I. Basic Table Printing

Base

head(iris)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

printr

library(printr)
head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
5.1 3.5 1.4 0.2 setosa
4.9 3.0 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5.0 3.6 1.4 0.2 setosa
5.4 3.9 1.7 0.4 setosa 
unloadNamespace('printr')

II. Descriptive Stats

Base

summary(iris)
##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
##

printr

library(printr)
summary(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100 setosa :50
1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300 versicolor:50
Median :5.800 Median :3.000 Median :4.350 Median :1.300 virginica :50
Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199 NA
3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800 NA
Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500 NA 
unloadNamespace('printr')

knitr::kable

knitr::kable(summary(iris),digits = 2)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100 setosa :50
1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300 versicolor:50
Median :5.800 Median :3.000 Median :4.350 Median :1.300 virginica :50
Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199 NA
3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800 NA
Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500 NA

stargazer

library(stargazer)
stargazer(iris, type='html')
Statistic N Mean St. Dev. Min Pctl(25) Pctl(75) Max
Sepal.Length 150 5.843 0.828 4.300 5.100 6.400 7.900
Sepal.Width 150 3.057 0.436 2.000 2.800 3.300 4.400
Petal.Length 150 3.758 1.765 1.000 1.600 5.100 6.900
Petal.Width 150 1.199 0.762 0.100 0.300 1.800 2.500

summarytools

library(summarytools)
dfSummary(iris, style='grid', plain.ascii = FALSE, graph.col = FALSE)

Data Frame Summary

iris

Dimensions: 150 x 5
Duplicates: 1

No Variable Stats / Values Freqs (% of Valid) Valid Missing

1

Sepal.Length
[numeric]

Mean (sd) : 5.8 (0.8)
min < med < max:
4.3 < 5.8 < 7.9
IQR (CV) : 1.3 (0.1)

35 distinct values

150
(100%)

0
(0%)

2

Sepal.Width
[numeric]

Mean (sd) : 3.1 (0.4)
min < med < max:
2 < 3 < 4.4
IQR (CV) : 0.5 (0.1)

23 distinct values

150
(100%)

0
(0%)

3

Petal.Length
[numeric]

Mean (sd) : 3.8 (1.8)
min < med < max:
1 < 4.3 < 6.9
IQR (CV) : 3.5 (0.5)

43 distinct values

150
(100%)

0
(0%)

4

Petal.Width
[numeric]

Mean (sd) : 1.2 (0.8)
min < med < max:
0.1 < 1.3 < 2.5
IQR (CV) : 1.5 (0.6)

22 distinct values

150
(100%)

0
(0%)

5

Species
[factor]

1. setosa
2. versicolor
3. virginica

50 (33.3%)
50 (33.3%)
50 (33.3%)

150
(100%)

0
(0%)

III. Models

Base

summary(linear.1)
## 
## Call:
## lm(formula = rating ~ complaints + privileges + learning + raises + 
##     critical, data = attitude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.9102  -5.2839   0.6959   5.8278  11.3886 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 11.01113   11.70394   0.941 0.356178    
## complaints   0.69205    0.14886   4.649 0.000101 ***
## privileges  -0.10356    0.13473  -0.769 0.449591    
## learning     0.24906    0.15962   1.560 0.131768    
## raises      -0.03346    0.20228  -0.165 0.869999    
## critical     0.01549    0.14725   0.105 0.917104    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.139 on 24 degrees of freedom
## Multiple R-squared:  0.7154, Adjusted R-squared:  0.6561 
## F-statistic: 12.06 on 5 and 24 DF,  p-value: 6.497e-06

printr

library(printr)
summary(linear.1)
## 
## Call:
## lm(formula = rating ~ complaints + privileges + learning + raises + 
##     critical, data = attitude)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -10.9102  -5.2839   0.6959   5.8278  11.3886 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 11.01113   11.70394   0.941 0.356178    
## complaints   0.69205    0.14886   4.649 0.000101 ***
## privileges  -0.10356    0.13473  -0.769 0.449591    
## learning     0.24906    0.15962   1.560 0.131768    
## raises      -0.03346    0.20228  -0.165 0.869999    
## critical     0.01549    0.14725   0.105 0.917104    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.139 on 24 degrees of freedom
## Multiple R-squared:  0.7154, Adjusted R-squared:  0.6561 
## F-statistic: 12.06 on 5 and 24 DF,  p-value: 6.497e-06
unloadNamespace('printr')

stargazer

library(stargazer)

stargazer( linear.1 , linear.2 , probit.model
 , title = "Results"
 , align = TRUE
 , type = 'html'
 , keep.stat=c("n","ser","adj.rsq"))
Results
Dependent variable:
rating high.rating
OLS probit
(1) (2) (3)
complaints 0.692*** 0.682***
(0.149) (0.129)
privileges -0.104 -0.103
(0.135) (0.129)
learning 0.249 0.238* 0.164***
(0.160) (0.139) (0.053)
raises -0.033
(0.202)
critical 0.015 -0.001
(0.147) (0.044)
advance -0.062
(0.042)
Constant 11.011 11.258 -7.476**
(11.704) (7.318) (3.570)
Observations 30 30 30
Adjusted R2 0.656 0.682
Residual Std. Error 7.139 (df = 24) 6.863 (df = 26)
Note: p<0.1; p<0.05; p<0.01