Plotting multiple graphs of regression into one figure

Question

I am not expert in R and trying my best. I appreciate to have some assistance.

I have data as follows:

    POPs: num[1:3000] 3,4,5,6,7,....
    PM1:  num[1:3000] 3,4,5,6,7,....
    PM2:  num[1:3000] 3,4,5,6,7,....
    PM3:  num[1:3000] 3,4,5,6,7,....
    PM4:  num[1:3000] 3,4,5,6,7,....
.. etc

I want to do regression analysis for each PMs (PM1, PM2, PM3, ..) and put them into one figure (as in the picture) . Also, adding into them the R2 , RMSE, MAE and the regression abline and 1:1 line.

The x is POPs and the y is PM1 and PM2 and PM3 ... etc.

I can do for each PMs (y-axis) individually in the code (aes(x=POPs, y=PM1)). However, it takes lot of figures and better to combine them in one figure. How I can add all the PMs into a single (y) in the code. I think some advance in looping which I am not into this level unfortunately.

ggplot(data =Plot,aes(x=POPs, y=PM1)) +
  stat_smooth(method = "lm", se=FALSE, color="black", formula = y ~ x) +
  geom_point(size=0.3) +
  stat_cor(aes(label = paste(..rr.label..)), # adds R^2 value
           r.accuracy = 0.01,
           label.x = 0, label.y = 375, size = 4) +
  stat_regline_equation(aes(label = ..eq.label..), # adds equation to linear regression
                        label.x = 0, label.y = 400, size = 4)

Based on Behnam Hedayat Answer below with some coding modification from my side and from Allan Cameron .. I can say now it worked 100% perfectly

# change format of df to longer
Plot %>%  pivot_longer(cols=starts_with("PEM"), names_to = "PEMs", values_to = "PEMs_value") -> df2

df2 %>% ggplot(aes(POPs, PEMs_value)) +
  geom_point(color = "#fe4300", size=0.3) +
  geom_abline()+
  geom_smooth(method='lm', se=FALSE, formula = y ~ x, color = "#1b14fd")+
  labs(y = expression(bold(PLF~PM["2.5"]~("u"*g/m^"3"))), x = expression(bold(POPS~PM["2.5"]~("u"*g/m^"3")))) +
  stat_cor(aes(label = paste(..rr.label..)), # adds R^2 value
           r.accuracy = 0.01,
           label.x = 0, label.y = 110, size = 3) +
  stat_regline_equation(aes(label = ..eq.label..), # adds equation to linear regression
                        label.x = 0, label.y = 100, size = 3) +
  facet_wrap(~PEMs, ncol=5)

Behnam Hedayat · Accepted Answer

You can use facet_wrap function of ggplot2, but first you have to reshape your dataset to longer format by pivot_longer() function of tidyverse.
To add regression metrics on plots, you can create a separate data frame containing metrics of each group of PMs variable, then use this data frame in geom_text function with x and y column created for x and y position respectively.

Here I also used caret package functions (R2, RMSE, MAE) to calculate regression metrics.

# caret for calculating R2, MAE and RMSE
# tidyverse to reshape data to longer format
libs <- c("ggplot2", "tidyverse","caret")
suppressMessages(invisible(sapply(libs, library, character.only=T)))

# sample dataset
df <- data.frame(POPs = sample(1:100, 100),
                 PM1 = sample(1:100, 100),
                 PM2 = sample(1:100, 100),
                 PM3 = sample(1:100, 100),
                 PM4 = sample(1:100,100),
                 PM5 = sample(1:100,100),
                 PM6 = sample(1:100,100),
                 PM7 = sample(1:100,100),
                 PM8 = sample(1:100,100))


# change format of df to longer
df %>%  pivot_longer(cols=starts_with("PM"),
                     names_to = "PMs", values_to = "PMs_value") -> df2

head(df2, 10)
#> # A tibble: 10 × 3
#>     POPs PMs   PMs_value
#>          
#>  1     5 PM1          88
#>  2     5 PM2          21
#>  3     5 PM3          51
#>  4     5 PM4          40
#>  5     5 PM5          40
#>  6     5 PM6           2
#>  7     5 PM7          30
#>  8     5 PM8          70
#>  9    52 PM1          13
#> 10    52 PM2          90

# create  a dataframe of summary of regression metrics
summary_df <- df2 %>%
  group_by(PMs) %>%
  summarise(R2 = R2(PMs_value, POPs),
            RMSE=RMSE(PMs_value, POPs),
            MAE=MAE(PMs_value, POPs)) %>%
  mutate_if(is.numeric, round,digits=2) %>%
  pivot_longer(cols = -PMs, names_to = "Metric", values_to = "Metric_value") %>%
  # add x column for x position of text and y column for y position
  mutate(x = rep(30, times =nrow(.)),
         y = rep(c(90,80,70), times=nrow(.)/3)) %>%
  unite("Metric", Metric:Metric_value, sep = " = ")

summary_df
#> # A tibble: 24 × 4
#>    PMs   Metric           x     y
#>              
#>  1 PM1   R2 = 0.03       30    90
#>  2 PM1   RMSE = 43.95    30    80
#>  3 PM1   MAE = 36.72     30    70
#>  4 PM2   R2 = 0.02       30    90
#>  5 PM2   RMSE = 37.83    30    80
#>  6 PM2   MAE = 29.76     30    70
#>  7 PM3   R2 = 0.02       30    90
#>  8 PM3   RMSE = 43.69    30    80
#>  9 PM3   MAE = 36.88     30    70
#> 10 PM4   R2 = 0.01       30    90
#> # … with 14 more rows

df2 %>% ggplot(aes(POPs, PMs_value)) +
  geom_point(size=0.3) +geom_abline()+
  geom_smooth(method='lm', se=FALSE)+
  facet_wrap(~PMs, ncol=4)+
  geom_text(data = summary_df,
            mapping = aes(x = x, y = y, label = Metric))
#> `geom_smooth()` using formula = 'y ~ x'

^{Created on 2023-02-12 with reprex v2.0.2}

Plotting multiple graphs of regression into one figure

Answers (2)

Related Questions