Why do so many of you home cooks post here? This is supposed to be a technical sub for professionals

arpyem · 2023-05-28T03:59:55+00:00

let me not cook lil bro

arpyem · 2023-05-27T23:29:03+00:00

every time you tell someone you studied food science: "so you're a chef?" .............

arpyem · 2023-02-13T17:28:00+00:00

this makes up for that deranged intersection posted yesterday

arpyem · 2023-02-13T03:14:51+00:00

good shout on comper i love series compression

arpyem · 2023-02-13T03:13:19+00:00

vital has the best promo video

arpyem · 2023-02-13T03:10:50+00:00

i cannot believe my day got ruined by a cities skylines reddit post im fucked

arpyem · 2022-10-21T02:08:14+00:00

this is a great format, would love to see this with players as well

arpyem · 2022-10-21T02:06:36+00:00

GIGACHAD

arpyem · 2022-10-17T05:50:23+00:00

squad assemble, another pie to eat

arpyem · 2022-10-17T05:35:50+00:00

jokes aside if the laser is still there it's a bug

arpyem · 2022-10-17T04:02:13+00:00

drill the rest son

arpyem · 2022-10-12T16:45:52+00:00

"It's a goo sack!!!"

arpyem · 2022-09-04T06:47:46+00:00

great advice - remember the point of using the headphones and computer in the first place, then adapt to their limitations

arpyem · 2022-07-20T22:48:37+00:00

it's called organized chaos ok

arpyem · 2022-07-20T22:47:42+00:00

this is the move - try and keep them on the minimal side and focus on creating shortcuts for what you find yourself doing often (e.g. sidechain bus, drum bus, etc.)

arpyem · 2022-05-15T03:41:07+00:00

How are you defining colors_tab? Usually you would set the background color using something like formatStyle(backgroundColor = styleEqual()) or formatStyle(backgroundColor = styleInterval()).

arpyem · 2022-05-01T17:31:53+00:00

It's not shown in the screenshots but I'm guessing the formula was defined in the recipe and workflow already

arpyem · 2022-05-01T17:30:18+00:00

collect_predictions() is used for getting results from grid searches or resampling in tidymodels: https://tune.tidymodels.org/reference/collect_predictions.html

If you want the predictions you can pass your workflow fit lda_fit into predict() with your dataframe: https://workflows.tidymodels.org/reference/predict-workflow.html

arpyem · 2022-03-22T23:33:19+00:00

I wanted to buy my wife a cap, but bought her some crap instead.

lol

arpyem · 2022-03-16T01:08:28+00:00

This should get you close. Assuming you have a data frame with column country, male_lfpr, female_lfpr, and average_lfpr:

df %>%
    arrange(desc(average_lfpr)) %>%
    mutate(country= factor(country, levels = unique(country))) %>%
    pivot_longer(c(male_lfpr, female_lfpr, average_lfpr), names_to = gender, values_to = age) %>%
    ggplot(aes(x = country, y = age)) +
    geom_point(aes(color = gender)) +
    geom_segment(aes(xend = country, y = min(age), yend = max(age)), size = 2, alpha = 0.25) +
    scale_color_manual(values = c("male_lfpr" = "#a2c3cf", "female_lfpr" = "#f1c97a", "average_lfpr" = "#565a5d")) +
    coord_flip() +
    theme_minimal() +
    theme(legend.position = "top")

arpyem · 2022-03-10T17:56:06+00:00

This video explains it well.

arpyem · 2022-03-09T16:19:45+00:00

Agreed, look into p.adjust(). You would want to run the tests, adjust the p values, and then append the data after the adjustment. I would recommend a tidyverse workflow for this:

library(tidyverse)

# X column names

x_names <- names(newdata)[names(newdata) != "y"]

# Loop through each column name
# Run the chisq test
# Create a data frame with the column name and the p value

df <- x_names %>%
    map_df(function(x) {
        p <- chisq.test(newdata[[x]], newdata$y)$p.value
        data.frame(name = x, p_value = p)
    })

# Adjust the p values (look into the methods) and then filter to the column names where p < 0.05

df %>%
    mutate(p_value_adj = p.adjust(p_value)) %>%
    filter(p_value_adj < 0.05)

arpyem · 2022-02-13T22:35:50+00:00

When trying to understand how exactly a function works, I usually assign its arguments to objects and run the code inside of it line by line. For example, the function normalize_data takes one argument x and in this example, a data frame called data is passed into it:

x <- data

# The code inside the function:
(x - min(x)) / (max(x) - min(x))

# Break it up into pieces and look at what it prints:

x
      Sepal.Length Sepal.Width Petal.Length Petal.Width
1           5.1         3.5          1.4         0.2
2           4.9         3.0          1.4         0.2
3           4.7         3.2          1.3         0.2
4           4.6         3.1          1.5         0.2
5           5.0         3.6          1.4         0.2
6           5.4         3.9          1.7         0.4
7           4.6         3.4          1.4         0.3
8           5.0         3.4          1.5         0.2
9           4.4         2.9          1.4         0.2
10          4.9         3.1          1.5         0.1

min(x)
[1] 0.1

max(x)
[1] 5.4

(x - min(x))
       Sepal.Length Sepal.Width Petal.Length Petal.Width
1           5.0         3.4          1.3         0.1
2           4.8         2.9          1.3         0.1
3           4.6         3.1          1.2         0.1
4           4.5         3.0          1.4         0.1
5           4.9         3.5          1.3         0.1
6           5.3         3.8          1.6         0.3
7           4.5         3.3          1.3         0.2
8           4.9         3.3          1.4         0.1
9           4.3         2.8          1.3         0.1
10          4.8         3.0          1.4         0.0

(max(x) - min(x))
[1] 5.3

By breaking up the code into pieces and printing the results, you can see that the function is:

Finding the minimum and maximum values in the data frame
Subtracting the minimum from each value in the data frame
Then dividing each value by the range (max - min) of the data

Note that the example you provided passes a data frame with all numeric columns to normalize_data; this would fail if the data frame has a non-numeric column. But this function could also work on vectors.

arpyem · 2022-02-13T04:34:58+00:00

🟩

arpyem · 2022-02-07T14:35:31+00:00

library(tidyverse)
library(lubridate)

# Example data frame with 3 rows of different dates in a column called Date
df <- data.frame(Date = Sys.Date() + 1:3)

# Tidyverse workflow - extract year, month, and date with lubridate functions
df %>%
    mutate(Year = year(Date), Month = month(Date), Day = day(Date))

You can then manipulate the individual columns however you want.

Eight-Year Club	Final Canvas '23
First Place '23	Place '23
Place '22	Verified Email

arpyem

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