generated from opensafely/research-template
/
table2.R
145 lines (105 loc) · 6 KB
/
table2.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
# # # # # # # # # # # # # # # # # # # # #
# This script:
# generate baseline table
# round to nearest 5
#
# # # # # # # # # # # # # # # # # # # # #
library('tidyverse')
library("ggplot2")
library("plyr")
library('dplyr')
library('lubridate')
library('stringr')
library("data.table")
library("ggpubr")
library("finalfit")
setwd(here::here("output"))
#setwd("/Users/yayang/Documents/GitHub/amr-uom-brit/output")
####### after matching ########
DF=readRDS("matched_outcome.rds")
DF=DF%>%dplyr::select("case","ethnicity_6", "bmi_cat", "CCI", "smoking_cat_3","imd","care_home_type","care_home","covrx_ever","flu_vaccine",
"cancer_comor","cerebrovascular_disease_comor", "chronic_obstructive_pulmonary_comor", "heart_failure_comor", "connective_tissue_comor", "dementia_comor", "diabetes_comor", "diabetes_complications_comor", "hemiplegia_comor", "metastatic_cancer_comor", "mild_liver_comor", "mod_severe_liver_comor", "mod_severe_renal_comor", "mi_comor", "peptic_ulcer_comor", "peripheral_vascular_comor","hiv_comor")
DF$case=as.factor(DF$case)
DF$imd=as.character(DF$imd)
case.num=sum(DF$case==1)
contr.num=sum(DF$case==0)
# select variables
explanatory<- c("ethnicity_6", "bmi_cat", "CCI", "smoking_cat_3","imd","care_home","covrx_ever","flu_vaccine",
"cancer_comor","cerebrovascular_disease_comor", "chronic_obstructive_pulmonary_comor", "heart_failure_comor", "connective_tissue_comor", "dementia_comor", "diabetes_comor", "diabetes_complications_comor", "hemiplegia_comor", "metastatic_cancer_comor", "mild_liver_comor", "mod_severe_liver_comor", "mod_severe_renal_comor", "mi_comor", "peptic_ulcer_comor", "peripheral_vascular_comor","hiv_comor")
dependent <- "case"
#table
tbl=DF%>% summary_factorlist(dependent, explanatory)
round_tbl=tbl
#remove percentage
round_tbl[,3]=gsub("\\(.*?\\)","",round_tbl[,3])
round_tbl[,4]=gsub("\\(.*?\\)","",round_tbl[,4])
#round to 5
round_tbl[,3]=as.numeric(round_tbl[,3])
round_tbl[,3]=plyr::round_any(round_tbl[,3], 5, f = round)
round_tbl[,4]=as.numeric(round_tbl[,4])
round_tbl[,4]=plyr::round_any(round_tbl[,4], 5, f = round)
#ethnicity
round_tbl[c(1:6),"percent_0"]=round_tbl[c(1:6),3]/sum(round_tbl[c(1:6),3])*100
round_tbl[c(1:6),"percent_1"]=round_tbl[c(1:6),4]/sum(round_tbl[c(1:6),4])*100
# bmi cat
round_tbl[c(7:11),"percent_0"]=round_tbl[c(7:11),3]/sum(round_tbl[c(7:11),3])*100
round_tbl[c(7:11),"percent_1"]=round_tbl[c(7:11),4]/sum(round_tbl[c(7:11),4])*100
#CCI
round_tbl[c(12:16),"percent_0"]=round_tbl[c(12:16),3]/sum(round_tbl[c(12:16),3])*100
round_tbl[c(12:16),"percent_1"]=round_tbl[c(12:16),4]/sum(round_tbl[c(12:16),4])*100
# smoking
round_tbl[c(17:20),"percent_0"]=round_tbl[c(17:20),3]/sum(round_tbl[c(17:20),3])*100
round_tbl[c(17:20),"percent_1"]=round_tbl[c(17:20),4]/sum(round_tbl[c(17:20),4])*100
# IMD
round_tbl[c(21:26),"percent_0"]=round_tbl[c(21:26),3]/sum(round_tbl[c(21:26),3])*100
round_tbl[c(21:26),"percent_1"]=round_tbl[c(21:26),4]/sum(round_tbl[c(21:26),4])*100
# care home, covid & flu vaccine
# commodities- 17 conditions
round_tbl[c(27:66),"percent_0"]=round_tbl[c(27:66),3]/contr.num
round_tbl[c(27:66),"percent_1"]=round_tbl[c(27:66),4]/case.num
write.csv(round_tbl,"table2_matched.csv")
rm(list=ls())
###### random sampling #######
DF <- read_rds("matched_outcome.rds")
DF=DF%>% dplyr::group_by(case,subclass)%>% sample_n(1)
DF=DF%>%dplyr::select("case","ethnicity_6", "bmi_cat", "CCI", "smoking_cat_3","imd","care_home_type","care_home","covrx_ever","flu_vaccine",
"cancer_comor","cerebrovascular_disease_comor", "chronic_obstructive_pulmonary_comor", "heart_failure_comor", "connective_tissue_comor", "dementia_comor", "diabetes_comor", "diabetes_complications_comor", "hemiplegia_comor", "metastatic_cancer_comor", "mild_liver_comor", "mod_severe_liver_comor", "mod_severe_renal_comor", "mi_comor", "peptic_ulcer_comor", "peripheral_vascular_comor","hiv_comor")
DF$case=as.factor(DF$case)
DF$imd=as.character(DF$imd)
case.num=sum(DF$case==1)
contr.num=sum(DF$case==0)
# select variables
explanatory<- c("ethnicity_6", "bmi_cat", "CCI", "smoking_cat_3","imd","care_home","covrx_ever","flu_vaccine",
"cancer_comor","cerebrovascular_disease_comor", "chronic_obstructive_pulmonary_comor", "heart_failure_comor", "connective_tissue_comor", "dementia_comor", "diabetes_comor", "diabetes_complications_comor", "hemiplegia_comor", "metastatic_cancer_comor", "mild_liver_comor", "mod_severe_liver_comor", "mod_severe_renal_comor", "mi_comor", "peptic_ulcer_comor", "peripheral_vascular_comor","hiv_comor")
dependent <- "case"
#table
tbl=DF%>% summary_factorlist(dependent, explanatory)
round_tbl=tbl
#remove percentage
round_tbl[,3]=gsub("\\(.*?\\)","",round_tbl[,3])
round_tbl[,4]=gsub("\\(.*?\\)","",round_tbl[,4])
#round to 5
round_tbl[,3]=as.numeric(round_tbl[,3])
round_tbl[,3]=plyr::round_any(round_tbl[,3], 5, f = round)
round_tbl[,4]=as.numeric(round_tbl[,4])
round_tbl[,4]=plyr::round_any(round_tbl[,4], 5, f = round)
#ethnicity
round_tbl[c(1:6),"percent_0"]=round_tbl[c(1:6),3]/sum(round_tbl[c(1:6),3])*100
round_tbl[c(1:6),"percent_1"]=round_tbl[c(1:6),4]/sum(round_tbl[c(1:6),4])*100
# bmi cat
round_tbl[c(7:11),"percent_0"]=round_tbl[c(7:11),3]/sum(round_tbl[c(7:11),3])*100
round_tbl[c(7:11),"percent_1"]=round_tbl[c(7:11),4]/sum(round_tbl[c(7:11),4])*100
#CCI
round_tbl[c(12:16),"percent_0"]=round_tbl[c(12:16),3]/sum(round_tbl[c(12:16),3])*100
round_tbl[c(12:16),"percent_1"]=round_tbl[c(12:16),4]/sum(round_tbl[c(12:16),4])*100
# smoking
round_tbl[c(17:20),"percent_0"]=round_tbl[c(17:20),3]/sum(round_tbl[c(17:20),3])*100
round_tbl[c(17:20),"percent_1"]=round_tbl[c(17:20),4]/sum(round_tbl[c(17:20),4])*100
# IMD
round_tbl[c(21:26),"percent_0"]=round_tbl[c(21:26),3]/sum(round_tbl[c(21:26),3])*100
round_tbl[c(21:26),"percent_1"]=round_tbl[c(21:26),4]/sum(round_tbl[c(21:26),4])*100
# care home, covid & flu vaccine
# commodities- 17 conditions
round_tbl[c(27:66),"percent_0"]=round_tbl[c(27:66),3]/contr.num
round_tbl[c(27:66),"percent_1"]=round_tbl[c(27:66),4]/case.num
write.csv(round_tbl,"table2_random.csv")