README.md
In decision-sciences/cds_dospert_package: dospert R package
Overview
dospert is a pacakge used to aid the analysis of DOSPERT data. It includes three functions:
- d_clean: d_clean function returns raw DOSPERT data in a panel format.
- d_sum: d_sum function returns sum of DOSPERT responses by domain and scales.
- d_score: d_score returns DOSPERT scores attached to the original input dataframe.
Installation
make sure you have devtools installed and run the following code:
library(devtools)
devtools::install_github("decision-sciences/cds_dospert_package")
DOSPERT domains and scales
DOSPERT has five risk domains and three risk types. These are taken as arguments for d_sum, and are used for variable names.
-
Risk domains are abbreviated as follows:
- financial: fin
- health/safety: hea or saf
- recreational: rec
- ethical: eth
- social: soc
-
Risk types are:
- risk taking: RT
- risk benefit: RB
- risk perception: RP
Data
Data for analysis is raw data downloaded from qualtrics. File formats supported are .csv and .xml, with .csv as default. The main difference is that when downloaded, .csv files include first row of column names/questions but .xml files do not. There is no need to manipulate the variable names before using any of the functions. However, for the responses of the dospert questions, variable names should be in "(domain)(risk type)_Question number" (e. g. finRT_1) format.
dcsv <- read.csv("pilot_data.csv", header = TRUE) # [d]ospert in [csv]
head(dcsv[, 1:10])
## V1 V2 V3 V4
## 1 ResponseID ResponseSet Name ExternalDataReference
## 2 R_3fYJHku6e1c8gjp Default Response Set Anonymous
## 3 R_cGbH7i8U2jwQYo1 Default Response Set Anonymous
## 4 R_1LNYShwUeHQ669I Default Response Set Anonymous
## 5 R_2UgfnYJCqZrhlBL Default Response Set Anonymous
## 6 R_XTE8ILQrqTEmpVv Default Response Set Anonymous
## V5 V6 V7 V8 V9 V10
## 1 EmailAddress IPAddress Status StartDate EndDate Finished
## 2 128.59.199.242 0 6/3/15 17:04 6/3/15 17:07 1
## 3 128.59.199.242 0 6/3/15 17:30 6/3/15 17:31 1
## 4 128.59.199.242 0 6/4/15 10:07 6/4/15 10:08 1
## 5 128.59.199.242 0 6/5/15 16:35 6/5/15 16:35 1
## 6 70.192.234.116 0 6/8/15 13:24 6/8/15 13:26 1
dxml <- xmlToDataFrame("pilot_data.xml", stringsAsFactors = F) %>% filter(uid != "")
# [d]ospert in [xml]
# unique identifying variable for this dataset is 'uid'
# removed observations without uid variable value
head(dxml[, 1:10])
## ResponseID ResponseSet Name ExternalDataReference
## 1 R_2q8GyJAKfgHxT3r Default Response Set Anonymous
## 2 R_33BaSpJt3vm38Hp Default Response Set Anonymous
## 3 R_1l5HCrHMeadd1Tj Default Response Set Anonymous
## 4 R_3psF61JNrj5d22M Default Response Set Anonymous
## 5 R_24w6c78hLXWE4Xk Default Response Set Anonymous
## 6 R_1JD6kEbdEIP7Xf7 Default Response Set Anonymous
## EmailAddress IPAddress Status StartDate
## 1 207.239.110.242 0 2015-06-26 14:01:42
## 2 128.59.199.242 0 2015-06-26 15:57:24
## 3 73.180.64.82 0 2015-07-28 10:17:49
## 4 64.184.91.34 0 2015-07-28 10:17:43
## 5 71.227.179.100 0 2015-07-28 10:18:22
## 6 66.151.30.140 0 2015-07-28 10:20:05
## EndDate Finished
## 1 2015-06-26 14:02:35 1
## 2 2015-06-26 15:58:18 1
## 3 2015-07-28 10:19:33 1
## 4 2015-07-28 10:20:18 1
## 5 2015-07-28 10:20:54 1
## 6 2015-07-28 10:25:19 1
1) d_clean
d_clean function takes three arguments: raw dataframe, unique identifying variable and file type and returns a panel format dataframe, which can be used, for example, for data inspection purposes.
In the sample .csv format dataframe, first column uniquely identifies the respondents.
csvdclean <- d_clean(dcsv, "V1", file_type = "csv")
head(csvdclean)
## Source: local data frame [6 x 7]
##
## unique_id domain Qnumber RT RP RB id
## (fctr) (chr) (chr) (dbl) (dbl) (dbl) (dbl)
## 1 R_1DUdsEimDpZTP4S fin 1 1 1 1 1
## 2 R_1DUdsEimDpZTP4S fin 2 3 2 2 1
## 3 R_1DUdsEimDpZTP4S fin 3 4 3 3 1
## 4 R_1DUdsEimDpZTP4S fin 4 5 4 4 1
## 5 R_1DUdsEimDpZTP4S fin 5 6 5 5 1
## 6 R_1DUdsEimDpZTP4S fin 6 7 6 6 1
For the .xml format dataframe, the usage is similar: unique identifying variable in this sample dataset is 'uid', and file type is xml.
xmldclean <- d_clean(dxml, "uid", file_type = "xml")
head(xmldclean)
## Source: local data frame [6 x 7]
##
## unique_id domain Qnumber RT RP RB id
## (chr) (chr) (chr) (dbl) (dbl) (dbl) (dbl)
## 1 002BNAWSGQT9YFW6PYQPBJYLKYPUYJUE fin 1 2 4 3 1
## 2 002BNAWSGQT9YFW6PYQPBJYLKYPUYJUE fin 2 5 2 4 1
## 3 002BNAWSGQT9YFW6PYQPBJYLKYPUYJUE fin 3 3 3 4 1
## 4 002BNAWSGQT9YFW6PYQPBJYLKYPUYJUE fin 4 2 6 5 1
## 5 002BNAWSGQT9YFW6PYQPBJYLKYPUYJUE fin 5 1 5 2 1
## 6 002BNAWSGQT9YFW6PYQPBJYLKYPUYJUE fin 6 4 4 5 1
2) d_sum
d_sum takes five arguments: raw dataframe, unique identifying variable, risk domain, risk type (or scale) and file type, and returns the sum of the respondents DOSPERT response of the designated risk domain and type by unique id.
csvdsum <- d_sum(dcsv, "V1", "fin", "RT", file_type = "csv")
head(csvdsum)
## unique_id finRT_sum
## 2 R_3fYJHku6e1c8gjp 10
## 3 R_cGbH7i8U2jwQYo1 23
## 4 R_1LNYShwUeHQ669I 16
## 5 R_2UgfnYJCqZrhlBL 42
## 6 R_XTE8ILQrqTEmpVv 20
## 7 R_1DUdsEimDpZTP4S 26
xmldsum <- d_sum(dxml, "uid", "fin", "RB", file_type = "xml")
head(xmldsum)
## unique_id finRB_sum
## 1 123456 21
## 2 1111111111111 21
## 3 BUK5HSGUUIMMFPI5IP286JFSNWKXYWIG 15
## 4 DZQ767VXX7XEDFZJFW6MU7CTWAJ3B1GM 24
## 5 BYF4NEUCXDYMXL2V5FQFGNTRFXU5DBV2 22
## 6 RJSE1JBXWWDLBHSHASKT5Y0I0GYPHREZ 18
3) d_score
In order to use d_score, make sure you have the package lm.beta installed and loaded into the environment using the following code:
install.packages(lm.beta)
library(lm.beta)
d_score takes same three arguments as d_clean, calculates the risk attitude coefficients and returns the results attached to the last columns of input dataframe. The risk attitude coefficients are named "(domain)_int'', "(domain)_RB'', "(domain)_RP''. It also calculates the standardized coefficents for "(domain)_RB'', and "(domain)_RP'' which are listed as "(domain).Standard_RB'', "(domain).Standard_RB'', and the r-squared for each participant for each domain, listed as "(domain).R_square''.
csvdscore <- d_score(dcsv, "V1", file_type = "csv")
head(csvdscore %>% dplyr::select(unique_ID, fin_int, fin_RB, fin_RP, fin.R_square, fin.Standard_RB, fin.Standard_RP))
unique_ID fin_int fin_RB fin_RP fin.R_square fin.Standard_RB
1 152068 5.893701e+00 -3.228346e-01 -4.763780e-01 0.9376136 -3.228346e-01
2 152069 7.333333e+00 -2.500000e-01 0.000000e+00 -0.2146226 4.435103e+00
3 152072 1.450389e-15 1.000000e+00 0.000000e+00 1.0000000 3.244752e-16
4 152074 1.000000e+00 -2.091908e-17 4.183815e-17 0.2753623 -Inf
5 152075 2.281690e+00 9.084507e-01 -3.239437e-01 0.2427404 8.323640e-01
6 152076 1.000000e+00 2.465190e-31 NaN 0.2647059 5.067452e-16
fin.Standard_RP
1 -6.788627e-01
2 7.324332e-01
3 7.166459e-17
4 Inf
5 -1.649440e-01
6 1.241267e-16
xmldscore <- d_score(dxml, "uid", file_type = "xml")
head(xmldscore %>% dplyr::select(unique_ID, rec_int, rec_RB, rec_RP, rec.R_square, rec.Standard_RB, rec.Standard_RP))
unique_ID rec_int rec_RB rec_RP rec.R_square rec.Standard_RB
1 152068 2.400000e+00 -4.000000e-01 0.000000e+00 0.2500000 6.782330e-01
2 152069 2.900779e-15 1.000000e+00 0.000000e+00 1.0000000 1.258875e-15
3 152072 8.870370e+00 -9.259259e-02 -1.111111e+00 0.6125731 -6.372659e-02
4 152074 1.000000e+00 1.485264e-30 1.359740e-16 0.3478261 Inf
5 152075 -1.066667e+00 1.266667e+00 -3.333333e-01 0.7333333 6.333333e-01
6 152076 3.500000e+00 1.500000e+00 -2.000000e+00 0.5833333 5.000000e-01
rec.Standard_RP
1 2.449490e-01
2 2.298380e-16
3 -9.190780e-01
4 Inf
5 -4.772607e-01
6 -7.071068e-01
decision-sciences/cds_dospert_package documentation built on May 15, 2019, 1:55 a.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Overview
dospert is a pacakge used to aid the analysis of DOSPERT data. It includes three functions:
- d_clean: d_clean function returns raw DOSPERT data in a panel format.
- d_sum: d_sum function returns sum of DOSPERT responses by domain and scales.
- d_score: d_score returns DOSPERT scores attached to the original input dataframe.
Installation
make sure you have devtools installed and run the following code:
library(devtools)
devtools::install_github("decision-sciences/cds_dospert_package")
DOSPERT domains and scales
DOSPERT has five risk domains and three risk types. These are taken as arguments for d_sum, and are used for variable names.
-
Risk domains are abbreviated as follows:
- financial: fin
- health/safety: hea or saf
- recreational: rec
- ethical: eth
- social: soc
-
Risk types are:
- risk taking: RT
- risk benefit: RB
- risk perception: RP
Data
Data for analysis is raw data downloaded from qualtrics. File formats supported are .csv and .xml, with .csv as default. The main difference is that when downloaded, .csv files include first row of column names/questions but .xml files do not. There is no need to manipulate the variable names before using any of the functions. However, for the responses of the dospert questions, variable names should be in "(domain)(risk type)_Question number" (e. g. finRT_1) format.
dcsv <- read.csv("pilot_data.csv", header = TRUE) # [d]ospert in [csv]
head(dcsv[, 1:10])
## V1 V2 V3 V4
## 1 ResponseID ResponseSet Name ExternalDataReference
## 2 R_3fYJHku6e1c8gjp Default Response Set Anonymous
## 3 R_cGbH7i8U2jwQYo1 Default Response Set Anonymous
## 4 R_1LNYShwUeHQ669I Default Response Set Anonymous
## 5 R_2UgfnYJCqZrhlBL Default Response Set Anonymous
## 6 R_XTE8ILQrqTEmpVv Default Response Set Anonymous
## V5 V6 V7 V8 V9 V10
## 1 EmailAddress IPAddress Status StartDate EndDate Finished
## 2 128.59.199.242 0 6/3/15 17:04 6/3/15 17:07 1
## 3 128.59.199.242 0 6/3/15 17:30 6/3/15 17:31 1
## 4 128.59.199.242 0 6/4/15 10:07 6/4/15 10:08 1
## 5 128.59.199.242 0 6/5/15 16:35 6/5/15 16:35 1
## 6 70.192.234.116 0 6/8/15 13:24 6/8/15 13:26 1
dxml <- xmlToDataFrame("pilot_data.xml", stringsAsFactors = F) %>% filter(uid != "")
# [d]ospert in [xml]
# unique identifying variable for this dataset is 'uid'
# removed observations without uid variable value
head(dxml[, 1:10])
## ResponseID ResponseSet Name ExternalDataReference
## 1 R_2q8GyJAKfgHxT3r Default Response Set Anonymous
## 2 R_33BaSpJt3vm38Hp Default Response Set Anonymous
## 3 R_1l5HCrHMeadd1Tj Default Response Set Anonymous
## 4 R_3psF61JNrj5d22M Default Response Set Anonymous
## 5 R_24w6c78hLXWE4Xk Default Response Set Anonymous
## 6 R_1JD6kEbdEIP7Xf7 Default Response Set Anonymous
## EmailAddress IPAddress Status StartDate
## 1 207.239.110.242 0 2015-06-26 14:01:42
## 2 128.59.199.242 0 2015-06-26 15:57:24
## 3 73.180.64.82 0 2015-07-28 10:17:49
## 4 64.184.91.34 0 2015-07-28 10:17:43
## 5 71.227.179.100 0 2015-07-28 10:18:22
## 6 66.151.30.140 0 2015-07-28 10:20:05
## EndDate Finished
## 1 2015-06-26 14:02:35 1
## 2 2015-06-26 15:58:18 1
## 3 2015-07-28 10:19:33 1
## 4 2015-07-28 10:20:18 1
## 5 2015-07-28 10:20:54 1
## 6 2015-07-28 10:25:19 1
1) d_clean
d_clean function takes three arguments: raw dataframe, unique identifying variable and file type and returns a panel format dataframe, which can be used, for example, for data inspection purposes.
In the sample .csv format dataframe, first column uniquely identifies the respondents.
csvdclean <- d_clean(dcsv, "V1", file_type = "csv")
head(csvdclean)
## Source: local data frame [6 x 7]
##
## unique_id domain Qnumber RT RP RB id
## (fctr) (chr) (chr) (dbl) (dbl) (dbl) (dbl)
## 1 R_1DUdsEimDpZTP4S fin 1 1 1 1 1
## 2 R_1DUdsEimDpZTP4S fin 2 3 2 2 1
## 3 R_1DUdsEimDpZTP4S fin 3 4 3 3 1
## 4 R_1DUdsEimDpZTP4S fin 4 5 4 4 1
## 5 R_1DUdsEimDpZTP4S fin 5 6 5 5 1
## 6 R_1DUdsEimDpZTP4S fin 6 7 6 6 1
For the .xml format dataframe, the usage is similar: unique identifying variable in this sample dataset is 'uid', and file type is xml.
xmldclean <- d_clean(dxml, "uid", file_type = "xml")
head(xmldclean)
## Source: local data frame [6 x 7]
##
## unique_id domain Qnumber RT RP RB id
## (chr) (chr) (chr) (dbl) (dbl) (dbl) (dbl)
## 1 002BNAWSGQT9YFW6PYQPBJYLKYPUYJUE fin 1 2 4 3 1
## 2 002BNAWSGQT9YFW6PYQPBJYLKYPUYJUE fin 2 5 2 4 1
## 3 002BNAWSGQT9YFW6PYQPBJYLKYPUYJUE fin 3 3 3 4 1
## 4 002BNAWSGQT9YFW6PYQPBJYLKYPUYJUE fin 4 2 6 5 1
## 5 002BNAWSGQT9YFW6PYQPBJYLKYPUYJUE fin 5 1 5 2 1
## 6 002BNAWSGQT9YFW6PYQPBJYLKYPUYJUE fin 6 4 4 5 1
2) d_sum
d_sum takes five arguments: raw dataframe, unique identifying variable, risk domain, risk type (or scale) and file type, and returns the sum of the respondents DOSPERT response of the designated risk domain and type by unique id.
csvdsum <- d_sum(dcsv, "V1", "fin", "RT", file_type = "csv")
head(csvdsum)
## unique_id finRT_sum
## 2 R_3fYJHku6e1c8gjp 10
## 3 R_cGbH7i8U2jwQYo1 23
## 4 R_1LNYShwUeHQ669I 16
## 5 R_2UgfnYJCqZrhlBL 42
## 6 R_XTE8ILQrqTEmpVv 20
## 7 R_1DUdsEimDpZTP4S 26
xmldsum <- d_sum(dxml, "uid", "fin", "RB", file_type = "xml")
head(xmldsum)
## unique_id finRB_sum
## 1 123456 21
## 2 1111111111111 21
## 3 BUK5HSGUUIMMFPI5IP286JFSNWKXYWIG 15
## 4 DZQ767VXX7XEDFZJFW6MU7CTWAJ3B1GM 24
## 5 BYF4NEUCXDYMXL2V5FQFGNTRFXU5DBV2 22
## 6 RJSE1JBXWWDLBHSHASKT5Y0I0GYPHREZ 18
3) d_score
In order to use d_score, make sure you have the package lm.beta installed and loaded into the environment using the following code:
install.packages(lm.beta)
library(lm.beta)
d_score takes same three arguments as d_clean, calculates the risk attitude coefficients and returns the results attached to the last columns of input dataframe. The risk attitude coefficients are named "(domain)_int'', "(domain)_RB'', "(domain)_RP''. It also calculates the standardized coefficents for "(domain)_RB'', and "(domain)_RP'' which are listed as "(domain).Standard_RB'', "(domain).Standard_RB'', and the r-squared for each participant for each domain, listed as "(domain).R_square''.
csvdscore <- d_score(dcsv, "V1", file_type = "csv")
head(csvdscore %>% dplyr::select(unique_ID, fin_int, fin_RB, fin_RP, fin.R_square, fin.Standard_RB, fin.Standard_RP))
unique_ID fin_int fin_RB fin_RP fin.R_square fin.Standard_RB
1 152068 5.893701e+00 -3.228346e-01 -4.763780e-01 0.9376136 -3.228346e-01
2 152069 7.333333e+00 -2.500000e-01 0.000000e+00 -0.2146226 4.435103e+00
3 152072 1.450389e-15 1.000000e+00 0.000000e+00 1.0000000 3.244752e-16
4 152074 1.000000e+00 -2.091908e-17 4.183815e-17 0.2753623 -Inf
5 152075 2.281690e+00 9.084507e-01 -3.239437e-01 0.2427404 8.323640e-01
6 152076 1.000000e+00 2.465190e-31 NaN 0.2647059 5.067452e-16
fin.Standard_RP
1 -6.788627e-01
2 7.324332e-01
3 7.166459e-17
4 Inf
5 -1.649440e-01
6 1.241267e-16
xmldscore <- d_score(dxml, "uid", file_type = "xml")
head(xmldscore %>% dplyr::select(unique_ID, rec_int, rec_RB, rec_RP, rec.R_square, rec.Standard_RB, rec.Standard_RP))
unique_ID rec_int rec_RB rec_RP rec.R_square rec.Standard_RB
1 152068 2.400000e+00 -4.000000e-01 0.000000e+00 0.2500000 6.782330e-01
2 152069 2.900779e-15 1.000000e+00 0.000000e+00 1.0000000 1.258875e-15
3 152072 8.870370e+00 -9.259259e-02 -1.111111e+00 0.6125731 -6.372659e-02
4 152074 1.000000e+00 1.485264e-30 1.359740e-16 0.3478261 Inf
5 152075 -1.066667e+00 1.266667e+00 -3.333333e-01 0.7333333 6.333333e-01
6 152076 3.500000e+00 1.500000e+00 -2.000000e+00 0.5833333 5.000000e-01
rec.Standard_RP
1 2.449490e-01
2 2.298380e-16
3 -9.190780e-01
4 Inf
5 -4.772607e-01
6 -7.071068e-01
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