Fast-track publishing using knitr: stitching it together (part V)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168

```{r Data_prep, echo=FALSE, message=FALSE, warning=FALSE}
# Moved this outside the document for easy of reading
# I often have those sections in here
source("Setup_and_munge.R")
```
 
```{r Versions}
info <- sessionInfo()
r_ver <- paste(info$R.version$major, info$R.version$minor, sep=".")
```
 
All analyses were performed using R (ver. `r r_ver`)[R Core Team, 2013] 
and packages rms (ver. `r info$otherPkgs$rms$Version`) [F. Harrell, 2014] 
for analysis, Gmisc for plot and table output (ver. `r info$otherPkgs$Gmisc$Version`), 
and knitr (ver `r info$otherPkgs$knitr$Version`) [Xie, 2013] for reproducible research.
 
Results
=======
 
We found `r nrow(melanoma)` patients with malignant melanoma between the years 
`r paste(range(melanoma$year), collapse=" and ")`. Patients were followed until 
the end of 1977, the median follow-up time was `r sprintf("%.1f", median(melanoma$time_years))` 
years (range `r paste(sprintf("%.1f", range(melanoma$time_years)), collapse=" to ")` years). 
Males were more common than females and had also a higher mortality rate.
 
```{r Table1, results='asis', cache=FALSE}
table_data <- list()
getT1Stat <- function(varname, digits=0){
  getDescriptionStatsBy(melanoma[, varname], melanoma$status, 
                        add_total_col=TRUE,
                        show_all_values=TRUE, 
                        hrzl_prop=TRUE,
                        statistics=FALSE, 
                        html=TRUE, 
                        digits=digits)
}
 
# Get the basic stats
table_data[["Sex"]] <- getT1Stat("sex")
table_data[["Age<sup>&dagger;</sup>"]] <- getT1Stat("age")
table_data[["Ulceration"]] <- getT1Stat("ulcer")
table_data[["Thickness<sup>&Dagger;</sup>"]] <- getT1Stat("thickness", digits=1)
 
# Now merge everything into a matrix
# and create the rgroup & n.rgroup variabels
rgroup <- c()
n.rgroup <- c()
output_data <- NULL
for (varlabel in names(table_data)){
  output_data <- rbind(output_data, table_data[[varlabel]])
  rgroup <- c(rgroup, varlabel)
  n.rgroup <- c(n.rgroup, nrow(table_data[[varlabel]]))
}
 
# Add a column spanner for the death columns
cgroup <- c("", "Death")
n.cgroup <- c(2, 2)
colnames(output_data) <- gsub("[ ]*death", "", colnames(output_data))
 
htmlTable(output_data, align="rrrr",
          rgroup=rgroup, n.rgroup=n.rgroup, 
          rgroupCSSseparator="", 
          cgroup = cgroup,
          n.cgroup = n.cgroup,
          rowlabel="", 
          caption="Baseline characteristics", 
          tfoot=paste0("<sup>&dagger;</sup> Age at the time of surgery.",
                       " <br/><sup>&Dagger;</sup> Tumour thicknes,",
                       " also known as Breslow thickness, measured in mm."), 
          ctable=TRUE)
```
 
Main results
------------
 
```{r C_and_A, results='asis'}
# Setup needed for the rms coxph wrapper
ddist <- datadist(melanoma)
options(datadist = "ddist")
 
# Do the cox regression model 
# for melanoma specific death
msurv <- Surv(melanoma$time_years, melanoma$status=="Melanoma death")
fit <- cph(msurv ~ sex + age + ulcer + thickness, data=melanoma)
 
# Print the model
printCrudeAndAdjustedModel(fit, desc_digits=0,
                           caption="Adjusted and unadjusted estimates for melanoma specific death.",
                           desc_column=TRUE,
                           add_references=TRUE, 
                           ctable=TRUE)
 
pvalues <- 
  1 - pchisq(coef(fit)^2/diag(vcov(fit)), df=1)
```
 
```{r}
pvalue_formatter <- function(pvalues, sig.limit = 0.001){
  sapply(pvalues, function(x, sig.limit){
    if (x < sig.limit)
      return(sprintf("&lt; %s", format(sig.limit))) 
    # &lt; stands for < and is needed
    # for the markdown/html to work
    # and the format is needed to avoid trailing zeros
 
    # High p-values you usually want two decimals
    # otherwise report only one
    if (x > 0.01)
      return(format(x, digits=2))
 
    return(format(x, digits=1))
    }, sig.limit=sig.limit)
}
```
 
After adjusting for the three variables, age, sex, tumor thickness 
and ulceration, only the latter two remained significant (p-value 
`r pvalue_formatter(pvalues["ulcer=Present"])` and 
`r pvalue_formatter(pvalues["thickness"])`), 
see table `r as.numeric(options("table_counter"))-1` 
and figure `r getNextFigureNo()`.
 
```{r Regression_forestplot, fig.height=3, fig.width=5, fig.cap="A foresplot comparing the regression coefficients."}
# I've adjusted the coefficient for age to be by 
forestplotRegrObj(update(fit, .~.-age+I(age/10)), 
                  order.regexps=c("Female", "age", "ulc", "thi"),
                  box.default.size=.25, xlog=TRUE,
                  new_page=TRUE, clip=c(.5, 6), rowname.fn=function(x){
  if (grepl("Female", x))
    return("Female")
 
  if (grepl("Present", x))
    return("Ulceration")
 
  if (grepl("age", x))
    return("Age/10 years")
 
  return(capitalize(x))
})
```
 
There was no strong indication for non-linearity for any of the 
continuous variables although the impact of thickness did 
seem to lessen above 4 mm, see figure `r getNextFigureNo()`.
 
```{r spline_plot, fig.cap=plotHR_cap}
plotHR_cap = paste0("The adjusted and unadjusted restricted cubic spline",
                    " for tumor thickness. Solid line and confidence interval",
                    " indicate the adjusted line while the dashed is",
                    " the unadjusted line. The grey area at ",
                    " the bottom indicates the density.")
# Generate adjusted and anuadjusted regression models
rcs_fit <- update(fit, .~.-thickness+rcs(thickness, 3))
rcs_fit_ua <- update(fit, .~+rcs(thickness, 3))
 
# Make sure the axes stay at the exact intended points
par(xaxs="i", yaxs="i")
plotHR(list(rcs_fit, rcs_fit_ua), col.dens="#00000033",
       lty.term=c(1, 2),
       col.term=c("blue", "#444444"), 
       col.se = c("#0000FF44", "grey"),
       polygon_ci=c(TRUE, FALSE),
       term="thickness", 
       xlab="Thickness (mm)", 
       ylim=c(.1, 4), xlim=c(min(melanoma$thickness), 4), 
       plot.bty="l", y.ticks=c(.1, .25, .5, 1, 2, 4))
legend(x=.1, y=1.1, legend=c("Adjusted", "Unadjusted"), fill=c("blue", "grey"), bty="n")
```

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141

##################
# Knitr settings #
##################
 
# Load the knitr package so that the code
# doesn't complain outside knitr
library(knitr) 
 
# Set some basic options. You usually do not
# want your code, messages, warnings etc
# to show in your actual manuscript
opts_chunk$set(warning=FALSE, 
               message=FALSE, 
               echo=FALSE, 
               dpi=96,
               fig.width=4, fig.height=4, # Default figure widths
               dev="png", dev.args=list(type="cairo"), # The png device
               # Change to dev="postscript" if you want the EPS-files
               # for submitting. Also remove the dev.args() as the postscript
               # doesn't accept the type="cairo" argument.
               error=FALSE)
 
# Evaluate the figure caption after the plot
opts_knit$set(eval.after='fig.cap')
 
# Avoid including base64_images - this only 
# works with the .RProfile setup
options(base64_images = "none")
 
# Use the table counter that the htmlTable() provides
options(table_counter = TRUE)
 
# Use the figure counter that we declare below
options(figure_counter = TRUE)
# Use roman letters (I, II, III, etc) for figures
options(figure_counter_roman = TRUE)
 
# Adding the figure number is a little tricky when the format is roman
getNextFigureNo <- function() as.character(as.roman(as.numeric(options("figure_counter"))))
 
# Add a figure counter function
knit_hooks$set(plot = function(x, options) {
  fig_fn = paste0(opts_knit$get("base.url"), 
                  paste(x, collapse = "."))
 
  # Some stuff from the default definition
  fig.cap <- knitr:::.img.cap(options)
 
  # Style and additional options that should be included in the img tag
  style=c("display: block",
          sprintf("margin: %s;",
                   switch(options$fig.align, 
                          left = 'auto auto auto 0', 
                          center = 'auto',
                          right = 'auto 0 auto auto')))
  # Certain arguments may not belong in style, 
  # for instance the width and height are usually
  # outside if the do not have a unit specified
  addon_args = ""
 
  # This is perhaps a little overly complicated prepared 
  # with the loop but it allows for a more out.parameters if necessary
  if (any(grepl("^out.(height|width)", names(options)))){
      on <- names(options)[grep("^out.(height|width)", names(options))]
      for(out_name in on){
          dimName <- substr(out_name, 5, nchar(out_name))
          if (grepl("[0-9]+(em|px|%|pt|pc|in|cm|mm)", out_name))
              style=append(style, paste0(dimName, ": ", options[[out_name]]))
          else if (length(options$out.width) > 0)
              addon_args = paste0(addon_args, dimName, "='", options[[out_name]], "'")
      }
  }
 
  # Add counter if wanted
  fig_number_txt <- ""
  cntr <- getOption("figure_counter", FALSE)
  if (cntr != FALSE){
    if (is.logical(cntr))
      cntr <- 1
    # The figure_counter_str allows for custom 
    # figure text, you may for instance want it in
    # bold: <b>Figure %s:</b>
    # The %s is so that you have the option of setting the
    # counter manually to 1a, 1b, etc if needed
    fig_number_txt <- 
      sprintf(getOption("figure_counter_str", "Figure %s: "), 
              ifelse(getOption("figure_counter_roman", FALSE), 
                     as.character(as.roman(cntr)), as.character(cntr)))
 
    if (is.numeric(cntr))
      options(figure_counter = cntr + 1)
  }
 
  # Put it all together
  paste0("<figure><img src='", fig_fn, "'", 
         " ", addon_args,
         paste0(" style='", paste(style, collapse="; "), "'"),
         ">",
         "<figcaption>", fig_number_txt, fig.cap, "</figcaption></figure>")
})
 
#################
# Load_packages #
#################
library(rms) # I use the cox regression from this package
library(boot) # The melanoma data set is used in this exampe
library(Gmisc) # Stuff I find convenient
 
##################
# Munge the data #
##################
 
# Here we go through and setup the variables so that
# they are in the proper format for the actual output
 
# Load the dataset - usually you would use read.csv
# or something similar
data("melanoma")
 
# Set time to years instead of days
melanoma$time_years <- 
  melanoma$time / 365.25
 
# Factor the basic variables that 
# we're interested in
melanoma$status <- 
  factor(melanoma$status, 
         levels=c(2, 1, 3),
         labels=c("Alive", # Reference
                  "Melanoma death", 
                  "Non-melanoma death"))
melanoma$sex <- 
  factor(melanoma$sex,
         labels=c("Male", # Reference
                  "Female"))
 
melanoma$ulcer <- 
  factor(melanoma$ulcer,
         levels=0:1,
         labels=c("Absent", # Reference
                  "Present"))