Module 11: Apply it to your data 10
Mitchell Cripps
Import your data
ads <- read.csv("../00_data/myData.csv")Chapter 15
Create a factor
x1 <- c("2018", "2020", "2006", "2018", "2003", "2020")
sort(x1)## [1] "2003" "2006" "2018" "2018" "2020" "2020"year_levels <- c("2015", "2016", "2017", "2018", "2019", "2020")y1 <- factor(x1, levels = year_levels)
y1## [1] 2018 2020 <NA> 2018 <NA> 2020
## Levels: 2015 2016 2017 2018 2019 2020sort(y1)## [1] 2018 2018 2020 2020
## Levels: 2015 2016 2017 2018 2019 2020Modify factor order
Make two bar charts here - one before ordering another after ### Unordered
like_count_summary <- ads %>%
group_by(brand) %>%
summarise(
avg_likes = mean(like_count, na.rm = TRUE)
)
like_count_summary## # A tibble: 10 × 2
## brand avg_likes
## <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.like_count_summary %>%
ggplot(aes(x = avg_likes, y = brand)) +
geom_point() +
#Labeling
labs(y = "BRAND", x = "average likes")
Modify factor levels
Ordered
# Reorder
like_count_summary %>%
ggplot(aes(x = avg_likes, y = fct_reorder(.f = brand, .x = avg_likes))) +
geom_point() +
#Labeling
labs(y = "BRAND", x = "average likes")
# Relevel
like_count_summary %>%
ggplot(aes(x = avg_likes,
y = fct_reorder(.f = brand, .x = avg_likes) %>%
fct_relevel("Doritos"))) +
geom_point() +
#Labeling
labs(y = "BRAND", x = "average likes")
# Recode
ads %>% distinct(brand)## brand
## 1 Toyota
## 2 Bud Light
## 3 Hynudai
## 4 Coca-Cola
## 5 Kia
## 6 Budweiser
## 7 NFL
## 8 Pepsi
## 9 Doritos
## 10 E-Tradeads %>%
mutate(drink = fct_recode(brand, "drink" = "Coca-Cola")) %>%
select(brand, drink) %>%
filter(brand == "Coca-Cola")## brand drink
## 1 Coca-Cola drink
## 2 Coca-Cola drink
## 3 Coca-Cola drink
## 4 Coca-Cola drink
## 5 Coca-Cola drink
## 6 Coca-Cola drink
## 7 Coca-Cola drink
## 8 Coca-Cola drink
## 9 Coca-Cola drink
## 10 Coca-Cola drink
## 11 Coca-Cola drink
## 12 Coca-Cola drink
## 13 Coca-Cola drink
## 14 Coca-Cola drink
## 15 Coca-Cola drink
## 16 Coca-Cola drink
## 17 Coca-Cola drink
## 18 Coca-Cola drink
## 19 Coca-Cola drink
## 20 Coca-Cola drink
## 21 Coca-Cola drink# Colapse multiple levels into one
ads %>%
mutate(drink = fct_collapse(brand, "drink"= c("Budweiser", "Bud Light"))) %>%
select(brand, drink) %>%
filter(brand != "Pepsi")## brand drink
## 1 Toyota Toyota
## 2 Bud Light drink
## 3 Bud Light drink
## 4 Hynudai Hynudai
## 5 Bud Light drink
## 6 Toyota Toyota
## 7 Coca-Cola Coca-Cola
## 8 Kia Kia
## 9 Hynudai Hynudai
## 10 Budweiser drink
## 11 Hynudai Hynudai
## 12 Bud Light drink
## 13 Budweiser drink
## 14 Budweiser drink
## 15 NFL NFL
## 16 NFL NFL
## 17 Bud Light drink
## 18 Bud Light drink
## 19 Doritos Doritos
## 20 Bud Light drink
## 21 Hynudai Hynudai
## 22 Budweiser drink
## 23 Kia Kia
## 24 Doritos Doritos
## 25 Hynudai Hynudai
## 26 NFL NFL
## 27 Coca-Cola Coca-Cola
## 28 Hynudai Hynudai
## 29 Bud Light drink
## 30 E-Trade E-Trade
## 31 Bud Light drink
## 32 Doritos Doritos
## 33 Budweiser drink
## 34 Coca-Cola Coca-Cola
## 35 Hynudai Hynudai
## 36 Kia Kia
## 37 NFL NFL
## 38 E-Trade E-Trade
## 39 NFL NFL
## 40 E-Trade E-Trade
## 41 E-Trade E-Trade
## 42 Budweiser drink
## 43 Doritos Doritos
## 44 Budweiser drink
## 45 Doritos Doritos
## 46 Budweiser drink
## 47 E-Trade E-Trade
## 48 Bud Light drink
## 49 Kia Kia
## 50 Bud Light drink
## 51 Bud Light drink
## 52 Hynudai Hynudai
## 53 Coca-Cola Coca-Cola
## 54 Hynudai Hynudai
## 55 Doritos Doritos
## 56 Doritos Doritos
## 57 Doritos Doritos
## 58 Coca-Cola Coca-Cola
## 59 Toyota Toyota
## 60 Budweiser drink
## 61 Coca-Cola Coca-Cola
## 62 Budweiser drink
## 63 Coca-Cola Coca-Cola
## 64 Budweiser drink
## 65 Hynudai Hynudai
## 66 Bud Light drink
## 67 Bud Light drink
## 68 Bud Light drink
## 69 Coca-Cola Coca-Cola
## 70 Doritos Doritos
## 71 Budweiser drink
## 72 Budweiser drink
## 73 Budweiser drink
## 74 Kia Kia
## 75 Budweiser drink
## 76 Budweiser drink
## 77 Coca-Cola Coca-Cola
## 78 Doritos Doritos
## 79 Toyota Toyota
## 80 NFL NFL
## 81 Hynudai Hynudai
## 82 Bud Light drink
## 83 Budweiser drink
## 84 Hynudai Hynudai
## 85 Doritos Doritos
## 86 Bud Light drink
## 87 Toyota Toyota
## 88 Bud Light drink
## 89 Budweiser drink
## 90 Bud Light drink
## 91 Coca-Cola Coca-Cola
## 92 Hynudai Hynudai
## 93 Hynudai Hynudai
## 94 Kia Kia
## 95 Budweiser drink
## 96 Bud Light drink
## 97 Hynudai Hynudai
## 98 NFL NFL
## 99 Budweiser drink
## 100 Bud Light drink
## 101 Bud Light drink
## 102 Budweiser drink
## 103 Budweiser drink
## 104 Bud Light drink
## 105 Coca-Cola Coca-Cola
## 106 Bud Light drink
## 107 Bud Light drink
## 108 Toyota Toyota
## 109 Bud Light drink
## 110 Kia Kia
## 111 Hynudai Hynudai
## 112 Bud Light drink
## 113 Bud Light drink
## 114 Budweiser drink
## 115 Bud Light drink
## 116 Doritos Doritos
## 117 Bud Light drink
## 118 Budweiser drink
## 119 Doritos Doritos
## 120 Bud Light drink
## 121 Doritos Doritos
## 122 Toyota Toyota
## 123 Doritos Doritos
## 124 Bud Light drink
## 125 Bud Light drink
## 126 E-Trade E-Trade
## 127 Bud Light drink
## 128 Coca-Cola Coca-Cola
## 129 Doritos Doritos
## 130 Kia Kia
## 131 NFL NFL
## 132 Bud Light drink
## 133 Hynudai Hynudai
## 134 Bud Light drink
## 135 Bud Light drink
## 136 Toyota Toyota
## 137 Bud Light drink
## 138 Coca-Cola Coca-Cola
## 139 Budweiser drink
## 140 Doritos Doritos
## 141 Bud Light drink
## 142 Doritos Doritos
## 143 Coca-Cola Coca-Cola
## 144 E-Trade E-Trade
## 145 Bud Light drink
## 146 Doritos Doritos
## 147 Kia Kia
## 148 Doritos Doritos
## 149 Coca-Cola Coca-Cola
## 150 Hynudai Hynudai
## 151 Coca-Cola Coca-Cola
## 152 Bud Light drink
## 153 Kia Kia
## 154 Bud Light drink
## 155 Kia Kia
## 156 Bud Light drink
## 157 Hynudai Hynudai
## 158 Bud Light drink
## 159 Bud Light drink
## 160 E-Trade E-Trade
## 161 Budweiser drink
## 162 Coca-Cola Coca-Cola
## 163 Bud Light drink
## 164 E-Trade E-Trade
## 165 Budweiser drink
## 166 Coca-Cola Coca-Cola
## 167 Kia Kia
## 168 Coca-Cola Coca-Cola
## 169 Budweiser drink
## 170 Bud Light drink
## 171 Bud Light drink
## 172 Budweiser drink
## 173 Kia Kia
## 174 Budweiser drink
## 175 E-Trade E-Trade
## 176 Bud Light drink
## 177 Toyota Toyota
## 178 Budweiser drink
## 179 Budweiser drink
## 180 Budweiser drink
## 181 Bud Light drink
## 182 E-Trade E-Trade
## 183 Budweiser drink
## 184 Coca-Cola Coca-Cola
## 185 Bud Light drink
## 186 Toyota Toyota
## 187 Hynudai Hynudai
## 188 Bud Light drink
## 189 Doritos Doritos
## 190 Budweiser drink
## 191 Bud Light drink
## 192 Bud Light drink
## 193 Bud Light drink
## 194 Bud Light drink
## 195 Bud Light drink
## 196 Bud Light drink
## 197 Budweiser drink
## 198 Bud Light drink
## 199 Bud Light drink
## 200 Budweiser drink
## 201 E-Trade E-Trade
## 202 Budweiser drink
## 203 Hynudai Hynudai
## 204 Budweiser drink
## 205 Coca-Cola Coca-Cola
## 206 NFL NFL
## 207 Doritos Doritos
## 208 Bud Light drink
## 209 Budweiser drink
## 210 Bud Light drink
## 211 Hynudai Hynudai
## 212 Toyota Toyota
## 213 Budweiser drink
## 214 Doritos Doritos
## 215 Doritos Doritos
## 216 Bud Light drink
## 217 Doritos Doritos
## 218 NFL NFL
## 219 NFL NFL
## 220 Budweiser drink
## 221 E-Trade E-Trade
## 222 Budweiser drink#Lump small levels into other levels
ads %>% 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 11ads %>% 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-TradeShow examples of three functions:
- fct_recode
- fct_collapse
- fct_lump
Chapter 16
No need to do anything here.