅▆▅▅▃▇▇
i_hernia 0 4650 4650 66.45 28.05 0.37 44.97 72.07 93 100 ▁▂▂▃▃▃▅▇
i_hodgkinlymph 0 4650 4650 42.31 31.21 0.27 15.98 25.3 73.04 100 ▂▇▂▁▂▂▂▃
i_hyperhd 0 4650 4650 55.36 24.06 0 36.71 52.78 77.84 99.99 ▁▃▆▇▆▅▇▃
i_ihd 0 4650 4650 57.17 23.12 0 40.06 56.51 75.51 100 ▁▃▅▇▇▇▆▅
i_leukemia 0 4650 4650 41.97 26.35 2.04 22.96 30.89 59.02 100 ▁▇▆▂▂▂▂▂
i_lris 0 4650 4650 47.08 20.41 0.00015 31.59 49.95 61.99 100 ▂▃▅▅▇▆▁▁
i_maternal 0 4650 4650 65.68 32.26 0.081 35.59 70.47 99.4 100 ▁▂▂▂▂▂▂▇
i_measles 0 4650 4650 73.16 32.25 0 41.88 99.19 100 100 ▁▁▂▂▁▁▁▇
i_neonatal 0 4650 4650 45.8 26.53 0.013 22.6 41.26 67.64 100 ▃▇▇▃▅▆▃▂
i_pud 0 4650 4650 55.68 24.06 0.00042 39.41 57.23 73.33 100 ▂▃▃▆▇▆▅▃
i_rheuhd 0 4650 4650 59.6 25.96 0 39.51 64.39 79.49 100 ▂▃▃▃▅▇▆▆
i_skincancer 0 4650 4650 44.34 32.48 0 13.22 33.42 80.47 100 ▇▆▅▂▁▁▇▅
i_stroke 0 4650 4650 49.12 24.48 0 29.57 48.45 68.87 100 ▃▆▇▆▆▇▅▂
i_tb 0 4650 4650 59.79 30.51 0 30.47 61.39 90.12 100 ▁▅▃▂▃▂▃▇
i_testcancer 0 4650 4650 49.92 32.89 0.37 18.28 41.54 84.46 100 ▃▇▃▂▂▂▅▆
i_tetanus 0 4650 4650 80.94 28.92 0 64.56 100 100 100 ▁▁▁▁▁▁▁▇
i_uris 0 4650 4650 97.94 8.11 15.86 99.98 100 100 100 ▁▁▁▁▁▁▁▇
i_uterinecancer 0 4650 4650 54.78 29.35 0 26.92 54.7 83.09 100 ▂▇▆▅▃▅▇▇
i_whoopcough 0 4650 4650 75.43 27.39 6.25 49.11 89.85 100 100 ▁▁▂▂▁▁▁▇
[[12]]
Skim summary statistics
n obs: 984
n variables: 12
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
cause_name 0 984 984 9 9 0 1
change_type 0 984 984 15 25 0 2
location_name 0 984 984 4 28 0 164
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
cause_id 0 984 984 100 0 100 100 100 100 100 ▁▁▁▇▁▁▁▁
cause_id.1 0 984 984 100 0 100 100 100 100 100 ▁▁▁▇▁▁▁▁
level 0 984 984 5.88 0.44 3 6 6 6 6 ▁▁▁▁▁▁▁▇
location_id 0 984 984 41216.89 11476.03 95 44669.75 44710.5 44751.25 44792 ▁▁▁▁▁▁▁▇
year_end_id 0 984 984 2010.67 7.55 2000 2000 2016 2016 2016 ▃▁▁▁▁▁▁▇
year_start_id 0 984 984 1993.33 4.72 1990 1990 1990 2000 2000 ▇▁▁▁▁▁▁▃
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
estimate 0 984 984 4.29 4.29 0.19 0.56 1.81 6.7 15.28 ▇▁▂▂▁▁▂▁
lower 0 984 984 2.84 3.23 -0.71 0.38 0.69 4.34 12.23 ▇▁▂▂▁▁▁▁
uppper 0 984 984 5.72 5.44 0.39 0.73 3.57 9.41 18.43 ▇▁▁▃▁▁▂▁
[[13]]
Skim summary statistics
n obs: 895968
n variables: 9
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
age_group 0 895968 895968 6 14 0 23
cause_name 0 895968 895968 7 50 0 26
location_name 0 895968 895968 4 28 0 153
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
cause_id 0 895968 895968 472.93 131.15 297 340 492 531 849 ▇▂▆▅▁▂▁▁
level 0 895968 895968 5.96 0.28 4 6 6 6 6 ▁▁▁▁▁▁▁▇
location_id 0 895968 895968 43876.65 5952.31 433 44678 44716 44754 44792 ▁▁▁▁▁▁▁▇
sex_id 0 895968 895968 1.5 0.5 1 1 1.5 2 2 ▇▁▁▁▁▁▁▇
year_id 0 895968 895968 2002.67 8.79 1990 1995 2002.5 2010 2016 ▇▇▁▇▇▁▇▇
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
joint_paf 0 895968 895968 0.25 0.36 -0.12 0 0 0.44 1 ▇▁▁▁▁▁▁▂
[[14]]
Skim summary statistics
n obs: 32472
n variables: 9
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
cause_name 0 32472 32472 6 50 0 33
location_name 0 32472 32472 4 28 0 164
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
cause_id 0 32472 32472 459.27 135.75 100 341 484 529 849 ▁▁▇▃▇▂▁▁
level 0 32472 32472 5.88 0.44 3 6 6 6 6 ▁▁▁▁▁▁▁▇
location_id 0 32472 32472 41216.89 11470.38 95 44669.75 44710.5 44751.25 44792 ▁▁▁▁▁▁▁▇
year_id 0 32472 32472 2002.67 8.79 1990 1995 2002.5 2010 2016 ▇▇▁▇▇▁▇▇
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
estimate 0 32472 32472 82.07 16.08 14.38 70.97 84.16 98.64 100 ▁▁▁▁▂▃▅▇
lower 0 32472 32472 76.73 19.03 7.17 62.9 77.96 95.02 100 ▁▁▁▂▃▅▅▇
upper 0 32472 32472 87.13 13.66 20.93 78.71 90.54 100 100 ▁▁▁▁▂▂▃▇
[[15]]
Skim summary statistics
n obs: 31488
n variables: 9
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
cause_name 0 31488 31488 6 50 0 32
location_name 0 31488 31488 4 28 0 164
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
cause_id 0 31488 31488 470.5 121.84 297 359.75 485.5 529.5 849 ▇▃▇▅▂▂▁▁
level 0 31488 31488 5.88 0.44 3 6 6 6 6 ▁▁▁▁▁▁▁▇
location_id 0 31488 31488 41216.89 11470.38 95 44669.75 44710.5 44751.25 44792 ▁▁▁▁▁▁▁▇
year_id 0 31488 31488 2002.67 8.79 1990 1995 2002.5 2010 2016 ▇▇▁▇▇▁▇▇
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
estimate 0 31488 31488 0.053 0.12 3.8e-09 2.4e-06 1.3e-05 0.004 0.86 ▇▁▁▁▁▁▁▁
lower 0 31488 31488 0.045 0.11 2.4e-09 1.8e-06 1e-05 0.0021 0.79 ▇▁▁▁▁▁▁▁
upper 0 31488 31488 0.062 0.14 5.4e-09 3.1e-06 1.7e-05 0.0051 0.95 ▇▁▁▁▁▁▁▁
[[16]]
Skim summary statistics
n obs: 624
n variables: 5
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
Column 1 623 624 0 2 8 616
Comments 0 624 624 0 189 36 2
Description 0 624 624 0 111 8 617
Field Type 0 624 624 0 9 8 4
Name of Field 0 624 624 0 32 8 617
[[17]]
Skim summary statistics
n obs: 7312
n variables: 616
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
CCG_CODE 0 7312 7312 0 3 1 208
CCG_NAME 0 7312 7312 0 54 1 208
HEE_REGION_CODE 0 7312 7312 0 5 1 14
HEE_REGION_NAME 0 7312 7312 0 46 1 14
PRAC_CODE 0 7312 7312 0 6 1 7312
PRAC_NAME 0 7312 7312 0 40 1 6887
REGION_CODE 0 7312 7312 0 3 1 5
REGION_GEOG_CODE 0 7312 7312 0 3 1 15
REGION_GEOG_NAME 0 7312 7312 0 48 1 15
REGION_NAME 0 7312 7312 0 28 1 5
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
FEMALE_ADMIN_APP_HC 1 7311 7312 0.1 0.42 0 0 0 0 12 ▇▁▁▁▁▁▁▁
FEMALE_ADMIN_ESTATES_ANC_HC 1 7311 7312 0.15 0.62 0 0 0 0 11 ▇▁▁▁▁▁▁▁
FEMALE_ADMIN_HC 1 7311 7312 10.5 7.92 0 5 9 14 114 ▇▂▁▁▁▁▁▁
FEMALE_ADMIN_HC_25TO29 1 7311 7312 0.6 1 0 0 0 1 13 ▇▁▁▁▁▁▁▁
FEMALE_ADMIN_HC_30TO34 1 7311 7312 0.62 0.99 0 0 0 1 16 ▇▁▁▁▁▁▁▁
FEMALE_ADMIN_HC_35TO39 1 7311 7312 0.65 0.96 0 0 0 1 10 ▇▁▁▁▁▁▁▁
FEMALE_ADMIN_HC_40TO44 1 7311 7312 0.83 1.09 0 0 0 1 11 ▇▁▁▁▁▁▁▁
FEMALE_ADMIN_HC_45TO49 1 7311 7312 1.38 1.58 0 0 1 2 23 ▇▂▁▁▁▁▁▁
FEMALE_ADMIN_HC_50TO54 1 7311 7312 1.84 1.97 0 0 1 3 19 ▇▂▁▁▁▁▁▁
FEMALE_ADMIN_HC_55TO59 1 7311 7312 1.86 1.95 0 0 1 3 20 ▇▃▁▁▁▁▁▁
FEMALE_ADMIN_HC_60TO64 1 7311 7312 1.3 1.54 0 0 1 2 12 ▇▃▁▁▁▁▁▁
FEMALE_ADMIN_HC_65TO69 1 7311 7312 0.42 0.75 0 0 0 1 10 ▇▁▁▁▁▁▁▁
FEMALE_ADMIN_HC_70PLUS 1 7311 7312 0.21 0.53 0 0 0 0 6 ▇▁▁▁▁▁▁▁
FEMALE_ADMIN_HC_UNDER25 1 7311 7312 0.7 1.16 0 0 0 1 14 ▇▂▁▁▁▁▁▁
FEMALE_ADMIN_HC_UNKNOWN_AGE 0 7312 7312 0.096 0.89 0 0 0 0 27 ▇▁▁▁▁▁▁▁
FEMALE_ADMIN_MANAGE_PTNR_HC 1 7311 7312 0.011 0.1 0 0 0 0 1 ▇▁▁▁▁▁▁▁
FEMALE_ADMIN_MANAGER_HC 1 7311 7312 1.11 0.94 0 1 1 2 8 ▇▂▁▁▁▁▁▁
FEMALE_ADMIN_MED_SECRETARY_HC 1 7311 7312 0.97 1.27 0 0 1 2 20 ▇▁▁▁▁▁▁▁
FEMALE_ADMIN_OTH_HC 1 7311 7312 2.23 3.36 0 0 1 3 40 ▇▁▁▁▁▁▁▁
FEMALE_ADMIN_RECEPT_HC 1 7311 7312 5.9 4.65 0 3 5 8 75 ▇▂▁▁▁▁▁▁
FEMALE_ADMIN_TELEPH_HC 1 7311 7312 0.033 0.39 0 0 0 0 14 ▇▁▁▁▁▁▁▁
FEMALE_DPC_APP_HCA_HC 1 7311 7312 0.098 0.45 0 0 0 0 5 ▇▁▁▁▁▁▁▁
FEMALE_DPC_APP_OTH_HC 1 7311 7312 0.003 0.068 0 0 0 0 3 ▇▁▁▁▁▁▁▁
FEMALE_DPC_APP_PHARMA_HC 1 7311 7312 0.00041 0.02 0 0 0 0 1 ▇▁▁▁▁▁▁▁
FEMALE_DPC_APP_PHLEB_FTE 1 7311 7312 0.00014 0.012 0 0 0 0 1 ▇▁▁▁▁▁▁▁
FEMALE_DPC_APP_PHLEB_HC 1 7311 7312 0.00014 0.012 0 0 0 0 1 ▇▁▁▁▁▁▁▁
FEMALE_DPC_APP_PHYSIO_FTE 1 7311 7312 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
FEMALE_DPC_APP_PHYSIO_HC 1 7311 7312 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
FEMALE_DPC_DISPENSER_HC 1 7311 7312 0.37 1.41 0 0 0 0 18 ▇▁▁▁▁▁▁▁
FEMALE_DPC_HC 1 7311 7312 1.87 2.32 0 0 1 2 26 ▇▁▁▁▁▁▁▁
FEMALE_DPC_HC_25TO29 1 7311 7312 0.11 0.38 0 0 0 0 9 ▇▁▁▁▁▁▁▁
FEMALE_DPC_HC_30TO34 1 7311 7312 0.14 0.41 0 0 0 0 5 ▇▁▁▁▁▁▁▁
FEMALE_DPC_HC_35TO39 1 7311 7312 0.17 0.44 0 0 0 0 5 ▇▁▁▁▁▁▁▁
FEMALE_DPC_HC_40TO44 1 7311 7312 0.18 0.46 0 0 0 0 5 ▇▁▁▁▁▁▁▁
FEMALE_DPC_HC_45TO49 1 7311 7312 0.28 0.6 0 0 0 0 6 ▇▂▁▁▁▁▁▁
FEMALE_DPC_HC_50TO54 1 7311 7312 0.35 0.7 0 0 0 1 11 ▇▁▁▁▁▁▁▁
FEMALE_DPC_HC_55TO59 1 7311 7312 0.31 0.66 0 0 0 0 8 ▇▁▁▁▁▁▁▁
FEMALE_DPC_HC_60TO64 1 7311 7312 0.17 0.46 0 0 0 0 4 ▇▁▁▁▁▁▁▁
FEMALE_DPC_HC_65TO69 1 7311 7312 0.05 0.23 0 0 0 0 2 ▇▁▁▁▁▁▁▁
FEMALE_DPC_HC_70PLUS 1 7311 7312 0.019 0.14 0 0 0 0 2 ▇▁▁▁▁▁▁▁
FEMALE_DPC_HC_UNDER25 1 7311 7312 0.075 0.33 0 0 0 0 5 ▇▁▁▁▁▁▁▁
FEMALE_DPC_HC_UNKNOWN_AGE 1 7311 7312 0.014 0.14 0 0 0 0 5 ▇▁▁▁▁▁▁▁
FEMALE_DPC_HCA_HC 1 7311 7312 1.05 1.2 0 0 1 2 22 ▇▁▁▁▁▁▁▁
FEMALE_DPC_NURSE_ASSOC_HC 1 7311 7312 0.0015 0.039 0 0 0 0 1 ▇▁▁▁▁▁▁▁
FEMALE_DPC_OTH_HC 1 7311 7312 0.058 0.33 0 0 0 0 9 ▇▁▁▁▁▁▁▁
FEMALE_DPC_PARAMED_HC 1 7311 7312 0.014 0.13 0 0 0 0 3 ▇▁▁▁▁▁▁▁
FEMALE_DPC_PHARMA_HC 1 7311 7312 0.074 0.31 0 0 0 0 4 ▇▁▁▁▁▁▁▁
FEMALE_DPC_PHLEB_HC 1 7311 7312 0.18 0.49 0 0 0 0 8 ▇▁▁▁▁▁▁▁
FEMALE_DPC_PHYSICIAN_ASSOC_HC 1 7311 7312 0.0064 0.091 0 0 0 0 2 ▇▁▁▁▁▁▁▁
FEMALE_DPC_PHYSIO_HC 1 7311 7312 0.004 0.094 0 0 0 0 5 ▇▁▁▁▁▁▁▁
FEMALE_DPC_PODIA_FTE 1 7311 7312 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
FEMALE_DPC_PODIA_HC 1 7311 7312 0 0 0 0 0 0 0 ▁▁▁▇▁▁▁▁
FEMALE_DPC_THERA_COU_HC 1 7311 7312 0.0018 0.051 0 0 0 0 2 ▇▁▁▁▁▁▁▁
FEMALE_DPC_THERA_OCC_HC 1 7311 7312 0.00014 0.012 0 0 0 0 1 ▇▁▁▁▁▁▁▁
FEMALE_DPC_THERA_OTH_HC 1 7311 7312 0.0042 0.082 0 0 0 0 3 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXL_HC 1 7311 7312 2.6 2.51 0 1 2 4 32 ▇▂▁▁▁▁▁▁
FEMALE_GP_EXL_HC_30TO34 1 7311 7312 0.42 0.81 0 0 0 1 10 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXL_HC_35TO39 1 7311 7312 0.5 0.84 0 0 0 1 8 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXL_HC_40TO44 1 7311 7312 0.47 0.77 0 0 0 1 7 ▇▃▁▁▁▁▁▁
FEMALE_GP_EXL_HC_45TO49 1 7311 7312 0.4 0.68 0 0 0 1 6 ▇▃▁▁▁▁▁▁
FEMALE_GP_EXL_HC_50TO54 1 7311 7312 0.36 0.65 0 0 0 1 5 ▇▂▁▁▁▁▁▁
FEMALE_GP_EXL_HC_55TO59 1 7311 7312 0.25 0.54 0 0 0 0 5 ▇▂▁▁▁▁▁▁
FEMALE_GP_EXL_HC_60TO64 1 7311 7312 0.073 0.27 0 0 0 0 3 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXL_HC_65TO69 1 7311 7312 0.026 0.16 0 0 0 0 2 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXL_HC_70PLUS 1 7311 7312 0.014 0.12 0 0 0 0 2 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXL_HC_UNDER30 1 7311 7312 0.068 0.3 0 0 0 0 4 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXL_HC_UNKNOWN_AGE 1 7311 7312 0.018 0.19 0 0 0 0 6 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXRL_HC 1 7311 7312 2.45 2.32 0 1 2 4 29 ▇▂▁▁▁▁▁▁
FEMALE_GP_EXRL_HC_30TO34 1 7311 7312 0.36 0.71 0 0 0 1 7 ▇▂▁▁▁▁▁▁
FEMALE_GP_EXRL_HC_35TO39 1 7311 7312 0.48 0.81 0 0 0 1 8 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXRL_HC_40TO44 1 7311 7312 0.46 0.76 0 0 0 1 7 ▇▃▁▁▁▁▁▁
FEMALE_GP_EXRL_HC_45TO49 1 7311 7312 0.39 0.68 0 0 0 1 6 ▇▃▁▁▁▁▁▁
FEMALE_GP_EXRL_HC_50TO54 1 7311 7312 0.36 0.65 0 0 0 1 5 ▇▂▁▁▁▁▁▁
FEMALE_GP_EXRL_HC_55TO59 1 7311 7312 0.25 0.54 0 0 0 0 5 ▇▂▁▁▁▁▁▁
FEMALE_GP_EXRL_HC_60TO64 1 7311 7312 0.072 0.27 0 0 0 0 3 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXRL_HC_65TO69 1 7311 7312 0.026 0.16 0 0 0 0 2 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXRL_HC_70PLUS 1 7311 7312 0.014 0.12 0 0 0 0 2 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXRL_HC_UNDER30 1 7311 7312 0.026 0.17 0 0 0 0 4 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXRL_HC_UNKNOWN_AGE 1 7311 7312 0.014 0.17 0 0 0 0 5 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXRRL_HC 1 7311 7312 2.42 2.3 0 1 2 4 29 ▇▂▁▁▁▁▁▁
FEMALE_GP_EXRRL_HC_30TO34 1 7311 7312 0.35 0.71 0 0 0 1 7 ▇▂▁▁▁▁▁▁
FEMALE_GP_EXRRL_HC_35TO39 1 7311 7312 0.47 0.8 0 0 0 1 8 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXRRL_HC_40TO44 1 7311 7312 0.45 0.75 0 0 0 1 7 ▇▃▁▁▁▁▁▁
[ reached getOption("max.print") -- omitted 378 rows ]
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
FEMALE_ADMIN_APP_FTE 1 7311 7312 0.092 0.36 0 0 0 0 7.51 ▇▁▁▁▁▁▁▁
FEMALE_ADMIN_ESTATES_ANC_FTE 1 7311 7312 0.06 0.29 0 0 0 0 8.81 ▇▁▁▁▁▁▁▁
FEMALE_ADMIN_FTE 1 7311 7312 7.23 5.63 0 3.52 6.29 10 80.67 ▇▂▁▁▁▁▁▁
FEMALE_ADMIN_MANAGE_PTNR_FTE 1 7311 7312 0.01 0.098 0 0 0 0 1.33 ▇▁▁▁▁▁▁▁
FEMALE_ADMIN_MANAGER_FTE 1 7311 7312 0.96 0.83 0 0.32 0.99 1.08 7.88 ▇▆▂▁▁▁▁▁
FEMALE_ADMIN_MED_SECRETARY_FTE 1 7311 7312 0.68 0.91 0 0 0.48 1 12.43 ▇▁▁▁▁▁▁▁
FEMALE_ADMIN_OTH_FTE 1 7311 7312 1.49 2.37 0 0 0.53 2.08 34.82 ▇▁▁▁▁▁▁▁
FEMALE_ADMIN_RECEPT_FTE 1 7311 7312 3.92 3.24 0 1.8 3.41 5.42 51.53 ▇▂▁▁▁▁▁▁
FEMALE_ADMIN_TELEPH_FTE 1 7311 7312 0.023 0.28 0 0 0 0 9.63 ▇▁▁▁▁▁▁▁
FEMALE_DPC_APP_HCA_FTE 1 7311 7312 0.062 0.29 0 0 0 0 4 ▇▁▁▁▁▁▁▁
FEMALE_DPC_APP_OTH_FTE 1 7311 7312 0.0023 0.053 0 0 0 0 2.03 ▇▁▁▁▁▁▁▁
FEMALE_DPC_APP_PHARMA_FTE 1 7311 7312 0.00038 0.019 0 0 0 0 1 ▇▁▁▁▁▁▁▁
FEMALE_DPC_DISPENSER_FTE 1 7311 7312 0.26 0.98 0 0 0 0 12.29 ▇▁▁▁▁▁▁▁
FEMALE_DPC_FTE 1 7311 7312 1.21 1.61 0 0 0.8 1.6 22.32 ▇▁▁▁▁▁▁▁
FEMALE_DPC_HCA_FTE 1 7311 7312 0.7 0.87 0 0 0.53 1 18.77 ▇▁▁▁▁▁▁▁
FEMALE_DPC_NURSE_ASSOC_FTE 1 7311 7312 0.0013 0.034 0 0 0 0 1 ▇▁▁▁▁▁▁▁
FEMALE_DPC_OTH_FTE 1 7311 7312 0.039 0.25 0 0 0 0 7.73 ▇▁▁▁▁▁▁▁
FEMALE_DPC_PARAMED_FTE 1 7311 7312 0.011 0.11 0 0 0 0 3 ▇▁▁▁▁▁▁▁
FEMALE_DPC_PHARMA_FTE 1 7311 7312 0.046 0.21 0 0 0 0 3.05 ▇▁▁▁▁▁▁▁
FEMALE_DPC_PHLEB_FTE 1 7311 7312 0.079 0.24 0 0 0 0 3.15 ▇▁▁▁▁▁▁▁
FEMALE_DPC_PHYSICIAN_ASSOC_FTE 1 7311 7312 0.005 0.076 0 0 0 0 2 ▇▁▁▁▁▁▁▁
FEMALE_DPC_PHYSIO_FTE 1 7311 7312 0.0015 0.041 0 0 0 0 2.32 ▇▁▁▁▁▁▁▁
FEMALE_DPC_THERA_COU_FTE 1 7311 7312 0.00068 0.022 0 0 0 0 1.2 ▇▁▁▁▁▁▁▁
FEMALE_DPC_THERA_OCC_FTE 1 7311 7312 0.00013 0.012 0 0 0 0 0.99 ▇▁▁▁▁▁▁▁
FEMALE_DPC_THERA_OTH_FTE 1 7311 7312 0.0014 0.029 0 0 0 0 1.12 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXL_FTE 1 7311 7312 1.84 1.83 0 0.53 1.44 2.67 29.81 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXRL_FTE 1 7311 7312 1.72 1.66 0 0.49 1.36 2.53 29.81 ▇▁▁▁▁▁▁▁
FEMALE_GP_EXRRL_FTE 1 7311 7312 1.71 1.65 0 0.49 1.34 2.51 29.81 ▇▁▁▁▁▁▁▁
FEMALE_GP_FTE 1 7311 7312 1.89 1.84 0 0.55 1.49 2.72 29.81 ▇▁▁▁▁▁▁▁
FEMALE_GP_LOCUM_ABS_FTE 1 7311 7312 0.012 0.097 0 0 0 0 2.88 ▇▁▁▁▁▁▁▁
FEMALE_GP_LOCUM_OTH_FTE 1 7311 7312 0.026 0.13 0 0 0 0 2.72 ▇▁▁▁▁▁▁▁
FEMALE_GP_LOCUM_VAC_FTE 1 7311 7312 0.011 0.091 0 0 0 0 2.13 ▇▁▁▁▁▁▁▁
FEMALE_GP_PTNR_PROV_FTE 1 7311 7312 0.83 1.06 0 0 0.53 1.33 9.17 ▇▂▁▁▁▁▁▁
FEMALE_GP_REG_F1_2_FTE 1 7311 7312 0.034 0.21 0 0 0 0 4.44 ▇▁▁▁▁▁▁▁
FEMALE_GP_REG_JUN_DOC_FTE 1 7311 7312 0.0022 0.054 0 0 0 0 2.13 ▇▁▁▁▁▁▁▁
FEMALE_GP_REG_ST3_4_FTE 1 7311 7312 0.091 0.36 0 0 0 0 4.44 ▇▁▁▁▁▁▁▁
FEMALE_GP_RET_FTE 1 7311 7312 0.012 0.081 0 0 0 0 2 ▇▁▁▁▁▁▁▁
FEMALE_GP_SAL_BY_OTH_FTE 1 7311 7312 0.0069 0.1 0 0 0 0 4.38 ▇▁▁▁▁▁▁▁
FEMALE_GP_SAL_BY_PRAC_FTE 1 7311 7312 0.69 1.02 0 0 0.29 1.07 19.31 ▇▁▁▁▁▁▁▁
FEMALE_GP_SEN_PTNR_FTE 1 7311 7312 0.18 0.44 0 0 0 0 4.9 ▇▁▁▁▁▁▁▁
FEMALE_N_ADV_NURSE_PRAC_FTE 1 7311 7312 0.36 0.71 0 0 0 0.64 10.96 ▇▁▁▁▁▁▁▁
FEMALE_N_DISTRICT_NURSE_FTE 1 7311 7312 0.0019 0.047 0 0 0 0 2.2 ▇▁▁▁▁▁▁▁
FEMALE_N_EXT_ROLE_NURSE_FTE 1 7311 7312 0.075 0.32 0 0 0 0 5.48 ▇▁▁▁▁▁▁▁
FEMALE_N_NURSE_DISP_FTE 1 7311 7312 0.0025 0.047 0 0 0 0 1.57 ▇▁▁▁▁▁▁▁
FEMALE_N_NURSE_PTNR_FTE 1 7311 7312 0.0033 0.055 0 0 0 0 1.6 ▇▁▁▁▁▁▁▁
FEMALE_N_NURSE_SPEC_FTE 1 7311 7312 0.061 0.31 0 0 0 0 7.68 ▇▁▁▁▁▁▁▁
FEMALE_N_PRAC_NURSE_FTE 1 7311 7312 1.42 1.21 0 0.59 1.15 1.97 15.44 ▇▂▁▁▁▁▁▁
FEMALE_N_TRAINEE_NURSE_FTE 1 7311 7312 0.021 0.15 0 0 0 0 2.6 ▇▁▁▁▁▁▁▁
FEMALE_NURSES_FTE 1 7311 7312 1.94 1.75 0 0.79 1.53 2.65 21.37 ▇▂▁▁▁▁▁▁
MALE_ADMIN_APP_FTE 1 7311 7312 0.013 0.12 0 0 0 0 3 ▇▁▁▁▁▁▁▁
MALE_ADMIN_ESTATES_ANC_FTE 1 7311 7312 0.013 0.1 0 0 0 0 2.79 ▇▁▁▁▁▁▁▁
MALE_ADMIN_FTE 1 7311 7312 0.43 0.77 0 0 0 0.8 12.88 ▇▁▁▁▁▁▁▁
MALE_ADMIN_MANAGE_PTNR_FTE 1 7311 7312 0.0047 0.068 0 0 0 0 2 ▇▁▁▁▁▁▁▁
MALE_ADMIN_MANAGER_FTE 1 7311 7312 0.16 0.38 0 0 0 0 3 ▇▁▁▁▁▁▁▁
MALE_ADMIN_MED_SECRETARY_FTE 1 7311 7312 0.007 0.079 0 0 0 0 1.6 ▇▁▁▁▁▁▁▁
MALE_ADMIN_OTH_FTE 1 7311 7312 0.11 0.36 0 0 0 0 4.57 ▇▁▁▁▁▁▁▁
MALE_ADMIN_RECEPT_FTE 1 7311 7312 0.12 0.38 0 0 0 0 4 ▇▁▁▁▁▁▁▁
MALE_ADMIN_TELEPH_FTE 1 7311 7312 0.0015 0.056 0 0 0 0 3.79 ▇▁▁▁▁▁▁▁
MALE_DPC_APP_HCA_FTE 1 7311 7312 0.0014 0.033 0 0 0 0 1 ▇▁▁▁▁▁▁▁
MALE_DPC_APP_OTH_FTE 1 7311 7312 0.00017 0.012 0 0 0 0 1 ▇▁▁▁▁▁▁▁
MALE_DPC_APP_PHLEB_FTE 1 7311 7312 0.00013 0.012 0 0 0 0 0.99 ▇▁▁▁▁▁▁▁
MALE_DPC_DISPENSER_FTE 1 7311 7312 0.0087 0.098 0 0 0 0 2.2 ▇▁▁▁▁▁▁▁
MALE_DPC_FTE 1 7311 7312 0.093 0.33 0 0 0 0 8 ▇▁▁▁▁▁▁▁
MALE_DPC_HCA_FTE 1 7311 7312 0.026 0.17 0 0 0 0 7.01 ▇▁▁▁▁▁▁▁
MALE_DPC_NURSE_ASSOC_FTE 1 7311 7312 0.00022 0.014 0 0 0 0 1 ▇▁▁▁▁▁▁▁
MALE_DPC_OTH_FTE 1 7311 7312 0.0092 0.098 0 0 0 0 2.77 ▇▁▁▁▁▁▁▁
MALE_DPC_PARAMED_FTE 1 7311 7312 0.015 0.13 0 0 0 0 2.03 ▇▁▁▁▁▁▁▁
MALE_DPC_PHARMA_FTE 1 7311 7312 0.026 0.16 0 0 0 0 3.95 ▇▁▁▁▁▁▁▁
MALE_DPC_PHLEB_FTE 1 7311 7312 0.0025 0.044 0 0 0 0 2 ▇▁▁▁▁▁▁▁
MALE_DPC_PHYSICIAN_ASSOC_FTE 1 7311 7312 0.0013 0.036 0 0 0 0 1.09 ▇▁▁▁▁▁▁▁
MALE_DPC_PHYSIO_FTE 1 7311 7312 0.00096 0.032 0 0 0 0 2 ▇▁▁▁▁▁▁▁
MALE_DPC_THERA_COU_FTE 1 7311 7312 0.00029 0.017 0 0 0 0 1 ▇▁▁▁▁▁▁▁
MALE_DPC_THERA_OTH_FTE 1 7311 7312 4e-04 0.013 0 0 0 0 0.53 ▇▁▁▁▁▁▁▁
MALE_GP_EXL_FTE 1 7311 7312 1.96 1.6 0 0.96 1.67 2.77 20.01 ▇▃▁▁▁▁▁▁
MALE_GP_EXRL_FTE 1 7311 7312 1.89 1.52 0 0.96 1.6 2.67 20.01 ▇▂▁▁▁▁▁▁
MALE_GP_EXRRL_FTE 1 7311 7312 1.88 1.52 0 0.96 1.6 2.67 20.01 ▇▂▁▁▁▁▁▁
MALE_GP_FTE 1 7311 7312 2.01 1.61 0 0.99 1.73 2.8 20.01 ▇▃▁▁▁▁▁▁
MALE_GP_LOCUM_ABS_FTE 1 7311 7312 0.0067 0.071 0 0 0 0 2 ▇▁▁▁▁▁▁▁
MALE_GP_LOCUM_OTH_FTE 1 7311 7312 0.037 0.18 0 0 0 0 3.29 ▇▁▁▁▁▁▁▁
MALE_GP_LOCUM_VAC_FTE 1 7311 7312 0.014 0.13 0 0 0 0 5.68 ▇▁▁▁▁▁▁▁
MALE_GP_PTNR_PROV_FTE 1 7311 7312 1.14 1.34 0 0 0.93 1.97 17.33 ▇▂▁▁▁▁▁▁
MALE_GP_REG_F1_2_FTE 1 7311 7312 0.017 0.14 0 0 0 0 2.77 ▇▁▁▁▁▁▁▁
MALE_GP_REG_JUN_DOC_FTE 1 7311 7312 0.0021 0.052 0 0 0 0 2.13 ▇▁▁▁▁▁▁▁
[ reached getOption("max.print") -- omitted 62 rows ]
[[18]]
Skim summary statistics
n obs: 152
n variables: 4
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
la name 0 152 152 4 28 0 152
ut la 0 152 152 9 9 0 152
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
per_capita_gp 0 152 152 50.24 26.59 31.34 43.37 48.01 53.03 365.73 ▇▁▁▁▁▁▁▁
per_capita_nurses 0 152 152 24.69 7.15 10.95 19.01 24.88 29.27 48.23 ▃▆▆▇▅▂▁▁
[[19]]
Skim summary statistics
n obs: 7392
n variables: 8
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
Gp post code 0 7392 7392 6 8 0 6644
Gpcode 0 7392 7392 6 6 0 7392
Gpname 0 7392 7392 4 40 0 6955
la name 0 7392 7392 4 28 0 152
lt la 0 7392 7392 9 9 0 326
region 0 7392 7392 9 9 0 9
region name 0 7392 7392 13 31 0 9
ut la 0 7392 7392 9 9 0 152
[[20]]
Skim summary statistics
n obs: 4650
n variables: 19
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
ihme_loc_id 0 4650 4650 1 9 0 775
location_name 0 4650 4650 3 48 0 771
region_name 0 4650 4650 0 28 48 22
sdi_quintile 174 4476 4650 7 15 0 5
super_region_name 0 4650 4650 0 48 6 8
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
age_group_id 0 4650 4650 27 0 27 27 27 27 27 ▁▁▁▇▁▁▁▁
cause_id 0 4650 4650 100 0 100 100 100 100 100 ▁▁▁▇▁▁▁▁
level 0 4650 4650 4.14 1.14 0 3 4 5 6 ▁▁▁▅▁▇▂▃
location_id 0 4650 4650 18216.26 20068.08 1 197 4735 43910 44792 ▇▁▁▁▁▁▂▃
region_id 48 4602 4650 100.24 54.63 5 73 100 159 199 ▃▁▇▂▂▂▆▁
sex_id 0 4650 4650 3 0 3 3 3 3 3 ▁▁▁▇▁▁▁▁
super_region_id 6 4644 4650 89.4 53.66 4 64 64 158 166 ▂▁▇▁▂▁▁▆
year_id 0 4650 4650 2002.67 8.79 1990 1995 2002.5 2010 2016 ▇▇▁▇▇▁▇▇
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
index 0 4650 4650 60.96 23.25 10.59 39.28 61.51 83.71 97.13 ▁▅▆▅▅▃▇▇
index_efa 0 4650 4650 60.42 22.52 11.16 39.83 60.04 82.42 96.96 ▁▅▆▅▅▃▇▆
index_geom_mean 0 4650 4650 56.53 23.34 9.37 35.55 53.65 79.53 97.06 ▁▅▇▅▃▃▇▅
index_lval 0 4650 4650 57.81 24.59 7.8 34.02 59.04 82.07 95.9 ▁▆▅▃▅▃▇▇
index_mean 0 4650 4650 60.49 20.76 15.2 42.27 58.61 80.58 96.32 ▁▅▆▆▃▃▇▅
index_uval 0 4650 4650 64.13 21.89 14.13 44.96 64.06 85.42 98.51 ▁▃▆▆▅▃▇▇
[[21]]
Skim summary statistics
n obs: 152
n variables: 3
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
area_code 0 152 152 9 9 0 152
area_name 0 152 152 4 28 0 152
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
index 0 152 152 50.33 28.96 1 25.75 50.5 75.25 100 ▇▇▇▇▇▇▇▇
[[22]]
Skim summary statistics
n obs: 152
n variables: 4
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
gp.la.name 0 152 152 4 28 0 152
nurses.la.name 0 152 152 4 28 0 152
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
gp.sumfte 0 152 152 186.68 142.76 5.74 97.31 133.15 202.41 777.94 ▆▇▂▂▁▁▁▁
nurses.sumfte 0 152 152 95.33 77.49 0.67 45.28 65.24 123.01 372.05 ▅▇▂▁▁▁▁▁
[[23]]
Skim summary statistics
n obs: 784
n variables: 4
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
ihme_loc_id 0 784 784 1 9 0 784
location_name 0 784 784 3 48 0 779
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
location_id 0 784 784 18521.05 20162.77 1 199.75 4739.5 43919.25 44800 ▇▁▁▁▁▁▂▅
V1 0 784 784 392.5 226.47 1 196.75 392.5 588.25 784 ▇▇▇▇▇▇▇▇
[[24]]
Skim summary statistics
n obs: 1288
n variables: 5
-- Variable type:character -----------------------------------------------------
variable missing complete n min max empty n_unique
AreaName 0 1288 1288 4 28 0 161
-- Variable type:integer -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
FYEAR 0 1288 1288 1364.5 231.51 1011 1187.75 1364.5 1541.25 1718 ▇▇▇▇▇▇▇▇
-- Variable type:numeric -------------------------------------------------------
variable missing complete n mean sd p0 p25 p50 p75 p100 hist
lowercl 9 1279 1288 29.45 8.49 9.04 23.94 28.51 33.6 70.68 ▁▅▇▅▂▁▁▁
uppercl 9 1279 1288 44.83 11.07 21.56 36.94 42.71 50.62 110.03 ▂▇▅▂▁▁▁▁
value 9 1279 1288 36.47 9.31 14.33 30.11 34.88 40.99 88.43 ▁▇▇▃▁▁▁▁Amenable codes
haq_files[1]
[[1]]NAPer capita beds
per_capita_beds <- haq_files[[2]] %>%
data.frame()
per_capita_beds %>%
ggplot(aes(per_capita_beds)) +
geom_density(fill = "blue") +
labs(title = "Distribution of per-capita beds by UTLA",
caption = "NB check Gloucestershire (low value)")
Hospitalisation rates
AE attendance rates
HAQ trends
haq %>%
ggplot(aes(year_id, mean_value, group = location_name)) +
geom_line(colour = "grey") +
gghighlight(mean_value == median(mean_value))
You set use_group_by = TRUE, but grouped calculation failed.
Falling back to ungrouped filter operation...label_key: location_name
HAQ link with deprivation
library(broom)
model <- lm(mean_value ~ IMDscore, data = filter(haq, year_id == 2016))
model_text <- tidy(model) %>%
mutate(model = paste("HAQ = ", round(.[1,2], 2), round(.[2,2],2), "* IMDscore; ", "p < 0.001; " ))
model_text_r2 <- glance(model) %>%
mutate(rsq = paste("r^2 = ", round(r.squared, 3)))
annotation <- paste(model_text$model[1], model_text_r2$rsq)
haq %>%
filter(year_id == 2016) %>%
ggplot(aes(mean_value, IMDscore)) +
geom_point() +
geom_smooth(method = "lm") +
labs(subtitle = paste(annotation))
Trend in association between deprivation and HAQ
dep_lookup %>%
left_join(haq) %>%
group_by(year_id) %>%
na.omit() %>%
do(broom::glance(lm(mean_value ~ IMDscore, data = .))) %>%
arrange(year_id) %>%
ggplot(aes(year_id, r.squared)) +
geom_line() +
geom_point() +
geom_smooth() +
labs(title = "Trend in relationship between IMD and HAQ",
subtitle = "Strength of association has weakened over time",
caption = "Strength of association measured by r^2",
x = "Year")Joining, by = c("AreaCode", "IMDscore", "decile")
Deprivation vs HAQ index components
dep_cause <- dep_lookup %>%
left_join(haq_files[[14]], by = c("AreaName" = "location_name")) %>%
group_by(year_id, cause_name) %>%
na.omit() %>%
do(broom::glance(lm(estimate~IMDscore, data = .))) %>%
select(cause_name, year_id, adj.r.squared) %>%
filter(year_id %in% c(2000, 2016))essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliabledep_cause %>%
ggplot() +
geom_point(aes(year_id, adj.r.squared), show.legend = FALSE) +
geom_line(aes(year_id, adj.r.squared, group = cause_name), show.legend = FALSE) +
geom_text(aes(year_id, adj.r.squared, label = cause_name), data = filter(dep_cause, year_id == 2000), hjust = 1.1, size = 3) +
geom_text(aes(year_id, adj.r.squared, label = cause_name), data = filter(dep_cause, year_id == 2016), hjust =- 0.1, size = 3) +
expand_limits(x = c(1990, 2025), c(0,1)) +
theme(panel.background = element_blank(),
axis.line = element_blank()) +
labs(title = "Change in strength of association between deprivation and cause between 2000 and 2016",
subtitle = "Has mostly reduced expect for skin cancer, hypertensive heart disease,\nand cervical cancer")
NAChange in HAQ index at LA level
haq_files[[12]] %>%
data.frame() %>%
filter(change_type == "Annualized rate of change", year_start_id == 2000, year_end_id == 2016) %>%
ggplot(aes(estimate, reorder(location_name, estimate))) +
geom_point(size = 2)
Trend in cause specific indices
haq_files[[14]] %>%
ggplot(aes(factor(year_id), estimate, fill= cause_name)) +
geom_violin(show.legend = FALSE) +
facet_wrap(~cause_name) +
theme(strip.text.x = element_text(size = 7))
Statistical significance plot
Cause significance
eng1 <- haq_cause %>%
filter(location_name == "England", year_id == 2016) %>%
gather(metric, value, estimate:IMDscore ) %>%
select(-c(decile, year_id))
haq_cause %>%
filter(location_name != "England", year_id == 2016) %>%
select(-c(year_id)) %>%
gather(metric, value, estimate:IMDscore) %>%
na.omit() %>%
#count(cause_name)>%
#select(-eng_metric) %>%
spread(metric, value) %>%
left_join(eng1, by = c("cause_name")) %>%
filter(metric == "estimate") %>%
mutate(sig = ifelse(lower > value, 1,
ifelse(upper < value, -1, 0))) %>%
ggplot(aes(cause_name, reorder(location_name.x, IMDscore), fill = factor(sig, labels = c("high", "NS", "low")))) +
geom_tile() +
coord_fixed() +
scale_fill_manual(values = c("red","white", "darkgreen")) +
scale_x_discrete(position = "top") +
theme(axis.text.y = element_text(size = 6),
axis.text.x.top = element_text(size = 5, angle = 90, hjust = 0)) +
labs(x ="",
y = "",
title = "Significance chart of HAQ by UTLA and cause",
caption = "UTLAs ordered by decreasing IMD score",
fill = "Statistical significance")
Per capita primary care staff
- HAQ score inversely correlated with deprivation ie better outcome/access in least deprived areas
- Other associations are weak - tendency to HAQ to increase with increasing per capita GPs
---
title: "HAQ figures and data"
output: html_notebook
---

```{r setup, include = FALSE}

library(pacman)
p_load(tidyverse, data.table, fingertipsR, phecharts, skimr, gghighlight)
theme_set(theme_phe())


```

# Datasets

```{r}

haq_data <- list.files(pattern = ".csv")[c(6:26, 28:30 )]

haq_files <- map(haq_data, fread )

haq_files %>%
  map(., skim)
```


```{r deprivation, cache=TRUE}
dep_scores <- fingertipsR::deprivation_decile()
prof_data <- fingertips_data(DomainID = 1938132694)

dep_lookup <- prof_data %>%
  group_by(IndicatorID, Age, Sex) %>%
  filter(TimeperiodSortable == max(TimeperiodSortable)) %>%
  left_join(dep_scores) %>%
  ungroup() %>%
  select(AreaName, AreaCode, IMDscore, decile) %>%
  filter(!is.na(IMDscore)) %>%
  distinct() 

haq <-  setDT(haq_files[[10]] %>% 
  data.frame() )

haq <- haq %>%
  mutate(location_name = case_when(location_name == "Bristol, City of" ~ "Bristol",
                                   location_name == "Herefordshire, County of" ~ "Herefordshire",
                                   location_name == "Kingston upon Hull, City of" ~ "Kingston upon Hull", 
                                   location_name == "St Helens" ~ "St. Helens", 
                                   TRUE ~ location_name)) %>%
  left_join(dep_lookup, by = c("location_name" = "AreaName")) %>%
  select(location_name, AreaCode, year_id, mean_value, lower_value, upper_value, IMDscore, decile) 
  
check <- haq %>%
  filter(year_id == 2016) %>%
  left_join(dep_lookup) %>%
  filter(!is.na(IMDscore))

haq_cause <- haq_files[[14]] %>%
  data.frame()

haq_cause <- haq_cause %>%
  mutate(location_name = case_when(location_name == "Bristol, City of" ~ "Bristol",
                                   location_name == "Herefordshire, County of" ~ "Herefordshire",
                                   location_name == "Kingston upon Hull, City of" ~ "Kingston upon Hull", 
                                   location_name == "St Helens" ~ "St. Helens", 
                                   TRUE ~ location_name)) %>%
  left_join(dep_lookup, by = c("location_name" = "AreaName")) %>%
  select(location_name, AreaCode, year_id, cause_name, estimate, lower, upper, IMDscore, decile) 

```


## Amenable codes

```{r}

haq_files[1]


```

## Per capita beds

```{r per-capita-beds}
per_capita_beds <- haq_files[[2]] %>% 
  data.frame() 

per_capita_beds %>%
  ggplot(aes(per_capita_beds)) +
  geom_density(fill = "blue") +
  labs(title = "Distribution of per-capita beds by UTLA", 
       caption = "NB check Gloucestershire (low value)")
```


## Hospitalisation rates

```{r dsrs}
dsr <- haq_files[[5]] %>% 
  data.frame() 

dsr %>%
  ggplot(aes(as.factor(FYEAR), value, group = FYEAR)) +
  geom_boxplot() +
  labs(title = "Hospitalisation rate", 
       subtitle = "Each person counted once per year", 
       x = "Financial year", 
       y = "DSR per 100,000")

```

## AE attendance rates

```{r ae-dsrs}
ae <- haq_files[[7]] %>% 
  data.frame() 

ae %>%
  ggplot(aes(factor(Year), value)) +
  geom_boxplot() +
  labs(title = "AE attendance rate", 
       subtitle = "Each person counted once per year", 
       x = "Financial year", 
       y = "DSR per 100,000") 

```


## HAQ trends

```{r haq-trends}
haq <-  setDT(haq_files[[10]] %>% 
  data.frame() )

haq <- haq %>%
  left_join(dep_lookup, by = c("location_name" = "AreaName") )

haq %>%
  ggplot(aes(year_id, mean_value, group = location_name)) +
  geom_line(colour = "grey") +
  gghighlight(mean_value == median(mean_value))


```

## HAQ link with deprivation

```{r}
library(broom)


model <- lm(mean_value ~ IMDscore, data = filter(haq, year_id == 2016))

model_text <- tidy(model) %>% 
  mutate(model = paste("HAQ = ", round(.[1,2], 2), round(.[2,2],2), "* IMDscore; ", "p < 0.001; " ))

model_text_r2 <- glance(model) %>%
  mutate(rsq = paste("r^2 = ", round(r.squared, 3)))

annotation <- paste(model_text$model[1], model_text_r2$rsq)

haq %>%
  filter(year_id == 2016) %>%
  ggplot(aes(mean_value, IMDscore)) + 
  geom_point() +
  geom_smooth(method = "lm") +
  labs(subtitle = paste(annotation))


```

## Trend in association between deprivation and HAQ

```{r dep-haq}


dep_lookup %>% 
  left_join(haq) %>%
  group_by(year_id) %>%
  na.omit() %>%
  do(broom::glance(lm(mean_value ~ IMDscore, data = .))) %>%
  arrange(year_id) %>%
  ggplot(aes(year_id, r.squared)) +
  geom_line() +
  geom_point() +
  geom_smooth() +
  labs(title = "Trend in relationship between IMD and HAQ",
       subtitle = "Strength of association has weakened over time", 
       caption = "Strength of association measured by r^2", 
       x = "Year")



```

## Deprivation vs HAQ index components

```{r}
dep_cause <- dep_lookup %>%
  left_join(haq_files[[14]], by = c("AreaName" = "location_name")) %>%
  group_by(year_id, cause_name) %>%
  na.omit() %>%
  do(broom::glance(lm(estimate~IMDscore, data = .))) %>%
  select(cause_name, year_id, adj.r.squared) %>%
  filter(year_id %in% c(2000, 2016))

dep_cause %>%
  ggplot() +
  geom_point(aes(year_id, adj.r.squared), show.legend = FALSE) +
  geom_line(aes(year_id, adj.r.squared, group = cause_name), show.legend = FALSE) +
  geom_text(aes(year_id, adj.r.squared, label = cause_name), data = filter(dep_cause, year_id == 2000), hjust = 1.1, size = 3) +
  geom_text(aes(year_id, adj.r.squared, label = cause_name), data = filter(dep_cause, year_id == 2016), hjust =- 0.1, size = 3) +
  expand_limits(x = c(1990, 2025), c(0,1)) +
  theme(panel.background = element_blank(), 
        axis.line = element_blank()) +
  labs(title = "Change in strength of association between deprivation and cause between 2000 and 2016", 
       subtitle = "Has mostly reduced expect for skin cancer, hypertensive heart disease,\nand cervical cancer")
  




```


## Change in HAQ index at LA level

```{r fig.height=14}


 haq_files[[12]] %>%
  data.frame() %>%
  filter(change_type == "Annualized rate of change", year_start_id == 2000, year_end_id == 2016) %>%
  ggplot(aes(estimate, reorder(location_name, estimate))) +
    geom_point(size = 2)



```

## Trend in cause specific indices

```{r fig.width=10}

haq_files[[14]] %>%
  ggplot(aes(factor(year_id), estimate, fill= cause_name)) +
  geom_violin(show.legend = FALSE) +
  facet_wrap(~cause_name) +
  theme(strip.text.x = element_text(size = 7))



```

## Statistical significance plot

```{r stat-sig, fig.height=12, fig.width= 10}

eng <- haq %>%
  filter(location_name == "England") %>%
  select(location_name, year_id, contains("value")) %>%
  gather(metric, value, mean_value)


haq %>%
  filter(location_name != "England") %>%
  select(location_name, year_id, contains("value"), IMDscore) %>%
  na.omit() %>%
  gather(metric, value, mean_value:IMDscore) %>%
  #full_join(eng, by = c("metric", "year_id")) %>%
  #count(year_id)
  mutate(eng_year = rep(eng$year_id, 600), 
          eng_metric = rep(eng$metric, each = 600),
          eng_value = rep(eng$value, 600)) %>%
  #select(-eng_metric) %>%
  spread(metric, value) %>%
  mutate(sig = ifelse(lower_value > eng_value, 1,
                      ifelse(upper_value < eng_value, -1, 0))) %>%
  ggplot(aes(year_id, fct_reorder(location_name, IMDscore), fill = factor(sig, labels = c("high", "NS", "low")))) +
  geom_tile() +
  coord_fixed() +
  scale_fill_manual(values = c("red","white", "darkgreen")) +
  #scale_x_discrete(position = "top") +
  theme(axis.text.y = element_text(size = 6)) +
  labs(x ="", 
       y = "", 
       title = "Significance chart of HAQ by UTLA and year", 
       caption = "UTLAs ordered by decreasing IMD score", 
       fill = "Statistical significance")





```

## Cause significance

```{r fig.height=12, fig.width=8}

eng1 <- haq_cause %>%
  filter(location_name == "England", year_id == 2016) %>%
  gather(metric, value, estimate:IMDscore ) %>%
  select(-c(decile, year_id))


haq_cause %>%
  filter(location_name != "England", year_id == 2016) %>%
  select(-c(year_id)) %>%
  gather(metric, value, estimate:IMDscore) %>% 
  na.omit() %>%
  #count(cause_name)>%
  #select(-eng_metric) %>%
  spread(metric, value) %>%
  left_join(eng1, by = c("cause_name")) %>% 
  filter(metric == "estimate") %>%
  mutate(sig = ifelse(lower > value, 1,
                      ifelse(upper < value, -1, 0))) %>%
  ggplot(aes(cause_name, reorder(location_name.x, IMDscore), fill = factor(sig, labels = c("high", "NS", "low")))) +
  geom_tile() +
  coord_fixed() +
  scale_fill_manual(values = c("red","white", "darkgreen")) +
  scale_x_discrete(position = "top") +
  theme(axis.text.y = element_text(size = 6), 
        axis.text.x.top = element_text(size = 5, angle = 90, hjust = 0)) +
  labs(x ="", 
       y = "", 
       title = "Significance chart of HAQ by UTLA and cause", 
       caption = "UTLAs ordered by decreasing IMD score", 
       fill = "Statistical significance")




```

## Per capita primary care staff

```{r}

corr <- list.files(pattern = "csv")[9] %>%
  fread()


corr %>% na.omit() %>%
  gather(metric, value) %>%
  ggplot(aes(metric, value, fill = metric), alpha = 0.6) +
  geom_boxplot()
  
corr %>% na.omit %>%
  cor() %>%
  corrplot::corrplot.mixed()


mod <- corr %>% na.omit
mod1 <- lm(estimate ~., data = mod)
mod2 <- lm(value ~., data = mod)

tidy(mod1)
tidy(mod2)

```


* HAQ score inversely correlated with deprivation ie better outcome/access in least deprived areas 
* Other associations are weak - tendency to HAQ to increase with increasing per capita GPs



