Il n’y a pas de conclusion…

CONTEXTE

Les données du #Tidytuesday de cette semaine proviennent de The Wallstreet Journal.

OBJECTIFS

Trouver une manière de présenter les données

IMPORTER

tuesdata <- tidytuesdayR::tt_load('2020-02-25')

measles <- tuesdata$measles

EXPLORER

glimpse(measles)

## Observations: 66,113  
## Variables: 16  
## $ index    <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 11, 12, 13, 14, 15, …  
## $ state    <chr> "Arizona", "Arizona", "Arizona", "Arizona", "Arizona", …  
## $ year     <chr> "2018-19", "2018-19", "2018-19", "2018-19", "2018-19", …  
## $ name     <chr> "A J Mitchell Elementary", "Academy Del Sol", "Academy …  
## $ type     <chr> "Public", "Charter", "Charter", "Charter", "Charter", "…  
## $ city     <chr> "Nogales", "Tucson", "Tucson", "Phoenix", "Phoenix", "P…  
## $ county   <chr> "Santa Cruz", "Pima", "Pima", "Maricopa", "Maricopa", "…  
## $ district <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…  
## $ enroll   <dbl> 51, 22, 85, 60, 43, 36, 24, 22, 26, 78, 78, 35, 54, 54,…  
## $ mmr      <dbl> 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, …  
## $ overall  <dbl> -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,…  
## $ xrel     <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…  
## $ xmed     <dbl> NA, NA, NA, NA, 2.33, NA, NA, NA, NA, NA, NA, 2.86, NA,…  
## $ xper     <dbl> NA, NA, NA, NA, 2.33, NA, 4.17, NA, NA, NA, NA, NA, NA,…  
## $ lat      <dbl> 31.34782, 32.22192, 32.13049, 33.48545, 33.49562, 33.43…  
## $ lng      <dbl> -110.9380, -110.8961, -111.1170, -112.1306, -112.2247, …

summary(measles)

##      index         state               year               name            ##  Min.   :   1   Length:66113       Length:66113       Length:66113        
##  1st Qu.: 429   Class :character   Class :character   Class :character    
##  Median : 997   Mode  :character   Mode  :character   Mode  :character    
##  Mean   :1608                                                             
##  3rd Qu.:2133                                                             
##  Max.   :8066                                                             
##                                                                           
##      type               city              county          district        
##  Length:66113       Length:66113       Length:66113       Mode:logical    
##  Class :character   Class :character   Class :character   NA's:66113      
##  Mode  :character   Mode  :character   Mode  :character                   
##                                                                           
##                                                                           
##                                                                           
##                                                                           
##      enroll            mmr            overall         xrel          
##  Min.   :   0.0   Min.   : -1.00   Min.   : -1.00   Mode:logical    
##  1st Qu.:  46.0   1st Qu.: -1.00   1st Qu.: -1.00   TRUE:109        
##  Median :  80.0   Median : 95.00   Median : 87.00   NA's:66004      
##  Mean   : 131.9   Mean   : 63.17   Mean   : 54.09                   
##  3rd Qu.: 129.0   3rd Qu.: 98.00   3rd Qu.: 96.10                   
##  Max.   :6222.0   Max.   :100.00   Max.   :100.00                   
##  NA's   :16260                                                      
##       xmed             xper             lat             lng           
##  Min.   :  0.04   Min.   :  0.17   Min.   :24.55   Min.   :-124.50    
##  1st Qu.:  1.00   1st Qu.:  2.84   1st Qu.:35.69   1st Qu.:-117.63    
##  Median :  2.00   Median :  5.00   Median :40.21   Median : -89.97    
##  Mean   :  2.91   Mean   :  6.78   Mean   :39.15   Mean   : -96.28    
##  3rd Qu.:  3.53   3rd Qu.:  7.55   3rd Qu.:42.18   3rd Qu.: -81.75    
##  Max.   :100.00   Max.   :169.23   Max.   :49.00   Max.   :  80.21    
##  NA's   :45122    NA's   :57560    NA's   :1549    NA's   :1549

measles %>%
     count(year) %>%
     knitr::kable()

year	n
2017	1939
2017-18	10418
2018-19	48075
null	5681

measles %>%
     count(type) %>%
     knitr::kable()

type	n
BOCES	47
Charter	276
Kindergarten	1488
Nonpublic	173
Private	6815
Public	20692
NA	36622

measles %>%
     filter(mmr>=0) %>%
     group_by(year, type) %>%
     count(mmr) %>%
     select(-mmr) %>%
     group_by(year,type) %>%
     summarise(n=sum(n)) %>%
     spread(type, n) %>%
     knitr::kable()

year	BOCES	Charter	Kindergarten	Nonpublic	Private	Public
2017	NA	NA	NA	NA	NA	NA	1939
2017-18	47	NA	1486	18	2232	3075	2848
2018-19	NA	217	NA	NA	2365	16687	11265
null	NA	NA	NA	NA	NA	NA	1978

measles %>%
     filter(mmr>=0) %>%
     group_by(state, year) %>%
     count(mmr) %>%
    select(-mmr) %>%
     group_by(year,state) %>%
     summarise(n=sum(n)) %>%
     spread(year, n) %>%
     knitr::kable()

state	2017	2017-18	2018-19	null
Arizona	NA	NA	1171	NA
Arkansas	NA	NA	567	NA
California	NA	NA	14225	NA
Colorado	NA	1507	NA	NA
Connecticut	NA	589	NA	NA
Illinois	NA	NA	7686	NA
Maine	NA	NA	357	NA
Massachusetts	NA	NA	954	NA
Minnesota	NA	1593	NA	NA
Missouri	NA	NA	700	NA
Montana	NA	522	NA	NA
New York	NA	4159	NA	NA
North Dakota	NA	360	NA	NA
Ohio	NA	NA	2919	NA
Oregon	NA	NA	806	NA
Pennsylvania	1939	NA	NA	NA
South Dakota	NA	373	NA	NA
Texas	NA	NA	811	NA
Utah	NA	603	NA	NA
Vermont	NA	NA	338	NA
Washington	NA	NA	NA	1978

plt1 <-measles %>%
     filter(mmr>=0) %>%
     ggplot(aes(x=" ", y = mmr)) +
     geom_boxplot(fill = "#FFFFFF", color = "black") + 
     coord_flip() +
     theme_classic() +
     xlab("") +
     ylab("taux de vaccination rougeole")+
    theme(axis.text.y=element_blank(),
          axis.ticks.y=element_blank())    

plt2 <-measles %>%
    filter(mmr>=0) %>%
    ggplot() +
    geom_histogram(aes(x = mmr, y = (..count..)/sum(..count..)),
                       position = "identity",
                       binwidth = 1,
                       fill = "#FFFFFF",
                       color = "black") +
     ylab("Fréquence Relative")+
     xlab("")+
    theme_classic()+
    theme(axis.text.x = element_blank())+
    theme(axis.ticks.x = element_blank())  

plt2 + plt1 + plot_layout(nrow = 2, heights = c(2, 1))

plt1 <-measles %>%
     filter(overall>0)  %>%
     ggplot(aes(x=" ", y = overall)) +
     geom_boxplot(fill = "#FFFFFF", color = "black") +
     coord_flip() +
    theme_classic() +
    xlab("") +
    ylab("taux de vaccination rougeole")+
    theme(axis.text.y=element_blank(),
          axis.ticks.y=element_blank())    

plt2 <-measles %>%
    filter(overall>0) %>%
     ggplot() +
     geom_histogram(aes(x = overall, y = (..count..)/sum(..count..)),
                         position = "identity", binwidth = 1,                          fill = "#FFFFFF", color = "black") +
     ylab("Fréquence Relative")+
     xlab("")+
    theme_classic()+
    theme(axis.text.x = element_blank())+
    theme(axis.ticks.x = element_blank())  

plt2 + plt1 + plot_layout(nrow = 2, heights = c(2, 1))

PRÉPARER

data<-measles %>%
    unique() %>% 
    filter(mmr>=0, overall>=0) %>%
    select(mmr, overall)

VISUALISER

#Graphique  
gg <- ggplot(data=data, aes(x=overall, y=mmr))  
gg<-gg + geom_point(size=1.5, alpha = 1)

Comment est-ce que le taux de vaccination pour la Rougeole peut être au dessus du taux de vaccination globale pour ces écoles des États-Unis???

Seulement si te taux de vaccination globale implique que se sont les étudiants qui ont reçu tous les vaccins requis par leur calendrier de vaccination.

Alors, tu veux en savoir plus sur ma démarche? Abonne-toi à mon infolettre pour savoir quand est-ce que le prochain épisode de podcast sera disponible. J’y expliquerai toute la réflexion et les concepts de data visualisation qui ont menés à la création de cette viz.

Johanie Fournier, agr.

Agriculture, données, visualisation

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