Explore and summarize data
explore(
dataset,
vars = "",
byvar = "",
fun = c("mean", "sd"),
top = "fun",
tabfilt = "",
tabsort = "",
tabslice = "",
nr = Inf,
data_filter = "",
arr = "",
rows = NULL,
envir = parent.frame()
)Dataset to explore
(Numeric) variables to summarize
Variable(s) to group data by
Functions to use for summarizing
Use functions ("fun"), variables ("vars"), or group-by variables as column headers
Expression used to filter the table (e.g., "Total > 10000")
Expression used to sort the table (e.g., "desc(Total)")
Expression used to filter table (e.g., "1:5")
Number of rows to display
Expression used to filter the dataset before creating the table (e.g., "price > 10000")
Expression to arrange (sort) the data on (e.g., "color, desc(price)")
Rows to select from the specified dataset
Environment to extract data from
A list of all variables defined in the function as an object of class explore
See https://radiant-rstats.github.io/docs/data/explore.html for an example in Radiant
See summary.explore to show summaries
explore(diamonds, c("price", "carat")) %>% str()
#> List of 13
#> $ tab :'data.frame': 2 obs. of 3 variables:
#> ..$ variable: Factor w/ 2 levels "price","carat": 1 2
#> ..$ mean : num [1:2] 3907.186 0.794
#> ..$ sd : num [1:2] 3956.915 0.474
#> ..- attr(*, "radiant_nrow")= int 2
#> $ df_name : chr "diamonds"
#> $ vars : chr [1:2] "price" "carat"
#> $ byvar : NULL
#> $ fun : chr [1:2] "mean" "sd"
#> $ top : chr "fun"
#> $ tabfilt : chr ""
#> $ tabsort : chr ""
#> $ tabslice : chr ""
#> $ nr : num Inf
#> $ data_filter: chr ""
#> $ arr : chr ""
#> $ rows : NULL
#> - attr(*, "class")= chr [1:2] "explore" "list"
explore(diamonds, "price:x")$tab
#> variable mean sd
#> 1 price 3.907186e+03 3956.9154001
#> 2 carat 7.942833e-01 0.4738263
#> 3 clarity 1.333333e-02 0.1147168
#> 4 cut 3.366667e-02 0.1803998
#> 5 color 1.273333e-01 0.3334016
#> 6 depth 6.175267e+01 1.4460279
#> 7 table 5.746533e+01 2.2411022
#> 8 x 5.721823e+00 1.1240545
explore(diamonds, c("price", "carat"), byvar = "cut", fun = c("n_missing", "skew"))$tab
#> cut variable n_missing skew
#> 1 Fair price 0 1.5741334
#> 2 Fair carat 0 0.9285670
#> 3 Good price 0 1.4885765
#> 4 Good carat 0 1.0207909
#> 5 Very Good price 0 1.6007752
#> 6 Very Good carat 0 0.9370738
#> 7 Premium price 0 1.4131786
#> 8 Premium carat 0 0.9299567
#> 9 Ideal price 0 1.7986601
#> 10 Ideal carat 0 1.3654745