els_covid
Lauren R. Borchers
12/22/2020
# load libraries
library("knitr")
library("ggplot2")
library("ggpubr")
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## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car## In case you would like cite this package, cite it as:
## Patil, I. (2018). ggstatsplot: "ggplot2" Based Plots with Statistical Details. CRAN.
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## viewlibrary("Hmisc")## Loading required package: lattice## Loading required package: survival## Loading required package: Formula##
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library("sjstats")##
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library("ggcorrplot")
library("sjmisc")##
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## add_caselibrary("lubridate")##
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## date, intersect, setdiff, unionlibrary("dplyr")# data file
covid = read.csv("/Volumes/group/users/borchersLR/COVID/github/covid.csv")
# excluding participants with no cesdc
covid = covid %>%
filter(!is.na(CESDC_total.TC))
# N = 109
head(covid)## ELS_ID Child_Sex.T1 Parent_Income.T1 KSADS_Child_Race_by_P.T1 Age_S1.T1
## 1 5 2 NA 1 11.14
## 2 6 2 NA 3 13.46
## 3 14 2 10 1 10.65
## 4 16 2 8 1 10.08
## 5 17 2 2 5 10.86
## 6 19 2 NA 1 11.06
## cdi_TOTAL.T1 sumsev_type_t1 Child_Age.TC PSS_total.TC CESDC_total.TC
## 1 10 11.5 17.68 18 12
## 2 0 14.5 19.98 25 38
## 3 3 11.5 17.05 34 51
## 4 0 2.5 16.42 33 50
## 5 1 5.0 17.11 23 18
## 6 4 8.5 17.30 29 41
## Covid_child_fever.TC Covid_child_cough.TC Covid_child_short_breath.TC
## 1 0 0 0
## 2 0 0 0
## 3 1 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## Covid_child_sore_throat.TC Covid_child_fatigue.TC
## 1 0 0
## 2 0 0
## 3 0 1
## 4 1 1
## 5 0 0
## 6 0 1
## Covid_child_loss_taste_smell.TC Covid_child_other_symptom.TC
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## Worry_self_infected.TC Worry_fam_infected.TC Self_phys_health_influence.TC
## 1 2 3 2
## 2 4 4 4
## 3 4 4 2
## 4 1 1 1
## 5 3 5 3
## 6 3 4 3
## Self_mental_health_influence.TC Read_talk_virus.TC Covid_positive_changes.TC
## 1 3 4 2
## 2 4 3 2
## 3 5 5 1
## 4 4 4 1
## 5 4 4 1
## 6 4 5 2
## Stress_of_restrictions.TC Contacts_outside_change.TC
## 1 3 1
## 2 2 2
## 3 5 4
## 4 5 5
## 5 4 4
## 6 4 5
## Diff_following_contact_rules.TC Quality_relations_family_change.TC
## 1 2 2
## 2 2 4
## 3 5 2
## 4 2 2
## 5 1 3
## 6 3 2
## Stress_relations_family_change.TC Quality_relations_friends_change.TC
## 1 3 3
## 2 4 3
## 3 5 4
## 4 4 2
## 5 1 3
## 6 3 4
## Stress_relations_friends_change.TC Cancelling_events_difficulty.TC
## 1 3 4
## 2 3 4
## 3 2 4
## 4 4 4
## 5 1 2
## 6 3 3
## Family_financial_problems.TC Worry_living_stability.TC Hope_crisis_end.TC
## 1 3 2 3
## 2 2 2 2
## 3 3 2 2
## 4 2 3 2
## 5 2 1 5
## 6 3 2 2
## Worried_2weeks.TC Happy_sad_2weeks.TC Relaxed_anxious_2weeks.TC
## 1 3 4 3
## 2 4 1 4
## 3 4 1 5
## 4 3 1 5
## 5 2 3 3
## 6 4 2 5
## Fatigued_tired_2weeks.TC Loneliness_2weeks.TC Concentration_2weeks.TC
## 1 3 2 3
## 2 3 4 4
## 3 4 5 5
## 4 2 4 5
## 5 3 1 2
## 6 3 5 4
## Neg_thoughts_2weeks.TC Irritability_2weeks.TC Mental_health_rating.TC
## 1 2 2 2
## 2 4 3 4
## 3 5 5 4
## 4 4 4 5
## 5 1 1 3
## 6 5 3 4
## Worry_no_food.TC Anhedonia_2weeks.TC Physical_health_rating.TC
## 1 0 2 2
## 2 0 2 3
## 3 1 1 3
## 4 1 2 3
## 5 1 1 3
## 6 0 3 2
## Fidgety_restless_2weeks.TC income_3levels
## 1 3 NA
## 2 1 NA
## 3 5 3
## 4 5 2
## 5 3 1
## 6 4 NA### key
# tc = covid-19 assessment time point
## sex
# 1 is male; 2 is female
## race
# 1 is white; 2 is black/african american; 3 is hispanic/latin-x; 4 is asian/asian american; 5 is biracial; 6 is other race
# els = early life stress# abstract
# number of male and female participants in the sample
# 1 is male; 2 is female
covid %>%
group_by(Child_Sex.T1) %>%
summarise(n=n())## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 2
## Child_Sex.T1 n
## <int> <int>
## 1 1 43
## 2 2 66# age range of participants
min(covid$Child_Age.TC)## [1] 12.82max(covid$Child_Age.TC)## [1] 19.98# years between baseline and tc assessment
covid$years <- covid$Child_Age.TC-covid$Age_S1.T1
summary(covid$years)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.020 4.510 5.070 5.064 5.810 6.540mean(covid$years)## [1] 5.064312sd(covid$years)## [1] 0.8592346# correlation between els and depression
cor.test(covid$sumsev_type_t1, covid$CESDC_total.TC)##
## Pearson's product-moment correlation
##
## data: covid$sumsev_type_t1 and covid$CESDC_total.TC
## t = 2.8112, df = 107, p-value = 0.005871
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07800399 0.42918713
## sample estimates:
## cor
## 0.2622577# sex differences in depression
t.test(CESDC_total.TC ~ Child_Sex.T1, data = covid, rm.na=TRUE, var.equal=T)##
## Two Sample t-test
##
## data: CESDC_total.TC by Child_Sex.T1
## t = -3.5573, df = 107, p-value = 0.0005598
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -11.569219 -3.289132
## sample estimates:
## mean in group 1 mean in group 2
## 16.11628 23.54545## methods
# dates not publicly available; identifying information
# dates <- read.csv('/Volumes/group/users/borchersLR/COVID/github/tc_dates.csv',sep=",",header=T)
# dates_sample <-left_join(covid, dates, by="ELS_ID")
# time between
# dates_sample$background_timestamp<-as.Date(dates_sample$background_timestamp, format = "%m/%d/%y")
# summary(dates_sample$background_timestamp)
# days between shelter in place March 17
# shelter<-as.Date(as.character("03/17/2020"), format="%m/%d/%y")
# dates_sample$date_diff <- dates_sample$background_timestamp- shelter
# summary(as.integer(dates_sample$date_diff))
# mean(as.integer(dates_sample$date_diff), na.rm=T)
# sd(as.integer(dates_sample$date_diff), na.rm=T)
# percent of participants who completed the covid-19 (TC) assessment
109/221## [1] 0.4932127# age of participants
min(covid$Child_Age.TC)## [1] 12.82max(covid$Child_Age.TC)## [1] 19.98mean(covid$Child_Age.TC)## [1] 16.29789sd(covid$Child_Age.TC)## [1] 1.473223# figure 1
# dates not publicly available; identifying information
cdate<-read.table('/Volumes/group/users/borchersLR/COVID/github/Bay_Area_Cases_Summed_Over_Time.csv',sep=",",header=T)
cdate$Date<-na.omit(cdate$Date)
cdate$Date<-as.Date(cdate$Date, format = "%m/%d/%y")
p<-ggplot(cdate, aes(x=Date, y=Bay_Area_Case_Count))+geom_line(size=2, linetype="solid")
tiff('Data_Collection_COVID_Cases.tiff', units="in", width=5, height=5, res=300)
p+labs(title="", x="Date", y="Bay Area COVID-19 Case Count")+
theme_classic()+
theme(axis.title.x=element_text(family="Arial",colour="Black", size=16),
axis.text.x=element_text(family="Arial",size=12, colour="black", angle=45, hjust=1))+
theme(axis.title.y=element_text(family="Arial",colour="Black", size=16),
axis.text.y=element_text(family="Arial", vjust=.5,size=12, colour="black"))+
theme(legend.text=element_text(family="Arial",size=12)) +
guides(colour = guide_legend(override.aes = list(size=8))) +
theme(legend.title=element_text(family="Arial", size=12))+
theme(legend.position = "none")+annotate("rect", xmin = as.Date("2020-04-03"), xmax = as.Date("2020-04-23"), ymin = 0, ymax = 8000, alpha = .2)+annotate("text", x = as.Date("2020-04-13"), y = 2000, label = "Data Collection Period", family="Arial") +
scale_x_date(name="Date", date_breaks="5 day", date_labels = "%b %d") ## Warning: Removed 218 row(s) containing missing values (geom_path).# cdi
# one participant did not complete the cdi at baseline
min(covid$cdi_TOTAL.T1, na.rm = TRUE)## [1] 0max(covid$cdi_TOTAL.T1, na.rm = TRUE)## [1] 10mean(covid$cdi_TOTAL.T1, na.rm = TRUE)## [1] 1.768519sd(covid$cdi_TOTAL.T1, na.rm = TRUE)## [1] 2.048964# crobach's alpha for cdi at baseline
cdi_t1 = read.csv("/Volumes/group/users/borchersLR/COVID/github/cdi_t1.csv")
cdi_t1 = cdi_t1 %>%
filter(!is.na(cdi_1.T1))
cdi_t1 %>%
select(
cdi_1.T1,
cdi_2_R.T1,
cdi_3.T1,
cdi_4_R.T1,
cdi_5_R.T1,
cdi_6_R.T1,
cdi_7.T1,
cdi_8.T1,
cdi_9.T1,
cdi_10_R.T1
) %>%
alpha()##
## Reliability analysis
## Call: alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.77 0.77 0.26 3.4 0.025 0.22 0.24 0.24
##
## lower alpha upper 95% confidence boundaries
## 0.7 0.75 0.8
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## cdi_1.T1 0.72 0.75 0.74 0.25 2.9 0.027 0.0082 0.24
## cdi_2_R.T1 0.74 0.76 0.76 0.26 3.2 0.026 0.0090 0.24
## cdi_3.T1 0.75 0.78 0.78 0.28 3.5 0.025 0.0071 0.28
## cdi_4_R.T1 0.73 0.74 0.74 0.24 2.9 0.027 0.0081 0.24
## cdi_5_R.T1 0.73 0.75 0.75 0.25 3.0 0.027 0.0083 0.24
## cdi_6_R.T1 0.74 0.76 0.76 0.26 3.2 0.026 0.0101 0.25
## cdi_7.T1 0.74 0.76 0.76 0.26 3.2 0.026 0.0102 0.27
## cdi_8.T1 0.71 0.74 0.74 0.24 2.9 0.029 0.0083 0.24
## cdi_9.T1 0.74 0.76 0.76 0.26 3.2 0.026 0.0094 0.26
## cdi_10_R.T1 0.72 0.74 0.73 0.24 2.8 0.028 0.0085 0.23
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## cdi_1.T1 211 0.60 0.63 0.58 0.48 0.185 0.39
## cdi_2_R.T1 211 0.57 0.54 0.46 0.40 0.332 0.50
## cdi_3.T1 211 0.35 0.41 0.29 0.25 0.071 0.26
## cdi_4_R.T1 211 0.60 0.64 0.60 0.51 0.095 0.29
## cdi_5_R.T1 210 0.58 0.60 0.54 0.47 0.114 0.35
## cdi_6_R.T1 211 0.61 0.54 0.46 0.41 0.488 0.63
## cdi_7.T1 211 0.55 0.52 0.43 0.37 0.346 0.52
## cdi_8.T1 211 0.67 0.66 0.62 0.53 0.223 0.48
## cdi_9.T1 211 0.54 0.53 0.45 0.38 0.242 0.44
## cdi_10_R.T1 211 0.63 0.67 0.64 0.55 0.090 0.29
##
## Non missing response frequency for each item
## 0 1 2 miss
## cdi_1.T1 0.82 0.18 0.00 0
## cdi_2_R.T1 0.68 0.30 0.01 0
## cdi_3.T1 0.93 0.07 0.00 0
## cdi_4_R.T1 0.91 0.09 0.00 0
## cdi_5_R.T1 0.90 0.10 0.01 0
## cdi_6_R.T1 0.58 0.35 0.07 0
## cdi_7.T1 0.68 0.30 0.02 0
## cdi_8.T1 0.81 0.17 0.03 0
## cdi_9.T1 0.76 0.23 0.00 0
## cdi_10_R.T1 0.91 0.09 0.00 0# cesdc
# internal consistency
# df with all cesdc and pss items at tc
items = read.csv("/Volumes/group/users/borchersLR/COVID/github/pss_cesd_items.csv")
# pss
# one participant did not complete the pss at tc
# reverse code 4, 5, 7, 8, then sum
items <-
items %>%
mutate_at(
vars(
covid_pss_c_4, covid_pss_c_5, covid_pss_c_7, covid_pss_c_8
),
funs(
. %>%
dplyr::recode(
"0" = 4,
"1" = 3,
"2" = 2,
"3" = 1,
"4" = 0
)
)
)## Warning: `funs()` is deprecated as of dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.items %>%
select(
covid_pss_c_1,
covid_pss_c_2,
covid_pss_c_3,
covid_pss_c_4,
covid_pss_c_5,
covid_pss_c_6,
covid_pss_c_7,
covid_pss_c_8,
covid_pss_c_9,
covid_pss_c_10
) %>%
alpha()##
## Reliability analysis
## Call: alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.84 0.87 0.35 5.3 0.016 1.8 0.66 0.33
##
## lower alpha upper 95% confidence boundaries
## 0.81 0.84 0.87
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## covid_pss_c_1 0.82 0.82 0.85 0.34 4.6 0.019 0.022 0.32
## covid_pss_c_2 0.83 0.83 0.86 0.35 4.9 0.018 0.021 0.32
## covid_pss_c_3 0.82 0.82 0.84 0.34 4.6 0.019 0.019 0.32
## covid_pss_c_4 0.83 0.83 0.86 0.36 5.1 0.017 0.020 0.34
## covid_pss_c_5 0.83 0.83 0.85 0.35 4.8 0.018 0.023 0.32
## covid_pss_c_6 0.83 0.83 0.86 0.35 4.9 0.018 0.023 0.34
## covid_pss_c_7 0.84 0.84 0.86 0.36 5.1 0.017 0.024 0.34
## covid_pss_c_8 0.83 0.82 0.85 0.34 4.7 0.018 0.021 0.32
## covid_pss_c_9 0.83 0.83 0.85 0.35 4.8 0.018 0.021 0.33
## covid_pss_c_10 0.81 0.81 0.83 0.32 4.2 0.020 0.020 0.29
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## covid_pss_c_1 108 0.69 0.69 0.65 0.60 1.8 0.97
## covid_pss_c_2 108 0.61 0.60 0.54 0.49 2.1 1.12
## covid_pss_c_3 108 0.70 0.68 0.65 0.60 2.1 1.08
## covid_pss_c_4 108 0.54 0.57 0.51 0.44 1.5 0.91
## covid_pss_c_5 108 0.60 0.62 0.58 0.50 2.0 0.91
## covid_pss_c_6 108 0.63 0.61 0.56 0.51 1.7 1.20
## covid_pss_c_7 108 0.54 0.56 0.48 0.43 1.8 0.90
## covid_pss_c_8 108 0.65 0.66 0.64 0.54 1.9 1.00
## covid_pss_c_9 108 0.64 0.63 0.58 0.52 1.9 1.11
## covid_pss_c_10 108 0.80 0.79 0.79 0.73 1.7 1.08
##
## Non missing response frequency for each item
## 0 1 2 3 4 miss
## covid_pss_c_1 0.09 0.25 0.44 0.19 0.04 0.48
## covid_pss_c_2 0.09 0.19 0.38 0.22 0.12 0.48
## covid_pss_c_3 0.06 0.21 0.35 0.26 0.11 0.48
## covid_pss_c_4 0.10 0.43 0.36 0.07 0.04 0.48
## covid_pss_c_5 0.04 0.28 0.40 0.25 0.04 0.48
## covid_pss_c_6 0.16 0.36 0.22 0.17 0.09 0.48
## covid_pss_c_7 0.06 0.32 0.42 0.17 0.03 0.48
## covid_pss_c_8 0.07 0.26 0.40 0.21 0.06 0.48
## covid_pss_c_9 0.16 0.18 0.35 0.28 0.04 0.48
## covid_pss_c_10 0.14 0.31 0.32 0.17 0.06 0.48# cesdc
# reverse code 4, 8, 12, 16, then sum
items <-
items %>%
mutate_at(
vars(
covid_cesd_c_4, covid_cesd_c_8, covid_cesd_c_12, covid_cesd_c_16
),
funs(
. %>%
dplyr::recode(
"0" = 3,
"1" = 2,
"2" = 1,
"3" = 0
)
)
)
items %>%
select(
covid_cesd_c_1,
covid_cesd_c_2,
covid_cesd_c_3,
covid_cesd_c_4,
covid_cesd_c_5,
covid_cesd_c_6,
covid_cesd_c_7,
covid_cesd_c_8,
covid_cesd_c_9,
covid_cesd_c_10,
covid_cesd_c_11,
covid_cesd_c_12,
covid_cesd_c_13,
covid_cesd_c_14,
covid_cesd_c_15,
covid_cesd_c_16,
covid_cesd_c_17,
covid_cesd_c_18,
covid_cesd_c_19,
covid_cesd_c_20
) %>%
alpha()##
## Reliability analysis
## Call: alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.91 0.91 0.94 0.34 10 0.0091 1 0.56 0.34
##
## lower alpha upper 95% confidence boundaries
## 0.89 0.91 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## covid_cesd_c_1 0.91 0.91 0.94 0.34 10.0 0.0094 0.021 0.35
## covid_cesd_c_2 0.91 0.91 0.94 0.35 10.0 0.0093 0.021 0.35
## covid_cesd_c_3 0.90 0.90 0.94 0.33 9.5 0.0097 0.021 0.33
## covid_cesd_c_4 0.91 0.91 0.94 0.35 10.1 0.0092 0.021 0.35
## covid_cesd_c_5 0.91 0.91 0.94 0.34 9.9 0.0094 0.021 0.35
## covid_cesd_c_6 0.90 0.90 0.93 0.33 9.4 0.0098 0.019 0.33
## covid_cesd_c_7 0.90 0.91 0.94 0.34 9.6 0.0097 0.022 0.33
## covid_cesd_c_8 0.91 0.91 0.94 0.35 10.0 0.0094 0.021 0.35
## covid_cesd_c_9 0.91 0.91 0.94 0.35 10.0 0.0093 0.021 0.34
## covid_cesd_c_10 0.91 0.91 0.94 0.35 10.2 0.0093 0.020 0.35
## covid_cesd_c_11 0.91 0.91 0.94 0.35 10.4 0.0090 0.019 0.35
## covid_cesd_c_12 0.90 0.91 0.94 0.33 9.5 0.0097 0.021 0.33
## covid_cesd_c_13 0.91 0.91 0.94 0.34 9.8 0.0095 0.021 0.33
## covid_cesd_c_14 0.90 0.91 0.94 0.34 9.6 0.0097 0.019 0.34
## covid_cesd_c_15 0.91 0.91 0.94 0.34 9.9 0.0094 0.018 0.34
## covid_cesd_c_16 0.90 0.91 0.94 0.33 9.5 0.0097 0.021 0.33
## covid_cesd_c_17 0.90 0.90 0.93 0.33 9.3 0.0100 0.020 0.33
## covid_cesd_c_18 0.90 0.90 0.93 0.33 9.3 0.0099 0.019 0.33
## covid_cesd_c_19 0.90 0.91 0.93 0.33 9.6 0.0097 0.019 0.33
## covid_cesd_c_20 0.90 0.90 0.94 0.33 9.4 0.0100 0.021 0.33
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## covid_cesd_c_1 109 0.55 0.55 0.52 0.49 0.99 0.98
## covid_cesd_c_2 109 0.54 0.52 0.49 0.47 0.91 1.02
## covid_cesd_c_3 109 0.69 0.70 0.68 0.65 0.54 0.76
## covid_cesd_c_4 109 0.51 0.51 0.48 0.43 1.27 1.03
## covid_cesd_c_5 109 0.56 0.55 0.52 0.50 1.61 0.97
## covid_cesd_c_6 109 0.72 0.74 0.74 0.69 1.02 0.83
## covid_cesd_c_7 109 0.66 0.65 0.63 0.60 1.16 0.97
## covid_cesd_c_8 109 0.52 0.52 0.49 0.46 1.85 0.85
## covid_cesd_c_9 109 0.52 0.52 0.48 0.46 0.93 0.90
## covid_cesd_c_10 109 0.45 0.46 0.43 0.40 0.70 0.71
## covid_cesd_c_11 109 0.42 0.41 0.37 0.34 0.74 1.01
## covid_cesd_c_12 109 0.67 0.68 0.66 0.62 0.95 0.79
## covid_cesd_c_13 109 0.60 0.59 0.57 0.54 1.06 1.00
## covid_cesd_c_14 109 0.66 0.67 0.66 0.61 0.88 1.04
## covid_cesd_c_15 109 0.56 0.56 0.55 0.50 0.52 0.87
## covid_cesd_c_16 109 0.67 0.68 0.67 0.63 1.18 0.82
## covid_cesd_c_17 109 0.75 0.75 0.75 0.71 0.97 0.98
## covid_cesd_c_18 109 0.74 0.75 0.75 0.70 1.10 0.83
## covid_cesd_c_19 109 0.67 0.67 0.68 0.62 0.68 0.99
## covid_cesd_c_20 109 0.73 0.73 0.72 0.68 1.56 1.06
##
## Non missing response frequency for each item
## 0 1 2 3 miss
## covid_cesd_c_1 0.39 0.33 0.19 0.09 0.48
## covid_cesd_c_2 0.48 0.23 0.20 0.09 0.48
## covid_cesd_c_3 0.60 0.29 0.08 0.03 0.48
## covid_cesd_c_4 0.27 0.37 0.20 0.17 0.48
## covid_cesd_c_5 0.17 0.25 0.40 0.18 0.48
## covid_cesd_c_6 0.29 0.43 0.24 0.04 0.48
## covid_cesd_c_7 0.29 0.37 0.23 0.11 0.48
## covid_cesd_c_8 0.06 0.25 0.46 0.23 0.48
## covid_cesd_c_9 0.39 0.36 0.20 0.06 0.48
## covid_cesd_c_10 0.43 0.46 0.09 0.02 0.48
## covid_cesd_c_11 0.56 0.25 0.08 0.11 0.48
## covid_cesd_c_12 0.31 0.44 0.23 0.02 0.48
## covid_cesd_c_13 0.35 0.37 0.17 0.12 0.48
## covid_cesd_c_14 0.50 0.24 0.16 0.11 0.48
## covid_cesd_c_15 0.67 0.19 0.08 0.06 0.48
## covid_cesd_c_16 0.23 0.39 0.36 0.03 0.48
## covid_cesd_c_17 0.41 0.28 0.24 0.07 0.48
## covid_cesd_c_18 0.23 0.50 0.20 0.06 0.48
## covid_cesd_c_19 0.61 0.20 0.10 0.09 0.48
## covid_cesd_c_20 0.18 0.32 0.25 0.25 0.48# calculating self-report covid symptoms
covid$covid_physs <-covid$Covid_child_fever.TC + covid$Covid_child_cough.TC + covid$Covid_child_short_breath.TC + covid$Covid_child_sore_throat.TC + covid$Covid_child_fatigue.TC + covid$Covid_child_loss_taste_smell.TC + covid$Covid_child_other_symptom.TC### table 1
## age baseline
t.test(Age_S1.T1 ~ Child_Sex.T1, data = covid, var.equal = T)##
## Two Sample t-test
##
## data: Age_S1.T1 by Child_Sex.T1
## t = 3.9542, df = 107, p-value = 0.0001382
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.355392 1.069992
## sample estimates:
## mean in group 1 mean in group 2
## 11.66512 10.95242# 1 is male; 2 is female
covid %>%
group_by(Child_Sex.T1) %>%
summarise(sd = sd(Age_S1.T1))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 2
## Child_Sex.T1 sd
## <int> <dbl>
## 1 1 0.914
## 2 2 0.923## els severity baseline
t.test(sumsev_type_t1 ~ Child_Sex.T1, data = covid, var.equal = T)##
## Two Sample t-test
##
## data: sumsev_type_t1 by Child_Sex.T1
## t = -0.28764, df = 107, p-value = 0.7742
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.999400 1.492705
## sample estimates:
## mean in group 1 mean in group 2
## 5.686047 5.939394# 1 is male; 2 is female
covid %>%
group_by(Child_Sex.T1) %>%
summarise(sd = sd(sumsev_type_t1))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 2
## Child_Sex.T1 sd
## <int> <dbl>
## 1 1 4.50
## 2 2 4.49## cdi baseline
# one male participant missing cdi at baseline
t.test(cdi_TOTAL.T1 ~ Child_Sex.T1, data = covid, rm.na = TRUE, var.equal = T)##
## Two Sample t-test
##
## data: cdi_TOTAL.T1 by Child_Sex.T1
## t = -2.1506, df = 106, p-value = 0.03378
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.63721329 -0.06654342
## sample estimates:
## mean in group 1 mean in group 2
## 1.255814 2.107692# 1 is male; 2 is female
covid %>%
group_by(Child_Sex.T1) %>%
summarise(sd = sd(cdi_TOTAL.T1, na.rm = TRUE))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 2
## Child_Sex.T1 sd
## <int> <dbl>
## 1 1 1.22
## 2 2 2.40## race
# 1 is white; 2 is black/african american; 3 is hispanic/latin-x; 4 is asian/asian american; 5 is biracial; 6 is other race
race <- data.frame(covid$Child_Sex.T1, covid$KSADS_Child_Race_by_P.T1)
race = table(covid$Child_Sex.T1, covid$KSADS_Child_Race_by_P.T1)
print(race)##
## 1 2 3 4 5 6
## 1 20 3 3 7 7 3
## 2 26 1 7 13 15 4print(chisq.test(race))## Warning in chisq.test(race): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: race
## X-squared = 3.5389, df = 5, p-value = 0.6175covid %>%
count(KSADS_Child_Race_by_P.T1, Child_Sex.T1, sort = TRUE) %>%
arrange(KSADS_Child_Race_by_P.T1, Child_Sex.T1)## KSADS_Child_Race_by_P.T1 Child_Sex.T1 n
## 1 1 1 20
## 2 1 2 26
## 3 2 1 3
## 4 2 2 1
## 5 3 1 3
## 6 3 2 7
## 7 4 1 7
## 8 4 2 13
## 9 5 1 7
## 10 5 2 15
## 11 6 1 3
## 12 6 2 4## parental income
covid_income <-
covid %>%
mutate_at(
vars(Parent_Income.T1
),
funs(
. %>%
dplyr::recode(
"10" = 3,
"9" = 2,
"8" = 2,
"7" = 2,
"6" = 2,
"5" = 1,
"4" = 1,
"3" = 1,
"2" = 1,
"1" = 1,
)
)
)
covid_income <- covid_income %>%
mutate(Parent_Income.T1 = if_else(is.na(Parent_Income.T1), 0, Parent_Income.T1))
income = table(covid_income$Child_Sex.T1, covid_income$Parent_Income.T1)
print(income)##
## 0 1 2 3
## 1 3 1 19 20
## 2 5 3 31 27print(chisq.test(income))## Warning in chisq.test(income): Chi-squared approximation may be incorrect##
## Pearson's Chi-squared test
##
## data: income
## X-squared = 0.59587, df = 3, p-value = 0.8974 covid_income %>%
count(Parent_Income.T1, Child_Sex.T1, sort = TRUE) %>%
arrange(Parent_Income.T1, Child_Sex.T1)## Parent_Income.T1 Child_Sex.T1 n
## 1 0 1 3
## 2 0 2 5
## 3 1 1 1
## 4 1 2 3
## 5 2 1 19
## 6 2 2 31
## 7 3 1 20
## 8 3 2 27## age tc
t.test(Child_Age.TC ~ Child_Sex.T1, data = covid, var.equal = T)##
## Two Sample t-test
##
## data: Child_Age.TC by Child_Sex.T1
## t = 3.1595, df = 107, p-value = 0.002055
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.3265441 1.4264228
## sample estimates:
## mean in group 1 mean in group 2
## 16.82860 15.95212# 1 is male; 2 is female
covid %>%
group_by(Child_Sex.T1) %>%
summarise(sd = sd(Child_Age.TC))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 2
## Child_Sex.T1 sd
## <int> <dbl>
## 1 1 1.22
## 2 2 1.53## pss tc
# one participant missing pss at tc
t.test(PSS_total.TC ~ Child_Sex.T1, data = covid, var.equal = T)##
## Two Sample t-test
##
## data: PSS_total.TC by Child_Sex.T1
## t = -3.7252, df = 106, p-value = 0.0003148
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -7.047525 -2.151610
## sample estimates:
## mean in group 1 mean in group 2
## 15.64286 20.24242# 1 is male; 2 is female
covid %>%
group_by(Child_Sex.T1) %>%
summarise(sd = sd(PSS_total.TC, na.rm = TRUE))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 2
## Child_Sex.T1 sd
## <int> <dbl>
## 1 1 6.14
## 2 2 6.33## cesdc tc
# one participant missing pss at tc
t.test(CESDC_total.TC ~ Child_Sex.T1, data = covid, var.equal = T)##
## Two Sample t-test
##
## data: CESDC_total.TC by Child_Sex.T1
## t = -3.5573, df = 107, p-value = 0.0005598
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -11.569219 -3.289132
## sample estimates:
## mean in group 1 mean in group 2
## 16.11628 23.54545# 1 is male; 2 is female
covid %>%
group_by(Child_Sex.T1) %>%
summarise(sd = sd(CESDC_total.TC))## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 2
## Child_Sex.T1 sd
## <int> <dbl>
## 1 1 9.24
## 2 2 11.5### sample characteristics
## race
# 1 is white; 2 is black/african american; 3 is hispanic/latin-x; 4 is asian/asian american; 5 is biracial; 6 is other race
# binarizing race
# covid <-
covid %>%
mutate_at(
vars(KSADS_Child_Race_by_P.T1
),
funs(
. %>%
dplyr::recode(
"1" = 1,
"2" = 0,
"3" = 0,
"4" = 0,
"5" = 0,
"6" = 0
)
)
)## ELS_ID Child_Sex.T1 Parent_Income.T1 KSADS_Child_Race_by_P.T1 Age_S1.T1
## 1 5 2 NA 1 11.14
## 2 6 2 NA 0 13.46
## 3 14 2 10 1 10.65
## 4 16 2 8 1 10.08
## 5 17 2 2 0 10.86
## 6 19 2 NA 1 11.06
## 7 20 1 6 0 10.63
## 8 22 1 9 1 11.18
## 9 23 2 8 0 11.88
## 10 25 1 6 0 11.59
## 11 26 2 NA 1 11.61
## 12 31 2 6 0 10.49
## 13 33 2 NA 0 11.49
## 14 34 2 9 0 11.45
## 15 35 1 10 0 11.69
## 16 36 2 7 1 11.15
## 17 37 1 10 1 13.24
## 18 40 2 7 0 12.39
## 19 42 1 10 1 11.83
## 20 43 2 10 0 11.64
## 21 47 1 9 1 12.10
## 22 48 1 8 1 11.23
## 23 53 1 9 1 10.32
## 24 54 1 10 0 11.75
## 25 55 2 10 1 10.73
## 26 58 1 NA 1 12.08
## 27 59 2 9 0 10.89
## 28 61 1 10 0 12.54
## 29 65 2 9 0 11.70
## 30 67 2 10 0 10.83
## 31 68 1 7 1 13.06
## 32 69 2 10 1 10.47
## 33 72 2 9 1 10.35
## 34 74 2 10 0 10.26
## 35 75 2 7 1 11.36
## 36 77 2 10 1 13.39
## 37 78 1 NA 0 10.87
## 38 79 2 10 1 11.84
## 39 81 1 10 0 11.20
## 40 84 2 5 0 10.20
## 41 85 1 9 0 11.81
## 42 86 2 10 1 12.41
## 43 87 1 9 1 11.05
## 44 88 2 10 1 12.01
## 45 96 1 10 0 13.65
## 46 99 2 10 1 11.32
## 47 100 2 10 0 12.14
## 48 101 1 10 0 11.66
## 49 102 1 10 1 13.08
## 50 103 2 9 1 12.48
## 51 106 2 9 1 11.20
## 52 108 1 5 0 11.52
## 53 109 1 10 0 12.53
## 54 111 1 10 0 10.60
## 55 112 1 NA 0 11.23
## 56 121 2 10 0 11.58
## 57 123 1 9 1 11.08
## 58 124 2 9 1 10.11
## 59 125 2 10 0 10.23
## 60 127 2 4 0 11.27
## 61 128 1 10 1 11.89
## 62 132 2 9 0 10.76
## 63 134 1 9 0 12.01
## 64 136 1 10 1 11.00
## 65 139 1 10 0 11.56
## 66 140 1 10 0 12.38
## 67 142 1 10 1 10.23
## 68 143 1 9 0 11.15
## 69 144 2 9 1 10.01
## 70 145 2 9 0 9.58
## 71 147 2 9 1 10.15
## 72 152 2 8 0 9.47
## 73 153 2 10 0 9.97
## 74 155 1 10 1 11.13
## 75 157 1 8 0 11.91
## 76 159 2 10 1 9.50
## 77 162 2 10 1 9.11
## 78 163 2 9 0 10.93
## 79 164 2 8 0 10.07
## 80 166 1 9 0 11.14
## 81 168 2 9 0 10.38
## 82 169 2 9 0 10.90
## 83 170 2 10 1 11.70
## 84 172 2 10 0 10.43
## 85 173 1 9 0 12.18
## 86 178 1 9 1 12.76
## 87 179 1 10 1 12.77
## 88 180 2 9 1 10.60
## 89 182 1 10 1 11.01
## 90 183 2 10 0 10.07
## 91 186 1 9 1 13.72
## 92 187 2 10 0 12.70
## 93 188 2 10 0 11.23
## 94 189 1 8 1 10.96
## 95 196 2 9 0 10.48
## 96 199 2 8 0 10.72
## 97 202 2 8 0 10.54
## 98 206 1 7 0 10.18
## 99 207 2 6 0 11.90
## 100 208 2 10 0 11.49
## 101 215 1 10 0 10.10
## 102 300 2 6 0 9.44
## 103 302 2 9 0 11.53
## 104 305 2 10 0 11.11
## 105 307 2 10 0 10.15
## 106 312 2 9 1 10.04
## 107 314 2 8 0 9.79
## 108 315 2 10 1 11.39
## 109 318 2 10 0 10.60
## cdi_TOTAL.T1 sumsev_type_t1 Child_Age.TC PSS_total.TC CESDC_total.TC
## 1 10 11.5 17.68 18 12
## 2 0 14.5 19.98 25 38
## 3 3 11.5 17.05 34 51
## 4 0 2.5 16.42 33 50
## 5 1 5.0 17.11 23 18
## 6 4 8.5 17.30 29 41
## 7 1 8.0 16.88 32 34
## 8 0 4.0 17.39 13 10
## 9 1 1.0 18.08 23 34
## 10 0 16.5 17.79 25 25
## 11 0 6.5 17.72 20 13
## 12 1 6.0 16.67 21 41
## 13 2 3.0 17.43 1 4
## 14 2 4.5 17.55 24 29
## 15 2 1.5 17.76 11 14
## 16 2 4.5 17.23 26 33
## 17 2 4.5 19.34 16 18
## 18 1 19.0 18.39 28 33
## 19 2 6.5 17.81 10 5
## 20 5 20.0 17.64 19 28
## 21 0 1.0 18.03 18 16
## 22 1 2.0 17.14 NA 25
## 23 3 5.0 16.21 14 11
## 24 0 7.5 17.63 11 10
## 25 0 1.0 16.62 13 18
## 26 1 0.5 17.93 17 21
## 27 0 2.5 16.73 25 13
## 28 1 5.5 18.35 18 30
## 29 2 12.0 17.50 24 27
## 30 3 7.5 16.62 30 45
## 31 2 15.5 18.84 17 24
## 32 0 0.0 16.21 12 8
## 33 0 1.5 16.03 25 22
## 34 1 2.0 15.94 20 22
## 35 2 2.0 17.02 13 12
## 36 1 8.5 19.03 20 13
## 37 2 2.5 16.51 20 32
## 38 0 3.0 17.46 19 19
## 39 1 3.0 16.77 17 18
## 40 4 8.0 15.75 20 37
## 41 4 4.5 17.35 23 26
## 42 NA 8.0 17.93 22 17
## 43 5 3.5 16.55 14 15
## 44 3 2.0 17.53 24 43
## 45 2 7.5 19.05 12 13
## 46 0 11.0 16.66 27 38
## 47 1 0.5 17.47 10 19
## 48 0 3.0 16.93 14 5
## 49 3 7.0 18.35 20 29
## 50 1 6.0 17.72 23 25
## 51 5 10.5 16.33 24 26
## 52 0 8.5 16.65 17 9
## 53 1 1.5 17.63 19 36
## 54 0 6.5 15.69 9 7
## 55 0 1.0 16.30 7 5
## 56 5 9.0 16.57 32 41
## 57 1 1.0 15.91 7 9
## 58 1 3.5 15.04 11 10
## 59 3 6.0 15.12 22 25
## 60 0 2.0 16.10 12 12
## 61 3 3.0 16.75 21 27
## 62 10 5.5 15.50 24 37
## 63 2 13.5 16.73 14 19
## 64 1 6.5 15.76 17 12
## 65 0 6.0 16.26 8 13
## 66 2 4.0 17.08 16 16
## 67 0 9.0 14.93 15 21
## 68 1 4.5 15.83 12 12
## 69 2 1.5 14.70 23 30
## 70 2 6.0 14.25 19 27
## 71 2 2.0 14.82 20 32
## 72 1 4.0 14.14 26 14
## 73 1 3.0 14.61 18 23
## 74 0 1.0 15.75 13 12
## 75 1 6.5 16.54 25 24
## 76 2 6.0 14.09 25 22
## 77 3 12.0 13.67 22 33
## 78 0 0.0 15.50 10 7
## 79 2 5.0 14.67 11 10
## 80 1 2.0 15.68 13 3
## 81 3 1.5 14.89 20 22
## 82 1 3.5 15.43 21 42
## 83 9 5.0 16.21 24 25
## 84 0 8.0 14.92 26 29
## 85 2 5.5 16.65 17 5
## 86 0 5.5 17.17 6 5
## 87 3 8.5 17.16 18 12
## 88 1 3.5 15.00 18 16
## 89 1 2.0 15.41 9 9
## 90 1 4.0 14.44 19 20
## 91 0 15.5 18.05 23 18
## 92 1 7.5 17.02 23 17
## 93 0 1.5 15.54 14 7
## 94 1 1.5 15.26 16 0
## 95 1 9.5 14.62 18 19
## 96 2 13.0 14.75 14 10
## 97 9 12.0 14.41 16 10
## 98 2 19.0 14.01 30 29
## 99 5 8.5 15.69 23 16
## 100 1 2.0 15.31 17 18
## 101 0 3.5 13.82 3 9
## 102 1 10.0 13.28 16 17
## 103 1 2.5 15.25 23 23
## 104 0 0.0 14.60 9 12
## 105 2 1.0 13.60 25 22
## 106 1 10.5 13.37 17 25
## 107 5 8.0 12.82 10 24
## 108 4 9.0 14.49 22 23
## 109 0 2.0 13.62 11 5
## Covid_child_fever.TC Covid_child_cough.TC Covid_child_short_breath.TC
## 1 0 0 0
## 2 0 0 0
## 3 1 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 0 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 0 0 0
## 17 0 0 0
## 18 0 0 0
## 19 0 0 0
## 20 0 0 0
## 21 0 0 0
## 22 0 0 0
## 23 0 0 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 1 1 0
## 30 0 0 0
## 31 0 0 0
## 32 0 0 0
## 33 0 1 1
## 34 0 0 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 0
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## 42 0 0 0
## 43 0 1 0
## 44 0 0 0
## 45 0 0 0
## 46 0 1 0
## 47 0 0 0
## 48 0 0 0
## 49 1 0 0
## 50 0 0 0
## 51 0 0 0
## 52 0 0 0
## 53 0 0 0
## 54 0 0 0
## 55 0 0 0
## 56 0 0 0
## 57 0 0 0
## 58 0 0 0
## 59 0 0 1
## 60 0 0 0
## 61 0 1 0
## 62 0 1 0
## 63 0 0 0
## 64 0 0 0
## 65 0 0 0
## 66 0 0 0
## 67 0 0 0
## 68 0 0 0
## 69 0 0 0
## 70 0 0 0
## 71 0 0 0
## 72 0 0 0
## 73 0 0 0
## 74 0 0 0
## 75 0 0 0
## 76 0 1 0
## 77 0 0 0
## 78 0 0 0
## 79 0 0 0
## 80 0 0 0
## 81 0 0 0
## 82 0 0 0
## 83 0 0 0
## 84 1 1 1
## 85 0 0 0
## 86 0 0 0
## 87 0 0 0
## 88 0 0 0
## 89 0 0 0
## 90 0 0 0
## 91 0 0 0
## 92 0 0 0
## 93 0 0 0
## 94 0 0 0
## 95 0 0 0
## 96 0 0 0
## 97 0 0 0
## 98 0 0 0
## 99 0 0 0
## 100 0 1 0
## 101 0 0 0
## 102 0 0 0
## 103 0 0 0
## 104 0 0 0
## 105 0 0 0
## 106 0 0 0
## 107 0 0 0
## 108 0 1 0
## 109 0 0 0
## Covid_child_sore_throat.TC Covid_child_fatigue.TC
## 1 0 0
## 2 0 0
## 3 0 1
## 4 1 1
## 5 0 0
## 6 0 1
## 7 0 0
## 8 0 0
## 9 0 1
## 10 0 0
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 1
## 17 0 0
## 18 0 0
## 19 0 0
## 20 0 0
## 21 0 0
## 22 0 0
## 23 0 1
## 24 0 0
## 25 0 0
## 26 0 0
## 27 0 0
## 28 0 0
## 29 1 1
## 30 0 0
## 31 0 0
## 32 0 0
## 33 1 0
## 34 0 0
## 35 0 0
## 36 0 0
## 37 0 0
## 38 0 0
## 39 0 0
## 40 0 0
## 41 0 0
## 42 1 0
## 43 0 0
## 44 0 1
## 45 0 0
## 46 1 1
## 47 0 0
## 48 0 0
## 49 0 0
## 50 1 0
## 51 0 0
## 52 0 0
## 53 0 0
## 54 0 0
## 55 0 0
## 56 0 1
## 57 0 0
## 58 0 0
## 59 0 0
## 60 0 0
## 61 0 1
## 62 0 0
## 63 0 0
## 64 0 0
## 65 0 1
## 66 0 0
## 67 0 0
## 68 0 0
## 69 0 1
## 70 0 0
## 71 0 1
## 72 0 0
## 73 0 0
## 74 0 0
## 75 0 0
## 76 0 1
## 77 1 0
## 78 0 0
## 79 0 0
## 80 0 0
## 81 0 0
## 82 0 0
## 83 0 0
## 84 0 1
## 85 0 0
## 86 0 0
## 87 1 0
## 88 0 0
## 89 0 0
## 90 1 1
## 91 0 0
## 92 0 0
## 93 0 0
## 94 0 0
## 95 0 0
## 96 0 0
## 97 0 0
## 98 0 0
## 99 0 0
## 100 0 1
## 101 0 0
## 102 0 0
## 103 0 0
## 104 0 0
## 105 0 1
## 106 0 0
## 107 0 0
## 108 0 0
## 109 0 0
## Covid_child_loss_taste_smell.TC Covid_child_other_symptom.TC
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## 12 0 0
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## Covid_positive_changes.TC Stress_of_restrictions.TC
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## Contacts_outside_change.TC Diff_following_contact_rules.TC
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## Quality_relations_family_change.TC Stress_relations_family_change.TC
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## Quality_relations_friends_change.TC Stress_relations_friends_change.TC
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## Cancelling_events_difficulty.TC Family_financial_problems.TC
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## Worry_living_stability.TC Hope_crisis_end.TC Worried_2weeks.TC
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## Happy_sad_2weeks.TC Relaxed_anxious_2weeks.TC Fatigued_tired_2weeks.TC
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## Loneliness_2weeks.TC Concentration_2weeks.TC Neg_thoughts_2weeks.TC
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## Irritability_2weeks.TC Mental_health_rating.TC Worry_no_food.TC
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## 49 2 4 0
## 50 3 4 0
## 51 3 4 0
## 52 2 2 0
## 53 5 4 0
## 54 2 2 0
## 55 1 1 0
## 56 5 5 0
## 57 1 2 0
## 58 3 1 0
## 59 2 3 0
## 60 2 2 0
## 61 4 3 0
## 62 4 4 0
## 63 2 1 0
## 64 2 3 0
## 65 1 2 0
## 66 1 1 0
## 67 5 4 0
## 68 1 2 0
## 69 3 4 0
## 70 4 3 0
## 71 4 2 0
## 72 3 3 0
## 73 2 3 0
## 74 1 2 0
## 75 4 3 0
## 76 3 2 0
## 77 5 3 0
## 78 2 2 0
## 79 1 3 1
## 80 2 3 0
## 81 2 2 0
## 82 3 5 0
## 83 3 3 0
## 84 4 2 0
## 85 2 1 0
## 86 1 2 0
## 87 2 1 0
## 88 1 2 0
## 89 2 3 0
## 90 2 3 0
## 91 3 2 0
## 92 3 2 0
## 93 1 1 0
## 94 1 1 0
## 95 2 2 0
## 96 3 3 0
## 97 1 2 0
## 98 3 2 0
## 99 1 4 1
## 100 2 2 0
## 101 1 1 0
## 102 1 2 0
## 103 4 2 0
## 104 1 3 0
## 105 4 4 0
## 106 2 2 0
## 107 2 4 0
## 108 2 2 0
## 109 2 2 0
## Anhedonia_2weeks.TC Physical_health_rating.TC Fidgety_restless_2weeks.TC
## 1 2 2 3
## 2 2 3 1
## 3 1 3 5
## 4 2 3 5
## 5 1 3 3
## 6 3 2 4
## 7 2 3 2
## 8 3 1 1
## 9 3 3 4
## 10 3 2 2
## 11 2 1 1
## 12 1 2 2
## 13 5 2 1
## 14 2 3 1
## 15 3 2 1
## 16 3 3 3
## 17 4 4 3
## 18 2 4 3
## 19 2 1 2
## 20 1 4 2
## 21 4 1 3
## 22 3 2 4
## 23 3 4 3
## 24 3 2 2
## 25 1 1 3
## 26 2 1 1
## 27 3 1 1
## 28 2 2 4
## 29 1 2 5
## 30 2 3 4
## 31 2 3 1
## 32 2 2 1
## 33 2 2 1
## 34 2 1 4
## 35 3 2 3
## 36 4 1 1
## 37 3 2 4
## 38 3 2 2
## 39 3 1 3
## 40 2 2 4
## 41 3 3 3
## 42 4 4 4
## 43 3 3 2
## 44 2 3 2
## 45 1 1 5
## 46 3 3 5
## 47 2 1 3
## 48 5 4 1
## 49 2 3 3
## 50 1 2 3
## 51 2 2 4
## 52 3 3 1
## 53 2 2 4
## 54 3 2 2
## 55 4 1 1
## 56 3 2 4
## 57 3 2 2
## 58 2 2 3
## 59 3 3 3
## 60 2 2 1
## 61 3 2 4
## 62 3 3 3
## 63 5 1 3
## 64 4 3 1
## 65 4 1 1
## 66 3 2 2
## 67 4 4 3
## 68 1 2 2
## 69 4 3 4
## 70 1 3 4
## 71 1 2 2
## 72 1 3 2
## 73 2 3 4
## 74 3 2 3
## 75 3 3 3
## 76 2 3 2
## 77 2 2 3
## 78 4 3 3
## 79 4 2 2
## 80 4 2 3
## 81 2 2 3
## 82 2 2 1
## 83 2 3 4
## 84 2 2 4
## 85 4 2 1
## 86 3 1 1
## 87 3 1 1
## 88 1 2 2
## 89 2 2 1
## 90 2 2 3
## 91 2 2 3
## 92 3 2 3
## 93 2 1 1
## 94 1 2 1
## 95 3 2 2
## 96 4 2 1
## 97 4 2 1
## 98 2 2 4
## 99 3 2 1
## 100 3 2 2
## 101 3 1 1
## 102 3 2 1
## 103 4 1 3
## 104 3 2 1
## 105 1 3 2
## 106 2 2 3
## 107 5 3 3
## 108 3 4 4
## 109 2 2 3
## income_3levels years covid_physs
## 1 NA 6.54 0
## 2 NA 6.52 0
## 3 3 6.40 2
## 4 2 6.34 2
## 5 1 6.25 0
## 6 NA 6.24 1
## 7 2 6.25 0
## 8 2 6.21 0
## 9 2 6.20 1
## 10 2 6.20 0
## 11 NA 6.11 0
## 12 2 6.18 0
## 13 NA 5.94 0
## 14 2 6.10 0
## 15 3 6.07 0
## 16 2 6.08 1
## 17 3 6.10 0
## 18 2 6.00 0
## 19 3 5.98 0
## 20 3 6.00 0
## 21 2 5.93 0
## 22 2 5.91 0
## 23 2 5.89 1
## 24 3 5.88 0
## 25 3 5.89 0
## 26 NA 5.85 0
## 27 2 5.84 0
## 28 3 5.81 0
## 29 2 5.80 4
## 30 3 5.79 0
## 31 2 5.78 0
## 32 3 5.74 0
## 33 2 5.68 3
## 34 3 5.68 0
## 35 2 5.66 0
## 36 3 5.64 0
## 37 NA 5.64 0
## 38 3 5.62 0
## 39 3 5.57 0
## 40 1 5.55 0
## 41 2 5.54 0
## 42 3 5.52 1
## 43 2 5.50 1
## 44 3 5.52 1
## 45 3 5.40 0
## 46 3 5.34 3
## 47 3 5.33 0
## 48 3 5.27 0
## 49 3 5.27 1
## 50 2 5.24 1
## 51 2 5.13 0
## 52 1 5.13 0
## 53 3 5.10 0
## 54 3 5.09 0
## 55 NA 5.07 0
## 56 3 4.99 1
## 57 2 4.83 0
## 58 2 4.93 0
## 59 3 4.89 1
## 60 1 4.83 0
## 61 3 4.86 2
## 62 2 4.74 1
## 63 2 4.72 0
## 64 3 4.76 0
## 65 3 4.70 1
## 66 3 4.70 0
## 67 3 4.70 1
## 68 2 4.68 0
## 69 2 4.69 1
## 70 2 4.67 0
## 71 2 4.67 1
## 72 2 4.67 0
## 73 3 4.64 0
## 74 3 4.62 0
## 75 2 4.63 0
## 76 3 4.59 2
## 77 3 4.56 1
## 78 2 4.57 0
## 79 2 4.60 0
## 80 2 4.54 0
## 81 2 4.51 0
## 82 2 4.53 0
## 83 3 4.51 0
## 84 3 4.49 4
## 85 2 4.47 0
## 86 2 4.41 0
## 87 3 4.39 1
## 88 2 4.40 0
## 89 3 4.40 0
## 90 3 4.37 2
## 91 2 4.33 0
## 92 3 4.32 0
## 93 3 4.31 0
## 94 2 4.30 0
## 95 2 4.14 0
## 96 2 4.03 0
## 97 2 3.87 0
## 98 2 3.83 0
## 99 2 3.79 0
## 100 3 3.82 2
## 101 3 3.72 0
## 102 2 3.84 0
## 103 2 3.72 0
## 104 3 3.49 0
## 105 3 3.45 1
## 106 2 3.33 0
## 107 2 3.03 0
## 108 3 3.10 1
## 109 3 3.02 0# covid %>%
# group_by(KSADS_Child_Race_by_P.T1) %>%
# summarise(n = n())
#calculation of non-white participants
63/109## [1] 0.5779817## income
# 2 is less than 10k; 10 is more than 150k
min(covid$Parent_Income.T1, na.rm = TRUE)## [1] 2max(covid$Parent_Income.T1, na.rm = TRUE)## [1] 10### sex differences crisis
x <- which(names(covid) == "Child_Sex.T1") # name of grouping variable
y <- which(names(covid) == "Mental_health_rating.TC" # names of variables to test
| names(covid) == "Worry_self_infected.TC"
| names(covid) == "Worry_fam_infected.TC"
| names(covid) == "Self_phys_health_influence.TC"
| names (covid) == "Self_mental_health_influence.TC"
| names (covid) == "Read_talk_virus.TC"
| names (covid) == "Covid_positive_changes.TC"
| names (covid) == "Stress_of_restrictions.TC"
| names (covid) == "Contacts_outside_change.TC"
| names (covid) == "Diff_following_contact_rules.TC"
| names (covid) == "Quality_relations_family_change.TC"
| names (covid) == "Stress_relations_family_change.TC"
| names (covid) == "Quality_relations_friends_change.TC"
| names (covid) == "Stress_relations_friends_change.TC"
| names (covid) == "Cancelling_events_difficulty.TC"
| names (covid) == "Family_financial_problems.TC"
| names (covid) == "Worry_living_stability.TC"
| names (covid) == "Hope_crisis_end.TC"
| names (covid) == "covid_physs")
method <- "t.test" # one of "wilcox.test" or "t.test"
paired <- FALSE
for (i in y) {
for (j in x) {
ifelse(paired == TRUE,
p <- ggpaired(covid,
x = colnames(covid[j]), y = colnames(covid[i]),
color = colnames(covid[j]), line.color = "gray", line.size = 0.4,
palette = "npg",
legend = "none",
xlab = colnames(covid[j]),
ylab = colnames(covid[i]),
add = "jitter"
),
p <- ggboxplot(covid,
x = colnames(covid[j]), y = colnames(covid[i]),
color = colnames(covid[j]),
palette = "npg",
legend = "none",
add = "jitter"
)
)
# Add p-value
print(p + stat_compare_means(aes(label = paste0(..method.., ", p-value = ", ..p.format..)),
method = method,
paired = paired,
# group.by = NULL,
ref.group = NULL
))
}
}
# significant sex differences on the crisis
t.test(covid_physs ~ Child_Sex.T1, data = covid, var.equal = TRUE)##
## Two Sample t-test
##
## data: covid_physs by Child_Sex.T1
## t = -2.4475, df = 107, p-value = 0.01601
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.70536132 -0.07406081
## sample estimates:
## mean in group 1 mean in group 2
## 0.1860465 0.5757576t.test(Worried_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)##
## Two Sample t-test
##
## data: Worried_2weeks.TC by Child_Sex.T1
## t = -2.2627, df = 107, p-value = 0.02568
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.74635316 -0.04927757
## sample estimates:
## mean in group 1 mean in group 2
## 2.162791 2.560606t.test(Happy_sad_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)##
## Two Sample t-test
##
## data: Happy_sad_2weeks.TC by Child_Sex.T1
## t = 3.4525, df = 107, p-value = 0.0007966
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.2681199 0.9912177
## sample estimates:
## mean in group 1 mean in group 2
## 3.372093 2.742424t.test(Relaxed_anxious_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)##
## Two Sample t-test
##
## data: Relaxed_anxious_2weeks.TC by Child_Sex.T1
## t = -2.075, df = 107, p-value = 0.04039
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.75720803 -0.01728105
## sample estimates:
## mean in group 1 mean in group 2
## 2.627907 3.015152t.test(Fatigued_tired_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)##
## Two Sample t-test
##
## data: Fatigued_tired_2weeks.TC by Child_Sex.T1
## t = -3.4128, df = 107, p-value = 0.0009088
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.9965369 -0.2642101
## sample estimates:
## mean in group 1 mean in group 2
## 1.930233 2.560606t.test(Loneliness_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)##
## Two Sample t-test
##
## data: Loneliness_2weeks.TC by Child_Sex.T1
## t = -2.8297, df = 107, p-value = 0.005566
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.0713971 -0.1886452
## sample estimates:
## mean in group 1 mean in group 2
## 2.279070 2.909091t.test(Concentration_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)##
## Two Sample t-test
##
## data: Concentration_2weeks.TC by Child_Sex.T1
## t = -2.5794, df = 107, p-value = 0.01126
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.0039271 -0.1313795
## sample estimates:
## mean in group 1 mean in group 2
## 3.023256 3.590909t.test(Neg_thoughts_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)##
## Two Sample t-test
##
## data: Neg_thoughts_2weeks.TC by Child_Sex.T1
## t = -2.0837, df = 107, p-value = 0.03957
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.86704455 -0.02160943
## sample estimates:
## mean in group 1 mean in group 2
## 2.116279 2.560606t.test(Self_mental_health_influence.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)##
## Two Sample t-test
##
## data: Self_mental_health_influence.TC by Child_Sex.T1
## t = -2.9393, df = 107, p-value = 0.004031
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.1245578 -0.2186416
## sample estimates:
## mean in group 1 mean in group 2
## 2.116279 2.787879t.test(Mental_health_rating.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)##
## Two Sample t-test
##
## data: Mental_health_rating.TC by Child_Sex.T1
## t = -2.5713, df = 107, p-value = 0.01151
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.9547513 -0.1234728
## sample estimates:
## mean in group 1 mean in group 2
## 2.279070 2.818182t.test(Cancelling_events_difficulty.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)##
## Two Sample t-test
##
## data: Cancelling_events_difficulty.TC by Child_Sex.T1
## t = -3.6008, df = 107, p-value = 0.0004826
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.3101518 -0.3797707
## sample estimates:
## mean in group 1 mean in group 2
## 2.488372 3.333333t.test(Worry_no_food.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)##
## Two Sample t-test
##
## data: Worry_no_food.TC by Child_Sex.T1
## t = -2.2379, df = 107, p-value = 0.0273
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.20001257 -0.01210864
## sample estimates:
## mean in group 1 mean in group 2
## 0.0000000 0.1060606t.test(Anhedonia_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)##
## Two Sample t-test
##
## data: Anhedonia_2weeks.TC by Child_Sex.T1
## t = 2.5575, df = 107, p-value = 0.01194
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1119646 0.8838071
## sample estimates:
## mean in group 1 mean in group 2
## 2.906977 2.409091# no sex differences
t.test(Physical_health_rating.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)##
## Two Sample t-test
##
## data: Physical_health_rating.TC by Child_Sex.T1
## t = -1.382, df = 107, p-value = 0.1698
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.54812725 0.09781012
## sample estimates:
## mean in group 1 mean in group 2
## 2.093023 2.318182t.test(Fidgety_restless_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)##
## Two Sample t-test
##
## data: Fidgety_restless_2weeks.TC by Child_Sex.T1
## t = -1.6416, df = 107, p-value = 0.1036
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.85566977 0.08047597
## sample estimates:
## mean in group 1 mean in group 2
## 2.279070 2.666667t.test(Irritability_2weeks.TC ~ Child_Sex.T1, data = covid, var.equal = TRUE)##
## Two Sample t-test
##
## data: Irritability_2weeks.TC by Child_Sex.T1
## t = -1.5114, df = 107, p-value = 0.1336
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.7558909 0.1019093
## sample estimates:
## mean in group 1 mean in group 2
## 2.279070 2.606061# data file for group comparisons of responders/non-responders
completers_noncompleters = read.csv("/Volumes/group/users/borchersLR/COVID/github/completers_noncompleters.csv")
completers_noncompleters = completers_noncompleters %>%
mutate(subsample = !is.na(CESDC_total.TC))
# 109 included in sample; 115 did not complete tc assessment
completers_noncompleters %>%
group_by(subsample) %>%
summarise(n=n())## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 2 x 2
## subsample n
## <lgl> <int>
## 1 FALSE 115
## 2 TRUE 109## group comparisons covid-19 survey completers vs. non-completers
# age at baseline
t.test(Age_S1.T1 ~ subsample, data = completers_noncompleters, var.equal=T)##
## Two Sample t-test
##
## data: Age_S1.T1 by subsample
## t = 1.5736, df = 222, p-value = 0.117
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.05545692 0.49490965
## sample estimates:
## mean in group FALSE mean in group TRUE
## 11.45330 11.23358# race
# 1 is white; 2 is african american; 3 is hispanic; 4 is asian; 5 is biracial; 6 is other
race <- data.frame(completers_noncompleters$subsample, completers_noncompleters$KSADS_Child_Race_by_P.T1)
race = table(completers_noncompleters$subsample, completers_noncompleters$KSADS_Child_Race_by_P.T1)
print(race)##
## 1 2 3 4 5 6
## FALSE 54 15 10 4 25 7
## TRUE 46 4 10 20 22 7print(chisq.test(race))##
## Pearson's Chi-squared test
##
## data: race
## X-squared = 17.719, df = 5, p-value = 0.003321# els baseline
t.test(sumsev_type_t1 ~ subsample, data = completers_noncompleters, var.equal=T)##
## Two Sample t-test
##
## data: sumsev_type_t1 by subsample
## t = 2.8344, df = 221, p-value = 0.005017
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.6235654 3.4694653
## sample estimates:
## mean in group FALSE mean in group TRUE
## 7.885965 5.839450t.test(cdi_TOTAL.T1 ~ subsample, data = completers_noncompleters, rm.na=TRUE, var.equal=T)##
## Two Sample t-test
##
## data: cdi_TOTAL.T1 by subsample
## t = 2.4216, df = 218, p-value = 0.01627
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.1427805 1.3916110
## sample estimates:
## mean in group FALSE mean in group TRUE
## 2.535714 1.768519# parental income
covid_income_all <-
completers_noncompleters %>%
mutate_at(
vars(Parent_Income.T1
),
funs(
. %>%
dplyr::recode(
"10" = 3,
"9" = 2,
"8" = 2,
"7" = 2,
"6" = 2,
"5" = 1,
"4" = 1,
"3" = 1,
"2" = 1,
"1" = 1,
)
)
)## Warning: Problem with `mutate()` input `Parent_Income.T1`.
## x Unreplaced values treated as NA as .x is not compatible. Please specify replacements exhaustively or supply .default
## ℹ Input `Parent_Income.T1` is ``%>%`(...)`.## Warning: Unreplaced values treated as NA as .x is not compatible. Please specify
## replacements exhaustively or supply .default# setting na values for income to 0
covid_income_all <- covid_income_all %>%
mutate(Parent_Income.T1 = if_else(is.na(Parent_Income.T1), 0, Parent_Income.T1))
income <- data.frame(covid_income_all$subsample, covid_income_all$Parent_Income.T1)
income = table(covid_income_all$subsample, covid_income_all$Parent_Income.T1)
print(income)##
## 0 1 2 3
## FALSE 12 16 56 31
## TRUE 8 4 50 47print(chisq.test(income))##
## Pearson's Chi-squared test
##
## data: income
## X-squared = 11.469, df = 3, p-value = 0.009441# figure 2
covid_plot = covid %>%
select(cdi_TOTAL.T1, sumsev_type_t1, PSS_total.TC, CESDC_total.TC,
covid_physs, Worry_no_food.TC, Mental_health_rating.TC,
Cancelling_events_difficulty.TC, Stress_of_restrictions.TC,
Stress_relations_family_change.TC, Stress_relations_friends_change.TC)
covid_plot <-covid_plot %>% rename('CDI-S Baseline' = cdi_TOTAL.T1, 'ELS Severity Baseline' = sumsev_type_t1, 'PSS COVID-19' = PSS_total.TC,
'CES-DC COVID-19' = CESDC_total.TC, 'Symptoms COVID-19' = covid_physs, 'Worry About Food COVID-19' = Worry_no_food.TC,
'Mental Health Impact COVID-19' = Mental_health_rating.TC, 'Difficulty Cancel Events COVID-19' = Cancelling_events_difficulty.TC,
'Stress of Restrictions COVID-19' = Stress_of_restrictions.TC, 'Stress Change Family Contact COVID-19' = Stress_relations_family_change.TC,
'Stress Change Social Contact COVID-19' = Stress_relations_friends_change.TC)
ggcorrmat(
covid,
cor.vars = c('CDI-S Baseline' = cdi_TOTAL.T1, 'ELS Severity Baseline' = sumsev_type_t1, 'PSS COVID-19' = PSS_total.TC, 'CES-DC COVID-19' = CESDC_total.TC, 'Symptoms COVID-19' =
covid_physs, 'Worry About Food COVID-19' = Worry_no_food.TC, 'Mental Health Impact COVID-19' = Mental_health_rating.TC, 'Difficulty Cancel Events COVID-19' =
Cancelling_events_difficulty.TC, 'Stress of Restrictions COVID-19' = Stress_of_restrictions.TC,
'Stress Change Family Contact COVID-19' =Stress_relations_family_change.TC, 'Stress Change Social Contact COVID-19' = Stress_relations_friends_change.TC),
sig.level = 0.05,
p.adjust.method = "fdr",
ggcorrplot.args = c(insig = "blank"),
colors = c("white", "grey80", "grey30"),
matrix.type = "lower",
title = "Correlations among Baseline and COVID-19 \n items/measures assessed in this study",
plot.title = element_text(size = 12, hjust = .5))
# ggsave("~/Desktop/figure2.tiff", height = 7, width = 9)# setting theme for graphs
base_size=9
theme_set(
theme_minimal(
base_size = 11,
base_family = "",
base_line_size = base_size/22,
base_rect_size = base_size/22
))
# figure 3a
cor.test(covid$PSS_total.TC, covid$sumsev_type_t1)##
## Pearson's product-moment correlation
##
## data: covid$PSS_total.TC and covid$sumsev_type_t1
## t = 3.8394, df = 106, p-value = 0.0002102
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1717782 0.5050365
## sample estimates:
## cor
## 0.3494098covid = covid %>%
mutate(Child_Sex.T1 = as.factor(Child_Sex.T1))
figure1.1=covid %>%
ggplot(mapping = aes(x = sumsev_type_t1,
y = PSS_total.TC,
color = Child_Sex.T1)) +
geom_point(alpha = .6, size = 3)+
geom_smooth(method = "lm", alpha=.1, size = 2) +
labs(x = "ELS Severity (Baseline)") +
labs(y = "Perceived Stress (COVID-19)") +
ggtitle(~italic(r)~"(106)=0.35,"~italic(p)~"0<.001") +
theme(plot.title = element_text(hjust = 0.5, size=30)) +
theme(axis.line = element_line(size = 1)) +
theme(legend.title = element_blank()) +
theme(legend.position = c(.92, .85)) +
labs(subtitle="A") +
theme(plot.subtitle=element_text(size=18, face="bold", color="black")) +
theme(axis.text=element_text(size=20)) +
theme(axis.title=element_text(size=18)) +
geom_hline(yintercept=14.2, color = "gray",
linetype="dashed") +
annotate("text", x = 16, y = 12, label = "Average PSS in adolescents", color="dark gray") +
scale_color_manual(labels = c("Male", "Female"), values = c("#4d4f53", "#8c1515"))+
xlim(0,20) +
ylim(0, 52)
# ggsave("~/Desktop/els_pss.tiff", height = 5, width = 6)
# figure 3b
cor.test(covid$CESDC_total.TC, covid$sumsev_type_t1)##
## Pearson's product-moment correlation
##
## data: covid$CESDC_total.TC and covid$sumsev_type_t1
## t = 2.8112, df = 107, p-value = 0.005871
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07800399 0.42918713
## sample estimates:
## cor
## 0.2622577figure1.2=covid %>%
ggplot(mapping = aes(x = sumsev_type_t1,
y = CESDC_total.TC,
color = Child_Sex.T1)) +
geom_point(alpha = .6, size = 3)+
geom_smooth(method = "lm", alpha=.1, size = 2) +
labs(x = "ELS Severity (Baseline)") +
labs(y = "Depression Symptoms (COVID-19)") +
ggtitle(~italic(r)~"(107)=0.26,"~italic(p)~"=0.006") +
theme(plot.title = element_text(hjust = 0.5, size=30)) +
theme(axis.line = element_line(size = 1)) +
theme(legend.title = element_blank()) +
theme(legend.position = c(.92, .85)) +
labs(subtitle="B") +
theme(plot.subtitle=element_text(size=18, face="bold", color="black")) +
theme(axis.text=element_text(size=20)) +
theme(axis.title=element_text(size=18)) +
geom_hline(yintercept=15, color = "gray",
linetype="dashed") +
annotate("text", x = 17, y = 12, label = "CES-DC cut-off score", color="dark gray") +
scale_color_manual(labels = c("Male", "Female"), values = c("#4d4f53", "#8c1515"))+
xlim(0,20) +
ylim(0, 52)
# ggsave("~/Desktop/els_cesdc.tiff", height = 5, width = 6)
# combined figure
require(gridExtra)## Loading required package: gridExtra##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinerequire(cowplot)## Loading required package: cowplot##
## ********************************************************## Note: As of version 1.0.0, cowplot does not change the## default ggplot2 theme anymore. To recover the previous## behavior, execute:
## theme_set(theme_cowplot())## ********************************************************##
## Attaching package: 'cowplot'## The following object is masked from 'package:lubridate':
##
## stamp## The following object is masked from 'package:ggthemes':
##
## theme_map## The following object is masked from 'package:ggpubr':
##
## get_legendgrid.arrange(figure1.1, figure1.2, ncol=2)## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).## `geom_smooth()` using formula 'y ~ x'
g <- arrangeGrob(figure1.1, figure1.2, ncol=2) #generates g## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).## `geom_smooth()` using formula 'y ~ x'ggsave(file="~/Desktop/figure3.tiff", g, height = 6, width = 12)# comparing scores in our sample to validated sample / clinical cutoff
mean(covid$PSS_total.TC, na.rm = TRUE)## [1] 18.4537# mean of pss in validated sample of 645 adolescents between 18-29 years old
14.2## [1] 14.2# difference between pss in our sample and the validated sample
18.45-14.2## [1] 4.25# number of participants above clinical cutoff of 15 on the cesdc
sum(covid$CESDC_total.TC > 15)## [1] 69# percent above cutoff in our sample
69/109## [1] 0.6330275## mediation analysis
# centering variables
covid$Age_S1.T1_z=scale(covid$Age_S1.T1, center=TRUE, scale =TRUE)
covid$CDI_total.T1_z=scale(covid$cdi_TOTAL.T1, center=TRUE, scale =TRUE)
covid$sumsev_type_t1_z=scale(covid$sumsev_type_t1, center=TRUE, scale =TRUE)
covid$Child_Age.TC_z=scale(covid$Child_Age.TC, center=TRUE, scale =TRUE)
covid$PSS_total.TC_z=scale(covid$PSS_total.TC, center=TRUE, scale =TRUE)
covid$CESDC_total.TC_z=scale(covid$CESDC_total.TC, center=TRUE, scale =TRUE)
covid = covid %>%
mutate(Child_Sex.T1 = as.numeric(Child_Sex.T1))
# # mediation
mediation_model <- mediate(CESDC_total.TC_z ~ sumsev_type_t1_z + (PSS_total.TC_z)- CDI_total.T1_z - Child_Sex.T1 - Age_S1.T1_z - Child_Age.TC_z - KSADS_Child_Race_by_P.T1 -
income_3levels, data =covid,n.iter=5000) 
summary(mediation_model)## Call: mediate(y = CESDC_total.TC_z ~ sumsev_type_t1_z + (PSS_total.TC_z) -
## CDI_total.T1_z - Child_Sex.T1 - Age_S1.T1_z - Child_Age.TC_z -
## KSADS_Child_Race_by_P.T1 - income_3levels, data = covid,
## n.iter = 5000)
##
## Direct effect estimates (traditional regression) (c')
## CESDC_total.TC_z* se t df Prob
## Intercept -0.65 0.37 -1.73 100 8.63e-02
## sumsev_type_t1_z 0.02 0.07 0.23 100 8.22e-01
## PSS_total.TC_z 0.69 0.07 9.41 100 1.92e-15
##
## R = 0.73 R2 = 0.53 F = 56.29 on 2 and 100 DF p-value: 4.21e-17
##
## Total effect estimates (c)
## CESDC_total.TC_z* se t df Prob
## Intercept -1.32 0.50 -2.62 101 0.0101
## sumsev_type_t1_z 0.23 0.09 2.60 101 0.0107
##
## 'a' effect estimates
## PSS_total.TC_z se t df Prob
## Intercept -0.97 0.50 -1.94 101 0.055000
## sumsev_type_t1_z 0.31 0.09 3.54 101 0.000605
##
## 'b' effect estimates
## CESDC_total.TC_z* se t df Prob
## PSS_total.TC_z 0.69 0.07 9.46 101 1.39e-15
##
## 'ab' effect estimates (through mediators)
## CESDC_total.TC_z* boot sd lower upper
## sumsev_type_t1_z 0.22 0.18 0 0.18 0.18