# 🤩 ggstatsplot | a high color R package that meets your daily statistical needs (IV)

## 1. Write in front

The point chart is very useful, which can show the distribution of variables, the correlation between variables, regression results, etc
This issue introduces the functions related to plotting DOTPLOT and scatterplot in the ggstatspot package

## 2. Package used

```rm(list=ls())
library(tidyverse)
library(ggstatsplot)
library(ggsci)
```

## 3. Sample data

```dat <- mpg
``` ## 4. dotplot shows sample distribution

#### 4.1 preliminary drawing

The function used is ggscatterstats

Because there are too many factors, we use the filter function here to filter

```df <- dplyr::filter(ggplot2::mpg, cyl %in% c("4", "6"))

## Generate enough colors
paletter_vector <-
paletteer::paletteer_d(
palette = "palettetown::venusaur",
n = nlevels(as.factor(df\$manufacturer)),
type = "discrete"
)

## Start drawing
ggdotplotstats(
data = df,
x = cty,
y = manufacturer,
xlab = "city miles per gallon",
ylab = "car manufacturer",
test.value = 15.5,
point.args = list(
shape = 16,
color = paletter_vector,
size = 5
),
title = "Distribution of mileage of cars",
#ggtheme = ggplot2::theme_dark()
)
``` #### 4.2 complex grouping drawing

The function used is grouped_ggdotplotstats

Let's take a look at the manufacturer distribution of different cyl and cty
Of course, you can also use the purrr package to batch draw. As mentioned in the previous issues,
I won't repeat it here

```grouped_ggdotplotstats(
## arguments relevant for ggdotplotstats
data = df,
grouping.var = cyl, ## grouping variable
x = cty,
y = manufacturer,
xlab = "city miles per gallon",
ylab = "car manufacturer",
type = "bayes", ## Bayesian test
test.value = 15.5,
## arguments relevant for `combine_plots`
annotation.args = list(title = "Fuel economy data"),
plotgrid.args = list(nrow = 2)
)
``` ## 5. scatterplot shows the correlation of variables

#### 5.1 preliminary drawing

The function used is ggscatterstats

```ggscatterstats(
data = dat,
x = displ,
y = hwy,
xlab = "displ", ## label for the x-axis
ylab = "hwy", ## label for the y-axis
label.var = manufacturer, ## variable to use for labeling data points
label.expression = displ > 5 & hwy> 24, ## which points to label
point.label.args = list(alpha = 0.7, size = 4, color = "grey50"),
xfill = "#CC79A7", ## fill for marginals on the x-axis
yfill = "#009E73", ## fill for marginals on the y-axis
title = "aaa",
caption = "Source"
)
``` #### 5.2 complex grouping drawing

The function used is grouped_ggscatterstats

Let's look at the correlation of hwy of displ of different cLys
Of course, the purrr package also supports batch drawing

```grouped_ggscatterstats(
## arguments relevant for ggscatterstats
data = df,
x = displ,
y = hwy,
grouping.var = cyl,
xlab = "displ", ## label for the x-axis
ylab = "hwy", ## label for the y-axis
label.var = manufacturer, ## variable to use for labeling data points
type = "r",
label.expression = displ > 5 & hwy> 24, ## which points to label
point.label.args = list(alpha = 0.7, size = 4, color = "grey50"),
xfill = "#CC79A7", ## fill for marginals on the x-axis
yfill = "#009E73", ## fill for marginals on the y-axis
# ggtheme = ggthemes::theme_tufte(),
## arguments relevant for combine_plots
annotation.args = list(
title = "title",
caption = "Source"
),
plotgrid.args = list(nrow = 2, ncol = 1)
)
```  Finally, I wish everyone an early end~

Order one and watch it, everyone ~ ✐ ɴɪ ᴄᴇ ᴅᴀ ʏ 〰

[external link image transfer failed. The source station may have anti-theft chain mechanism. It is recommended to save the image and upload it directly (img-ekfb1p9f-1660733459651)( https://picbed-1312756706.cos.ap-nanjing.myqcloud.com/img/202208170104716.png )]

## 1. Write in front

The point chart is very useful, which can show the distribution of variables, the correlation between variables, regression results, etc
This issue introduces the functions related to plotting DOTPLOT and scatterplot in the ggstatspot package

## 2. Package used

```rm(list=ls())
library(tidyverse)
library(ggstatsplot)
library(ggsci)
```

## 3. Sample data

```dat <- mpg
``` ## 4. dotplot shows sample distribution

#### 4.1 preliminary drawing

The function used is ggscatterstats

Because there are too many factors, we use the filter function here to filter

```df <- dplyr::filter(ggplot2::mpg, cyl %in% c("4", "6"))

## Generate enough colors
paletter_vector <-
paletteer::paletteer_d(
palette = "palettetown::venusaur",
n = nlevels(as.factor(df\$manufacturer)),
type = "discrete"
)

## Start drawing
ggdotplotstats(
data = df,
x = cty,
y = manufacturer,
xlab = "city miles per gallon",
ylab = "car manufacturer",
test.value = 15.5,
point.args = list(
shape = 16,
color = paletter_vector,
size = 5
),
title = "Distribution of mileage of cars",
#ggtheme = ggplot2::theme_dark()
)
``` #### 4.2 complex grouping drawing

The function used is grouped_ggdotplotstats

Let's take a look at the manufacturer distribution of different cyl and cty
Of course, you can also use the purrr package to batch draw. As mentioned in the previous issues,
I won't repeat it here

```grouped_ggdotplotstats(
## arguments relevant for ggdotplotstats
data = df,
grouping.var = cyl, ## grouping variable
x = cty,
y = manufacturer,
xlab = "city miles per gallon",
ylab = "car manufacturer",
type = "bayes", ## Bayesian test
test.value = 15.5,
## arguments relevant for `combine_plots`
annotation.args = list(title = "Fuel economy data"),
plotgrid.args = list(nrow = 2)
)
``` ## 5. scatterplot shows the correlation of variables

#### 5.1 preliminary drawing

The function used is ggscatterstats

```ggscatterstats(
data = dat,
x = displ,
y = hwy,
xlab = "displ", ## label for the x-axis
ylab = "hwy", ## label for the y-axis
label.var = manufacturer, ## variable to use for labeling data points
label.expression = displ > 5 & hwy> 24, ## which points to label
point.label.args = list(alpha = 0.7, size = 4, color = "grey50"),
xfill = "#CC79A7", ## fill for marginals on the x-axis
yfill = "#009E73", ## fill for marginals on the y-axis
title = "aaa",
caption = "Source"
)
``` #### 5.2 complex grouping drawing

The function used is grouped_ggscatterstats

Let's look at the correlation of hwy of displ of different cLys
Of course, the purrr package also supports batch drawing

```grouped_ggscatterstats(
## arguments relevant for ggscatterstats
data = df,
x = displ,
y = hwy,
grouping.var = cyl,
xlab = "displ", ## label for the x-axis
ylab = "hwy", ## label for the y-axis
label.var = manufacturer, ## variable to use for labeling data points
type = "r",
label.expression = displ > 5 & hwy> 24, ## which points to label
point.label.args = list(alpha = 0.7, size = 4, color = "grey50"),
xfill = "#CC79A7", ## fill for marginals on the x-axis
yfill = "#009E73", ## fill for marginals on the y-axis
# ggtheme = ggthemes::theme_tufte(),
## arguments relevant for combine_plots
annotation.args = list(
title = "title",
caption = "Source"
),
plotgrid.args = list(nrow = 2, ncol = 1)
)
```  Finally, I wish everyone an early end~

Order one and watch it, everyone ~ ✐ ɴɪ ᴄᴇ ᴅᴀ ʏ 〰

# 🤩 ggstatsplot | a high color R package that meets your daily statistical needs (IV)

## 1. Write in front

The point chart is very useful, which can show the distribution of variables, the correlation between variables, regression results, etc
This issue introduces the functions related to plotting DOTPLOT and scatterplot in the ggstatspot package

## 2. Package used

```rm(list=ls())
library(tidyverse)
library(ggstatsplot)
library(ggsci)
```

## 3. Sample data

```dat <- mpg
``` ## 4. dotplot shows sample distribution

#### 4.1 preliminary drawing

The function used is ggscatterstats

Because there are too many factors, we use the filter function here to filter

```df <- dplyr::filter(ggplot2::mpg, cyl %in% c("4", "6"))

## Generate enough colors
paletter_vector <-
paletteer::paletteer_d(
palette = "palettetown::venusaur",
n = nlevels(as.factor(df\$manufacturer)),
type = "discrete"
)

## Start drawing
ggdotplotstats(
data = df,
x = cty,
y = manufacturer,
xlab = "city miles per gallon",
ylab = "car manufacturer",
test.value = 15.5,
point.args = list(
shape = 16,
color = paletter_vector,
size = 5
),
title = "Distribution of mileage of cars",
#ggtheme = ggplot2::theme_dark()
)
``` #### 4.2 complex grouping drawing

The function used is grouped_ggdotplotstats

Let's take a look at the manufacturer distribution of different cyl and cty
Of course, you can also use the purrr package to batch draw. As mentioned in the previous issues,
I won't repeat it here

```grouped_ggdotplotstats(
## arguments relevant for ggdotplotstats
data = df,
grouping.var = cyl, ## grouping variable
x = cty,
y = manufacturer,
xlab = "city miles per gallon",
ylab = "car manufacturer",
type = "bayes", ## Bayesian test
test.value = 15.5,
## arguments relevant for `combine_plots`
annotation.args = list(title = "Fuel economy data"),
plotgrid.args = list(nrow = 2)
)
``` ## 5. scatterplot shows the correlation of variables

#### 5.1 preliminary drawing

The function used is ggscatterstats

```ggscatterstats(
data = dat,
x = displ,
y = hwy,
xlab = "displ", ## label for the x-axis
ylab = "hwy", ## label for the y-axis
label.var = manufacturer, ## variable to use for labeling data points
label.expression = displ > 5 & hwy> 24, ## which points to label
point.label.args = list(alpha = 0.7, size = 4, color = "grey50"),
xfill = "#CC79A7", ## fill for marginals on the x-axis
yfill = "#009E73", ## fill for marginals on the y-axis
title = "aaa",
caption = "Source"
)
``` #### 5.2 complex grouping drawing

The function used is grouped_ggscatterstats

Let's look at the correlation of hwy of displ of different cLys
Of course, the purrr package also supports batch drawing

```grouped_ggscatterstats(
## arguments relevant for ggscatterstats
data = df,
x = displ,
y = hwy,
grouping.var = cyl,
xlab = "displ", ## label for the x-axis
ylab = "hwy", ## label for the y-axis
label.var = manufacturer, ## variable to use for labeling data points
type = "r",
label.expression = displ > 5 & hwy> 24, ## which points to label
point.label.args = list(alpha = 0.7, size = 4, color = "grey50"),
xfill = "#CC79A7", ## fill for marginals on the x-axis
yfill = "#009E73", ## fill for marginals on the y-axis
# ggtheme = ggthemes::theme_tufte(),
## arguments relevant for combine_plots
annotation.args = list(
title = "title",
caption = "Source"
),
plotgrid.args = list(nrow = 2, ncol = 1)
)
```  Finally, I wish everyone an early end~

Order one and watch it, everyone ~ ✐ ɴɪ ᴄᴇ ᴅᴀ ʏ 〰 Posted by antray on Thu, 18 Aug 2022 17:59:46 +0530