skimr | pipeable approach to dealing with summary statistics | Data Visualization library

I am trying to add percentages of levels of factor to skimr::skim output. I tried to use the table function but it did not work as intended. I can I get the percentages of the different species in the correct format, similar to top_count?

I just finished a course to learn R for Data Analysis and now I am working on my own on a case study.

Since I am a beginner, please help me understand this problem I did not have during the course.

I have imported csv files and I want to assign them to variables with better names.

I am using following packades: tidyverse, readr, lubridate, ggplot2, janitor, tidyr, skimr.

This is my code:

Hi everyone based on the wage-dataset (wage being the dependent variable) and on the workflow created below, I would like to find out the following:

1. What is the predicted wage of a person with age equal to 30 for each piecewise model?
2. Considering the flexible pw6_wf_fit model configuration and in particular the six breakpoints above: Exceeding which (approximate) value of age correlates strongest with wage?

I tried to use versions of extract but so far I don´t know how to apply it in R. Helpful for any comment

The code I use is the following:

The fitted values returned from speedglm() look really different from those returned from glm() and i don't know why. For example, if I run this:

I am working on a report that will display the results of some Likert scale data. I want to use the skim() function from the skimr package to utilize the spark graphs/histogram visual. The issue is that my response options range from 1 to 5 on each question, but some of my questions only collected responses in the 3 to 5 range (response options 1 and 2 were not selected). The histogram shows five columns and the range seems to represent 3, 3.5, 4, 4.5, 5 rather than from 1 to 5. How do I tell skimr to display option 1 through 5? Thanks for any help in advance.

Example:

Data:

I'm trying to develop a simple logistic regression model using Tidymodels with the Spark engine. My code works fine when I specify set_engine = "glm", but fails when I attempt to set the engine to spark. Any advice would be much appreciated!

Apologies if this has been asked elsewhere / if I am using the wrong terms, I have been trying to search for the correct way to do this but with no success so far.

I have an experimental design with 3 experimental conditions using repeated measures outcomes (each participant completes 4 trials). The data I have currently is in long format (each participant ID is repeated 4 times). I am trying to calculate summary statistics for the demographic variables (age, gender, condition etc.) but I cannot figure out how to, for lack of a better word, collapse/merge the rows for each participant together to get the frequency data and/or summary stats.

Below I have a simulated dataset

Say, I have a dataset called iris. I want to create an indicator variable called sepal_length_group in this dataset. The values of this indicator will be p25, p50, p75, and p100. For example, I want sepal_length_group to be equal to "p25" for an observation if the Species is "setosa" and if the Sepal.Length is equal to or less than the 25th percentile for all species classified as "setosa". I wrote the following codes, but it generates all NAs:

When I run the default skimr command in the console on a linux RStudio server I get the following partial output and error:

library(skimr) skim(iris)

── Data Summary ──────────────────────── Values Name iris
Number of rows 150
Number of columns 5

Column type frequency:
factor 1
numeric 4

Group variables None
Error in check_dots_used(action = warn) : unused argument (action = warn)

However, the same code will run just fine when I knit it in an RMarkdown document.

The same code will also run fine on my Mac OSX laptop instance of RStudio, both in the console and an RMarkdown document.

I can assign the output of the skimr command and View the assigned output object just fine on the server instance:

out <- skim(iris) View(out) class(out) 1 "skim_df" "tbl_df" "tbl" "data.frame"

but print(out) generates the same error again

Here's the sessionInfo.

sessionInfo()

R version 3.5.1 (2018-07-02)

Platform: x86_64-redhat-linux-gnu (64-bit)

Running under: CentOS Linux 7 (Core)

Matrix products: default

BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so

locale: 1 LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
[6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:

1 stats graphics grDevices utils datasets methods base

other attached packages: 1 skimr_2.1.2

loaded via a namespace (and not attached):

1 Rcpp_1.0.2 rstudioapi_0.13 knitr_1.31 magrittr_1.5 tidyselect_1.1.0 R6_2.3.0 rlang_0.4.10 fansi_0.4.0 stringr_1.4.0
[10] dplyr_1.0.4 tools_3.5.1 xfun_0.20 utf8_1.1.4 cli_2.3.0 DBI_1.0.0 withr_2.4.1 htmltools_0.3.6 ellipsis_0.2.0.1 [19] yaml_2.2.0 rprojroot_1.3-2 assertthat_0.2.0 digest_0.6.18 tibble_3.0.6 lifecycle_0.2.0 crayon_1.3.4 tidyr_1.1.2 purrr_0.3.4
[28] repr_1.1.3 base64enc_0.1-3 vctrs_0.3.6 evaluate_0.12 glue_1.4.2 rmarkdown_1.10 stringi_1.2.4 compiler_3.5.1 pillar_1.4.7
[37] backports_1.1.2 generics_0.1.0 jsonlite_1.6 pkgconfig_2.0.2