- Use the summary function to gain an overview of the data set. Then
display the mean and median for at least two attributes.
# import file, set header = FALSE to get row name header
penguins <- read.csv (file = 'C:\\Users\\Home\\penguins.csv', header = TRUE, sep = ",")
head (penguins) #get a glimpse of the data
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## 1 Adelie Torgersen 39.1 18.7 181 3750
## 2 Adelie Torgersen 39.5 17.4 186 3800
## 3 Adelie Torgersen 40.3 18.0 195 3250
## 4 Adelie Torgersen NA NA NA NA
## 5 Adelie Torgersen 36.7 19.3 193 3450
## 6 Adelie Torgersen 39.3 20.6 190 3650
## sex year
## 1 male 2007
## 2 female 2007
## 3 female 2007
## 4 <NA> 2007
## 5 female 2007
## 6 male 2007
sapply(penguins, class) # data type of columns
## species island bill_length_mm bill_depth_mm
## "character" "character" "numeric" "numeric"
## flipper_length_mm body_mass_g sex year
## "integer" "integer" "character" "integer"
summary (penguins)
## species island bill_length_mm bill_depth_mm
## Length:344 Length:344 Min. :32.10 Min. :13.10
## Class :character Class :character 1st Qu.:39.23 1st Qu.:15.60
## Mode :character Mode :character Median :44.45 Median :17.30
## Mean :43.92 Mean :17.15
## 3rd Qu.:48.50 3rd Qu.:18.70
## Max. :59.60 Max. :21.50
## NA's :2 NA's :2
## flipper_length_mm body_mass_g sex year
## Min. :172.0 Min. :2700 Length:344 Min. :2007
## 1st Qu.:190.0 1st Qu.:3550 Class :character 1st Qu.:2007
## Median :197.0 Median :4050 Mode :character Median :2008
## Mean :200.9 Mean :4202 Mean :2008
## 3rd Qu.:213.0 3rd Qu.:4750 3rd Qu.:2009
## Max. :231.0 Max. :6300 Max. :2009
## NA's :2 NA's :2
all_penguins_mean <- mean (penguins$body_mass_g, na.rm = TRUE) #mean for body mass in grams
all_penguins_median <- median (penguins$bill_length_mm, na.rm = TRUE) #median for bill length in mm
- Create a new data frame with a subset of the columns and rows. Make
sure to name it
# Get Female penguins only for 2008, and save dataframe
girl_penguins <-subset(penguins, sex == 'female' & year == '2008')
head (girl_penguins) #get a glimpse of the data
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## 51 Adelie Biscoe 39.6 17.7 186 3500
## 53 Adelie Biscoe 35.0 17.9 190 3450
## 55 Adelie Biscoe 34.5 18.1 187 2900
## 57 Adelie Biscoe 39.0 17.5 186 3550
## 59 Adelie Biscoe 36.5 16.6 181 2850
## 61 Adelie Biscoe 35.7 16.9 185 3150
## sex year
## 51 female 2008
## 53 female 2008
## 55 female 2008
## 57 female 2008
## 59 female 2008
## 61 female 2008
summary(girl_penguins)
## species island bill_length_mm bill_depth_mm
## Length:56 Length:56 Min. :33.10 Min. :13.30
## Class :character Class :character 1st Qu.:36.48 1st Qu.:14.20
## Mode :character Mode :character Median :42.65 Median :16.60
## Mean :41.49 Mean :16.13
## 3rd Qu.:45.73 3rd Qu.:17.50
## Max. :50.50 Max. :19.00
## flipper_length_mm body_mass_g sex year
## Min. :178.0 Min. :2700 Length:56 Min. :2008
## 1st Qu.:187.0 1st Qu.:3400 Class :character 1st Qu.:2008
## Median :195.0 Median :3700 Mode :character Median :2008
## Mean :198.8 Mean :3888 Mean :2008
## 3rd Qu.:210.0 3rd Qu.:4412 3rd Qu.:2008
## Max. :220.0 Max. :5200 Max. :2008
- Create new column names for the new data frame.
#ensure use of rename () function in plyer due to "Error in `chr_as_locations()`:"
girl_penguinsnew <- plyr::rename(girl_penguins, c(
"bill_length_mm" = "Beak_length_mm",
"bill_depth_mm" = "Beak_depth_mm",
"flipper_length_mm" = "Flipper_length_mm",
"body_mass_g" = "Body_Mass_grams"))
print (girl_penguinsnew)
## species island Beak_length_mm Beak_depth_mm Flipper_length_mm
## 51 Adelie Biscoe 39.6 17.7 186
## 53 Adelie Biscoe 35.0 17.9 190
## 55 Adelie Biscoe 34.5 18.1 187
## 57 Adelie Biscoe 39.0 17.5 186
## 59 Adelie Biscoe 36.5 16.6 181
## 61 Adelie Biscoe 35.7 16.9 185
## 63 Adelie Biscoe 37.6 17.0 185
## 65 Adelie Biscoe 36.4 17.1 184
## 67 Adelie Biscoe 35.5 16.2 195
## 69 Adelie Torgersen 35.9 16.6 190
## 71 Adelie Torgersen 33.5 19.0 190
## 73 Adelie Torgersen 39.6 17.2 196
## 75 Adelie Torgersen 35.5 17.5 190
## 77 Adelie Torgersen 40.9 16.8 191
## 79 Adelie Torgersen 36.2 16.1 187
## 81 Adelie Torgersen 34.6 17.2 189
## 83 Adelie Torgersen 36.7 18.8 187
## 85 Adelie Dream 37.3 17.8 191
## 88 Adelie Dream 36.9 18.6 189
## 90 Adelie Dream 38.9 18.8 190
## 91 Adelie Dream 35.7 18.0 202
## 93 Adelie Dream 34.0 17.1 185
## 95 Adelie Dream 36.2 17.3 187
## 97 Adelie Dream 38.1 18.6 190
## 99 Adelie Dream 33.1 16.1 178
## 187 Gentoo Biscoe 49.1 14.8 220
## 189 Gentoo Biscoe 42.6 13.7 213
## 191 Gentoo Biscoe 44.0 13.6 208
## 193 Gentoo Biscoe 42.7 13.7 208
## 195 Gentoo Biscoe 45.3 13.7 210
## 198 Gentoo Biscoe 43.6 13.9 217
## 199 Gentoo Biscoe 45.5 13.9 210
## 201 Gentoo Biscoe 44.9 13.3 213
## 203 Gentoo Biscoe 46.6 14.2 210
## 205 Gentoo Biscoe 45.1 14.4 210
## 207 Gentoo Biscoe 46.5 14.4 217
## 209 Gentoo Biscoe 43.8 13.9 208
## 211 Gentoo Biscoe 43.2 14.5 208
## 213 Gentoo Biscoe 45.3 13.8 208
## 215 Gentoo Biscoe 45.7 13.9 214
## 217 Gentoo Biscoe 45.8 14.2 219
## 221 Gentoo Biscoe 43.5 14.2 220
## 223 Gentoo Biscoe 47.7 15.0 216
## 226 Gentoo Biscoe 46.5 14.8 217
## 227 Gentoo Biscoe 46.4 15.0 216
## 229 Gentoo Biscoe 47.5 14.2 209
## 231 Gentoo Biscoe 45.2 13.8 215
## 303 Chinstrap Dream 50.5 18.4 200
## 305 Chinstrap Dream 46.4 17.8 191
## 307 Chinstrap Dream 40.9 16.6 187
## 309 Chinstrap Dream 42.5 16.7 187
## 312 Chinstrap Dream 47.5 16.8 199
## 313 Chinstrap Dream 47.6 18.3 195
## 315 Chinstrap Dream 46.9 16.6 192
## 318 Chinstrap Dream 46.2 17.5 187
## 320 Chinstrap Dream 45.5 17.0 196
## Body_Mass_grams sex year
## 51 3500 female 2008
## 53 3450 female 2008
## 55 2900 female 2008
## 57 3550 female 2008
## 59 2850 female 2008
## 61 3150 female 2008
## 63 3600 female 2008
## 65 2850 female 2008
## 67 3350 female 2008
## 69 3050 female 2008
## 71 3600 female 2008
## 73 3550 female 2008
## 75 3700 female 2008
## 77 3700 female 2008
## 79 3550 female 2008
## 81 3200 female 2008
## 83 3800 female 2008
## 85 3350 female 2008
## 88 3500 female 2008
## 90 3600 female 2008
## 91 3550 female 2008
## 93 3400 female 2008
## 95 3300 female 2008
## 97 3700 female 2008
## 99 2900 female 2008
## 187 5150 female 2008
## 189 4950 female 2008
## 191 4350 female 2008
## 193 3950 female 2008
## 195 4300 female 2008
## 198 4900 female 2008
## 199 4200 female 2008
## 201 5100 female 2008
## 203 4850 female 2008
## 205 4400 female 2008
## 207 4900 female 2008
## 209 4300 female 2008
## 211 4450 female 2008
## 213 4200 female 2008
## 215 4400 female 2008
## 217 4700 female 2008
## 221 4700 female 2008
## 223 4750 female 2008
## 226 5200 female 2008
## 227 4700 female 2008
## 229 4600 female 2008
## 231 4750 female 2008
## 303 3400 female 2008
## 305 3700 female 2008
## 307 3200 female 2008
## 309 3350 female 2008
## 312 3900 female 2008
## 313 3850 female 2008
## 315 2700 female 2008
## 318 3650 female 2008
## 320 3500 female 2008
- Use the summary function to create an overview of your new data
frame. Print the mean and the median for the same two attributes. Please
compare
summary(girl_penguinsnew)
## species island Beak_length_mm Beak_depth_mm
## Length:56 Length:56 Min. :33.10 Min. :13.30
## Class :character Class :character 1st Qu.:36.48 1st Qu.:14.20
## Mode :character Mode :character Median :42.65 Median :16.60
## Mean :41.49 Mean :16.13
## 3rd Qu.:45.73 3rd Qu.:17.50
## Max. :50.50 Max. :19.00
## Flipper_length_mm Body_Mass_grams sex year
## Min. :178.0 Min. :2700 Length:56 Min. :2008
## 1st Qu.:187.0 1st Qu.:3400 Class :character 1st Qu.:2008
## Median :195.0 Median :3700 Mode :character Median :2008
## Mean :198.8 Mean :3888 Mean :2008
## 3rd Qu.:210.0 3rd Qu.:4412 3rd Qu.:2008
## Max. :220.0 Max. :5200 Max. :2008
girl_penguinsnew_mean <- mean (girl_penguinsnew$Body_Mass_grams, na.rm = TRUE)
sprintf(paste("The mean of all Penguins body mass in grams is ", all_penguins_mean))
## [1] "The mean of all Penguins body mass in grams is 4201.75438596491"
sprintf(paste("The mean of Girl Penguins body mass for 2008 in grams is ", girl_penguinsnew_mean))
## [1] "The mean of Girl Penguins body mass for 2008 in grams is 3887.5"
girl_penguins_median <- median (girl_penguinsnew$Beak_length_mm, na.rm = TRUE)
sprintf(paste("The median of all Penguins' bill length in mm is ", all_penguins_median))
## [1] "The median of all Penguins' bill length in mm is 44.45"
sprintf(paste("The median of all Girl Penguins' Beak length in mm is ", girl_penguins_median))
## [1] "The median of all Girl Penguins' Beak length in mm is 42.65"
- For at least 3 values in a column, please rename so that every value
in that column is renamed.
# Change Biscoe island to Phillip
girl_penguinsnew$island [girl_penguinsnew$island == "Biscoe"] <- "Phillip"
# Change Dream island to Galapagos
girl_penguinsnew$island [girl_penguinsnew$island == "Dream"] <- "Galapagos"
#Change Torgersen islant to Falkland
girl_penguinsnew$island [girl_penguinsnew$island == "Torgersen"] <- "Falkland"
girl_penguinsnew1 <- girl_penguinsnew
print (girl_penguinsnew1)
## species island Beak_length_mm Beak_depth_mm Flipper_length_mm
## 51 Adelie Phillip 39.6 17.7 186
## 53 Adelie Phillip 35.0 17.9 190
## 55 Adelie Phillip 34.5 18.1 187
## 57 Adelie Phillip 39.0 17.5 186
## 59 Adelie Phillip 36.5 16.6 181
## 61 Adelie Phillip 35.7 16.9 185
## 63 Adelie Phillip 37.6 17.0 185
## 65 Adelie Phillip 36.4 17.1 184
## 67 Adelie Phillip 35.5 16.2 195
## 69 Adelie Falkland 35.9 16.6 190
## 71 Adelie Falkland 33.5 19.0 190
## 73 Adelie Falkland 39.6 17.2 196
## 75 Adelie Falkland 35.5 17.5 190
## 77 Adelie Falkland 40.9 16.8 191
## 79 Adelie Falkland 36.2 16.1 187
## 81 Adelie Falkland 34.6 17.2 189
## 83 Adelie Falkland 36.7 18.8 187
## 85 Adelie Galapagos 37.3 17.8 191
## 88 Adelie Galapagos 36.9 18.6 189
## 90 Adelie Galapagos 38.9 18.8 190
## 91 Adelie Galapagos 35.7 18.0 202
## 93 Adelie Galapagos 34.0 17.1 185
## 95 Adelie Galapagos 36.2 17.3 187
## 97 Adelie Galapagos 38.1 18.6 190
## 99 Adelie Galapagos 33.1 16.1 178
## 187 Gentoo Phillip 49.1 14.8 220
## 189 Gentoo Phillip 42.6 13.7 213
## 191 Gentoo Phillip 44.0 13.6 208
## 193 Gentoo Phillip 42.7 13.7 208
## 195 Gentoo Phillip 45.3 13.7 210
## 198 Gentoo Phillip 43.6 13.9 217
## 199 Gentoo Phillip 45.5 13.9 210
## 201 Gentoo Phillip 44.9 13.3 213
## 203 Gentoo Phillip 46.6 14.2 210
## 205 Gentoo Phillip 45.1 14.4 210
## 207 Gentoo Phillip 46.5 14.4 217
## 209 Gentoo Phillip 43.8 13.9 208
## 211 Gentoo Phillip 43.2 14.5 208
## 213 Gentoo Phillip 45.3 13.8 208
## 215 Gentoo Phillip 45.7 13.9 214
## 217 Gentoo Phillip 45.8 14.2 219
## 221 Gentoo Phillip 43.5 14.2 220
## 223 Gentoo Phillip 47.7 15.0 216
## 226 Gentoo Phillip 46.5 14.8 217
## 227 Gentoo Phillip 46.4 15.0 216
## 229 Gentoo Phillip 47.5 14.2 209
## 231 Gentoo Phillip 45.2 13.8 215
## 303 Chinstrap Galapagos 50.5 18.4 200
## 305 Chinstrap Galapagos 46.4 17.8 191
## 307 Chinstrap Galapagos 40.9 16.6 187
## 309 Chinstrap Galapagos 42.5 16.7 187
## 312 Chinstrap Galapagos 47.5 16.8 199
## 313 Chinstrap Galapagos 47.6 18.3 195
## 315 Chinstrap Galapagos 46.9 16.6 192
## 318 Chinstrap Galapagos 46.2 17.5 187
## 320 Chinstrap Galapagos 45.5 17.0 196
## Body_Mass_grams sex year
## 51 3500 female 2008
## 53 3450 female 2008
## 55 2900 female 2008
## 57 3550 female 2008
## 59 2850 female 2008
## 61 3150 female 2008
## 63 3600 female 2008
## 65 2850 female 2008
## 67 3350 female 2008
## 69 3050 female 2008
## 71 3600 female 2008
## 73 3550 female 2008
## 75 3700 female 2008
## 77 3700 female 2008
## 79 3550 female 2008
## 81 3200 female 2008
## 83 3800 female 2008
## 85 3350 female 2008
## 88 3500 female 2008
## 90 3600 female 2008
## 91 3550 female 2008
## 93 3400 female 2008
## 95 3300 female 2008
## 97 3700 female 2008
## 99 2900 female 2008
## 187 5150 female 2008
## 189 4950 female 2008
## 191 4350 female 2008
## 193 3950 female 2008
## 195 4300 female 2008
## 198 4900 female 2008
## 199 4200 female 2008
## 201 5100 female 2008
## 203 4850 female 2008
## 205 4400 female 2008
## 207 4900 female 2008
## 209 4300 female 2008
## 211 4450 female 2008
## 213 4200 female 2008
## 215 4400 female 2008
## 217 4700 female 2008
## 221 4700 female 2008
## 223 4750 female 2008
## 226 5200 female 2008
## 227 4700 female 2008
## 229 4600 female 2008
## 231 4750 female 2008
## 303 3400 female 2008
## 305 3700 female 2008
## 307 3200 female 2008
## 309 3350 female 2008
## 312 3900 female 2008
## 313 3850 female 2008
## 315 2700 female 2008
## 318 3650 female 2008
## 320 3500 female 2008
- Display enough rows to see examples of all steps 1-5 above.