Have your collaborators been remiss in their data collection?
Have you experienced characters in your numeric columns?
Do rows ever get incorrectly entered or swapped?
The overarching goal of the remice package is to account for remiss data, providing a way to identify and solve those problems.
You can install the development version of remice like so:
install_github("erictong2/remice")If you prefer, you can also go to the Github website (https://github.com/EricTong2/remice/) and download the package from there.
These are the functions included in the package:
-
analyze_type()takes a column of characters and determines if any values in that row are not of a specified type. While this does not directly fix the types of the data, it creates a table to highlight areas in which the data could cause errors for other functions, allowing for those rows to be manually fixed. -
table_different()takes a column of characters and creates a table of strings that are uncommonly found in a dataset. This is aimed to mitigate various typos found by either entering data or from survey responses. For example, if all of the column values are “True” except for a single “Ture”, it is likely for a typo to have occurred. This function does not fix any problems, but it highlights the row numbers where the data should be checked. -
separate_list()takes a column of characters and creates a table of the most common responses when multiple responses could exist in one row. For example, if two of the responses are “Apple, Banana, and Orange” and “Banana, Orange”, this would output a table of “Apple: 1, Banana: 2, Orange: 2.” This can be used on a survey about comfort foods, where a response might be -
plot_outliers()takes multiple columns of numeric data that was longitudinally collected and checks for any abnormally large changes in the data. This could be used to check for potential outliers or areas where the data was mis-input: for example, recording “11” grams instead of “21” grams. In addition, the user inputs a percent change, and any points that have a change greater than the input for any two points would be plotted in a graph.
food_datais a survey of 125 individuals from Mercyhurst University. There are three columns, all with 125 character entries:comfort_foodis a list of students’ comfort foodsfood_childhoodis a list of students’ childhood foodsmeals_dinner_friendis a list of foods eaten when friends came over
mouse_data_birthis a dataset from 32 mice that has four columns, describing the mouseID,Sex,Number, and theTreatmenttypemouse_data_bw_dirtyis a dataset from the same 32 mice that has seven columns, the first being the sameIDasmouse_data_birth. The other six columns describe three dates over which the data was collected alternating between theBody Weightof the mouse and theDateof which theBody Weightwas collectedmouse_data_bw_cleanprovides the same information asmouse_data_bw_dirtybut includes cleaned data to provide examples of functions.
This is an example of separate_list() using the Comfort Food column
from food_data
library(remice)
separate_list(food_data, "comfort_food", 5)
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| Separated Word Frequencies | |
| Word | Frequency |
|---|---|
| ice cream | 45 |
| pizza | 37 |
| chips | 26 |
| chocolate | 25 |
| cookies | 17 |
| cheese | 16 |
| mac | 11 |
| pasta | 9 |
| cake | 7 |
| candy | 7 |
| french fries | 7 |
| popcorn | 7 |
| pretzels | 5 |
This is an example of plot_outliers() using the mouse_data_bw_clean
dataset:
library(remice)
table_different(mouse_data_birth, "Treatment", 5)
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}
#ozslihfktx .gt_footnotes {
color: #333333;
background-color: #FFFFFF;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
}
#ozslihfktx .gt_footnote {
margin: 0px;
font-size: 90%;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 5px;
padding-right: 5px;
}
#ozslihfktx .gt_sourcenotes {
color: #333333;
background-color: #FFFFFF;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
}
#ozslihfktx .gt_sourcenote {
font-size: 90%;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 5px;
padding-right: 5px;
}
#ozslihfktx .gt_left {
text-align: left;
}
#ozslihfktx .gt_center {
text-align: center;
}
#ozslihfktx .gt_right {
text-align: right;
font-variant-numeric: tabular-nums;
}
#ozslihfktx .gt_font_normal {
font-weight: normal;
}
#ozslihfktx .gt_font_bold {
font-weight: bold;
}
#ozslihfktx .gt_font_italic {
font-style: italic;
}
#ozslihfktx .gt_super {
font-size: 65%;
}
#ozslihfktx .gt_footnote_marks {
font-size: 75%;
vertical-align: 0.4em;
position: initial;
}
#ozslihfktx .gt_asterisk {
font-size: 100%;
vertical-align: 0;
}
#ozslihfktx .gt_indent_1 {
text-indent: 5px;
}
#ozslihfktx .gt_indent_2 {
text-indent: 10px;
}
#ozslihfktx .gt_indent_3 {
text-indent: 15px;
}
#ozslihfktx .gt_indent_4 {
text-indent: 20px;
}
#ozslihfktx .gt_indent_5 {
text-indent: 25px;
}
#ozslihfktx .katex-display {
display: inline-flex !important;
margin-bottom: 0.75em !important;
}
#ozslihfktx div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
height: 0px !important;
}
</style>
| Inconsistent Strings | |
| Row Number | String |
|---|---|
| 11 | Placobo |
| 20 | Trmt |