photosynthesis | word.camera - lexograph /ˈleksəʊɡɹɑːf/ | Computer Vision library
kandi X-RAY | photosynthesis Summary
kandi X-RAY | photosynthesis Summary
lexograph /ˈleksəʊɡɹɑːf/ (n.) A text document generated from digital image data.
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QUESTION
I have a tibble that nests named lists. I want to apply a cleanup function (e.g., janitor::make_clean_names()
) over the named lists: both the names and the values. Based on my basic understanding of purrr
package, I think that map()
is appropriate for such a task.
However, I don't know how to perform such a cleanup in-place, similar to the functionality of dplyr
's mutate(across(...))
.
Here's a tibble for example:
...ANSWER
Answered 2021-Feb-04 at 08:38You can clean up names and values separately and combine them together.
QUESTION
When I run this program it gives me an error in the text and I don't know why, I have tried to run it also from a file and it also gives me an error, how can it work?
...ANSWER
Answered 2020-May-07 at 05:38The problem with your first code snippet is that the text you're passing as a parameter to the HTTP call is too long, if you print the response object you'll see:
that corresponds to
414 URI Too Long
Reference
If you pass a smaller text, dbpedia-spotlight will be able to annotate the entities for you.
For the second code that you put, you have two problems, the first one is that dbpedia-spotlight may respond with 403 status after consecutive calls to the annotate service, to check that I suggest you to do:
QUESTION
Basically, I have a text file: -
Plants are mainly multi-cellular. Green plants obtain most of their energy from sunlight via photosynthesis. There are about 320,000 species of plants. Some 260–290 thousand, produce seeds. Green plants produce oxygen.
Green plants occupy a significant amount of land today. We should conserve this greenery around us.
I wanted the output to be:-
oxygen. produce plants Green seeds. produce thousand, 260-290 Some plants. of species 320,000 about are There photosynthesis. via sunlight from energy their of most obtain plants Green multi-cellular. mainly are Plants
us. around greenery this conserve should we today. land of amount significant a occupy plants Green.
I used split()
and then used .join()
to combine the file, but it ended up reversing the whole thing and not paragraph-wise.
ANSWER
Answered 2020-Jan-20 at 05:43Change
open("testp.txt")
toopen("[path to your file]")
QUESTION
I have a long-form dataframe subsetted below. What I need to do is adjust the value column based on both the Response and Date columns. I need to adjust the value column for rows where Response = Photosynthesis. If Response = Photosynthesis, I need to subtract the value where Response = Respiration. I also have repeated measures on different dates so the Respiration and Photosynthesis values need to be from the same date. I think ifelse will work but I can't get the syntax right.
...ANSWER
Answered 2019-Dec-01 at 22:43We can just do a diff
as the data is already grouped and have only 2 observations per group
QUESTION
I have two continuous variables Light and Temperature and I am studying rates of photosynthesis against these variables.
I am looking to split/subset my data into small ranges of light and temperature which I will then plot photosynthesis against.
I was wondering how can I split up my data into these light and temperature range combinations. For example, I would like the following:
Dataset1: (Light >100 & <200) & (Temperature >15 and <20)
Dataset2: (Light >100 & <200) & (Temperature >20 and <25)
... etc. which will be continued for each light range and each temperature range.
I want to specify that I am only interested in the subsetting of my data by temperature and light. This is because I will be plotting the data in that data set by another measured variable (Photosynthesis). I am not looking to plot light or temperature itself.
My outcome: Calculate average photosynthesis in two treatments (ambient CO2 and elevated CO2) in these different microclimate ranges.
So far I have tried: subset() %inrange% %between%
Any help is appreciated.
...ANSWER
Answered 2019-Apr-03 at 10:23With the use of the base function cut
and the ggplot function facet_wrap
you can do what you need plus have the flexibility of trying out multiple arrangements and without the proliferation of multiple data frames.
QUESTION
ANSWER
Answered 2018-Feb-13 at 13:17Use this
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