Easy Examples
Here are just a couple examples of tables that TexTables makes extremely easy to produce and export. These are mostly proof of concept: TexTables provides a backend that makes the code to write these convenience methods extremely compact.
Regression Tables
using TexTables, StatsModels, GLM, RDatasets
df = dataset("datasets", "attitude");
m1 = lm(@formula( Rating ~ 1 + Raises ), df);
m2 = lm(@formula( Rating ~ 1 + Raises + Learning), df);
m3 = lm(@formula( Rating ~ 1 + Raises + Learning + Privileges), df);
m4 = lm(@formula( Rating ~ 1 + Raises + Learning + Privileges
+ Complaints), df);
m5 = lm(@formula( Rating ~ 1 + Raises + Learning + Privileges
+ Complaints + Critical), df);
table = regtable(m1, m2, m3, m4, m5)
# output
| (1) | (2) | (3) | (4) | (5)
-------------------------------------------------------------------
(Intercept) | 19.978* | 15.809 | 14.167 | 11.834 | 11.011
| (11.688) | (11.084) | (11.519) | (8.535) | (11.704)
Raises | 0.691*** | 0.379* | 0.352 | -0.026 | -0.033
| (0.179) | (0.217) | (0.224) | (0.184) | (0.202)
Learning | | 0.432** | 0.394* | 0.246 | 0.249
| | (0.193) | (0.204) | (0.154) | (0.160)
Privileges | | | 0.105 | -0.103 | -0.104
| | | (0.168) | (0.132) | (0.135)
Complaints | | | | 0.691*** | 0.692***
| | | | (0.146) | (0.149)
Critical | | | | | 0.015
| | | | | (0.147)
-------------------------------------------------------------------
N | 30 | 30 | 30 | 30 | 30
$R^2$ | 0.348 | 0.451 | 0.459 | 0.715 | 0.715Grouped Regression Tables
We can add a add a hierarchical structure by passing the model objects as pairs of Strings/Symbols and model objects/tuples of model objects:
grouped_table = regtable( "Group 1"=>(m1,m2,m3),
"Group 2"=>(m4, m5))
# output
| Group 1 | Group 2
| (1) | (2) | (3) | (4) | (5)
-------------------------------------------------------------------
(Intercept) | 19.978* | 15.809 | 14.167 | 11.834 | 11.011
| (11.688) | (11.084) | (11.519) | (8.535) | (11.704)
Raises | 0.691*** | 0.379* | 0.352 | -0.026 | -0.033
| (0.179) | (0.217) | (0.224) | (0.184) | (0.202)
Learning | | 0.432** | 0.394* | 0.246 | 0.249
| | (0.193) | (0.204) | (0.154) | (0.160)
Privileges | | | 0.105 | -0.103 | -0.104
| | | (0.168) | (0.132) | (0.135)
Complaints | | | | 0.691*** | 0.692***
| | | | (0.146) | (0.149)
Critical | | | | | 0.015
| | | | | (0.147)
-------------------------------------------------------------------
N | 30 | 30 | 30 | 30 | 30
$R^2$ | 0.348 | 0.451 | 0.459 | 0.715 | 0.715
Exporting to Latex
All of these commands return subtypes of the abstract TexTable type. Any TexTable can be printed as either an ascii table (as shown above) with the method to_ascii or as a latex table with the method to_tex:
to_tex(grouped_table) |> print
# output
\begin{tabular}{r|ccc|cc}
\toprule
& \multicolumn{3}{c}{Group 1} & \multicolumn{2}{c}{Group 2} \\
& (1) & (2) & (3) & (4) & (5) \\ \hline
(Intercept) & 19.978* & 15.809 & 14.167 & 11.834 & 11.011 \\
& (11.688) & (11.084) & (11.519) & (8.535) & (11.704) \\
Raises & 0.691*** & 0.379* & 0.352 & -0.026 & -0.033 \\
& (0.179) & (0.217) & (0.224) & (0.184) & (0.202) \\
Learning & & 0.432** & 0.394* & 0.246 & 0.249 \\
& & (0.193) & (0.204) & (0.154) & (0.160) \\
Privileges & & & 0.105 & -0.103 & -0.104 \\
& & & (0.168) & (0.132) & (0.135) \\
Complaints & & & & 0.691*** & 0.692*** \\
& & & & (0.146) & (0.149) \\
Critical & & & & & 0.015 \\
& & & & & (0.147) \\ \hline
N & 30 & 30 & 30 & 30 & 30 \\
$R^2$ & 0.348 & 0.451 & 0.459 & 0.715 & 0.715 \\
\bottomrule
\end{tabular}It's as simple as that. As you can see, higher level groupings will be separated with vertical bars, and their headings will be printed as \multicolumn environments. In tables with row-groupings, TexTables will automatically use \multirow environments. TableTex will automatically handle printing it in a way that is well aligned and can be read even from the raw tex file, and will align the multi-columns and multi-indexes for you.
You can write the table to a tex file yourself, or you can use the convenience wrapper write_tex(fpath::String, t::TexTable).
Summary Tables
Making summary tables is similarly easy:
df = dataset("datasets", "iris");
summarize(df)
# output
| Obs | Mean | Std. Dev. | Min | Max
------------------------------------------------------
SepalLength | 150 | 5.843 | 0.828 | 4.300 | 7.900
SepalWidth | 150 | 3.057 | 0.436 | 2.000 | 4.400
PetalLength | 150 | 3.758 | 1.765 | 1.000 | 6.900
PetalWidth | 150 | 1.199 | 0.762 | 0.100 | 2.500
Species | | | | |To choose only a subset of variables, and get a more detailed summary table:
summarize(df, [:SepalLength, :SepalWidth], detail=true)
# output
| Obs | Mean | Std. Dev. | Min | p10 | p25 | p50 | p75 | p90 | Max
----------------------------------------------------------------------------------------------
SepalLength | 150 | 5.843 | 0.828 | 4.300 | 4.800 | 5.100 | 5.800 | 6.400 | 6.900 | 7.900
SepalWidth | 150 | 3.057 | 0.436 | 2.000 | 2.500 | 2.800 | 3.000 | 3.300 | 3.610 | 4.400To group by another variable in the DataFrame, use the summarize_by function:
c1 = summarize_by(df, :Species, [:SepalLength, :SepalWidth])
# output
| | Obs | Mean | Std. Dev. | Min | Max
-------------------------------------------------------------------
setosa | SepalLength | 50 | 5.006 | 0.352 | 4.300 | 5.800
| SepalWidth | 50 | 3.428 | 0.379 | 2.300 | 4.400
-------------------------------------------------------------------
versicolor | SepalLength | 50 | 5.936 | 0.516 | 4.900 | 7.000
| SepalWidth | 50 | 2.770 | 0.314 | 2.000 | 3.400
-------------------------------------------------------------------
virginica | SepalLength | 50 | 6.588 | 0.636 | 4.900 | 7.900
| SepalWidth | 50 | 2.974 | 0.322 | 2.200 | 3.800