In manateelab/EMAeval-R-Package: Identification of Careless Responses and Responders in EMA Data
knitr::opts_chunk$set(echo = TRUE)
EMAeval R Markdown
The R package EMAeval contains functions created to help researchers identify careless responses as well as responders ine EMA data. An example dataset is included in the package to help the user better understand the uses of each function. This dataset "EMAeval_Data" is used in the example code below.
There are 4 "participants," each having 50 assessments. Each was asked 8 questions per assessment.
library(EMAeval)
library(knitr)
library(kableExtra)
knitr::opts_chunk$set(opts.label="kill_prefix")
EMAeval_Data <- EMAeval::EMAeval_Data
kable(EMAeval_Data[c(1,56,111,172),], caption = "EMAeval_Data Example", row.names = TRUE) %>%
kable_styling()
Functions
Below are the functions in the R package EMAeval. The functions are:
- flagging_df
- flagging_plots
- TPI_cutoff
- SD_cutoff
- Perc_Mode_cutoff
- Combined_cutoff
- Combined_cutoff_percent
Each section will be dedicated to a particular function, giving an example of the usage with the EMAeval_Data and showing the output.
flagging_df
*This function creates a dataframe that reports Time to Complete (TTC), Time per Item (TPI), Item Standard Deviation (SD), and Longstring. If the Longstring returns NA, then there was no longstring response because all item responses were different. The partial results of the flagging_df function are based from the EMAeval_Data Example above. *
``` {r eval = FALSE}
flaggingDF <- flagging_df(EMAeval_Data,
ttc.colnames = c("StartDate", "EndDate"),
item.colnames = colnames(EMAeval_Data[,4:11]))
```r
library(EMAeval)
library(knitr)
library(kableExtra)
EMAeval_Data <- EMAeval::EMAeval_Data
flaggingDF <- flagging_df(EMAeval_Data, ttc.colnames = c("StartDate", "EndDate"), item.colnames = colnames(EMAeval_Data[,4:11]))
kable(flaggingDF[c(1,56,111,172),], caption = "Flagging Dataframe Example", row.names = TRUE) %>%
kable_styling()
flagging_plots
This function creates a histograms of each of the calculations reported in the flagging_df function. This can be used to help users identify the cutoff values for TPI and SD.
``` {r eval = FALSE}
flagging_plots(EMAeval_Data,
ttc.colnames = c("StartDate", "EndDate"),
item.colnames = colnames(EMAeval_Data[,4:11]),
number.items = 8)
```r
library(EMAeval)
library(ggplot2)
library(knitr)
library(kableExtra)
EMAeval_Data <- EMAeval::EMAeval_Data
flagging_plots(EMAeval_Data, ttc.colnames = c("StartDate", "EndDate"), item.colnames = colnames(EMAeval_Data[,4:11]),number.items = 8)
Note: the Longstring histogram has a much smaller scale for the Count. This is due to the lack of longstring values because many assessments do not have a Longstring value because all item responses differ.
TPI_cutoff
This function creates a dataframe of ID and data indices in which the assessment met the cutoff criterion for Time per Item. The user inputs their own cutoff for TPI. The user can also specify what type of comparison they would like to complete with the cutoff value using condition =... If responses to all items are mandatory, then the following response should be included:
mandatory.response = TRUE
Below is the code for the function.
``` {r eval = FALSE}
TPI_cutoff(EMAeval_Data,
cutoff = 1,
condition = "<=",
ttc.colnames = c("StartDate", "EndDate"),
number.items = 8,
mandatory.response = TRUE,
item.colnames = colnames(EMAeval_Data[,4:11]),
ID.colname = "ID")
```r
library(EMAeval)
library(knitr)
library(kableExtra)
EMAeval_Data <- EMAeval::EMAeval_Data
TPI_cutoff_DF <- TPI_cutoff(EMAeval_Data, cutoff = 1, condition = "<=", ttc.colnames = c("StartDate", "EndDate"), number.items = 8, mandatory.response = TRUE, item.colnames = colnames(EMAeval_Data[,4:11]), ID.colname = "ID")
kable(TPI_cutoff_DF, caption = "Assessments Flagged by TPI Cutoff", row.names = FALSE) %>%
kable_styling() %>%
scroll_box(width = "100%" , height = "400px")
SD_cutoff
This function creates a dataframe of ID and data indices in which the assessment met the cutoff criterion for Item Score Standard Deviation. The user inputs their own cutoff for SD. The user can also specify what type of comparison they would like to complete with the cutoff value using condition =...
Below is the code for the function.
``` {r eval = FALSE}
SD_cutoff(EMAeval_Data,
cutoff = 5,
condition = "<=",
item.colnames = colnames(EMAeval_Data[,4:11]),
ID.colname = "ID")
```r
library(EMAeval)
library(knitr)
library(kableExtra)
EMAeval_Data <- EMAeval::EMAeval_Data
SD_cutoff_DF <- SD_cutoff(EMAeval_Data, cutoff = 5, condition = "<=", item.colnames = colnames(EMAeval_Data[,4:11]), ID.colname = "ID")
kable(SD_cutoff_DF, caption = "Assessments Flagged by SD Cutoff", row.names = FALSE) %>%
kable_styling() %>%
scroll_box(width = "100%" , height = "400px")
Perc_Mode_cutoff
This function creates a dataframe of ID and data indices in which the assessment met the cutoff criterion for the Percent of Items at Mode. The user inputs their own cutoff for Percent of Items at Mode. The user can also specify what type of comparison they would like to complete with the cutoff value using condition =...
Below is the code for the function.
``` {r eval = FALSE}
Perc_Mode_cutoff(EMAeval_Data,
cutoff = 0.7,
condition = ">=",
item.colnames = colnames(EMAeval_Data[,4:11]),
ID.colname = "ID")
```r
library(EMAeval)
library(knitr)
library(kableExtra)
EMAeval_Data <- EMAeval::EMAeval_Data
Perc_Mode_cutoff_DF <- Perc_Mode_cutoff(EMAeval_Data, cutoff = 0.7, condition = ">=", item.colnames = colnames(EMAeval_Data[,4:11]), ID.colname = "ID")
kable(Perc_Mode_cutoff_DF, caption = "Assessments Flagged by PErcent of Items at Mode Cutoff", row.names = FALSE) %>%
kable_styling() %>%
scroll_box(width = "100%" , height = "400px")
Combined_cutoff
This function creates a dataframe of ID and data indices in which the assessment met the cutoff criteria for Time per Item OR Item Score Standard Deviation OR Percent of Items at Mode. The user inputs their own cutoff for TPI, SD and Percent of Items at Mode. The user can also specify what type of comparison they would like to complete with each cutoff value using either SD.condition =... and TPI.condition=... and Perc.Mode.condition=... Users can also specify the logical component for the criteria, specifying with Combined.logic = ... If responses to all items are mandatory, then the following response should be included:
mandatory.response = TRUE
Below is the code for the function.
``` {r eval = FALSE}
Combined_cutoff(EMAeval_Data,
SD.cutoff = 5, SD.condition = "<=",
TPI.cutoff = 1, TPI.condition = "<=",
Perc.Mode.cutoff = 0.7, Perc.Mode.condition = ">=",
Combined.logic = "or",
ttc.colnames = c("StartDate", "EndDate"),
number.items = 8, mandatory.response = TRUE,
item.colnames = colnames(EMAeval_Data[,4:11]),
ID.colname = "ID")
```r
library(EMAeval)
library(knitr)
library(kableExtra)
EMAeval_Data <- EMAeval::EMAeval_Data
Combined_cutoff_DF <- Combined_cutoff(EMAeval_Data,SD.cutoff = 5, SD.condition = "<=", TPI.cutoff = 1, TPI.condition = "<=", Perc.Mode.cutoff = 0.7, Perc.Mode.condition = ">=", Combined.logic = "or", ttc.colnames = c("StartDate", "EndDate"), number.items = 8, mandatory.response = TRUE, item.colnames = colnames(EMAeval_Data[,4:11]), ID.colname = "ID")
kable(Combined_cutoff_DF, caption = "Assessments Flagged by TPI, SD or Percent of Items at Mode Cutoffs", row.names = FALSE) %>%
kable_styling() %>%
scroll_box(width = "100%" , height = "400px")
Combined_cutoff_percent
This function creates a dataframe of ID and percent of responses in which assessments met the cutoff criteria for Time per Item OR Item Score Standard Deviation OR Percent of Items at Mode. The user inputs their own cutoff for TPI, SD and Percent of Items at Mode. The user can also specify what type of comparison they would like to complete with each cutoff value using either SD.condition =... and TPI.condition=... and Perc.Mode.condition=... Users can also specify the logical component for the criteria, specifying with Combined.logic = ... If responses to all items are mandatory, then the following response should be included:
mandatory.response = TRUE
Below is the code for the function.
``` {r eval = FALSE}
Combined_cutoff_percent(EMAeval_Data,
SD.cutoff = 5, SD.condition = "<=",
TPI.cutoff = 1, TPI.condition = "<=",
Perc.Mode.cutoff = 0.7, Perc.Mode.condition = ">=",
Combined.logic = "or",
ttc.colnames = c("StartDate", "EndDate"),
number.items = 8, mandatory.response = TRUE,
item.colnames = colnames(EMAeval_Data[,4:11]),
ID.colname = "ID")
```r
library(EMAeval)
library(knitr)
library(kableExtra)
EMAeval_Data <- EMAeval::EMAeval_Data
Combined_cutoff_percent_DF <- Combined_cutoff_percent(EMAeval_Data, SD.cutoff = 5, SD.condition = "<=", TPI.cutoff = 1, TPI.condition = "<=", Perc.Mode.cutoff = 0.7, Perc.Mode.condition = ">=", Combined.logic = "or", ttc.colnames = c("StartDate", "EndDate"), number.items = 8, mandatory.response = TRUE, item.colnames = colnames(EMAeval_Data[,4:11]), ID.colname = "ID")
kable(Combined_cutoff_percent_DF, caption = "Percentage of Assessments Flagged by TPI, SD or Percent of Items at Mode Cutoffs", row.names = FALSE) %>%
kable_styling()
manateelab/EMAeval-R-Package documentation built on Oct. 13, 2021, 6:48 a.m.
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knitr::opts_chunk$set(echo = TRUE)
EMAeval R Markdown
The R package EMAeval contains functions created to help researchers identify careless responses as well as responders ine EMA data. An example dataset is included in the package to help the user better understand the uses of each function. This dataset "EMAeval_Data" is used in the example code below.
There are 4 "participants," each having 50 assessments. Each was asked 8 questions per assessment.
library(EMAeval) library(knitr) library(kableExtra) knitr::opts_chunk$set(opts.label="kill_prefix") EMAeval_Data <- EMAeval::EMAeval_Data kable(EMAeval_Data[c(1,56,111,172),], caption = "EMAeval_Data Example", row.names = TRUE) %>% kable_styling()
Functions
Below are the functions in the R package EMAeval. The functions are:
- flagging_df
- flagging_plots
- TPI_cutoff
- SD_cutoff
- Perc_Mode_cutoff
- Combined_cutoff
- Combined_cutoff_percent
Each section will be dedicated to a particular function, giving an example of the usage with the EMAeval_Data and showing the output.
flagging_df
*This function creates a dataframe that reports Time to Complete (TTC), Time per Item (TPI), Item Standard Deviation (SD), and Longstring. If the Longstring returns NA, then there was no longstring response because all item responses were different. The partial results of the flagging_df function are based from the EMAeval_Data Example above. *
``` {r eval = FALSE} flaggingDF <- flagging_df(EMAeval_Data, ttc.colnames = c("StartDate", "EndDate"), item.colnames = colnames(EMAeval_Data[,4:11]))
```r library(EMAeval) library(knitr) library(kableExtra) EMAeval_Data <- EMAeval::EMAeval_Data flaggingDF <- flagging_df(EMAeval_Data, ttc.colnames = c("StartDate", "EndDate"), item.colnames = colnames(EMAeval_Data[,4:11])) kable(flaggingDF[c(1,56,111,172),], caption = "Flagging Dataframe Example", row.names = TRUE) %>% kable_styling()
flagging_plots
This function creates a histograms of each of the calculations reported in the flagging_df function. This can be used to help users identify the cutoff values for TPI and SD.
``` {r eval = FALSE} flagging_plots(EMAeval_Data, ttc.colnames = c("StartDate", "EndDate"), item.colnames = colnames(EMAeval_Data[,4:11]), number.items = 8)
```r library(EMAeval) library(ggplot2) library(knitr) library(kableExtra) EMAeval_Data <- EMAeval::EMAeval_Data flagging_plots(EMAeval_Data, ttc.colnames = c("StartDate", "EndDate"), item.colnames = colnames(EMAeval_Data[,4:11]),number.items = 8)
Note: the Longstring histogram has a much smaller scale for the Count. This is due to the lack of longstring values because many assessments do not have a Longstring value because all item responses differ.
TPI_cutoff
This function creates a dataframe of ID and data indices in which the assessment met the cutoff criterion for Time per Item. The user inputs their own cutoff for TPI. The user can also specify what type of comparison they would like to complete with the cutoff value using condition =... If responses to all items are mandatory, then the following response should be included:
mandatory.response = TRUE
Below is the code for the function.
``` {r eval = FALSE}
TPI_cutoff(EMAeval_Data,
cutoff = 1,
condition = "<=",
ttc.colnames = c("StartDate", "EndDate"),
number.items = 8,
mandatory.response = TRUE,
item.colnames = colnames(EMAeval_Data[,4:11]),
ID.colname = "ID")
```r library(EMAeval) library(knitr) library(kableExtra) EMAeval_Data <- EMAeval::EMAeval_Data TPI_cutoff_DF <- TPI_cutoff(EMAeval_Data, cutoff = 1, condition = "<=", ttc.colnames = c("StartDate", "EndDate"), number.items = 8, mandatory.response = TRUE, item.colnames = colnames(EMAeval_Data[,4:11]), ID.colname = "ID") kable(TPI_cutoff_DF, caption = "Assessments Flagged by TPI Cutoff", row.names = FALSE) %>% kable_styling() %>% scroll_box(width = "100%" , height = "400px")
SD_cutoff
This function creates a dataframe of ID and data indices in which the assessment met the cutoff criterion for Item Score Standard Deviation. The user inputs their own cutoff for SD. The user can also specify what type of comparison they would like to complete with the cutoff value using condition =...
Below is the code for the function.
``` {r eval = FALSE}
SD_cutoff(EMAeval_Data,
cutoff = 5,
condition = "<=",
item.colnames = colnames(EMAeval_Data[,4:11]),
ID.colname = "ID")
```r library(EMAeval) library(knitr) library(kableExtra) EMAeval_Data <- EMAeval::EMAeval_Data SD_cutoff_DF <- SD_cutoff(EMAeval_Data, cutoff = 5, condition = "<=", item.colnames = colnames(EMAeval_Data[,4:11]), ID.colname = "ID") kable(SD_cutoff_DF, caption = "Assessments Flagged by SD Cutoff", row.names = FALSE) %>% kable_styling() %>% scroll_box(width = "100%" , height = "400px")
Perc_Mode_cutoff
This function creates a dataframe of ID and data indices in which the assessment met the cutoff criterion for the Percent of Items at Mode. The user inputs their own cutoff for Percent of Items at Mode. The user can also specify what type of comparison they would like to complete with the cutoff value using condition =...
Below is the code for the function.
``` {r eval = FALSE}
Perc_Mode_cutoff(EMAeval_Data,
cutoff = 0.7,
condition = ">=",
item.colnames = colnames(EMAeval_Data[,4:11]),
ID.colname = "ID")
```r library(EMAeval) library(knitr) library(kableExtra) EMAeval_Data <- EMAeval::EMAeval_Data Perc_Mode_cutoff_DF <- Perc_Mode_cutoff(EMAeval_Data, cutoff = 0.7, condition = ">=", item.colnames = colnames(EMAeval_Data[,4:11]), ID.colname = "ID") kable(Perc_Mode_cutoff_DF, caption = "Assessments Flagged by PErcent of Items at Mode Cutoff", row.names = FALSE) %>% kable_styling() %>% scroll_box(width = "100%" , height = "400px")
Combined_cutoff
This function creates a dataframe of ID and data indices in which the assessment met the cutoff criteria for Time per Item OR Item Score Standard Deviation OR Percent of Items at Mode. The user inputs their own cutoff for TPI, SD and Percent of Items at Mode. The user can also specify what type of comparison they would like to complete with each cutoff value using either SD.condition =... and TPI.condition=... and Perc.Mode.condition=... Users can also specify the logical component for the criteria, specifying with Combined.logic = ... If responses to all items are mandatory, then the following response should be included:
mandatory.response = TRUE
Below is the code for the function. ``` {r eval = FALSE} Combined_cutoff(EMAeval_Data, SD.cutoff = 5, SD.condition = "<=", TPI.cutoff = 1, TPI.condition = "<=", Perc.Mode.cutoff = 0.7, Perc.Mode.condition = ">=", Combined.logic = "or", ttc.colnames = c("StartDate", "EndDate"), number.items = 8, mandatory.response = TRUE, item.colnames = colnames(EMAeval_Data[,4:11]), ID.colname = "ID")
```r library(EMAeval) library(knitr) library(kableExtra) EMAeval_Data <- EMAeval::EMAeval_Data Combined_cutoff_DF <- Combined_cutoff(EMAeval_Data,SD.cutoff = 5, SD.condition = "<=", TPI.cutoff = 1, TPI.condition = "<=", Perc.Mode.cutoff = 0.7, Perc.Mode.condition = ">=", Combined.logic = "or", ttc.colnames = c("StartDate", "EndDate"), number.items = 8, mandatory.response = TRUE, item.colnames = colnames(EMAeval_Data[,4:11]), ID.colname = "ID") kable(Combined_cutoff_DF, caption = "Assessments Flagged by TPI, SD or Percent of Items at Mode Cutoffs", row.names = FALSE) %>% kable_styling() %>% scroll_box(width = "100%" , height = "400px")
Combined_cutoff_percent
This function creates a dataframe of ID and percent of responses in which assessments met the cutoff criteria for Time per Item OR Item Score Standard Deviation OR Percent of Items at Mode. The user inputs their own cutoff for TPI, SD and Percent of Items at Mode. The user can also specify what type of comparison they would like to complete with each cutoff value using either SD.condition =... and TPI.condition=... and Perc.Mode.condition=... Users can also specify the logical component for the criteria, specifying with Combined.logic = ... If responses to all items are mandatory, then the following response should be included:
mandatory.response = TRUE
Below is the code for the function.
``` {r eval = FALSE}
Combined_cutoff_percent(EMAeval_Data,
SD.cutoff = 5, SD.condition = "<=",
TPI.cutoff = 1, TPI.condition = "<=",
Perc.Mode.cutoff = 0.7, Perc.Mode.condition = ">=",
Combined.logic = "or",
ttc.colnames = c("StartDate", "EndDate"),
number.items = 8, mandatory.response = TRUE,
item.colnames = colnames(EMAeval_Data[,4:11]),
ID.colname = "ID")
```r library(EMAeval) library(knitr) library(kableExtra) EMAeval_Data <- EMAeval::EMAeval_Data Combined_cutoff_percent_DF <- Combined_cutoff_percent(EMAeval_Data, SD.cutoff = 5, SD.condition = "<=", TPI.cutoff = 1, TPI.condition = "<=", Perc.Mode.cutoff = 0.7, Perc.Mode.condition = ">=", Combined.logic = "or", ttc.colnames = c("StartDate", "EndDate"), number.items = 8, mandatory.response = TRUE, item.colnames = colnames(EMAeval_Data[,4:11]), ID.colname = "ID") kable(Combined_cutoff_percent_DF, caption = "Percentage of Assessments Flagged by TPI, SD or Percent of Items at Mode Cutoffs", row.names = FALSE) %>% kable_styling()
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