Add regression line equation and R 2 on graph

Question

I wonder how to add regression line equation and R 2 on the ggplot  My code is   library ggplot2   df  lt - data frame x   c 1 100   df y  lt - 2   3   df x   rnorm 100  sd   40  p  lt - ggplot data   df  aes x   x  y   y                 geom smooth method    lm   se FALSE  color  black   formula   y   x                geom point   p   Any help will be highly appreciated

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I changed a few lines of the source of stat smooth and related functions to make a new function that adds the fit equation and R squared value  This will work on facet plots too   library devtools  source gist  524eade46135f6348140   df   data frame x   c 1 100   df y   2   5   df x   rnorm 100  sd   40  df class   rep 1 2 50  ggplot data   df  aes x   x  y   y  label y       stat smooth func geom  text  method  lm  hjust 0 parse TRUE      geom smooth method  lm  se FALSE      geom point     facet wrap  class      I used the code in  Ramnath s answer to format the equation  The stat smooth func function isn t very robust  but it shouldn t be hard to play around with it   https   gist github com kdauria 524eade46135f6348140  Try updating ggplot2 if you get an error

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Inspired by the equation style provided in this answer  a more generic approach  more than one predictor   latex output as option  can be    print equation  function model  latex  FALSE            dots  lt - list          cc  model coefficients     var sign  as character sign cc -1     gt  gsub  1         gt  gsub  -    -          var sign var sign                  f args abs  f args  dots     f args x  cc     f args abs x  abs cc      cc   do call format  args  f args      cc abs  do call format  args  f args abs      pred vars          cc abs  gt           paste    x vars  sep  star   gt           paste var sign     gt  paste    collapse           if latex           star      cdot           y var  strsplit as character model call formula         2    gt               paste0    hat        i             x vars  names cc   -1   gt  paste0      i         else          star                y var  strsplit as character model call formula         2                   x vars  names cc   -1             equ  paste y var     cc  1  pred vars      if latex           equ  paste0 equ       hat   varepsilon  i     quad where   quad   varepsilon   sim   mathcal N  0                        summary MetamodelKdifEryth  sigma       gt  paste0                      cat equ      The model argument expects an lm object  the latex argument is a boolean to ask for a simple character or a latex-formated equation  and the     argument pass its values to the format function   I also added an option to output it as latex so you can use this function in a rmarkdown like this         r echo FALSE  results  asis   print equation model   lm mod  latex   TRUE          Now using it   df  lt - data frame x   c 1 100   df y  lt - 2   3   df x   rnorm 100  sd   40  df z  lt - 8   3   df x   rnorm 100  sd   40  lm mod  lm y x z  data   df   print equation model   lm mod  latex   FALSE    This code yields   y   11 3382963933174     2 5893419   x    0 1002227   z  And if we ask for a latex equation  rounding the parameters to 3 digits   print equation model   lm mod  latex   TRUE  digits  3    This yields

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Using ggpubr   library ggpubr     reproducible data set seed 1  df  lt - data frame x   c 1 100   df y  lt - 2   3   df x   rnorm 100  sd   40     By default showing Pearson R ggscatter df  x    x   y    y   add    reg line       stat cor label y   300      stat regline equation label y   280        Use R2 instead of R ggscatter df  x    x   y    y   add    reg line       stat cor label y   300              aes label   paste   rr label      p label    sep                  stat regline equation label y   280      compare R2 with accepted answer   m  lt - lm y   x  df    round summary m  r squared  2     1  0 85

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Here s the most simplest code for everyone  Note  Showing Pearson s Rho and not R 2   library ggplot2  library ggpubr   df  lt - data frame x   c 1 100  df y  lt - 2   3   df x   rnorm 100  sd   40  p  lt - ggplot data   df  aes x   x  y   y             geom smooth method    lm   se FALSE  color  black   formula   y   x            geom point            stat cor label y   35    this means at 35th unit in the y axis  the r squared and p value will be shown         stat regline equation label y   30   this means at 30th unit regresion line equation will be shown  p

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I included a statistics stat poly eq   in my package ggpmisc that allows this answer   library ggplot2  library ggpmisc  df  lt - data frame x   c 1 100   df y  lt - 2   3   df x   rnorm 100  sd   40  my formula  lt - y   x p  lt - ggplot data   df  aes x   x  y   y        geom smooth method    lm   se FALSE  color  black   formula   my formula       stat poly eq formula   my formula                   aes label   paste   eq label      rr label    sep                             parse   TRUE                geom point   p     This statistic works with any polynomial with no missing terms  and hopefully has enough flexibility to be generally useful  The R 2 or adjusted R 2 labels can be used with any model formula fitted with lm    Being a ggplot statistic it behaves as expected both with groups and facets   The  ggpmisc  package is available through CRAN   Version 0 2 6 was just accepted to CRAN    It addresses comments by  shabbychef and  MYaseen208    MYaseen208 this shows how to add a hat   library ggplot2  library ggpmisc  df  lt - data frame x   c 1 100   df y  lt - 2   3   df x   rnorm 100  sd   40  my formula  lt - y   x p  lt - ggplot data   df  aes x   x  y   y        geom smooth method    lm   se FALSE  color  black   formula   my formula       stat poly eq formula   my formula                  eq with lhs    italic hat y                          aes label   paste   eq label      rr label    sep                             parse   TRUE                geom point   p      shabbychef Now it is possible to match the variables in the equation to those used for the axis-labels  To replace the x with say z and y with h one would use   p  lt - ggplot data   df  aes x   x  y   y        geom smooth method    lm   se FALSE  color  black   formula   my formula       stat poly eq formula   my formula                  eq with lhs    italic h                         eq x rhs     italic z                    aes label     eq label                      parse   TRUE        labs x   expression italic z    y   expression italic h                   geom point   p     Being these normal R parsed expressions greek letters can now also be used both in the lhs and rhs of the equation    2017-03-08   elarry Edit to more precisely address the original question  showing how to add a comma between the equation- and R2-labels   p  lt - ggplot data   df  aes x   x  y   y       geom smooth method    lm   se FALSE  color  black   formula   my formula      stat poly eq formula   my formula                 eq with lhs    italic hat y                         aes label   paste   eq label      rr label    sep     plain                             parse   TRUE               geom point   p      2019-10-20   helen h I give below examples of use of stat poly eq   with grouping   library ggpmisc  df  lt - data frame x   c 1 100   df y  lt - 20   c 0  1    3   df x   rnorm 100  sd   40  df group  lt - factor rep c  A    B    50   my formula  lt - y   x p  lt - ggplot data   df  aes x   x  y   y  colour   group       geom smooth method    lm   se FALSE  formula   my formula      stat poly eq formula   my formula                  aes label   paste   eq label      rr label    sep                            parse   TRUE               geom point   p  p  lt - ggplot data   df  aes x   x  y   y  linetype   group       geom smooth method    lm   se FALSE  formula   my formula      stat poly eq formula   my formula                  aes label   paste   eq label      rr label    sep                            parse   TRUE               geom point   p        2020-01-21   Herman It may be a bit counter-intuitive at first sight  but to obtain a single equation when using grouping one needs to follow the grammar of graphics  Either restrict the mapping that creates the grouping to individual layers  shown below  or keep the default mapping and override it with a constant value in the layer where you do not want the grouping  e g  colour    black     Continuing from previous example   p  lt - ggplot data   df  aes x   x  y   y       geom smooth method    lm   se FALSE  formula   my formula      stat poly eq formula   my formula                  aes label   paste   eq label      rr label    sep                            parse   TRUE               geom point aes colour   group   p      2020-01-22  For the sake of completeness an example with facets  demonstrating that also in this case the expectations of the grammar of graphics are fulfilled   library ggpmisc  df  lt - data frame x   c 1 100   df y  lt - 20   c 0  1    3   df x   rnorm 100  sd   40  df group  lt - factor rep c  A    B    50   my formula  lt - y   x  p  lt - ggplot data   df  aes x   x  y   y       geom smooth method    lm   se FALSE  formula   my formula      stat poly eq formula   my formula                  aes label   paste   eq label      rr label    sep                            parse   TRUE               geom point       facet wrap  group  p

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Here is one solution    GET EQUATION AND R-SQUARED AS STRING   SOURCE  https   groups google com forum   topic ggplot2 1TgH-kG5XMA  lm eqn  lt - function df       m  lt - lm y   x  df       eq  lt - substitute italic y     a   b     italic x       italic r  2     r2            list a   format unname coef m  1    digits   2                 b   format unname coef m  2    digits   2                r2   format summary m  r squared  digits   3        as character as expression eq       p1  lt - p   geom text x   25  y   300  label   lm eqn df   parse   TRUE    EDIT  I figured out the source from where I picked this code  Here is the link to the original post in the ggplot2 google groups

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really love  Ramnath solution  To allow use to customize the regression formula  instead of fixed as y and x as literal variable names   and added the p-value into the printout as well  as  Jerry T commented   here is the mod   lm eqn  lt - function df  y  x       formula   as formula sprintf   s    s   y  x       m  lt - lm formula  data df         formating the values into a summary string to print out         give some space  but equal size and comma need to be quoted     eq  lt - substitute italic target     a   b     italic input       italic r  2     r2      p     italic pvalue             list target   y                input   x                a   format as vector coef m  1    digits   2                  b   format as vector coef m  2    digits   2                 r2   format summary m  r squared  digits   3                  getting the pvalue is painful              pvalue   format summary m  coefficients 2  Pr  gt  t      digits 1                                as character as expression eq                        geom point       ggrepel  geom text repel label rownames mtcars       geom text x 3 y 300 label lm eqn mtcars   hp   wt   color  red  parse T      geom smooth method  lm      Unfortunately  this doesn t work with facet wrap or facet grid

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I ve modified Ramnath s post to a  make more generic so it accepts a linear model as a parameter rather than the data frame and b  displays negatives more appropriately   lm eqn   function m       l  lt - list a   format coef m  1   digits   2         b   format abs coef m  2    digits   2         r2   format summary m  r squared  digits   3       if  coef m  2   gt   0         eq  lt - substitute italic y     a   b     italic x       italic r  2     r2 l      else       eq  lt - substitute italic y     a - b     italic x       italic r  2     r2 l             as character as expression eq                         Usage would change to   p1   p   geom text aes x   25  y   300  label   lm eqn lm y   x  df     parse   TRUE

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Another option would be to create a custom function generating the equation using dplyr and broom libraries  get formula  lt - function model         broom  tidy model    1 2    gt       mutate sign   ifelse sign estimate     1           -       gt    coeff signs     mutate if is numeric    abs round    2      gt    for improving formatting     mutate a   ifelse term      Intercept    paste0  y      estimate   paste0 sign  estimate         term      gt       summarise formula   paste a  collapse          gt       as character       lm y   x  data   df  - gt  model get formula model    quot y   6 22   3 16   x quot   scales  percent summary model  r squared  accuracy   0 01  - gt  r squared  Now we need to add the text to the plot  p      geom text x   20  y   300              label   get formula model               color    red       geom text x   20  y   285              label   r squared              color    blue

[r] Add regression line equation and R^2 on graph

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