- Data analysis tool for excel for mac pdf#
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- Data analysis tool for excel for mac plus#
- Data analysis tool for excel for mac series#
RStudio add-in offers a menu for R Markdown formatting commands, so you no longer need to remember and/or type code for things like making an HTML list or embedding a YouTube video. Yihui Xie & others (knitr), RStudio (rmarkdown) See the Minimal Examples knitr page and RStudio's R Markdown page. A must-have if you're interested in reproducible research and automating the journey from data analysis to report creation. CRAN.Īdd R to a markdown document and easily generate reports in HTML, Word and other formats. Super easy way to convert data between different R time-series data formats: xts, data frame, zoo, tsibble, and more.
Data analysis tool for excel for mac series#
Robust package with a slew of functions for dealing with time series data I like the handy rollmean function with its align=right and fill=NA options for calculating moving averages. Not sure where to get started looking at a data set? Want to get a basic handle on that data without running multiple commands like str() and plot()? DataExplorer attempts to offer one-click report generation to show and visualize basics about a data set, such as distributions and missing data. Garrett Grolemund, Hadley Wickham & others Mdy("") + months(1) More examples in the package vignette CRAN.Įverything you ever wanted to do with date arithmetic, although understanding & using available functionality can be somewhat complex. Some of my favorites: str_pad and str_trim.
Some are similar to existing base R functions but in a more standard format, including working with regular expressions. Numerous functions for text manipulation. CRAN.ĭata.table in 5 minutes video, The ultimate data.table cheat sheet, Intro vignette While I often prefer dplyr for basic analysis, data.table has become my go-to for large data sets or when speed is critical (such as in Shiny apps). Popular package for heavy-duty data wrangling and computation. See the testing chapter of Hadley Wickham's book on R packages. Package that makes it easy to write unit tests for your R code. Intuitive data validation based on rules you can define, save and re-use. as a placeholder for the original object being operated upon. Myjson % symbol for chaining R operations, but it's got other useful operators such as %% for mutating a data frame in place and and. Parse json within R or turn R data frames into json. Sqldf("select * from mydf where mycol > 4") CRAN.ĭf_paste() to create a data frame, vector_paste() to create a vector.ĭo you know a great SQL query you'd use if your R data frame were in a SQL database? Run SQL queries on your data frame with sqldf. It includes RStudio add-ins as well as command-line functions for transposing data, turning it into markdown format, and more. If you've copied data from the Web, a spreadsheet, or other source into your clipboard, datapasta lets you paste it into R as an R object, with the code to reproduce it. CRAN.ĭata copy and paste: Meet reproducible research. Cs(so, it, goes) creates c("so", "it", "goes"). Two of my favorites: describe, a more robust summary function, and Cs, which creates a vector of quoted character strings from unquoted comma-separated text. There are a number of useful functions in here. Each cell is imported in its own row, with information about data type, position, and color, not just value, allowing you to reshape the data from there. If you've ever wanted to tear your hair out over an Excel file with merged cells, data in column headers, headers mixed in data, and key information in color coding, this is the package for you. Rio has a good idea: Pull a lot of separate data-reading packages into one, so you just need to remember 2 functions: import and export. Hadley Wickham (readr), Jim Hester (vroom) Read_csv(myfile.csv) or vroom(myfile.csv) data.table's fread() is another useful alternative. readr has been around for awhile vroom is a speedier alternative, useful for larger data sets. Read_excel("my-spreadsheet.xls", sheet = 1)īase R handles most of these functions but if you have huge files, these packages offer faster and standardized way to read CSVs and similar files into R.
Data analysis tool for excel for mac pdf#
Map_df(mylist, myfunction) More: Charlotte Wickham's purr tutorial video, the purrr cheat sheet PDF download, easy error checking with purrr's possibly.įast way to read Excel files in R, without dependencies such as Java.
Data analysis tool for excel for mac plus#
And, its functions are more standardized than base R's apply family - plus it's got functions for tasks like error-checking. It's more complex to learn than the older plyr package, but also more robust. Purrr makes it easy to apply a function to each item in a list and return results in the format of your choice. Especially useful for operating on data by categories.
The essential data-munging R package when working with data frames.
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