I'm still learning R and I'm not sure why there is NA data in my graph. Considering that I have used the table function to check the variables in the column.
graph
Any suggestions to remove the NA variable in my graph?
Please find below sample of code(not actual dataset):
*Install and load relevant packages
install.packages("tidyverse")
install.packages("lubridate")
install.packages("ggplot2")
install.packages("tibble")
library(tidyverse)
library(lubridate)
library(ggplot2)
library(tibble)
library(dplyr)
*data frame
all_trips <- tribble(~start, ~end, ~start_name, ~type,
"2020-03-22 03:20:20", "2020-03-22 04:10:15", "A", "member",
"2020-03-25 01:01:07", "2020-03-25 05:09:45", NA, "member",
"2020-03-26 07:09:55", "2020-03-26 08:10:20", "B", "casual",
"2020-03-29 09:10:30", "2020-03-29 09:00:20", "A", "casual",
"2020-03-30 11:09:18", "2020-03-30 03:40:10", "B", "member")
*generate new columns
all_trips$date <- as.Date(all_trips$start) #The default format is yyyy-mm-dd
all_trips$month <- format(as.Date(all_trips$date), "%m")
all_trips$day <- format(as.Date(all_trips$date), "%d")
all_trips$year <- format(as.Date(all_trips$date), "%Y")
all_trips$day_of_week <- format(as.Date(all_trips$date), "%A")
all_trips$ride_length <- difftime(all_trips$end,all_trips$start)
is.factor(all_trips$ride_length)
all_trips$ride_length <- as.numeric(as.character(all_trips$ride_length))
is.numeric(all_trips$ride_length)
*data cleaning
all_trips_v2 <- all_trips[!(all_trips$start_name == "NA" |
all_trips$ride_length<0),]
*data viz
all_trips_v2 %>%
mutate(weekday = wday(start, label = TRUE)) %>% #creates weekday field using wday()
group_by(type, weekday) %>% #groups by usertype and weekday
summarise(number_of_rides = n() #calculates the number of rides and average duration
,average_duration = mean(ride_length)) %>% # calculates the average duration
arrange(type, weekday) %>% # sorts
ggplot(aes(x = weekday, y = number_of_rides, fill = type)) +
geom_col(position = "dodge", na.rm = TRUE) +
scale_x_discrete(na.translate = FALSE)
Bar Chart:
Click here
Adding na.rmand na.translate arguments will remove missing values from bar chart without a warning message as shown here:
tibble(x = rep(c('One', 'Two', 'Two', NA),2), Group=rep(c("A","B"),each=4)) %>%
ggplot(aes(x, fill=Group)) +
labs(title="Sample Group Bar Chart with NA's Removed") +
geom_bar(stat="Count", position=position_dodge(), na.rm = TRUE) +
scale_x_discrete(na.translate = FALSE)
Related
I have a dataset that I am modeling with a gam. Because there are two continuous varaibles in the gam, I have centred and scaled these variables before adding them to the model. Therefore, when I use the built-in features in gratia to show the results, the x values are not the same as the original scale. I'd like to plot the results using the scale of the original data.
An example:
library(tidyverse)
library(mgcv)
library(gratia)
set.seed(42)
df <- data.frame(
doy = sample.int(90, 300, replace = TRUE),
year = sample(c(1980:2020), size = 300, replace = TRUE),
site = c(rep("A", 150), rep("B", 80), rep("C", 70)),
sex = sample(c("F", "M"), size = 300, replace = TRUE),
mass = rnorm(300, mean = 500, sd = 50)) %>%
mutate(doy.s = scale(doy, center = TRUE, scale = TRUE),
year.s = scale(year, center = TRUE, scale = TRUE),
across(c(sex, site), as.factor))
m1 <- gam(mass ~
s(year.s, site, bs = "fs", by = sex, k = 5) +
s(doy.s, site, bs = "fs", by = sex, k = 5) +
s(sex, bs = "re"),
data = df, method = "REML", family = gaussian)
draw(m1)
How do I re-plot the last two panels in this figure to show the relationship between year and mass with ggplot?
You can't do this with gratia::draw automatically (unless I'm mistaken).* But you can use gratia::smooth_estimates to get a dataframe which you can then do whatever you like with.
To answer your specific question: to re-plot the last two panels of the plot you provided, but with year unscaled, you can do the following
# Get a tibble of smooth estimates from the model
sm <- gratia::smooth_estimates(m1)
# Add a new column for the unscaled year
sm <- sm %>% mutate(year = mean(df$year) + (year.s * sd(df$year)))
# Plot the smooth s(year.s,site) for sex=F with year unscaled
pF <- sm %>% filter(smooth == "s(year.s,site):sexF" ) %>%
ggplot(aes(x = year, y = est, color=site)) +
geom_line() +
theme(legend.position = "none") +
labs(y = "Partial effect", title = "s(year.s,site)", subtitle = "By: sex; F")
# Plot the smooth s(year.s,site) for sex=M with year unscaled
pM <- sm %>% filter(smooth == "s(year.s,site):sexM" ) %>%
ggplot(aes(x = year, y = est, color=site)) +
geom_line() +
theme(legend.position = "none") +
labs(y = "Partial effect", title = "s(year.s,site)", subtitle = "By: sex; M")
library(patchwork) # use `patchwork` just for easy side-by-side plots
pF + pM
to get:
EDIT: If you also want to shift result on the y-axis as #GavinSimpson (who is the author and maintainer of gratia) mentioned, you can do this with add_constant, adding this code before plotting above:
sm <- sm %>%
add_constant(coef(m1)["(Intercept)"]) %>%
transform_fun(inv_link(m1))
[You should also in general untransform the smooth by the inverse of the model's link function. In your case this is just the identity, so it is not necessary, but in general it would be. That's what the second step above is doing.]
In your example, this results in:
*As mentioned in the custom-plotting vignette for gratia, the goal of draw not to be fully customizable, but just to be useful default. See there for recommendations about custom plots.
Let's say I'm creating the grouped barplot by something like this:
data <- data.frame(time = factor(1:3), type = LETTERS[1:4], values = runif(24)*10)
ggplot(data, aes(x = type, y = values, fill = time)) +
stat_summary(fun=mean, geom='bar', width=0.55, size = 1, position=position_dodge(0.75))
Inside each type I want to connect all bar tops (meaning to connect 3 bars for A, 3 bars for B, and so on) with the line.
I'd like to get something like that as a result:
Is there a way to do that ?
Thank you!
I changed the code to another logic that I prefer, that is to prepare the data before using ggplot().
Code
library(dplyr)
library(ggplot2)
data <- data.frame(time = factor(1:3), type = LETTERS[1:4], values = runif(24)*10)
pdata <- data %>% group_by(type,time) %>% summarise(values = mean(values,na.rm = TRUE)) %>% ungroup()
pdata %>%
ggplot(aes(x = type, y = values)) +
geom_col(
mapping = aes(fill = time, group = time),
width = 0.55,
size = 1,
position = position_dodge(0.75)
)+
geom_line(
mapping = aes(group = type),
size = 1,
position = position_dodge2(.75)
)
Output
So, I've done my searches but cannot find the solution to this problem i have with a bar plot in ggplot.
I'm trying to make the bars be in percentage of the total number of cases in each group in grouping variable 2.
Right now i have it visualising the number of counts,
Dataframe = ASAP
Grouping variable 1 - cc_groups (seen in top of the graph)
(counts number of cases within a range (steps of 20) in a score from 0-100.)
grouping variable 2 - asap
( binary variable with either intervention or control, number of controls and interventions are not the same)
Initial code
``` r
ggplot(ASAP, aes(x = asap, fill = asap)) + geom_bar(position = "dodge") +
facet_grid(. ~ cc_groups) + scale_fill_manual(values = c("red",
"darkgray"))
#> Error in ggplot(ASAP, aes(x = asap, fill = asap)): could not find function "ggplot"
```
Created on 2020-05-19 by the reprex package (v0.3.0)
this gives me the following graph which is a visualisation of the counts in each subgroup.
enter image description here
I have manually calculated the different percentages that actually needs to be visualised:
table_groups <- matrix(c(66/120,128/258,34/120,67/258,10/120,30/258,2/120,4/258,0,1/258,8/120,28/258),ncol = 2, byrow = T)
colnames(table_groups) <- c("ASAP","Control")
rownames(table_groups) <- c("0-10","20-39","40-59","60-79","80-99","100")
ASAP Control
0-10 0.55000 0.496124
20-39 0.28333 0.259690
40-59 0.08333 0.116279
60-79 0.01667 0.015504
80-99 0.00000 0.003876
100 0.06667 0.108527
When i use the solution provided by Stefan below (which was an excellent answer but didn't do the actual trick. i get the following output
``` r
ASAP %>% count(cc_groups, asap) %>% group_by(cc_groups) %>% mutate(pct = n/sum(n)) %>%
ggplot(aes(x = asap, y = pct, fill = asap)) + geom_col(position = "dodge") +
facet_grid(~cc_groups) + scale_fill_manual(values = c("red",
"darkgray"))
#> Error in ASAP %>% count(cc_groups, asap) %>% group_by(cc_groups) %>% mutate(pct = n/sum(n)) %>% : could not find function "%>%"
```
<sup>Created on 2020-05-19 by the [reprex package](https://reprex.tidyverse.org) (v0.3.0)</sup>
enter image description here
whereas (when i go analogue) id like it to show the percentages as above like this.
enter image description here
Im SO sorry about that drawing.. :) and reprex kept feeding me errors, im sure im using it incorrectly.
The easiest way to achieve this is via aggregating the data before plotting, i.e. manually computing counts and percentages:
library(ggplot2)
library(dplyr)
ASAP %>%
count(cc_groups, asap) %>%
group_by(asap) %>%
mutate(pct = n / sum(n)) %>%
ggplot(aes(x = asap, y = pct, fill=asap)) +
geom_col(position="dodge")+
facet_grid(~cc_groups)+
scale_fill_manual(values = c("red","darkgray"))
Using ggplot2::mpg as example data:
library(ggplot2)
library(dplyr)
# example data
mpg2 <- mpg %>%
filter(cyl %in% c(4, 6)) %>%
mutate(cyl = factor(cyl))
# Manually compute counts and percentages
mpg3 <- mpg2 %>%
count(class, cyl) %>%
group_by(class) %>%
mutate(pct = n / sum(n))
# Plot
ggplot(mpg3, aes(x = cyl, y = pct, fill = cyl)) +
geom_col(position = "dodge") +
facet_grid(~ class) +
scale_fill_manual(values = c("red","darkgray"))
Created on 2020-05-18 by the reprex package (v0.3.0)
I have a ggplot of countries (X axis) over two different time periods (Y axis), so double bar for each country.
I would like to see the values of each bar. I used geom_text but I get the values on the same line so they are not in place. How can I use geom_text for this type of plot ?
Rcountry %>%
gather("Type", "Value",-Country) %>%
ggplot(aes(Country, Value, fill = Type)) +
geom_bar(position = "dodge", stat = "identity") +
coord_flip()+
theme_minimal()+scale_fill_grey()+
theme(legend.position="bottom")+
theme(legend.title = element_blank())+
scale_fill_manual(values=c("darkslategray4", "darkslategrey"))+
labs(x="Country", y="Stock of robots per thousands worker in '000")+
geom_text(aes(label=c(X2010, X2018)), size=3.5)```
Thank you
This can be achieved by adding position = position_dodge(.9) to geom_text, i.e. you have to the positioning used in geom_bar to geom_text to get the labels right. Using mtcars as example data, try this:
library(ggplot2)
library(dplyr)
mtcars2 <- mtcars %>%
group_by(cyl, gear) %>%
summarise(mpg = mean(mpg)) %>%
ungroup()
ggplot(mtcars2, aes(x = factor(cyl), mpg, fill = factor(gear))) +
geom_bar(position = "dodge", stat = "identity") +
theme_minimal() +
scale_fill_grey() +
theme(legend.position="bottom")+
theme(legend.title = element_blank())+
labs(x="Country", y="Stock of robots per thousands worker in '000")+
geom_text(aes(label = mpg), position = position_dodge(.9), size=3.5) +
coord_flip()
Created on 2020-04-15 by the reprex package (v0.3.0)
I am looking to create a stacked bar chart where my y-axis measures the value but the table shows the % of total bar.
I think I need to add a pct column to my table then use that but am not sure how to get the pct column either.
Df for example is:
date, type, value, pct
Jan 1, A, 5, 45% (5/11)
Jan 1, B, 6, 55% (6/11)
table and chart image
Maybe something like this?
library(dplyr)
library(ggplot2)
test.df <- data.frame(date = c("2020-01-01", "2020-01-01", "2020-01-02", "2020-01-02"),
type = c("A", "B", "A", "B"),
val = c(5:6, 1, 7))
test.df <- test.df %>%
group_by(date) %>%
mutate(
type.num = as.numeric(type),
prop = val/sum(val),
y_text_pos = ifelse(type=="B", val, sum(val))) %>%
ungroup()
ggplot(data = test.df, aes(x = as.Date(date), y = val, fill = type)) +
geom_col() +
geom_text(aes(y = y_text_pos, label = paste0(round(prop*100,1), "%")), color = "black", vjust = 1.1)
With the output: