WK06-Analyzation_of_Case_Study_2
Priyanka Veeranki
2022-10-23
Team member: Priyanka Veeranki Sneha Vanguri Amod Ashok Panchal Sai Sudha Upadrasta
#Loading necessary Packages
library(ggplot2)
library(ggcorrplot)## Warning: package 'ggcorrplot' was built under R version 4.2.1library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.2.1## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ tibble 3.1.7 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ✔ purrr 0.3.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()#Reading Data Initially we need to read the data. We are provided the excel file Final_Nutrition2.xlsx
Nut.df <- read_csv("C:/Users/Public/Final_Nutrition2.csv") %>%
as.data.frame()## Rows: 8789 Columns: 42
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Shrt_Desc, Long_Desc, FdGrp_Desc, GmWt_Desc1, GmWt_Desc2
## dbl (37): NDB_No, Water_g, Energ_Kcal, Protein_g, Lipid_Tot_g, Fiber_TD_g, S...
##
## 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.#Merging After loading the data, we will see if we can merge different data sets. In this case study we are provided just one data set - Final_Nutrition2.csv. Now, check structure of the data before doing EDA.
Nut.df %>% str()## 'data.frame': 8789 obs. of 42 variables:
## $ NDB_No : num 1001 1002 1003 1004 1005 ...
## $ Shrt_Desc : chr "BUTTER,WITH SALT" "BUTTER,WHIPPED,W/ SALT" "BUTTER OIL,ANHYDROUS" "CHEESE,BLUE" ...
## $ Long_Desc : chr "Butter, salted" "Butter, whipped, with salt" "Butter oil, anhydrous" "Cheese, blue" ...
## $ FdGrp_Desc : chr "Dairy and Egg Products" "Dairy and Egg Products" "Dairy and Egg Products" "Dairy and Egg Products" ...
## $ Water_g : num 15.87 16.72 0.24 42.41 41.11 ...
## $ Energ_Kcal : num 717 718 876 353 371 334 300 376 404 387 ...
## $ Protein_g : num 0.85 0.49 0.28 21.4 23.24 ...
## $ Lipid_Tot_g : num 81.1 78.3 99.5 28.7 29.7 ...
## $ Fiber_TD_g : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Sugar_Tot_g : num 0.06 0.06 0 0.5 0.51 0.45 0.46 NA 0.48 NA ...
## $ Calcium_mg : num 24 23 4 528 674 184 388 673 710 643 ...
## $ Iron_mg : num 0.02 0.05 0 0.31 0.43 0.5 0.33 0.64 0.14 0.21 ...
## $ Magnesium_mg : num 2 1 0 23 24 20 20 22 27 21 ...
## $ Phosphorus_mg : num 24 24 3 387 451 188 347 490 455 464 ...
## $ Potassium_mg : num 24 41 5 256 136 152 187 93 76 95 ...
## $ Sodium_mg : num 643 583 2 1146 560 ...
## $ Zinc_mg : num 0.09 0.05 0.01 2.66 2.6 2.38 2.38 2.94 3.64 2.79 ...
## $ Copper_mg : num 0 0.01 0.001 0.04 0.024 0.019 0.021 0.024 0.03 0.042 ...
## $ Manganese_mg : num 0 0.001 0 0.009 0.012 0.034 0.038 0.021 0.027 0.012 ...
## $ Selenium_g : num 1 0 0 14.5 14.5 14.5 14.5 14.5 28.5 14.5 ...
## $ Vit_C_mg : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Thiamin_mg : num 0.005 0.007 0.001 0.029 0.014 0.07 0.028 0.031 0.029 0.046 ...
## $ Riboflavin_mg : num 0.034 0.064 0.005 0.382 0.351 0.52 0.488 0.45 0.428 0.293 ...
## $ Niacin_mg : num 0.042 0.022 0.003 1.016 0.118 ...
## $ Vit_B6_mg : num 0.003 0.008 0.001 0.166 0.065 0.235 0.227 0.074 0.066 0.074 ...
## $ Folate_Tot_g : num 3 4 0 36 20 65 62 18 27 18 ...
## $ Folic_Acid_g : num 0 0 0 0 0 0 0 0 0 0 ...
## $ Food_Folate_g : num 3 4 0 36 20 65 62 18 27 18 ...
## $ Choline_Tot__mg: num 18.8 18.8 22.3 15.4 15.4 15.4 15.4 NA 16.5 NA ...
## $ Vit_B12_g : num 0.17 0.07 0.01 1.22 1.26 1.65 1.3 0.27 1.1 0.83 ...
## $ Lycopene_g : num 0 0 0 0 0 0 0 NA 0 NA ...
## $ Vit_E_mg : num 2.32 1.37 2.8 0.25 0.26 0.24 0.21 NA 0.71 NA ...
## $ Vit_D_g : num 0 0 0 0.5 0.5 0.5 0.4 NA 0.6 NA ...
## $ Vit_K_g : num 7 4.6 8.6 2.4 2.5 2.3 2 NA 2.4 NA ...
## $ FA_Sat_g : num 51.4 45.4 61.9 18.7 18.8 ...
## $ FA_Mono_g : num 21.02 19.87 28.73 7.78 8.6 ...
## $ FA_Poly_g : num 3.043 3.331 3.694 0.8 0.784 ...
## $ Cholestrl_mg : num 215 225 256 75 94 100 72 93 99 103 ...
## $ GmWt_1 : num 5 3.8 12.8 28.4 132 ...
## $ GmWt_Desc1 : chr "1 pat, (1\" sq, 1/3\" high)" "1 pat, (1\" sq, 1/3\" high)" "1 tbsp" "1 oz" ...
## $ GmWt_2 : num 14.2 9.4 205 17 113 144 246 NA 244 NA ...
## $ GmWt_Desc2 : chr "1 tbsp" "1 tbsp" "1 cup" "1 cubic inch" ...From the Nutrition dataset structure above, we can see what the dataset contains 42 variables. The database has 8,789 rows. The change we’re going to make the forecast on “Energ_Kcal” shows the calories of different foods. This is let’s look at the basic data for food data.
#now we are going to choose few columns along with the response variable
Nut.df <- Nut.df %>%
select(Energ_Kcal, Protein_g, Lipid_Tot_g,
Fiber_TD_g, Potassium_mg, Sodium_mg,
Manganese_mg )#Calculate summary statistics of the subset of data
summary(Nut.df)## Energ_Kcal Protein_g Lipid_Tot_g Fiber_TD_g
## Min. : 0.0 Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.: 91.0 1st Qu.: 2.38 1st Qu.: 0.95 1st Qu.: 0.000
## Median :191.0 Median : 8.02 Median : 5.13 Median : 0.700
## Mean :226.3 Mean :11.35 Mean : 10.55 Mean : 2.188
## 3rd Qu.:337.0 3rd Qu.:19.88 3rd Qu.: 13.72 3rd Qu.: 2.600
## Max. :902.0 Max. :88.32 Max. :100.00 Max. :79.000
## NA's :594
## Potassium_mg Sodium_mg Manganese_mg
## Min. : 0.0 Min. : 0.0 Min. : 0.0000
## 1st Qu.: 127.0 1st Qu.: 41.0 1st Qu.: 0.0150
## Median : 229.0 Median : 88.0 Median : 0.0820
## Mean : 279.4 Mean : 312.6 Mean : 0.6582
## 3rd Qu.: 335.5 3rd Qu.: 405.0 3rd Qu.: 0.3260
## Max. :16500.0 Max. :38758.0 Max. :328.0000
## NA's :426 NA's :83 NA's :2160Basic information gives us quick insights into the identity of the data. exchange. The types of changes for food information are very different from each other. This will be required to provide us with specific information. This is very fast.
#Checking Missing values count
apply(Nut.df, 2, FUN = function(x) sum(is.na(x)))## Energ_Kcal Protein_g Lipid_Tot_g Fiber_TD_g Potassium_mg Sodium_mg
## 0 0 0 594 426 83
## Manganese_mg
## 2160From the analysis above, we can see that many things are missing from it. Differences in Fiber_TD_g, Sugar_Tot_g, Calcium_mg and Iron_mg
#Removing the NA records
Nut.df <- Nut.df %>% na.omit()#Check dimension of the dataset againg after removing the missing values.
dim(Nut.df)## [1] 6209 7We have to seen, after removing NA records there are 6209 rows available
#Let’s check the distribution of the response variable
ggplot(data = Nut.df,
mapping = aes(Energ_Kcal)) +
geom_histogram(binwidth = 50, bins = 30, fill = "coral")
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## - attr(*, "complete")= logi TRUE
## - attr(*, "validate")= logi TRUEFrom the graph above we can see our calorie distribution policy changes. We can see that there is a lot of food available low calorie.
#Scatter plot with linear fitted lines
gather(Nut.df[, -1], "Variable", "Value") %>%
ggplot(mapping = aes(y=Value, fill = Variable)) +
facet_wrap(~ Variable, scales = "free_y") +
geom_boxplot(color = "green") +
# geom_smooth(method = "lm", col = "coral") +
theme_minimal() +
theme(legend.position = "none")
From the above plots, we can see there are so many extreme points are
there. It’s very difficult to get an accurate values
#Scatter plot with linear fitted lines
gather(Nut.df, "Variable", "Value", -Energ_Kcal) %>%
ggplot(mapping = aes(Value, Energ_Kcal)) +
facet_wrap(~ Variable, scales = "free_x") +
geom_point(color = "green") +
geom_smooth(method = "lm", col = "coral") +
theme_minimal()## `geom_smooth()` using formula 'y ~ x'
From the above plots, the independent variables are positively
correlated with response variable.
#correlation matrix
cm <- round(cor(Nut.df), 2)
cm## Energ_Kcal Protein_g Lipid_Tot_g Fiber_TD_g Potassium_mg Sodium_mg
## Energ_Kcal 1.00 0.17 0.80 0.22 0.12 0.05
## Protein_g 0.17 1.00 0.13 -0.10 0.21 -0.01
## Lipid_Tot_g 0.80 0.13 1.00 0.01 -0.01 0.02
## Fiber_TD_g 0.22 -0.10 0.01 1.00 0.36 -0.01
## Potassium_mg 0.12 0.21 -0.01 0.36 1.00 0.00
## Sodium_mg 0.05 -0.01 0.02 -0.01 0.00 1.00
## Manganese_mg 0.05 0.03 0.00 0.12 0.15 0.00
## Manganese_mg
## Energ_Kcal 0.05
## Protein_g 0.03
## Lipid_Tot_g 0.00
## Fiber_TD_g 0.12
## Potassium_mg 0.15
## Sodium_mg 0.00
## Manganese_mg 1.00#Plot the resulting matrix
ggcorrplot(corr = cm,lab = T, type= "lower")
The heatmap and correlation matrix provide complete information it is
not important that there is a large relationship between variables.
Some data preprocessing and recognized the reason for the absence. We removed the missing goods. Once upon a time we We had world data, we did a special EDA on nutrition data and The distribution was made for the version that shows Calories.We then performed a correlation analysis and found no significance by reason of the variables