read in and format data

df <- read.csv("maggie2.csv"); #this imports 262 cases from the original dateframe
df <- df[!is.na(df$fellowship_yn),]; #this removes any NA responses for `fellowship_yn` = 257 cases

df2 <- df[c(2,7,20,22:23,25:28,31:33,35,37:39)];
df2$fellow_cat <- ifelse(df2$fellowship_yn==1|df2$fellowship_yn==2, 'Probably or Definitely Yes', ifelse(df2$fellowship_yn==4|df2$fellowship_yn==5, "Probably or Definitely No", "Undecided"))

#df2$age_cat <- ifelse(df2$age==2, '22-25 y/o', ifelse(df2$age==3, "26-30 y/o", ifelse(df2$age==4, "31-35 y/o", "36-45 y/o")))
#complete race categories
#df2$race_cat <- ifelse(df2$race==1, 'Native American or Alaska Native', ifelse(df2$race==2, "Asian", ifelse(df2$race==3, "Black or African American", ifelse(df2$race==4, "Native Hawaiian or other Pacific Islander", ifelse(df2$race==5, "White", "Other/No Response")))))
#remove non cis gender responses, Keep NA, keep 7
#this brings the dataframe down to 230 cases
df2 <- df2 %>%
  filter(is.na(gender) | gender == 1 | gender == 2 | gender == 7)

DF FOR FULL TABLES (DF2)

#dataframe for full tables
#df2 is for full

#the code below is replacing all "no response" responses with NA

df2$debt[df2$debt == 6] <- NA
df2$gender[df2$gender == 7] <- NA
df2$race[df2$race == 7] <- NA
df2$marital_status[df2$marital_status == 7] <- NA
df2$children[df2$children == 5] <- NA

DF FOR CONDENSED TABLES (DF3)

#dataframe for condensed tables
#df3 is condensed
df3 <- df2 

#year of training
df3$current_year <- ifelse(df3$current_year<4, "PGY123", "PGY4567")

#debt
df3$debt <- ifelse(df3$debt == 1, "under100k","over100k")

#race
df3$race <- ifelse(df3$race == 5, "white","notwhite")

#age
df3$age[df3$age == 1 |df3$age == 2|df3$age == 3] <- "under30"
df3$age[df3$age == 4 |df3$age == 5|df3$age == 6] <- "30plus"

#marital status
df3$marital_status <- ifelse(df3$marital_status == 3, "married","notmarried")

#children
df3$children[df3$children == 1 |df3$children == 2] <- "have_plan_kids12"
df3$children[df3$children == 3 |df3$children == 4] <- "no_kids34"

1a. Characteristics of Residents Planning Versus Not Planning to Complete Fellowship

#group 1 vs group 5
df2[,c(1,10:17)] %>%
  filter(fellowship_yn == 1 | fellowship_yn == 5) %>%
  tbl_summary(by=fellowship_yn) %>%
  add_p()
Characteristic 1, N = 1491 5, N = 31 p-value2
current_year 0.10
    1 7 (5.1%) 0 (0%)
    2 16 (12%) 0 (0%)
    3 44 (32%) 0 (0%)
    4 27 (20%) 0 (0%)
    5 12 (8.7%) 0 (0%)
    6 15 (11%) 1 (33%)
    7 17 (12%) 2 (67%)
    Unknown 11 0
debt 0.12
    1 33 (23%) 1 (33%)
    2 57 (39%) 0 (0%)
    3 16 (11%) 0 (0%)
    4 6 (4.1%) 1 (33%)
    5 33 (23%) 1 (33%)
    Unknown 4 0
gender 0.5
    1 43 (31%) 1 (50%)
    2 94 (69%) 1 (50%)
    Unknown 12 1
race >0.9
    1 1 (0.7%) 0 (0%)
    2 14 (10%) 0 (0%)
    3 3 (2.2%) 0 (0%)
    5 110 (82%) 2 (100%)
    6 6 (4.5%) 0 (0%)
    Unknown 15 1
age 0.2
    2 17 (12%) 0 (0%)
    3 64 (44%) 0 (0%)
    4 49 (34%) 2 (67%)
    5 13 (8.9%) 1 (33%)
    6 3 (2.1%) 0 (0%)
    Unknown 3 0
marital_status 0.032
    1 34 (25%) 0 (0%)
    2 22 (16%) 0 (0%)
    3 80 (58%) 2 (67%)
    4 0 (0%) 1 (33%)
    6 2 (1.4%) 0 (0%)
    Unknown 11 0
children >0.9
    1 39 (29%) 1 (33%)
    2 71 (53%) 2 (67%)
    3 4 (3.0%) 0 (0%)
    4 21 (16%) 0 (0%)
    Unknown 14 0
fellow_cat <0.001
    Probably or Definitely No 0 (0%) 3 (100%)
    Probably or Definitely Yes 149 (100%) 0 (0%)
1 n (%)
2 Fisher's exact test 

#groups 1&2 vs groups 4 and 5
df2[,c(10:17)] %>%
  filter(!fellow_cat == "Undecided") %>%
  tbl_summary(by=fellow_cat) %>%
  add_p()
Characteristic Probably or Definitely No, N = 101 Probably or Definitely Yes, N = 2001 p-value2
current_year 0.2
    1 0 (0%) 15 (8.0%)
    2 1 (10%) 30 (16%)
    3 1 (10%) 60 (32%)
    4 3 (30%) 35 (19%)
    5 2 (20%) 12 (6.4%)
    6 1 (10%) 18 (9.6%)
    7 2 (20%) 18 (9.6%)
    Unknown 0 12
debt 0.004
    1 4 (40%) 42 (22%)
    2 0 (0%) 73 (37%)
    3 0 (0%) 30 (15%)
    4 2 (20%) 9 (4.6%)
    5 4 (40%) 41 (21%)
    Unknown 0 5
gender 0.3
    1 4 (44%) 51 (28%)
    2 5 (56%) 133 (72%)
    Unknown 1 16
race >0.9
    1 0 (0%) 1 (0.6%)
    2 1 (11%) 17 (9.4%)
    3 0 (0%) 16 (8.8%)
    5 8 (89%) 139 (77%)
    6 0 (0%) 8 (4.4%)
    Unknown 1 19
age 0.3
    2 0 (0%) 19 (9.8%)
    3 5 (50%) 87 (45%)
    4 3 (30%) 72 (37%)
    5 1 (10%) 13 (6.7%)
    6 1 (10%) 3 (1.5%)
    Unknown 0 6
marital_status 0.4
    1 1 (10%) 48 (26%)
    2 1 (10%) 32 (17%)
    3 7 (70%) 99 (53%)
    4 1 (10%) 4 (2.2%)
    6 0 (0%) 3 (1.6%)
    Unknown 0 14
children 0.5
    1 4 (40%) 46 (25%)
    2 6 (60%) 100 (54%)
    3 0 (0%) 8 (4.3%)
    4 0 (0%) 31 (17%)
    Unknown 0 15
1 n (%)
2 Fisher's exact test

1b. Characteristics of Residents Planning Versus Not Planning to Complete Fellowship - Condensed

#group 1 vs group 5
df3[,c(1,10:16)] %>%
  filter(fellowship_yn == 1 | fellowship_yn == 5) %>%
  tbl_summary(by=fellowship_yn) %>%
  add_p()
Characteristic 1, N = 1491 5, N = 31 p-value2
current_year 0.2
    PGY123 67 (49%) 0 (0%)
    PGY4567 71 (51%) 3 (100%)
    Unknown 11 0
debt 0.5
    over100k 112 (77%) 2 (67%)
    under100k 33 (23%) 1 (33%)
    Unknown 4 0
gender 0.5
    1 43 (31%) 1 (50%)
    2 94 (69%) 1 (50%)
    Unknown 12 1
race >0.9
    notwhite 24 (18%) 0 (0%)
    white 110 (82%) 2 (100%)
    Unknown 15 1
age 0.093
    30plus 65 (45%) 3 (100%)
    under30 81 (55%) 0 (0%)
    Unknown 3 0
marital_status >0.9
    married 80 (58%) 2 (67%)
    notmarried 58 (42%) 1 (33%)
    Unknown 11 0
children >0.9
    have_plan_kids12 110 (81%) 3 (100%)
    no_kids34 25 (19%) 0 (0%)
    Unknown 14 0
1 n (%)
2 Fisher's exact test 

#groups 1&2 vs groups 45
df3[,c(10:17)] %>%
  filter(!fellow_cat == "Undecided") %>%
  tbl_summary(by=fellow_cat) %>%
  add_p()
Characteristic Probably or Definitely No, N = 101 Probably or Definitely Yes, N = 2001 p-value2
current_year 0.046
    PGY123 2 (20%) 105 (56%)
    PGY4567 8 (80%) 83 (44%)
    Unknown 0 12
debt 0.2
    over100k 6 (60%) 153 (78%)
    under100k 4 (40%) 42 (22%)
    Unknown 0 5
gender 0.3
    1 4 (44%) 51 (28%)
    2 5 (56%) 133 (72%)
    Unknown 1 16
race 0.7
    notwhite 1 (11%) 42 (23%)
    white 8 (89%) 139 (77%)
    Unknown 1 19
age >0.9
    30plus 5 (50%) 88 (45%)
    under30 5 (50%) 106 (55%)
    Unknown 0 6
marital_status 0.3
    married 7 (70%) 99 (53%)
    notmarried 3 (30%) 87 (47%)
    Unknown 0 14
children 0.2
    have_plan_kids12 10 (100%) 146 (79%)
    no_kids34 0 (0%) 39 (21%)
    Unknown 0 15
1 n (%)
2 Fisher's exact test

2a. Characteristics of Residents Choosing Enfolded Versus Postgraduate Fellowship*

#enfolded vs postgraduate
df2[,c(2,10:17)] %>%
  filter(enfolded_postgrad_yn == 1 | enfolded_postgrad_yn ==2) %>%
  tbl_summary(by=enfolded_postgrad_yn ) %>%
  add_p()
Characteristic 1, N = 521 2, N = 1131 p-value2
current_year 0.2
    1 4 (8.7%) 5 (4.6%)
    2 4 (8.7%) 22 (20%)
    3 16 (35%) 44 (41%)
    4 7 (15%) 17 (16%)
    5 6 (13%) 5 (4.6%)
    6 4 (8.7%) 8 (7.4%)
    7 5 (11%) 7 (6.5%)
    Unknown 6 5
debt <0.001
    1 14 (27%) 15 (14%)
    2 9 (17%) 56 (51%)
    3 9 (17%) 15 (14%)
    4 6 (12%) 3 (2.7%)
    5 14 (27%) 21 (19%)
    Unknown 0 3
gender 0.2
    1 11 (24%) 36 (34%)
    2 35 (76%) 69 (66%)
    Unknown 6 8
race 0.030
    1 1 (2.3%) 0 (0%)
    2 4 (9.1%) 6 (5.8%)
    3 4 (9.1%) 8 (7.7%)
    4 3 (6.8%) 0 (0%)
    5 31 (70%) 89 (86%)
    6 1 (2.3%) 1 (1.0%)
    Unknown 8 9
age 0.5
    2 4 (7.7%) 15 (14%)
    3 23 (44%) 53 (49%)
    4 20 (38%) 34 (31%)
    5 4 (7.7%) 5 (4.6%)
    6 1 (1.9%) 1 (0.9%)
    Unknown 0 5
marital_status 0.011
    1 14 (30%) 18 (17%)
    2 3 (6.5%) 26 (25%)
    3 24 (52%) 57 (54%)
    4 5 (11%) 4 (3.8%)
    6 0 (0%) 1 (0.9%)
    Unknown 6 7
children 0.004
    1 11 (24%) 27 (26%)
    2 19 (41%) 67 (64%)
    3 6 (13%) 4 (3.8%)
    4 10 (22%) 7 (6.7%)
    Unknown 6 8
fellow_cat <0.001
    Probably or Definitely No 3 (5.8%) 0 (0%)
    Probably or Definitely Yes 40 (77%) 111 (98%)
    Undecided 9 (17%) 2 (1.8%)
1 n (%)
2 Fisher's exact test; Pearson's Chi-squared test 

#1 = enfolded only
#2 = postgrad only

2b. Characteristics of Residents Choosing Enfolded Versus Postgraduate Fellowship – Condensed*

df3[,c(2,10:17)] %>%
  filter(enfolded_postgrad_yn == 1 | enfolded_postgrad_yn == 2) %>%
  tbl_summary(by=enfolded_postgrad_yn ) %>%
  add_p()
Characteristic 1, N = 521 2, N = 1131 p-value2
current_year 0.11
    PGY123 24 (52%) 71 (66%)
    PGY4567 22 (48%) 37 (34%)
    Unknown 6 5
debt 0.039
    over100k 38 (73%) 95 (86%)
    under100k 14 (27%) 15 (14%)
    Unknown 0 3
gender 0.2
    1 11 (24%) 36 (34%)
    2 35 (76%) 69 (66%)
    Unknown 6 8
race 0.032
    notwhite 13 (30%) 15 (14%)
    white 31 (70%) 89 (86%)
    Unknown 8 9
age 0.2
    30plus 25 (48%) 40 (37%)
    under30 27 (52%) 68 (63%)
    Unknown 0 5
marital_status 0.9
    married 24 (52%) 57 (54%)
    notmarried 22 (48%) 49 (46%)
    Unknown 6 7
children <0.001
    have_plan_kids12 30 (65%) 94 (90%)
    no_kids34 16 (35%) 11 (10%)
    Unknown 6 8
fellow_cat <0.001
    Probably or Definitely No 3 (5.8%) 0 (0%)
    Probably or Definitely Yes 40 (77%) 111 (98%)
    Undecided 9 (17%) 2 (1.8%)
1 n (%)
2 Pearson's Chi-squared test; Fisher's exact test

3a. Characteristics of Residents Choosing Academic Versus Private Practice*

#academic vs private 
df2[,c(3,10:17)] %>%
  filter(private_academic == 1 | private_academic ==2) %>%
  tbl_summary(by=private_academic ) %>%
  add_p()
Characteristic 1, N = 1021 2, N = 531 p-value2
current_year 0.2
    1 7 (7.4%) 0 (0%)
    2 17 (18%) 9 (17%)
    3 34 (36%) 20 (38%)
    4 17 (18%) 9 (17%)
    5 4 (4.2%) 6 (11%)
    6 7 (7.4%) 7 (13%)
    7 9 (9.5%) 2 (3.8%)
    Unknown 7 0
debt 0.009
    1 19 (19%) 4 (7.5%)
    2 45 (46%) 18 (34%)
    3 12 (12%) 14 (26%)
    4 4 (4.1%) 8 (15%)
    5 18 (18%) 9 (17%)
    Unknown 4 0
gender 0.012
    1 33 (34%) 7 (14%)
    2 64 (66%) 42 (86%)
    Unknown 5 4
race 0.007
    1 1 (1.1%) 0 (0%)
    2 5 (5.3%) 3 (5.9%)
    3 6 (6.4%) 10 (20%)
    4 0 (0%) 3 (5.9%)
    5 77 (82%) 35 (69%)
    6 5 (5.3%) 0 (0%)
    Unknown 8 2
age 0.2
    2 15 (15%) 5 (9.4%)
    3 40 (40%) 30 (57%)
    4 37 (37%) 14 (26%)
    5 6 (6.1%) 2 (3.8%)
    6 1 (1.0%) 2 (3.8%)
    Unknown 3 0
marital_status 0.002
    1 19 (20%) 8 (15%)
    2 17 (18%) 13 (25%)
    3 57 (60%) 23 (43%)
    4 1 (1.1%) 9 (17%)
    6 1 (1.1%) 0 (0%)
    Unknown 7 0
children 0.002
    1 32 (34%) 7 (13%)
    2 48 (51%) 33 (62%)
    3 2 (2.1%) 8 (15%)
    4 12 (13%) 5 (9.4%)
    Unknown 8 0
fellow_cat 0.002
    Probably or Definitely No 1 (1.0%) 5 (9.4%)
    Probably or Definitely Yes 96 (94%) 40 (75%)
    Undecided 5 (4.9%) 8 (15%)
1 n (%)
2 Fisher's exact test; Pearson's Chi-squared test

3b. Characteristics of Residents Choosing Academic Versus Private Practice – Condensed*

df3[,c(3,10:17)] %>%
  filter(private_academic == 1 | private_academic ==2) %>%
  tbl_summary(by=private_academic ) %>%
  add_p()
Characteristic 1, N = 1021 2, N = 531 p-value2
current_year 0.5
    PGY123 58 (61%) 29 (55%)
    PGY4567 37 (39%) 24 (45%)
    Unknown 7 0
debt 0.053
    over100k 79 (81%) 49 (92%)
    under100k 19 (19%) 4 (7.5%)
    Unknown 4 0
gender 0.012
    1 33 (34%) 7 (14%)
    2 64 (66%) 42 (86%)
    Unknown 5 4
race 0.068
    notwhite 17 (18%) 16 (31%)
    white 77 (82%) 35 (69%)
    Unknown 8 2
age 0.2
    30plus 44 (44%) 18 (34%)
    under30 55 (56%) 35 (66%)
    Unknown 3 0
marital_status 0.052
    married 57 (60%) 23 (43%)
    notmarried 38 (40%) 30 (57%)
    Unknown 7 0
children 0.15
    have_plan_kids12 80 (85%) 40 (75%)
    no_kids34 14 (15%) 13 (25%)
    Unknown 8 0
fellow_cat 0.002
    Probably or Definitely No 1 (1.0%) 5 (9.4%)
    Probably or Definitely Yes 96 (94%) 40 (75%)
    Undecided 5 (4.9%) 8 (15%)
1 n (%)
2 Pearson's Chi-squared test; Fisher's exact test

4a. Characteristics of Residents Based on Q21. Did CAST accreditation affect DECISION TO PURSUE FELLOWSHIP

df2[,c(6,10:17)] %>%
  filter(CAST_fellowship == 1 | CAST_fellowship == 5) %>%
  tbl_summary(by=CAST_fellowship) %>%
  add_p()
Characteristic 1, N = 571 5, N = 261 p-value2
current_year 0.023
    1 5 (8.8%) 1 (3.8%)
    2 16 (28%) 4 (15%)
    3 18 (32%) 2 (7.7%)
    4 6 (11%) 5 (19%)
    5 4 (7.0%) 5 (19%)
    6 4 (7.0%) 3 (12%)
    7 4 (7.0%) 6 (23%)
debt <0.001
    1 8 (14%) 10 (38%)
    2 30 (53%) 2 (7.7%)
    3 8 (14%) 2 (7.7%)
    4 3 (5.3%) 1 (3.8%)
    5 8 (14%) 11 (42%)
gender 0.5
    1 27 (48%) 10 (40%)
    2 29 (52%) 15 (60%)
    Unknown 1 1
race 0.8
    2 5 (9.1%) 3 (12%)
    3 5 (9.1%) 1 (3.8%)
    5 44 (80%) 21 (81%)
    6 1 (1.8%) 1 (3.8%)
    Unknown 2 0
age 0.007
    2 1 (1.8%) 1 (3.8%)
    3 36 (65%) 7 (27%)
    4 15 (27%) 15 (58%)
    5 2 (3.6%) 2 (7.7%)
    6 1 (1.8%) 1 (3.8%)
    Unknown 2 0
marital_status 0.3
    1 18 (33%) 6 (23%)
    2 6 (11%) 3 (12%)
    3 30 (55%) 15 (58%)
    4 1 (1.8%) 0 (0%)
    6 0 (0%) 2 (7.7%)
    Unknown 2 0
children 0.14
    1 24 (43%) 6 (23%)
    2 18 (32%) 14 (54%)
    3 2 (3.6%) 2 (7.7%)
    4 12 (21%) 4 (15%)
    Unknown 1 0
fellow_cat 0.089
    Probably or Definitely No 1 (1.8%) 2 (7.7%)
    Probably or Definitely Yes 56 (98%) 23 (88%)
    Undecided 0 (0%) 1 (3.8%)
1 n (%)
2 Fisher's exact test; Pearson's Chi-squared test 


df2[,c(6,10:17)] %>%
  mutate(CAST_cat = ifelse(df2$CAST_fellowship==1|df2$CAST_fellowship==2, 'Groups 1 and 2', ifelse(df2$CAST_fellowship==4|df2$CAST_fellowship==5, "Groups 4 and 5", "Other"))) %>%
filter(!CAST_cat == "Other") %>%
  tbl_summary(by=CAST_cat) %>%
  add_p()
Characteristic Groups 1 and 2, N = 1241 Groups 4 and 5, N = 381 p-value2
CAST_fellowship <0.001
    1 57 (46%) 0 (0%)
    2 67 (54%) 0 (0%)
    4 0 (0%) 12 (32%)
    5 0 (0%) 26 (68%)
current_year 0.003
    1 9 (7.5%) 1 (2.6%)
    2 25 (21%) 6 (16%)
    3 41 (34%) 4 (11%)
    4 23 (19%) 9 (24%)
    5 5 (4.2%) 5 (13%)
    6 10 (8.3%) 5 (13%)
    7 7 (5.8%) 8 (21%)
    Unknown 4 0
debt <0.001
    1 18 (15%) 15 (39%)
    2 59 (49%) 4 (11%)
    3 20 (17%) 3 (7.9%)
    4 4 (3.3%) 3 (7.9%)
    5 19 (16%) 13 (34%)
    Unknown 4 0
gender 0.7
    1 36 (31%) 12 (33%)
    2 82 (69%) 24 (67%)
    Unknown 6 2
race 0.078
    1 1 (0.8%) 0 (0%)
    2 7 (5.9%) 8 (22%)
    3 8 (6.8%) 2 (5.4%)
    5 99 (84%) 26 (70%)
    6 3 (2.5%) 1 (2.7%)
    Unknown 6 1
age 0.006
    2 17 (14%) 1 (2.6%)
    3 61 (52%) 12 (32%)
    4 34 (29%) 22 (58%)
    5 4 (3.4%) 2 (5.3%)
    6 2 (1.7%) 1 (2.6%)
    Unknown 6 0
marital_status 0.10
    1 28 (24%) 12 (32%)
    2 22 (19%) 4 (11%)
    3 67 (57%) 20 (53%)
    4 1 (0.8%) 0 (0%)
    6 0 (0%) 2 (5.3%)
    Unknown 6 0
children 0.3
    1 35 (29%) 7 (18%)
    2 61 (51%) 19 (50%)
    3 5 (4.2%) 3 (7.9%)
    4 18 (15%) 9 (24%)
    Unknown 5 0
fellow_cat 0.038
    Probably or Definitely No 1 (0.8%) 2 (5.3%)
    Probably or Definitely Yes 119 (96%) 32 (84%)
    Undecided 4 (3.2%) 4 (11%)
1 n (%)
2 Fisher's exact test; Pearson's Chi-squared test

4b. Characteristics of Residents Based on Q21. Did CAST accreditation affect DECISION TO PURSUE FELLOWSHIP - Condensed

df3[,c(6,10:17)] %>%
  filter(CAST_fellowship == 1 | CAST_fellowship == 5) %>%
  tbl_summary(by=CAST_fellowship) %>%
  add_p()
Characteristic 1, N = 571 5, N = 261 p-value2
current_year <0.001
    PGY123 39 (68%) 7 (27%)
    PGY4567 18 (32%) 19 (73%)
debt 0.012
    over100k 49 (86%) 16 (62%)
    under100k 8 (14%) 10 (38%)
gender 0.5
    1 27 (48%) 10 (40%)
    2 29 (52%) 15 (60%)
    Unknown 1 1
race >0.9
    notwhite 11 (20%) 5 (19%)
    white 44 (80%) 21 (81%)
    Unknown 2 0
age 0.002
    30plus 18 (33%) 18 (69%)
    under30 37 (67%) 8 (31%)
    Unknown 2 0
marital_status 0.8
    married 30 (55%) 15 (58%)
    notmarried 25 (45%) 11 (42%)
    Unknown 2 0
children 0.9
    have_plan_kids12 42 (75%) 20 (77%)
    no_kids34 14 (25%) 6 (23%)
    Unknown 1 0
fellow_cat 0.089
    Probably or Definitely No 1 (1.8%) 2 (7.7%)
    Probably or Definitely Yes 56 (98%) 23 (88%)
    Undecided 0 (0%) 1 (3.8%)
1 n (%)
2 Pearson's Chi-squared test; Fisher's exact test 

df3[,c(6,10:17)] %>%
  mutate(CAST_cat = ifelse(df2$CAST_fellowship==1|df2$CAST_fellowship==2, 'Groups 1 and 2', ifelse(df2$CAST_fellowship==4|df2$CAST_fellowship==5, "Groups 4 and 5", "Other"))) %>%
filter(!CAST_cat == "Other") %>%
  tbl_summary(by=CAST_cat) %>%
  add_p()
Characteristic Groups 1 and 2, N = 1241 Groups 4 and 5, N = 381 p-value2
CAST_fellowship <0.001
    1 57 (46%) 0 (0%)
    2 67 (54%) 0 (0%)
    4 0 (0%) 12 (32%)
    5 0 (0%) 26 (68%)
current_year <0.001
    PGY123 75 (62%) 11 (29%)
    PGY4567 45 (38%) 27 (71%)
    Unknown 4 0
debt 0.001
    over100k 102 (85%) 23 (61%)
    under100k 18 (15%) 15 (39%)
    Unknown 4 0
gender 0.7
    1 36 (31%) 12 (33%)
    2 82 (69%) 24 (67%)
    Unknown 6 2
race 0.067
    notwhite 19 (16%) 11 (30%)
    white 99 (84%) 26 (70%)
    Unknown 6 1
age <0.001
    30plus 40 (34%) 25 (66%)
    under30 78 (66%) 13 (34%)
    Unknown 6 0
marital_status 0.7
    married 67 (57%) 20 (53%)
    notmarried 51 (43%) 18 (47%)
    Unknown 6 0
children 0.11
    have_plan_kids12 96 (81%) 26 (68%)
    no_kids34 23 (19%) 12 (32%)
    Unknown 5 0
fellow_cat 0.038
    Probably or Definitely No 1 (0.8%) 2 (5.3%)
    Probably or Definitely Yes 119 (96%) 32 (84%)
    Undecided 4 (3.2%) 4 (11%)
1 n (%)
2 Fisher's exact test; Pearson's Chi-squared test

5a. Characteristics of Residents Based on Q34. Did CAST accreditation affect SUBSPECIALTY CHOICE

df2[,c(7,10:17)] %>%
  filter(CAST_subspecialty == 1 | CAST_subspecialty == 5) %>%
  tbl_summary(by=CAST_subspecialty) %>%
  add_p()
Characteristic 1, N = 381 5, N = 471 p-value2
current_year 0.002
    1 2 (5.3%) 3 (6.4%)
    2 7 (18%) 5 (11%)
    3 16 (42%) 4 (8.5%)
    4 7 (18%) 10 (21%)
    5 2 (5.3%) 6 (13%)
    6 1 (2.6%) 9 (19%)
    7 3 (7.9%) 10 (21%)
debt <0.001
    1 5 (13%) 20 (43%)
    2 26 (68%) 3 (6.4%)
    3 3 (7.9%) 4 (8.5%)
    4 0 (0%) 5 (11%)
    5 4 (11%) 15 (32%)
gender 0.2
    1 6 (16%) 12 (27%)
    2 31 (84%) 32 (73%)
    Unknown 1 3
race 0.3
    1 1 (2.6%) 0 (0%)
    2 2 (5.3%) 7 (16%)
    3 4 (11%) 2 (4.4%)
    5 30 (79%) 34 (76%)
    6 1 (2.6%) 2 (4.4%)
    Unknown 0 2
age <0.001
    2 10 (26%) 1 (2.1%)
    3 18 (47%) 12 (26%)
    4 8 (21%) 31 (66%)
    5 1 (2.6%) 3 (6.4%)
    6 1 (2.6%) 0 (0%)
marital_status 0.12
    1 9 (24%) 15 (32%)
    2 9 (24%) 4 (8.5%)
    3 20 (53%) 25 (53%)
    6 0 (0%) 3 (6.4%)
children 0.9
    1 8 (21%) 11 (24%)
    2 21 (55%) 23 (51%)
    3 1 (2.6%) 3 (6.7%)
    4 8 (21%) 8 (18%)
    Unknown 0 2
fellow_cat 0.2
    Probably or Definitely No 0 (0%) 2 (4.3%)
    Probably or Definitely Yes 38 (100%) 43 (91%)
    Undecided 0 (0%) 2 (4.3%)
1 n (%)
2 Fisher's exact test; Pearson's Chi-squared test 


df2[,c(7,10:17)] %>%
  mutate(CAST_cat = ifelse(df2$CAST_subspecialty==1|df2$CAST_subspecialty==2, 'Groups 1 and 2', ifelse(df2$CAST_subspecialty==4|df2$CAST_subspecialty==5, "Groups 4 and 5", "Other"))) %>%
filter(!CAST_cat == "Other") %>%
  tbl_summary(by=CAST_cat) %>%
  add_p(test.args = current_year ~ list(workspace=2e8))
Characteristic Groups 1 and 2, N = 1091 Groups 4 and 5, N = 631 p-value2
CAST_subspecialty <0.001
    1 38 (35%) 0 (0%)
    2 71 (65%) 0 (0%)
    4 0 (0%) 16 (25%)
    5 0 (0%) 47 (75%)
current_year <0.001
    1 6 (5.7%) 4 (6.3%)
    2 26 (25%) 6 (9.5%)
    3 42 (40%) 10 (16%)
    4 20 (19%) 12 (19%)
    5 3 (2.9%) 6 (9.5%)
    6 4 (3.8%) 12 (19%)
    7 4 (3.8%) 13 (21%)
    Unknown 4 0
debt <0.001
    1 11 (10%) 24 (39%)
    2 65 (62%) 5 (8.1%)
    3 13 (12%) 8 (13%)
    4 2 (1.9%) 7 (11%)
    5 14 (13%) 18 (29%)
    Unknown 4 1
gender 0.5
    1 29 (28%) 20 (33%)
    2 75 (72%) 40 (67%)
    Unknown 5 3
race 0.027
    1 1 (1.0%) 0 (0%)
    2 5 (4.8%) 11 (19%)
    3 9 (8.6%) 6 (11%)
    5 87 (83%) 38 (67%)
    6 3 (2.9%) 2 (3.5%)
    Unknown 4 6
age <0.001
    2 17 (16%) 2 (3.3%)
    3 57 (54%) 15 (25%)
    4 24 (23%) 40 (66%)
    5 4 (3.8%) 3 (4.9%)
    6 3 (2.9%) 1 (1.6%)
    Unknown 4 2
marital_status <0.001
    1 20 (19%) 20 (33%)
    2 25 (24%) 6 (9.8%)
    3 60 (57%) 27 (44%)
    4 0 (0%) 5 (8.2%)
    6 0 (0%) 3 (4.9%)
    Unknown 4 2
children 0.070
    1 28 (27%) 11 (18%)
    2 59 (56%) 31 (51%)
    3 2 (1.9%) 6 (9.8%)
    4 16 (15%) 13 (21%)
    Unknown 4 2
fellow_cat 0.10
    Probably or Definitely No 0 (0%) 3 (4.8%)
    Probably or Definitely Yes 104 (95%) 58 (92%)
    Undecided 5 (4.6%) 2 (3.2%)
1 n (%)
2 Pearson's Chi-squared test; Fisher's exact test

5b. Characteristics of Residents Based on Q34. Did CAST accreditation affect SUBSPECIALTY CHOICE - Condensed

df3[,c(7,10:17)] %>%
  filter(CAST_subspecialty == 1 | CAST_subspecialty == 5) %>%
  tbl_summary(by=CAST_subspecialty) %>%
  add_p()
Characteristic 1, N = 381 5, N = 471 p-value2
current_year <0.001
    PGY123 25 (66%) 12 (26%)
    PGY4567 13 (34%) 35 (74%)
debt 0.003
    over100k 33 (87%) 27 (57%)
    under100k 5 (13%) 20 (43%)
gender 0.2
    1 6 (16%) 12 (27%)
    2 31 (84%) 32 (73%)
    Unknown 1 3
race 0.7
    notwhite 8 (21%) 11 (24%)
    white 30 (79%) 34 (76%)
    Unknown 0 2
age <0.001
    30plus 10 (26%) 34 (72%)
    under30 28 (74%) 13 (28%)
marital_status >0.9
    married 20 (53%) 25 (53%)
    notmarried 18 (47%) 22 (47%)
children >0.9
    have_plan_kids12 29 (76%) 34 (76%)
    no_kids34 9 (24%) 11 (24%)
    Unknown 0 2
fellow_cat 0.2
    Probably or Definitely No 0 (0%) 2 (4.3%)
    Probably or Definitely Yes 38 (100%) 43 (91%)
    Undecided 0 (0%) 2 (4.3%)
1 n (%)
2 Pearson's Chi-squared test; Fisher's exact test 

df3[,c(7,10:17)] %>%
  mutate(CAST_cat = ifelse(df2$CAST_subspecialty==1|df2$CAST_subspecialty==2, 'Groups 1 and 2', ifelse(df2$CAST_subspecialty==4|df2$CAST_subspecialty==5, "Groups 4 and 5", "Other"))) %>%
filter(!CAST_cat == "Other") %>%
  tbl_summary(by=CAST_cat) %>%
  add_p()
Characteristic Groups 1 and 2, N = 1091 Groups 4 and 5, N = 631 p-value2
CAST_subspecialty <0.001
    1 38 (35%) 0 (0%)
    2 71 (65%) 0 (0%)
    4 0 (0%) 16 (25%)
    5 0 (0%) 47 (75%)
current_year <0.001
    PGY123 74 (70%) 20 (32%)
    PGY4567 31 (30%) 43 (68%)
    Unknown 4 0
debt <0.001
    over100k 94 (90%) 38 (61%)
    under100k 11 (10%) 24 (39%)
    Unknown 4 1
gender 0.5
    1 29 (28%) 20 (33%)
    2 75 (72%) 40 (67%)
    Unknown 5 3
race 0.019
    notwhite 18 (17%) 19 (33%)
    white 87 (83%) 38 (67%)
    Unknown 4 6
age <0.001
    30plus 31 (30%) 44 (72%)
    under30 74 (70%) 17 (28%)
    Unknown 4 2
marital_status 0.11
    married 60 (57%) 27 (44%)
    notmarried 45 (43%) 34 (56%)
    Unknown 4 2
children 0.037
    have_plan_kids12 87 (83%) 42 (69%)
    no_kids34 18 (17%) 19 (31%)
    Unknown 4 2
fellow_cat 0.10
    Probably or Definitely No 0 (0%) 3 (4.8%)
    Probably or Definitely Yes 104 (95%) 58 (92%)
    Undecided 5 (4.6%) 2 (3.2%)
1 n (%)
2 Pearson's Chi-squared test; Fisher's exact test

6a. Characteristics of Residents Based on Q35. Did CAST accreditation affect FELLOWSHIP PROGRAMS APPLIED TO

df2[,c(8,10:17)] %>%
  filter(CAST_apply == 1 | CAST_apply == 5) %>%
  tbl_summary(by=CAST_apply) %>%
  add_p()
Characteristic 1, N = 381 5, N = 341 p-value2
current_year <0.001
    1 3 (7.9%) 1 (2.9%)
    2 8 (21%) 1 (2.9%)
    3 16 (42%) 2 (5.9%)
    4 5 (13%) 6 (18%)
    5 2 (5.3%) 8 (24%)
    6 2 (5.3%) 8 (24%)
    7 2 (5.3%) 8 (24%)
debt <0.001
    1 4 (11%) 16 (47%)
    2 22 (58%) 2 (5.9%)
    3 6 (16%) 2 (5.9%)
    4 2 (5.3%) 5 (15%)
    5 4 (11%) 9 (26%)
gender 0.9
    1 8 (21%) 7 (23%)
    2 30 (79%) 24 (77%)
    Unknown 0 3
race 0.7
    2 4 (11%) 3 (9.4%)
    3 3 (7.9%) 2 (6.2%)
    5 30 (79%) 24 (75%)
    6 1 (2.6%) 3 (9.4%)
    Unknown 0 2
age <0.001
    2 5 (13%) 1 (2.9%)
    3 21 (55%) 5 (15%)
    4 9 (24%) 26 (76%)
    5 2 (5.3%) 2 (5.9%)
    6 1 (2.6%) 0 (0%)
marital_status 0.3
    1 11 (29%) 10 (29%)
    2 5 (13%) 3 (8.8%)
    3 22 (58%) 17 (50%)
    4 0 (0%) 2 (5.9%)
    6 0 (0%) 2 (5.9%)
children 0.10
    1 7 (19%) 10 (29%)
    2 21 (57%) 14 (41%)
    3 0 (0%) 4 (12%)
    4 9 (24%) 6 (18%)
    Unknown 1 0
fellow_cat 0.045
    Probably or Definitely No 0 (0%) 1 (2.9%)
    Probably or Definitely Yes 38 (100%) 30 (88%)
    Undecided 0 (0%) 3 (8.8%)
1 n (%)
2 Fisher's exact test; Pearson's Chi-squared test 


df2[,c(8,10:17)] %>%
  mutate(CAST_cat = ifelse(df2$CAST_apply==1|df2$CAST_apply==2, 'Groups 1 and 2', ifelse(df2$CAST_apply==4|df2$CAST_apply==5, "Groups 4 and 5", "Other"))) %>%
filter(!CAST_cat == "Other") %>%
  tbl_summary(by=CAST_cat) %>%
  add_p(test.args = current_year ~ list(workspace=2e8))
Characteristic Groups 1 and 2, N = 1081 Groups 4 and 5, N = 571 p-value2
CAST_apply <0.001
    1 38 (35%) 0 (0%)
    2 70 (65%) 0 (0%)
    4 0 (0%) 23 (40%)
    5 0 (0%) 34 (60%)
current_year <0.001
    1 6 (5.7%) 1 (1.8%)
    2 22 (21%) 4 (7.0%)
    3 45 (42%) 9 (16%)
    4 18 (17%) 10 (18%)
    5 4 (3.8%) 11 (19%)
    6 6 (5.7%) 13 (23%)
    7 5 (4.7%) 9 (16%)
    Unknown 2 0
debt <0.001
    1 15 (14%) 24 (42%)
    2 58 (55%) 5 (8.8%)
    3 16 (15%) 7 (12%)
    4 4 (3.8%) 9 (16%)
    5 13 (12%) 12 (21%)
    Unknown 2 0
gender 0.5
    1 27 (26%) 11 (20%)
    2 78 (74%) 43 (80%)
    Unknown 3 3
race <0.001
    2 6 (5.8%) 9 (17%)
    3 7 (6.7%) 6 (11%)
    4 0 (0%) 3 (5.7%)
    5 89 (86%) 31 (58%)
    6 2 (1.9%) 4 (7.5%)
    Unknown 4 4
age <0.001
    2 17 (16%) 4 (7.3%)
    3 56 (53%) 11 (20%)
    4 25 (24%) 36 (65%)
    5 5 (4.7%) 3 (5.5%)
    6 3 (2.8%) 1 (1.8%)
    Unknown 2 2
marital_status <0.001
    1 24 (23%) 13 (24%)
    2 21 (20%) 6 (11%)
    3 61 (58%) 25 (45%)
    4 0 (0%) 9 (16%)
    6 0 (0%) 2 (3.6%)
    Unknown 2 2
children 0.033
    1 28 (27%) 11 (19%)
    2 55 (53%) 28 (49%)
    3 3 (2.9%) 9 (16%)
    4 18 (17%) 9 (16%)
    Unknown 4 0
fellow_cat 0.014
    Probably or Definitely No 0 (0%) 1 (1.8%)
    Probably or Definitely Yes 105 (97%) 49 (86%)
    Undecided 3 (2.8%) 7 (12%)
1 n (%)
2 Pearson's Chi-squared test; Fisher's exact test

6b. Characteristics of Residents Based on Q35. Did CAST accreditation affect FELLOWSHIP PROGRAMS APPLIED TO - Condensed

df3[,c(8,10:17)] %>%
  filter(CAST_apply == 1 | CAST_apply == 5) %>%
  tbl_summary(by=CAST_apply) %>%
  add_p()
Characteristic 1, N = 381 5, N = 341 p-value2
current_year <0.001
    PGY123 27 (71%) 4 (12%)
    PGY4567 11 (29%) 30 (88%)
debt <0.001
    over100k 34 (89%) 18 (53%)
    under100k 4 (11%) 16 (47%)
gender 0.9
    1 8 (21%) 7 (23%)
    2 30 (79%) 24 (77%)
    Unknown 0 3
race 0.7
    notwhite 8 (21%) 8 (25%)
    white 30 (79%) 24 (75%)
    Unknown 0 2
age <0.001
    30plus 12 (32%) 28 (82%)
    under30 26 (68%) 6 (18%)
marital_status 0.5
    married 22 (58%) 17 (50%)
    notmarried 16 (42%) 17 (50%)
children 0.6
    have_plan_kids12 28 (76%) 24 (71%)
    no_kids34 9 (24%) 10 (29%)
    Unknown 1 0
fellow_cat 0.045
    Probably or Definitely No 0 (0%) 1 (2.9%)
    Probably or Definitely Yes 38 (100%) 30 (88%)
    Undecided 0 (0%) 3 (8.8%)
1 n (%)
2 Pearson's Chi-squared test; Fisher's exact test 

df3[,c(8,10:17)] %>%
  mutate(CAST_cat = ifelse(df2$CAST_apply==1|df2$CAST_apply==2, 'Groups 1 and 2', ifelse(df2$CAST_apply==4|df2$CAST_apply==5, "Groups 4 and 5", "Other"))) %>%
filter(!CAST_cat == "Other") %>%
  tbl_summary(by=CAST_cat) %>%
  add_p()
Characteristic Groups 1 and 2, N = 1081 Groups 4 and 5, N = 571 p-value2
CAST_apply <0.001
    1 38 (35%) 0 (0%)
    2 70 (65%) 0 (0%)
    4 0 (0%) 23 (40%)
    5 0 (0%) 34 (60%)
current_year <0.001
    PGY123 73 (69%) 14 (25%)
    PGY4567 33 (31%) 43 (75%)
    Unknown 2 0
debt <0.001
    over100k 91 (86%) 33 (58%)
    under100k 15 (14%) 24 (42%)
    Unknown 2 0
gender 0.5
    1 27 (26%) 11 (20%)
    2 78 (74%) 43 (80%)
    Unknown 3 3
race <0.001
    notwhite 15 (14%) 22 (42%)
    white 89 (86%) 31 (58%)
    Unknown 4 4
age <0.001
    30plus 33 (31%) 40 (73%)
    under30 73 (69%) 15 (27%)
    Unknown 2 2
marital_status 0.14
    married 61 (58%) 25 (45%)
    notmarried 45 (42%) 30 (55%)
    Unknown 2 2
children 0.11
    have_plan_kids12 83 (80%) 39 (68%)
    no_kids34 21 (20%) 18 (32%)
    Unknown 4 0
fellow_cat 0.014
    Probably or Definitely No 0 (0%) 1 (1.8%)
    Probably or Definitely Yes 105 (97%) 49 (86%)
    Undecided 3 (2.8%) 7 (12%)
1 n (%)
2 Pearson's Chi-squared test; Fisher's exact test
---
title: "Maggie Data v3 Tables"
output: html_notebook
---

```{r ,echo=FALSE, message=FALSE}
rm(list = ls())

library(ggplot2);library(Matching);library(readxl);library(tibble);library(gridExtra);library("ggpubr");library(caret);library(gtsummary);library(ggridges);library(dplyr);library(foreign);library(nnet);library(VGAM);library(data.table);library(scales);library(tidyr)
```

#### read in and format data
```{r}
df <- read.csv("maggie2.csv"); #this imports 262 cases from the original dateframe
df <- df[!is.na(df$fellowship_yn),]; #this removes any NA responses for `fellowship_yn` = 257 cases

df2 <- df[c(2,7,20,22:23,25:28,31:33,35,37:39)];
```


```{r}
df2$fellow_cat <- ifelse(df2$fellowship_yn==1|df2$fellowship_yn==2, 'Probably or Definitely Yes', ifelse(df2$fellowship_yn==4|df2$fellowship_yn==5, "Probably or Definitely No", "Undecided"))

#df2$age_cat <- ifelse(df2$age==2, '22-25 y/o', ifelse(df2$age==3, "26-30 y/o", ifelse(df2$age==4, "31-35 y/o", "36-45 y/o")))
```


```{r}
#complete race categories
#df2$race_cat <- ifelse(df2$race==1, 'Native American or Alaska Native', ifelse(df2$race==2, "Asian", ifelse(df2$race==3, "Black or African American", ifelse(df2$race==4, "Native Hawaiian or other Pacific Islander", ifelse(df2$race==5, "White", "Other/No Response")))))
```


```{r}
#remove non cis gender responses, Keep NA, keep 7
#this brings the dataframe down to 230 cases
df2 <- df2 %>%
  filter(is.na(gender) | gender == 1 | gender == 2 | gender == 7)
```


DF FOR FULL TABLES (DF2)
```{r}
#dataframe for full tables
#df2 is for full

#the code below is replacing all "no response" responses with NA

df2$debt[df2$debt == 6] <- NA
df2$gender[df2$gender == 7] <- NA
df2$race[df2$race == 7] <- NA
df2$marital_status[df2$marital_status == 7] <- NA
df2$children[df2$children == 5] <- NA
```


DF FOR CONDENSED TABLES (DF3)
```{r}
#dataframe for condensed tables
#df3 is condensed
df3 <- df2 

#year of training
df3$current_year <- ifelse(df3$current_year<4, "PGY123", "PGY4567")

#debt
df3$debt <- ifelse(df3$debt == 1, "under100k","over100k")

#race
df3$race <- ifelse(df3$race == 5, "white","notwhite")

#age
df3$age[df3$age == 1 |df3$age == 2|df3$age == 3] <- "under30"
df3$age[df3$age == 4 |df3$age == 5|df3$age == 6] <- "30plus"

#marital status
df3$marital_status <- ifelse(df3$marital_status == 3, "married","notmarried")

#children
df3$children[df3$children == 1 |df3$children == 2] <- "have_plan_kids12"
df3$children[df3$children == 3 |df3$children == 4] <- "no_kids34"
```


### 1a. Characteristics of Residents Planning Versus Not Planning to Complete Fellowship  
```{r}
#group 1 vs group 5
df2[,c(1,10:17)] %>%
  filter(fellowship_yn == 1 | fellowship_yn == 5) %>%
  tbl_summary(by=fellowship_yn) %>%
  add_p()

#groups 1&2 vs groups 4 and 5
df2[,c(10:17)] %>%
  filter(!fellow_cat == "Undecided") %>%
  tbl_summary(by=fellow_cat) %>%
  add_p()
```



### 1b. Characteristics of Residents Planning Versus Not Planning to Complete Fellowship - Condensed 
```{r}
#group 1 vs group 5
df3[,c(1,10:16)] %>%
  filter(fellowship_yn == 1 | fellowship_yn == 5) %>%
  tbl_summary(by=fellowship_yn) %>%
  add_p()

#groups 1&2 vs groups 45
df3[,c(10:17)] %>%
  filter(!fellow_cat == "Undecided") %>%
  tbl_summary(by=fellow_cat) %>%
  add_p()
```


### 2a. Characteristics of Residents Choosing Enfolded Versus Postgraduate Fellowship*  
```{r}
#enfolded vs postgraduate
df2[,c(2,10:17)] %>%
  filter(enfolded_postgrad_yn == 1 | enfolded_postgrad_yn ==2) %>%
  tbl_summary(by=enfolded_postgrad_yn ) %>%
  add_p()

#1 = enfolded only
#2 = postgrad only
```

### 2b.  Characteristics of Residents Choosing Enfolded Versus Postgraduate Fellowship – Condensed*
```{r}
df3[,c(2,10:17)] %>%
  filter(enfolded_postgrad_yn == 1 | enfolded_postgrad_yn == 2) %>%
  tbl_summary(by=enfolded_postgrad_yn ) %>%
  add_p()
```


### 3a. Characteristics of Residents Choosing Academic Versus Private Practice*  
```{r}
#academic vs private 
df2[,c(3,10:17)] %>%
  filter(private_academic == 1 | private_academic ==2) %>%
  tbl_summary(by=private_academic ) %>%
  add_p()
```


### 3b. Characteristics of Residents Choosing Academic Versus Private Practice – Condensed*
```{r}
df3[,c(3,10:17)] %>%
  filter(private_academic == 1 | private_academic ==2) %>%
  tbl_summary(by=private_academic ) %>%
  add_p()
```


### 4a. Characteristics of Residents Based on Q21. Did CAST accreditation affect DECISION TO PURSUE FELLOWSHIP  
```{r}
df2[,c(6,10:17)] %>%
  filter(CAST_fellowship == 1 | CAST_fellowship == 5) %>%
  tbl_summary(by=CAST_fellowship) %>%
  add_p()


df2[,c(6,10:17)] %>%
  mutate(CAST_cat = ifelse(df2$CAST_fellowship==1|df2$CAST_fellowship==2, 'Groups 1 and 2', ifelse(df2$CAST_fellowship==4|df2$CAST_fellowship==5, "Groups 4 and 5", "Other"))) %>%
filter(!CAST_cat == "Other") %>%
  tbl_summary(by=CAST_cat) %>%
  add_p()
```


### 4b. Characteristics of Residents Based on Q21. Did CAST accreditation affect DECISION TO PURSUE FELLOWSHIP - Condensed
```{r}
df3[,c(6,10:17)] %>%
  filter(CAST_fellowship == 1 | CAST_fellowship == 5) %>%
  tbl_summary(by=CAST_fellowship) %>%
  add_p()

df3[,c(6,10:17)] %>%
  mutate(CAST_cat = ifelse(df2$CAST_fellowship==1|df2$CAST_fellowship==2, 'Groups 1 and 2', ifelse(df2$CAST_fellowship==4|df2$CAST_fellowship==5, "Groups 4 and 5", "Other"))) %>%
filter(!CAST_cat == "Other") %>%
  tbl_summary(by=CAST_cat) %>%
  add_p()
```


### 5a. Characteristics of Residents Based on Q34. Did CAST accreditation affect SUBSPECIALTY CHOICE 
```{r}
df2[,c(7,10:17)] %>%
  filter(CAST_subspecialty == 1 | CAST_subspecialty == 5) %>%
  tbl_summary(by=CAST_subspecialty) %>%
  add_p()


df2[,c(7,10:17)] %>%
  mutate(CAST_cat = ifelse(df2$CAST_subspecialty==1|df2$CAST_subspecialty==2, 'Groups 1 and 2', ifelse(df2$CAST_subspecialty==4|df2$CAST_subspecialty==5, "Groups 4 and 5", "Other"))) %>%
filter(!CAST_cat == "Other") %>%
  tbl_summary(by=CAST_cat) %>%
  add_p(test.args = current_year ~ list(workspace=2e8))
```

### 5b. Characteristics of Residents Based on Q34. Did CAST accreditation affect SUBSPECIALTY CHOICE - Condensed
```{r}
df3[,c(7,10:17)] %>%
  filter(CAST_subspecialty == 1 | CAST_subspecialty == 5) %>%
  tbl_summary(by=CAST_subspecialty) %>%
  add_p()

df3[,c(7,10:17)] %>%
  mutate(CAST_cat = ifelse(df2$CAST_subspecialty==1|df2$CAST_subspecialty==2, 'Groups 1 and 2', ifelse(df2$CAST_subspecialty==4|df2$CAST_subspecialty==5, "Groups 4 and 5", "Other"))) %>%
filter(!CAST_cat == "Other") %>%
  tbl_summary(by=CAST_cat) %>%
  add_p()
```


### 6a. Characteristics of Residents Based on Q35. Did CAST accreditation affect FELLOWSHIP PROGRAMS APPLIED TO
```{r}
df2[,c(8,10:17)] %>%
  filter(CAST_apply == 1 | CAST_apply == 5) %>%
  tbl_summary(by=CAST_apply) %>%
  add_p()


df2[,c(8,10:17)] %>%
  mutate(CAST_cat = ifelse(df2$CAST_apply==1|df2$CAST_apply==2, 'Groups 1 and 2', ifelse(df2$CAST_apply==4|df2$CAST_apply==5, "Groups 4 and 5", "Other"))) %>%
filter(!CAST_cat == "Other") %>%
  tbl_summary(by=CAST_cat) %>%
  add_p(test.args = current_year ~ list(workspace=2e8))
```

### 6b. Characteristics of Residents Based on Q35. Did CAST accreditation affect FELLOWSHIP PROGRAMS APPLIED TO - Condensed 
```{r}
df3[,c(8,10:17)] %>%
  filter(CAST_apply == 1 | CAST_apply == 5) %>%
  tbl_summary(by=CAST_apply) %>%
  add_p()

df3[,c(8,10:17)] %>%
  mutate(CAST_cat = ifelse(df2$CAST_apply==1|df2$CAST_apply==2, 'Groups 1 and 2', ifelse(df2$CAST_apply==4|df2$CAST_apply==5, "Groups 4 and 5", "Other"))) %>%
filter(!CAST_cat == "Other") %>%
  tbl_summary(by=CAST_cat) %>%
  add_p()
```


