differences

library(readr)
library(ggplot2)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggpubr)
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

Tonnages au cours du temps

donnees_base %>%
  group_by(year,SECT_COD_SACROIS_NIV5)%>%
  summarise(sum_MONTANT_EUROS_SACROIS = sum(MONTANT_EUROS_SACROIS, na.rm = TRUE)) %>%
  ggplot(aes(x = year, y = sum_MONTANT_EUROS_SACROIS, group = SECT_COD_SACROIS_NIV5, color = SECT_COD_SACROIS_NIV5)) +
  geom_line() + theme(legend.position = "none")

## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.

Cha_Fil_Lig_def %>%
  group_by(year,SECT_COD_SACROIS_NIV5)%>%
  summarise(sum_MONTANT_EUROS_SACROIS = sum(MONTANT_EUROS_SACROIS, na.rm = TRUE)) %>%
  ggplot(aes(x = year, y = sum_MONTANT_EUROS_SACROIS, group = SECT_COD_SACROIS_NIV5, color = SECT_COD_SACROIS_NIV5)) +
  geom_line() + theme(legend.position = "none")

## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.

new_cha <- Cha_Fil_Lig_def %>%
  select(year,SECT_COD_SACROIS_NIV5,MONTANT_EUROS_SACROIS)

new_donnees <- donnees_base %>%
  select(year,SECT_COD_SACROIS_NIV5,MONTANT_EUROS_SACROIS) 

df<-new_cha %>%
  bind_rows(new_donnees) %>%
  group_by(year,SECT_COD_SACROIS_NIV5)%>%
  summarise(sum_MONTANT_EUROS_SACROIS = sum(MONTANT_EUROS_SACROIS, na.rm = TRUE)) 

## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.

gg3 <- ggplot(df, aes(x = year, y = sum_MONTANT_EUROS_SACROIS, group = SECT_COD_SACROIS_NIV5, color = SECT_COD_SACROIS_NIV5)) +
  geom_line() + theme(legend.position = "none")

ggplotly(gg3)

Engin utilisés

donnees_base

summary(as.factor(donnees_base$ENGIN_COD))

##    FPO    FWR    GEN    GES     GN    GNC    GND    GNS    GTN    GTR    LHM 
##    940      3     76   1162    908    508    443  79575      6 106023    340 
##    LHP    LLD    LLS    LNB    LTL    LVS    MIS    OTB    OTM     PS    PS1 
##    947    213  47279     11    836      3      2 185864     32   5117   4381 
##    PTB    PTM 
##   4119   8509

Cha_Fil_Lig_def

summary(droplevels(as.factor(Cha_Fil_Lig_def$ENGIN_COD)))

##    DRB    FDV    FPO    GEN    GES     GN    GNC    GND    GNS    GTN    GTR 
##   6149      3   9075  21784   3938  16769   1218   1101 392331  12045 295583 
##    HES    LHM    LHP     LL    LLD    LLF    LLS    LNS    LTL    LVD    LVS 
##      5    143   4491  11847  13722    204  99873     76   3244    184   1695 
##     LX    MIS    OTB    OTM    OTT     PS    PS1     PT    PTB    PTM     SB 
##     55   2008 961352   3104  92351  19781  19223      8   5717  47334    161 
##    SDV     TX     SV    DRH    FIX    SDN    SSC     SX           FSN    GNF 
##    198    978     53    174     38     40     29   3894      7      2     19 
##    LNP     OT     TB    FCN 
##      2   7192   1408      2

gg1 <-ggplot(donnees_base, aes(x = ENGIN_COD)) +
  geom_bar()
gg2 <-ggplot(Cha_Fil_Lig_def, aes(x = ENGIN_COD)) +
  geom_bar()
ggarrange(gg1,gg2, ncol =1)