kable(x, format, digits = getOption(“digits”), row.names = NA, col.names = NA, align, caption = NULL, label = NULL, format.args = list(), escape = TRUE, …)
The function kable is a simple table generator, from library knitr
knitr::kable(mtcars,
digits = 2,
caption = "table 1 caption")| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Mazda RX4 | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.62 | 16.46 | 0 | 1 | 4 | 4 |
| Mazda RX4 Wag | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.88 | 17.02 | 0 | 1 | 4 | 4 |
| Datsun 710 | 22.8 | 4 | 108.0 | 93 | 3.85 | 2.32 | 18.61 | 1 | 1 | 4 | 1 |
| Hornet 4 Drive | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.21 | 19.44 | 1 | 0 | 3 | 1 |
| Hornet Sportabout | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.44 | 17.02 | 0 | 0 | 3 | 2 |
| Valiant | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.46 | 20.22 | 1 | 0 | 3 | 1 |
| Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.57 | 15.84 | 0 | 0 | 3 | 4 |
| Merc 240D | 24.4 | 4 | 146.7 | 62 | 3.69 | 3.19 | 20.00 | 1 | 0 | 4 | 2 |
| Merc 230 | 22.8 | 4 | 140.8 | 95 | 3.92 | 3.15 | 22.90 | 1 | 0 | 4 | 2 |
| Merc 280 | 19.2 | 6 | 167.6 | 123 | 3.92 | 3.44 | 18.30 | 1 | 0 | 4 | 4 |
| Merc 280C | 17.8 | 6 | 167.6 | 123 | 3.92 | 3.44 | 18.90 | 1 | 0 | 4 | 4 |
| Merc 450SE | 16.4 | 8 | 275.8 | 180 | 3.07 | 4.07 | 17.40 | 0 | 0 | 3 | 3 |
| Merc 450SL | 17.3 | 8 | 275.8 | 180 | 3.07 | 3.73 | 17.60 | 0 | 0 | 3 | 3 |
| Merc 450SLC | 15.2 | 8 | 275.8 | 180 | 3.07 | 3.78 | 18.00 | 0 | 0 | 3 | 3 |
| Cadillac Fleetwood | 10.4 | 8 | 472.0 | 205 | 2.93 | 5.25 | 17.98 | 0 | 0 | 3 | 4 |
| Lincoln Continental | 10.4 | 8 | 460.0 | 215 | 3.00 | 5.42 | 17.82 | 0 | 0 | 3 | 4 |
| Chrysler Imperial | 14.7 | 8 | 440.0 | 230 | 3.23 | 5.34 | 17.42 | 0 | 0 | 3 | 4 |
| Fiat 128 | 32.4 | 4 | 78.7 | 66 | 4.08 | 2.20 | 19.47 | 1 | 1 | 4 | 1 |
| Honda Civic | 30.4 | 4 | 75.7 | 52 | 4.93 | 1.62 | 18.52 | 1 | 1 | 4 | 2 |
| Toyota Corolla | 33.9 | 4 | 71.1 | 65 | 4.22 | 1.84 | 19.90 | 1 | 1 | 4 | 1 |
| Toyota Corona | 21.5 | 4 | 120.1 | 97 | 3.70 | 2.46 | 20.01 | 1 | 0 | 3 | 1 |
| Dodge Challenger | 15.5 | 8 | 318.0 | 150 | 2.76 | 3.52 | 16.87 | 0 | 0 | 3 | 2 |
| AMC Javelin | 15.2 | 8 | 304.0 | 150 | 3.15 | 3.44 | 17.30 | 0 | 0 | 3 | 2 |
| Camaro Z28 | 13.3 | 8 | 350.0 | 245 | 3.73 | 3.84 | 15.41 | 0 | 0 | 3 | 4 |
| Pontiac Firebird | 19.2 | 8 | 400.0 | 175 | 3.08 | 3.85 | 17.05 | 0 | 0 | 3 | 2 |
| Fiat X1-9 | 27.3 | 4 | 79.0 | 66 | 4.08 | 1.94 | 18.90 | 1 | 1 | 4 | 1 |
| Porsche 914-2 | 26.0 | 4 | 120.3 | 91 | 4.43 | 2.14 | 16.70 | 0 | 1 | 5 | 2 |
| Lotus Europa | 30.4 | 4 | 95.1 | 113 | 3.77 | 1.51 | 16.90 | 1 | 1 | 5 | 2 |
| Ford Pantera L | 15.8 | 8 | 351.0 | 264 | 4.22 | 3.17 | 14.50 | 0 | 1 | 5 | 4 |
| Ferrari Dino | 19.7 | 6 | 145.0 | 175 | 3.62 | 2.77 | 15.50 | 0 | 1 | 5 | 6 |
| Maserati Bora | 15.0 | 8 | 301.0 | 335 | 3.54 | 3.57 | 14.60 | 0 | 1 | 5 | 8 |
| Volvo 142E | 21.4 | 4 | 121.0 | 109 | 4.11 | 2.78 | 18.60 | 1 | 1 | 4 | 2 |
datatable(data, options = list(), class = “display”, callback = JS(“return table;”), rownames, colnames, container, caption = NULL, filter = c(“none”, “bottom”, “top”), escape = TRUE, style = “default”, width = NULL, height = NULL, elementId = NULL, fillContainer = getOption(“DT.fillContainer”, NULL), autoHideNavigation = getOption(“DT.autoHideNavigation”, NULL), selection = c(“multiple”, “single”, “none”), extensions = list(), plugins = NULL, editable = FALSE)
library(DT)
longley <- data.frame(longley)
datatable(longley, rownames = FALSE, filter="top", options = list(pageLength = 6, scrollX=T) )tibble(…, .rows = NULL, .name_repair = c(“check_unique”, “unique”, “universal”, “minimal”))
library(tibble)
as_tibble(longley)## # A tibble: 16 x 7
## GNP.deflator GNP Unemployed Armed.Forces Population Year Employed
## <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl>
## 1 83 234. 236. 159 108. 1947 60.3
## 2 88.5 259. 232. 146. 109. 1948 61.1
## 3 88.2 258. 368. 162. 110. 1949 60.2
## 4 89.5 285. 335. 165 111. 1950 61.2
## 5 96.2 329. 210. 310. 112. 1951 63.2
## 6 98.1 347. 193. 359. 113. 1952 63.6
## 7 99 365. 187 355. 115. 1953 65.0
## 8 100 363. 358. 335 116. 1954 63.8
## 9 101. 397. 290. 305. 117. 1955 66.0
## 10 105. 419. 282. 286. 119. 1956 67.9
## 11 108. 443. 294. 280. 120. 1957 68.2
## 12 111. 445. 468. 264. 122. 1958 66.5
## 13 113. 483. 381. 255. 123. 1959 68.7
## 14 114. 503. 393. 251. 125. 1960 69.6
## 15 116. 518. 481. 257. 128. 1961 69.3
## 16 117. 555. 401. 283. 130. 1962 70.6tibble(longley$GNP, longley$Unemployed, total = longley$GNP + longley$Unemployed)## # A tibble: 16 x 3
## `longley$GNP` `longley$Unemployed` total
## <dbl> <dbl> <dbl>
## 1 234. 236. 470.
## 2 259. 232. 492.
## 3 258. 368. 626.
## 4 285. 335. 620.
## 5 329. 210. 539.
## 6 347. 193. 540.
## 7 365. 187 552.
## 8 363. 358. 721.
## 9 397. 290. 688.
## 10 419. 282. 701.
## 11 443. 294. 736.
## 12 445. 468. 913.
## 13 483. 381. 864.
## 14 503. 393. 896.
## 15 518. 481. 999.
## 16 555. 401. 956.rmarkdown::paged_table(mtcars)xtable(x, caption = NULL, label = NULL, align = NULL, digits = NULL, display = NULL, auto = FALSE, …)
library(xtable)
f1 <- aov(Year ~ GNP + Unemployed + Population, data = longley) #latex input
xtable(f1)## % latex table generated in R 3.6.1 by xtable 1.8-4 package
## % Sat Aug 24 09:14:03 2019
## \begin{table}[ht]
## \centering
## \begin{tabular}{lrrrrr}
## \hline
## & Df & Sum Sq & Mean Sq & F value & Pr($>$F) \\
## \hline
## GNP & 1 & 336.79 & 336.79 & 6298.74 & 0.0000 \\
## Unemployed & 1 & 2.39 & 2.39 & 44.76 & 0.0000 \\
## Population & 1 & 0.17 & 0.17 & 3.20 & 0.0987 \\
## Residuals & 12 & 0.64 & 0.05 & & \\
## \hline
## \end{tabular}
## \end{table}stargazer( …, type = “latex”, title = “”, style = “default”, summary = NULL, out = NULL, out.header = FALSE, column.labels = NULL, column.separate = NULL, covariate.labels = NULL, dep.var.caption = NULL, dep.var.labels = NULL, dep.var.labels.include = TRUE, align = FALSE, coef = NULL, se = NULL, t = NULL, p = NULL, t.auto = TRUE, p.auto = TRUE, ci = FALSE, ci.custom = NULL, ci.level = 0.95, ci.separator = NULL, add.lines = NULL, apply.coef = NULL, apply.se = NULL, summary.logical = TRUE, summary.stat = NULL, nobs = TRUE, mean.sd = TRUE, min.max = TRUE, median = FALSE, iqr = FALSE )
library(stargazer)
f1 <- lm(mpg ~ wt, mtcars) #2 OLS models
f2 <- lm(mpg ~ wt + hp, mtcars)
f3 <- lm(mpg ~ wt + hp + cyl, mtcars)
stargazer(f1, f2, f3, type = 'html')| Dependent variable: | |||
| mpg | |||
| (1) | (2) | (3) | |
| wt | -5.344*** | -3.878*** | -3.167*** |
| (0.559) | (0.633) | (0.741) | |
| hp | -0.032*** | -0.018 | |
| (0.009) | (0.012) | ||
| cyl | -0.942* | ||
| (0.551) | |||
| Constant | 37.285*** | 37.227*** | 38.752*** |
| (1.878) | (1.599) | (1.787) | |
| Observations | 32 | 32 | 32 |
| R2 | 0.753 | 0.827 | 0.843 |
| Adjusted R2 | 0.745 | 0.815 | 0.826 |
| Residual Std. Error | 3.046 (df = 30) | 2.593 (df = 29) | 2.512 (df = 28) |
| F Statistic | 91.375*** (df = 1; 30) | 69.211*** (df = 2; 29) | 50.171*** (df = 3; 28) |
| Note: | p<0.1; p<0.05; p<0.01 | ||
library(stargazer)
f1 <- lm(mpg ~ wt, mtcars) #2 OLS models
f2 <- lm(mpg ~ wt + hp, mtcars)
f3 <- lm(mpg ~ wt + hp + cyl, mtcars)
stargazer(f1, f2, f3, type = 'html')| Dependent variable: | |||
| mpg | |||
| (1) | (2) | (3) | |
| wt | -5.344*** | -3.878*** | -3.167*** |
| (0.559) | (0.633) | (0.741) | |
| hp | -0.032*** | -0.018 | |
| (0.009) | (0.012) | ||
| cyl | -0.942* | ||
| (0.551) | |||
| Constant | 37.285*** | 37.227*** | 38.752*** |
| (1.878) | (1.599) | (1.787) | |
| Observations | 32 | 32 | 32 |
| R2 | 0.753 | 0.827 | 0.843 |
| Adjusted R2 | 0.745 | 0.815 | 0.826 |
| Residual Std. Error | 3.046 (df = 30) | 2.593 (df = 29) | 2.512 (df = 28) |
| F Statistic | 91.375*** (df = 1; 30) | 69.211*** (df = 2; 29) | 50.171*** (df = 3; 28) |
| Note: | p<0.1; p<0.05; p<0.01 | ||
correlation.matrix <- cor(mtcars) #correlation matrix
stargazer(correlation.matrix, type="html")| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
| mpg | 1 | -0.852 | -0.848 | -0.776 | 0.681 | -0.868 | 0.419 | 0.664 | 0.600 | 0.480 | -0.551 |
| cyl | -0.852 | 1 | 0.902 | 0.832 | -0.700 | 0.782 | -0.591 | -0.811 | -0.523 | -0.493 | 0.527 |
| disp | -0.848 | 0.902 | 1 | 0.791 | -0.710 | 0.888 | -0.434 | -0.710 | -0.591 | -0.556 | 0.395 |
| hp | -0.776 | 0.832 | 0.791 | 1 | -0.449 | 0.659 | -0.708 | -0.723 | -0.243 | -0.126 | 0.750 |
| drat | 0.681 | -0.700 | -0.710 | -0.449 | 1 | -0.712 | 0.091 | 0.440 | 0.713 | 0.700 | -0.091 |
| wt | -0.868 | 0.782 | 0.888 | 0.659 | -0.712 | 1 | -0.175 | -0.555 | -0.692 | -0.583 | 0.428 |
| qsec | 0.419 | -0.591 | -0.434 | -0.708 | 0.091 | -0.175 | 1 | 0.745 | -0.230 | -0.213 | -0.656 |
| vs | 0.664 | -0.811 | -0.710 | -0.723 | 0.440 | -0.555 | 0.745 | 1 | 0.168 | 0.206 | -0.570 |
| am | 0.600 | -0.523 | -0.591 | -0.243 | 0.713 | -0.692 | -0.230 | 0.168 | 1 | 0.794 | 0.058 |
| gear | 0.480 | -0.493 | -0.556 | -0.126 | 0.700 | -0.583 | -0.213 | 0.206 | 0.794 | 1 | 0.274 |
| carb | -0.551 | 0.527 | 0.395 | 0.750 | -0.091 | 0.428 | -0.656 | -0.570 | 0.058 | 0.274 | 1 |