-
Notifications
You must be signed in to change notification settings - Fork 45
/
GW_implement.r
310 lines (251 loc) · 11.4 KB
/
GW_implement.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
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
# Implementation Considerations
library(ggplot2)
library(graphics)
library(ggplot2)
library(MASS)
library(scales) #scale_x_date()
library(gridExtra)
## Set theme
theme_set(theme_minimal(base_size=18))
th <- theme(panel.grid.minor=element_blank(),
panel.grid.major=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank(),
strip.background = element_rect(fill = "lightgrey", colour = "lightgrey"),
panel.background = element_rect(fill = "white", colour = "lightgrey")
)
############################################################
## Simple scatter plot - cause and effect
############################################################
## Cause and effect
# For xy plots display the cause on the x-axis and the effect on the y-axis.
set.seed(12345666)
my_data <- data.frame(
Bodyweight = 90 + 20*c(-runif(50), runif(50))
)
my_data$AUC <- ((my_data$Bodyweight/90)^-0.75)*exp(0.1*rnorm(length(my_data$Bodyweight)))
ggplot(my_data, aes(x = AUC, y = Bodyweight)) +
geom_smooth(method = "lm") +
geom_point() +
theme_minimal(base_size = 18 ) +
theme(panel.grid.minor=element_blank(),
#panel.grid.major=element_blank(),
legend.position="none",
axis.ticks = element_blank(),
axis.text.y=element_blank(),
axis.text.x=element_blank(),
panel.background = element_rect(fill = "white", colour = "lightgrey")
)
ggsave(file="GWImp_a1.png", width = 80, height = 80, units = "mm", dpi = 300)
ggplot(my_data, aes(x = Bodyweight, y = AUC)) +
geom_smooth(method = "lm")+
geom_point() + theme_minimal(base_size = 18 ) +
theme(panel.grid.minor=element_blank(),
#panel.grid.major=element_blank(),
legend.position="none",
axis.ticks = element_blank(),
axis.text.y=element_blank(),
axis.text.x=element_blank(),
panel.background = element_rect(fill = "white", colour = "lightgrey")
)
ggsave(file="GWImp_a2.png", width = 80, height = 80, units = "mm", dpi = 300)
###################################################################
## Simple scatter plot - Aspect ratio
####################################################################
# The selection of the aspect ratio can influence the interpretation of a line graph.
# Try to display lesser height versus greater length.
# Aim to scale the representation to have approximately a 45 degree angle of change.
set.seed(12345666)
my_data <- data.frame(
x = 50*rnorm(1000)
)
my_data$y <- (0.2*my_data$x+0.8*50*rnorm(length(my_data$x)))
p <- ggplot(my_data, aes(x = x, y = y)) +
geom_point() + theme_minimal(base_size = 18 ) +
theme_bw(base_size=16) +
theme(panel.grid.minor=element_blank(),
panel.grid.major=element_blank(),
legend.position="none",
axis.title.y=element_blank(),
axis.title.x = element_blank(),
axis.ticks = element_blank(),
axis.text.y=element_blank(),
axis.text.x=element_blank()
)
p + coord_fixed(0.5)
ggsave(file="GWImp_b1.png", width = 80, height = 80, units = "mm", dpi = 300)
p + coord_fixed(ratio=3)
ggsave(file="GWImp_b2.png", width = 80, height = 80, units = "mm", dpi = 300)
################################################################
## Simple bar chart and dot plot
################################################################
## Plotting bars on a non-linear axis
## Avoid using comparisons based on length (i.e. a bar chart)
## when displaying data on a non-linear scale (i.e. a log scale or percentage change).
## Instead display comparisons using position (i.e.a dot chart).
ggplot(OrchardSprays,aes(x=treatment,y=decrease)) +
stat_summary(fun.data=mean_cl_normal,geom="bar") +
theme_minimal(base_size = 18 )+
scale_y_log10(limits = c(1, 100), breaks = c(1,10,100)) +
xlab("Treatment") +
ylab("Effect")
ggsave(file="GWImp_c1.png", width = 80, height = 80, units = "mm", dpi = 300)
mm1 <- function(...) {
mean_cl_normal(...,mult=1)
}
mm2 <- function(...) {
mean_cl_normal(...,mult=2)
}
ggplot(OrchardSprays,aes(x=treatment,y=decrease)) +
stat_summary(fun.data=mm1,geom="point", color = "black",size=4) +
theme_minimal(base_size = 18)+
scale_y_log10(limits = c(1, 100), breaks = c(1,10,100))+
xlab("Treatment")+
ylab("Effect")
ggsave(file="GWImp_c2.png", width = 80, height = 80, units = "mm", dpi = 300)
######################################################
## Simple dot plot including uncertainty estimates
######################################################
## Log Scales
## Do not plot log-normally distributed variables on a linear scale
## (e.g. hazards ratio, odds ratio, AUC, CL)
DF <- data.frame(Est = c(1,2,3,4,5),
Point = c(-0.7, 0.7, 0.2, 0.1, -0.3),
SE = c(0.1,0.1,0.1,0.8,0.9))
gg <- ggplot(data = DF) +
geom_point(aes(x=Est, y= exp(Point)), size = 3) +
geom_errorbar(aes(x = Est, ymin = exp(Point-2*SE),
ymax = exp(Point+2*SE)), width = 0.5) +
scale_y_continuous(breaks = c(0,1,2,3,4,5,6,7,8)) +
geom_hline(yintercept = 1, linetype = "dashed", color = rgb(0.5,0.5,0.5)) +
geom_vline(xintercept = 0, linetype = "solid", color = "black") +
ylab("Odds Ratio") +
theme_minimal(base_size = 18 ) +
theme(panel.grid.minor=element_blank(),
panel.grid.major=element_blank(),
legend.position="none",
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.text.x=element_blank()
)
gg
ggsave(file="GWImp_d1.png", width = 80, height = 80, units = "mm", dpi = 300)
gg + scale_y_log10(breaks = c(0.125,0.25,0.5,1,2,4,8), labels = c("1/8","1/4","1/2","1","2","4","8")) +
geom_hline(yintercept = 1, linetype = "dashed", color = rgb(0.5,0.5,0.5))
ggsave(file="GWImp_d2.png", width = 80, height = 80, units = "mm", dpi = 300)
#############################################################################
## Simple panelled line plot
#############################################################################
## Consistent scales
set.seed(12345666)
Time = c(c(0.5,1,2,4,8,12,18,23.99), 3*24 + c(0.5,1,2,4,8,12,18,23.99))
DAY = floor(Time/24)
DOSE = (1+DAY)*100
K1 = 0.2
K2 = 0.8
Concentration = DOSE*(K2*K1/(K2-K1))*(exp(-K1*(Time-DAY*24)) - exp(-K2*(Time-DAY*24)) )
my.data <- data.frame(Time = Time, Concentration = Concentration, DAY = DAY, DOSE = DOSE)
gg <- ggplot(data = my.data, aes(x = Time-DAY*24, y = Concentration)) +
geom_point(size = 4) +
geom_line(aes(group = DAY), size = 1) + scale_y_log10() +
theme_minimal(base_size = 18) +
theme(panel.grid.minor=element_blank(),
panel.grid.major=element_blank(),
legend.position="none",
axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.ticks = element_blank(),
strip.background = element_rect(fill = "lightgrey", colour = "grey50"),
panel.background = element_rect(fill = "white", colour = "grey50"),
axis.text.x=element_blank())
gg + facet_wrap(~DOSE, scales = "free_y")
ggsave(file="GWImp_e1.png", width = 80, height = 80, units = "mm", dpi = 300)
gg + facet_wrap(~DOSE)
ggsave(file="GWImp_e2.png", width = 80, height = 80, units = "mm", dpi = 300)
#########################################################################
## Simple line plot with disconnected trend
#########################################################################
## Disconnected scale
set.seed(12345666)
Time = c(c(0,0.5,1,2,4,8,12,18,23.99), 24 + seq(1,2,1)*24, 3*24 + c(0,0.5,1,2,4,8,12,18,23.99))
DAY = floor(Time/24)
K1 = 0.2
K2 = 0.8
Concentration = 100*(K2*K1/(K2-K1))*(exp(-K1*(Time-DAY*24)) - exp(-K2*(Time-DAY*24)) + 0.25*Time/100/(Time/100 + 1))
my.data <- data.frame(Time = Time, Concentration = Concentration, DAY = DAY)
gg <- ggplot(data = my.data, aes(x = Time, y = Concentration)) +
geom_point(size = 4) +
geom_line(aes(group = DAY), size = 1) +
scale_y_log10(lim = c(0.1,100)) +
theme_minimal(base_size = 18) +
theme(panel.grid.minor=element_blank(),
panel.grid.major=element_blank(),
panel.background = element_rect(fill = "white", colour = "lightgrey"),
legend.position="none",
axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.ticks = element_blank(),
axis.text.y=element_blank(),
axis.text.x=element_blank()
)
gg + geom_line(size = 1)
ggsave(file="GWImp_f1.png", width = 80, height = 80, units = "mm", dpi = 300)
gg
ggsave(file="GWImp_f2.png", width = 80, height = 80, units = "mm", dpi = 300)
##############################################################
## Simple line plot with uncertainty intervals and band
##############################################################
## Inferences
set.seed(12345666)
Dose = seq(0,10,0.1)
DAY = floor(Dose/24)
K1 = 0.2
K2 = 0.8
Response = 100*(Dose/1)/(Dose/1 + 1)
my.data <- data.frame(Dose = Dose, Response = Response,
ymin = Response - 0.1*Response - 5,
ymax = Response + 0.1*Response + 5,
obs = Response +
5*rnorm(length(Response)) +
0.1*rnorm(length(Response))*Response,
DAY = DAY)
gg <- ggplot(data = my.data, aes(x = Dose, y = Response)) +
theme_minimal(base_size = 18) +
geom_line(size = 1) +
coord_cartesian(ylim = c(-10,120)) +
theme(panel.grid.minor=element_blank(),
panel.grid.major=element_blank(),
legend.position="none",
axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.ticks = element_blank(),
axis.text.y=element_blank(),
axis.text.x=element_blank(),
panel.background = element_rect(fill = "white", colour = "lightgrey")
)
gg
ggsave(file="GWImp_g1.png", width = 80, height = 80, units = "mm", dpi = 300)
gg + geom_ribbon(aes(ymin = ymin, ymax=ymax), fill = rgb(0.5,0.5,0.5), alpha = 0.5) +
geom_point(data = my.data[Dose%in%c(0.1,1,2.5,5,10),],
aes(x=Dose, y=obs),size = 4) +
geom_errorbar(data = my.data[Dose%in%c(0.1,1,2.5,5,10),],
aes(x=Dose, ymin=obs-5-0.1*obs, ymax = obs+5+0.1*obs),size = 1)
ggsave(file="GWImp_g2.png", width = 80, height = 80, units = "mm", dpi = 300)
#############################################################
## Simple boxplots on a linear scale
#############################################################
## Continuous Time Scales
grl <- data.frame(x=rep(c(0,1,2,4,8,12,24),200))
grl$y <- 100*(exp(-0.5*grl$x+0.5*rnorm(length(grl$x)))+0.5*rnorm(length(grl$x)))
ggplot(grl, aes(x=factor(x),y=y)) +
geom_boxplot(aes(group = factor(x)),size=1, color = "black", outlier.size = 3) +
theme_minimal(base_size=26) +
th
ggsave(file="GWImp_h1.png", width = 80, height = 80, units = "mm", dpi = 300)
ggplot(grl, aes(x=x,y=y)) +
geom_boxplot(aes(group = factor(x)),size=1, color = "black", outlier.size = 3) +
theme_minimal(base_size=26) +
th
ggsave(file="GWImp_h2.png", width = 80, height = 80, units = "mm", dpi = 300)