Show data as an interactive table
Choose one of the datasets from the Datasets dropdown.
Files are loaded into Radiant through the Data > Manage
tab.
There are several ways to select a subset of the data to view. The
Filter data box on the left (click the check-box) can be
used with > and < symbols. You can also
combine subset commands, for example, x > 3 & y == 2
would show only those rows for which the variable x has
values larger than 3 AND for which y is
equal to 2. Note that in R, and most other programming languages,
= is used to assign a value and == to
determine if values are equal to each other. In contrast,
!= is used to determine if two values are unequal.
You can also use expressions that have an OR condition.
For example, to select rows where Salary is smaller than
$100,000 OR larger than $20,000 use
Salary > 20000 | Salary < 100000. | is
the symbol for OR and & is the symbol
for AND
It is also possible to filter using dates. For example, to select
rows with dates before June 1st, 2014 enter
date < "2014-6-1" into the filter box and press
return.
You can also use string matching to select rows. For example, type
grepl('ood', cut) to select rows with Good or
Very good cut. This search is case sensitive by default.
For case insensitive search use
grepl("GOOD", cut, ignore.case = TRUE). Type your statement
in the Filter box and press return to see the result on
screen or an error below the box if the expression is invalid.
It is important to note that these filters are persistent
and will be applied to any analysis conducted through in Radiant. To
deactivate a filter un-check the Filter data check-box. To
remove a filter simply delete it.
| Operator | Description | Example |
|---|---|---|
<
|
less than |
price < 5000
|
<=
|
less than or equal to |
carat <= 2
|
>
|
greater than |
price > 1000
|
>=
|
greater than or equal to |
carat >= 2
|
==
|
exactly equal to |
cut == 'Fair'
|
!=
|
not equal to |
cut != 'Fair'
|
|
|
x OR y |
price > 10000 | cut == 'Premium'
|
&
|
x AND y |
carat < 2 & cut == 'Fair'
|
%in%
|
x is one of y |
cut %in% c('Fair', 'Good')
|
| is.na | is missing |
is.na(price)
|
Filters can also be used with R-code to quickly view a sample from
the selected dataset. For example, runif(n()) > .9 could
be used to sample approximately 10% of the rows in the data and
1:n() < 101 would select only the first 100 rows in the
data.
By default all columns in the data are shown. Click on any variable to focus on it alone. To select several variables use the SHIFT and ARROW keys on your keyboard. On a mac the CMD key can also be used to select multiple variables. The same effect is achieved on windows using the CTRL key. To select all variable use CTRL-A (or CMD-A on mac).
By default only 10 rows of data are shown at a time. You can change
this setting through the Show ... entries dropdown. Press
the Next and Previous buttons at the
bottom-right of the screen to page through the data.
Click on a column header in the table to sort the data. Clicking again will toggle between sorting in ascending and descending order. To sort on multiple columns at once press shift and then click on the 2nd, 3rd, etc. column to sort by.
For variables that have a limited number of different values (i.e., a
factor) you can select the levels to keep from the column filter below
the variable name. For example, to filter on rows with ideal cut click
in the box below the cut column header and select
Ideal from the dropdown menu shown. You can also type a
string into these column filters and then press return. Note that
matching is case-insensitive. In fact, typing eal would
produce the same result because the search will match any part of a
string. Similarly, you can type a string to select rows based on
character variables (e.g., street names).
For numeric variables the column filter boxes have some special
features that make them almost as powerful as the
Filter data box. For numeric and integer variables you can
use ... to indicate a range. For example, to select
price values between $500 and $2000 type
500 ... 2000 and press return. The range is inclusive of
the values typed. Furthermore, if we want to filter on
carat 0.32 ... will show only diamonds with
carat values larger than or equal to 0.32. Numeric variables also have a
slider that you can use to define the range of values to keep.
If you want to get really fancy you can use the search box
on the top right to search across all columns in the data using
regular expressions. For example, to find all rows that
have an entry in any column ending with the number 72 type
72$ (i.e., the $ sign is used to indicate the
end of an entry). For all rows with entries that start with 60 use
^60 (i.e., the ^ is used to indicate the first
character in an entry). Regular expressions are incredibly powerful for
search but this is a big topic area. To learn more about
regular expressions see this
tutorial.
It is important to note that column sorting, column filters, and
search are not persistent. To store these settings for
use in other parts of Radiant press the Store button. You
can store the data and settings under a different dataset name by
changing the value in the text input to the left of the
Store button. This feature can also be used to select a
subset of variables to keep. Just select the ones you want to keep and
press the Store button. For more control over the variables
you want to keep or remove and to specify their order in the dataset use
the Data > Transform tab.
To download the data in csv format click the icon on the top right of your screen.
Click the report ()
icon on the bottom left of your screen or press ALT-enter
on your keyboard to add the filter and sort commands used by Radiant to
a (reproducible) report in
Report
> Rmd.
For an overview of related R-functions used by Radiant to view, search, and filter data see Data > View