I have a line plot with errorbars in ggplot2 and I would like to add lines and asterisks to show which comparisons were significant. I have done my testing in a different program, so I am looking for something other than "ggsignif" because it wouldn't reflect my significance testing.
This is a minimal example of my code so far:
data = structure(list(group = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("no", "yes"), class = "factor"),
factorA = structure(c(2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L), .Label = c("circle", "square"), class = "factor"),
factorB = structure(c(3L, 1L, 2L, 4L, 3L, 1L, 2L, 4L, 3L, 1L, 2L, 4L, 3L, 1L, 2L, 4L), .Label = c("blue", "green", "red", "yellow"), class = "factor"),
rating = c(0.4, 0.1, 0.3, 0.7, 0.3, 0.15, 0.5, 0.47, 0.56, 0.34, 0.48, 0.74, 0.60, 0.45, 0.37, 0.57),
se = c(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)),
row.names = c(NA, -16L), class = "data.frame")
pd <- position_dodge(0.1)
ggplot(data, aes(x=factorA, y=rating, colour=group, group=group)) +
facet_grid(. ~ factorB) +
geom_errorbar(aes(ymin=rating-se, ymax=rating+se), colour="black", width=.25) +
geom_line() +
geom_point()
And this is kind of what I am looking for:
I am also open to other suggestions how to reflect these significant comparisons:
no versus yes in the blue and the red condition
circle versus square in the yellow condition
Two thoughts:
For specifying comparisons and significance within plots, then stat_compare_means from the ggpubr package is fantastic. Vignette here - either with a ggpubr plot (eg ggline) or can add to any other ggplot object.
# Pairwise comparisons: Specify the comparisons you want
my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )
ggboxplot(ToothGrowth, x = "dose", y = "len",
color = "dose", palette = "npg")+
# Add pairwise comparisons p-value
stat_compare_means(comparisons = my_comparisons, label.y = c(29, 35, 40))+
stat_compare_means(label.y = 45) # Add global Anova p-value
However more broadly, it's a busy plot (with rating v factorA, facet by factorB and grouped by group) and I'm not sure it's clear which comparisons you are making eg. in facet yellow, the p-val is the aggregate difference between circle & square, but the error bars are circle vs square, subset by group yes/no also
Then within blue and red: appears to be comparing yes v no
I am plotting the proportion of deep sleep (y axis) vs days (x axis). I would like to add vertical shaded area for a better understanding (e.g. grey for week-ends, orange for sick period...).
I have tried using geom_ribbon (I created a variable taking the value of 30, with is the top of my y axis if the data is during the WE - information given in another column), but instead of getting rectangles, I get trapezes.
In another post, someone proposed the use of "geom_rect", or "annotate" if one's know the x and y coordinates, but I don't see how to adapt it in my case, when I want to have the colored area repeated to all week-end (it is not exactly every 7 days because some data are missing).
Do you have any idea ?
Many thanks in advance !
ggplot(Sleep.data, aes(x = DATEID)) +
geom_line(aes(y = P.DEEP, group = 1), col = "deepskyblue3") +
geom_point(aes(y = P.DEEP, group = 1, col = Sign.deep)) +
guides(col=FALSE) +
geom_ribbon(aes(ymin = min, ymax = max.WE), fill = '#6495ED80') +
facet_grid(MONTH~.) +
geom_hline(yintercept = 15, col = "forestgreen") +
geom_hline(yintercept = 20, col = "forestgreen", linetype = "dashed") +
geom_vline(xintercept = c(7,14,21,28), col = "grey") +
scale_x_continuous(breaks=seq(0,28,7)) +
scale_y_continuous(breaks=seq(0,30,5)) +
labs(x = "Days",y="Proportion of deep sleep stage", title = "Deep sleep")
Proportion of deep sleep vs time
Head(Sleep.data)
> dput(head(Sleep.data))
structure(list(DATE = structure(c(1L, 4L, 7L, 10L, 13L, 16L), .Label = c("01-Dec-17",
"01-Feb-18", "01-Jan-18", "02-Dec-17", "02-Feb-18", "02-Jan-18",
"03-Dec-17", "03-Feb-18", "03-Jan-18", "04-Dec-17", "04-Feb-18",
"04-Jan-18", "05-Dec-17", "05-Feb-18", "05-Jan-18", "06-Dec-17",
"06-Feb-18", "06-Jan-18", "07-Dec-17", "07-Feb-18", "07-Jan-18",
"08-Dec-17", "08-Jan-18", "09-Dec-17", "09-Feb-18", "09-Jan-18",
"10-Dec-17", "10-Jan-18", "11-Dec-17", "11-Feb-18", "11-Jan-18",
"12-Dec-17", "12-Jan-18", "13-Dec-17", "13-Feb-18", "13-Jan-18",
"14-Dec-17", "14-Feb-18", "14-Jan-18", "15-Dec-17", "15-Jan-18",
"16-Dec-17", "16-Jan-18", "17-Dec-17", "17-Jan-18", "18-Dec-17",
"18-Jan-18", "19-Dec-17", "19-Jan-18", "20-Dec-17", "21-Dec-17",
"21-Jan-18", "22-Dec-17", "22-Jan-18", "23-Dec-17", "23-Jan-18",
"24-Dec-17", "24-Jan-18", "25-Dec-17", "25-Jan-18", "26-Dec-17",
"26-Jan-18", "27-Dec-17", "27-Jan-18", "28-Dec-17", "28-Jan-18",
"29-Dec-17", "29-Jan-18", "30-Dec-17", "30-Jan-18", "31-Dec-17",
"31-Jan-18"), class = "factor"), DATEID = 1:6, MONTH = structure(c(1L,
1L, 1L, 1L, 1L, 1L), .Label = c("Decembre", "Janvier", "Février"
), class = "factor"), DURATION = c(8.08, 7.43, 6.85, 6.23, 7.27,
6.62), D.DEEP = c(1.67, 1.37, 1.62, 1.75, 1.95, 0.9), P.DEEP = c(17L,
17L, 21L, 24L, 25L, 12L), STIMS = c(0L, 0L, 0L, 0L, 390L, 147L
), D.REM = c(1.7, 0.95, 0.95, 1.43, 1.47, 0.72), P.REM = c(17L,
11L, 12L, 20L, 19L, 9L), D.LIGHT = c(4.7, 5.12, 4.27, 3.05, 3.83,
4.98), P.LIGHT = c(49L, 63L, 55L, 43L, 49L, 66L), D.AWAKE = c(1.45,
0.58, 0.47, 0.87, 0.37, 0.85), P.AWAKE = c(15L, 7L, 6L, 12L,
4L, 11L), WAKE.UP = c(-2L, 0L, 2L, -1L, 3L, 1L), AGITATION = c(-1L,
-3L, -1L, -2L, 2L, -1L), FRAGMENTATION = c(1L, -2L, 2L, 1L, 0L,
-1L), PERIOD = structure(c(3L, 3L, 4L, 4L, 4L, 4L), .Label = c("HOLIDAYS",
"SICK", "WE", "WORK"), class = "factor"), SPORT = structure(c(2L,
1L, 2L, 2L, 2L, 1L), .Label = c("", "Day", "Evening"), class = "factor"),
ACTIVITY = structure(c(6L, 1L, 3L, 4L, 5L, 1L), .Label = c("",
"Bkool", "eBike", "Gym", "Natation", "Run"), class = "factor"),
TABLETS = c(0.5, 0.5, 0.5, 0.5, 0.5, 0.5), Ratio = c(1.15,
2.36, 3.45, 2.01, 5.27, 1.06), Sign = structure(c(2L, 2L,
2L, 2L, 2L, 2L), .Label = c("0", "1"), class = "factor"),
Sign.ratio = structure(c(2L, 2L, 2L, 2L, 2L, 2L), .Label = c("0",
"1"), class = "factor"), Sign.deep = structure(c(2L, 2L,
2L, 2L, 2L, 1L), .Label = c("0", "1"), class = "factor"),
Sign.awake = structure(c(1L, 2L, 2L, 1L, 2L, 1L), .Label = c("0",
"1"), class = "factor"), Sign.light = structure(c(2L, 1L,
1L, 2L, 2L, 1L), .Label = c("0", "1"), class = "factor"),
index = structure(c(1L, 1L, 1L, 1L, 2L, 1L), .Label = c("0",
"1"), class = "factor"), min = c(0, 0, 0, 0, 0, 0), max.WE = c(30,
30, 0, 0, 0, 0)), .Names = c("DATE", "DATEID", "MONTH", "DURATION",
"D.DEEP", "P.DEEP", "STIMS", "D.REM", "P.REM", "D.LIGHT", "P.LIGHT",
"D.AWAKE", "P.AWAKE", "WAKE.UP", "AGITATION", "FRAGMENTATION",
"PERIOD", "SPORT", "ACTIVITY", "TABLETS", "Ratio", "Sign", "Sign.ratio",
"Sign.deep", "Sign.awake", "Sign.light", "index", "min", "max.WE"
), row.names = c(NA, 6L), class = "data.frame")
Thanks for adding the data, that makes it easier to understand exactly what you're working with and to confirm that an answer actually addresses your question.
I thought it would be helpful to make a separate table with just the start and end of each contiguous set of rows with the same PERIOD. I did this using dplyr::case_when, assuming we should mark dates as a "start" if they are the first row in the table (row_number() == 1), or they have a different PERIOD value than the prior row. I mark dates as an "end" if they are the last row of the table, or have a different PERIOD than the next row. I only keep the starts and ends, and spread these into new columns called start and end.
library(tidyverse)
Period_ranges <- Sleep.data %>%
mutate(period_status = case_when(row_number() == 1 ~ "start",
PERIOD != lag(PERIOD) ~ "start",
row_number() == n() ~ "end",
PERIOD != lead(PERIOD) ~ "end",
TRUE ~ "other")) %>%
filter(period_status %in% c("start", "end")) %>%
select(DATEID, PERIOD, period_status) %>%
mutate(PERIOD_NUM = cumsum(PERIOD != lag(PERIOD) | row_number() == 1)) %>%
spread(period_status, DATEID)
# Output based on sample data only. If there's a problem with the full data, please add more. To share full data, use `dput(Sleep.data)` or to share 20 rows use `dput(head(Sleep.data, 20))`.
>Period_ranges
PERIOD PERIOD_NUM end start
1 WE 1 2 1
2 WORK 2 6 3
We can now use that in the plot. If you want to toggle the inclusion or fiddle with the appearance separately of different PERIOD types, you could modify the code below with Period_ranges %>% filter(PERIOD == "WE"),
ggplot(Sleep.data, aes(x = DATEID)) +
# Here I specify that this geom should use its own data.
# I start the rectangles half a day before and end half a day after to fill the space.
geom_rect(data = Period_ranges, inherit.aes = F,
aes(xmin = start - 0.5, xmax = end + 0.5,
ymin = 0, ymax = 30,
fill = PERIOD), alpha = 0.5) +
# Here we can specify the shading color for each type of PERIOD
scale_fill_manual(values = c(
"WE" = '#6495ED80',
"WORK" = "gray60"
)) +
# rest of your code
Chart based on data sample:
I am trying to calculate a CAGR value, defined as (Ending/Beginning)^(1/number of years)-1.
I have a df which has columns "Stock", "date", "Annual.Growth.Rate". To quickly note: I was trying to do this using the lag function, however, I wasn't able to change the recursive formula at the beginning of each stocks. It'll make more sense looking at the dput:
structure(list(Stock = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L), .Label = c("A", "B"), class = "factor"),
date = structure(c(6L, 2L, 3L, 4L, 5L, 1L, 12L, 8L, 9L, 10L,
11L, 7L), .Label = c("3/28/16", "3/29/12", "3/29/13", "3/29/14",
"3/29/15", "3/30/11", "6/28/16", "6/29/12", "6/29/13", "6/29/14",
"6/29/15", "6/30/11"), class = "factor"), Annual.Growth.Rate = c(0.1,
0.2, 0.1, 0.1, 0.1, 0.1, 0.3, 0.2, 0.14, 0.14, 0.14, 0.14
), Growth = c(110, 132, 145.2, 159.72, 175.692, 193.2612,
130, 156, 177.84, 202.7376, 231.120864, 263.477785), CAGR = c(0.098479605,
0.098479605, 0.098479605, 0.098479605, 0.098479605, 0.098479605,
0.125, 0.125, 0.125, 0.125, 0.125, 0.125)), .Names = c("Stock",
"date", "Annual.Growth.Rate", "Growth.on.100", "CAGR"), class = "data.frame", row.names = c(NA,
-12L))
This is the expected output. Before there was the stock, date, and growth). The growth on 100 is not all a "lag" from before. Since the first available date is multiplied by a given starter, in this case 100, (1+.1)*100, and then the following growth value is the future value (110) * the next growth rate. I can figure out how to do the CAGR using dplyr, but I'm really stuck on growth on 100.
You could use cumprod in a mutate. Also the starting 100 value is arbitrary. It is all a product. You can calculate the rest of the product then multiply by the starter.
starter <- 100
my.data <- data.frame(stock=c('a','a','a','b','b','b'), growth = c(.1,.2,.1,.1,.1,.1), date = c(1,2,3,1,2,3)) #example Data
my.data
my.data %>%
group_by(stock) %>%
mutate(growth.unit = order_by(date,cumprod(1+growth)),
growth = growth.unit*starter) -> new.data
For a sample data.frame:
df <- structure(list(region = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L), .Label = c("a", "b", "c", "d"), class = "factor"),
result = c(0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L), weight = c(0.126,
0.5, 0.8, 1.5, 5.3, 2.2, 3.2, 1.1, 0.1, 1.3, 2.5)), .Names = c("region",
"result", "weight"), row.names = c(NA, 11L), class = "data.frame")
df$region <- factor(df$region)
result <- xtabs(weight ~ region + result, data=df)
result
How would I go about changing the order of the xtab (I don't want to switch the axes which I asked previously)? For example ensuring 1 was always the first result or b was the first region.
If anyone has any ideas, I would appreciate it. I am doing some followup statistics on this data and I need to ensure my values are the right way round.
Try the following, using relevel, which resets the base level of a factor:
df$region <- relevel(df$region, "b")
xtabs(weight ~ region + result, data=df)
Perhaps the best way to do this if you want to order multiple levels is to set these up initially when first using factor:
df$region2 <- df$region <- factor(df$region, levels=c("b", "c", "a"))
or whatever order you want.
For a sample dataframe:
df <- structure(list(region = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L), .Label = c("a", "b", "c", "d"), class = "factor"),
result = c(1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L), weight = c(0.126,
0.5, 0.8, 1.5, 5.3, 2.2, 3.2, 1.1, 0.1, 1.3, 2.5)), .Names = c("region",
"result", "weight"), row.names = c(NA, 11L), class = "data.frame")
I draw a cross tabulation using:
df$region <- factor(df$region)
result <- xtabs(weight ~ region + result, data=df)
result
However I want to ensure the regions of the xtab are in order of magnitude of percentage 1s in sample. (i.e. 1s represent 29% of region a and 33% of region b). Therefore I would like the xtab to be reordered, so region b is first, then a.
I know I could use relevel, however this would be dependent on me looking at the result and re-levelling where appropriate.
Instead I want this to be automatic in the code and not dependent on the user (as this code will be running lots of times, and completing further analysis on the resulting xtab).
If anyone has any ideas, I would greatly appreciate it.
You can reorder the xtab on the values of the second column using order as follows:
result[order(result[, 2], decreasing=T),]
order ranks the values, adding decreasing=T ranks from top to bottom.