**DataScience+**, and kindly contributed to R-bloggers)

In this post I will present a simple way how to export your regression results (or output) from R into Microsoft Word. Previously, I have written a tutorial how to create Table 1 with study characteristics and to export into Microsoft Word. These posts are especially useful for researchers who prepare their manuscript for publication in peer-reviewed journals.

## Get the results from Cox Regression Analysis

As an example to illustrate this post, I will compute a survival analysis. Survival analysis is statistical methods for analyzing data where the outcome variable is the time until the occurrence of an event. The event can be a occurrence of a disease or death, etc. In R we compute the survival analysis with the `survival`

package. The function for Cox regression analysis is `coxph()`

. I will use the `veteran`

data which come with `survival`

package.

## Load survival package library(survival) # Load veteran data data(veteran) # Show first 6 rows head(veteran)trt celltype time status karno diagtime age prior 1 1 squamous 72 1 60 7 69 0 2 1 squamous 411 1 70 5 64 10 3 1 squamous 228 1 60 3 38 0 4 1 squamous 126 1 60 9 63 10 5 1 squamous 118 1 70 11 65 10 6 1 squamous 10 1 20 5 49 0# Data description help(veteran, package="survival")trt: 1=standard 2=test celltype: 1=squamous, 2=smallcell, 3=adeno, 4=large time: survival time status: censoring status karno: Karnofsky performance score (100=good) diagtime: months from diagnosis to randomisation age: in years prior: prior therapy 0=no, 1=yes

Now let say that we are interested to know the risk of dying (status) from different *cell type* (celltype) and *treatment* (trt) when we adjust for other variables (karno, age prior, diagtime).

This is the model:

# Fit the COX model fit = coxph(Surv(time, status) ~ age + celltype + prior + karno + diagtime + trt, data=veteran)

And the output:

summary(fit)Call: coxph(formula = Surv(time, status) ~ age + celltype + prior + karno + diagtime + trt, data = veteran) n= 137, number of events= 128 coef exp(coef) se(coef) z Pr(>|z|) age -8.706e-03 9.913e-01 9.300e-03 -0.936 0.34920 celltypesmallcell 8.616e-01 2.367e+00 2.753e-01 3.130 0.00175 ** celltypeadeno 1.196e+00 3.307e+00 3.009e-01 3.975 7.05e-05 *** celltypelarge 4.013e-01 1.494e+00 2.827e-01 1.420 0.15574 prior 7.159e-03 1.007e+00 2.323e-02 0.308 0.75794 karno -3.282e-02 9.677e-01 5.508e-03 -5.958 2.55e-09 *** diagtime 8.132e-05 1.000e+00 9.136e-03 0.009 0.99290 trt 2.946e-01 1.343e+00 2.075e-01 1.419 0.15577 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 exp(coef) exp(-coef) lower .95 upper .95 age 0.9913 1.0087 0.9734 1.0096 celltypesmallcell 2.3669 0.4225 1.3799 4.0597 celltypeadeno 3.3071 0.3024 1.8336 5.9647 celltypelarge 1.4938 0.6695 0.8583 2.5996 prior 1.0072 0.9929 0.9624 1.0541 karno 0.9677 1.0334 0.9573 0.9782 diagtime 1.0001 0.9999 0.9823 1.0182 trt 1.3426 0.7448 0.8939 2.0166 Concordance= 0.736 (se = 0.03 ) Rsquare= 0.364 (max possible= 0.999 ) Likelihood ratio test= 62.1 on 8 df, p=1.799e-10 Wald test = 62.37 on 8 df, p=1.596e-10 Score (logrank) test = 66.74 on 8 df, p=2.186e-11

As we see there are “a lot” of results. In manuscript we often report only the Hazard ratio and 95% Confidence interval and only for the variables of interest. For example in this case I am interested for the *cell types* and *treatment*. Note: I will not comment for the regression coefficients since is not the aim of this post.

### Prepare the table by creating the columns

# Prepare the columns HR <- round(exp(coef(fit)), 2) CI <- round(exp(confint(fit)), 2) # Names the columns of CI colnames(CI) <- c("Lower", "Higher") # Bind columns together as dataset table2 <- as.data.frame(cbind(HR, CI)) table2HR Lower Higher age 0.99 0.97 1.01 celltypesmallcell 2.37 1.38 4.06 celltypeadeno 3.31 1.83 5.96 celltypelarge 1.49 0.86 2.60 prior 1.01 0.96 1.05 karno 0.97 0.96 0.98 diagtime 1.00 0.98 1.02 trt 1.34 0.89 2.02

### Select variables of interest from the table

As I mentioned earlier, I am interested only for 2 variables (cell type and treatment). With the code below I will select those variables.

# select variables you want to present in table table2 <- table2[c("celltypesmallcell","celltypeadeno","celltypelarge","trt"),] table2HR Lower Higher celltypesmallcell 2.37 1.38 4.06 celltypeadeno 3.31 1.83 5.96 celltypelarge 1.49 0.86 2.60 trt 1.34 0.89 2.02

### Format the table

In the manuscript we present the confidence intervals within brackets. Therefore, with the code below I will add the brackets.

# add brackes and line for later use in table table2$a <- "("; table2$b <- "-"; table2$c <- ")" # order the columns table2 <- table2[,c("HR","a","Lower","b","Higher","c")] table2HR a Lower b Higher c celltypesmallcell 2.37 ( 1.38 - 4.06 ) celltypeadeno 3.31 ( 1.83 - 5.96 ) celltypelarge 1.49 ( 0.86 - 2.60 ) trt 1.34 ( 0.89 - 2.02 )

### Finalize the table and make it ready for Microsoft Word

The table is almost ready, now I will merge in one column by using package `tidyr`

with function `unite()`

.

# Merge all columns in one library(tidyr) table2 = unite(table2, "HR (95%CI)", c(HR, a, Lower, b, Higher, c), sep = "", remove=T) # add space between the estimates of HR and CI table2[,1] <- gsub("\(", " (", table2[,1]) table2HR (95%CI) celltypesmallcell 2.37 (1.38-4.06) celltypeadeno 3.31 (1.83-5.96) celltypelarge 1.49 (0.86-2.6) trt 1.34 (0.89-2.02)

## Export Table from R to Microsoft Word

To export table from R to Microsoft Word I will use the function `FlexTable()`

from the package ReporteRs. I found a very good script in StackOverflow to achieve this task. I am sharing the code below. (Credits to the author in StackOverflow).

# Load the packages library(ReporteRs) library(magrittr) # The script docx( ) %>% addFlexTable(table2 %>% FlexTable(header.cell.props = cellProperties( background.color = "#003366"), header.text.props = textBold(color = "white"), add.rownames = TRUE ) %>% setZebraStyle(odd = "#DDDDDD", even = "#FFFFFF")) %>% writeDoc(file = "table2.docx")

This is the table in Microsoft Word:

If you have any comment or feedback feel free to post a comment below.

Related Post

- Learn R by Intensive Practice
- Learn R from the Ground Up
- Table 1 and the Characteristics of Study Population
- Learn R From Scratch – Part 3
- Learn R From Scratch – Part 2

**leave a comment**for the author, please follow the link and comment on their blog:

**DataScience+**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...