Do all of this in markdown
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.4 v dplyr 1.0.7
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.1.1 v forcats 0.5.1
## Warning: package 'tidyr' was built under R version 4.1.2
## Warning: package 'readr' was built under R version 4.1.2
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
head(mpg)
## # A tibble: 6 x 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto(l5) f 18 29 p compa~
## 2 audi a4 1.8 1999 4 manual(m5) f 21 29 p compa~
## 3 audi a4 2 2008 4 manual(m6) f 20 31 p compa~
## 4 audi a4 2 2008 4 auto(av) f 21 30 p compa~
## 5 audi a4 2.8 1999 6 auto(l5) f 16 26 p compa~
## 6 audi a4 2.8 1999 6 manual(m5) f 18 26 p compa~
ggplot(mpg, aes(cty, displ)) +
geom_point() +
geom_smooth(method = "lm", se = F)
## `geom_smooth()` using formula 'y ~ x'
library(kableExtra)
## Warning: package 'kableExtra' was built under R version 4.1.2
##
## Attaching package: 'kableExtra'
## The following object is masked from 'package:dplyr':
##
## group_rows
m1 <- lm(displ ~ cty, data = mpg)
d2 <- coef(summary(m1))
row.names(d2) <- c("Intercept", "City MPG")
knitr::kable(d2, caption = "Table 1. Cty MPG vs displacement", col.names = c("Estimate", "Std. Error", "t value", "p value")) %>%
kable_classic(full_width = F, html_font = "Cambria", position = "left")
Table 1. Cty MPG vs displacement
|
|
Estimate
|
Std. Error
|
t value
|
p value
|
|
Intercept
|
7.5584856
|
0.2085784
|
36.23810
|
0
|
|
City MPG
|
-0.2424045
|
0.0119972
|
-20.20515
|
0
|