Project 2b - Chickens
Sheyla Daccarett
Data Import
Download chickens.csv to your working directory. Make sure to set your working directory appropriately! This dataset was created by modifying the R built-in dataset chickwts.
Import the chickens.csv data into R. Store it in a data.frame named ch_df and print out the entire ch_df to the screen.
library(readr)
ch_df <- read_csv("C:/Users/Mitcheyla$/Desktop/DATA 101, Fall Semester/chickens.csv")## Rows: 71 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): weight, feed
##
## 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.View(ch_df)Clean Missing Values
There are some missing values in this dataset. Unfortunately they are represented in a number of different ways.
- Clean up this dataset by doing the following:
- Calculate how many elements in the original ch_df are recognized as NA by R.
- Change all of the missing elements to NA in ch_df.
- You do NOT have to fill in the missing values. Just leave them as NA.
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ dplyr 1.0.10
## ✔ tibble 3.1.8 ✔ stringr 1.4.1
## ✔ tidyr 1.2.1 ✔ forcats 0.5.2
## ✔ purrr 0.3.5
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()sum(is.na(ch_df))## [1] 12ch_df <- ch_df %>%
mutate(weight = replace(weight, weight == "na", NA)) %>%
mutate(weight = replace(weight, weight == "N/A", NA)) %>%
mutate(weight = replace(weight, weight == "", NA)) %>%
mutate(weight = replace(weight, weight == "?", NA)) %>%
mutate(weight = replace(weight, weight == "-", NA)) %>%
mutate(weight = replace(weight, weight == "NA", NA))
ch_df <- ch_df %>%
mutate(feed = replace(feed, feed == "na", NA)) %>%
mutate(feed = replace(feed, feed == "N/A", NA)) %>%
mutate(feed = replace(feed, feed == "", NA)) %>%
mutate(feed = replace(feed, feed == "?", NA)) %>%
mutate(feed = replace(feed, feed == "NA", NA))Now that the dataset is clean, let’s see what percentage of our data is missing.
- Calculate the percentage of missing data from the weight column, the feed column, and the entire dataset. Print out each result in the following format: “Percentage of missing data in [fill in the column or dataset name]: [fill in percentage]%.”
((sum(is.na(ch_df$weight))/(length(ch_df$weight))*100))## [1] 21.12676print("Percentage of missing data in the weight column: 21.12676%")## [1] "Percentage of missing data in the weight column: 21.12676%"((sum(is.na(ch_df$feed))/(length(ch_df$feed))*100))## [1] 14.08451print("Percentage of missing data in the feed column: 14.08451%")## [1] "Percentage of missing data in the feed column: 14.08451%"((sum(is.na(ch_df))/(length(ch_df$weight)+length(ch_df$feed))*100))## [1] 17.60563print("Percentage of missing data in the entire dataset: 17.60563%")## [1] "Percentage of missing data in the entire dataset: 17.60563%"EXTRA CREDIT (Optional): Figure out how to create these print statements so that the name and percentage number are not hard-coded into the statement. In other words, so that the name and percentage number are read in dynamically (for example, from a variable, from a function call, etc.) instead of just written in the statement. Please ask me for clarification if necessary.
x<-((sum(is.na(ch_df$weight))/(length(ch_df$weight))*100))
y<-((sum(is.na(ch_df$feed))/(length(ch_df$feed))*100))
z<-((sum(is.na(ch_df))/(length(ch_df$weight)+length(ch_df$feed))*100))
cat("weight column:", x,"\n")## weight column: 21.12676cat("feed column:", y,"\n")## feed column: 14.08451cat("entire dataset:", z, "\n")## entire dataset: 17.60563Data Investigation
- Group the data by feed and find the mean and median weight for each group. Your result should be a new data frame with the group means in a column named weight_mean and the group medians in a column named weight_median. Save this new data frame; you can name the data frame as you wish. (Remember that variable names should be somewhat descriptive of what they contain.)
ch_df$weight<-as.character(ch_df$weight)
ch_df$weight<-as.numeric(ch_df$weight)ch_df2<-ch_df %>%
group_by(feed)%>%
summarise(weight_mean=mean(weight,na.rm = TRUE),weight_median=median(weight,na.rm = TRUE))ch_df2## # A tibble: 9 × 3
## feed weight_mean weight_median
## <chr> <dbl> <dbl>
## 1 casein 314. 325
## 2 horsebean 161. 160
## 3 linseed 232. 236.
## 4 meatmeal 304. 315
## 5 not sure 329 329
## 6 soybean 242. 249
## 7 sunflower 353. 340
## 8 unknown 263 263
## 9 <NA> 241. 217- Find the feed that has the maximum median chicken weight.
which.max(ch_df2$weight_median)## [1] 7- Create a quick histogram of the weight from the original data frame using the Base R Plotting package.
ch_df## # A tibble: 71 × 2
## weight feed
## <dbl> <chr>
## 1 206 meatmeal
## 2 140 horsebean
## 3 NA <NA>
## 4 318 sunflower
## 5 332 casein
## 6 NA horsebean
## 7 216 <NA>
## 8 143 horsebean
## 9 271 soybean
## 10 315 meatmeal
## # … with 61 more rowshist(ch_df$weight, col="blue", main="The Weight of Chickens",xlab="Weight in Grams",ylab ="Frequency")
- Create a box plot with feed type as the X axis.
boxplot(weight ~ feed, data = ch_df, col=c("blue","brown","green","orange","red","yellow"),
varwidth = TRUE, notch = TRUE, main = "chicken data",
ylab = "Weight in gram")## Warning in (function (z, notch = FALSE, width = NULL, varwidth = FALSE, : some
## notches went outside hinges ('box'): maybe set notch=FALSE
- What do these charts tell you? Does the box plot confirm you mean and median calculations? If yes, how so? Are there any outliers displayed in either chart? Confirm this using the five number summary for specific feed types and the IQR.
The histogram shows there is one distinct peak indicating the most frequent number of 200-250 and 300-350, and there is no significant skew. Also, it does not show any outlier.
The box plot shows sunflower has the maximum mean and median chicken weight, follows by casein, and the horsebean has the least mean and median. Also, we can see an outlier on the horsebean feed.
summary(ch_df)## weight feed
## Min. :108.0 Length:71
## 1st Qu.:211.2 Class :character
## Median :261.5 Mode :character
## Mean :264.1
## 3rd Qu.:325.5
## Max. :423.0
## NA's :15ch_df %>%
group_by(feed) %>%
summarize(min = min(weight, na.rm = TRUE),
q1 = quantile(weight, 0.25, na.rm = TRUE),
median = median(weight, na.rm = TRUE),
mean = mean(weight, na.rm = TRUE),
q3 = quantile(weight, 0.75, na.rm = TRUE),
max = max(weight, na.rm = TRUE))## # A tibble: 9 × 7
## feed min q1 median mean q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 casein 222 277. 325 314. 356 379
## 2 horsebean 108 142. 160 161. 174. 227
## 3 linseed 148 205 236. 232. 263. 309
## 4 meatmeal 206 280. 315 304. 334. 380
## 5 not sure 329 329 329 329 329 329
## 6 soybean 158 225 249 242. 268 327
## 7 sunflower 318 328 340 353. 366. 423
## 8 unknown 263 263 263 263 263 263
## 9 <NA> 141 169 217 241. 295 404- Recreate the box plot above using ggplot and add x and y axis labels as well as a title. Describe any differences you notice about the two plots.
library(ggplot2)ch_dfplot<-ggplot(ch_df, aes(x=feed, y=weight, fill=feed)) +
geom_boxplot()+
labs(title="Interrelationship between Type of Feed and Weight of Chickens",x="Feed", y = "Weight of Chickens")
ch_dfplot + scale_fill_brewer(palette="Spectral") + theme_light()## Warning: Removed 15 rows containing non-finite values (`stat_boxplot()`).
In the above charts,we have less details. GGplot gives access to change the position and the appearance of the graph. Also, in the boxplot above, we could only see one outlier, but in this one, we can see two outliers.