Project 2b - Chickens
Dong-Min Kim
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(tidyverse)## Warning: package 'tidyverse' was built under R version 4.1.3## -- 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## Warning: package 'ggplot2' was built under R version 4.1.2## Warning: package 'tibble' was built under R version 4.1.2## Warning: package 'tidyr' was built under R version 4.1.2## Warning: package 'readr' was built under R version 4.1.2## Warning: package 'purrr' was built under R version 4.1.2## Warning: package 'dplyr' was built under R version 4.1.2## Warning: package 'stringr' was built under R version 4.1.2## Warning: package 'forcats' was built under R version 4.1.2## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()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.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.
I would like to explore the data frame.
class(ch_df)## [1] "spec_tbl_df" "tbl_df" "tbl" "data.frame"dim(ch_df) # the dataset contains 71 observations and 2 variables.## [1] 71 2summary(ch_df)## weight feed
## Length:71 Length:71
## Class :character Class :character
## Mode :character Mode :characteris.na(ch_df)## weight feed
## [1,] FALSE FALSE
## [2,] FALSE FALSE
## [3,] TRUE TRUE
## [4,] FALSE FALSE
## [5,] FALSE FALSE
## [6,] FALSE FALSE
## [7,] FALSE FALSE
## [8,] FALSE FALSE
## [9,] FALSE FALSE
## [10,] FALSE FALSE
## [11,] FALSE FALSE
## [12,] FALSE FALSE
## [13,] FALSE FALSE
## [14,] FALSE FALSE
## [15,] FALSE FALSE
## [16,] FALSE FALSE
## [17,] FALSE FALSE
## [18,] FALSE FALSE
## [19,] FALSE FALSE
## [20,] FALSE FALSE
## [21,] FALSE TRUE
## [22,] FALSE FALSE
## [23,] FALSE FALSE
## [24,] FALSE FALSE
## [25,] FALSE FALSE
## [26,] FALSE FALSE
## [27,] FALSE FALSE
## [28,] FALSE FALSE
## [29,] FALSE FALSE
## [30,] TRUE FALSE
## [31,] TRUE FALSE
## [32,] FALSE FALSE
## [33,] FALSE FALSE
## [34,] FALSE FALSE
## [35,] FALSE FALSE
## [36,] FALSE FALSE
## [37,] FALSE FALSE
## [38,] FALSE FALSE
## [39,] FALSE FALSE
## [40,] FALSE FALSE
## [41,] TRUE FALSE
## [42,] FALSE FALSE
## [43,] FALSE TRUE
## [44,] FALSE FALSE
## [45,] FALSE FALSE
## [46,] FALSE FALSE
## [47,] FALSE FALSE
## [48,] FALSE FALSE
## [49,] FALSE FALSE
## [50,] FALSE FALSE
## [51,] FALSE FALSE
## [52,] TRUE FALSE
## [53,] FALSE FALSE
## [54,] FALSE FALSE
## [55,] FALSE FALSE
## [56,] FALSE FALSE
## [57,] FALSE FALSE
## [58,] FALSE FALSE
## [59,] FALSE FALSE
## [60,] TRUE FALSE
## [61,] FALSE FALSE
## [62,] FALSE TRUE
## [63,] FALSE FALSE
## [64,] FALSE FALSE
## [65,] FALSE FALSE
## [66,] FALSE FALSE
## [67,] FALSE FALSE
## [68,] FALSE FALSE
## [69,] FALSE TRUE
## [70,] FALSE FALSE
## [71,] TRUE FALSEsum(is.na(ch_df$weight))## [1] 7sum(is.na(ch_df$feed))## [1] 5colSums(is.na(ch_df))## weight feed
## 7 5Clean “na” to NA.
ch_df <-replace(ch_df,is.na(ch_df),"NA")
ch_df[ch_df=="na"] <- "NA" # I consider
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 rowsconvert the weight variable to numeric
ch_df$weight <- as.numeric(as.character(ch_df$weight))## Warning: NAs introduced by coercionch_df## # A tibble: 71 x 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 rowssum(is.na(ch_df))## [1] 15Now 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]%.”
# Percentage of missing data in the weight column : 7.042252 %
# Percentage of missing data in the feed column : 4.225352 %
# Percentage of missing data in the mod_ch_df dataframe : 11.26761 %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.
totalcells = prod(dim(ch_df)) # calculate the product of dimensions of the dataframe.
totalcells## [1] 142missingcells = sum(is.na(ch_df$weight)) # calculate the missing cells in the weight column.
missingcells## [1] 15percentage = (missingcells*100)/(totalcells) # calculate the percentage of the missing cells in the weight column.
percentage## [1] 10.56338missingcells = sum(is.na(ch_df$feed)) # calculate the missing cells in the weight column.
missingcells## [1] 0percentage = (missingcells*100)/(totalcells) # calculate the percentage of the missing cells in the weight column.
percentage## [1] 0missingcells = sum(is.na(ch_df)) # calculate the missing cells in the weight column.
missingcells## [1] 15percentage = (missingcells*100)/(totalcells) # calculate the percentage of the missing cells in the weight column.
percentage## [1] 10.56338Data 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$weight <- as.numeric(as.character(ch_df$weight))
sapply(ch_df, class)## weight feed
## "numeric" "character"suppressWarnings(weight <- as.numeric(ch_df$weight))
summary(ch_df)## weight feed
## Min. :108.0 Length:71
## 1st Qu.:211.2 Class :character
## Median :261.5 Mode :character
## Mean :264.1
## 3rd Qu.:325.5
## Max. :423.0
## NA's :15- Find the feed that has the maximum median chicken weight.
weight_mean1 <- lm(formula = weight ~ feed, ch_df)
summary(weight_mean1)##
## Call:
## lm(formula = weight ~ feed, data = ch_df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -98.43 -32.15 0.00 30.34 122.00
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 189.50 27.41 6.912 1.23e-08 ***
## feedcasein 124.75 33.58 3.716 0.000548 ***
## feedhorsebean -28.79 34.37 -0.838 0.406566
## feedlinseed 42.37 33.58 1.262 0.213279
## feedmeatmeal 114.93 34.37 3.344 0.001648 **
## feedNA 92.50 36.78 2.515 0.015463 *
## feednot sure 139.50 61.30 2.276 0.027568 *
## feedsoybean 52.38 33.58 1.560 0.125632
## feedsunflower 163.36 34.37 4.754 2.00e-05 ***
## feedunknown 73.50 61.30 1.199 0.236659
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 54.83 on 46 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.5934, Adjusted R-squared: 0.5138
## F-statistic: 7.459 on 9 and 46 DF, p-value: 1.268e-06- Create a quick histogram of the weight from the original data frame using the Base R Plotting package.
Weight <-ch_df$weight
hist(Weight)
- Create a box plot with feed type as the X axis.
library(ggplot2)
p <- ggplot(data=ch_df, aes(x=feed,y=weight, fill = feed))+geom_boxplot()+stat_summary(fun = mean, geom = "point", shape = 20, size = 3,show.legend = FALSE)+theme(legend.position = "none")+scale_fill_brewer(palette = "Set1")
p## Warning: Removed 15 rows containing non-finite values (stat_boxplot).## Warning: Removed 15 rows containing non-finite values (stat_summary).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors
- 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 mean is the avearage number of the data set and the median is the middle number in the data set. In the boxplot, A vertical line goes through the box at the median. The point in the box represents the mean.
# Yes. There is outliers in the chart.- 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.
# I have created a boxplot with x and y axis in the number 8.