For example, a trimmed mean. It is certainly risky if you are not expecting it :). Alternatively, you can use the out argument to save the output in a .tex or .txt file. Amisc is a great package for summary statistics tables. does the most popular category contain 10% or 99% of the data? Usage Thanks for the kind comments, and the link to stargazer. : It also does not work with kable and is not tidy. Arguments with a value of NULL will use the default settings of the requested style. R – Risk and Compliance Survey: we need your help! B. Sorry, your blog cannot share posts by email. We will also see how to use outreg2 in the outreg2 package, which is less flexible but is slightly less work to use for standard tables that are basically summarize but nicer-looking and output to a file. In addition to that it is also possible to put p-values as a separate column at the end of the table. In order for you to follow my code, I used the gapminder data set from the gapminder package. The summarytools package also includes a fancier, more comprehensive, summarising function called dfSummary, intended to summarise a whole dataframe – which is often exactly what I want to do with this type of summarisation. In the next code block, we are customizing our table. If FALSE, the count component is displayed. Is it possible to export table to Word (or Excel), if table was made with “table1”? Great post. A common way to do this, which allows you to show information about many variables at once, is a “Summary statistics table” or “descriptive statistics table” in which each row is one variable in your data, and the columns include things like number of observations, mean, median, standard deviation, and range. a logical value that indicates whether stargazer should calculate the p-values, using the standard normal distribution, if coefficients or standard errors are supplied by the user (from arguments coef and se) or modified by a function (from arguments apply.coef or How strange! Can you let me knwo how to fix it? I did not know tableone, but at a glance it looks very interesting. purrr::flatten_lgl() %>% You can include the produced tables in your paper by inserting stargazer LaTeX output into your publication's TeX source. It is becoming a bit boring to see the same data again and again. In the code below, we are showing how to create a table without stratification by any group. After that I divided the population by one million to make the table more readable. dplyr::group_by_all() %>% You can look at a package such as skimr on CRAN. I will be consulting this page extensively for my senior thesis project in sociology , I am glad I can be of help. For categorical data, produce at least these types of summary stats: A list of the categories – perhaps restricted to the most popular if there are a high number. unname() %>% This function can deal with both categorical and numeric variables and provides a pretty output in the console with all of the most used summary stats, info on sample sizes and missingness. An empty string (i.e., "") will lead stargazer to omit the caption. by(data, data$type, Hmisc::describe). Do any of these packages do this? There are many ways to do this, but two common ones are. Again, a bit modified and with the introduction of missing values. Here’s how we’d use it if we want the stats for each “type” in my dataset, A – E. Everything you need is there, subject to the limitations of the basic describe(). This clearly provides the count of variables and observations. This package unfortunately has only the option to show the missing values as percentages. Unfortunately, there is not much documentation about this package. It does not seem to have got any reply... Just trying my luck here. The first thing I note is that this is another one of the summary functions that (deliberately) only works with numerical data. One of three strings can be used: "l" for left alignment, "r" for right alignment, and "c" for centering. I also spent several hours evaluating different packages and arsenal is the most flexible library I have used so far. This time it was a bit faster, taking around 5 minutes, but still not the few seconds that you mentioned it should take. To include stargazer tables in Microsoft Word documents (e.g., .doc or .docx), please follow the following procedure: Use the out argument to save output into an .htm or .html file. Then we are calculating the total missing cylinder values for each column. I just tried it again, where my dataset has just 64 rows and 4 columns, and it took around 6 minutes to complete the dfSummary (into the console). For the remaining tables, we are using the mtcars data set. R 3.5.0 is released! 2. a character string that specifies how notes should be aligned under the table. This is visually less pleasant, but it does enable you to produce a potentially useful dataframe, which you could tidy up or use to produce group comparisons downstream, if you don’t mind a little bit of post-processing. Is there any way to use stargazer to create a table of descriptive statistics by group such as the one below? Thank you for your comment, I am glad it helped out. Stargazer’s default will produce a table with both of these measures as well as Standard Deviation, Minimum and Maximum values. I hope you all have enjoyed this post and that you have found a package which suits your needs. This argument is not case-sensitive. Glad it helped Martin! I think I’ll take another look at it after your reminder, thanks! a character vector that specifies which summary statistics should be omitted from summary statistics table output. an integer that indicates how many decimal places should be used. and then knit it to html/word/pdf. #TidyTuesday, How to Easily Create Descriptive Summary Statistics Tables in R Studio – By Group, Assumption Checking of LDA vs. QDA – R Tutorial (Pima Indians Data Set). These graphs are not as beautiful as the sparklines that the skimr function tries to show, but have the advantage that they work right away on Windows machines. purrr::flatten_chr() -> my_p_values, Here is a link to the vignette: I took a look at the vignette and it looks like it might be incredibly fully featured, yet easy to use even for people who don’t enjoy coding. The out commented section is how the insert_row() function works. If one of the aforementioned letters is followed by an asterisk ("*"), significance stars will be reported next to the corresponding statistic. On the downside, the function seems very slow to perform its calculations at the moment. This is what you expect from the output, it gives you LaTeX code that will create a table when compiled (you do have to compile it within a LaTeX document). This has been a guest post by Marek Hlavac, the author of the {stargazer} R package for beautiful LaTeX tables from R's statistical models' outputs.