This data is from the CDC website at https://data.cdc.gov/Foodborne-Waterborne-and-Related-Diseases/Botulism/66i6-hisz and has counts of confirmed botulism cases in the United States by state, year, type of botulism toxin, and transmission type. I will be cleaning an working with all 5 variables.
#read in the data and assign to botulismdatabotulismdata<-read_csv("dataanalysis-exercise/rawdata/Botulism.csv")
Rows: 2280 Columns: 5
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): State, BotType, ToxinType
dbl (2): Year, Count
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#checking structure and summary of data setstr(botulismdata)
State Year BotType ToxinType
Length:2280 Min. :1899 Length:2280 Length:2280
Class :character 1st Qu.:1976 Class :character Class :character
Mode :character Median :1993 Mode :character Mode :character
Mean :1986
3rd Qu.:2006
Max. :2017
Count
Min. : 1.000
1st Qu.: 1.000
Median : 1.000
Mean : 3.199
3rd Qu.: 3.000
Max. :59.000
Change to Factors
BotType and ToxinType are both classified as characters but would be better represented as factors so I will change them.
#change BotType and ToxinType to factor variablesbotulismdata$BotType <-as.factor(botulismdata$BotType)botulismdata$ToxinType <-as.factor(botulismdata$ToxinType)#confirm they were both changedstr(botulismdata)
#Check is there is any missing datacolSums(is.na(botulismdata))
State Year Transmission Type Toxin Type
34 0 0 0
Count
0
Remove NAs
All the NA entries appear to be in the state column. There are 2280 observations so I decided to just remove the 34 NA values since they don’t make up a large percentage of the data overall.
#Drop the NA values in the state columnbotulismdata <-drop_na(botulismdata, State)#Confirm number of observations dropped from 2280 to 2246str(botulismdata)
##install and load psych package to use describe to see a summary of data, also run summarylibrary(psych)
Attaching package: 'psych'
The following objects are masked from 'package:ggplot2':
%+%, alpha
summary(botulismdata)
State Year Transmission Type Toxin Type
Length:2246 Min. :1899 Foodborne: 899 A :958
Class :character 1st Qu.:1976 Infant :1124 B :778
Mode :character Median :1992 Other : 72 Unknown:369
Mean :1985 Wound : 151 E : 72
3rd Qu.:2006 F : 41
Max. :2017 Bf : 8
(Other): 20
Count
Min. : 1.000
1st Qu.: 1.000
Median : 1.000
Mean : 3.223
3rd Qu.: 3.000
Max. :59.000
describe(botulismdata)
vars n mean sd median trimmed mad min max range
State* 1 2246 23.72 15.98 24 23.36 25.20 1 50 49
Year 2 2246 1985.50 26.60 1992 1989.05 22.24 1899 2017 118
Transmission Type* 3 2246 1.77 0.80 2 1.62 0.00 1 4 3
Toxin Type* 4 2246 5.83 4.83 7 5.42 8.90 1 14 13
Count 5 2246 3.22 4.66 1 2.05 0.00 1 59 58
skew kurtosis se
State* 0.06 -1.51 0.34
Year -1.05 0.23 0.56
Transmission Type* 1.22 1.51 0.02
Toxin Type* 0.47 -1.09 0.10
Count 3.90 21.46 0.10
AKbotulism<-readRDS("cleanbotulismdata.RData") #load() the dataset was shooting errors so I renamed it to read it inhead(AKbotulism)
# A tibble: 6 × 5
State Year `Transmission Type` `Toxin Type` Count
<chr> <dbl> <fct> <fct> <dbl>
1 Alaska 1947 Foodborne Unknown 3
2 Alaska 1948 Foodborne Unknown 4
3 Alaska 1950 Foodborne E 5
4 Alaska 1952 Foodborne E 1
5 Alaska 1956 Foodborne E 5
6 Alaska 1959 Foodborne E 10
I’m interested to see how the toxin types and counts varied by year.
Grouped By State
library(ggplot2)ggplot()+geom_line(aes(x=Year, y=Count, group = State), data=AKbotulism, alpha =0.2)+facet_wrap(.~`Toxin Type`) +theme_bw()
`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?
`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?
`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?
`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?
Well that was confusing, let’s try it grouped by Transmission Type.
Grouped by Transmission Type
ggplot()+geom_line(aes(x=Year, y=Count, group =`Transmission Type`), data=AKbotulism, alpha =0.2)+facet_wrap(.~`Toxin Type`) +theme_bw()
`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?
`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?
`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?
We can see some better patterns here if we squint really hard, but faceting by toxin type allows for too many options to get a good idea of what is happening within the data. Toxins A, B, E, and Unknown seem to be the most prevelant types, so I want to focus in on them
ggplot()+geom_line(aes(x=Year, y=Count, group =`Transmission Type`), data=ABE, alpha =0.35)+facet_wrap(.~`Toxin Type`) +theme_bw()
Awesome, now we can start to see the patterns and the ranges better among the data. It seems that Type A is popular and only continues to grow in prevalence. B seems to as well though on a smaller scale. I’m not really sure what’s going on with E, and the number of unknown seems to be decreasing. This may be because these cases are becoming better identified and may account for some of the increase in A and B types (all right around 1975).
While I grouped by Transmission Type to better see the data, that grouping isn’t telling us much at the moments, so let’s see if there’s any patterns within it.
ggplot()+geom_point(aes(x=Year, y=Count, color =`Transmission Type`), data=ABE, alpha =0.35)+facet_wrap(.~`Toxin Type`) +theme_bw()
This is pretty cool! Most of the new cases for A and B are transmitted by infants with foodborne transmission nearly dying out overnight. The number of unknown types of foodborne transmission also snuffed out around the same time - I wonder if there was new food safety legislation in place that would explain the mass decrease. However, Type E remains mainly foodborne and pretty constant across time. Also with the drastic increase in infant transmission around when foodborne transmission ended I wonder if the classifications for transmission were changed. There’s no instance of “infant” transmission until 1975.
I don’t know enough about botulism to provide more commentary, but this posed some interesting questions I’ll keep in mind if this topic arises again.
