Project 2b - Chickens
FILL IN YOUR NAME
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(dplyr)
library(ggplot2)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("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.print(ch_df)## # A tibble: 71 x 2
## weight feed
## <chr> <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 rowsClean 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.
sum(is.na(ch_df))## [1] 12replace_na(ch_df)## # A tibble: 71 x 2
## weight feed
## <chr> <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 rowsNow 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]%.”
mean(is.na(ch_df$weight)) * 100## [1] 9.859155mean(is.na(ch_df$feed)) * 100## [1] 7.042254mean(is.na(ch_df)) * 100## [1] 8.450704EXTRA 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.
# fill in your code hereData 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)## Warning: NAs introduced by coercionch_dfmedmen <- ch_df %>%
group_by(feed) %>%
summarise(mean_weight= mean(weight, na.rm = TRUE), median_weight= median(weight, na.rm = TRUE))
ch_dfmedmen## # A tibble: 11 x 3
## feed mean_weight median_weight
## <chr> <dbl> <dbl>
## 1 ? 190. 161
## 2 casein 314. 325
## 3 horsebean 161. 160
## 4 linseed 232. 236.
## 5 meatmeal 304. 315
## 6 na 216 216
## 7 not sure 329 329
## 8 soybean 242. 249
## 9 sunflower 353. 340
## 10 unknown 263 263
## 11 <NA> 298. 286.- Find the feed that has the maximum median chicken weight.
ch_dfmedmen[which.max(ch_dfmedmen$median_weight),]## # A tibble: 1 x 3
## feed mean_weight median_weight
## <chr> <dbl> <dbl>
## 1 sunflower 353. 340- Create a quick histogram of the weight from the original data frame using the Base R Plotting package.
class(ch_df$weight)## [1] "numeric"weight_chdf <- as.numeric(ch_df$weight)
hist(weight_chdf)
- Create a box plot with feed type as the X axis.
ch_df$weight <- as.numeric(ch_df$weight)p1 <- ch_df %>%
ggplot(aes(x= feed, y= weight, group = feed)) + geom_boxplot()
p1## Warning: Removed 15 rows containing non-finite values (stat_boxplot).
- 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.
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 :15#Group by FEED types (note to self)
ch_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: 11 x 7
## feed min Q1 Median Mean Q3 Max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ? 141 150 161 190. 200. 295
## 2 casein 222 277. 325 314. 356 379
## 3 horsebean 108 142. 160 161. 174. 227
## 4 linseed 148 205 236. 232. 263. 309
## 5 meatmeal 206 280. 315 304. 334. 380
## 6 na 216 216 216 216 216 216
## 7 not sure 329 329 329 329 329 329
## 8 soybean 158 225 249 242. 268 327
## 9 sunflower 318 328 340 353. 366. 423
## 10 unknown 263 263 263 263 263 263
## 11 <NA> 217 247 286. 298. 338 404- 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.
p2 <- p1 + ggtitle("Feed and Weights - Chickens") +
xlab("Feed Type") + ylab ("Weight")
p2## Warning: Removed 15 rows containing non-finite values (stat_boxplot).