I have a data set with several species that shall be visualised via geom_jitter. In every species, there is one observation that I want to highlight as red point (i.e. this study). However, I couldn't find a useful explanation and hope anybody here can help to fix this.
The data have 7 variables and 65 observations.
The 7 variables are
Genus
Species
Energy
Organics
CN
CP
Reference
The variable "Reference" contains many different things, among others "this study".
The code looks like this:
``
ggplot(Genus,aes(x=Genus,y=Organics,colour=Genus))+
geom_jitter(position=position_jitter(0.15),alpha=1,size=2)+
labs(x="\nGenus",y="AFDW % DW\n")+
theme(axis.title.x=element_text(size=18),
axis.text.x=element_blank(),
axis.title.y=element_text(size=18),
legend.title=element_text(colour="black",size=14),
legend.text=element_text(face="italic",colour="black",size=14),
axis.text.y=element_text(size=14,colour="black"),
axis.ticks=element_blank())
p23
``
Does anybody has an idea how to highlight the points belonging to "this study" in every species scatter plot?
Without any relevent example data I can't be more specific as to how to achieve this. Here is how you could achieve it on some dummy data, as mentioned in a comment:
library(ggplot2)
df <- data.frame(
Genus = rep(LETTERS[1:2], each = 50),
Organics = rnorm(100),
Reference = sample(LETTERS, 100, replace = T),
stringsAsFactors = FALSE
)
# Pick out 2 points in across groups to be highlighted
df$Reference[rpois(1, 20) + c(0, 50)] <- "This Study"
ggplot(df, aes(Genus, Organics)) +
geom_jitter(position = position_jitter(0.15),
aes(colour = ifelse(Reference == "This Study",
"Highlight", "Don't Highlight"))) +
labs(colour = "Highlight?")
You can choose the highlight colours by adding + scale_colour_manual(values = c("my_colour_1", "my_colour_2"))
Related
Ive written a R-chunk which should provide me a coloured ggplot boxplot. All needed templates are loaded, so is the Data.
The Data for „Healthy“ & „BodyTemperature“ is based inside the Data „Hospital“.
For Healthy there can be only 0 oder 1.
It should plott two Boxplots next to each other on the x-axis, one showing Healthy (0) the other one Unhealthy (1) compared to the BodyTemperature of the patients on y-axis.
The Boxplot should be coloured with the Template „Brewer“.
Everytime i try to run this chunk, a warning occours. Whats the solution?
colour:
colour <- brewer.pal(n = 2, name = "Set1")
colour
Warnung: minimal value for n is 3, returning requested palette with 3 different levels
[1] "#E41A1C" "#377EB8" "#4DAF4A"
R-Chunk
colour = brewer.pal(n = 2, name = "Set1")
ggplot(Hospital, aes(x = Healthy, y = BodyTemperature)) +
geom_boxplot(fill=c(colour)) +
ylab("Temperature") +
xlab("Healthy") +
ggtitle("Health compared to Temperature")
Warning ocours:
Error in `check_aesthetics()`:
! Aesthetics must be either length 1 or the same as the data (1): fill
Backtrace:
1. base (local) `<fn>`(x)
2. ggplot2:::print.ggplot(x)
4. ggplot2:::ggplot_build.ggplot(x)
5. ggplot2 (local) by_layer(function(l, d) l$compute_geom_2(d))
6. ggplot2 (local) f(l = layers[[i]], d = data[[i]])
7. l$compute_geom_2(d)
8. ggplot2 (local) f(..., self = self)
9. self$geom$use_defaults(data, self$aes_params, modifiers)
10. ggplot2 (local) f(..., self = self)
11. ggplot2:::check_aesthetics(params[aes_params], nrow(data))
Error in check_aesthetics(params[aes_params], nrow(data)) :
As you want to color your boxplots by the value of Healthy you could do so by mapping Healthy on the fill aesthetic. Also to use one of the Brewer palettes ggplot2 already offers some convenience functions which in case of the fill aes is called scale_fill_brewer. Not sure whether you want a legend but IMHO it does not make sense so I removed it via guides. Finally as you provided no data it's not clear whether your Healthy column is a numeric or a categorical variable. For this reason I wrapped in factor to make it categorical.
Using some fake random example data:
set.seed(123)
library(ggplot2)
Hospital <- data.frame(
Healthy = rep(c(0, 1), 50),
BodyTemperature = runif(100)
)
ggplot(Hospital, aes(x = factor(Healthy), y = BodyTemperature)) +
geom_boxplot(aes(fill = factor(Healthy))) +
scale_fill_brewer(palette = "Set1") +
ylab("Temperature") +
xlab("Healthy") +
ggtitle("Health compared to Temperature") +
guides(fill = "none")
I have a network diagram that looks like this:
I made it using ggraph and added the labels using geom_nodelabel_repel() from ggnetwork:
( ggraph_plot <- ggraph(layout) +
geom_edge_fan(aes(color = as.factor(responses), edge_width = as.factor(responses))) +
geom_node_point(aes(color = as.factor(group)), size = 10) +
geom_nodelabel_repel(aes(label = name, x=x, y=y), segment.size = 1, segment.color = "black", size = 5) +
scale_color_manual("Group", values = c("#2b83ba", "#d7191c", "#fdae61")) +
scale_edge_color_manual("Frequency of Communication", values = c("Once a week or more" = "#444444","Monthly" = "#777777",
"Once every 3 months" = "#888888", "Once a year" = "#999999"),
limits = c("Once a week or more", "Monthly", "Once every 3 months", "Once a year")) +
scale_edge_width_manual("Frequency of Communication", values = c("Once a week or more" = 3,"Monthly" = 2,
"Once every 3 months" = 1, "Once a year" = 0.25),
limits = c("Once a week or more", "Monthly", "Once every 3 months", "Once a year")) +
theme_void() +
theme(legend.text = element_text(size=16, face="bold"),
legend.title = element_text(size=16, face="bold")) )
I want to have the labels on the left side of the plot be off to the left, and the labels on the right side of the plot to be off to the right. I want to do this because the actual labels are quite long (organization names) and they get in the way of the lines in the actual plot.
How can I do this using geom_nodelabel_repel()? i've tried different combinations of box_padding and point_padding, as well as h_just and v_just but these apply to all labels and it doesn't seem like there is a way to subset or position specific points.
Apologies for not providing a reproducible example but I wasn't sure how to do this without compromising the identities of respondents from my survey.
Well, there is always the manually-intensive, yet effective method of separately adding the geom_node_label_repel function for the nodes on the "left" vs. the "right" of the plot. It's not at all elegant and probably bad coding practice, but I've done similar things myself when I can't figure out an elegant solution. It works really well when you don't have a very large dataset to begin with and if you are not planning to make the same plot over and over again. Basically, it would entail:
Identifying if there exists a property in your dataset that places points on the "left" vs. the "right". In this case, it doesn't look like it, so you would just have to create a list manually of those entries on the "left" vs. "right" of your plot.
Using separate calls to geom_node_label_repel with different nudge_x values. Use any reasonable method to subset the "left" and "right datapoints. You can create a new column in the dataset, or use formatting in-line like data = subset(your.data.frame, property %in% left.list)
For example, if you created a column called subset.side, being either "left" or "right" in your data.frame (here: your.data.frame), your calls to geom_node_label_repel might look something like:
geom_node_label_repel(
data=subset(your.data.frame, subset.side=='left'),
aes(label=name, x=x, y=y), segment.size=1, segment.color='black', size=5,
nudge_x=-10
) +
geom_node_label_repel(
data=subset(your.data.frame, subset.side=='right'),
aes(label=name, x=x, y=y), segment.size=1, segment.color='black', size=5,
nudge_x=10
) +
Alternatively, you can create a list based on the label name itself--let's say you called those lists names.left and names.right, where you can subset accordingly by swapping in as represented in the pseudo code below:
geom_node_label_repel(
data=subset(your.data.frame, name %in% names.left),...
nudge_x = -10, ...
) +
geom_node_label_repel(
data=subset(your.data.frame, name %in% names.right),...
nudge_x = 10, ...
)
To be fair, I have not worked with the node geoms before, so I am assuming here that the positioning of the labels will not affect the mapping (as it would not with other geoms).
I have a model that predicts 10 words for a particular course in order of likelihood, and I'd like the first 5 words of those words that appear in the course's description.
This is the format of the data:
course_name course_title course_description predicted_word_10 predicted_word_9 predicted_word_8 predicted_word_7 predicted_word_6 predicted_word_5 predicted_word_4 predicted_word_3 predicted_word_2 predicted_word_1
Xmath 32 Precalculus Polynomial and rational functions, exponential... directed scholars approach build african different visual cultures placed global
Xphilos 2 Morality Introduction to ethical and political philosop... make presentation weekly european ways general range questions liberal speakers
My idea is for each row to start iterating from predicted_word_1 until I get the first 5 that are in the description. I'd like to save those words in the order they appear into additional columns description_word_1 ... description_word_5. (If there are <5 predicted words in the description I plan to return NAN in the corresponding columns).
To clarify with an example: if the course_description of a course is 'Polynomial and rational functions, exponential and logarithmic functions, trigonometry and trigonometric functions. Complex numbers, fundamental theorem of algebra, mathematical induction, binomial theorem, series, and sequences. ' and its first few predicted words are irrelevantword1, induction, exponential, logarithmic, irrelevantword2, polynomial, algebra...
I would want to return induction, exponential, logarithmic, polynomial, algebra for that in that order and do the same for the rest of the courses.
My attempt was to define an apply function that will take in a row and iterate from the first predicted word until it finds the first 5 that are in the description, but the part I am unable to figure out is how to create these additional columns that have the correct words for each course. This code will currently only keep the words for one course for all the rows.
def find_top_description_words(row):
print(row['course_title'])
description_words_index=1
for i in range(num_words_per_course):
description = row.loc['course_description']
word_i = row.loc['predicted_word_' + str(i+1)]
if (word_i in description) & (description_words_index <=5) :
print(description_words_index)
row['description_word_' + str(description_words_index)] = word_i
description_words_index += 1
df.apply(find_top_description_words,axis=1)
The end goal of this data manipulation is to keep the top 10 predicted words from the model and the top 5 predicted words in the description so the dataframe would look like:
course_name course_title course_description top_description_word_1 ... top_description_word_5 predicted_word_1 ... predicted_word_10
Any pointers would be appreciated. Thank you!
If I understand correctly:
Create new DataFrame with just 100 predicted words:
pred_words_lists = df.apply(lambda x: list(x[3:].dropna())[::-1], axis = 1)
Please note that, there are lists in each row with predicted words. The order is nice, I mean the first, not empty, predicted word is on the first place, the second on the second place and so on.
Now let's create a new DataFrame:
pred_words_df = pd.DataFrame(pred_words_lists.tolist())
pred_words_df.columns = df.columns[:2:-1]
And The final DataFrame:
final_df = df[['course_name', 'course_title', 'course_description']].join(pred_words_df.iloc[:,0:11])
Hope this works.
EDIT
def common_elements(xx, yy):
temp = pd.Series(range(0, len(xx)), index= xx)
return list(df.reindex(yy).sort_values()[0:10].dropna().index)
pred_words_lists = df.apply(lambda x: common_elements(x[2].replace(',','').split(), list(x[3:].dropna())), axis = 1)
Does it satisfy your requirements?
Adapted solution (OP):
def get_sorted_descriptions_words(course_description, predicted_words, k):
description_words = course_description.replace(',','').split()
predicted_words_list = list(predicted_words)
predicted_words = pd.Series(range(0, len(predicted_words_list)), index=predicted_words_list)
predicted_words = predicted_words[~predicted_words.index.duplicated()]
ordered_description = predicted_words.reindex(description_words).dropna().sort_values()
ordered_description_list = pd.Series(ordered_description.index).unique()[:k]
return ordered_description_list
df.apply(lambda x: get_sorted_descriptions_words(x['course_description'], x.filter(regex=r'predicted_word_.*'), k), axis=1)
let's say i have a dataframe of 80 columns and 1 target column,
for example a bank account table with 80 attributes for each record (account) and 1 target column which decides if the client stays or leaves.
what steps and algorithms should i follow to select the most effective columns with the higher impact on the target column ?
There are a number of steps you can take, I'll give some examples to get you started:
A correlation coefficient, such as Pearson's Rho (for parametric data) or Spearman's R (for ordinate data).
Feature importances. I like XGBoost for this, as it includes the handy xgb.ggplot.importance / xgb.plot_importance methods.
One of the many feature selection options, such as python's sklearn.feature_selection methods.
This one way to do it using the Pearson correlation coefficient in Rstudio, I used it once when exploring the red_wine dataset my targeted variable or column was the quality and I wanted to know the effect of the rest of the columns on it.
see below figure shows the output of the code as you can see the blue color represents positive relation and red represents negative relations and the closer the value to 1 or -1 the darker the color
c <- cor(
red_wine %>%
# first we remove unwanted columns
dplyr::select(-X) %>%
dplyr::select(-rating) %>%
mutate(
# now we translate quality to a number
quality = as.numeric(quality)
)
)
corrplot(c, method = "color", type = "lower", addCoef.col = "gray", title = "Red Wine Variables Correlations", mar=c(0,0,1,0), tl.cex = 0.7, tl.col = "black", number.cex = 0.9)
I have 2 dataframes in R and I want to do a query using the dataframe "y" like parameter to dataframe "x".
I have this code:
x <- c('The book is on the table','I hear birds outside','The electricity
came back')
x <- data.frame(x)
colnames(x) <- c('text')
x
y <- c('book','birds','electricity')
y <- data.frame(y)
colnames(y) <- c('search')
y
r <- sqldf("select * from x where text IN (select search from y)")
r
I think to use "like" here, but I don´t know.
Can you helpme ?
If you want a sqldf solution, I think that this would work:
sqldf("select x.text, y.search FROM x JOIN y on x.text LIKE '%' || y.search || '%'")
## text search
## 1 The book is on the table book
## 2 I hear birds outside birds
## 3 The electricity \ncame back electricity
You could use the fuzzyjoin package:
library(dplyr)
library(fuzzyjoin)
regex_join(
mutate_if(x, is.factor, as.character),
mutate_if(y, is.factor, as.character),
by = c("text" = "search")
)
# text search
# 1 The book is on the table book
# 2 I hear birds outside birds
# 3 The electricity \ncame back electricity
It's hard to know if this is what you want without a more varied fixture. To add a little bit of variation, I added an extra word to y$search - y = c('book','birds','electricity', 'cat'). More variation would further clarify
Just know which words are in which statements? sapply and grepl
> m = sapply(y$search, grepl, x$text)
> rownames(m) = x$text
> colnames(m) = y$search
> m
book birds electricity cat
The book is on the table TRUE FALSE FALSE FALSE
I hear birds outside FALSE TRUE FALSE FALSE
The electricity \ncame back FALSE FALSE TRUE FALSE
Pulling out just the matching rows?
> library(magrittr) # To use the pipe, "%>%"
> x %>% data.table::setDT() # To return the result as a table easily
>
> x[(sapply(y$search, grepl, x$text) %>% rowSums() %>% as.logical()) * (1:nrow(x)), ]
text
1: The book is on the table
2: I hear birds outside
3: The electricity \ncame back
#Aurèle's solution will give the best result for matching text and the text it match to. Note that if back was also in y$search, the text The electricity \ncame back would get reported twice in the result for the different search terms matched, so this is better in the case that uniqueness is not important.
So it largely depends on your desired output.