Baseline characteristics

Tip

To run the code from this article as a Python script:

python3 examples/example-baseline.py

This article reproduces vignettes/example-basechar.Rmd in the r2rtf package.

Imports

from importlib.resources import files

import pandas as pd

import rtflite as rtf

Ingest data

Load data from CSV file:

data_path = files("rtflite.data").joinpath("baseline.csv")
df = pd.read_csv(data_path, na_filter=False)
print(df)

                   var         1 1_pct         2 2_pct         3 3_pct      9999 9999_pct    var_label
0               Female        53  10.4        50   9.8        40   7.9       143     28.1       Gender
1                 Male        33   6.5        34   6.7        44   8.7       111     21.9       Gender
2                  <65        14   2.8         8   1.6        11   2.2        33      6.5  Age (Years)
3                65-80        42   8.3        47   9.3        55  10.8       144     28.3  Age (Years)
4                  >80        30   5.9        29   5.7        18   3.5        77     15.2  Age (Years)
5                                                                                          Age (Years)
6   Subjects with data        86              84              84             254           Age (Years)
7                 Mean      75.2            75.7            74.4            75.1           Age (Years)
8                   SD       8.6             8.3             7.9             8.2           Age (Years)
9               Median      76.0            77.5            76.0            77.0           Age (Years)
10               Range  52 to 89        51 to 88        56 to 88        51 to 89           Age (Years)
11               White        78  15.4        78  15.4        74  14.6       230     45.3         Race
12               Black         8   1.6         6   1.2         9   1.8        23      4.5         Race
13               Other         0   0.0         0   0.0         1   0.2         1      0.2         Race

Create header data frames:

header1 = pd.DataFrame([["", "Placebo", "Drug Low Dose", "Drug High Dose", "Total"]])
header2 = pd.DataFrame([["", "n", "(%)", "n", "(%)", "n", "(%)", "n", "(%)"]])

Compose RTF

Create RTF document:

doc = rtf.RTFDocument(
    df=df,
    rtf_title=rtf.RTFTitle(
        text=["Demographic and Anthropometric Characteristics", "ITT Subjects"]
    ),
    rtf_column_header=[
        rtf.RTFColumnHeader(df=header1, col_rel_width=[3] + [2] * 4),
        rtf.RTFColumnHeader(
            df=header2,
            col_rel_width=[3] + [1.2, 0.8] * 4,
            border_top=[""] + ["single"] * 8,
            border_left=["single"] + ["single", ""] * 4,
        ),
    ],
    rtf_body=rtf.RTFBody(
        page_by=["var_label"],
        col_rel_width=[3] + [1.2, 0.8] * 4 + [3],
        text_justification=["l"] + ["c"] * 8 + ["l"],
        text_format=[""] * 9 + ["b"],
        border_left=["single"] + ["single", ""] * 4 + ["single"],
        border_top=[""] * 9 + ["single"],
        border_bottom=[""] * 9 + ["single"],
    ),
)

doc.write_rtf("output.rtf")

Convert to PDF

converter = rtf.LibreOfficeConverter()
converter.convert(
    input_files="output.rtf", output_dir=".", format="pdf", overwrite=True
)