flanker | Python email address and Mime | Email library
kandi X-RAY | flanker Summary
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- Parse an address
- Unquote a string
- Lift a parse result
- Convert val to text
- Validate a list
- Validate an address specification
- Lookup the mail exchange for a given domain
- Get the global cache
- Signs a message
- Parse an email address
- Return the size in bytes
- Match style parameters
- Match new style
- Get the content type
- Set the charset
- Create an instance from a stream
- Generate a new email address
- Return a list of MessageId
- Write headers to stream
- Sign a message
- Validate an email address
- Create an attachment
- Suggest an address for an address
- Apply a function to the headers
- The body
- Converts a message into a string
- Returns the filename of the MIME header
flanker Key Features
flanker Examples and Code Snippets
Lines of Code : 7License : Permissive (Apache-2.0)
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train_11-1-1-1-1_total_pool_contrast_areafactor_lr0.001.sh train_11-1-1-1-1_total_pool_contrast_areafactor_lr0.01.sh train_11-1-1-1-1_total_pool_contrast_areafactor_lr0.1.sh train_11-1-1-1-1_total_pool_contrast_None_lr0.001.sh train_11-1-1-1-1_total_pool_contrast_None_lr0.01.sh train_11-1-1-1-1_total_pool_contrast_None_lr0.1.sh train_all.shTrending Discussions on flanker
Trending Discussions on flanker
QUESTION
I'm fairly new to programming and am looking for some guidance. Any help is appreciated.
Here's what I'm trying to do: I have a large number of .txt files from a cognitive experiment (Flanker task, if curious) that I need to compute means for based on condition. The files have no headers and look like below:
XXXXX 1 1 675
XXYXX 0 1 844
YYYYY 1 1 599
YYXYY 0 1 902
I would like to compute means for miliseconds (rightmost column; c4) based on the experimental condition (0 or 1; c2). I would also need the file name of each .txt file (my participant ID) included in the output.
I'm most familiar with R but really just for data analysis. I also have a little experience with Python and Matlab if those (or something else) better suit my needs. Again, a point in any direction would be greatly appreciated.
Thanks
ANSWER
Answered 2021-Dec-16 at 00:46The Tidyverse collection of packages specially the dplyr and readr can easy do this task for you on a grammar likely SQL.
Something like
#loading packages
library(tidyverse)
#importing data
df <- read_delim("file.txt", delim="|", col_names=c("col1", "col2", "col3", "col4"))
#dealing with data
#only mean for col2 == 1
df %>%
filter(col2 == 1) %>%
summarize(mean_exp = mean(col4))
#mean considering grouping by col2
df %>%
group_by(col2) %>%
summarize(mean_exp = mean(col4))
I may suggest you search for cheatsheets available on the links above. They are very easy to understand and reproduce the code.
QUESTION
I am trying to label my individual boxplots in this graph as "Cong." and "Incong." I am drawing from a df "flanker.Summary.ID.RT", and using the column in this df "Type" for the boxplot x-axis, and the column "Flanker.RT" for the boxplot y-axis. I am currently trying this code:
flanker.A.1 <- ggplot() +
geom_line(data=flanker.Summary.ID.RT, aes(x=Type, y=Flanker.RT, group=ID),scale_x_discrete(labels=c("Cong.","Incong.")),
color="gray") +
geom_point(data=flanker.Summary.ID.RT, aes(x=Type, y=Flanker.RT, group=ID), color="gray") +
geom_boxplot(data=flanker.Summary.ID.RT, aes(x=Type, y=Flanker.RT),
width=0.4, alpha=0.6, fill="#CEB888", outlier.colour = "gray") +
theme_cowplot(font_size=24) +
scale_x_discrete(name="Trial type") +
scale_y_continuous(name="RT (ms)", limits=c(300, 630), breaks=c(300, 400, 500, 600)) +
theme(panel.grid.major.y = element_line(colour="grey"))
flanker.A.1
As you can see, I am trying to use the scale_x_discrete to create labels, but the boxplot labels being produced from this code are drawing from the names in the "Type" and "Flanker.RT" columns in the original df instead. What am I doing wrong?
ANSWER
Answered 2021-Apr-21 at 21:09To relabel values on the x axis with scale_x_discrete() you need to access the labels argument. Here's a demonstration:
set.seed(1234)
x <- c(sample(LETTERS[1:3], 100, replace=TRUE))
p <- ggplot(as.data.frame(x), aes(x=x)) + geom_bar(aes(fill=x))
p
If you want to relabel the bars on the x axis, you use scale_x_discrete() and pass a vector to the labels argument. The name argument is the title of the axis. If you pass a normal vector to the labels argument, the order of the vector will be mapped according to the order of the x axis items. You can specify the mapping if you pass a named vector, like I show here:
p + scale_x_discrete(
name="new axis name",
labels=c("B" = "BBB", 'A'= 'AAA', 'C'= 'CCC')
)
QUESTION
I want to make the font bigger on the axes on a collated plot. I'd like both the axes on the collated plot, as well as the axes on each individual plot to be bigger. Is there an easy way to do this without individually going into each of the plots I've collated together and changing the font size- for example, can I add anything to the plot_grid() function to do this? Code for context is included below.
# Make Figure 4.
# Flanker.
flanker.Training <- ggplot(data=correlations, aes(x=`Flanker.Con-Incon`, y=Training.ACC)) +
geom_smooth(method = "lm", color="#CEB888") +
geom_point() +
theme_cowplot(font_size=16) +
scale_x_continuous(name="Flanker") +
scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
theme(legend.position="none") +
labs(title="Training") +
theme(panel.grid.major.y = element_line(colour="grey"))
flanker.Training
flanker.Pretest <- ggplot(data=correlations, aes(x=`Flanker.Con-Incon`, y=`Pre-test.ACC`)) +
geom_smooth(method = "lm", color="#CEB888") +
geom_point() +
theme_cowplot(font_size=16) +
scale_x_continuous(name="Flanker") +
scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
theme(legend.position="none") +
labs(title="Pre-test") +
theme(panel.grid.major.y = element_line(colour="grey"))
flanker.Pretest
flanker.Posttest <- ggplot(data=correlations, aes(x=`Flanker.Con-Incon`, y=`Post-test.ACC`)) +
geom_smooth(method = "lm", color="#CEB888") +
geom_point() +
theme_cowplot(font_size=16) +
scale_x_continuous(name="Flanker") +
scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
theme(legend.position="none") +
labs(title="Post-test") +
theme(panel.grid.major.y = element_line(colour="grey"))
flanker.Posttest
flanker.PostPre <- ggplot(data=correlations, aes(x=`Flanker.Con-Incon`, y=`Post-Pre.ACC`)) +
geom_smooth(method = "lm", color="#CEB888") +
geom_point() +
theme_cowplot(font_size=16) +
scale_x_continuous(name="Flanker") +
scale_y_continuous(name=expression(Delta~p(Correct))) +
theme(legend.position="none") +
labs(title="Learning") +
theme(panel.grid.major.y = element_line(colour="grey"))
flanker.PostPre
# Pitch.
pitch.Training <- ggplot(data=correlations, aes(x=Pitch.Dprime, y=Training.ACC)) +
geom_smooth(method = "lm", color="#004369") +
geom_point() +
theme_cowplot(font_size=16) +
scale_x_continuous(name="Pitch perception (d')", limits=c(-0.6, 4.2)) +
scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
theme(legend.position="none") +
theme(panel.grid.major.y = element_line(colour="grey"))
pitch.Training
pitch.Pretest <- ggplot(data=correlations, aes(x=Pitch.Dprime, y=`Pre-test.ACC`)) +
geom_smooth(method = "lm", color="#004369") +
geom_point() +
theme_cowplot(font_size=16) +
scale_x_continuous(name="Pitch perception (d')", limits=c(-0.6, 4.2)) +
scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
theme(legend.position="none") +
theme(panel.grid.major.y = element_line(colour="grey"))
pitch.Pretest
pitch.Posttest <- ggplot(data=correlations, aes(x=Pitch.Dprime, y=`Post-test.ACC`)) +
geom_smooth(method = "lm", color="#004369") +
geom_point() +
theme_cowplot(font_size=16) +
scale_x_continuous(name="Pitch perception (d')", limits=c(-0.6, 4.2)) +
scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
theme(legend.position="none") +
theme(panel.grid.major.y = element_line(colour="grey"))
pitch.Posttest
pitch.PostPre <- ggplot(data=correlations, aes(x=Pitch.Dprime, y=`Post-Pre.ACC`)) +
geom_smooth(method = "lm", color="#004369") +
geom_point() +
theme_cowplot(font_size=16) +
scale_x_continuous(name="Pitch perception (d')", limits=c(-0.6, 4.2)) +
scale_y_continuous(name=expression(Delta~p(Correct))) +
theme(legend.position="none") +
theme(panel.grid.major.y = element_line(colour="grey"))
pitch.PostPre
# Identification slope.
id.Training <- ggplot(data=correlations, aes(x=ID.Slope, y=Training.ACC)) +
geom_smooth(method = "lm", color="#BAA892") +
geom_point() +
theme_cowplot(font_size=16) +
scale_x_continuous(name="Identification slope", limits=c(0, 0.3)) +
scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
theme(legend.position="none") +
theme(panel.grid.major.y = element_line(colour="grey"))
id.Training
id.Pretest <- ggplot(data=correlations, aes(x=ID.Slope, y=`Pre-test.ACC`)) +
geom_smooth(method = "lm", color="#BAA892") +
geom_point() +
theme_cowplot(font_size=16) +
scale_x_continuous(name="Identification slope", limits=c(0, 0.3)) +
scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
theme(legend.position="none") +
theme(panel.grid.major.y = element_line(colour="grey"))
id.Pretest
id.Posttest <- ggplot(data=correlations, aes(x=ID.Slope, y=`Post-test.ACC`)) +
geom_smooth(method = "lm", color="#BAA892") +
geom_point() +
theme_cowplot(font_size=16) +
scale_x_continuous(name="Identification slope", limits=c(0, 0.3)) +
scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
theme(legend.position="none") +
theme(panel.grid.major.y = element_line(colour="grey"))
id.Posttest
id.PostPre <- ggplot(data=correlations, aes(x=ID.Slope, y=`Post-Pre.ACC`)) +
geom_smooth(method = "lm", color="#BAA892") +
geom_point() +
theme_cowplot(font_size=16) +
scale_x_continuous(name="Identification slope", limits=c(0, 0.3)) +
scale_y_continuous(name=expression(Delta~p(Correct))) +
theme(legend.position="none") +
theme(panel.grid.major.y = element_line(colour="grey"))
id.PostPre
# Within-category discrimination.
discrimination.Training <- ggplot(data=correlations, aes(x=Discrimination.Dprime, y=Training.ACC)) +
geom_smooth(method = "lm", color="#79AFBA") +
geom_point() +
theme_cowplot(font_size=16) +
scale_x_continuous(name="Discrimination (d')", limits=c(-0.6, 4.2)) +
scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
theme(legend.position="none") +
theme(panel.grid.major.y = element_line(colour="grey"))
discrimination.Training
discrimination.Pretest <- ggplot(data=correlations, aes(x=Discrimination.Dprime, y=`Pre-test.ACC`)) +
geom_smooth(method = "lm", color="#79AFBA") +
geom_point() +
theme_cowplot(font_size=16) +
scale_x_continuous(name="Discrimination (d')", limits=c(-0.6, 4.2)) +
scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
theme(legend.position="none") +
theme(panel.grid.major.y = element_line(colour="grey"))
discrimination.Pretest
discrimination.Posttest <- ggplot(data=correlations, aes(x=Discrimination.Dprime, y=`Post-test.ACC`)) +
geom_smooth(method = "lm", color="#79AFBA") +
geom_point() +
theme_cowplot(font_size=16) +
scale_x_continuous(name="Discrimination (d')", limits=c(-0.6, 4.2)) +
scale_y_continuous(name="p(Correct)", limits=c(0.4, 1)) +
theme(legend.position="none") +
theme(panel.grid.major.y = element_line(colour="grey"))
discrimination.Posttest
discrimination.PostPre <- ggplot(data=correlations, aes(x=Discrimination.Dprime, y=`Post-Pre.ACC`)) +
geom_smooth(method = "lm", color="#79AFBA") +
geom_point() +
theme_cowplot(font_size=16) +
scale_x_continuous(name="Discrimination (d')", limits=c(-0.6, 4.2)) +
scale_y_continuous(name=expression(Delta~p(Correct))) +
theme(legend.position="none") +
theme(panel.grid.major.y = element_line(colour="grey"))
discrimination.PostPre
# Collate to columns.
F4.C1 <- plot_grid(flanker.Training, pitch.Training, id.Training, discrimination.Training,
align="v", ncol=1,
rel_heights = c(0.28, 0.24, 0.24, 0.24))
F4.C1
F4.C2 <- plot_grid(flanker.Pretest, pitch.Pretest, id.Pretest, discrimination.Pretest,
align="v", ncol=1,
rel_heights = c(0.28, 0.24, 0.24, 0.24))
F4.C2
F4.C3 <- plot_grid(flanker.Posttest, pitch.Posttest, id.Posttest, discrimination.Posttest,
align="v", ncol=1,
rel_heights = c(0.28, 0.24, 0.24, 0.24))
F4.C3
F4.C4 <- plot_grid(flanker.PostPre, pitch.PostPre, id.PostPre, discrimination.PostPre,
align="v", ncol=1,
rel_heights = c(0.28, 0.24, 0.24, 0.24))
F4.C4
# Collate/print Figure 3.
Figure4 <- plot_grid(F4.C1, F4.C2, F4.C3, F4.C4,
align="h", nrow=1)
Figure4
pdf("Figure4.pdf", 16, 16, bg="transparent")
plot(Figure4)
dev.off()
ANSWER
Answered 2021-Apr-21 at 14:22If you're willing to switch to the patchwork package for plot composition, you can easily set global theme elements with the & theme(...) operation. Simplified example below.
library(patchwork)
library(ggplot2)
p <- ggplot(mpg, aes(displ, hwy)) +
geom_point()
p + p + p + p + plot_layout(ncol = 2, nrow = 2) &
theme(axis.text = element_text(size = rel(2)))
Created on 2021-04-21 by the reprex package (v1.0.0)
I didn't understand what you meant with 'making axes bigger', so I've ignored that bit of the question.
QUESTION
I am trying to implement the Websters Dictionary into this python code so that I can look up the definition of a word.
As Trigonom pointed out I can search for "shortdef" in the JSON
@bot.command()
async def define(ctx, *, search):
with urllib.request.urlopen('https://dictionaryapi.com/api/v3/references/collegiate/json/' + search + '?key=632c5b56-d2ec-4c66-a432-93c5a5994748') as url:
lk = url.read()
word_dict = json.loads(lk)
defin = word_dict[0]['shortdef']
print (defin)
I get this output when I search for "shotgun":
['a usually smoothbore shoulder weapon capable of firing shot at short ranges', 'an offensive football formation in which the quarterback plays a few yards behind the line of scrimmage and the other backs are scattered as flankers or slotbacks']
How can I get the first definition from that
https://dictionaryapi.com/products/api-collegiate-dictionary
ANSWER
Answered 2020-Nov-03 at 00:23As the error indicates, the JSON result is a list. In particular, a list of objects.
There are multiple shortdefs, and you need to parse each object out
results = [x['shortdef'] for x in json.loads(lk)]
example output
[['a small vessel for travel on water',
'ship',
'a boat-shaped container, utensil, or device'],
['to place in or bring into a boat', 'to go by boat'],
['a pole-handled hook with a point or knob on the back used especially to pull or push a boat, raft, or log into place'],
['refugees fleeing by boat'],
['a low-cut shoe with a slip-resistant sole'],
['an express train for transporting passengers between a port and a city'],
['a small portable boat used in an amphibious military attack or in land warfare for crossing rivers or lakes'],
['a seaplane with a hull designed for floating'],
["a ship's boat of medium size used for general-purpose work"],
['pt boat']]
QUESTION
I would like to overlay rainfall data (column) over a Gantt chart that contains 'suggested sowing windows' and actual sowing dates. From the dataset, I can create both separately but not on one chart. Any pointers greatly appreciated.
## plot Gantt chart with suggested sowing dates and actual sowing dates
sowdate.df$Element <- factor(sowdate.df$Element,levels=c("SOWING DATE","Dart","Spitfire","Suntop","Beckom","Flanker","Lancer","Sunmax","Kittyhawk"))
ggplot(sowdate.df, aes(Date1, Element, Color=Category, group=Item)) +
geom_line(size = 10)
## plot rainfall
ggplot(sowdate.df, aes(Date1, rain)) + geom_col()
## combine Gantt and rainfall
ggplot(sowdate.df) +
geom_col(aes(Date1, rain), size = 1, color = "darkblue", fill = "white") +
geom_line(aes(Date1, Element, Color=Category, group=Item), size = 1.5, color="red", group = 1)
Item Element Category Start-End Date1 rain
1 1 Beckom Variety Start 2018-05-07 NA
2 2 Dart Variety Start 2018-06-01 NA
3 3 Flanker Variety Start 2018-05-01 NA
4 4 Kittyhawk Variety Start 2018-04-01 NA
5 5 Lancer Variety Start 2018-05-01 NA
6 6 SOWING DATE Sowing date Start 2018-06-06 NA
7 7 SOWING DATE Sowing date Start 2018-06-26 NA
8 8 SOWING DATE Sowing date Start 2018-07-03 NA
9 9 SOWING DATE Sowing date Start 2018-07-12 NA
10 10 Spitfire Variety Start 2018-05-21 NA
11 11 Sunmax Variety Start 2018-04-15 NA
12 12 Suntop Variety Start 2018-05-07 NA
13 1 Beckom Variety End 2018-05-31 NA
14 2 Dart Variety End 2018-06-30 NA
15 3 Flanker Variety End 2018-05-21 NA
16 4 Kittyhawk Variety End 2018-05-07 NA
17 5 Lancer Variety End 2018-05-21 NA
18 6 SOWING DATE Sowing date End 2018-06-07 NA
19 7 SOWING DATE Sowing date End 2018-06-27 NA
20 8 SOWING DATE Sowing date End 2018-07-04 NA
21 9 SOWING DATE Sowing date End 2018-07-13 NA
22 10 Spitfire Variety End 2018-06-21 NA
23 11 Sunmax Variety End 2018-05-07 NA
24 12 Suntop Variety End 2018-06-07 NA
25 13 Rainfall 2018-04-14 3.0
26 14 Rainfall 2018-03-30 7.0
27 15 Rainfall 2018-06-10 3.5
28 16 Rainfall 2018-06-18 4.0
29 17 Rainfall 2018-06-28 13.5
30 18 Rainfall 2018-07-23 3.0
31 19 Rainfall 2018-08-05 6.0
32 20 Rainfall 2018-08-25 23.0
33 21 Rainfall 2018-09-10 5.0
ANSWER
Answered 2020-Jan-22 at 08:51As you can see on the image that you have posted - the plot you are shown just overlays two plots. Although this is also possible to do with ggplot2, I don't find this very elegant, and can be very tricky, because you need to find the exact positions of both plots so that it looks neat.
Your workaround using geom_line with your factor levels as y values is interesting, but I am not sure if so desirable.
In any case - this is probably the core of your problem. You are mixing different y measures - and they are of different classes. Factor levels for one plot, numeric / integer for the other. This is problematic. I would not try hard and force those into one y-axis, but I would rather create two plots and combine them with one of the plot combining packages such as patchwork. Like so
I have renamed your columns, am using a package from GitHub user @alisdaire47 for reading your data and also change some columns in order to achieve the plot. Key is using the right classes: Dates as dates, numerics as numerics.
First read your data:
sowdate.df <- read.so::read_so('Item Element Category Start_End Date1 rain
1 1 Beckom Variety Start 2018-05-07 NA
2 2 Dart Variety Start 2018-06-01 NA
3 3 Flanker Variety Start 2018-05-01 NA
4 4 Kittyhawk Variety Start 2018-04-01 NA
5 5 Lancer Variety Start 2018-05-01 NA
6 6 SOWING DATE Sowing date Start 2018-06-06 NA
7 7 SOWING DATE Sowing date Start 2018-06-26 NA
8 8 SOWING DATE Sowing date Start 2018-07-03 NA
9 9 SOWING DATE Sowing date Start 2018-07-12 NA
10 10 Spitfire Variety Start 2018-05-21 NA
11 11 Sunmax Variety Start 2018-04-15 NA
12 12 Suntop Variety Start 2018-05-07 NA
13 1 Beckom Variety End 2018-05-31 NA
14 2 Dart Variety End 2018-06-30 NA
15 3 Flanker Variety End 2018-05-21 NA
16 4 Kittyhawk Variety End 2018-05-07 NA
17 5 Lancer Variety End 2018-05-21 NA
18 6 SOWING DATE Sowing date End 2018-06-07 NA
19 7 SOWING DATE Sowing date End 2018-06-27 NA
20 8 SOWING DATE Sowing date End 2018-07-04 NA
21 9 SOWING DATE Sowing date End 2018-07-13 NA
22 10 Spitfire Variety End 2018-06-21 NA
23 11 Sunmax Variety End 2018-05-07 NA
24 12 Suntop Variety End 2018-06-07 NA
25 13 Rainfall 2018-04-14 3.0
26 14 Rainfall 2018-03-30 7.0
27 15 Rainfall 2018-06-10 3.5
28 16 Rainfall 2018-06-18 4.0
29 17 Rainfall 2018-06-28 13.5
30 18 Rainfall 2018-07-23 3.0
31 19 Rainfall 2018-08-05 6.0
32 20 Rainfall 2018-08-25 23.0
33 21 Rainfall 2018-09-10 5.0')
#> Warning: 8 parsing failures.
#> row col expected actual file
#> 6 -- 6 columns 8 columns literal data
#> 7 -- 6 columns 8 columns literal data
#> 8 -- 6 columns 8 columns literal data
#> 9 -- 6 columns 8 columns literal data
#> 18 -- 6 columns 8 columns literal data
#> ... ... ......... ......... ............
#> See problems(...) for more details.
now the plots
library(tidyverse)
library(patchwork)
Prepare the data (the messiness is due to value scaling to your factor levels)
sowdate <- sowdate.df %>% mutate(element_f = factor(Element,levels=c("SOWING DATE","Dart","Spitfire","Suntop","Beckom","Flanker","Lancer","Sunmax","Kittyhawk")),
date = as.Date(Date1),
rain = as.numeric(rain),
rain_scaled = rain*max(length(levels(element_f))/max(rain, na.rm = TRUE)))
#> Warning: NAs introduced by coercion
Method 1 - combine plots using patchwork. I recommend this, in order not to mix different classes into one y.
p1 <- ggplot(sowdate, aes(date, element_f, Color = Category, group = Item)) +
geom_line(size = 10) +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
plot.margin = margin(b = 0))
p2 <- ggplot(sowdate) +
geom_col(aes(date, rain)) +
theme(plot.margin = margin(t = 0))
p1 + p2 + plot_layout(nrow = 2, )
#> Warning: Removed 8 rows containing missing values (geom_path).
#> Warning: Removed 24 rows containing missing values (position_stack).
I removed the axis text and title and ticks from the first plot and lower and upper plot margins to bring them closer together
Method 2 Combine different variable classes (I don't recommend that. This gets quite messy as you can see above and below). You'll need to scale your rain values to your factor levels, so that the columns overlap and don't get too long. Now this then requires a second y axis. For this you have to make your factor levels numeric, than create breaks and labels for the left y-axis and then re-transform the rain values to their real values, and hope that the breaks kind of works. I don't think a second y-axis really helps to read the graph.
max_rain <- max(sowdate$rain,na.rm = TRUE)
breaks_ax <- 1:length(levels(sowdate$element_f)) - sum(is.na(levels(sowdate$element_f)))
labels_ax <- as.character(levels(sowdate$element_f)[which(!is.na(levels(sowdate$element_f)))])
ggplot(sowdate, aes(date, as.numeric(element_f), Color = Category, group=Item)) +
geom_line(size = 10) +
geom_col(aes(date, rain_scaled)) +
scale_y_continuous(breaks = breaks_ax, labels = labels_ax,
sec.axis = sec_axis(~ .*max_rain/ max(length(levels(sowdate$element_f))))) +
labs(y = 'Element')
#> Warning: Removed 24 rows containing missing values (position_stack).
#> Warning: Removed 17 rows containing missing values (geom_path).
Created on 2020-01-22 by the reprex package (v0.3.0)
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install flanker
You can use flanker like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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