SPARS
Experiment 1 – Participant descriptive statistics
Peter Kamerman
24 Nov 2018
This script generates descriptive statistics of the SPARS (trial A) cohort, and includes assessments of:
- Age
- Sex
- Pain Catastrophizing Scale (PCS)
- Positive and Negative Affect Schedule (PANAS)
- Depression, Anxiety, and Stress Scale (DASS-42)
Import and inspect data
# Import
data <- read_rds('./data-cleaned/SPARS_A.rds')
# Inspect
dim(data)## [1] 1927 19names(data)## [1] "PID" "block" "block_order"
## [4] "trial_number" "intensity" "intensity_char"
## [7] "rating" "rating_positive" "EDA"
## [10] "age" "sex" "panas_positive"
## [13] "panas_negative" "dass42_depression" "dass42_anxiety"
## [16] "dass42_stress" "pcs_magnification" "pcs_rumination"
## [19] "pcs_helplessness"head(data)## # A tibble: 6 x 19
## PID block block_order trial_number intensity intensity_char rating
## <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 ID01 A 4 79 3 3.00 -40
## 2 ID01 A 4 80 2.25 2.25 -25
## 3 ID01 A 4 81 4 4.00 10
## 4 ID01 A 4 82 3.25 3.25 2
## 5 ID01 A 4 83 2.75 2.75 -10
## 6 ID01 A 4 84 2.25 2.25 -25
## # ... with 12 more variables: rating_positive <dbl>, EDA <dbl>, age <dbl>,
## # sex <dbl>, panas_positive <dbl>, panas_negative <dbl>,
## # dass42_depression <dbl>, dass42_anxiety <dbl>, dass42_stress <dbl>,
## # pcs_magnification <dbl>, pcs_rumination <dbl>, pcs_helplessness <dbl>tail(data)## # A tibble: 6 x 19
## PID block block_order trial_number intensity intensity_char rating
## <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 ID19 D 4 99 2 2.00 -25
## 2 ID19 D 4 100 3.25 3.25 -3
## 3 ID19 D 4 101 3.5 3.50 -8
## 4 ID19 D 4 102 1.5 1.50 -17
## 5 ID19 D 4 103 1.5 1.50 -7
## 6 ID19 D 4 104 2.75 2.75 -8
## # ... with 12 more variables: rating_positive <dbl>, EDA <dbl>, age <dbl>,
## # sex <dbl>, panas_positive <dbl>, panas_negative <dbl>,
## # dass42_depression <dbl>, dass42_anxiety <dbl>, dass42_stress <dbl>,
## # pcs_magnification <dbl>, pcs_rumination <dbl>, pcs_helplessness <dbl>glimpse(data)## Observations: 1,927
## Variables: 19
## $ PID <chr> "ID01", "ID01", "ID01", "ID01", "ID01", "ID0...
## $ block <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A",...
## $ block_order <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,...
## $ trial_number <dbl> 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, ...
## $ intensity <dbl> 3.00, 2.25, 4.00, 3.25, 2.75, 2.25, 2.75, 4....
## $ intensity_char <chr> "3.00", "2.25", "4.00", "3.25", "2.75", "2.2...
## $ rating <dbl> -40, -25, 10, 2, -10, -25, -20, 10, -25, -50...
## $ rating_positive <dbl> 10, 25, 60, 52, 40, 25, 30, 60, 25, 0, 25, 3...
## $ EDA <dbl> 75270.55, 43838.67, 35967.67, 26720.61, 1931...
## $ age <dbl> 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, ...
## $ sex <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,...
## $ panas_positive <dbl> 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, ...
## $ panas_negative <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, ...
## $ dass42_depression <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ dass42_anxiety <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,...
## $ dass42_stress <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
## $ pcs_magnification <dbl> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,...
## $ pcs_rumination <dbl> 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, ...
## $ pcs_helplessness <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, ...Clean data
# Select participant descriptive data
data %<>%
select(PID, age:pcs_helplessness)
# Filter unique data for each individual
data %<>%
group_by(PID) %>%
distinct() %>%
ungroup()
# Quick inspection for duplicates
data## # A tibble: 19 x 11
## PID age sex panas_positive panas_negative dass42_depressi…
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ID01 21 2 36 10 0
## 2 ID02 20 1 34 11 3
## 3 ID03 19 1 22 12 3
## 4 ID04 21 2 17 10 0
## 5 ID05 25 2 17 10 0
## 6 ID06 22 2 30 10 0
## 7 ID07 27 1 33 10 1
## 8 ID08 24 2 23 11 0
## 9 ID09 26 2 26 13 6
## 10 ID10 20 2 24 10 0
## 11 ID11 24 2 18 11 1
## 12 ID12 18 2 29 10 18
## 13 ID13 25 2 34 10 4
## 14 ID14 29 2 24 14 7
## 15 ID15 24 1 25 12 2
## 16 ID16 30 2 29 16 5
## 17 ID17 24 1 25 12 2
## 18 ID18 31 1 24 10 7
## 19 ID19 22 1 19 14 13
## # ... with 5 more variables: dass42_anxiety <dbl>, dass42_stress <dbl>,
## # pcs_magnification <dbl>, pcs_rumination <dbl>, pcs_helplessness <dbl># Remove ID15 duplicate (row 16, one miscoded value of 13 for dass42_anxiety)
data <- data[-16, ]Age
# Tabulate
data %>%
select(age) %>%
summarise(Mean = round(mean(age), 1),
Median = median(age),
iqr = paste(round(quantile(age, 0.25)), '-',
round(quantile(age, 0.75))),
range = paste(min(age), '-', max(age))) %>%
knitr::kable(x = .,
caption = 'Summary table of age in years',
col.names = c('Mean', 'Median', 'IQR', 'Range'),
align = 'rrrr')| Mean | Median | IQR | Range |
|---|---|---|---|
| 23.4 | 24 | 21 - 25 | 18 - 31 |
# Plot
data %>%
select(age) %>%
ggplot(data = .) +
aes(x = 'Age',
y = age) +
geom_boxplot(outlier.size = -1,
fill = '#cccccc') +
geom_point(shape = 21,
size = 2,
fill = '#FFFFFF',
position = position_jitter(height = 0)) +
labs(title = "Summary plot of participants' ages",
y = 'Age (years)') +
theme_bw() +
theme(axis.title.x = element_blank())
Sex
# Process data
sex <- data %>%
select(sex) %>%
mutate(sex = factor(sex),
sex = fct_recode(sex,
male = '1',
female = '2')) %>%
group_by(sex) %>%
summarise(count = n(),
proportion = round(count / nrow(.), 2))
# Tabulate data
knitr::kable(x = sex,
caption = 'Summary table sex ratio',
col.names = c('', 'Count', 'Proportion'),
align = 'lrr')| Count | Proportion | |
|---|---|---|
| male | 7 | 0.39 |
| female | 11 | 0.61 |
# Plot
ggplot(data = sex) +
aes(x = 'sex',
y = count,
fill = sex) +
geom_col(position = position_fill()) +
scale_fill_grey(start = 0.4) +
labs(title = "Summary plot of sex ratio",
y = 'Proportion') +
theme_bw() +
theme(axis.title.x = element_blank())
Pain Catastrophizing Scale (PCS)
The Pain Catastrophizing Scale (PCS) 1 is a 13-item scale, with each item rated on a 5-point Likert scale: 0 (not at all) to 4 (all the time). The PCS consists of three subscales being magnification (3 items), rumination (4 items), and helplessness (6 items).
# Process data
pcs <- data %>%
select(starts_with('pcs')) %>%
mutate(pcs_total = rowSums(.))
# Tabulate
pcs %>%
gather(key = subscale,
value = score) %>%
group_by(subscale) %>%
summarise(Mean = round(mean(score), 1),
Median = median(score),
iqr = paste(round(quantile(score, 0.25)), '-',
round(quantile(score, 0.75))),
range = paste(min(score), '-', max(score))) %>%
knitr::kable(x = .,
caption = 'Summary table of PCS total and subscale scores',
col.names = c('', 'Mean', 'Median', 'IQR', 'Range'),
align = 'lrrrr')| Mean | Median | IQR | Range | |
|---|---|---|---|---|
| pcs_helplessness | 5.8 | 5.0 | 4 - 9 | 0 - 11 |
| pcs_magnification | 3.5 | 3.5 | 1 - 6 | 0 - 9 |
| pcs_rumination | 6.7 | 7.0 | 4 - 9 | 0 - 13 |
| pcs_total | 16.1 | 15.5 | 10 - 23 | 1 - 31 |
# Plot
total <- pcs %>%
select(pcs_total) %>%
ggplot(data = .) +
aes(x = 'Total',
y = pcs_total) +
geom_boxplot(outlier.size = -1,
fill = '#cccccc') +
geom_point(shape = 21,
size = 2,
fill = '#FFFFFF',
position = position_jitter(height = 0)) +
geom_hline(yintercept = 30,
linetype = 2) +
lims(y = c(0, 52)) +
labs(title = 'Summary plot of PCS total and subscale scores',
subtitle = 'Total score (normal < 30)',
y = 'Score [0 - 52]') +
theme_bw() +
theme(axis.title.x = element_blank())
rumination <- pcs %>%
select(pcs_rumination) %>%
ggplot(data = .) +
aes(x = 'Rumination',
y = pcs_rumination) +
geom_boxplot(outlier.size = -1,
fill = '#cccccc') +
geom_point(shape = 21,
size = 2,
fill = '#FFFFFF',
position = position_jitter(height = 0)) +
lims(y = c(0, 16)) +
labs(title = '',
subtitle = 'Rumination score\n(max score = 16)',
y = 'Score [0 - 16]') +
theme_bw() +
theme(axis.title.x = element_blank())
magnification <- pcs %>%
select(pcs_magnification) %>%
ggplot(data = .) +
aes(x = 'Magnification',
y = pcs_magnification) +
geom_boxplot(outlier.size = -1,
fill = '#cccccc') +
geom_point(shape = 21,
size = 2,
fill = '#FFFFFF',
position = position_jitter(height = 0)) +
scale_y_continuous(breaks = c(0, 3, 6, 9, 12), limits = c(0, 12)) +
labs(title = '',
subtitle = 'Magnification score\n(max score = 12)',
y = 'Score [0 - 12]') +
theme_bw() +
theme(axis.title.x = element_blank())
helplessness <- pcs %>%
select(pcs_helplessness) %>%
ggplot(data = .) +
aes(x = 'Helplessness',
y = pcs_helplessness) +
geom_boxplot(outlier.size = -1,
fill = '#cccccc') +
geom_point(shape = 21,
size = 2,
fill = '#FFFFFF',
position = position_jitter(height = 0)) +
lims(y = c(0, 24)) +
labs(title = '',
subtitle = 'Helplessness score\n(max score = 24)',
y = 'Score [0 - 24]') +
theme_bw() +
theme(axis.title.x = element_blank())
# Patch plots together
total + {magnification + rumination + helplessness} + plot_layout(nrow = 2)
Positive and Negative Affect Schedule (PANAS)
The Positive and Negative Affect Schedule (PANAS) 2 is a self-report questionnaire that consists of two 10-item scales to measure positive and negative affect. Each item is rated on a Likert scale of 1 (not at all) to 5 (very much).
# Process
panas <- data %>%
select(PID, starts_with('panas')) %>%
gather(key = affect,
value = score,
-PID) %>%
mutate(total_score = as.integer(score),
affect = fct_recode(affect,
Negative = 'panas_negative',
Positive = 'panas_positive'))
# Tabulate
panas %>%
group_by(affect) %>%
summarise(median = median(score),
iqr = paste(round(quantile(score, 0.25)), '-',
round(quantile(score, 0.75))),
range = paste(min(score), '-', max(score))) %>%
knitr::kable(x = .,
caption = 'Summary table of PANAS subscale scores',
col.names = c('Affect', 'Median', 'IQR', 'Range'),
align = 'lrrr')| Affect | Median | IQR | Range |
|---|---|---|---|
| Negative | 10.5 | 10 - 12 | 10 - 14 |
| Positive | 24.5 | 22 - 30 | 17 - 36 |
# Plot
ggplot(data = panas) +
aes(x = affect,
y = score,
fill = affect) +
geom_boxplot() +
geom_point(shape = 21,
size = 2,
fill = '#FFFFFF',
position = position_jitter(height = 0)) +
scale_fill_grey(start = 0.4) +
lims(y = c(10, 50)) +
labs(title = 'Summary plot of PANAS subscale scores',
x = 'Affect',
y = 'Score [10 - 50]',
subtitle = 'Negative scale: lower values are better\nPositive Scale: greater values are better') +
theme_bw()
Depression, Anxiety and Stress Scale (DASS-42)
The Depression Anxiety Stress Scales 3 is a 42 self-report item scale assessing negative emotional symptoms. Each item is rated on a four-point Likert scale of frequency or severity of the participants’ experiences over the last week with the intention of emphasising states over traits. These scores ranged from 0 (did not apply at all) to 4 (applied to them very much/most of the time).
# Tabulate
data %>%
select(PID, dass42_depression, dass42_anxiety, dass42_stress) %>%
rename(depression = dass42_depression,
anxiety = dass42_anxiety,
stress = dass42_stress) %>%
gather(key = dimension,
value = score,
-PID) %>%
group_by(dimension) %>%
summarise(median = median(score),
iqr = paste(round(quantile(score, 0.25)), '-',
round(quantile(score, 0.75))),
range = paste(min(score), '-', max(score))) %>%
knitr::kable(x = .,
caption = 'Summary table of DASS-42 dimension scores',
col.names = c('Dimension', 'Median', 'IQR', 'Range'),
align = 'lrrr')| Dimension | Median | IQR | Range |
|---|---|---|---|
| anxiety | 1.5 | 0 - 4 | 0 - 10 |
| depression | 2.0 | 0 - 6 | 0 - 18 |
| stress | 5.0 | 1 - 8 | 0 - 24 |
# Process plots
depression <- data %>%
select(PID, dass42_depression) %>%
rename(depression = dass42_depression) %>%
ggplot(data = .) +
aes(x = 'Depression',
y = depression) +
geom_boxplot(outlier.size = -1,
fill = '#cccccc') +
geom_point(shape = 21,
size = 2,
fill = '#FFFFFF',
position = position_jitter(height = 0)) +
geom_hline(yintercept = 9,
linetype = 2) +
lims(y = c(0, 56)) +
labs(title = 'Summary plot of DASS-42',
subtitle = 'Depression (normal < 10)',
y = 'Score [0 - 56]') +
theme_bw() +
theme(axis.title.x = element_blank())
anxiety <- data %>%
select(PID, dass42_anxiety) %>%
rename(anxiety = dass42_anxiety) %>%
ggplot(data = .) +
aes(x = 'Anxiety',
y = anxiety) +
geom_boxplot(outlier.size = -1,
fill = '#cccccc') +
geom_point(shape = 21,
size = 2,
fill = '#FFFFFF',
position = position_jitter(height = 0)) +
geom_hline(yintercept = 7,
linetype = 2) +
lims(y = c(0, 56)) +
labs(title = '',
subtitle = 'Anxiety (normal < 8)',
y = '') +
theme_bw() +
theme(axis.title.x = element_blank())
stress <- data %>%
select(PID, dass42_stress) %>%
rename(stress = dass42_stress) %>%
ggplot(data = .) +
aes(x = 'Stress',
y = stress) +
geom_boxplot(outlier.size = -1,
fill = '#cccccc') +
geom_point(shape = 21,
size = 2,
fill = '#FFFFFF',
position = position_jitter(height = 0)) +
geom_hline(yintercept = 14,
linetype = 2) +
lims(y = c(0, 56)) +
labs(title = '',
subtitle = 'Stress (normal < 15)',
y = '') +
theme_bw() +
theme(axis.title.x = element_blank())
# Patch them together
depression + anxiety + stress + plot_layout(ncol = 3)
Session information
sessionInfo()## R version 3.5.1 (2018-07-02)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 9 (stretch)
##
## Matrix products: default
## BLAS: /usr/lib/openblas-base/libblas.so.3
## LAPACK: /usr/lib/libopenblasp-r0.2.19.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] bindrcpp_0.2.2 patchwork_0.0.1 forcats_0.3.0 stringr_1.3.1
## [5] dplyr_0.7.6 purrr_0.2.5 readr_1.1.1 tidyr_0.8.1
## [9] tibble_1.4.2 ggplot2_3.0.0 tidyverse_1.2.1 magrittr_1.5
##
## loaded via a namespace (and not attached):
## [1] tidyselect_0.2.4 haven_1.1.2 lattice_0.20-35 colorspace_1.3-2
## [5] htmltools_0.3.6 yaml_2.2.0 utf8_1.1.4 rlang_0.2.2
## [9] pillar_1.3.0 glue_1.3.0 withr_2.1.2 modelr_0.1.2
## [13] readxl_1.1.0 bindr_0.1.1 plyr_1.8.4 munsell_0.5.0
## [17] gtable_0.2.0 cellranger_1.1.0 rvest_0.3.2 evaluate_0.11
## [21] labeling_0.3 knitr_1.20 fansi_0.3.0 highr_0.7
## [25] broom_0.5.0 Rcpp_0.12.18 scales_1.0.0 backports_1.1.2
## [29] jsonlite_1.5 hms_0.4.2 digest_0.6.16 stringi_1.2.4
## [33] grid_3.5.1 rprojroot_1.3-2 cli_1.0.0 tools_3.5.1
## [37] lazyeval_0.2.1 crayon_1.3.4 pkgconfig_2.0.2 xml2_1.2.0
## [41] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.10 httr_1.3.1
## [45] rstudioapi_0.7 R6_2.2.2 nlme_3.1-137 compiler_3.5.1Sullivan M, Bishop SR, Pivik J. The pain catastrophizing scale: development and validation. Psychol. Assess. 7: 524, 1995. Download↩
Crawford JR, Henry JD. The positive and negative affect schedule (PANAS): construct validity, measurement properties and normative data in a large non-clinical sample. Br J Clin Psychol 43: 245-65, 2004. PMID: 15333231.↩
Crawford JR, Henry JD. The Depression Anxiety Stress Scales (DASS): normative data and latent structure in a large non-clinical sample. Br J Clin Psychol 42: 111-31, 2003. PMID: 12828802↩