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I am using R cld() function with emmeans, but the order of factor level in the output is different from what I set. Before calling cld(), the by.years output is also in the desired order (screenshot), but when I do cld(), the output is in the alphabetical order of Light - Moderate - No(screenshot). I also checked cld.years$Grazing.intensity, the levels are correct. Is there a way to specify the order of factor levels in the cld() output? Any help is appreciated.
# sample data
plants <- structure(list(Grazing.intensity = structure(c(3L, 2L, 3L, 3L, 3L, 1L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 3L, 3L), .Label = c("Light-grazing", "Moderate-grazing", "No-grazing"), class = "factor"), Grazing.intensity1 = structure(c(3L, 2L, 3L, 3L, 3L, 1L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 3L, 3L), .Label = c("LG", "MG", "NG"), class = "factor"), Years = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L), .Label = c("Dry-year", "Wet-year"), class = "factor"), Month = structure(c(2L, 2L, 2L, 1L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 2L, 2L, 3L), .Label = c("Aug.", "Jul.", "Sept."), class = "factor"), Plots = c(1L, 3L, 8L, 6L, 9L, 7L, 2L, 2L, 10L, 10L, 7L, 7L, 9L, 4L, 2L), Species.richness = c(8L, 6L, 10L, 11L, 9L, 5L, 7L, 13L, 10L, 6L, 5L, 5L, 14L, 8L, 10L)), class = "data.frame", row.names = c(NA, -15L))
# set the order of factor levels
plants$Grazing.intensity <- factor(plants$Grazing.intensity, levels =
c('No-grazing','Light-grazing','Moderate-grazing'))
attach(plants)
lmer.mod <- lmer(Species.richness ~ Grazing.intensity*Years + (1|Month), data = plants)
by.years <- emmeans(lmer.mod, specs = ~ Grazing.intensity:Years, by = 'Years', type = "response")
# display cld
cld.years <- cld(by.years, Letters = letters)
This is my first time posting sample data in StackOverflow, so it may be wrong.. I used dput().
I solved the issue. The order changed because the levels are displayed in the increasing order of emmean. I set sort = FALSE, and the result was displayed in the default order. I should have read the documentations more thoroughly.
I am working with ggstatsplot to get visual representations of my statistical analyses.
I have numerous datasets, all very similar in make-up. Some work just fine, while others don't. data1 is a working example, and data2 doesn't work.
data1 <- structure(list(
treatment = structure(c(1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L),
.Label = c("negative_ctrl", "positive_ctrl", "treatmentA", "treatmentB", "treatmentC", "treatmentD"), class = "factor"),
value = c(1.74501, 2.04001, 1.89501, 1.84001,
1.89501, 9.75001, 8.50001, 8.80001, 11.50001, 10.25001, 7.90001,
9.25001, 11.45001, 7.75001, 7.75001, 7.55001, 8.70001, 8.20001,
6.95001, 6.60001, 7.40001, 7.15001, 8.25001, 9.20001, 8.95001,
6.45001, 6.05001, 5.40001, 7.95001, 6.80001, 4.65001, 6.40001,
6.40001, 6.70001, 5.40001, 3.20001, 2.70001, 4.30001, 4.10001,
3.60001, 4.00001, 3.00001, 4.70001, 3.10001, 3.50001, 6.45001,
5.45001, 4.90001, 7.25001, 4.55001, 4.70001, 6.25001, 5.65001,
6.00001, 5.10001)),
row.names = c(NA, -55L), class = c("tbl_df", "tbl", "data.frame"))
data2 <- structure(list(
treatment = structure(c(1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L),
.Label = c("negative_ctrl", "positive_ctrl", "treatmentA", "treatmentB", "treatmentC", "treatmentD"), class = "factor"),
value = c(1.00001, 1.00001, 1.00001, 1.00001, 1.00001, 6.77501,
5.68751, 5.99201, 8.24501, 7.01251, 4.79501, 5.99126, 8.26276,
5.35376, 5.38751, 4.60251, 5.38901, 4.85201, 4.44401, 5.20501,
6.20701, 5.77001, 4.05201, 3.65126, 3.02401, 4.68351, 3.90001,
2.56951, 3.70001, 3.61901, 3.96401, 2.93601, 1.53901, 1.40801,
2.05601, 2.08501, 1.89701, 1.79501, 1.50001, 2.09151, 1.53551,
1.57501, 3.88851, 3.09151, 2.75501, 4.40626, 2.42001, 2.60951,
3.83501, 3.37151, 3.70001, 2.92701)),
row.names = c(NA, -52L), class = c("tbl_df", "tbl", "data.frame"))
I call the most basic analysis for both datasets:
library(Rmpfr)
library(ggstatsplot)
ggstatsplot::ggbetweenstats(
data = data1,
x = treatment,
y = value,
messages = FALSE )
ggstatsplot::ggbetweenstats(
data = data2,
x = treatment,
y = value,
messages = FALSE )
For data1 I get this:
for data2 I get:
> Error in stats::optim(par = 1.1 * rep(lambda, 2), fn = function(x) { : non-finite value supplied by optim
At first I thought the issue might be a few zeros that I passed on in the negative control, but I first upped them by a tiny amount and then by 1 to make sure the range of the values is not an issue. The only discrepancy I can see is that I only have 7 instead of 10 measurements for treatmentA (level 3) in data2 but 10 in data1 (had to remove a few NAs due to sample failure). However, in both cases the negative control (level 1) only has 5 values, and I don't think that in this type of analysis there is an issue with different sample sizes between the groups.
It's a good idea to try basic plots out in these cases eg isolate the boxplots:
So comparing the two datasets:
boxplot(value ~ treatment, data=data1)
boxplot(value ~ treatment, data=data2)
data2 has a treatment with no variability ("negative_ctrl"), 0 SD. I'm guessing this function is doing some tests that require variation. You will need to read the documentation for the function to see if this is brought up but you can get views either by removing these treatments, or forcing a very small amount of variation eg
# run without negative_ctrl
ggstatsplot::ggbetweenstats(
data = data2[data2$treatment != "negative_ctrl",],
x = treatment,
y = value,
messages = FALSE )
# add some tiny fake variation to force it through (this is a hack)
data3 <- data2
data3[data3$treatment=="negative_ctrl",][1,][["value"]] <- 1.0001
ggstatsplot::ggbetweenstats(
data = data3,
x = treatment,
y = value,
messages = FALSE )
I tried to use is.na() in mutate_if() but I get an error:
Error in is_logical(.p) : object 'n_day' not found
n_day indeed in my dataframe and I thought because of the argument set of is.na() that I can not use it in mutate_if() but I don't know how to solve it.
Here's the idea if the value in n_day is NA, replace it with the value in n_cum at the same day.
Any help will be highly appreciated!
My code like this:
library(tidyverse)
t <- structure(list(city = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("a", "b"), class = "factor"),
time = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L), .Label = c("2012/1/1", "2012/1/2",
"2012/1/3", "2012/1/4", "2012/2/1", "2012/2/2", "2012/2/3",
"2012/2/4"), class = "factor"), n_cum = c(1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L)), class = "data.frame", row.names = c(NA,
-16L))
t
t2 <- t %>% group_by(city) %>%
mutate(n_day = n_cum - lag(n_cum))
t2 %>% mutate_if(is.na(n_day), n_day = n_cum)
mutate_if is used to do operations on multiple columns at once(See documentation), this is not what you are looking for here as you only want to change one column.
The question can be solved using mutate and if_else :
t2 %>% mutate(n_day = if_else(is.na(n_day),n_cum,n_day))
Use mutate_at + if condition instead,
t2 %>% mutate_at(vars(n_day), ~ ifelse(is.na(.), n_cum, .))
In the case of multiple variables selection, just add them respectively into vars helper.
I have data that I want to know the number of specific rows that are with specific character. The data looks like the following
df<-structure(list(Gene.refGene = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 4L), .Label = c("A1BG", "A1BG-AS1", "A1CF",
"A1CF;PRKG1"), class = "factor"), Chr = structure(c(2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("chr10", "chr19"
), class = "factor"), Start = c(58858232L, 58858615L, 58858676L,
58859052L, 58859055L, 58859066L, 58859510L, 58863162L, 58864479L,
58864150L, 58864867L, 58864879L, 58865857L, 52566433L, 52569637L,
52571047L, 52573510L, 52576068L, 52580561L, 52603659L, 52619845L,
52625849L, 52642500L, 52650951L, 52675605L, 52703952L, 52723140L,
52723638L), End = c(58858232L, 58858615L, 58858676L, 58859052L,
58859055L, 58859066L, 58859510L, 58863166L, 58864479L, 58864150L,
58864867L, 58864879L, 58865857L, 52566433L, 52569637L, 52571047L,
52573510L, 52576068L, 52580561L, 52603659L, 52619845L, 52625849L,
52642500L, 52650958L, 52675605L, 52703952L, 52723140L, 52723638L
), Ref = structure(c(3L, 5L, 2L, 2L, 3L, 2L, 5L, 7L, 6L, 6L,
2L, 1L, 5L, 6L, 5L, 3L, 2L, 5L, 6L, 3L, 3L, 6L, 3L, 4L, 3L, 6L,
6L, 3L), .Label = c("-", "A", "C", "CTCTCTCT", "G", "T", "TTTTT"
), class = "factor"), Alt_df1 = structure(c(1L, 1L, 4L, 4L, 1L,
4L, 5L, 1L, 3L, 3L, 4L, 4L, 3L, 1L, 2L, 5L, 1L, 2L, 1L, 5L, 5L,
2L, 5L, 1L, 4L, 3L, 4L, 2L), .Label = c("-", "A", "C", "G", "T"
), class = "factor")), class = "data.frame", row.names = c(NA,
-28L))
I want to know how many rows of the column named "alt_df1" is missing or - or NA
Here is an answer using which and utilising base R's LETTERS data:
length(which(!df$Alt_df1%in%LETTERS))
#[1] 8
Or using just which:
length(which(df$Alt_df1=="-"))
#[1] 8
One way would be to create a logical vector using %in% and then sum over them to count the number of occurrences.
sum(df$Alt_df1 %in% c("-", NA))
#[1] 8
Or we can also subset and count the number of rows.
nrow(subset(df, Alt_df1 %in% c("-", NA)))
which can also be done in dplyr by
library(dplyr)
df %>% filter(Alt_df1 %in% c("-", NA)) %>% nrow
Another option using grepl
with(df, sum(grepl("-", Alt_df1)) + sum(is.na(Alt_df1)))
and I am sure there are multiple other ways.
I am new to ggplot2 and trying to plot a continuous histogram showing the evolution of reviews by date and rating.
My data set look like this:
date rating reviews
1 2017-11-24 1 some text here
2 2017-11-24 1 some text here
3 2017-12-02 5 some text here
4 2017-11-24 3 some text here
5 2017-11-24 3 some text here
6 2017-11-24 4 some text here
What I want to get is something like this:
for rating == 1
date count
1 2017-11-24 2
2 2017-11-25 7
.
.
.
and so on for rating == 2 and 3
I've tried
ggplot(aes(x = date, y = rating), data = df) + geom_line()
but it gives me only rating on the y axis and not counts:
You can use dplyr to get the desired dataset and pass that into ggplot();
library(dplyr)
library(ggplot2)
sample_data %>% group_by(rating,date) %>% summarise(n=n()) %>%
ggplot(aes(x=date, y=n, group=rating, color=as.factor(rating))) +
geom_line(size=1.5) + geom_point()
Data:
sample_data <- structure(list(id = c(1L, 2L, 2L, 3L, 4L, 5L, 5L, 6L, 6L, 1L,
2L, 3L, 3L, 4L, 5L, 6L, 1L, 2L, 2L, 2L, 3L, 4L, 5L, 6L), date = structure(c(1L,
1L, 3L, 7L, 1L, 1L, 1L, 1L, 5L, 2L, 3L, 8L, 8L, 3L, 4L, 5L, 5L,
6L, 6L, 6L, 9L, 6L, 6L, 6L), .Label = c("2017-11-24", "2017-11-25",
"2017-11-26", "2017-11-27", "2017-11-28", "2017-11-29", "2017-12-02",
"2017-12-04", "2017-12-08"), class = "factor"), rating = c(1L,
1L, 1L, 5L, 3L, 3L, 3L, 4L, 4L, 1L, 1L, 5L, 5L, 3L, 3L, 4L, 1L,
1L, 1L, 1L, 5L, 3L, 3L, 4L), reviews = 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), .Label = "review", class = "factor")), .Names = c("id",
"date", "rating", "reviews"), row.names = c(NA, 24L), class = "data.frame")
Just using some dummy data:
library(tidyverse)
set.seed(999)
df <- data.frame(date = sample(seq(as.Date('2017/01/01'), as.Date('2017/04/01'), by="day"), 2000, replace = T),
rating = sample(1:5,2000,replace = T))
df$rating <- as.factor(df$rating)
df %>%
group_by(date,rating) %>%
summarise(n = length(rating)) %>%
ggplot(aes(date,n, color = rating)) +
geom_line() +
geom_point()