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Knit directory: Fiber_Intervention_Study/
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This page contains the summary statistics of participants’ data across the study.
# load packages
source("code/load_packages.R")Loading required package: permuteLoading required package: latticeThis is vegan 2.5-6Loading required package: Matrix
Attaching package: 'lmerTest'The following object is masked from 'package:lme4':
lmerThe following object is masked from 'package:stats':
step-- Attaching packages -------------------------------------- tidyverse 1.3.0 --v ggplot2 3.3.0 v purrr 0.3.4
v tibble 3.0.1 v dplyr 0.8.5
v tidyr 1.1.0 v stringr 1.4.0
v readr 1.3.1 v forcats 0.5.0-- Conflicts ----------------------------------------- tidyverse_conflicts() --
x tidyr::expand() masks Matrix::expand()
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
x tidyr::pack() masks Matrix::pack()
x tidyr::unpack() masks Matrix::unpack()
Attaching package: 'data.table'The following objects are masked from 'package:dplyr':
between, first, lastThe following object is masked from 'package:purrr':
transpose------------------------------------------------------------------------------You have loaded plyr after dplyr - this is likely to cause problems.
If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)------------------------------------------------------------------------------
Attaching package: 'plyr'The following objects are masked from 'package:dplyr':
arrange, count, desc, failwith, id, mutate, rename, summarise,
summarizeThe following object is masked from 'package:purrr':
compact
Attaching package: 'kableExtra'The following object is masked from 'package:dplyr':
group_rows
Attaching package: 'gridExtra'The following object is masked from 'package:dplyr':
combineLoading required package: viridisLiteLoading required package: carDataRegistered S3 methods overwritten by 'car':
method from
influence.merMod lme4
cooks.distance.influence.merMod lme4
dfbeta.influence.merMod lme4
dfbetas.influence.merMod lme4
Attaching package: 'car'The following object is masked from 'package:dplyr':
recodeThe following object is masked from 'package:purrr':
some
microbiome R package (microbiome.github.com)
Copyright (C) 2011-2019 Leo Lahti,
Sudarshan Shetty et al. <microbiome.github.io>
Attaching package: 'microbiome'The following object is masked from 'package:ggplot2':
alphaThe following object is masked from 'package:vegan':
diversityThe following object is masked from 'package:base':
transform
********************************************************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:patchwork':
align_plots# get data
source("code/get_cleaned_data.R")Joining, by = c("SubjectID", "Age", "Ethnicity", "Gender", "Intervention")Warning: Column `Ethnicity` joining factor and character vector, coercing into
character vectorWarning: Column `Gender` joining factor and character vector, coercing into
character vectorWarning: Column `Intervention` joining factor and character vector, coercing
into character vectorJoining, by = "SubjectID"Joining, by = c("SubjectID", "Week")mydata <- microbiome_data$meta.dat %>%
mutate(female = ifelse(Gender == "F", 1, 0),
c.age = Age - mean(Age),
IntB = ifelse(Intervention == "B", 1, 0),
Stress = Stress.Scale,
hispanic = ifelse(Ethnicity %in% c("White", "Asian", "Native America"), 1, 0),
BMI = Weight_pre/((Height_cm/100)**2))
sumTab <- mydata %>%
dplyr::group_by(Week) %>%
dplyr::summarise(N = n(),
Age_Mean = mean(Age),
Age_SD = sd(Age),
Weight_kg_M = mean(Weight_kg),
Weight_kg_SD = sd(Weight_kg),
Height_cm_M = mean(Height_cm),
Height_cm_SD = sd(Height_cm))
kable(sumTab, format="html", digits=1) %>%
kable_styling(full_width = T)| Week | N | Age_Mean | Age_SD | Weight_kg_M | Weight_kg_SD | Height_cm_M | Height_cm_SD |
|---|---|---|---|---|---|---|---|
| 1 | 11 | 27.8 | 2.0 | 71.2 | 10.7 | 164.0 | 8.2 |
| 4 | 10 | 28.0 | 2.1 | 71.5 | 11.3 | 164.1 | 8.6 |
| 8 | 7 | 27.9 | 2.3 | 73.3 | 8.8 | 162.6 | 5.7 |
| 12 | 9 | 28.0 | 2.2 | 69.8 | 10.5 | 161.9 | 5.2 |
sumTab <- mydata %>%
dplyr::filter(Week==1) %>%
dplyr::group_by(Intervention) %>%
dplyr::summarise(N = n(),
PercentMale = (1 - mean(female))*100,
Age_Mean = mean(Age),
Age_SD = sd(Age),
Weight_kg_M = mean(Weight_kg),
Weight_kg_SD = sd(Weight_kg),
Height_cm_M = mean(Height_cm),
Height_cm_SD = sd(Height_cm),
BMI_M = mean(BMI),
BMI_SD = sd(BMI),
VFL_M = mean(Visceral_Fat_Level_pre),
VFL_SD = sd(Visceral_Fat_Level_pre),
PercBF_M = mean(Perc_Body_Fat_pre),
PercBF_SD = sd(Perc_Body_Fat_pre),
PercLF_M = mean(LBM_pre),
PercLF_SD = sd(LBM_pre),
HEI_Total_M = mean(HEI_Total, na.rm=T),
HEI_Total_SD = sd(HEI_Total, na.rm=T))
sumTab <- t(sumTab)
M <- sumTab[rownames(sumTab) %like% "_M",]
SD <- sumTab[rownames(sumTab) %like% "_SD",]
tab <- cbind(M, SD)
tab <- tab[, c(1,3,2,4)]
Ng <- c(sumTab[2,1], NA, sumTab[2,2], NA)
PercentMale <- c(sumTab[3,1], NA, sumTab[3,2], NA)
tab <- rbind(Ng, PercentMale, tab)
colnames(tab) <- c("GroupA_Mean","GroupA_SD", "GroupB_Mean", "GroupB_SD")
tab <- apply(tab, 1:2, as.numeric)
#tabNext, we conducted Mann-Whitney-U test to compare distributions across intervention vs. placebo groups.
dat <- filter(mydata, Week == 1)
VAR <- c("Age", "Weight_kg", "Height_cm", "BMI", "Visceral_Fat_Level_pre", "Perc_Body_Fat_pre", "LBM_pre", "HEI_Total")
out <- numeric(length(VAR))
i <- 1
for(i in 1:length(VAR)){
fit <- wilcox.test(dat[,VAR[i]] ~ dat$IntB)
out[i] <- fit$p.value
}Warning in wilcox.test.default(x = c(27, 28, 30, 26, 26, 27), y = c(32, : cannot
compute exact p-value with tiesWarning in wilcox.test.default(x = c(157.5, 184.2, 161.3, 163.8, 161.3, : cannot
compute exact p-value with tiesWarning in wilcox.test.default(x = c(17, 7, 6, 9, 9, 5), y = c(12, 13, 7, :
cannot compute exact p-value with tiesWarning in wilcox.test.default(x = c(44.3, 20.1, 24.6, 30.8, 33.4, 25.3), :
cannot compute exact p-value with tiesWarning in wilcox.test.default(x = c(22.2, 39.4, 26.1, 29.8, 22.2, 21.5), :
cannot compute exact p-value with tiesWarning in wilcox.test.default(x = c(88.8, 89.4, 89.13, 89.49, 90), y = c(90, :
cannot compute exact p-value with tiesout <- c(NA, NA, out)
out <- matrix(out, ncol=1)
colnames(out) <- "P_Value"
tab <- cbind(tab, out)
kable(tab, format="html", digits=3) %>%
kable_styling(full_width = T)| GroupA_Mean | GroupA_SD | GroupB_Mean | GroupB_SD | P_Value | |
|---|---|---|---|---|---|
| Ng | 6.000 | NA | 5.000 | NA | NA |
| PercentMale | 33.333 | NA | 40.000 | NA | NA |
| Age_Mean | 27.333 | 1.506 | 28.400 | 2.608 | 0.638 |
| Weight_kg_M | 68.967 | 12.199 | 73.880 | 9.238 | 0.537 |
| Height_cm_M | 164.267 | 10.067 | 163.584 | 6.309 | 0.927 |
| BMI_M | 25.460 | 3.127 | 27.516 | 1.724 | 0.247 |
| VFL_M | 8.833 | 4.309 | 10.200 | 2.950 | 0.405 |
| PercBF_M | 29.750 | 8.551 | 32.340 | 4.999 | 0.464 |
| PercLF_M | 26.867 | 6.908 | 27.860 | 3.996 | 0.407 |
| HEI_Total_M | 89.364 | 0.446 | 89.817 | 0.365 | 0.201 |
tab <- as.data.frame(tab)
#write.csv(tab,paste0(w.d, "/tab/table_1_results.csv"))asa24 <- microbiome_data$meta.dat
# If the recall number is missing, then they didn't response so we need to exclude them in the count.
asa24 %>%
dplyr::group_by(Week)%>%
filter(is.na(RecallNo)==F)%>%
dplyr::summarise(N=n())# A tibble: 4 x 2
Week N
<fct> <int>
1 1 9
2 4 7
3 8 4
4 12 2HEI scores were only gathered at onetime point. So, we need to subset to only 1 week then exclude the missing observations.
ffq <- microbiome_data$meta.dat
ffq %>%
dplyr::filter(Week == 1, is.na(HEI_Total)==F)%>%
dplyr::summarise(N = n()) N
1 9Nvec <- matrix(ncol=1, nrow=5, dimnames = list(c("Total", "Week1", "Week4", "Week8", "Week12"), c("N")))
# total number of samples
Nvec[1,1] <- nsamples(phylo_data)
# week 1
subph <- subset_samples(phylo_data, Week == 1)
Nvec[2,1] <- nsamples(subph)
# week 4
subph <- subset_samples(phylo_data, Week == 4)
Nvec[3,1] <- nsamples(subph)
# week 8
subph <- subset_samples(phylo_data, Week == 8)
Nvec[4,1] <- nsamples(subph)
# week 12
subph <- subset_samples(phylo_data, Week == 12)
Nvec[5,1] <- nsamples(subph)
Nvec N
Total 37
Week1 11
Week4 10
Week8 7
Week12 9# Blood Samples
blood_data <- read_excel("data/Blood measures data/Copy of Fiber Study Blood Results.xlsx")
meta_data <- microbiome_data$meta.dat
keepVar <- c("SubjectID","Week", "Intervention", "Stress.Scale", "Ethnicity", "Gender", "Age")
meta_data <- meta_data [, keepVar] %>%
filter(Week == 1)
blood_data <- full_join(blood_data, meta_data)Joining, by = "SubjectID"blood_data <- distinct(blood_data, SubjectID, time,.keep_all = T)
blood_data %>%
dplyr::group_by(time) %>%
dplyr::summarise(N=n())# A tibble: 2 x 2
time N
<chr> <int>
1 Post 11
2 Pre 11
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] cowplot_1.0.0 microbiome_1.8.0 car_3.0-8 carData_3.0-4
[5] gvlma_1.0.0.3 patchwork_1.0.0 viridis_0.5.1 viridisLite_0.3.0
[9] gridExtra_2.3 xtable_1.8-4 kableExtra_1.1.0 plyr_1.8.6
[13] data.table_1.12.8 readxl_1.3.1 forcats_0.5.0 stringr_1.4.0
[17] dplyr_0.8.5 purrr_0.3.4 readr_1.3.1 tidyr_1.1.0
[21] tibble_3.0.1 ggplot2_3.3.0 tidyverse_1.3.0 lmerTest_3.1-2
[25] lme4_1.1-23 Matrix_1.2-18 vegan_2.5-6 lattice_0.20-38
[29] permute_0.9-5 phyloseq_1.30.0
loaded via a namespace (and not attached):
[1] Rtsne_0.15 minqa_1.2.4 colorspace_1.4-1
[4] rio_0.5.16 ellipsis_0.3.1 rprojroot_1.3-2
[7] XVector_0.26.0 fs_1.4.1 rstudioapi_0.11
[10] fansi_0.4.1 lubridate_1.7.8 xml2_1.3.2
[13] codetools_0.2-16 splines_3.6.3 knitr_1.28
[16] ade4_1.7-15 jsonlite_1.6.1 workflowr_1.6.2
[19] nloptr_1.2.2.1 broom_0.5.6 cluster_2.1.0
[22] dbplyr_1.4.4 BiocManager_1.30.10 compiler_3.6.3
[25] httr_1.4.1 backports_1.1.7 assertthat_0.2.1
[28] cli_2.0.2 later_1.0.0 htmltools_0.4.0
[31] tools_3.6.3 igraph_1.2.5 gtable_0.3.0
[34] glue_1.4.1 reshape2_1.4.4 Rcpp_1.0.4.6
[37] Biobase_2.46.0 cellranger_1.1.0 vctrs_0.3.0
[40] Biostrings_2.54.0 multtest_2.42.0 ape_5.3
[43] nlme_3.1-144 iterators_1.0.12 xfun_0.14
[46] openxlsx_4.1.5 rvest_0.3.5 lifecycle_0.2.0
[49] statmod_1.4.34 zlibbioc_1.32.0 MASS_7.3-51.5
[52] scales_1.1.1 hms_0.5.3 promises_1.1.0
[55] parallel_3.6.3 biomformat_1.14.0 rhdf5_2.30.1
[58] curl_4.3 yaml_2.2.1 stringi_1.4.6
[61] highr_0.8 S4Vectors_0.24.4 foreach_1.5.0
[64] BiocGenerics_0.32.0 zip_2.0.4 boot_1.3-24
[67] rlang_0.4.6 pkgconfig_2.0.3 evaluate_0.14
[70] Rhdf5lib_1.8.0 tidyselect_1.1.0 magrittr_1.5
[73] R6_2.4.1 IRanges_2.20.2 generics_0.0.2
[76] DBI_1.1.0 foreign_0.8-75 pillar_1.4.4
[79] haven_2.3.0 whisker_0.4 withr_2.2.0
[82] mgcv_1.8-31 abind_1.4-5 survival_3.1-8
[85] modelr_0.1.8 crayon_1.3.4 utf8_1.1.4
[88] rmarkdown_2.1 grid_3.6.3 blob_1.2.1
[91] git2r_0.27.1 reprex_0.3.0 digest_0.6.25
[94] webshot_0.5.2 httpuv_1.5.2 numDeriv_2016.8-1.1
[97] stats4_3.6.3 munsell_0.5.0