lesser known stars of the tidyverse

Emily Robinson has just joined DataCamp and writes a great blog called www.hookedondata.org. She talked at the 2018 New York R conference recently and shared some of her favourite (less well known) stars of the Tidyverse. Here are her slides www.tiny.cc/nyrtalk and my notes…

1.use as_tibble()

Tibble = modern dataframe. Use instead of printing your dataset to the console.

as_tibble() will only print the first 10 rows and columns that fit on the screen.

2. examine your NAs

is.na() returns TRUE or FALSE, TRUE is 1 and FALSE is 0, which means you can use sum to count the number of NAs in a given variable.

summarise(numberNA = sum(is.na(variable))

What about all variables? Purrr can help. map_df() maps a function across all variables and gives a dataframe (hence the df) back. The dot says this is where the column/function should go; the tilde ~ stands for anonymous function.

map_df(~sum(is.na(.)))

What if cells are empty rather than NA? You can convert empty cells to NA using na.if()

na_if(“”)

3. look at numeric variables

Look just a numeric columns with select_if(is.numeric) then pipe in to a skim() to get mean, SD, missing, and little histograms.

4. examine data in a single column

When you have more than 1 piece of information in a column, using stringr package str_split(column, ",") to split the string at the point where the , is.

In ggplot, mhen x axis labels are all bunchy, use coord_flip() to fip the graph on its side. Also if you want to sort the bars, use fct_reorder() from forcat to sort by n.

5. make a mimimal reproducible example

When you want help, if it helpful to helpers if you create a minimal reproudicule example so that they can see and run the code using your data.

Optional. Use as.tribble() to make a fake data set, if there is a reason you can’t show helpers real data.

  1. Use reprex() to find problems that may prevent helpers from running your code for reasons that aren’t related to why your code won’t run for you.

  2. Use reprex() to post your question/issue

Emily’s favourite resources

  1. www.r4ds.co.nz R for Data Science

  2. Twitter #rstats

  3. Rstats cheat sheets

  4. www.DataCamp.com