Tables

1. Using the mpg data, create a data frame called my_mpg that capitalizes the manufacturer.

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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✔ forcats   1.0.0     ✔ stringr   1.5.1
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✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

my_mpg <- mpg |> 
  mutate(manufacturer = str_to_sentence(manufacturer))

2. Calculate mean highway and city fuel efficiency for each manufacturer and return a table with the caption “Highway and city fuel efficiency” and column names “Manufacturer”, “Highway”, and “City”.

library(knitr)
my_mpg |> 
  summarise(mean_hwy = mean(hwy), mean_cty = mean(cty), .by = c(manufacturer)) |> 
  kable(digits = 1, 
        col.names = c("Manufacturer", "Highway", "City"),
        caption = "Highway and city fuel efficiency")

Highway and city fuel efficiency

Manufacturer	Highway	City
Audi	26.4	17.6
Chevrolet	21.9	15.0
Dodge	17.9	13.1
Ford	19.4	14.0
Honda	32.6	24.4
Hyundai	26.9	18.6
Jeep	17.6	13.5
Land rover	16.5	11.5
Lincoln	17.0	11.3
Mercury	18.0	13.2
Nissan	24.6	18.1
Pontiac	26.4	17.0
Subaru	25.6	19.3
Toyota	24.9	18.5
Volkswagen	29.2	20.9

3. Repeat the table from #2 but add a header that spans Highway and City that says “Fuel efficiency”. Reminder, you’ll need to load {kableExtra} to do this.

library(kableExtra)
my_mpg |> 
  summarise(mean_hwy = mean(hwy), mean_cty = mean(cty), .by = c(manufacturer)) |> 
  kable(digits = 1, 
        col.names = c("Manufacturer", "Highway", "City"),
        caption = "Highway and city fuel efficiency") |> 
  add_header_above(c(" " = 1, "Fuel efficiency" = 2))

Highway and city fuel efficiency

	Fuel efficiency
Manufacturer	Highway	City
Audi	26.4	17.6
Chevrolet	21.9	15.0
Dodge	17.9	13.1
Ford	19.4	14.0
Honda	32.6	24.4
Hyundai	26.9	18.6
Jeep	17.6	13.5
Land rover	16.5	11.5
Lincoln	17.0	11.3
Mercury	18.0	13.2
Nissan	24.6	18.1
Pontiac	26.4	17.0
Subaru	25.6	19.3
Toyota	24.9	18.5
Volkswagen	29.2	20.9

4. Calculate mean highway and city fuel efficiency for each manufacturer and year. Order the data frame by year, then remove the year column and add labels for each year that spans the rows for each year. Also, add a footnote that says “Source: mpg data set.”.

my_mpg |> 
  summarise(mean_hwy = mean(hwy), mean_cty = mean(cty), .by = c(year, manufacturer)) |> 
  arrange(year) |> 
  select(-year) |> 
  kable(digits = 1, 
        col.names = c("Manufacturer", "Highway", "City"),
        caption = "Highway and city fuel efficiency") |> 
  pack_rows("1999", 1, 15) |> 
  pack_rows("2008", 16, 30) |> 
  footnote(general = "Source: mpg data set.")

Highway and city fuel efficiency

Manufacturer	Highway	City
1999
Audi	26.1	17.1
Chevrolet	21.6	15.1
Dodge	18.4	13.4
Ford	18.6	13.9
Honda	31.6	24.8
Hyundai	26.7	18.3
Jeep	18.5	14.5
Land rover	15.0	11.0
Lincoln	16.5	11.0
Mercury	17.0	13.5
Nissan	23.5	17.7
Pontiac	26.3	17.0
Subaru	25.5	19.0
Toyota	24.0	18.1
Volkswagen	29.7	21.2
2008
Audi	26.8	18.1
Chevrolet	22.1	14.9
Dodge	17.6	13.0
Ford	20.5	14.1
Honda	33.8	24.0
Hyundai	27.0	18.9
Jeep	17.3	13.2
Land rover	18.0	12.0
Lincoln	18.0	12.0
Mercury	19.0	13.0
Nissan	25.6	18.4
Pontiac	26.5	17.0
Subaru	25.6	19.5
Toyota	26.1	19.1
Volkswagen	28.5	20.5
Note:
Source: mpg data set.

5. Conduct a linear regression called my_model of the effects of displacement, cylinder, and year on highway fuel efficiency: lm(hwy ~ displ + cyl + year, my_mpg). Apply the summary() function to the model object, then return the coefficients table from the summary.

my_model <- lm(hwy ~ displ + cyl + year, my_mpg)
my_model_summary <- summary(my_model)$coefficients
kable(my_model_summary)

	Estimate	Std. Error	t value	Pr(>|t|)
(Intercept)	-259.1221270	109.1594125	-2.373795	0.0184288
displ	-2.0912178	0.5146232	-4.063590	0.0000663
cyl	-1.3065333	0.4111205	-3.177982	0.0016866
year	0.1484984	0.0545146	2.724013	0.0069446

6. Extract the table after applying {papaja}’s apa_print() to my_model, then pass this to apa_table().

library(papaja)
apa_table(apa_print(my_model)$table)

(#tab:unnamed-chunk-6)

Predictor	\(b\)	95% CI	\(t\)	\(\mathit{df}\)	\(p\)
Intercept	-259.12	[-474.20, -44.04]	-2.37	230	.018
Displ	-2.09	[-3.11, -1.08]	-4.06	230	< .001
Cyl	-1.31	[-2.12, -0.50]	-3.18	230	.002
Year	0.15	[0.04, 0.26]	2.72	230	.007