So, I have this tibble from which I am trying to make a multiple bar graph that shows how much was spent supporting(for) or opposing(against) each of these candidates
However, I am completely lost on how to go about doing it, and I think I want to rearrange this tibble to make it simpler to create a graph. Any pointers would be very helpful.
A tibble: 5 x 5
type clinton sanders omalley fa_camp
<chr> <dbl> <dbl> <dbl> <chr>
1 24A 51937848 859337 0 against
2 24C 15106530 900 0 for
3 24E 29651626 5307952 374821 for
4 24F 5096083 304153 0 for
5 24N 10139 0 0 against
I am hoping to eventually achieve a result that looks like this:
The different colored bars would be for/against, and the y-axis would be the amount spent.
Before plotting, would put into long format.
library(tidyverse)
library(scales)
df %>%
pivot_longer(cols = -c(type, fa_camp), names_to = "candidate", values_to = "amount_spent") %>%
ggplot(aes(x = candidate, y = amount_spent, group = fa_camp, fill = fa_camp)) +
geom_bar(stat = "identity", position = "dodge") +
scale_y_continuous(labels = dollar)
Plot
Related
I have two separate data frames - each representing a feature (activity, and sleep) and the amount of days that each of these features were recorded by each id number. The amount of days need to reflect on the y-axis and the feature itself needs to reflect on the x-axis.
I managed to draw the boxplots separately, showing the outliers clearly esp for the one set, however if I want to place the two boxplots next to each other, the outliers do not show up clearly. Also, how do I get the names of the two features (activity and sleep) on my x-axis?
The dataframe for the "sleep "feature:
head(idday)
A tibble: 6 x 2
id days
<dbl> <int>
1 1503960366 25
2 1644430081 4
3 1844505072 3
4 1927972279 5
5 2026352035 28
6 2320127002 1
The dataframe for the "activity "feature:
head(iddaya)
A tibble: 6 x 2
id days
<dbl> <int>
1 1503960366 31
2 1624580081 31
3 1644430081 30
4 1844505072 31
5 1927972279 31
6 2022484408 31
My attempt for sleep:
ggplot(idday, aes(y = days), boxwex = 0.05) +
stat_boxplot(geom = "errorbar",
width = 0.2) +
geom_boxplot(alpha=0.9, outlier.color="red")
and for activity:
ggplot(iddaya, aes(y = days), boxwex = 0.05) +
stat_boxplot(geom = "errorbar",
width = 0.2) +
geom_boxplot(alpha=0.9, outlier.color="red")
I then combined them:
boxplot(summary(idday$days), summary(iddaya$days))
In this final image the outliers do not show clearly, and I want to name my x-axis and y-axis.
There are several ways to achieve your task. One way could be:
If your dataframes are coalled df_sleep and df_activity then we could combine them in a named list and add a new column feature, then plot:
df_sleep
df_activity
library(tidyverse)
bind_rows(list(sleep = df_sleep, activity = df_activity), .id = 'feature') %>%
ggplot(aes(x = feature, y=days, fill=feature))+
geom_boxplot()
If you want to compare these two boxplots with each other I recommend to use the same range for your y-axis. To achieve this you first have to combine both data frames. You can do this with inner_join() from the dplyr package.
data_combined <- inner_join(idday, iddaya,
by = "id",
suffix = c("_sleep", "_activity"))
Then you need to transform your data frame into long-format with pivot_longer() from the tidyr package:
data_combined_long <- data_combined %>%
pivot_longer(days_sleep:days_activity,
names_to = "features",
names_prefix = "days_",
values_to = "days")
After that you can again use ggplot() to create your boxplot. But now you have to define that you want your x-axis to represent your features:
ggplot(data_combined_long, aes(y = days, x = features), boxwex = 0.05)+
stat_boxplot(geom = "errorbar",
width = 0.5) +
geom_boxplot(alpha=0.9, outlier.color="red")
Your plot should then look like this:
I want to visualise two variables in the same graph.
the variables look like this
> head(intp.trust_male)
# A tibble: 1 × 1
average_intp.trust
<dbl>
1 2.33
and
> head(intp.trust_fem)
# A tibble: 1 × 1
average_intp.trust
<dbl>
1 2.34
I have tried merge to put them in the same data frame, but it doesn't seem to work
Q5 <- merge(intp.trust_fem, intp.trust_male)
ggplot(data = Q5)+
aes(fill = percent_owned) +
geom_sf() +
scale_fill_viridis_c()
can anyone help me out here, please?
Thank you :)
I think what you want to do is stack your data frames. You can do this with dplyr::bind_rows. It's not clear from your question what you're trying to accomplish because percent_owned is not a variable in the data you've shown. Generally, you could do (using geom_point):
library(dplyr)
library(ggplot2)
intp.trust_male <- mutate(intp.trust_male, label = "intp.trust_male")
intp.trust_fem <- mutate(intp.trust_fem, label = "intp.trust_fem")
df <- bind_rows(intp.trust_male, intp.trust_fem)
ggplot(df, aes(x = label, y = average_intp.trust)) +
geom_point()
I have some experience with base R but am trying to learn tidyverse and ggplot. I have a dataframe with 4 columns of data. I want a simple x-y plot, where the first column of data is on the x-axis, and the data in the other 3 columns is plotted on the y-axis, resulting in 3 lines on one plot. The first 15 lines of my data look like this (sorry about the image - I don't know how to insert a sample of my data):
screen shot - first 15 rows of data
I tried to plot the second and third columns of data as follows: ,
ggplot(data=SWRC_SL, aes(x=SWRC_SL$pressure_head, y=SWRC_SL$UNSODA_theta)) +
geom_line(colour="red") + scale_x_log10() +
ggplot(data=SWRC_SL, aes(x=SWRC_SL$pressure_head, y=SWRC_SL$Vrugt_theta)) +
geom_line(colour="blue") + scale_x_log10()
I get this error:
Error: Don't know how to add ggplot(data = SWRC_SL, aes(x = SWRC_SL$pressure_head, y = SWRC_SL$Vrugt_theta)) to a plot
I believe I should be using something like "group=" to indicate which columns should be plotted, but I haven't been able to find an example that shows how you can use gglot to plot data across multiple columns. What am I missing ?
ggplot() is only ever called once when you create a chart. Try with the following:
ggplot() +
geom_line(data=SWRC_SL, aes(x=pressure_head, y=UNSODA_theta), colour="red") +
geom_line(data=SWRC_SL, aes(x=pressure_head, y=Vrugt_theta), colour="blue") +
scale_x_log10()
A better method would be to turn your data to long, where the UNSODA_theta and Vrugt_theta data are in the same column (say thetas), and have another column (say type_theta) indicating whether the data is for UNSODA_theta or Vrugt_theta. Then you could do the following:
ggplot(data=SWRC_SL, aes(x=pressure_head, y=thetas, colour=type_theta)) +
geom_line() +
scale_x_log10()
This is more desirable because ggplot2 will include a legend indicating what type of theta the colours are applied to.
As suggested by #Marius, the most efficient way to plot your data is to convert them into a long format.
Using tidyverse, you can have the use of pivot_longer function (from tidyr package) and write the following code:
library(tidyverse)
SWRC_SL %>% pivot_longer(.,-pressure_head, names_to = "variable", values_to = "value") %>%
ggplot(aes(x = pressure_head, y = value, color = variable))+
geom_line()+
scale_x_log10()
EDIT: Illustrating example
Using this dummy dataset:
pressure UNSODA_theta Vrugt_theta Cassel_theta
1 0 -1.4672500 1.4119747 -2.0553118
2 1 0.5210227 0.6189239 1.4817574
3 2 -0.1587546 1.4094018 2.2796175
4 3 1.4645873 2.6888733 -0.4631109
5 4 -0.7660820 2.5865884 -1.8799346
6 5 -0.4302118 0.6690922 0.9633620
First, you pivot your data into a long format:
df %>% pivot_longer(.,-pressure, names_to = "variable", values_to = "value")
# A tibble: 45 x 3
pressure variable value
<int> <chr> <dbl>
1 0 UNSODA_theta -1.47
2 0 Vrugt_theta 1.41
3 0 Cassel_theta -2.06
4 1 UNSODA_theta 0.521
5 1 Vrugt_theta 0.619
6 1 Cassel_theta 1.48
7 2 UNSODA_theta -0.159
8 2 Vrugt_theta 1.41
9 2 Cassel_theta 2.28
10 3 UNSODA_theta 1.46
# … with 35 more rows
Now, your data are suitable for the plotting with ggplot2, you can directly add ggplot command to the previous command by adding a "pipe" (%>%) between them:
library(tidyverse)
df %>% pivot_longer(.,-pressure, names_to = "variable", values_to = "value") %>%
ggplot(aes(x = pressure, y = value, color = variable))+
geom_line()+
scale_x_log10()
And you get the following plot with legend included:
Data example
structure(list(pressure = 0:14, UNSODA_theta = c(-1.46725002909224,
0.521022742648139, -0.158754604716016, 1.4645873119698, -0.766081999604665,
-0.430211753928547, -0.926109497377437, -0.17710396143654, 0.402011779486338,
-0.731748173119606, 0.830373167981674, -1.20808278630446, -1.04798441280774,
1.44115770684428, -1.01584746530465), Vrugt_theta = c(1.41197471231751,
0.61892394889108, 1.40940183965093, 2.68887328620405, 2.58658843344197,
0.669092199317234, -1.28523553529247, 3.49766158983416, 1.66706616676549,
1.5413273359637, 0.986600476854091, 1.51010842295293, 0.835624168230333,
1.42069464325451, 0.599753256022356), Cassel_theta = c(-2.05531181632119,
1.48175740118232, 2.27961753824932, -0.46311085383842, -1.87993463341154,
0.963361958516736, -0.0670637053409687, -2.59982761023726, 0.00319778952040447,
-0.945450500892219, -0.511452869790608, -1.73485854395378, 2.7047128618762,
-0.496698054586832, -2.40827011837962)), class = "data.frame", row.names = c(NA,
-15L))
Sorry if this question already exists - was googling for a while now already and didn't find anything.
I am relatively new to R and learning while doing all of this.
I'm supposed to create some PDF via r markdown that analyses patient-data with specific main-diagnosis and secondary-diagnosis. For this I'm supposed to plot some numbers via ggplot (geom_bar and geom_boxplot).
So what I do so far is, I retrieve data-sets that include both codes via SQL and load them into data.table-objects afterwards. Afterwards I join them to get the data I need.
After this I add columns that consist sub-strings of those codes and others that consist the count of those certain sub-strings (so I can plot the occurrences of every code).
I wanted now for example to put certain data.table into a geom_bar or geom_boxplot and make it visible. This actually works, but my y-axis has a weird scale that doesn't fit the numbers it actually should show. The proportions of the bars are also not accurate.
For example: one diagnoses appears 600 times and the other one 1000 times. The y-axis shows steps of 0 - 500.000 - 1.000.000 - 1.500.000 - ....
The Bar that shows 600 is super small and the bar with 1000 goes up to 1.500.000
If I create a new variable before and count what I need via count() and plot this it just works. The rows I put for the y-axis have in both variable the same datatype (integer)
So here is just how I create the data.table that I use for plotting
exazerbationsHdComorbiditiesNd <- allExazerbationsHd[allComorbiditiesNd, on="encounter_num", nomatch=0]
exazerbationsHdComorbiditiesNd <- exazerbationsHdComorbiditiesNd[, c("i.DurationGroup", "i.DurationInDays", "i.start_date", "i.end_date", "i.duration", "i.patient_num"):=NULL]
exazerbationsHdComorbiditiesNd[ , IcdHdCodeCount := .N, by = concept_cd]
exazerbationsHdComorbiditiesNd[ , IcdHdCodeClassCount := .N, by = IcdHdClass]
If I want to bar-plot now for example IcdHdClass by IcdHdCodeClassCount I do following:
ggplot(exazerbationsHdComorbiditiesNd, aes(exazerbationsHdComorbiditiesNd$IcdHdClass, exazerbationsHdComorbiditiesNd$IcdHdCodeClassCount, label=exazerbationsHdComorbiditiesNd$IcdHdCodeClassCount)) + geom_bar(stat = "identity") + geom_text(vjust = 0, size = 5)
It outputs said bar-plot with weird proportions.
If I do first:
plotTest <- count(exazerbationsHdComorbiditiesNd, exazerbationsHdComorbiditiesNd$IcdHdClass)
And then bar-plot it:
ggplot(plotTest, aes(plotTest$`exazerbationsHdComorbiditiesNd$IcdHdClass`, plotTest$n, label=plotTest$n)) + geom_bar(stat = "identity") + geom_text(vjust = 0, size = 5)
Its all perfect and works.
I checked also data-types of the columns I needed:
sapply(exazerbationsHdComorbiditiesNd, class)
sapply(plotTest, class)
In both variables the columns I need are of the type character and integer
Edit:
Unfortunately I cant post images. So here are just the links to those.
Here is a screenshot of the plot with wrong y-axis:
https://ibb.co/CbxX1n7
And here is a screenshot of the plot shown right:
https://ibb.co/Xb8gyx1
Here is some example-data that I copied out the data.table object:
Exampledata
Since you added the class counts as an additional column--rather than aggregating--what’s happening is that for each row in your data, the class counts get stacked on top of each other:
library(tidyverse)
set.seed(42)
df <- tibble(class = sample(letters[1:3], 10, replace = TRUE)) %>%
add_count(class, name = "count")
df # this is essentially what your data looks like
#> # A tibble: 10 x 2
#> class count
#> <chr> <int>
#> 1 a 5
#> 2 a 5
#> 3 a 5
#> 4 a 5
#> 5 b 3
#> 6 b 3
#> 7 b 3
#> 8 a 5
#> 9 c 2
#> 10 c 2
ggplot(df, aes(class, count)) + geom_bar(stat = "identity")
You could use position = "identity" so that the bars don’t get stacked:
ggplot(df, aes(class, count)) +
geom_bar(stat = "identity", position = "identity")
However, that creates a whole bunch of unnecessary layers in your plot that you can’t see. A better approach would be to drop the extra rows from your data before plotting:
df %>%
distinct(class, count)
#> # A tibble: 3 x 2
#> class count
#> <chr> <int>
#> 1 a 5
#> 2 b 3
#> 3 c 2
df %>%
distinct(class, count) %>%
ggplot(aes(class, count)) +
geom_bar(stat = "identity")
Created on 2019-09-05 by the reprex package (v0.3.0.9000)
Long time fan of this site, first time user though.
Been searching for a similar/working result for this question.
I am trying to show the PROPORTION that each level of a 2 level factor appear at three locations. All in a side by side bar chart in ggplot.
Here is the code I've been using to (try) to create the chart. The result has been two charts: one using geom_bar and geom_col, respectively. What I'd like is essentially a combination of the two. The first, but with the colors and Y axis of the second.
Thank you!
ggplot(df,aes(x = Stream,fill = death)) +
geom_bar(position = "dodge")+
scale_fill_manual(values = c(rep(c("gray45", "gray75"))))+
labs(fill="Time of Death")
death_stream <-df %>%
group_by(Stream,Tree_Death)%>%
summarise (n = n()) %>%
mutate(rel.freq = paste0(round(100 * n/sum(n), 0), "%"))
death_stream %>%
ggplot(aes(x = Stream,y = rel.freq)) +
geom_col(position = "dodge",fill = "grey50", colour = "black")+
labs(fill="Time of Death")
Thanks Axeman, I figured it out.
the "class" of "rel.freq." was character. I tried specifying as numeric, but instead of
<int>
it produced
<dbl>
turns out all i had to do was revert the tibble BACK to data.frame and specify as numeric. Another way is to export as excel file and change the column "rel.freq" to NUMBERS in Excel.
death_stream
# A tibble: 6 x 4
Stream Tree_Death n percent
<int> <int> <int> <int>
1 1 0 25 33
2 1 1 50 67
3 2 0 17 30
4 2 1 40 70
5 3 0 120 70
6 3 1 51 30