Tabular summary statistics of baseline variables (time = 0 weeks) for the whole cohort, and stratified by sex and study site.
# Demographic data
demo <- read_rds('data-cleaned/demographics.rds')
# BDI
bdi <- read_rds('data-cleaned/bdi.rds')
# BPI
bpi <- read_rds('data-cleaned/bpi.rds')
# EQ5D
eq5d <- read_rds('data-cleaned/eq5d.rds')
# SE6
se6 <- read_rds('data-cleaned/se6.rds')
demo %<>%
# Convert sex to factor
mutate(Sex = factor(Sex)) %>%
# Rename Years_on_ART
rename(Years_on_HAART = Years_on_ART) %>%
# Convert SOS_mnemonic to factor
mutate(SOS_mnemonic = factor(SOS_mnemonic)) %>%
# Transfer CD4_nadir data to CD4_recent if missing CD4_recent data
# (i.e. get the most updated CD4 count available)
mutate(CD4_most_recent =
ifelse(is.na(CD4_recent),
yes = CD4_nadir,
no = CD4_recent)) %>%
# Categorise years of schooling into 7 years or less, 8-12 years,
# and more than 12 years of education, factorize, and then order
mutate(Education = case_when(
Years_education <= 7 ~ '0-7 years',
Years_education > 7 & Years_education <= 12 ~ '8-12 years',
Years_education > 12 ~ 'More than 12 years'),
Education = factor(Education,
levels = c('0-7 years',
'8-12 years',
'More than 12 years'),
ordered = TRUE)) %>%
# Recode HAART and order
mutate(HAART = case_when(
HAART == 'first-line' ~ 'first-line HAART',
HAART == 'second-line' ~ 'second-line HAART',
HAART == 'monitoring' ~ 'no HAART'),
HAART = factor(HAART,
levels = c('no HAART',
'first-line HAART',
'second-line HAART'),
ordered = TRUE)) %>%
# Recode and order occupation categories
mutate(Employment = str_replace_all(Occupation,
pattern = '^unemployed - .+',
replacement = 'unemployed'),
Employment = factor(Employment,
levels = c('employed', 'unemployed',
'student/volunteer',
'unable to work - disability grant'))) %>%
# Select required columns
select(ID, Study_site, Sex, Age_years, Years_on_HAART,
CD4_most_recent, HAART, Education, Employment,
SOS_mnemonic)
# Make a site/sex filter
sorter <- demo %>%
select(ID, Study_site, Sex)
bpi %<>%
# Capitalize IDs
mutate(ID = stringr::str_to_upper(ID)) %>%
# Select baseline values
select(ID,
ends_with('BL')) %>%
# Select columns
select(ID, 3:6, 9:15) %>%
# Calculate Pain Severity Score (PSS) at baseline
mutate(PSS = rowMeans(.[2:5], na.rm = TRUE),
# Treat PSS as a discrete scale
PSS = round(PSS)) %>%
# Calculate Pain Interference Index (PIS) at baseline
mutate(PIS = rowMeans(.[6:12], na.rm = TRUE),
# Treat PIS as a discrete scale
PIS = round(PIS)) %>%
#remove unwanted columns
select(ID, PSS, PIS) %>%
left_join(sorter)
bdi %<>%
# Capitalize IDs
mutate(ID = stringr::str_to_upper(ID)) %>%
# Make a total score column
mutate_at(2:ncol(bdi),
as.numeric) %>%
mutate(BDI = rowSums(.[2:ncol(bdi)], na.rm = TRUE),
# Treat BDI as a discrete scale
BDI = round(BDI)) %>%
select(ID, BDI) %>%
left_join(sorter)
eq5d %<>%
# Capitalize IDs
mutate(ID = stringr::str_to_upper(ID))
# Calculate eq5d index score
## Create basic term = 1 for all cases in new column
eq5d$index_core <- 1
## Sum all rows for total index score
eq5d %<>%
mutate(index_sum = rowSums(.[2:6], na.rm = TRUE))
# Create constant term to subtract for domain scores > 1 (i.e. sum > 5)
eq5d %<>%
mutate(index_constant = ifelse(index_sum > 5,
yes = 0.081,
no = 0))
## Create variable for subtraction for each domain
eq5d %<>%
mutate(Mobility_index = ifelse(Mobility.BL == 2,
yes = 0.069,
no = ifelse(Mobility.BL == 3,
yes = 0.314,
no = 0))) %>%
mutate(Self_care_index = ifelse(Self_care.BL == 2,
yes = 0.104,
no = ifelse(Self_care.BL == 3,
yes = 0.214,
no = 0))) %>%
mutate(Usual_activities_index = ifelse(Usual_activities.BL == 2,
yes = 0.036,
no = ifelse(Usual_activities.BL == 3,
yes = 0.094,
no = 0))) %>%
mutate(Pain_index = ifelse(Pain.BL == 2,
yes = 0.123,
no = ifelse(Pain.BL == 3,
yes = 0.386,
no = 0))) %>%
mutate(Anxiety_depression_index = ifelse(Anxiety_and_depression.BL == 2,
yes = 0.071,
no = ifelse(Anxiety_and_depression.BL == 3,
yes = 0.236,
no = 0)))
## Compute the index score using:
## index = index_core - constant_index - Mobility_index...
eq5d %<>%
mutate(EQ5D_index = index_core - index_constant - Mobility_index
- Self_care_index - Usual_activities_index - Pain_index
- Anxiety_depression_index) %>%
# Convert State_of_health VAS to double
mutate(EQ5D_VAS = as.numeric(State_of_health.BL))
# Select columns
eq5d %<>% select(ID,
EQ5D_index,
EQ5D_VAS) %>%
left_join(sorter)
se6 %<>%
# Capitalize IDs
mutate(ID = stringr::str_to_upper(ID)) %>%
# Calculate SE6 at baseline
mutate(SE6 = rowMeans(.[2:7], na.rm = TRUE),
# Treat SE6 as a discrete scale
SE6 = round(SE6)) %>%
#remove unwanted columns
select(ID,
SE6) %>%
left_join(sorter)
demo %>%
select_if(is.numeric) %>%
skim_to_wide(.) %>%
kable(., caption = 'Whole cohort',
align = 'llrrrrrrrrrr')
type | variable | missing | complete | n | mean | sd | median | q25 | q75 | min | max |
---|---|---|---|---|---|---|---|---|---|---|---|
numeric | Age_years | 0 | 160 | 160 | 35.23 | 5.65 | 35 | 32 | 38 | 18 | 58 |
numeric | CD4_most_recent | 8 | 152 | 160 | 406.45 | 249.51 | 376 | 224.75 | 547 | 3 | 1189 |
numeric | Years_on_HAART | 78 | 82 | 160 | 3.56 | 2.83 | 3 | 1 | 5.06 | 0.25 | 13 |
demo %>%
select(Study_site, Age_years, Years_on_HAART, CD4_most_recent) %>%
group_by(Study_site) %>%
skim_to_wide(.) %>%
kable(., caption = 'By study site',
align = 'lllrrrrrrrrrr')
type | Study_site | variable | missing | complete | n | mean | sd | median | q25 | q75 | min | max |
---|---|---|---|---|---|---|---|---|---|---|---|---|
numeric | R1 | Age_years | 0 | 47 | 47 | 35.28 | 2.99 | 36 | 33 | 38 | 28 | 40 |
numeric | R1 | CD4_most_recent | 0 | 47 | 47 | 415.43 | 195.84 | 397 | 277.5 | 538 | 114 | 1180 |
numeric | R1 | Years_on_HAART | 29 | 18 | 47 | 3.92 | 2.05 | 4.17 | 2.31 | 5.46 | 0.67 | 8.3 |
numeric | R2 | Age_years | 0 | 49 | 49 | 32.9 | 4.63 | 35 | 30 | 36 | 18 | 40 |
numeric | R2 | CD4_most_recent | 2 | 47 | 49 | 450.53 | 241.69 | 407 | 268.5 | 562.5 | 36 | 1120 |
numeric | R2 | Years_on_HAART | 2 | 47 | 49 | 3.97 | 3.25 | 3 | 1 | 6 | 0.25 | 13 |
numeric | U1 | Age_years | 0 | 47 | 47 | 39.34 | 6.27 | 38 | 35 | 43.5 | 27 | 58 |
numeric | U1 | CD4_most_recent | 6 | 41 | 47 | 302.73 | 284.63 | 206 | 113 | 368 | 3 | 1189 |
numeric | U1 | Years_on_HAART | 47 | 0 | 47 | NaN | NA | NA | NA | NA | Inf | -Inf |
numeric | U2 | Age_years | 0 | 17 | 17 | 30.41 | 4.77 | 30 | 26 | 34 | 23 | 37 |
numeric | U2 | CD4_most_recent | 0 | 17 | 17 | 509.94 | 248.8 | 471 | 414 | 648 | 119 | 1097 |
numeric | U2 | Years_on_HAART | 0 | 17 | 17 | 2.07 | 1.69 | 1 | 0.67 | 3.42 | 0.33 | 5.25 |
demo %>%
select(Sex, Age_years, Years_on_HAART, CD4_most_recent) %>%
group_by(Sex) %>%
skim_to_wide(.) %>%
kable(., caption = 'By sex',
align = 'lllrrrrrrrrrr')
type | Sex | variable | missing | complete | n | mean | sd | median | q25 | q75 | min | max |
---|---|---|---|---|---|---|---|---|---|---|---|---|
numeric | female | Age_years | 0 | 97 | 97 | 34.23 | 5.97 | 35 | 30 | 37 | 18 | 58 |
numeric | female | CD4_most_recent | 5 | 92 | 97 | 433.73 | 273.51 | 410.5 | 244.75 | 570.5 | 3 | 1189 |
numeric | female | Years_on_HAART | 33 | 64 | 97 | 3.46 | 3.03 | 3 | 0.96 | 5 | 0.25 | 13 |
numeric | male | Age_years | 0 | 63 | 63 | 36.76 | 4.76 | 36 | 33.5 | 39 | 27 | 50 |
numeric | male | CD4_most_recent | 3 | 60 | 63 | 364.63 | 202.5 | 335 | 210.5 | 491.75 | 30 | 1180 |
numeric | male | Years_on_HAART | 45 | 18 | 63 | 3.92 | 2.05 | 4.17 | 2.31 | 5.46 | 0.67 | 8.3 |
demo %>%
select_if(is.factor) %>%
skim_to_wide(.) %>%
kable(., caption = 'Whole cohort',
align = 'llrrrrr')
type | variable | missing | complete | n | n_unique | top_counts |
---|---|---|---|---|---|---|
factor | Education | 2 | 158 | 160 | 3 | 8-1: 112, 0-7: 44, Mor: 2 |
factor | Employment | 3 | 157 | 160 | 4 | une: 96, emp: 51, una: 8, stu: 2 |
factor | HAART | 4 | 156 | 160 | 3 | fir: 115, sec: 36, no : 5 |
factor | Sex | 0 | 160 | 160 | 2 | fem: 97, mal: 63 |
factor | SOS_mnemonic | 47 | 113 | 160 | 2 | low: 78, hea: 35 |
demo %>%
select(Study_site, Education, Employment, HAART, Sex, SOS_mnemonic) %>%
group_by(Study_site) %>%
skim_to_wide(.) %>%
kable(., caption = 'By study site',
align = 'lllrrrrr')
type | Study_site | variable | missing | complete | n | n_unique | top_counts |
---|---|---|---|---|---|---|---|
factor | R1 | Education | 0 | 47 | 47 | 2 | 8-1: 30, 0-7: 17, Mor: 0 |
factor | R1 | Employment | 2 | 45 | 47 | 3 | emp: 21, une: 20, una: 4, stu: 0 |
factor | R1 | HAART | 0 | 47 | 47 | 2 | fir: 43, sec: 4, no : 0 |
factor | R1 | Sex | 0 | 47 | 47 | 1 | mal: 47, fem: 0 |
factor | R1 | SOS_mnemonic | 0 | 47 | 47 | 2 | hea: 28, low: 19 |
factor | R2 | Education | 0 | 49 | 49 | 2 | 8-1: 30, 0-7: 19, Mor: 0 |
factor | R2 | Employment | 0 | 49 | 49 | 3 | une: 40, emp: 8, una: 1, stu: 0 |
factor | R2 | HAART | 0 | 49 | 49 | 3 | fir: 40, sec: 5, no : 4 |
factor | R2 | Sex | 0 | 49 | 49 | 1 | fem: 49, mal: 0 |
factor | R2 | SOS_mnemonic | 0 | 49 | 49 | 1 | low: 49, hea: 0 |
factor | U1 | Education | 2 | 45 | 47 | 3 | 8-1: 41, 0-7: 2, Mor: 2 |
factor | U1 | Employment | 1 | 46 | 47 | 4 | une: 25, emp: 19, stu: 1, una: 1 |
factor | U1 | HAART | 4 | 43 | 47 | 3 | sec: 24, fir: 18, no : 1 |
factor | U1 | Sex | 0 | 47 | 47 | 2 | fem: 31, mal: 16 |
factor | U1 | SOS_mnemonic | 47 | 0 | 47 | 0 | hea: 0, low: 0 |
factor | U2 | Education | 0 | 17 | 17 | 2 | 8-1: 11, 0-7: 6, Mor: 0 |
factor | U2 | Employment | 0 | 17 | 17 | 4 | une: 11, emp: 3, una: 2, stu: 1 |
factor | U2 | HAART | 0 | 17 | 17 | 2 | fir: 14, sec: 3, no : 0 |
factor | U2 | Sex | 0 | 17 | 17 | 1 | fem: 17, mal: 0 |
factor | U2 | SOS_mnemonic | 0 | 17 | 17 | 2 | low: 10, hea: 7 |
demo %>%
select(Sex, Education, Employment, HAART, Sex, SOS_mnemonic) %>%
group_by(Sex) %>%
skim_to_wide(.) %>%
kable(., caption = 'By sex',
align = 'lllrrrrr')
type | Sex | variable | missing | complete | n | n_unique | top_counts |
---|---|---|---|---|---|---|---|
factor | female | Education | 1 | 96 | 97 | 3 | 8-1: 68, 0-7: 26, Mor: 2 |
factor | female | Employment | 1 | 96 | 97 | 4 | une: 65, emp: 26, una: 3, stu: 2 |
factor | female | HAART | 3 | 94 | 97 | 3 | fir: 65, sec: 24, no : 5 |
factor | female | SOS_mnemonic | 31 | 66 | 97 | 2 | low: 59, hea: 7 |
factor | male | Education | 1 | 62 | 63 | 2 | 8-1: 44, 0-7: 18, Mor: 0 |
factor | male | Employment | 2 | 61 | 63 | 3 | une: 31, emp: 25, una: 5, stu: 0 |
factor | male | HAART | 1 | 62 | 63 | 2 | fir: 50, sec: 12, no : 0 |
factor | male | SOS_mnemonic | 16 | 47 | 63 | 2 | hea: 28, low: 19 |
bpi %>%
select_if(is.numeric) %>%
skim_to_wide(.) %>%
kable(., caption = 'Whole cohort',
align = 'llrrrrrrrrrr')
type | variable | missing | complete | n | mean | sd | median | q25 | q75 | min | max |
---|---|---|---|---|---|---|---|---|---|---|---|
numeric | PIS | 17 | 143 | 160 | 5.12 | 2.58 | 5 | 3 | 7 | 0 | 10 |
numeric | PSS | 16 | 144 | 160 | 5.03 | 2.14 | 5 | 4 | 6 | 0 | 10 |
bpi %>%
select(Study_site, PIS, PSS) %>%
group_by(Study_site) %>%
skim_to_wide(.) %>%
kable(., caption = 'By study site',
align = 'lllrrrrrrrrrr')
type | Study_site | variable | missing | complete | n | mean | sd | median | q25 | q75 | min | max |
---|---|---|---|---|---|---|---|---|---|---|---|---|
numeric | R1 | PIS | 12 | 35 | 47 | 4.69 | 3.19 | 5 | 1.5 | 7 | 0 | 9 |
numeric | R1 | PSS | 12 | 35 | 47 | 5 | 3.01 | 5 | 3 | 8 | 0 | 10 |
numeric | R2 | PIS | 1 | 48 | 49 | 4.79 | 2.25 | 5 | 3 | 6.25 | 0 | 10 |
numeric | R2 | PSS | 0 | 49 | 49 | 4.61 | 1.74 | 5 | 4 | 6 | 2 | 10 |
numeric | U1 | PIS | 4 | 43 | 47 | 5.12 | 2.31 | 5 | 3.5 | 7 | 0 | 9 |
numeric | U1 | PSS | 4 | 43 | 47 | 5 | 1.6 | 5 | 4 | 6 | 0 | 8 |
numeric | U2 | PIS | 0 | 17 | 17 | 6.94 | 2.05 | 7 | 6 | 9 | 3 | 10 |
numeric | U2 | PSS | 0 | 17 | 17 | 6.41 | 1.84 | 6 | 6 | 7 | 3 | 10 |
bpi %>%
select(Sex, PIS, PSS) %>%
group_by(Sex) %>%
skim_to_wide(.) %>%
kable(., caption = 'By sex',
align = 'lllrrrrrrrrrr')
type | Sex | variable | missing | complete | n | mean | sd | median | q25 | q75 | min | max |
---|---|---|---|---|---|---|---|---|---|---|---|---|
numeric | female | PIS | 5 | 92 | 97 | 5.32 | 2.39 | 5 | 4 | 7 | 0 | 10 |
numeric | female | PSS | 4 | 93 | 97 | 5.05 | 1.89 | 5 | 4 | 6 | 0 | 10 |
numeric | male | PIS | 12 | 51 | 63 | 4.76 | 2.88 | 5 | 2 | 7 | 0 | 9 |
numeric | male | PSS | 12 | 51 | 63 | 5 | 2.56 | 5 | 4 | 6 | 0 | 10 |
bdi %>%
select_if(is.numeric) %>%
skim_to_wide(.) %>%
kable(., caption = 'Whole cohort',
align = 'llrrrrrrrrrr')
type | variable | missing | complete | n | mean | sd | median | q25 | q75 | min | max |
---|---|---|---|---|---|---|---|---|---|---|---|
numeric | BDI | 0 | 160 | 160 | 20.07 | 13.22 | 18.5 | 10 | 29 | 0 | 55 |
bdi %>%
select(Study_site, BDI) %>%
group_by(Study_site) %>%
skim_to_wide(.) %>%
kable(., caption = 'By study site',
align = 'lllrrrrrrrrrr')
type | Study_site | variable | missing | complete | n | mean | sd | median | q25 | q75 | min | max |
---|---|---|---|---|---|---|---|---|---|---|---|---|
numeric | R1 | BDI | 0 | 47 | 47 | 13.47 | 12.1 | 13 | 0 | 21.5 | 0 | 45 |
numeric | R2 | BDI | 0 | 49 | 49 | 25.73 | 11.12 | 27 | 17 | 33 | 5 | 49 |
numeric | U1 | BDI | 0 | 47 | 47 | 17.51 | 12.39 | 16 | 8 | 25.5 | 0 | 46 |
numeric | U2 | BDI | 0 | 17 | 17 | 29.06 | 13.15 | 31 | 22 | 35 | 0 | 55 |
bdi %>%
select(Sex, BDI) %>%
group_by(Sex) %>%
skim_to_wide(.) %>%
kable(., caption = 'By sex',
align = 'lllrrrrrrrrrr')
type | Sex | variable | missing | complete | n | mean | sd | median | q25 | q75 | min | max |
---|---|---|---|---|---|---|---|---|---|---|---|---|
numeric | female | BDI | 0 | 97 | 97 | 24.26 | 12.65 | 25 | 16 | 33 | 0 | 55 |
numeric | male | BDI | 0 | 63 | 63 | 13.62 | 11.44 | 13 | 4 | 20 | 0 | 45 |
eq5d %>%
select_if(is.numeric) %>%
skim_to_wide(.) %>%
kable(., caption = 'Whole cohort',
align = 'llrrrrrrrrrr')
type | variable | missing | complete | n | mean | sd | median | q25 | q75 | min | max |
---|---|---|---|---|---|---|---|---|---|---|---|
numeric | EQ5D_index | 18 | 142 | 160 | 0.62 | 0.19 | 0.69 | 0.49 | 0.76 | -0.21 | 1 |
numeric | EQ5D_VAS | 16 | 144 | 160 | 59.52 | 21.17 | 60 | 50 | 76.25 | 0 | 100 |
eq5d %>%
select(Study_site, EQ5D_index, EQ5D_VAS) %>%
group_by(Study_site) %>%
skim_to_wide(.) %>%
kable(., caption = 'By study site',
align = 'lllrrrrrrrrrr')
type | Study_site | variable | missing | complete | n | mean | sd | median | q25 | q75 | min | max |
---|---|---|---|---|---|---|---|---|---|---|---|---|
numeric | R1 | EQ5D_index | 12 | 35 | 47 | 0.67 | 0.21 | 0.73 | 0.66 | 0.78 | -0.21 | 1 |
numeric | R1 | EQ5D_VAS | 12 | 35 | 47 | 62.46 | 23.1 | 69 | 50 | 80 | 10 | 100 |
numeric | R2 | EQ5D_index | 0 | 49 | 49 | 0.66 | 0.18 | 0.73 | 0.62 | 0.8 | -0.05 | 0.8 |
numeric | R2 | EQ5D_VAS | 0 | 49 | 49 | 59.9 | 16.6 | 60 | 50 | 70 | 20 | 90 |
numeric | U1 | EQ5D_index | 6 | 41 | 47 | 0.56 | 0.17 | 0.5 | 0.43 | 0.73 | 0.36 | 1 |
numeric | U1 | EQ5D_VAS | 4 | 43 | 47 | 59.77 | 20.64 | 60 | 55 | 75 | 0 | 90 |
numeric | U2 | EQ5D_index | 0 | 17 | 17 | 0.53 | 0.22 | 0.52 | 0.46 | 0.66 | 0.008 | 0.85 |
numeric | U2 | EQ5D_VAS | 0 | 17 | 17 | 51.76 | 29.04 | 60 | 40 | 75 | 0 | 80 |
eq5d %>%
select(Sex, EQ5D_index, EQ5D_VAS) %>%
group_by(Sex) %>%
skim_to_wide(.) %>%
kable(., caption = 'By sex',
align = 'lllrrrrrrrrrr')
type | Sex | variable | missing | complete | n | mean | sd | median | q25 | q75 | min | max |
---|---|---|---|---|---|---|---|---|---|---|---|---|
numeric | female | EQ5D_index | 5 | 92 | 97 | 0.61 | 0.19 | 0.66 | 0.47 | 0.76 | -0.05 | 1 |
numeric | female | EQ5D_VAS | 4 | 93 | 97 | 58.12 | 21.02 | 60 | 50 | 75 | 0 | 90 |
numeric | male | EQ5D_index | 13 | 50 | 63 | 0.64 | 0.2 | 0.73 | 0.51 | 0.76 | -0.21 | 1 |
numeric | male | EQ5D_VAS | 12 | 51 | 63 | 62.08 | 21.42 | 70 | 50 | 80 | 10 | 100 |
se6 %>%
select_if(is.numeric) %>%
skim_to_wide(.) %>%
kable(., caption = 'Whole cohort',
align = 'llrrrrrrrrrr')
type | variable | missing | complete | n | mean | sd | median | q25 | q75 | min | max |
---|---|---|---|---|---|---|---|---|---|---|---|
numeric | SE6 | 18 | 142 | 160 | 6.93 | 2.25 | 7.5 | 5 | 9 | 1 | 10 |
se6 %>%
select(Study_site, SE6) %>%
group_by(Study_site) %>%
skim_to_wide(.) %>%
kable(., caption = 'By study site',
align = 'lllrrrrrrrrrr')
type | Study_site | variable | missing | complete | n | mean | sd | median | q25 | q75 | min | max |
---|---|---|---|---|---|---|---|---|---|---|---|---|
numeric | R1 | SE6 | 12 | 35 | 47 | 6.4 | 2.24 | 7 | 5 | 8 | 1 | 10 |
numeric | R2 | SE6 | 0 | 49 | 49 | 6.16 | 2.32 | 6 | 5 | 8 | 1 | 10 |
numeric | U1 | SE6 | 6 | 41 | 47 | 8.54 | 1.43 | 9 | 8 | 10 | 4 | 10 |
numeric | U2 | SE6 | 0 | 17 | 17 | 6.35 | 1.8 | 6 | 5 | 8 | 2 | 9 |
se6 %>%
select(Sex, SE6) %>%
group_by(Sex) %>%
skim_to_wide(.) %>%
kable(., caption = 'By sex',
align = 'lllrrrrrrrrrr')
type | Sex | variable | missing | complete | n | mean | sd | median | q25 | q75 | min | max |
---|---|---|---|---|---|---|---|---|---|---|---|---|
numeric | female | SE6 | 5 | 92 | 97 | 6.9 | 2.31 | 8 | 5 | 9 | 1 | 10 |
numeric | male | SE6 | 13 | 50 | 63 | 6.98 | 2.16 | 7 | 6 | 8.75 | 1 | 10 |
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] knitr_1.22 skimr_1.0.5 magrittr_1.5 forcats_0.4.0
## [5] stringr_1.4.0 dplyr_0.8.0.1 purrr_0.3.2 readr_1.3.1
## [9] tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1 tidyverse_1.2.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.1 highr_0.8 cellranger_1.1.0 pillar_1.3.1
## [5] compiler_3.6.0 plyr_1.8.4 tools_3.6.0 digest_0.6.18
## [9] lubridate_1.7.4 jsonlite_1.6 evaluate_0.13 nlme_3.1-139
## [13] gtable_0.3.0 lattice_0.20-38 pkgconfig_2.0.2 rlang_0.3.4
## [17] cli_1.1.0 rstudioapi_0.10 yaml_2.2.0 haven_2.1.0
## [21] xfun_0.6 withr_2.1.2.9000 xml2_1.2.0 httr_1.4.0
## [25] hms_0.4.2 generics_0.0.2 grid_3.6.0 tidyselect_0.2.5
## [29] glue_1.3.1 R6_2.4.0 readxl_1.3.1 rmarkdown_1.12
## [33] modelr_0.1.4 backports_1.1.4 scales_1.0.0 htmltools_0.3.6
## [37] rvest_0.3.3 assertthat_0.2.1 colorspace_1.4-1 stringi_1.4.3
## [41] lazyeval_0.2.2 munsell_0.5.0 broom_0.5.2 crayon_1.3.4