Module 11: Apply it to your data 10
Daniel Lee
Import your data
data <- read.csv("../00_data/myData.csv")Chapter 15
Create a factor
Modify factor order
Make two bar charts here - one before ordering another after
unordered factor levels
# Transform data: calculate average ty hours by religion
like_count_by_brand <- data %>%
group_by(brand) %>%
summarise(
avg_like_count = mean(like_count, na.rm = TRUE)
)
like_count_by_brand## # A tibble: 10 × 2
## brand avg_like_count
## <chr> <dbl>
## 1 Bud Light 1864.
## 2 Budweiser 2399.
## 3 Coca-Cola 8012.
## 4 Doritos 14180.
## 5 E-Trade 219.
## 6 Hynudai 191.
## 7 Kia 177.
## 8 NFL 24918.
## 9 Pepsi 616.
## 10 Toyota 532.# Plot
like_count_by_brand %>%
ggplot(aes(x = avg_like_count, y = brand)) +
geom_point()
Ordered factor levels
like_count_by_brand %>%
ggplot(aes(x = avg_like_count, y = fct_reorder(.f = brand, .x = avg_like_count)))+
geom_point() +
#Labeling
labs(y = NULL, x = "Mean Like Count")
Modify factor levels
Show examples of three functions:
- fct_recode
data %>%
#rename levels
mutate(brand_rev = fct_recode(brand, "Drink" = "Pepsi")) %>%
select(brand, brand_rev) %>%
filter(brand == "Pepsi")## brand brand_rev
## 1 Pepsi Drink
## 2 Pepsi Drink
## 3 Pepsi Drink
## 4 Pepsi Drink
## 5 Pepsi Drink
## 6 Pepsi Drink
## 7 Pepsi Drink
## 8 Pepsi Drink
## 9 Pepsi Drink
## 10 Pepsi Drink
## 11 Pepsi Drink
## 12 Pepsi Drink
## 13 Pepsi Drink
## 14 Pepsi Drink
## 15 Pepsi Drink
## 16 Pepsi Drink
## 17 Pepsi Drink
## 18 Pepsi Drink
## 19 Pepsi Drink
## 20 Pepsi Drink
## 21 Pepsi Drink
## 22 Pepsi Drink
## 23 Pepsi Drink
## 24 Pepsi Drink
## 25 Pepsi Drink- fct_collapse
# Collapse multiple levels into one
data %>%
mutate(brand_col = fct_collapse(brand, "Drink" = c("Pepsi", "Coca-Cola" ))) %>%
select(brand, brand_col) %>%
filter(brand != "Bud Light")## brand brand_col
## 1 Toyota Toyota
## 2 Hynudai Hynudai
## 3 Toyota Toyota
## 4 Coca-Cola Drink
## 5 Kia Kia
## 6 Hynudai Hynudai
## 7 Budweiser Budweiser
## 8 Hynudai Hynudai
## 9 Budweiser Budweiser
## 10 Budweiser Budweiser
## 11 NFL NFL
## 12 NFL NFL
## 13 Pepsi Drink
## 14 Doritos Doritos
## 15 Pepsi Drink
## 16 Hynudai Hynudai
## 17 Budweiser Budweiser
## 18 Kia Kia
## 19 Doritos Doritos
## 20 Pepsi Drink
## 21 Hynudai Hynudai
## 22 NFL NFL
## 23 Coca-Cola Drink
## 24 Pepsi Drink
## 25 Hynudai Hynudai
## 26 E-Trade E-Trade
## 27 Doritos Doritos
## 28 Budweiser Budweiser
## 29 Coca-Cola Drink
## 30 Hynudai Hynudai
## 31 Pepsi Drink
## 32 Kia Kia
## 33 Pepsi Drink
## 34 NFL NFL
## 35 E-Trade E-Trade
## 36 NFL NFL
## 37 E-Trade E-Trade
## 38 E-Trade E-Trade
## 39 Budweiser Budweiser
## 40 Doritos Doritos
## 41 Budweiser Budweiser
## 42 Doritos Doritos
## 43 Budweiser Budweiser
## 44 E-Trade E-Trade
## 45 Kia Kia
## 46 Pepsi Drink
## 47 Hynudai Hynudai
## 48 Coca-Cola Drink
## 49 Hynudai Hynudai
## 50 Doritos Doritos
## 51 Doritos Doritos
## 52 Doritos Doritos
## 53 Coca-Cola Drink
## 54 Toyota Toyota
## 55 Budweiser Budweiser
## 56 Coca-Cola Drink
## 57 Budweiser Budweiser
## 58 Coca-Cola Drink
## 59 Budweiser Budweiser
## 60 Hynudai Hynudai
## 61 Coca-Cola Drink
## 62 Pepsi Drink
## 63 Doritos Doritos
## 64 Budweiser Budweiser
## 65 Budweiser Budweiser
## 66 Budweiser Budweiser
## 67 Kia Kia
## 68 Budweiser Budweiser
## 69 Budweiser Budweiser
## 70 Coca-Cola Drink
## 71 Doritos Doritos
## 72 Toyota Toyota
## 73 NFL NFL
## 74 Hynudai Hynudai
## 75 Budweiser Budweiser
## 76 Hynudai Hynudai
## 77 Doritos Doritos
## 78 Toyota Toyota
## 79 Budweiser Budweiser
## 80 Coca-Cola Drink
## 81 Hynudai Hynudai
## 82 Hynudai Hynudai
## 83 Kia Kia
## 84 Budweiser Budweiser
## 85 Hynudai Hynudai
## 86 NFL NFL
## 87 Pepsi Drink
## 88 Budweiser Budweiser
## 89 Budweiser Budweiser
## 90 Budweiser Budweiser
## 91 Coca-Cola Drink
## 92 Toyota Toyota
## 93 Kia Kia
## 94 Hynudai Hynudai
## 95 Budweiser Budweiser
## 96 Doritos Doritos
## 97 Budweiser Budweiser
## 98 Doritos Doritos
## 99 Pepsi Drink
## 100 Doritos Doritos
## 101 Toyota Toyota
## 102 Doritos Doritos
## 103 E-Trade E-Trade
## 104 Coca-Cola Drink
## 105 Pepsi Drink
## 106 Doritos Doritos
## 107 Kia Kia
## 108 NFL NFL
## 109 Hynudai Hynudai
## 110 Toyota Toyota
## 111 Coca-Cola Drink
## 112 Budweiser Budweiser
## 113 Doritos Doritos
## 114 Doritos Doritos
## 115 Coca-Cola Drink
## 116 Pepsi Drink
## 117 E-Trade E-Trade
## 118 Doritos Doritos
## 119 Kia Kia
## 120 Doritos Doritos
## 121 Coca-Cola Drink
## 122 Hynudai Hynudai
## 123 Coca-Cola Drink
## 124 Kia Kia
## 125 Kia Kia
## 126 Pepsi Drink
## 127 Hynudai Hynudai
## 128 E-Trade E-Trade
## 129 Budweiser Budweiser
## 130 Pepsi Drink
## 131 Coca-Cola Drink
## 132 Pepsi Drink
## 133 E-Trade E-Trade
## 134 Budweiser Budweiser
## 135 Coca-Cola Drink
## 136 Kia Kia
## 137 Coca-Cola Drink
## 138 Budweiser Budweiser
## 139 Budweiser Budweiser
## 140 Kia Kia
## 141 Budweiser Budweiser
## 142 E-Trade E-Trade
## 143 Toyota Toyota
## 144 Budweiser Budweiser
## 145 Budweiser Budweiser
## 146 Budweiser Budweiser
## 147 E-Trade E-Trade
## 148 Budweiser Budweiser
## 149 Coca-Cola Drink
## 150 Toyota Toyota
## 151 Hynudai Hynudai
## 152 Pepsi Drink
## 153 Pepsi Drink
## 154 Pepsi Drink
## 155 Doritos Doritos
## 156 Budweiser Budweiser
## 157 Pepsi Drink
## 158 Pepsi Drink
## 159 Budweiser Budweiser
## 160 Budweiser Budweiser
## 161 E-Trade E-Trade
## 162 Pepsi Drink
## 163 Budweiser Budweiser
## 164 Hynudai Hynudai
## 165 Budweiser Budweiser
## 166 Pepsi Drink
## 167 Coca-Cola Drink
## 168 NFL NFL
## 169 Doritos Doritos
## 170 Pepsi Drink
## 171 Budweiser Budweiser
## 172 Hynudai Hynudai
## 173 Toyota Toyota
## 174 Budweiser Budweiser
## 175 Doritos Doritos
## 176 Doritos Doritos
## 177 Doritos Doritos
## 178 NFL NFL
## 179 Pepsi Drink
## 180 NFL NFL
## 181 Budweiser Budweiser
## 182 Pepsi Drink
## 183 E-Trade E-Trade
## 184 Budweiser Budweiser- fct_lump
#Lump small levels into other levels
data %>% count(brand)## brand n
## 1 Bud Light 63
## 2 Budweiser 43
## 3 Coca-Cola 21
## 4 Doritos 25
## 5 E-Trade 13
## 6 Hynudai 22
## 7 Kia 13
## 8 NFL 11
## 9 Pepsi 25
## 10 Toyota 11data %>% mutate(brand_lump = fct_lump(brand)) %>% distinct(brand_lump)## brand_lump
## 1 Toyota
## 2 Bud Light
## 3 Hynudai
## 4 Coca-Cola
## 5 Kia
## 6 Budweiser
## 7 NFL
## 8 Pepsi
## 9 Doritos
## 10 E-TradeChapter 16
No need to do anything here.