Effect size plot OLS

# Effect size plot OLS
## Using flipbookr and xaringan
### Me

---

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---

```r
*knitr::opts_chunk$set(echo = TRUE)
```
]
 
.panel2-setup-auto[

]

---
count: false

```r
knitr::opts_chunk$set(echo = TRUE)
*library(tidyverse)
```
]
 
.panel2-setup-auto[

]

---
count: false

```r
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
```
]
 
.panel2-setup-auto[

]

---

```r
*mtcars
```
]
 
.panel2-cars-auto[

```
                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
```
]

---
count: false

```r
mtcars %>%
* select(mpg, cyl, hp, disp)
```
]
 
.panel2-cars-auto[

```
                     mpg cyl  hp  disp
Mazda RX4           21.0   6 110 160.0
Mazda RX4 Wag       21.0   6 110 160.0
Datsun 710          22.8   4  93 108.0
Hornet 4 Drive      21.4   6 110 258.0
Hornet Sportabout   18.7   8 175 360.0
Valiant             18.1   6 105 225.0
Duster 360          14.3   8 245 360.0
Merc 240D           24.4   4  62 146.7
Merc 230            22.8   4  95 140.8
Merc 280            19.2   6 123 167.6
Merc 280C           17.8   6 123 167.6
Merc 450SE          16.4   8 180 275.8
Merc 450SL          17.3   8 180 275.8
Merc 450SLC         15.2   8 180 275.8
Cadillac Fleetwood  10.4   8 205 472.0
Lincoln Continental 10.4   8 215 460.0
Chrysler Imperial   14.7   8 230 440.0
Fiat 128            32.4   4  66  78.7
Honda Civic         30.4   4  52  75.7
Toyota Corolla      33.9   4  65  71.1
Toyota Corona       21.5   4  97 120.1
Dodge Challenger    15.5   8 150 318.0
AMC Javelin         15.2   8 150 304.0
Camaro Z28          13.3   8 245 350.0
Pontiac Firebird    19.2   8 175 400.0
Fiat X1-9           27.3   4  66  79.0
Porsche 914-2       26.0   4  91 120.3
Lotus Europa        30.4   4 113  95.1
Ford Pantera L      15.8   8 264 351.0
Ferrari Dino        19.7   6 175 145.0
Maserati Bora       15.0   8 335 301.0
Volvo 142E          21.4   4 109 121.0
```
]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
* remove_missing()
```
]
 
.panel2-cars-auto[

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
* mutate(mpg_sd = mpg/sd(mpg))
```
]
 
.panel2-cars-auto[

```
                     mpg cyl  hp  disp   mpg_sd
Mazda RX4           21.0   6 110 160.0 3.484351
Mazda RX4 Wag       21.0   6 110 160.0 3.484351
Datsun 710          22.8   4  93 108.0 3.783009
Hornet 4 Drive      21.4   6 110 258.0 3.550719
Hornet Sportabout   18.7   8 175 360.0 3.102731
Valiant             18.1   6 105 225.0 3.003178
Duster 360          14.3   8 245 360.0 2.372677
Merc 240D           24.4   4  62 146.7 4.048484
Merc 230            22.8   4  95 140.8 3.783009
Merc 280            19.2   6 123 167.6 3.185692
Merc 280C           17.8   6 123 167.6 2.953402
Merc 450SE          16.4   8 180 275.8 2.721112
Merc 450SL          17.3   8 180 275.8 2.870441
Merc 450SLC         15.2   8 180 275.8 2.522006
Cadillac Fleetwood  10.4   8 205 472.0 1.725583
Lincoln Continental 10.4   8 215 460.0 1.725583
Chrysler Imperial   14.7   8 230 440.0 2.439045
Fiat 128            32.4   4  66  78.7 5.375855
Honda Civic         30.4   4  52  75.7 5.044012
Toyota Corolla      33.9   4  65  71.1 5.624737
Toyota Corona       21.5   4  97 120.1 3.567311
Dodge Challenger    15.5   8 150 318.0 2.571783
AMC Javelin         15.2   8 150 304.0 2.522006
Camaro Z28          13.3   8 245 350.0 2.206755
Pontiac Firebird    19.2   8 175 400.0 3.185692
Fiat X1-9           27.3   4  66  79.0 4.529656
Porsche 914-2       26.0   4  91 120.3 4.313958
Lotus Europa        30.4   4 113  95.1 5.044012
Ford Pantera L      15.8   8 264 351.0 2.621559
Ferrari Dino        19.7   6 175 145.0 3.268653
Maserati Bora       15.0   8 335 301.0 2.488822
Volvo 142E          21.4   4 109 121.0 3.550719
```
]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
  mutate(mpg_sd = mpg/sd(mpg)) %>%
* mutate(cyl_sd = cyl/sd(cyl))
```
]
 
.panel2-cars-auto[

```
                     mpg cyl  hp  disp   mpg_sd  cyl_sd
Mazda RX4           21.0   6 110 160.0 3.484351 3.35961
Mazda RX4 Wag       21.0   6 110 160.0 3.484351 3.35961
Datsun 710          22.8   4  93 108.0 3.783009 2.23974
Hornet 4 Drive      21.4   6 110 258.0 3.550719 3.35961
Hornet Sportabout   18.7   8 175 360.0 3.102731 4.47948
Valiant             18.1   6 105 225.0 3.003178 3.35961
Duster 360          14.3   8 245 360.0 2.372677 4.47948
Merc 240D           24.4   4  62 146.7 4.048484 2.23974
Merc 230            22.8   4  95 140.8 3.783009 2.23974
Merc 280            19.2   6 123 167.6 3.185692 3.35961
Merc 280C           17.8   6 123 167.6 2.953402 3.35961
Merc 450SE          16.4   8 180 275.8 2.721112 4.47948
Merc 450SL          17.3   8 180 275.8 2.870441 4.47948
Merc 450SLC         15.2   8 180 275.8 2.522006 4.47948
Cadillac Fleetwood  10.4   8 205 472.0 1.725583 4.47948
Lincoln Continental 10.4   8 215 460.0 1.725583 4.47948
Chrysler Imperial   14.7   8 230 440.0 2.439045 4.47948
Fiat 128            32.4   4  66  78.7 5.375855 2.23974
Honda Civic         30.4   4  52  75.7 5.044012 2.23974
Toyota Corolla      33.9   4  65  71.1 5.624737 2.23974
Toyota Corona       21.5   4  97 120.1 3.567311 2.23974
Dodge Challenger    15.5   8 150 318.0 2.571783 4.47948
AMC Javelin         15.2   8 150 304.0 2.522006 4.47948
Camaro Z28          13.3   8 245 350.0 2.206755 4.47948
Pontiac Firebird    19.2   8 175 400.0 3.185692 4.47948
Fiat X1-9           27.3   4  66  79.0 4.529656 2.23974
Porsche 914-2       26.0   4  91 120.3 4.313958 2.23974
Lotus Europa        30.4   4 113  95.1 5.044012 2.23974
Ford Pantera L      15.8   8 264 351.0 2.621559 4.47948
Ferrari Dino        19.7   6 175 145.0 3.268653 3.35961
Maserati Bora       15.0   8 335 301.0 2.488822 4.47948
Volvo 142E          21.4   4 109 121.0 3.550719 2.23974
```
]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
  mutate(mpg_sd = mpg/sd(mpg)) %>%
  mutate(cyl_sd = cyl/sd(cyl)) %>%
* mutate(disp_sd = disp/sd(disp))
```
]
 
.panel2-cars-auto[

```
                     mpg cyl  hp  disp   mpg_sd  cyl_sd   disp_sd
Mazda RX4           21.0   6 110 160.0 3.484351 3.35961 1.2909608
Mazda RX4 Wag       21.0   6 110 160.0 3.484351 3.35961 1.2909608
Datsun 710          22.8   4  93 108.0 3.783009 2.23974 0.8713986
Hornet 4 Drive      21.4   6 110 258.0 3.550719 3.35961 2.0816744
Hornet Sportabout   18.7   8 175 360.0 3.102731 4.47948 2.9046619
Valiant             18.1   6 105 225.0 3.003178 3.35961 1.8154137
Duster 360          14.3   8 245 360.0 2.372677 4.47948 2.9046619
Merc 240D           24.4   4  62 146.7 4.048484 2.23974 1.1836497
Merc 230            22.8   4  95 140.8 3.783009 2.23974 1.1360455
Merc 280            19.2   6 123 167.6 3.185692 3.35961 1.3522815
Merc 280C           17.8   6 123 167.6 2.953402 3.35961 1.3522815
Merc 450SE          16.4   8 180 275.8 2.721112 4.47948 2.2252937
Merc 450SL          17.3   8 180 275.8 2.870441 4.47948 2.2252937
Merc 450SLC         15.2   8 180 275.8 2.522006 4.47948 2.2252937
Cadillac Fleetwood  10.4   8 205 472.0 1.725583 4.47948 3.8083345
Lincoln Continental 10.4   8 215 460.0 1.725583 4.47948 3.7115124
Chrysler Imperial   14.7   8 230 440.0 2.439045 4.47948 3.5501423
Fiat 128            32.4   4  66  78.7 5.375855 2.23974 0.6349914
Honda Civic         30.4   4  52  75.7 5.044012 2.23974 0.6107858
Toyota Corolla      33.9   4  65  71.1 5.624737 2.23974 0.5736707
Toyota Corona       21.5   4  97 120.1 3.567311 2.23974 0.9690275
Dodge Challenger    15.5   8 150 318.0 2.571783 4.47948 2.5657847
AMC Javelin         15.2   8 150 304.0 2.522006 4.47948 2.4528256
Camaro Z28          13.3   8 245 350.0 2.206755 4.47948 2.8239768
Pontiac Firebird    19.2   8 175 400.0 3.185692 4.47948 3.2274021
Fiat X1-9           27.3   4  66  79.0 4.529656 2.23974 0.6374119
Porsche 914-2       26.0   4  91 120.3 4.313958 2.23974 0.9706412
Lotus Europa        30.4   4 113  95.1 5.044012 2.23974 0.7673148
Ford Pantera L      15.8   8 264 351.0 2.621559 4.47948 2.8320453
Ferrari Dino        19.7   6 175 145.0 3.268653 3.35961 1.1699333
Maserati Bora       15.0   8 335 301.0 2.488822 4.47948 2.4286201
Volvo 142E          21.4   4 109 121.0 3.550719 2.23974 0.9762891
```
]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
  mutate(mpg_sd = mpg/sd(mpg)) %>%
  mutate(cyl_sd = cyl/sd(cyl)) %>%
  mutate(disp_sd = disp/sd(disp)) %>%
* mutate(hp_sd = hp/sd(hp))
```
]
 
.panel2-cars-auto[

```
                     mpg cyl  hp  disp   mpg_sd  cyl_sd   disp_sd     hp_sd
Mazda RX4           21.0   6 110 160.0 3.484351 3.35961 1.2909608 1.6043669
Mazda RX4 Wag       21.0   6 110 160.0 3.484351 3.35961 1.2909608 1.6043669
Datsun 710          22.8   4  93 108.0 3.783009 2.23974 0.8713986 1.3564193
Hornet 4 Drive      21.4   6 110 258.0 3.550719 3.35961 2.0816744 1.6043669
Hornet Sportabout   18.7   8 175 360.0 3.102731 4.47948 2.9046619 2.5524020
Valiant             18.1   6 105 225.0 3.003178 3.35961 1.8154137 1.5314412
Duster 360          14.3   8 245 360.0 2.372677 4.47948 2.9046619 3.5733627
Merc 240D           24.4   4  62 146.7 4.048484 2.23974 1.1836497 0.9042796
Merc 230            22.8   4  95 140.8 3.783009 2.23974 1.1360455 1.3855896
Merc 280            19.2   6 123 167.6 3.185692 3.35961 1.3522815 1.7939739
Merc 280C           17.8   6 123 167.6 2.953402 3.35961 1.3522815 1.7939739
Merc 450SE          16.4   8 180 275.8 2.721112 4.47948 2.2252937 2.6253277
Merc 450SL          17.3   8 180 275.8 2.870441 4.47948 2.2252937 2.6253277
Merc 450SLC         15.2   8 180 275.8 2.522006 4.47948 2.2252937 2.6253277
Cadillac Fleetwood  10.4   8 205 472.0 1.725583 4.47948 3.8083345 2.9899566
Lincoln Continental 10.4   8 215 460.0 1.725583 4.47948 3.7115124 3.1358081
Chrysler Imperial   14.7   8 230 440.0 2.439045 4.47948 3.5501423 3.3545854
Fiat 128            32.4   4  66  78.7 5.375855 2.23974 0.6349914 0.9626202
Honda Civic         30.4   4  52  75.7 5.044012 2.23974 0.6107858 0.7584280
Toyota Corolla      33.9   4  65  71.1 5.624737 2.23974 0.5736707 0.9480350
Toyota Corona       21.5   4  97 120.1 3.567311 2.23974 0.9690275 1.4147599
Dodge Challenger    15.5   8 150 318.0 2.571783 4.47948 2.5657847 2.1877731
AMC Javelin         15.2   8 150 304.0 2.522006 4.47948 2.4528256 2.1877731
Camaro Z28          13.3   8 245 350.0 2.206755 4.47948 2.8239768 3.5733627
Pontiac Firebird    19.2   8 175 400.0 3.185692 4.47948 3.2274021 2.5524020
Fiat X1-9           27.3   4  66  79.0 4.529656 2.23974 0.6374119 0.9626202
Porsche 914-2       26.0   4  91 120.3 4.313958 2.23974 0.9706412 1.3272490
Lotus Europa        30.4   4 113  95.1 5.044012 2.23974 0.7673148 1.6481224
Ford Pantera L      15.8   8 264 351.0 2.621559 4.47948 2.8320453 3.8504807
Ferrari Dino        19.7   6 175 145.0 3.268653 3.35961 1.1699333 2.5524020
Maserati Bora       15.0   8 335 301.0 2.488822 4.47948 2.4286201 4.8860266
Volvo 142E          21.4   4 109 121.0 3.550719 2.23974 0.9762891 1.5897818
```
]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
  mutate(mpg_sd = mpg/sd(mpg)) %>%
  mutate(cyl_sd = cyl/sd(cyl)) %>%
  mutate(disp_sd = disp/sd(disp)) %>%
  mutate(hp_sd = hp/sd(hp)) %>%
* lm(mpg_sd ~ cyl_sd + disp_sd + hp_sd, data = .)
```
]
 
.panel2-cars-auto[

```

Call:
lm(formula = mpg_sd ~ cyl_sd + disp_sd + hp_sd, data = .)

Coefficients:
(Intercept)       cyl_sd      disp_sd        hp_sd  
     5.6720      -0.3637      -0.3874      -0.1670  
```
]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
  mutate(mpg_sd = mpg/sd(mpg)) %>%
  mutate(cyl_sd = cyl/sd(cyl)) %>%
  mutate(disp_sd = disp/sd(disp)) %>%
  mutate(hp_sd = hp/sd(hp)) %>%
  lm(mpg_sd ~ cyl_sd + disp_sd + hp_sd, data = .) ->
*m1
```
]
 
.panel2-cars-auto[

]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
  mutate(mpg_sd = mpg/sd(mpg)) %>%
  mutate(cyl_sd = cyl/sd(cyl)) %>%
  mutate(disp_sd = disp/sd(disp)) %>%
  mutate(hp_sd = hp/sd(hp)) %>%
  lm(mpg_sd ~ cyl_sd + disp_sd + hp_sd, data = .) ->
m1

*confint(m1)
```
]
 
.panel2-cars-auto[

```
                 2.5 %    97.5 %
(Intercept)  4.7914718 6.5525513
cyl_sd      -0.8476508 0.1202260
disp_sd     -0.8256293 0.0508532
hp_sd       -0.5083989 0.1744132
```
]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
  mutate(mpg_sd = mpg/sd(mpg)) %>%
  mutate(cyl_sd = cyl/sd(cyl)) %>%
  mutate(disp_sd = disp/sd(disp)) %>%
  mutate(hp_sd = hp/sd(hp)) %>%
  lm(mpg_sd ~ cyl_sd + disp_sd + hp_sd, data = .) ->
m1

confint(m1) %>%
* as_tibble()
```
]
 
.panel2-cars-auto[

```
# A tibble: 4 × 2
  `2.5 %` `97.5 %`
    <dbl>    <dbl>
1   4.79    6.55  
2  -0.848   0.120 
3  -0.826   0.0509
4  -0.508   0.174 
```
]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
  mutate(mpg_sd = mpg/sd(mpg)) %>%
  mutate(cyl_sd = cyl/sd(cyl)) %>%
  mutate(disp_sd = disp/sd(disp)) %>%
  mutate(hp_sd = hp/sd(hp)) %>%
  lm(mpg_sd ~ cyl_sd + disp_sd + hp_sd, data = .) ->
m1

confint(m1) %>%
  as_tibble()  ->
*confint_m1
```
]
 
.panel2-cars-auto[

]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
  mutate(mpg_sd = mpg/sd(mpg)) %>%
  mutate(cyl_sd = cyl/sd(cyl)) %>%
  mutate(disp_sd = disp/sd(disp)) %>%
  mutate(hp_sd = hp/sd(hp)) %>%
  lm(mpg_sd ~ cyl_sd + disp_sd + hp_sd, data = .) ->
m1

confint(m1) %>%
  as_tibble()  ->
confint_m1

*m1
```
]
 
.panel2-cars-auto[

```

Call:
lm(formula = mpg_sd ~ cyl_sd + disp_sd + hp_sd, data = .)

Coefficients:
(Intercept)       cyl_sd      disp_sd        hp_sd  
     5.6720      -0.3637      -0.3874      -0.1670  
```
]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
  mutate(mpg_sd = mpg/sd(mpg)) %>%
  mutate(cyl_sd = cyl/sd(cyl)) %>%
  mutate(disp_sd = disp/sd(disp)) %>%
  mutate(hp_sd = hp/sd(hp)) %>%
  lm(mpg_sd ~ cyl_sd + disp_sd + hp_sd, data = .) ->
m1

confint(m1) %>%
  as_tibble()  ->
confint_m1

m1 %>%
* broom::tidy(conf.int = T)
```
]
 
.panel2-cars-auto[

```
# A tibble: 4 × 7
  term        estimate std.error statistic  p.value conf.low conf.high
  <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
1 (Intercept)    5.67      0.430     13.2  1.54e-13    4.79     6.55  
2 cyl_sd        -0.364     0.236     -1.54 1.35e- 1   -0.848    0.120 
3 disp_sd       -0.387     0.214     -1.81 8.09e- 2   -0.826    0.0509
4 hp_sd         -0.167     0.167     -1.00 3.25e- 1   -0.508    0.174 
```
]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
  mutate(mpg_sd = mpg/sd(mpg)) %>%
  mutate(cyl_sd = cyl/sd(cyl)) %>%
  mutate(disp_sd = disp/sd(disp)) %>%
  mutate(hp_sd = hp/sd(hp)) %>%
  lm(mpg_sd ~ cyl_sd + disp_sd + hp_sd, data = .) ->
m1

confint(m1) %>%
  as_tibble()  ->
confint_m1

m1 %>%
  broom::tidy(conf.int = T) %>%
* slice(-1)
```
]
 
.panel2-cars-auto[

```
# A tibble: 3 × 7
  term    estimate std.error statistic p.value conf.low conf.high
  <chr>      <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
1 cyl_sd    -0.364     0.236     -1.54  0.135    -0.848    0.120 
2 disp_sd   -0.387     0.214     -1.81  0.0809   -0.826    0.0509
3 hp_sd     -0.167     0.167     -1.00  0.325    -0.508    0.174 
```
]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
  mutate(mpg_sd = mpg/sd(mpg)) %>%
  mutate(cyl_sd = cyl/sd(cyl)) %>%
  mutate(disp_sd = disp/sd(disp)) %>%
  mutate(hp_sd = hp/sd(hp)) %>%
  lm(mpg_sd ~ cyl_sd + disp_sd + hp_sd, data = .) ->
m1

confint(m1) %>%
  as_tibble()  ->
confint_m1

m1 %>%
  broom::tidy(conf.int = T) %>%
  slice(-1) %>%
* mutate(term = fct_reorder(term, estimate) %>%
*          fct_rev())
```
]
 
.panel2-cars-auto[

```
# A tibble: 3 × 7
  term    estimate std.error statistic p.value conf.low conf.high
  <fct>      <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
1 cyl_sd    -0.364     0.236     -1.54  0.135    -0.848    0.120 
2 disp_sd   -0.387     0.214     -1.81  0.0809   -0.826    0.0509
3 hp_sd     -0.167     0.167     -1.00  0.325    -0.508    0.174 
```
]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
  mutate(mpg_sd = mpg/sd(mpg)) %>%
  mutate(cyl_sd = cyl/sd(cyl)) %>%
  mutate(disp_sd = disp/sd(disp)) %>%
  mutate(hp_sd = hp/sd(hp)) %>%
  lm(mpg_sd ~ cyl_sd + disp_sd + hp_sd, data = .) ->
m1

confint(m1) %>%
  as_tibble()  ->
confint_m1

m1 %>%
  broom::tidy(conf.int = T) %>%
  slice(-1) %>%
  mutate(term = fct_reorder(term, estimate) %>%
           fct_rev()) %>%
* ggplot()
```
]
 
.panel2-cars-auto[
![](broom_viz_flipbook_files/figure-html/cars_auto_17_output-1.png)
]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
  mutate(mpg_sd = mpg/sd(mpg)) %>%
  mutate(cyl_sd = cyl/sd(cyl)) %>%
  mutate(disp_sd = disp/sd(disp)) %>%
  mutate(hp_sd = hp/sd(hp)) %>%
  lm(mpg_sd ~ cyl_sd + disp_sd + hp_sd, data = .) ->
m1

confint(m1) %>%
  as_tibble()  ->
confint_m1

m1 %>%
  broom::tidy(conf.int = T) %>%
  slice(-1) %>%
  mutate(term = fct_reorder(term, estimate) %>%
           fct_rev()) %>%
  ggplot() +
* aes(y = term, x = estimate)
```
]
 
.panel2-cars-auto[
![](broom_viz_flipbook_files/figure-html/cars_auto_18_output-1.png)
]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
  mutate(mpg_sd = mpg/sd(mpg)) %>%
  mutate(cyl_sd = cyl/sd(cyl)) %>%
  mutate(disp_sd = disp/sd(disp)) %>%
  mutate(hp_sd = hp/sd(hp)) %>%
  lm(mpg_sd ~ cyl_sd + disp_sd + hp_sd, data = .) ->
m1

confint(m1) %>%
  as_tibble()  ->
confint_m1

m1 %>%
  broom::tidy(conf.int = T) %>%
  slice(-1) %>%
  mutate(term = fct_reorder(term, estimate) %>%
           fct_rev()) %>%
  ggplot() +
  aes(y = term, x = estimate) +
* geom_point()
```
]
 
.panel2-cars-auto[
![](broom_viz_flipbook_files/figure-html/cars_auto_19_output-1.png)
]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
  mutate(mpg_sd = mpg/sd(mpg)) %>%
  mutate(cyl_sd = cyl/sd(cyl)) %>%
  mutate(disp_sd = disp/sd(disp)) %>%
  mutate(hp_sd = hp/sd(hp)) %>%
  lm(mpg_sd ~ cyl_sd + disp_sd + hp_sd, data = .) ->
m1

confint(m1) %>%
  as_tibble()  ->
confint_m1

m1 %>%
  broom::tidy(conf.int = T) %>%
  slice(-1) %>%
  mutate(term = fct_reorder(term, estimate) %>%
           fct_rev()) %>%
  ggplot() +
  aes(y = term, x = estimate) +
  geom_point() +
* geom_segment(aes(x = conf.low, xend = conf.high, yend = term))
```
]
 
.panel2-cars-auto[
![](broom_viz_flipbook_files/figure-html/cars_auto_20_output-1.png)
]

---
count: false

```r
mtcars %>%
  select(mpg, cyl, hp, disp) %>%
  remove_missing() %>%
  mutate(mpg_sd = mpg/sd(mpg)) %>%
  mutate(cyl_sd = cyl/sd(cyl)) %>%
  mutate(disp_sd = disp/sd(disp)) %>%
  mutate(hp_sd = hp/sd(hp)) %>%
  lm(mpg_sd ~ cyl_sd + disp_sd + hp_sd, data = .) ->
m1

confint(m1) %>%
  as_tibble()  ->
confint_m1

m1 %>%
  broom::tidy(conf.int = T) %>%
  slice(-1) %>%
  mutate(term = fct_reorder(term, estimate) %>%
           fct_rev()) %>%
  ggplot() +
  aes(y = term, x = estimate) +
  geom_point() +
  geom_segment(aes(x = conf.low, xend = conf.high, yend = term)) +
* geom_vline(xintercept = 0, linetype = "dashed")
```
]
 
.panel2-cars-auto[
![](broom_viz_flipbook_files/figure-html/cars_auto_21_output-1.png)
]