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Did you run this line near the top of your code? library(tidylog) You may also need to use these options where appropriate, but normally putting library(tidylog) at the top of your code is fine: # turn logging-output on options("tidylog.display" = NULL) # turn logging-output off options("tidylog.display" = list()) (more details on those here: rdrr.io/github/elbersb/tidylog/f/README.Rmd) One possibility is that another package you are using is overriding tidylog, but that is unlikely.

@@tomhenry-datasciencewithr6047 thank you Tom for the prompt reply Ll give it a go Any interesting packages, add-ins and/or tips using R? A video is due:) Appreciate the efforts, they really make a difference

To filter rows in R where depth is between 100 and 150, you can use: library(tidyverse) # assuming you've loaded this earlier filtered_data <- your_data %>% filter(depth >= 100 & depth <= 150) # if you want to include 150, too filtered_data <- your_data %>% filter(depth >= 100 & depth < 150) # if you don't want to include 150 For more information on the filter function and other data manipulation tools in R, check out the book "R for Data Science" by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund, available online at r4ds.hadley.nz/data-transform#filter :)

Glad it was helpful!

You're very welcome!

It depends - are you trying to do time series forecasting or do you just want the plot to show a particular way? I would recommend checking out tsibble.tidyverts.org/reference/fill_gaps.html .

@@tomhenry-datasciencewithr6047 thank you for this. I am trying to do both but starting with making a plot. I recall there is an command like complete() are you on linkedIn? we could connect

@@ahmed007Jaber For time series data, I would recommend tsibble.tidyverts.org/reference/fill_gaps.html However, if using tsibble is not an option, you could create a table of dates, e.g. dates_desired <- tibble(date = seq(lubridate::ymd("2022-01-01"), lubridate::ymd("2022-12-31"), by = "1 day")) and join your dateset onto it: data_with_all_dates <- dates_desired %>% left_join(your_data_with_missing_dates, by = "date") PS - I'm not on LinkedIn unfortunately!

@@tomhenry-datasciencewithr6047 thank you will check this out for sure. I have timeseries data and I am searching how to know do a shiny dashboard to interact with data LinkedIn community is good and I really have learnt a lot through connectionss and posts. what is the best way to reach you out? interested to grow my network and I like your content.

Glad to hear!!!!

What is your goal? Depending on what you are aiming to do, there may be more specific methods. However, a general approach works like this: your_data %>% group_by(specific_value) %>% sample_frac(0.5) %>% ungroup()

@@tomhenry-datasciencewithr6047 Hey Tom thank you for your answer. I try but it din't work. Instead i used the following approach! tmp_data <- data %>% filter(x == ma_valeur) %>% slice_sample(prop = 0.5) data %>% anti_join(tmp_data)

It depends on the exact scenario. Assuming you have one row per person (i.e., not multiple rows per person), you can do it like this: your_data <- your_data %>% mutate(variable_of_interest.percentile = percent_rank(variable_of_interest)) your_data %>% filter(variable_of_interest.percentile <= 0.01) # lowest 1% your_data %>% filter(variable_of_interest.percentile > 0.99) # top 1%

If you are interested only in excluding the top and bottom 1%, you could do your_data %>% filter(variable_of_interest.percentile >= 0.01 & variable_of_interest.percentile < 0.99) **However** - my guess is that you are trying to filter out outliers - is that right? If so, depending on your situation I would usually recommend two alternatives: 1. retaining outliers but using a technique that is more resilient to outliers (e.g., quantile regression using the quantreg package - basically quantile regression optimizes for the median, whereas regular linear regression optimizes for the mean) e.g. # install.packages("quantreg") library(quantreg) rq(y ~ height + age, data = ...) (same kind of interface as linear regression) 2. figuring out non-arbitrary absolute cut-offs - e.g. if you know that a customer spending more than a certain amount is unrealistic or a patient will never have a lab value less than a particular amount, then just filter out records where they exceed those values. The reason is that in many datasets, the top 1% (say) may have some invalid records but also a lot of valid records, so it's best to be explicit about your inclusion/exclusion criteria rather than using a % criteria for exclusion, where possible.

@@tomhenry-datasciencewithr6047 Wow thank you so much for your help! Amazing!!! Love from the UK

@@tomhenry-datasciencewithr6047 You got it exactly right, I needed to get rid of outliers and I will take these better suggestions from you on board. Thank you so much

@@JC-wt5fw Hope it goes well! :)

You can do this like so: your_data %>% filter((x == 1 & y > 2000) | (x == 2 & y > 3000) | (x == 3 & y > 4000)) (OR is the `|` symbol, and AND is the `&` symbol). There are other ways of doing it which depend on your use-cases and which could be better, but this is the general method.

@@tomhenry-datasciencewithr6047 This is SO helpful. Thanks Tom. I look forward to watching more of your videos. They are super clear!

You are asking exactly the questions that would go into a more detailed analysis! There is a useful function called SnowballC::wordStem() which reduces words down to a common 'stem.' For example, it produces: "then there is the matter of word associ and similar and deplur that should be done i saw player and player textual the same content also a pair of review negat but no review negat" For a more rigorous look into this subject see Julia Silge and David Robinson's excellent 'Tidy Text Mining in R' which is free online: www.tidytextmining.com Text analysis is always imperfect (and will remain so forever, I suspect), but it can yield good insights when applied to a large dataset, provided a human is in the loop!

@@tomhenry-datasciencewithr6047 Something like this would be PERFECT for what I am doing, but it is honestly above me in complexity, at the moment. I was looking for a formulated way to form a sense of textual hierarchy. One which could be used to help new entries "find a logical category level", where it belongs with other similar associated words. In a basic sense... object => transportation => vehicle => automobile => car => gasoline_engine => hatch_back => ford => mustang => 1985 => cherry_red_paint So a "truck", which, by similar associations, would be at the level of "car". transportation => vehicle, as opposed to skates (accessory vs something you drive) or a ski-lift (not drivable) vehicle => automobile, as opposed to a bicycle or skateboard (non-motorized vehicles) etc... The hierarchy being assumed by known relations, and/or by simple volume of appearance and order. People tend to say, "my 1985 mustang", classed in reverse, "specific => generic". However, by volume, 1985 appears less than mustang and ford appears more than that. Continuing up the chain to automobiles, vehicles and transportation, which has progressively more and more "objects" that they are identified with. While knowing the relation, without needing to know the specifics of any one car... ford can be aligned with lexus, chevy, mazda, etc... Because of the similar preceding and following similar groupings. Why turn the entire language into a form of "word tree of origins"? Partly for use with AI classification of image contents. Knowing that cars have rims, wheels, paint, body styles, manufactures. Partly for extracting and isolating the correlating subject matter and "emotion/opinions" within descriptions. Partly for assisting the extraction and isolation of "finer details", such as the more rare descriptions of the types of tires, ground effects, antenna types, rim types. I could go on, but my primary goal in my knowledge-quest, was for the things just mentioned, as a whole. Extending to the final purpose of being used as a guide for helping others "classify images contents", with more valuable information that AI can use for digestion. (All in relation for text2image and image2image AI created art, which is assisted by "human textual prompts". They have a rough system in place, but it is hardly extensive or adequate enough to be used with any form of accuracy or repeatability.) P.S. Looking into this further, because of this video you posted. (Yet another language to consume my brain-cells.)

I also went through your tutorial in this video and had the same error message. When I get to line 30 I receive an error that "object 'volcano_type_counts' not found". For some reason my assigned variables disappear. Any advice?

Hi James! Can you post the code snippets you are using here? (for both getting the counts and plotting the count results)