March 2019
exploreR is an R package loaded with methods to help explore and explain the contents of a dataframe.
To install exploreR, follow these instructions:
- Input the following into the console:
devtools::install_github("UBC-MDS/exploreR", build_opts = c("--no-resave-data", "--no-manual"))
- The package is now installed and ready for use.
Load the package.
library(exploreR)
The function variable_summary will take a data frame as input and provide the total quantity of each type of variable present in the data frame. The output of the function will be a dataframe of size 5 x 2 and will have one row for each variable type with its corresponding quantity. The function will look to identify 5 different types of variables: numerical, character, boolean, date, and an other category.
example usage of variable_summary:
toy_data <- data.frame("letters" = c("a", "b", NA, "d"),
"numbers" = c(1, 4, 6, NA),
"logical" = c(NA, FALSE, NA, TRUE),
"dates" = as.Date(c("2003-01-02", "2002-02-02", "2004-03-03", "2005-04-04")),
"integers" = c(2L, 3L, 4L, 5L),
stringsAsFactors = FALSE)
variable_summary(toy_data)
example output of variable_summary:
| variable_type | count |
|---|---|
| numeric | 1 |
| character | 1 |
| logical | 1 |
| date | 1 |
| other | 1 |
For each column/variable in the dataframe, this function will count the number of missing values present and report back on that number per column. The function missing_values will accept a dataframe as input and output a corresponding dataframe with the above information detailing the counts of missing values per column/variable. If the input is of size n x d, the output size will be d x 3.
example usage of missing_values:
toy_data <- data.frame("letters" = c("a", "b", NA, "d"),
"numbers" = c(1, 4, 6, NA),
"logical" = c(NA, FALSE, NA, TRUE),
"dates" = as.Date(c("2003-01-02", "2002-02-02", "2004-03-03", "2005-04-04")),
"integers" = c(2L, 3L, 4L, 5L),
stringsAsFactors = FALSE)
missing_values(toy_data)
example output of missing_values:
| variable | missing_values | percent_missing |
|---|---|---|
| letters | 1 | 0.25 |
| numbers | 1 | 0.25 |
| logical | 2 | 0.50 |
| dates | 0 | 0.00 |
| integers | 0 | 0.00 |
The function size will take in a dataframe and print the shape and size of the dataframe. For the size, the function will print how much memory the dataframe consumes in bytes. The output of the function will be a dataframe of size 1 x 3.
example usage of size:
toy_data <- data.frame("letters" = c("a", "b", NA, "d"),
"numbers" = c(1, 4, 6, NA),
"logical" = c(NA, FALSE, NA, TRUE),
"dates" = as.Date(c("2003-01-02", "2002-02-02", "2004-03-03", "2005-04-04")),
"integers" = c(2L, 3L, 4L, 5L),
stringsAsFactors = FALSE)
size(toy_data)
example output of size:
| rows | columns | size_in_memory |
|---|---|---|
| 4 | 5 | 1760 |
Check out the package vignette for more information by entering the following in the console:
vignette("explorer") for viewing inside the RStudio viewer
or
browseVignettes(package="exploreR") for viewing in a browser
The following are existing functions in R that are similar to those developed within our project.
dim(): used to obtain the shape of a dataframe.
ncol() and nrow(): used to get the number of rows and columns in a dataframe.
str(): provides summary information about the dataframe, including some of the same information as above (i.e. dim, ncol and nrow). str() provides descriptive information about variable and data types in the dataframe.
is.na(): provides the number of missing values in the columns of the data frame.
| name | github handle |
|---|---|
| Rachel K. Riggs | @rachelkriggs |
| Milos Milic | @milicmil |
| Arzan Irani | @nazra-inari |
| James Pushor | @jpush1773 |
