I have some data, see subset below. For each Method I want to calculate the difference in the mean Cq between the 2uL and 4 uL and the 4 uL and the 8uL.
I have a function to calculate the mean for each Method, grouped by Volume. But I can't figure out how to add another column with the difference. I think I might have to summarize the summ table, but I"m getting confused. Any help appreciated. Thanks
dat_summ<-
dat %>%
group_by(Volume,Method) %>%
summarise(mean_Cq = mean(Cq,na.rm=T), sd_Cq=sd(Cq,na.rm=T),
CV=(sd(Cq,na.rm=T)/mean(Cq,na.rm=T))*100)
what I want but know if wrong:
dat_summ<-
dat %>%
group_by(Volume,Method) %>%
summarise(mean_Cq = mean(Cq,na.rm=T), sd_Cq=sd(Cq,na.rm=T),
CV=(sd(Cq,na.rm=T)/mean(Cq,na.rm=T))*100)+
**mutate(delta_doub=mean_Cq_for2uL-meanCq_for4uL)**
current output:
> dat_summ
# A tibble: 12 × 5
# Groups: Volume [3]
Volume Method mean_Cq sd_Cq CV
<chr> <fct> <dbl> <dbl> <dbl>
1 2ul 2ew 20.0 0.295 1.47
2 2ul 3ew 21.9 1.79 8.18
3 2ul Manual 22.2 0.248 1.12
4 2ul WN2ew 20.5 0.604 2.94
5 4ul 2ew 19.3 0.278 1.44
6 4ul 3ew 21.2 1.33 6.29
7 4ul Manual 22.2 0.139 0.627
8 4ul WN2ew 19.9 0.493 2.48
9 8ul 2ew 18.8 0.270 1.43
10 8ul 3ew 20.8 1.21 5.81
11 8ul Manual 23.7 1.50 6.35
12 8ul WN2ew 19.5 0.463 2.38
subset of dat:
sample Method Volume Cq
1 Sample 1 2ew 2ul 20.11
2 Sample 2 2ew 2ul 20.12
3 Sample 3 2ew 2ul 19.76
17 Sample 1 WN2ew 2ul 19.89
18 Sample 2 WN2ew 2ul 20.62
19 Sample 3 WN2ew 2ul 21.07
20 Sample 4 WN2ew 2ul 20.08
52 Sample 1 2ew 4ul 19.30
53 Sample 2 2ew 4ul 19.33
54 Sample 3 2ew 4ul 19.16
68 Sample 1 WN2ew 4ul 19.49
69 Sample 2 WN2ew 4ul 19.46
70 Sample 3 WN2ew 4ul 20.42
103 Sample 1 2ew 8ul 18.91
104 Sample 2 2ew 8ul 18.60
105 Sample 3 2ew 8ul 18.42
119 Sample 1 WN2ew 8ul 18.66
120 Sample 2 WN2ew 8ul 19.13
121 Sample 3 WN2ew 8ul 19.52
> dput(dat)
structure(list(sample = c("Sample 1", "Sample 2", "Sample 3",
"Sample 4", "Sample 5", "Sample 6", "Sample 7", "Sample 8", "Sample 9",
"Sample 10", "Sample 11", "Sample 12", "Sample 13", "Sample 14",
"Sample 15", "Sample 16", "Sample 1", "Sample 2", "Sample 3",
"Sample 4", "Sample 5", "Sample 6", "Sample 7", "Sample 8", "Sample 9",
"Sample 10", "Sample 11", "Sample 12", "Sample 13", "Sample 14",
"Sample 15", "Sample 16", "Sample 1", "Sample 2", "Sample 3",
"Sample 4", "Sample 5", "Sample 6", "Sample 7", "Sample 8", "Sample 10",
"Sample 11", "Sample 12", "Sample 13", "Sample 14", "Sample 15",
"Sample 16", "Sample 1", "Sample 2", "Sample 3", "Sample 4",
"Sample 1", "Sample 2", "Sample 3", "Sample 4", "Sample 5", "Sample 6",
"Sample 7", "Sample 8", "Sample 9", "Sample 10", "Sample 11",
"Sample 12", "Sample 13", "Sample 14", "Sample 15", "Sample 16",
"Sample 1", "Sample 2", "Sample 3", "Sample 4", "Sample 5", "Sample 6",
"Sample 7", "Sample 8", "Sample 9", "Sample 10", "Sample 11",
"Sample 12", "Sample 13", "Sample 14", "Sample 15", "Sample 16",
"Sample 1", "Sample 2", "Sample 3", "Sample 4", "Sample 5", "Sample 6",
"Sample 7", "Sample 8", "Sample 10", "Sample 11", "Sample 12",
"Sample 13", "Sample 14", "Sample 15", "Sample 16", "Sample 1",
"Sample 2", "Sample 3", "Sample 4", "Sample 1", "Sample 2", "Sample 3",
"Sample 4", "Sample 5", "Sample 6", "Sample 7", "Sample 8", "Sample 9",
"Sample 10", "Sample 11", "Sample 12", "Sample 13", "Sample 14",
"Sample 15", "Sample 16", "Sample 1", "Sample 2", "Sample 3",
"Sample 4", "Sample 5", "Sample 6", "Sample 7", "Sample 8", "Sample 9",
"Sample 10", "Sample 11", "Sample 12", "Sample 13", "Sample 14",
"Sample 15", "Sample 16", "Sample 1", "Sample 2", "Sample 3",
"Sample 4", "Sample 5", "Sample 6", "Sample 7", "Sample 8", "Sample 10",
"Sample 11", "Sample 12", "Sample 13", "Sample 14", "Sample 15",
"Sample 16", "Sample 1", "Sample 2", "Sample 3", "Sample 4"),
Method = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L), .Label = c("2ew", "3ew",
"Manual", "WN2ew"), class = "factor"), Volume = c("2ul",
"2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul",
"2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul",
"2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul",
"2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul",
"2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul", "2ul",
"2ul", "2ul", "2ul", "2ul", "2ul", "4ul", "4ul", "4ul", "4ul",
"4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul",
"4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul",
"4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul",
"4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul",
"4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul", "4ul",
"4ul", "4ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul",
"8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul",
"8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul",
"8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul",
"8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul",
"8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul", "8ul"),
Cq = c(20.11, 20.12, 19.76, 20.07, 20.19, 19.87, 20.33, 19.81,
20.15, 19.79, 19.67, 20.23, 19.9, 20.9, 19.93, 19.96, 19.89,
20.62, 21.07, 20.08, 21.32, 21.15, 21.07, 20.85, 21.16, 21.03,
20.79, 19.39, 20.25, 19.6, 20.14, 20.32, 26.35, 21.36, 21.67,
21.13, 21.28, 21.27, 21.36, 21.08, 20.56, 26.18, 21.31, 21.35,
21.06, 21.15, 21.28, 22.2, 22.18, 21.96, 22.56, 19.3, 19.33,
19.16, 19.27, 19.42, 19.16, 19.53, 19.1, 19.38, 19.08, 19.2,
19.44, 19.18, 20.11, 19.43, 18.81, 19.49, 19.46, 20.42, 19.21,
20.69, 20.39, 20.19, 20.13, 20.29, 20.49, 20.09, 19.19, 19.63,
19.27, 19.82, 19.76, 25.57, 20.45, 20.83, 20.68, 20.72, 21.25,
21.14, 21.06, 20.47, 22.51, 20.49, 20.9, 20.47, 20.24, 20.71,
22.09, 22.07, 22.13, 22.37, 18.91, 18.6, 18.42, 18.64, 19.14,
18.77, 18.77, 18.71, 19.39, 18.7, 18.67, 19.18, 18.79, 19.22,
18.73, NA, 18.66, 19.13, 19.52, 19.02, 20.25, 19.66, 19.78,
19.71, 19.89, 20.25, 19.47, 19.06, 19.49, 18.84, 19.27, 19.22,
24.97, 20.05, 20.33, 20.05, 20.59, 20.39, 20.08, 20.73, 20.3,
20.76, 21.12, 20.81, 20.22, 20.32, 20.69, 22.15, 25.2, 24.69,
22.63)), row.names = c(NA, -153L), class = "data.frame")
If the mean delta should be for each 'Method', then create the column first grouped by 'Method' (or if it is based across all the Method, then we do not need any grouping), get the mean difference of 'Cq' where 'Volume is '2ul' and '4ul' respectively, use that in grouping for calculating the rest of the summarised columns
library(dplyr)
dat %>%
group_by(Method) %>%
mutate(delta_doub =mean(Cq[Volume == '2ul'], na.rm = TRUE) -
mean(Cq[Volume=='4ul'], na.rm = TRUE) ) %>%
group_by(Volume, Method, delta_doub) %>%
summarise(mean_Cq = mean(Cq,na.rm=TRUE), sd_Cq=sd(Cq,na.rm=TRUE),
CV=(sd(Cq,na.rm=TRUE)/mean(Cq,na.rm=TRUE))*100, .groups = "drop")
-output
# A tibble: 12 × 6
Volume Method delta_doub mean_Cq sd_Cq CV
<chr> <fct> <dbl> <dbl> <dbl> <dbl>
1 2ul 2ew 0.743 20.0 0.295 1.47
2 2ul 3ew 0.727 21.9 1.79 8.18
3 2ul Manual 0.0600 22.2 0.248 1.12
4 2ul WN2ew 0.638 20.5 0.604 2.94
5 4ul 2ew 0.743 19.3 0.278 1.44
6 4ul 3ew 0.727 21.2 1.33 6.29
7 4ul Manual 0.0600 22.2 0.139 0.627
8 4ul WN2ew 0.638 19.9 0.493 2.48
9 8ul 2ew 0.743 18.8 0.270 1.43
10 8ul 3ew 0.727 20.8 1.21 5.81
11 8ul Manual 0.0600 23.7 1.50 6.35
12 8ul WN2ew 0.638 19.5 0.463 2.38
Or it can be
dat %>%
group_by(Volume,Method) %>%
summarise(mean_Cq = mean(Cq,na.rm=TRUE), sd_Cq=sd(Cq,na.rm=TRUE),
CV=(sd(Cq,na.rm=TRUE)/mean(Cq,na.rm=TRUE))*100,
.groups = 'drop') %>%
mutate(delta_doub_2_4 = mean(mean_Cq[Volume == '2ul']) -
mean(mean_Cq[Volume == '4ul']),
delta_doub_4_8 = mean(mean_Cq[Volume == '4ul']) -
mean(mean_Cq[Volume == '8ul']))
-output
# A tibble: 12 × 7
Volume Method mean_Cq sd_Cq CV delta_doub_2_4 delta_doub_4_8
<chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 2ul 2ew 20.0 0.295 1.47 0.542 -0.0443
2 2ul 3ew 21.9 1.79 8.18 0.542 -0.0443
3 2ul Manual 22.2 0.248 1.12 0.542 -0.0443
4 2ul WN2ew 20.5 0.604 2.94 0.542 -0.0443
5 4ul 2ew 19.3 0.278 1.44 0.542 -0.0443
6 4ul 3ew 21.2 1.33 6.29 0.542 -0.0443
7 4ul Manual 22.2 0.139 0.627 0.542 -0.0443
8 4ul WN2ew 19.9 0.493 2.48 0.542 -0.0443
9 8ul 2ew 18.8 0.270 1.43 0.542 -0.0443
10 8ul 3ew 20.8 1.21 5.81 0.542 -0.0443
11 8ul Manual 23.7 1.50 6.35 0.542 -0.0443
12 8ul WN2ew 19.5 0.463 2.38 0.542 -0.0443
Related
I'm looking for a way to place my dunn's test results on my ggplot:
either directly by somehow implementing the necessary code lines on the plot regarding the results of the dunn test
or indirectly by placing just the letters regarding significance by the order in which i know they appear
In the example graphic I attached, I put in the letters myself through a 2nd party drawing software, to give you an idea of what I'm looking for - ideally i would want the lower case letters to represent significant differences only within the same variable called "type" but a different "day" and capitals for the opposite. I do however recognize this is a tall order, and would be happy with just the differences within the same type.
here is my dput and str:
> dput(table5)
structure(list(day = c("day 00", "day 00", "day 00", "day 00",
"day 00", "day 00", "day 00", "day 00", "day 07", "day 07", "day 07",
"day 07", "day 07", "day 07", "day 07", "day 07", "day 14", "day 14",
"day 14", "day 14", "day 14", "day 14", "day 14", "day 14", "day 21",
"day 21", "day 21", "day 21", "day 21", "day 21", "day 21", "day 21",
"day 28", "day 28", "day 28", "day 28", "day 28", "day 28", "day 28",
"day 28", "day 00", "day 00", "day 00", "day 00", "day 00", "day 00",
"day 00", "day 00", "day 07", "day 07", "day 07", "day 07", "day 07",
"day 07", "day 07", "day 07", "day 14", "day 14", "day 14", "day 14",
"day 14", "day 14", "day 14", "day 14", "day 21", "day 21", "day 21",
"day 21", "day 21", "day 21", "day 21", "day 21", "day 28", "day 28",
"day 28", "day 28", "day 28", "day 28", "day 28", "day 28", "day 00",
"day 00", "day 00", "day 00", "day 00", "day 00", "day 00", "day 00",
"day 07", "day 07", "day 07", "day 07", "day 07", "day 07", "day 07",
"day 07", "day 14", "day 14", "day 14", "day 14", "day 14", "day 14",
"day 14", "day 14", "day 21", "day 21", "day 21", "day 21", "day 21",
"day 21", "day 21", "day 21", "day 28", "day 28", "day 28", "day 28",
"day 28", "day 28", "day 28", "day 28", "day 00", "day 00", "day 00",
"day 00", "day 00", "day 00", "day 00", "day 00", "day 07", "day 07",
"day 07", "day 07", "day 07", "day 07", "day 07", "day 07", "day 14",
"day 14", "day 14", "day 14", "day 14", "day 14", "day 14", "day 14",
"day 21", "day 21", "day 21", "day 21", "day 21", "day 21", "day 21",
"day 21", "day 28", "day 28", "day 28", "day 28", "day 28", "day 28",
"day 28", "day 28", "day 00", "day 00", "day 00", "day 00", "day 00",
"day 00", "day 00", "day 00", "day 07", "day 07", "day 07", "day 07",
"day 07", "day 07", "day 07", "day 07", "day 14", "day 14", "day 14",
"day 14", "day 14", "day 14", "day 14", "day 14", "day 21", "day 21",
"day 21", "day 21", "day 21", "day 21", "day 21", "day 21", "day 28",
"day 28", "day 28", "day 28", "day 28", "day 28", "day 28", "day 28",
"day 00", "day 00", "day 00", "day 00", "day 00", "day 00", "day 00",
"day 00", "day 07", "day 07", "day 07", "day 07", "day 07", "day 07",
"day 07", "day 07", "day 14", "day 14", "day 14", "day 14", "day 14",
"day 14", "day 14", "day 14", "day 21", "day 21", "day 21", "day 21",
"day 21", "day 21", "day 21", "day 21", "day 28", "day 28", "day 28",
"day 28", "day 28", "day 28", "day 28", "day 28", "day 00", "day 00",
"day 00", "day 00", "day 00", "day 00", "day 00", "day 00", "day 07",
"day 07", "day 07", "day 07", "day 07", "day 07", "day 07", "day 07",
"day 14", "day 14", "day 14", "day 14", "day 14", "day 14", "day 14",
"day 14", "day 21", "day 21", "day 21", "day 21", "day 21", "day 21",
"day 21", "day 21", "day 28", "day 28", "day 28", "day 28", "day 28",
"day 28", "day 28", "day 28"), type = c("control", "control",
"control", "control", "control", "control", "control", "control",
"control", "control", "control", "control", "control", "control",
"control", "control", "control", "control", "control", "control",
"control", "control", "control", "control", "control", "control",
"control", "control", "control", "control", "control", "control",
"control", "control", "control", "control", "control", "control",
"control", "control", "nZn1_", "nZn1_", "nZn1_", "nZn1_", "nZn1_",
"nZn1_", "nZn1_", "nZn1_", "nZn1_", "nZn1_", "nZn1_", "nZn1_",
"nZn1_", "nZn1_", "nZn1_", "nZn1_", "nZn1_", "nZn1_", "nZn1_",
"nZn1_", "nZn1_", "nZn1_", "nZn1_", "nZn1_", "nZn1_", "nZn1_",
"nZn1_", "nZn1_", "nZn1_", "nZn1_", "nZn1_", "nZn1_", "nZn1_",
"nZn1_", "nZn1_", "nZn1_", "nZn1_", "nZn1_", "nZn1_", "nZn1_",
"nZn10_", "nZn10_", "nZn10_", "nZn10_", "nZn10_", "nZn10_", "nZn10_",
"nZn10_", "nZn10_", "nZn10_", "nZn10_", "nZn10_", "nZn10_", "nZn10_",
"nZn10_", "nZn10_", "nZn10_", "nZn10_", "nZn10_", "nZn10_", "nZn10_",
"nZn10_", "nZn10_", "nZn10_", "nZn10_", "nZn10_", "nZn10_", "nZn10_",
"nZn10_", "nZn10_", "nZn10_", "nZn10_", "nZn10_", "nZn10_", "nZn10_",
"nZn10_", "nZn10_", "nZn10_", "nZn10_", "nZn10_", "nZn100_",
"nZn100_", "nZn100_", "nZn100_", "nZn100_", "nZn100_", "nZn100_",
"nZn100_", "nZn100_", "nZn100_", "nZn100_", "nZn100_", "nZn100_",
"nZn100_", "nZn100_", "nZn100_", "nZn100_", "nZn100_", "nZn100_",
"nZn100_", "nZn100_", "nZn100_", "nZn100_", "nZn100_", "nZn100_",
"nZn100_", "nZn100_", "nZn100_", "nZn100_", "nZn100_", "nZn100_",
"nZn100_", "nZn100_", "nZn100_", "nZn100_", "nZn100_", "nZn100_",
"nZn100_", "nZn100_", "nZn100_", "Zn1_", "Zn1_", "Zn1_", "Zn1_",
"Zn1_", "Zn1_", "Zn1_", "Zn1_", "Zn1_", "Zn1_", "Zn1_", "Zn1_",
"Zn1_", "Zn1_", "Zn1_", "Zn1_", "Zn1_", "Zn1_", "Zn1_", "Zn1_",
"Zn1_", "Zn1_", "Zn1_", "Zn1_", "Zn1_", "Zn1_", "Zn1_", "Zn1_",
"Zn1_", "Zn1_", "Zn1_", "Zn1_", "Zn1_", "Zn1_", "Zn1_", "Zn1_",
"Zn1_", "Zn1_", "Zn1_", "Zn1_", "Zn10_", "Zn10_", "Zn10_", "Zn10_",
"Zn10_", "Zn10_", "Zn10_", "Zn10_", "Zn10_", "Zn10_", "Zn10_",
"Zn10_", "Zn10_", "Zn10_", "Zn10_", "Zn10_", "Zn10_", "Zn10_",
"Zn10_", "Zn10_", "Zn10_", "Zn10_", "Zn10_", "Zn10_", "Zn10_",
"Zn10_", "Zn10_", "Zn10_", "Zn10_", "Zn10_", "Zn10_", "Zn10_",
"Zn10_", "Zn10_", "Zn10_", "Zn10_", "Zn10_", "Zn10_", "Zn10_",
"Zn10_", "Zn100_", "Zn100_", "Zn100_", "Zn100_", "Zn100_", "Zn100_",
"Zn100_", "Zn100_", "Zn100_", "Zn100_", "Zn100_", "Zn100_", "Zn100_",
"Zn100_", "Zn100_", "Zn100_", "Zn100_", "Zn100_", "Zn100_", "Zn100_",
"Zn100_", "Zn100_", "Zn100_", "Zn100_", "Zn100_", "Zn100_", "Zn100_",
"Zn100_", "Zn100_", "Zn100_", "Zn100_", "Zn100_", "Zn100_", "Zn100_",
"Zn100_", "Zn100_", "Zn100_", "Zn100_", "Zn100_", "Zn100_"),
TAC = c(0.0134723395589115, 0.0161888871061509, 0.0146337654145718,
0.0153067871292595, 0.012800314735395, 0.0160841665978896,
0.0140621616691814, 0.0135425580967982, 0.0132198270328205,
0.0138496077219653, 0.0135775493518084, 0.0126333962864469,
0.0164821881641534, 0.0132516331108305, 0.0157791571175251,
0.0129960024291699, 0.0146323678504021, 0.0134451215151322,
0.0143262838325461, 0.0153573779185249, 0.0139773746147923,
0.0159350865128266, 0.0156720782857077, 0.0155096081292032,
0.013476349735956, 0.0140104181996115, 0.0129878390010014,
0.0147239859165112, 0.015160930718777, 0.0148955399340424,
0.013274378116328, 0.0153663044374496, 0.0145472559523844,
0.0132898660703847, 0.0139871399975842, 0.0124985111701027,
0.0149240276338179, 0.0129573902698069, 0.0147729343794709,
0.0128674264777598, 0.0147815872982594, 0.0139767796824041,
0.0144185398405766, 0.0155799146991459, 0.0135417909851351,
0.015988596586438, 0.0139603963976125, 0.0126397298299191,
0.013297964384596, 0.012347536157165, 0.0152573470818857,
0.0136566619097667, 0.0125192707022401, 0.0141156296691061,
0.0139603724286662, 0.0141388938152221, 0.0127749097766803,
0.0142082519110294, 0.0149398326676766, 0.0143207529313558,
0.0144381103787128, 0.0149147414885484, 0.0139224295866318,
0.0161358891403436, 0.0151690152511571, 0.0120945286936824,
0.0153132383654698, 0.0131770823852777, 0.0136750345235747,
0.0129352436377984, 0.0162120454010317, 0.0155409171425954,
0.0135940425474181, 0.0142951343511937, 0.0143779323175896,
0.0136891451722703, 0.0140286347004686, 0.0122667606250391,
0.0152446224172418, 0.013442306549535, 0.0129068996979612,
0.0147404146947943, 0.013688825582269, 0.0130193063055386,
0.01285971255513, 0.0151660181611206, 0.0138280467330508,
0.0135147736966651, 0.0158580706409006, 0.0149366602534351,
0.0106554950909403, 0.0179654260106192, 0.0120425346368713,
0.0145387164119486, 0.0139546280207597, 0.0121871897075845,
0.0150418870034593, 0.0148117380734173, 0.0139690179111281,
0.0170751257982307, 0.0129661477952429, 0.0144612227917873,
0.0146065893466387, 0.0126241343210384, 0.0170751257982307,
0.0130964557093226, 0.0134570968344701, 0.0165480203562944,
0.0151921149184481, 0.0130666062376204, 0.012722050697886,
0.0155582048904096, 0.0125288074742436, 0.016985639190516,
0.0176528351294189, 0.0138432089287227, 0.013890319218671,
0.017035215335001, 0.0168839977227436, 0.0133203267470888,
0.013892777179513, 0.0155216139064973, 0.0130076218759369,
0.013903958340264, 0.0135000204009635, 0.0148519977852621,
0.0153029154169557, 0.0141832966293512, 0.0176005510379328,
0.0180687740940438, 0.0177789446952697, 0.0182099087520794,
0.0184723827329167, 0.022483746075728, 0.0196648164641345,
0.0170131886149416, 0.0215058343136062, 0.0211259597744559,
0.0196373761289472, 0.0206737739206, 0.020532594441278, 0.0193494766153245,
0.0211617300063814, 0.0213333413267872, 0.0202163436360403,
0.0236752367085596, 0.0231873026647459, 0.0228522660496144,
0.0238366734630018, 0.0264524093818515, 0.0268093919646026,
0.0252668406573153, 0.0258403852690662, 0.0223986018317785,
0.0272147558779617, 0.0225116847733454, 0.0247724813762193,
0.022691182948792, 0.0235805783268122, 0.0270689051186104,
0.0126334908832258, 0.0164665820507107, 0.0129386884401034,
0.0119158011756844, 0.0130928729787235, 0.0149940706645974,
0.0129535502638655, 0.0162831996423606, 0.0176755444192191,
0.0161755659998132, 0.0174173101524856, 0.0155714069341957,
0.01433383826834, 0.0143819293817603, 0.0185494616259894,
0.0140319779691521, 0.0144114680062016, 0.0174497227904159,
0.0180907703704672, 0.0157478259355293, 0.0158958906812569,
0.0147163839619763, 0.0146701443994308, 0.0180369287296324,
0.0149336258279806, 0.0186097801562105, 0.0137231521985133,
0.0153650910635747, 0.0138998273293687, 0.0155199902217533,
0.0163903022171882, 0.015754928008943, 0.0171808546793322,
0.0154244829039175, 0.0134954450270778, 0.0147187179502944,
0.0160939056001929, 0.0145497150558122, 0.0154571534643691,
0.015511148172344, 0.0132885919777709, 0.0138910418368534,
0.0152496449072613, 0.0132820365830201, 0.013480084079182,
0.016683045565325, 0.0176337406920335, 0.0151657804062655,
0.0125455114843902, 0.0118102856445592, 0.0116410665300014,
0.0146556231989517, 0.014464999427952, 0.0121229802720933,
0.0146834533301593, 0.0121645122630423, 0.0136816673389857,
0.0135984961089614, 0.0164906141382343, 0.0149265724276527,
0.0163311308492402, 0.017967595623527, 0.0143263172313383,
0.0145117513172078, 0.0149694356038913, 0.0136478358101476,
0.0148523043836901, 0.0140267859486034, 0.0136857372651645,
0.0161384954212, 0.0171836598216303, 0.0165288287203719,
0.0163703032374203, 0.0149628937118673, 0.0167639896711626,
0.0144140290861155, 0.0164700832677882, 0.017097353142466,
0.0177233791174971, 0.016410406871025, 0.0145656397252108,
0.0127795571441824, 0.0139787766512734, 0.0145603577832239,
0.0130325210010334, 0.0157142193796273, 0.0165295708322065,
0.0154878492755022, 0.0176888974165639, 0.0186435561581489,
0.0177330425080685, 0.0182856446463086, 0.0219973970170363,
0.0217533371623466, 0.0176290655250839, 0.0202192044566584,
0.01917805317661, 0.0186277616395779, 0.0170154664932417,
0.0195884686724334, 0.0201420675026667, 0.0183148068985733,
0.020836323932372, 0.0207067552945439, 0.018534989031893,
0.019680916901509, 0.0219673944081694, 0.0236890701508884,
0.0235543150426157, 0.0234233849979097, 0.0210565415662947,
0.0232511101944444, 0.0227186732866978, 0.0225332903957415,
0.0234773944195847, 0.0229988542468931, 0.022618525386521,
0.0197686090869307, 0.0186686467858637, 0.0189525178016395
), conditions = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L,
24L, 24L, 24L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 26L,
26L, 26L, 26L, 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L, 27L,
27L, 27L, 27L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 28L, 29L,
29L, 29L, 29L, 29L, 29L, 29L, 29L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 31L, 32L,
32L, 32L, 32L, 32L, 32L, 32L, 32L, 33L, 33L, 33L, 33L, 33L,
33L, 33L, 33L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 35L,
35L, 35L, 35L, 35L, 35L, 35L, 35L), levels = c("controlday 00",
"controlday 07", "controlday 14", "controlday 21", "controlday 28",
"nZn1_day 00", "nZn1_day 07", "nZn1_day 14", "nZn1_day 21",
"nZn1_day 28", "nZn10_day 00", "nZn10_day 07", "nZn10_day 14",
"nZn10_day 21", "nZn10_day 28", "nZn100_day 00", "nZn100_day 07",
"nZn100_day 14", "nZn100_day 21", "nZn100_day 28", "Zn1_day 00",
"Zn1_day 07", "Zn1_day 14", "Zn1_day 21", "Zn1_day 28", "Zn10_day 00",
"Zn10_day 07", "Zn10_day 14", "Zn10_day 21", "Zn10_day 28",
"Zn100_day 00", "Zn100_day 07", "Zn100_day 14", "Zn100_day 21",
"Zn100_day 28"), class = "factor")), class = "data.frame", row.names = c(NA,
-280L))
> str(table5)
'data.frame': 280 obs. of 4 variables:
$ day : chr "day 00" "day 00" "day 00" "day 00" ...
$ type : chr "control" "control" "control" "control" ...
$ TAC : num 0.0135 0.0162 0.0146 0.0153 0.0128 ...
$ conditions: Factor w/ 35 levels "controlday 00",..: 1 1 1 1 1 1 1 1 2 2 ...
and my ggplot plus dunn codes:
library(plotrix)
library(ggplot2)
aa <- aggregate(CAT ~ day + type, data=table5, FUN=mean)
bb <- aggregate(CAT ~ day + type, data=table5, FUN=sd)
ee <- aggregate(CAT ~ day + type, data=table5, FUN=std.error)
cc <- merge(aa, ee, by=c("day", "type"))
colnames(cc)[3:4] <- c("mean", "se")
ggplot(cc, aes(x = type, y = mean, fill = day))+
geom_bar(stat="identity", position= "dodge") +
scale_fill_brewer(palette="Paired")+
theme_minimal() +
labs(x="", y="ratio", title = "CAT") +
theme(panel.background = element_blank(),
axis.line = element_line(colour = "black"),
panel.grid=element_blank(),
plot.title = element_text(size=40, hjust = 0),
legend.text = element_text(size=30),
legend.title = element_text(size=32),
axis.title.x = element_text(size = 36),
axis.text.x = element_text(size = 34),
axis.title.y = element_text(size = 36),
axis.text.y = element_text(size = 28)
) +
geom_errorbar(aes(ymin = mean-se,
ymax = mean+se),
position = "dodge")
library(dunn.test)
dunn.test(table5$CAT, table5$conditions, method = "bh")
All my thanks for any help provided.
I am trying to run through an old coworkers script and I am hoping someone can help inform me of what exactly he did during this code segment. Earlier in the script we calculated the intake rate for several prey species and now it appears that we are grouping them based on unique locations. The section of code after this requires that there be 41 rows ( 1 row for each unique location in the complete dataset). I believe that the code subsets the data based on latitude and then adds an 'alpha' column. The main issue that I am having is what is this line calculating: x= x + d$Intakerate_kjday[j]*d$alpha[j]. For locations that had several prey items (profit.fall.38.4.959) is this code summing up "intakerate_kjday" and "alpha" and then multiplying them together? When the code is performed I receive the error Error in
`[<-.data.frame`(`*tmp*`, k, , value = c("2", "Bishop's Head", : replacement has 6 items, need 7
I would really appreciate any insight into what he was trying to calculate and a potential work around. Thank you.
dput(profit)
structure(list(Sample.ID = structure(c(5L, 19L, 27L, 28L, 30L,
38L, 12L, 62L, 49L, 29L, 25L, 17L, 61L, 67L, 27L, 26L, 32L, 9L,
47L, 45L, 5L, 26L, 27L, 44L, 45L, 4L, 1L, 43L, 19L, 35L), .Label = c("Barren Island Mud 1",
"BH High 1", "BH High 2", "BH Low 1", "BH Low 2", "BH Low 3",
"BHH 1 C", "BHH 2 E", "BHL 1 E", "BHL 2", "BHL 3 (B)", "BHM 1 C",
"BI High 1", "BI Low 1", "BI Low 2C", "BI Low 3", "BI Mud", "BIHI High B",
"BIL1 (low) E", "BIL1E", "BIL2 E", "BIL2E", "BW Fresh 1", "BW Fresh 2",
"BW High 1", "BW High 2", "BW High 5", "BW Low 3", "BW Money Stump",
"BW Mud 1", "BW SAV 1", "BW SAV 2", "BWH 1 D", "BWH 2", "BWH 3",
"BWH 5", "BWL 1", "BWL 2", "BWL 3", "BWM 1", "BWMS D", "EN High 2",
"EN High 4", "EN High 5", "EN Low 1", "EN Low 2", "EN Mud 2",
"ENH3 A High", "ENH4 A High", "ENH5 A High", "ENL1 Low E", "ENM1 A Mud",
"ENS1 SAV", "ENS2 SAV 2C", "ENS3 SAV 3E", "High 3C", "MWP 29 Low 1",
"MWP 30 Mud 1", "MWP 31 Low 2", "MWP 32 Mud 2", "MWP 33 Low 3",
"MWP 34 Low 4", "PWRC Fresh", "WP 27 HM-MARC", "WP 28 HM-MARC",
"WP 30 IT MARE", "WP29 LM-MARC"), class = "factor"), Season = structure(c(2L,
3L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L,
3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 3L, 3L), .Label = c("",
"Fall", "Spring", "Spring?"), class = "factor"), Refuge = structure(c(3L,
2L, 5L, 5L, 5L, 5L, 4L, 7L, 6L, 5L, 5L, 2L, 7L, 7L, 5L, 5L, 5L,
4L, 6L, 6L, 3L, 5L, 5L, 6L, 6L, 3L, 2L, 6L, 2L, 5L), .Label = c("",
"Barren Island", "Bishop's Head", "Bishops Head", "Blackwater",
"Eastern Neck", "Martin", "PWRC"), class = "factor"), Habitat.Type = structure(c(3L,
3L, 2L, 3L, 4L, 3L, 4L, 3L, 2L, 3L, 2L, 4L, 3L, 3L, 2L, 2L, 5L,
3L, 4L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 4L, 2L, 3L, 2L), .Label = c("Fresh",
"High", "Low", "Mud", "SAV"), class = "factor"), Longitude = c(-76.03896,
-76.26205, -76.05714, -76.06332, -76.14641, -76.23522, -76.03869,
-75.99733, -76.21661, -76.23491, -76.22003, -76.26163, -75.99354,
-76.01407, -76.05714, -76.01762, -76.10363, -76.04883, -76.21547,
-76.23986, -76.03896, -76.01762, -76.05714, -76.2181, -76.23986,
-76.04883, -76.26163, -76.21661, -76.26205, -76.0235), Latitude = c(38.22447,
38.33905, 38.40959, 38.39708, 38.41795, 38.43055, 38.23255, 37.99369,
39.03264, 38.43141, 38.41026, 38.33606, 37.98833, 38.01108, 38.40959,
38.41913, 38.40351, 38.22694, 39.04036, 39.02677, 38.22447, 38.41913,
38.40959, 39.03887, 39.02677, 38.22694, 38.33606, 39.03264, 38.33905,
38.39138), Prey = structure(c(11L, 41L, 35L, 30L, 41L, 41L, 41L,
3L, 18L, 31L, 40L, 9L, 41L, 38L, 30L, 13L, 35L, 41L, 20L, 27L,
4L, 40L, 13L, 35L, 41L, 5L, 5L, 15L, 22L, 20L), .Label = c("Hydrobia",
"Hydrobia genus", "Hydrobia sp.", "Hydrobia spp", "Melampus bidentatus",
"Ruppia (maritima or rostellata)", "Ruppia genus", "Ruppia maritima",
"Schoenoplectus pungens", "Schoenoplectus robustus", "Schoenoplectus spp",
"Schoenoplectus spp.", "Scirpus acutus", "Scirpus acutus?", "Scirpus americanus",
"Scirpus fluviatilis", "Scirpus genus", "Scirpus genus 1", "Scirpus genus 1?",
"Scirpus genus 2", "Scirpus genus 3", "Scirpus genus?", "Scirpus heterochaetus",
"Scirpus meterochaetus", "Scirpus mevadensis", "Scirpus olney?",
"Scirpus olneyi", "Scirpus paludosis", "Scirpus paludosus", "Scirpus robustus",
"Scirpus robustus?", "Scirpus species", "Scirpus subterminalis",
"Scirpus subtermiralis", "Scirpus validus", "Spartina alterniflora",
"Spartina genus", "Spartina genus?", "Spartina patens", "Spartina pectinata",
"Zannichallia palustris"), class = "factor"), Density = c(2.36e-05,
0.000101477, 0.000335244, 1.17e-05, 1.91e-06, 2.8e-06, 1.72e-05,
1.34e-05, 2.71e-05, 0.000107843, 2.16e-06, 4.46e-06, 1.22e-05,
6.61e-05, 0.000263052, 3.91e-05, 0.00034925, 3.69e-06, 8.02e-06,
2.04e-05, 2.9e-05, 2.05e-05, 0.000564046, 0.001912535, 2.04e-05,
0.001117905, 0.00255132, 9.03e-05, 4.23e-05, 0.000248282), Intakerate_kcals = c(-3.5399430250046e-07,
7.6382794280604e-14, -5.02872205332896e-06, -1.7549698484651e-07,
2.70599529637464e-17, 5.81535679492809e-17, 2.19440708445348e-15,
4.34155540862746e-08, -4.06493587341127e-07, -1.61763139817e-06,
-3.23994151550826e-08, -6.68988064422799e-08, 1.10402768540446e-15,
-9.91487886840506e-07, -3.94580269988612e-06, -5.8649138992111e-07,
-5.23882134070119e-06, 1.00998060784975e-16, -1.2029789281118e-07,
-3.05994985702607e-07, 9.3958523768985e-08, -3.07494963928282e-07,
-8.46097103856411e-06, -2.86925082960488e-05, 3.08688633134856e-15,
3.62058033172122e-06, 8.25888178764606e-06, -1.35448644277712e-06,
-6.34490870510011e-07, -3.72424640639279e-06), Intakerate_kjs = c(-1.48111216166192e-06,
3.19585611270047e-13, -2.10401730711284e-05, -7.34279384597799e-07,
1.13218843200315e-16, 2.43314528299791e-16, 9.18139924135334e-15,
1.81650678296973e-07, -1.70076916943527e-06, -6.76816976994329e-06,
-1.35559153008866e-07, -2.79904606154499e-07, 4.61925183573226e-15,
-4.14838531854068e-06, -1.65092384963235e-05, -2.45387997542992e-06,
-2.19192284894938e-05, 4.22575886324335e-16, -5.03326383521979e-07,
-1.28028302017971e-06, 3.93122463449433e-07, -1.28655892907593e-06,
-3.54007028253523e-05, -0.000120049454710668, 1.29155324103624e-14,
1.51485081079216e-05, 3.45551613995111e-05, -5.66717127657947e-06,
-2.65470980221389e-06, -1.55822469643474e-05), Intakerate_kjday = c(-0.12796809076759,
2.76121968137321e-08, -1.81787095334549, -0.0634417388292498,
9.78210805250721e-12, 2.1022375245102e-11, 7.93272894452929e-10,
0.0156946186048585, -0.146946456239208, -0.584769868123101, -0.011712310819966,
-0.0241837579717487, 3.99103358607267e-10, -0.358420491521915,
-1.42639820608235, -0.212015229877145, -1.89382134149226, 3.65105565784226e-11,
-0.043487399536299, -0.110616452943527, 0.033965780842031, -0.111158691472161,
-3.05862072411043, -10.3722728870017, 1.11590200025531e-09, 1.30883110052443,
2.98556594491776, -0.489643598296466, -0.22936692691128, -1.34630613771962
)), row.names = c(NA, -30L), class = "data.frame")
lat=unique(profit$Latitude)
## for each location I am calculating the weight for Fall only
nfall=0
latfall<-c(double())
for(i in lat){
name = paste0("profit.fall.",round(i,5))
x = subset(profit,Latitude==i & Season=="Fall")
if(nrow(x)>=1){
for(j in 1:nrow(x)){
x$alpha[j]<- 1 # used to be this x$Density[j]/sum(x$Density)
}
nfall= nfall+1
assign(name, data.frame(x))
latfall<-c(latfall,round(i,5))
print(name)
}
}
View(profit.fall.38.4.959)
profit.fall.all <- data.frame(matrix(ncol=7,nrow=nfall))
names(profit.fall.all)[1]<-'Id'
names(profit.fall.all)[2]<-'Refuge'
names(profit.fall.all)[3]<-'Season'
names(profit.fall.all)[4]<-'HType'
names(profit.fall.all)[5]<-'Lat'
names(profit.fall.all)[6]<-'Long'
names(profit.fall.all)[7]<-'IntakeEnergy'
View(profit.fall.all)
k=0
lat=latfall
for(i in lat){
df=as.name(paste0('profit.fall.',i))
d=get(as.character(df))
x=0
for(j in 1:nrow(d)){
x= x + d$Intakerate_kjday[j]*d$alpha[j]
}
k=k+1
new_row <- c(k,as.character(d$Refuge[1]),as.character(d$Season[1]),as.character(d$Habitat.Type[1]),as.numeric(d$Latitude[1]),as.numeric(d$Longtitude[1]),as.numeric(x))
#names(new_row)<-c("id","Refuge","Season","HType","Lat","Long","Intakerate_kjday")
#profit.spring.all <- rbind(profit.spring.all, new_row)
profit.fall.all[k,] <- new_row
}
View(profit.fall.all)
The code in question apparently computes (very inefficiently and inaccurately)
sum(d$Intakerate_kjday * d$alpha)
Your error however suggests, that a column is missing in one of the data frames.
Take a look at new_row here:
for(i in lat){
df=as.name(paste0('profit.fall.',i))
d=get(as.character(df))
x=0
for(j in 1:nrow(d)){
x= x + d$Intakerate_kjday[j]*d$alpha[j]
}
k=k+1
new_row <- c(k,as.character(d$Refuge[1]),as.character(d$Season[1]),as.character(d$Habitat.Type[1]),as.numeric(d$Latitude[1]),as.numeric(d$Longtitude[1]),as.numeric(x))
#names(new_row)<-c("id","Refuge","Season","HType","Lat","Long","Intakerate_kjday")
#profit.spring.all <- rbind(profit.spring.all, new_row)
if (length(new_row) != ncol(profit.fall.all)) {
# Catch the bad df
browser()
}
profit.fall.all[k,] <- new_row
}
I have a huge dataset and I need to plot in facets with one nested facets in x axis(alpha and de id of replicate) and in y axis (beta)
fen_ale_all_rep$alpha <- factor(
fen_ale_all_rep$alpha,
levels = c("alpha 0.1", "alpha 0.2", "alpha 0.4", "alpha 0.8", "alpha 1.6"),
labels = c(
"alpha '0.1'",
"alpha '0.2'",
"alpha '0.4'",
"alpha '0.8'",
"alpha '1.6'"
)
)
fen_ale_rep5$beta <- factor(
fen_ale_rep5$beta,
levels = c(
"beta 0.05",
"beta 0.1",
"beta 0.2",
"beta 0.4",
"beta 0.8",
"beta 1.6",
"beta 3.2",
"beta 6.4",
"beta 12.8",
"beta 25.6",
"beta 51.2",
"beta 102.4"
),
labels = c(
"beta 0.05",
"beta 0.1",
"beta 0.2",
"beta 0.4",
"beta 0.8",
"beta 1.6",
"beta 3.2",
"beta 6.4",
"beta 12.8",
"beta 25.6",
"beta 51.2",
"beta 102.4"
)
)
structure:
tbl <- structure(list(FEN = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), .Label = c("Plants", "Insects"), class = "factor"),
NSEL = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1), rep = c("rep 1", "rep 1", "rep 1", "rep 1",
"rep 1", "rep 1", "rep 1", "rep 1", "rep 1", "rep 1", "rep 1",
"rep 1", "rep 1", "rep 1", "rep 1", "rep 1", "rep 1", "rep 1",
"rep 1", "rep 1", "rep 1", "rep 1", "rep 1", "rep 1", "rep 1",
"rep 1", "rep 1", "rep 1", "rep 1", "rep 1", "rep 1", "rep 1",
"rep 1", "rep 1", "rep 1", "rep 1", "rep 1", "rep 1", "rep 1",
"rep 1", "rep 1", "rep 1", "rep 1", "rep 1", "rep 1", "rep 1",
"rep 1", "rep 1", "rep 1", "rep 1", "rep 1", "rep 1", "rep 1",
"rep 1", "rep 1", "rep 1", "rep 1", "rep 1", "rep 1", "rep 1"
), alpha = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L), .Label = c("alpha 0.1", "alpha 0.2",
"alpha 0.4", "alpha 0.8", "alpha 1.6"), class = "factor"),
beta = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L), .Label = c("beta 0.05", "beta 0.1",
"beta 0.2", "beta 0.4", "beta 0.8", "beta 1.6", "beta 3.2",
"beta 6.4", "beta 12.8", "beta 25.6", "beta 51.2", "beta 102.4"
), class = "factor"), TEMPO = c(1L, 200L, 400L, 600L, 800L,
1000L, 1200L, 1400L, 1600L, 1800L, 2000L, 2200L, 2400L, 2600L,
2800L, 3000L, 3200L, 3400L, 3600L, 3800L, 4000L, 4200L, 4400L,
4600L, 4800L, 5000L, 5200L, 5400L, 5600L, 5800L, 6000L, 6200L,
6400L, 6600L, 6800L, 7000L, 7200L, 7400L, 7600L, 7800L, 8000L,
8200L, 8400L, 8600L, 8800L, 9000L, 9200L, 9400L, 9600L, 9800L,
10000L, 1L, 200L, 400L, 600L, 800L, 1000L, 1200L, 1400L,
1600L), ab = c("0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05",
"0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05",
"0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05",
"0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05",
"0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05",
"0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05",
"0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05",
"0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05",
"0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05",
"0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05", "0.1 0.05",
"0.1 0.05", "0.1 0.05", "0.1 0.1", "0.1 0.1", "0.1 0.1",
"0.1 0.1", "0.1 0.1", "0.1 0.1", "0.1 0.1", "0.1 0.1", "0.1 0.1"
), especie = c("1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1"), trait = c(0.59532188, 0.63023233, 0.59210289,
0.58660778, 0.55932657, 0.57032034, 0.56379332, 0.61973847,
0.61705717, 0.62310601, 0.61690676, 0.6508817, 0.64581712,
0.5890861, 0.66424559, 0.64007846, 0.52734623, 0.65276626,
0.57025516, 0.59734711, 0.59672837, 0.57971204, 0.51274562,
0.60679023, 0.56463537, 0.64716034, 0.60257023, 0.68582648,
0.62075915, 0.46707366, 0.65330306, 0.59301249, 0.70620201,
0.5260134, 0.51619464, 0.65620395, 0.48348558, 0.62189428,
0.60360017, 0.55274833, 0.56492948, 0.61039593, 0.75225962,
0.54349533, 0.47189362, 0.52454647, 0.56282583, 0.63985652,
0.58380379, 0.59214658, 0.73188778, 0.62808414, 0.59229323,
0.6978987, 0.61346245, 0.49995094, 0.4661158, 0.57436886,
0.58524132, 0.60717574)), row.names = c(NA, 60L), class = "data.frame")
I can produce this plot:
But, it doesn't show the replicate id. and the betas doesn't follow the crescent order.
I would like something like:
or one single facet with replicate and de columns with each alpha value (0.1,0.2,....)
for(i in 1:10) {
grid1 <- print(
ggplot(
fen_ale_all_rep,
aes(
x = TEMPO,
y = trait,
group = interaction(FEN, ab, especie, rep),
colour = FEN
)
) +
geom_line() +
labs(x = "Time",
y = "Mean phenotypes") +
scale_color_viridis(discrete=TRUE,
name = NULL
) +
theme_pubclean() + theme(
axis.text.x = element_text(
size = 15,
angle = 0,
vjust = 0.3
),
axis.text.y = element_text(size = 15),
plot.title = element_text(size = 15),
legend.text = element_text(size = 15),
axis.title.x = element_text(size = 15),
axis.title.y = element_text(size = 15),
strip.text.x = element_text(size = 15),
strip.text.y = element_text(size = 15),
plot.caption = element_text(size=15),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
axis.ticks = element_blank(),
legend.position = "top"
) +
facet_grid_paginate(
beta ~ alpha+rep,
ncol = 5,
nrow = 6,
page = i, labeller = label_bquote(beta == .(beta), alpha == .(alpha)))))
}
How can I achieve that?
thanks in advance! and sorry about my english :)
Running into something strange as I try to use dplyr's select command to reduce the number of columns I have. I name three columns but I keep getting 4. Aside from the star trek chain of command flashbacks I find this behaviour odd and not sure how to get around it. Also, why is this happening?
Here is my dataframe expressing the number of occurrences at in each block of time in a day. It's also pretty verbose for just 6 rows of data.
library(dplyr)
library(tidyr)
test <- structure(list(Day = c("Dec 10", "Dec 10", "Dec 10", "Dec 10",
"Dec 11", "Dec 11"), Number = c(10L, 10L, 10L, 10L, 11L, 11L),
time = c("08:30", "12:00", "15:30", "19:00", "08:30", "12:00"
), Start = structure(c(1544430600, 1544443200, 1544455800,
1544468400, 1544517000, 1544529600), class = c("POSIXct",
"POSIXt"), tzone = "UTC"), n = c(29L, 74L, 20L, 26L, 29L,
32L)), class = c("grouped_df", "tbl_df", "tbl", "data.frame"
), row.names = c(NA, -6L), vars = c("Day", "Number", "time"), drop = TRUE, indices = list(
0L, 1L, 2L, 3L, 4L, 5L), group_sizes = c(1L, 1L, 1L, 1L,
1L, 1L), biggest_group_size = 1L, labels = structure(list(Day = c("Dec 10",
"Dec 10", "Dec 10", "Dec 10", "Dec 11", "Dec 11"), Number = c(10L,
10L, 10L, 10L, 11L, 11L), time = c("08:30", "12:00", "15:30",
"19:00", "08:30", "12:00")), class = "data.frame", row.names = c(NA,
-6L), vars = c("Day", "Number", "time"), drop = TRUE, indices = list(
0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L,
26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L,
38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L,
50L, 51L, 52L), group_sizes = c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), biggest_group_size = 1L, labels = structure(list(
Day = c("Dec 10", "Dec 10", "Dec 10", "Dec 10", "Dec 11",
"Dec 11", "Dec 11", "Dec 11", "Dec 12", "Dec 12", "Dec 12",
"Dec 12", "Dec 13", "Dec 13", "Dec 13", "Dec 13", "Dec 14",
"Dec 14", "Dec 14", "Dec 14", "Dec 15", "Dec 15", "Dec 15",
"Dec 17", "Dec 17", "Dec 17", "Dec 17", "Dec 18", "Dec 18",
"Dec 18", "Dec 18", "Dec 19", "Dec 19", "Dec 19", "Dec 4",
"Dec 4", "Dec 4", "Dec 4", "Dec 5", "Dec 5", "Dec 5", "Dec 5",
"Dec 6", "Dec 6", "Dec 6", "Dec 6", "Dec 7", "Dec 7", "Dec 7",
"Dec 7", "Dec 8", "Dec 8", "Dec 8"), Number = c(10L, 10L,
10L, 10L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 13L, 13L,
13L, 13L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 17L, 17L, 17L,
17L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L),
time = c("08:30", "12:00", "15:30", "19:00", "08:30", "12:00",
"15:30", "19:00", "08:30", "12:00", "15:30", "19:00", "08:30",
"12:00", "15:30", "19:00", "08:30", "12:00", "15:30", "19:00",
"08:30", "12:00", "15:30", "08:30", "12:00", "15:30", "19:00",
"08:30", "12:00", "15:30", "19:00", "08:30", "12:00", "15:30",
"08:30", "12:00", "15:30", "19:00", "08:30", "12:00", "15:30",
"19:00", "08:30", "12:00", "15:30", "19:00", "08:30", "12:00",
"15:30", "19:00", "08:30", "12:00", "15:30")), class = "data.frame", row.names = c(NA,
-53L), vars = c("Day", "Number", "time"), drop = TRUE)))
You can see in the output that there's only 3 variables listed but oddly shows more. And when I select for specific variables or subtract others it won't work.
test %>%
select(Day, time, n)
The tibble should be ungrouped before selecting variables, as described here "Adding missing grouping variables" message in dplyr in R:
Without ungrouping:
test %>%
select(Day, time, n)
> test %>%
+ select(Day, time, n)
Adding missing grouping variables: `Number`
# A tibble: 6 x 4
# Groups: Day, Number, time [6]
Number Day time n
<int> <chr> <chr> <int>
1 10 Dec 10 08:30 29
2 10 Dec 10 12:00 74
3 10 Dec 10 15:30 20
4 10 Dec 10 19:00 26
5 11 Dec 11 08:30 29
6 11 Dec 11 12:00 32
With ungrouping
test %>%
ungroup() %>%
select(Day, time, n)
> test %>%
+ ungroup() %>%
+ select(Day, time, n)
# A tibble: 6 x 3
Day time n
<chr> <chr> <int>
1 Dec 10 08:30 29
2 Dec 10 12:00 74
3 Dec 10 15:30 20
4 Dec 10 19:00 26
5 Dec 11 08:30 29
6 Dec 11 12:00 32
Ungrouping the dataframe fixes it all.
I'm assigning hexadecimal colors which are in a df column to ggplot.
However in the plot it shows the names of the hexadecimal colors but not the right colors, colors don't match the hexadecimal colors.
Dataframe and plot code:
str(Trun)
'data.frame': 1043 obs. of 12 variables:
$ X : int 1 2 3 4 5 6 7 8 9 10 ...
$ DE : num 36.5 37 40.2 36.8 38.8 ...
$ hex : chr NA NA NA NA ...
unique(Trun$hex)
[1] NA "#8A8F8C" "#507085" "#3F7767" "#917652" "#DBAD5D"
ggplot(data=Trun, aes(x=X, y=DE, colour=hex)) + geom_point() + ggtitle("DE with 35* sd values")
Below the data reworked with dput
structure(list(X = 1:50, Sample = structure(c(1L, 12L, 23L, 34L,
45L, 47L, 48L, 49L, 50L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 24L, 25L,
26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 35L, 36L, 37L, 38L, 39L,
40L, 41L, 42L, 43L, 44L, 46L), .Label = c("Sample 1", "Sample 10",
"Sample 11", "Sample 12", "Sample 13", "Sample 14", "Sample 15",
"Sample 16", "Sample 17", "Sample 18", "Sample 19", "Sample 2",
"Sample 20", "Sample 21", "Sample 22", "Sample 23", "Sample 24",
"Sample 25", "Sample 26", "Sample 27", "Sample 28", "Sample 29",
"Sample 3", "Sample 30", "Sample 31", "Sample 32", "Sample 33",
"Sample 34", "Sample 35", "Sample 36", "Sample 37", "Sample 38",
"Sample 39", "Sample 4", "Sample 40", "Sample 41", "Sample 42",
"Sample 43", "Sample 44", "Sample 45", "Sample 46", "Sample 47",
"Sample 48", "Sample 49", "Sample 5", "Sample 50", "Sample 6",
"Sample 7", "Sample 8", "Sample 9"), class = "factor"), Time = structure(1:50, .Label = c("10:51:04 AM",
"10:51:05 AM", "10:51:06 AM", "10:51:07 AM", "10:51:08 AM", "10:51:09 AM",
"10:51:10 AM", "10:51:11 AM", "10:51:12 AM", "10:51:13 AM", "10:51:14 AM",
"10:51:15 AM", "10:51:16 AM", "10:51:17 AM", "10:51:18 AM", "10:51:19 AM",
"10:51:20 AM", "10:51:21 AM", "10:51:22 AM", "10:51:23 AM", "10:51:24 AM",
"10:51:25 AM", "10:51:26 AM", "10:51:27 AM", "10:51:28 AM", "10:51:29 AM",
"10:51:30 AM", "10:51:31 AM", "10:51:32 AM", "10:51:33 AM", "10:51:34 AM",
"10:51:35 AM", "10:51:36 AM", "10:51:37 AM", "10:51:38 AM", "10:51:39 AM",
"10:51:40 AM", "10:51:41 AM", "10:51:42 AM", "10:51:43 AM", "10:51:44 AM",
"10:51:45 AM", "10:51:46 AM", "10:51:47 AM", "10:51:48 AM", "10:51:49 AM",
"10:51:50 AM", "10:51:51 AM", "10:51:52 AM", "10:51:53 AM"), class = "factor"),
L = c(57.61, 57.16, 53.96, 57.3, 55.27, 57.9, 59.05, 55.13,
53.8, 57.59, 52.23, 57.93, 58.59, 56.27, 58.62, 61.25, 56.76,
56.64, 58.49, 53.99, 53.17, 56.77, 57.35, 53.43, 55.19, 54.5,
53.17, 53.88, 55.15, 61.81, 57.03, 55.97, 54.83, 59.53, 54.29,
56.84, 53.53, 55.38, 57.84, 58.32, 54.67, 52.72, 53.94, 55.17,
58.15, 53.55, 58.75, 56.07, 58.46, 60.33), C = c(4.56, 4.17,
5.14, 3.9, 3.63, 3.47, 4.3, 4.95, 5.76, 3.49, 4.7, 4.64,
5.64, 3.76, 2.25, 4.66, 5.96, 4.13, 5.32, 4.45, 4.11, 3.88,
5.47, 4.17, 5.92, 2.71, 5.2, 4.24, 5.78, 5.37, 4.71, 4.39,
3.83, 5.01, 4.62, 5.08, 4.74, 3.62, 3.59, 4.09, 3.32, 4.06,
4.09, 5.16, 3.1, 5.59, 3.06, 3.67, 4.56, 6.75), h = c(219.98,
226.13, 233.39, 221.78, 213.56, 214.16, 230.93, 229.57, 236.17,
230.59, 235.2, 237.58, 240.43, 228.53, 206.55, 234.13, 241.12,
231.82, 227.03, 231.32, 218.68, 230.31, 228.59, 229.36, 235.56,
197.32, 232.49, 228.39, 244.63, 235.78, 231.22, 228.92, 231.17,
244.2, 228.49, 234.01, 227.85, 226.29, 210.04, 232.38, 222.71,
220.69, 226.08, 233.15, 215.76, 236.26, 206.3, 219.79, 232.37,
246.63), L1 = c(57.61, 57.16, 53.96, 57.3, 55.27, 57.9, 59.05,
55.13, 53.8, 57.59, 52.23, 57.93, 58.59, 56.27, 58.62, 61.25,
56.76, 56.64, 58.49, 53.99, 53.17, 56.77, 57.35, 53.43, 55.19,
54.5, 53.17, 53.88, 55.15, 61.81, 57.03, 55.97, 54.83, 59.53,
54.29, 56.84, 53.53, 55.38, 57.84, 58.32, 54.67, 52.72, 53.94,
55.17, 58.15, 53.55, 58.75, 56.07, 58.46, 60.33), a = c(-3.49,
-2.89, -3.06, -2.91, -3.03, -2.87, -2.71, -3.21, -3.21, -2.22,
-2.68, -2.49, -2.79, -2.49, -2.01, -2.73, -2.88, -2.55, -3.63,
-2.78, -3.21, -2.48, -3.62, -2.72, -3.35, -2.58, -3.16, -2.81,
-2.48, -3.02, -2.95, -2.88, -2.4, -2.18, -3.06, -2.99, -3.18,
-2.5, -3.11, -2.5, -2.44, -3.08, -2.84, -3.1, -2.51, -3.1,
-2.74, -2.82, -2.79, -2.68), b = c(-2.93, -3.01, -4.13, -2.6,
-2.01, -1.95, -3.34, -3.77, -4.79, -2.7, -3.86, -3.92, -4.91,
-2.82, -1, -3.78, -5.22, -3.24, -3.89, -3.47, -2.57, -2.99,
-4.11, -3.17, -4.88, -0.81, -4.12, -3.17, -5.22, -4.44, -3.67,
-3.31, -2.99, -4.51, -3.46, -4.11, -3.52, -2.61, -1.8, -3.24,
-2.25, -2.64, -2.94, -4.13, -1.81, -4.65, -1.35, -2.35, -3.61,
-6.2), DE = c(36.52, 36.95, 40.24, 36.78, 38.77, 36.13, 35.08,
39.04, 40.47, 36.47, 41.92, 36.24, 35.72, 37.8, 35.35, 32.94,
37.57, 37.46, 35.74, 40.14, 40.9, 37.31, 36.9, 40.67, 39.11,
39.48, 41.02, 40.22, 39.15, 32.48, 37.13, 38.15, 39.24, 34.72,
39.84, 37.37, 40.61, 38.67, 36.2, 35.79, 39.35, 41.35, 40.14,
39.03, 35.86, 40.7, 35.25, 37.98, 35.69, 34.2), heihgtmm = c(53.1,
67.01, 80.16, 85.3, 86.37, 92.36, 91.12, 90.56, 91.02, 94.25,
96.79, 94.36, 96.26, 95.4, 93, 91.58, 91.92, 89, 81.52, 68.57,
53.54, 49.43, 54.31, 72.51, 81.95, 82.85, 86.77, 85.47, 90.1,
87.95, 90.1, 86.97, 89.42, 89.65, 87.56, 83.48, 76.43, 63.15,
54.92, 64.79, 67.47, 64.77, 61.6, 63.58, 69.27, 79.75, 83.41,
85.56, 88.47, 90.57), hex = structure(c(1L, 1L, 1L, 1L, 3L,
3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 3L, 1L, 1L, 2L
), .Label = c("", "#507085", "#8A8F8C"), class = "factor")), .Names = c("X",
"Sample", "Time", "L", "C", "h", "L1", "a", "b", "DE", "heihgtmm",
"hex"), class = "data.frame", row.names = c(NA, -50L))
I created a new variable containing hex-values as character and added that variable into the aes.
#create a new variable, substituting red for missings and turning factor into string (you can add your own colour)
Trun$hex2 <- ifelse(Trun$hex=="","#CC0000",as.character(Trun$hex))
#plot
ggplot(data=Trun, aes(x=X, y=DE, colour=hex2)) + geom_point(size=3) + #larger vor visibility
ggtitle("DE with 35* sd values") +
scale_colour_identity()