broom | Convert statistical analysis objects from R into tidy format | Data Visualization library
kandi X-RAY | broom Summary
kandi X-RAY | broom Summary
broom summarizes key information about models in tidy tibble()s. broom provides three verbs to make it convenient to interact with model objects:. For a detailed introduction, please see vignette("broom"). broom tidies 100+ models from popular modelling packages and almost all of the model objects in the stats package that comes with base R. vignette("available-methods") lists method availability. If you aren’t familiar with tidy data structures and want to know how they can make your life easier, we highly recommend reading Hadley Wickham’s Tidy Data.
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QUESTION
I have two large-ish data frames I am trying to append...
In df1, I have state codes, county codes, state names (Alabama, Alaska, etc.), county names, and years from 2010:2020.
In df2, I have county names, state abbreviations (AL, AK), and data for the year 2010 (which I am trying to merge into df1. The issue lies in that without specifying the state name and simply merging df1 and df2, some of the data which I am trying to get into df1 is duplicated due to there being some counties with the same name...hence, I am trying to also join by state to prevent this, but I have state abbreviations, and state names.
Is there any way in which I can make either the state names in df1 abbreviations, or the state names in df2 full names? Please let me know! Thank you for the help.
Edit: dput(df2)
...ANSWER
Answered 2022-Apr-18 at 03:52Here's one way you could turn state abbreviations into state names using R's built in state vectors:
QUESTION
I have an issue that might be silly in some ways, but following this question:
Linear Regression and group by in R
I tried to install the broom package in order to "retrieve the coefficients and Rsquared/p.value".
I know that the previous question is 12 years old but this package is still listed in my RStudio for installation, but then I have this error message and I am lost on what to do to make it work properly:
library(broom) Error in value[3L]: Package 'broom' version 0.7.12 cannot be loaded: Error in unloadNamespace(package): namespace 'broom' is imported by 'modelr', 'tidyverse', 'rstatix' and therefore cannot be unloaded
So my question is straightforward: what does it mean? Did broom become a dependancy of the 3 packages cited? How to make it work?
Thank you very much for your help.
EDIT: screenshot of the output to know why some numbers appear in red.
...ANSWER
Answered 2022-Apr-17 at 08:28Given your comments, you should be able to purrr::map broom::tidy over your list column of models.
QUESTION
Here is my toy data.
...ANSWER
Answered 2022-Apr-15 at 19:43To carry out the same operation multiple times, we can use a for-loop or an apply function.
To keep the code tidy, I first made a function out of the code to repeat, with the value of .before as a parameter. Then lapply() executes that function multiple times. Then do.call(rbind) binds the resulting dataframes together.
QUESTION
I have a several lm objects that I would like to loop into broom::tidy using purrr::map. Is this possible to do?
ANSWER
Answered 2022-Apr-10 at 20:47You need to keep your models in a list (list()), not in a vector (c()):
QUESTION
I am trying to run a loop which takes different columns of a dataset as the dependent variable and remaining variables as the independent variables and run the lm command. Here's my code
...ANSWER
Answered 2022-Mar-24 at 17:53We could change the line of fit with
QUESTION
here the datasets with repeated measure
...ANSWER
Answered 2022-Jan-26 at 14:33 alt %>%
group_by(groupter) %>%
mutate(id_row = row_number()) %>%
pivot_longer(-c(id_row, groupter)) %>%
nest() %>%
mutate(result = map(data, ~friedman.test(value ~ name | id_row, data = .x))) %>%
mutate(out = map(result, broom::tidy)) %>%
select(-c(data, result)) %>%
ungroup() %>%
unnest(out)
groupter statistic p.value parameter method
1 1 14.5 0.0129 5 Friedman rank sum test
2 2 11.7 0.0389 5 Friedman rank sum test
QUESTION
I installed broom.mixed package via conda conda install -c conda-forge r-broom.mixed, however, it doesn't import and shows error message:
ANSWER
Answered 2022-Jan-17 at 14:18I believe the issue being encountered is primarily driven by mixing the defaults channel (specifically the r channel) and the conda-forge channel. This is known to lead to missing libraries and missing symbol references in shared libraries because Anaconda and Conda Forge use different build stacks and sometimes different recipes.
In this case, r-broom.mixed depends on r-tmb, which on Conda Forge depends on libblas and liblapack, but on the r channel does not have these dependencies.
Generally, I recommend that Conda users who want R environments should only use Conda Forge and avoid using the r channel. This is because the r channel has mostly been abandoned from what I can tell (e.g., no R version 4 releases, and most packages have not been updated for over a year).
Furthermore, I would discourage the use of the r-essentials package. Analogous to the Anaconda distribution of Python (anaconda package), the r-essentials package bundles together many packages that are anticipated to be used by data scientists, but some of it simply seems bloated to me. Something specific that troubles me about it is that it ends up pulling in Python in addition to R. No one should need to have Python mixed in with an R environment. This is due to including notebook, which if users really want to load an R environment as a kernel, they only need r-irkernel (as demonstrated below).
In summary, one should be fine simply doing:
QUESTION
With code below (edited basing on code from here) I generates two example tables with gt package:
ANSWER
Answered 2022-Jan-06 at 13:23I can offer to you this solution:
1. We take your data:
QUESTION
I'm trying to run a simple single linear regression over a large number of variables, grouped according to another variable. Using the mtcars dataset as an example, I'd like to run a separate linear regression between mpg and each other variable (mpg ~ disp, mpg ~ hp, etc.), grouped by another variable (for example, cyl).
Running lm over each variable independently can easily be done using purrr::map (modified from this great tutorial - https://sebastiansauer.github.io/EDIT-multiple_lm_purrr_EDIT/):
...ANSWER
Answered 2021-Dec-12 at 22:18IIUC, you can use group_by and group_modify, with a map inside that iterates over predictors.
If you can isolate your predictor variables in advance, it'll make it easier, as with ivs in this solution.
QUESTION
I'm currently trying to run tbl_regression with an imputed dataset from mice run through a logistic glm. I'm having trouble trying to combine the custom tidiers pool_and_tidy_mice and tidy_standardize in order to get the regression output from the pooled mice results and the standardized odds ratio estimates.
Is there a way to get the standardized odds ratios with pooling imputed data with tbl_regression or possibly another step I can take to get them?
Using the surival package as an example, I can get standardized odds ratio with non-imputed data with this code:
ANSWER
Answered 2021-Dec-10 at 09:41Unlike the unimputed data, mice doesn't output a table, so it must be transformed using complete afterwards. What about this:
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