library (modeest) #allows me to find the mode for #1B
library(tidyverse) #for other questions in this HW ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.6
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.2 ✔ tibble 3.3.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.2.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors#1: Import Regis Dataset
relig<-read.csv("relig-baseline.csv") #imported the dataset after setting the appropriate working directory from my computer. #1A: How many rows and columns of data are there in the data set?
dim(relig) #this tells us the dimensional of the dataset; there are 138 rows *and* 373 columns## [1] 138 373nrow(relig) #this specifically tells us there are 138 rows## [1] 138ncol(relig) #this specifically tells us there are 373 columns## [1] 373#1B Are there any missing values in the participants’ ages?
na_age<-sum(is.na(relig$age)) #this adds up the number of N/A entries in the "age" column.
print(na_age) #this tells us there are 0 missing values in the "ages" column## [1] 0#1B - Average age of participants in dataset
mean(relig$age) #this tells us the average age of participants in dataset, which is 23.59.## [1] 23.5942#1B - Most frequent age of participants in dataset
mfv(relig$age) #the mode tells us that the most frequently occurring age in this dataset, which 21 years-old## [1] 21#1C: In the dataset, the variables depress1, depress2, …, depress21 are measures of depression. Select these columns and save as a subset called relig_depress using the select() and starts_with() functions in Tidyverse.
relig_depress<-relig%>%
dplyr::select(starts_with("depress")) #this creates a new relig dataset that only contains the columns that start with 'depress' from the original religion dataset
relig_depress #this is the dataset with only depress 1, depress 2, etc. as its columns## depress1 depress2 depress3 depress4 depress5 depress6 depress7 depress8
## 1 2 2 1 2 2 1 2 2
## 2 0 0 0 0 0 0 0 3
## 3 0 1 0 0 0 0 0 1
## 4 0 0 1 0 0 0 0 0
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## 104 NA NA NA NA NA NA NA NA
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## depress9 depress10 depress11 depress12 depress13 depress14 depress15
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## 96 0 0 1 0 0 0 1
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## 100 0 0 1 0 0 0 0
## 101 0 5 1 1 1 2 0
## 102 0 0 0 0 0 0 0
## 103 0 0 0 0 0 0 0
## 104 NA NA NA NA NA NA NA
## 105 0 0 0 0 0 0 1
## 106 0 0 0 0 0 0 1
## 107 0 1 0 0 0 0 1
## 108 0 1 0 0 0 0 1
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## depress16 depress17 depress18 depress19 depress20 depress21
## 1 2 0 2 0 1 2
## 2 3 0 1 1 1 0
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## 103 0 0 1 1 0 0
## 104 NA NA NA NA NA NA
## 105 0 0 0 1 0 0
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## 135 1 1 1 0 1 0
## 136 2 0 0 1 2 1
## 137 0 0 0 0 1 0
## 138 1 0 1 1 0 0#1D: Calculate the total score of depression by adding the 21 depression variables together for each participant (set na.rm = TRUE) and add this total score as a new variable to the relig_depress subset, and save the new subset as relig_depress_total
relig_depress_total <-relig_depress %>%
mutate(relig_depress_sum=rowSums(select(.,starts_with("depress")), na.rm=TRUE))
#'mutate' allows us to create a new column (relig_depress_sum) where each item is the sum of what was in the previous columns with 'depress' in the title. na.rm = TRUE ensures that all N/A entries are ignored during calculation
relig_depress_total #lets us view the dataset with this new column ## depress1 depress2 depress3 depress4 depress5 depress6 depress7 depress8
## 1 2 2 1 2 2 1 2 2
## 2 0 0 0 0 0 0 0 3
## 3 0 1 0 0 0 0 0 1
## 4 0 0 1 0 0 0 0 0
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## 6 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0
## 8 1 1 1 1 1 0 0 0
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## 10 0 0 1 0 0 0 0 1
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## 12 0 1 1 1 0 1 1 1
## 13 1 1 0 1 0 3 3 2
## 14 0 0 0 0 0 0 0 0
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## 103 0 0 1 0 0 0 0 1
## 104 NA NA NA NA NA NA NA NA
## 105 0 1 1 0 0 0 1 1
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## 107 0 1 1 0 1 0 0 2
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## 117 1 0 1 1 1 1 0 0
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## depress9 depress10 depress11 depress12 depress13 depress14 depress15
## 1 0 5 2 1 0 0 2
## 2 1 1 0 0 3 0 1
## 3 0 0 1 0 1 0 0
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## 5 0 0 1 0 0 0 0
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## 84 0 0 0 0 0 0 0
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## 87 0 0 1 0 0 0 0
## 88 0 0 0 0 1 0 2
## 89 0 1 2 1 2 2 1
## 90 0 0 0 1 0 0 1
## 91 0 0 0 0 1 0 0
## 92 0 0 1 3 0 1 2
## 93 0 1 0 0 0 0 0
## 94 0 0 2 1 3 2 0
## 95 1 1 0 0 3 0 1
## 96 0 0 1 0 0 0 1
## 97 0 5 0 1 1 0 2
## 98 0 0 0 0 0 0 1
## 99 0 0 1 0 0 0 0
## 100 0 0 1 0 0 0 0
## 101 0 5 1 1 1 2 0
## 102 0 0 0 0 0 0 0
## 103 0 0 0 0 0 0 0
## 104 NA NA NA NA NA NA NA
## 105 0 0 0 0 0 0 1
## 106 0 0 0 0 0 0 1
## 107 0 1 0 0 0 0 1
## 108 0 1 0 0 0 0 1
## 109 0 1 0 0 0 1 0
## 110 0 0 0 0 0 0 0
## 111 0 0 0 0 1 0 0
## 112 1 1 1 1 1 1 1
## 113 0 0 0 0 0 2 1
## 114 0 0 0 0 1 0 1
## 115 0 0 0 0 0 2 1
## 116 0 1 3 1 3 2 2
## 117 0 0 0 3 0 0 0
## 118 0 1 0 0 0 0 0
## 119 0 0 0 0 0 0 1
## 120 0 0 0 0 0 1 0
## 121 0 0 0 0 0 0 1
## 122 0 0 1 0 0 0 0
## 123 0 0 0 0 1 1 1
## 124 0 0 1 0 0 2 0
## 125 0 0 0 0 0 0 0
## 126 0 0 3 3 1 0 3
## 127 0 0 2 1 0 0 0
## 128 0 0 0 0 0 0 0
## 129 0 0 0 0 0 0 0
## 130 1 0 1 0 1 1 1
## 131 0 0 0 0 1 0 1
## 132 1 5 1 2 2 3 2
## 133 0 0 0 0 0 0 0
## 134 0 1 1 1 0 0 0
## 135 0 0 0 0 0 0 1
## 136 0 0 0 0 0 0 1
## 137 0 0 0 0 0 0 1
## 138 1 5 0 0 0 1 0
## depress16 depress17 depress18 depress19 depress20 depress21
## 1 2 0 2 0 1 2
## 2 3 0 1 1 1 0
## 3 1 2 0 1 0 1
## 4 1 0 0 0 0 0
## 5 0 0 1 0 0 0
## 6 0 0 0 0 0 0
## 7 0 0 0 0 0 0
## 8 1 2 1 0 2 0
## 9 1 1 0 1 1 0
## 10 0 1 0 0 1 1
## 11 1 1 1 1 1 0
## 12 1 1 1 1 1 1
## 13 0 2 0 2 3 0
## 14 0 1 0 0 0 0
## 15 1 0 1 2 0 0
## 16 0 0 0 0 0 0
## 17 1 0 0 1 0 0
## 18 1 2 1 2 1 1
## 19 0 0 0 0 1 0
## 20 1 0 0 2 1 0
## 21 0 1 0 0 0 0
## 22 0 0 1 0 1 0
## 23 1 0 1 0 1 0
## 24 0 0 1 0 1 0
## 25 2 0 0 0 1 1
## 26 1 1 1 0 1 1
## 27 1 1 0 0 0 0
## 28 1 1 1 0 0 0
## 29 0 0 0 0 0 0
## 30 0 0 0 0 0 0
## 31 0 0 0 0 0 0
## 32 1 0 1 NA 1 0
## 33 0 0 0 0 0 0
## 34 1 0 0 1 1 0
## 35 3 1 2 1 0 0
## 36 1 0 1 0 1 0
## 37 1 1 2 1 2 0
## 38 2 0 0 0 1 0
## 39 0 0 1 0 0 0
## 40 0 1 0 0 1 0
## 41 1 2 2 1 1 2
## 42 0 1 0 1 0 0
## 43 0 1 1 1 0 0
## 44 0 0 1 0 1 1
## 45 0 1 0 1 0 1
## 46 0 0 2 1 0 1
## 47 1 2 1 2 2 3
## 48 0 1 1 1 1 0
## 49 1 1 0 0 0 0
## 50 1 0 0 0 0 0
## 51 0 1 1 0 2 0
## 52 1 1 1 1 1 0
## 53 0 0 3 1 1 0
## 54 0 1 1 0 0 0
## 55 1 1 0 1 1 0
## 56 1 0 1 0 1 1
## 57 1 2 1 2 3 2
## 58 0 0 1 1 0 0
## 59 1 1 1 1 1 0
## 60 1 1 0 0 1 0
## 61 1 0 0 0 1 0
## 62 0 0 1 0 1 0
## 63 1 0 0 0 0 0
## 64 0 0 2 2 1 0
## 65 0 0 0 0 0 0
## 66 1 0 0 0 0 0
## 67 1 1 0 1 1 0
## 68 1 0 3 0 1 1
## 69 2 0 1 1 1 1
## 70 0 1 0 1 1 1
## 71 1 0 1 0 0 3
## 72 1 1 0 0 1 0
## 73 1 0 0 0 0 1
## 74 1 1 1 1 1 0
## 75 0 0 1 0 0 0
## 76 0 1 0 0 0 0
## 77 1 0 0 0 0 0
## 78 1 0 1 1 1 0
## 79 1 0 0 1 1 0
## 80 1 0 0 2 1 0
## 81 1 0 0 1 1 0
## 82 1 0 1 2 1 0
## 83 3 0 3 3 3 3
## 84 0 0 0 0 0 0
## 85 0 0 0 0 0 0
## 86 1 1 1 1 2 0
## 87 1 0 1 1 1 1
## 88 0 0 1 1 1 1
## 89 3 1 1 2 1 1
## 90 1 2 2 0 0 1
## 91 0 0 1 1 1 0
## 92 1 2 1 2 3 0
## 93 1 0 0 0 1 0
## 94 1 1 3 2 0 0
## 95 0 1 0 0 1 0
## 96 1 0 3 2 1 0
## 97 0 0 1 3 0 1
## 98 0 0 0 0 0 0
## 99 1 1 1 0 1 0
## 100 0 0 1 0 0 0
## 101 1 1 0 1 1 0
## 102 0 0 0 0 1 0
## 103 0 0 1 1 0 0
## 104 NA NA NA NA NA NA
## 105 0 0 0 1 0 0
## 106 1 1 0 1 1 0
## 107 2 1 0 1 2 0
## 108 1 0 0 0 1 0
## 109 0 0 0 0 0 0
## 110 1 0 1 1 0 0
## 111 0 0 1 0 0 0
## 112 1 1 0 1 1 0
## 113 1 0 1 1 1 0
## 114 2 1 0 0 1 0
## 115 1 1 1 1 0 0
## 116 0 2 2 3 3 0
## 117 1 2 0 2 1 0
## 118 2 0 0 0 0 0
## 119 0 0 0 0 1 0
## 120 0 0 2 0 0 0
## 121 1 0 1 0 1 0
## 122 2 0 0 1 1 0
## 123 1 0 1 0 2 1
## 124 0 0 0 0 0 0
## 125 0 0 0 0 0 0
## 126 3 3 1 0 3 1
## 127 1 1 1 2 1 1
## 128 0 0 0 0 0 0
## 129 0 0 0 1 1 0
## 130 2 1 1 2 1 1
## 131 1 0 1 1 1 0
## 132 2 2 3 2 2 2
## 133 0 1 3 0 0 0
## 134 1 0 3 1 1 0
## 135 1 1 1 0 1 0
## 136 2 0 0 1 2 1
## 137 0 0 0 0 1 0
## 138 1 0 1 1 0 0
## relig_depress_sum
## 1 31
## 2 15
## 3 9
## 4 2
## 5 4
## 6 0
## 7 0
## 8 14
## 9 10
## 10 6
## 11 8
## 12 17
## 13 32
## 14 2
## 15 14
## 16 4
## 17 3
## 18 15
## 19 3
## 20 5
## 21 4
## 22 8
## 23 6
## 24 3
## 25 6
## 26 14
## 27 5
## 28 11
## 29 0
## 30 0
## 31 5
## 32 4
## 33 0
## 34 4
## 35 8
## 36 4
## 37 21
## 38 3
## 39 2
## 40 3
## 41 28
## 42 16
## 43 3
## 44 7
## 45 6
## 46 5
## 47 24
## 48 20
## 49 3
## 50 3
## 51 7
## 52 11
## 53 25
## 54 2
## 55 13
## 56 8
## 57 25
## 58 8
## 59 9
## 60 5
## 61 2
## 62 3
## 63 7
## 64 24
## 65 0
## 66 2
## 67 9
## 68 8
## 69 12
## 70 11
## 71 17
## 72 9
## 73 4
## 74 19
## 75 1
## 76 3
## 77 3
## 78 6
## 79 6
## 80 11
## 81 5
## 82 8
## 83 41
## 84 0
## 85 0
## 86 14
## 87 6
## 88 8
## 89 28
## 90 9
## 91 6
## 92 19
## 93 3
## 94 20
## 95 12
## 96 9
## 97 21
## 98 2
## 99 8
## 100 4
## 101 21
## 102 1
## 103 4
## 104 0
## 105 6
## 106 6
## 107 13
## 108 6
## 109 5
## 110 3
## 111 4
## 112 19
## 113 10
## 114 7
## 115 9
## 116 38
## 117 14
## 118 3
## 119 4
## 120 5
## 121 4
## 122 6
## 123 16
## 124 8
## 125 0
## 126 26
## 127 13
## 128 0
## 129 2
## 130 18
## 131 7
## 132 50
## 133 4
## 134 13
## 135 5
## 136 9
## 137 2
## 138 12#1E: Save the relig_depress_total subset into .csv data file. [Use write.csv function here.]
write.csv(relig_depress_total,file="relig_depress.csv") #this exports the dataframe into a csv file in the folder I set the working directory to. #2A: The iris dataset is pre-installed in R. Please convert it to a tibble by using the as_tibble function and save the tibble as an object called iris_dat.
iris_dat<-as_tibble(iris) #tibble #2B: Use the select() function to keep only three variables—Sepal.Length, Petal.Length, and Species—and arrange them in this order: Species, Sepal.Length, Petal.Length. Save this subset as an object called iris_subset.
iris_subset<-iris_dat%>%
dplyr::select(Species, Sepal.Length, Petal.Length) #This creates a new Iris dataset that only contains the columns that contains Species, Sepal Length, Petal Length in that order
iris_subset #this prints the new dataset## # A tibble: 150 × 3
## Species Sepal.Length Petal.Length
## <fct> <dbl> <dbl>
## 1 setosa 5.1 1.4
## 2 setosa 4.9 1.4
## 3 setosa 4.7 1.3
## 4 setosa 4.6 1.5
## 5 setosa 5 1.4
## 6 setosa 5.4 1.7
## 7 setosa 4.6 1.4
## 8 setosa 5 1.5
## 9 setosa 4.4 1.4
## 10 setosa 4.9 1.5
## # ℹ 140 more rows#2C: Within iris_subset, filter out rows where Sepal.Length is greater than 6.
iris_subset_6<-iris_subset%>%
dplyr::filter(Sepal.Length>6) #this creates a new Iris dataset that only contains the columns that contains rows where the Sepal.Length is greater than 6, everything else is excluded
iris_subset_6 #this prints the new dataset## # A tibble: 61 × 3
## Species Sepal.Length Petal.Length
## <fct> <dbl> <dbl>
## 1 versicolor 7 4.7
## 2 versicolor 6.4 4.5
## 3 versicolor 6.9 4.9
## 4 versicolor 6.5 4.6
## 5 versicolor 6.3 4.7
## 6 versicolor 6.6 4.6
## 7 versicolor 6.1 4.7
## 8 versicolor 6.7 4.4
## 9 versicolor 6.2 4.5
## 10 versicolor 6.1 4
## # ℹ 51 more rows#2D: Within iris_subset, compute average Petal.Length by species using group_by() and summarize().
iris_subset_average_group<-iris_subset%>%
group_by(Species)%>%
summarize(average_petal_length = mean(Petal.Length, na.rm=TRUE)) #this creates a new Iris dataset where all of the species rows (ex: Setosa) is collapsed into one row in one column with the average petal length as a seperate column
iris_subset_average_group #this prints the new dataset## # A tibble: 3 × 2
## Species average_petal_length
## <fct> <dbl>
## 1 setosa 1.46
## 2 versicolor 4.26
## 3 virginica 5.55#2E: Add the average Petal.Length as a new variable (column) to iris_subset.
iris_subset_average_petal <- iris_subset %>%
group_by(Species) %>%
mutate(average_petal_length = mean(Petal.Length, na.rm = TRUE)) %>%
ungroup() #this creates a new Iris dataset where there is a new column that computes the average petal length in one column based on the species name in the other column
iris_subset_average_petal #prints the new dataset## # A tibble: 150 × 4
## Species Sepal.Length Petal.Length average_petal_length
## <fct> <dbl> <dbl> <dbl>
## 1 setosa 5.1 1.4 1.46
## 2 setosa 4.9 1.4 1.46
## 3 setosa 4.7 1.3 1.46
## 4 setosa 4.6 1.5 1.46
## 5 setosa 5 1.4 1.46
## 6 setosa 5.4 1.7 1.46
## 7 setosa 4.6 1.4 1.46
## 8 setosa 5 1.5 1.46
## 9 setosa 4.4 1.4 1.46
## 10 setosa 4.9 1.5 1.46
## # ℹ 140 more rows