Script publication Analyste R

Comparaison efficace de modèles de régression: Gagnez du temps avec mtable()

#fit first regression model
model1 <- lm(mpg ~ disp + carb + hp + cyl, data = mtcars)

#fit second regression model
model2 <- lm(mpg ~ disp + carb, data = mtcars)


library(memisc)

Loading required package: lattice

Loading required package: MASS

Attaching package: 'memisc'

The following objects are masked from 'package:stats':

    contr.sum, contr.treatment, contrasts

The following object is masked from 'package:base':

    as.array

#create table to compare coefficient values from both regression models
(tmtable=mtable("Model 1"=model1,"Model 2"=model2))

Calls:
Model 1: lm(formula = mpg ~ disp + carb + hp + cyl, data = mtcars)
Model 2: lm(formula = mpg ~ disp + carb, data = mtcars)

=====================================
                Model 1    Model 2   
-------------------------------------
  (Intercept)  34.022***  31.153***  
               (2.523)    (1.264)    
  disp         -0.027*    -0.036***  
               (0.011)    (0.005)    
  carb         -0.927     -0.956*    
               (0.579)    (0.359)    
  hp            0.009                
               (0.021)               
  cyl          -1.049                
               (0.784)               
-------------------------------------
  R-squared     0.788      0.774     
  N            32         32         
=====================================
  Significance: *** = p < 0.001;   
                ** = p < 0.01;   
                * = p < 0.05  

Ajout d’une note de bas de page à un graphique avec ggplot2

library(conflicted)
library(sjPlot)

Learn more about sjPlot with 'browseVignettes("sjPlot")'.

library(ggplot2)
library(palmerpenguins)
#Données
data("penguins")
#Graphique avec sjplot
grpfrq=plot_frq(penguins,species)
#Ajout du pied de page avec labs de ggplot2

grpfrq<-grpfrq+labs( caption ="Source: IPERSO|Analyste R, 2024" )
print(grpfrq)

Analyse de la corrélation : Transformer les corrélations en informations avec correlationfunnel

library(conflicted)
library(correlationfunnel)

══ Using correlationfunnel? ════════════════════════════════════════════════════
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library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ lubridate 1.9.3     ✔ tibble    3.2.1
✔ purrr     1.0.2     ✔ tidyr     1.3.1

library(palmerpenguins)
library(ggplot2)


#Analyse de la correlation
##Données
data("penguins")
## Dichotomisation des  variables  

###La fonction binarize ne fonctionne pas correctement en présence de NA, il faut les exclure  

penguinsbin=binarize(na.omit(penguins))
##Calcul ne de correlation Funnem

corr=correlate(penguinsbin,target=bill_depth_mm__17.3_18.7)
 ##visualition graphique
grcorr= plot_correlation_funnel(corr,interactive =FALSE )
grcorr<-grcorr+labs(caption="Source:Analyste R,2024",y="Variables explicatives")
#ggsave("plot_correlation_funnel.png", path="C:/IPERSO/0.Formation/Journée R burkina/Analyste R Burkina/ARB")

print(grcorr)

skimr pour une exploration rapide de vos données

library(conflicted)
library(palmerpenguins)
library(flextable)
library(skimr)
# Les données
data("penguins")
#exploration avec skim
skim=skim(penguins)
#Mise en forme  avec flextable
fskim=flextable(skim)
#ajout d'un pied de page
fskim=add_footer_lines(fskim, value="Source: IPERSO|Analyste R,2024")
#Mise en gras du pied de page
fskim=bold(fskim,part="footer")
fskim

skim_type	skim_variable	n_missing	complete_rate	factor.ordered	factor.n_unique	factor.top_counts	numeric.mean	numeric.sd	numeric.p0	numeric.p25	numeric.p50	numeric.p75	numeric.p100	numeric.hist
factor	species	0	1.0000000	FALSE	3	Ade: 152, Gen: 124, Chi: 68
factor	island	0	1.0000000	FALSE	3	Bis: 168, Dre: 124, Tor: 52
factor	sex	11	0.9680233	FALSE	2	mal: 168, fem: 165
numeric	bill_length_mm	2	0.9941860				43.92193	5.4595837	32.1	39.225	44.45	48.5	59.6	▃▇▇▆▁
numeric	bill_depth_mm	2	0.9941860				17.15117	1.9747932	13.1	15.600	17.30	18.7	21.5	▅▅▇▇▂
numeric	flipper_length_mm	2	0.9941860				200.91520	14.0617137	172.0	190.000	197.00	213.0	231.0	▂▇▃▅▂
numeric	body_mass_g	2	0.9941860				4,201.75439	801.9545357	2,700.0	3,550.000	4,050.00	4,750.0	6,300.0	▃▇▆▃▂
numeric	year	0	1.0000000				2,008.02907	0.8183559	2,007.0	2,007.000	2,008.00	2,009.0	2,009.0	▇▁▇▁▇
Source: IPERSO|Analyste R,2024