# carica pacchettilibrary(readxl)library(tableone)library(DescTools)DescToolsOptions(Fmt(digits=1)) # questo per aggiustare cifre decimalilibrary(chron)library(tidyr)library(ggplot2)library(knitr)library(lubridate)
Caricamento pacchetto: 'lubridate'
I seguenti oggetti sono mascherati da 'package:chron':
days, hours, minutes, seconds, years
I seguenti oggetti sono mascherati da 'package:base':
date, intersect, setdiff, union
library(plotly)
Caricamento pacchetto: 'plotly'
Il seguente oggetto è mascherato da 'package:ggplot2':
last_plot
Il seguente oggetto è mascherato da 'package:stats':
filter
Il seguente oggetto è mascherato da 'package:graphics':
layout
library(dplyr)
Caricamento pacchetto: 'dplyr'
I seguenti oggetti sono mascherati da 'package:stats':
filter, lag
I seguenti oggetti sono mascherati da 'package:base':
intersect, setdiff, setequal, union
library(countrycode)library(qwraps2)db <-read_excel("dataset_survey.xlsx")db <-as.data.frame(db)db_original <- dbdb$Year <-as.factor(db$Year)db1 <- db[,c("Age", "Haemoglobin", "WBC", "Creatinine_Clearance", "Prior_bleeding","Score", "Cluster_risk", "Stent_type", "Stent_brand", "Stent_brand2","Stent_Size", "Stent_Lenght", "Polymer", "Drug", "Stent_number","strategy_change", "dapt_duration", "DAPT_Long_Short", "Concordance","Antiplatelet_agents", "Year", "alternative_score")] # rinomino così ho un db pulito da date/timedb1$hbr <-if_else (db$Cluster_risk =="High", 1, 0, missing =NULL)db1 <-as.data.frame(db1)tone <-TOne(db1, add.length =TRUE, intref ="high", grp = db1$hbr,fmt =list(abs =Fmt("abs"), num =Fmt("num"), per =Fmt("per"), TEST =TRUE,pval =as.fmt(fmt ="*", na.form ="")) )
Warning in chisq.test(table(x, g)): L'approssimazione al Chi-quadrato potrebbe
essere inesatta
Warning in chisq.test(table(x, g)): L'approssimazione al Chi-quadrato potrebbe
essere inesatta
Warning in chisq.test(table(x, g)): L'approssimazione al Chi-quadrato potrebbe
essere inesatta
Warning in chisq.test(table(x, g)): L'approssimazione al Chi-quadrato potrebbe
essere inesatta
Warning in chisq.test(table(x, g)): L'approssimazione al Chi-quadrato potrebbe
essere inesatta
kable(tone)
var
total
0
1
n
1’215
511 (42.1%)
704 (57.9%)
Age
68 (13)
62 (11)
72 (13)
*** ’
Haemoglobin
12 (2)
14 (2)
12 (2)
*** ’
WBC
9 (8)
9 (6)
10 (10)
*** ’
Creatinine_Clearance
58 (31)
81 (19)
42 (28)
*** ’
Prior_bleeding (= 1)
201 (16.5%)
0 (0.0%)
201 (28.6%)
*** ”
Score
30 (18)
14 (7)
42 (14)
*** ’
Cluster_risk
*** “”
High
704 (57.9%)
0 (0.0%)
704 (100.0%)
Low
327 (26.9%)
327 (64.0%)
0 (0.0%)
Moderate
184 (15.1%)
184 (36.0%)
0 (0.0%)
Stent_type
“”
BMS
57 (5.5%)
24 (5.3%)
33 (5.6%)
BRS
10 (1.0%)
4 (0.9%)
6 (1.0%)
DES
974 (93.6%)
424 (93.8%)
550 (93.4%)
Stent_brand
. “”
Absorb
1 (0.4%)
1 (0.7%)
0 (0.0%)
Angiolite
1 (0.4%)
0 (0.0%)
1 (0.7%)
BioFreedom
14 (5.0%)
1 (0.7%)
13 (9.0%)
Biomatrix
4 (1.4%)
3 (2.2%)
1 (0.7%)
Biomime
3 (1.1%)
1 (0.7%)
2 (1.4%)
Combo
4 (1.4%)
1 (0.7%)
3 (2.1%)
Cre8
10 (3.6%)
2 (1.5%)
8 (5.6%)
Cypher
3 (1.1%)
0 (0.0%)
3 (2.1%)
Driver
1 (0.4%)
0 (0.0%)
1 (0.7%)
Evermine
2 (0.7%)
0 (0.0%)
2 (1.4%)
Firebird
7 (2.5%)
3 (2.2%)
4 (2.8%)
Firehawk
1 (0.4%)
1 (0.7%)
0 (0.0%)
Orsiro
23 (8.3%)
12 (9.0%)
11 (7.6%)
Promus
27 (9.7%)
16 (11.9%)
11 (7.6%)
Rebel
1 (0.4%)
0 (0.0%)
1 (0.7%)
Resolute
56 (20.1%)
32 (23.9%)
24 (16.7%)
Stentys
1 (0.4%)
0 (0.0%)
1 (0.7%)
Synergy
31 (11.2%)
17 (12.7%)
14 (9.7%)
Tsunami Gold
1 (0.4%)
1 (0.7%)
0 (0.0%)
Ultimaster
29 (10.4%)
13 (9.7%)
16 (11.1%)
Xience
58 (20.9%)
30 (22.4%)
28 (19.4%)
Stent_brand2
“”
BioFreedom Ultra
2 (2.4%)
0 (0.0%)
2 (5.3%)
Cre8 Evo
3 (3.6%)
1 (2.2%)
2 (5.3%)
Endeavor Resolute
1 (1.2%)
0 (0.0%)
1 (2.6%)
Orsiro Mission
1 (1.2%)
1 (2.2%)
0 (0.0%)
Promus Element
7 (8.4%)
4 (8.9%)
3 (7.9%)
Promus Premiere
11 (13.3%)
8 (17.8%)
3 (7.9%)
Rebel
1 (1.2%)
0 (0.0%)
1 (2.6%)
Resolute Integrity
7 (8.4%)
4 (8.9%)
3 (7.9%)
Resolute Onyx
31 (37.3%)
19 (42.2%)
12 (31.6%)
Ultimaster Tansei
1 (1.2%)
1 (2.2%)
0 (0.0%)
Xience Alpine
1 (1.2%)
1 (2.2%)
0 (0.0%)
Xience Sierra
1 (1.2%)
0 (0.0%)
1 (2.6%)
Xience Alpine
4 (4.8%)
2 (4.4%)
2 (5.3%)
Xience Pro
2 (2.4%)
1 (2.2%)
1 (2.6%)
Xience Sierra
6 (7.2%)
1 (2.2%)
5 (13.2%)
Xience v
1 (1.2%)
0 (0.0%)
1 (2.6%)
Xience Xpedition
3 (3.6%)
2 (4.4%)
1 (2.6%)
Stent_Size
3 (0)
3 (0)
3 (0)
’
Stent_Lenght
24 (8)
26 (8)
23 (8)
’
Polymer
** “”
Anti CD-3
4 (2.7%)
1 (1.5%)
3 (3.7%)
Bioresorbable
37 (24.8%)
19 (27.9%)
18 (22.2%)
Durable
84 (56.4%)
45 (66.2%)
39 (48.1%)
No
24 (16.1%)
3 (4.4%)
21 (25.9%)
Drug
. “”
Amphillimus
10 (6.4%)
2 (2.9%)
8 (9.2%)
Biolimus
18 (11.5%)
4 (5.7%)
14 (16.1%)
Everolimus
48 (30.6%)
22 (31.4%)
26 (29.9%)
Paclitaxel
1 (0.6%)
1 (1.4%)
0 (0.0%)
Sirolimus
41 (26.1%)
18 (25.7%)
23 (26.4%)
Zotarolimus
39 (24.8%)
23 (32.9%)
16 (18.4%)
Stent_number
2 (1)
2 (1)
2 (1)
* ’
strategy_change (= Yes)
642 (62.1%)
222 (49.4%)
420 (71.8%)
*** ”
dapt_duration
10 (7)
12 (8)
9 (5)
*** ’
DAPT_Long_Short
*** “”
<12
426 (37.4%)
126 (25.1%)
300 (47.0%)
>12
110 (9.7%)
82 (16.4%)
28 (4.4%)
12
603 (52.9%)
293 (58.5%)
310 (48.6%)
Concordance
*** “”
Concordance
666 (58.5%)
375 (74.9%)
291 (45.6%)
Discordance Less
126 (11.1%)
126 (25.1%)
0 (0.0%)
Discordance More
347 (30.5%)
0 (0.0%)
347 (54.4%)
Antiplatelet_agents
*** “”
ASA+Clopidogrel
570 (53.7%)
221 (47.0%)
349 (59.0%)
ASA+Prasugrel
61 (5.7%)
29 (6.2%)
32 (5.4%)
ASA+Ticagrelor
380 (35.8%)
198 (42.1%)
182 (30.7%)
Other
51 (4.8%)
22 (4.7%)
29 (4.9%)
Year
“”
2017
299 (24.6%)
121 (23.7%)
178 (25.3%)
2018
220 (18.1%)
86 (16.8%)
134 (19.0%)
2019
206 (17.0%)
96 (18.8%)
110 (15.6%)
2020
164 (13.5%)
78 (15.3%)
86 (12.2%)
2021
229 (18.8%)
87 (17.0%)
142 (20.2%)
2022
97 (8.0%)
43 (8.4%)
54 (7.7%)
alternative_score
32 (21)
13 (8)
46 (16)
*** ’
hbr (= 1)
704 (57.9%)
0 (0.0%)
704 (100.0%)
*** ”
#inzio figure#aggiungo dati per anno a tabella# tone1 <- TOne(db1, add.length = TRUE, intref = "high", grp = db1$strategy_change,# fmt = list(abs = Fmt("abs"), num = Fmt("num"), per = Fmt("per"), TEST = TRUE,# pval = as.fmt(fmt = "*", na.form = "")) )# kable(tone1)# date_table <- data.frame(table(db$Year))# date_table$strategy_no <- c(106, 51, 68, 55, 81, 31)# date_table$strategy_si <- c(111, 138, 119, 92, 123, 59)# date_table$daptless12 <- c(71, 91, 73, 59, 90, 42)# date_table$dapt12 <- c(135, 111, 100, 88, 120, 49)# date_table$daptmore12 <- c(29, 16, 30, 13, 17, 5)# date_table$concordance <- c(133, 128, 113, 98, 130, 64)# date_table$discordanceless <- c(25, 23, 27, 18, 23, 10)# date_table$discordancemore <- c(77, 67, 63, 44, 74, 22)# # # # #FIGURE INIZIALI NON DEFINITIVE -> GRAFICI A LINEE# figure1 <- ggplot(date_table, aes(x=Var1, y=strategy_no)) +# geom_line(aes(x = Var1, y = strategy_no), size = 1, color="red", group = 1) +# geom_line(aes(x = Var1, y = strategy_si), size = 1, color="blue", group = 1) # # figure1 <- figure1 + theme(panel.background = element_rect(fill = 'white', color = 'black')) # figure1 <- figure1 + labs (y= "Did Score change yout attitude?", x = "")# figure1# # # figure2 <- ggplot(date_table) +# geom_line(aes(x = Var1, y = daptless12), size = 1, color="red", group = 1) +# geom_line(aes(x = Var1, y = dapt12), size = 1, color="blue", group = 1) +# geom_line(aes(x = Var1, y = daptmore12), size = 1, color="green", group = 1) # # figure2 <- figure2 + theme(panel.background = element_rect(fill = 'white', color = 'black')) # figure2 <- figure2 + labs (y= "Treatment duration assigned", x = "")# figure2# # figure3 <- ggplot(date_table) +# geom_line(aes(x = Var1, y = concordance), size = 1, color="red", group = 1) +# geom_line(aes(x = Var1, y = discordanceless), size = 1, color="blue", group = 1) +# geom_line(aes(x = Var1, y = discordancemore), size = 1, color="green", group = 1) # # figure3 <- figure3 + theme(panel.background = element_rect(fill = 'white', color = 'black')) # figure3 <- figure3 + labs (y= "Concordance/Discordance", x = "")# figure3# # library("writexl")# write_xlsx(date_table,"date-table-survey.xlsx")#INIZIO FIGURE CON PERCENTUALI E DEFINITIVEdate_table_per <-read_excel("date-table-per-survey.xlsx")figure1per <-ggplot(date_table_per, aes(x=Var1, y=strategy_si)) +geom_line(aes(x = Var1, y = strategy_si), size =1, color="#5c67ae", group =1) +geom_point(aes(x = Var1, y = strategy_si), size =3, color="#5b79b8", group =1)
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
figure1per <- figure1per +theme_light() figure1per <- figure1per +ggtitle("Did Score change your attitude?") +theme(plot.title =element_text(hjust =0.5)) +labs (y="Changing DAPT Strategy (%)", x ="")# figura 1 survey definitivafigure1per
