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library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.3 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.2
## ── 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# Menyimpan URL raw data GitHub ke dalam variabel
url <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-01/key_crop_yields.csv"
# Membaca data read_csv
df_crop <- read_csv(url)## Rows: 13075 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Entity, Code
## dbl (12): Year, Wheat (tonnes per hectare), Rice (tonnes per hectare), Maize...
##
## 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.# Melihat data
glimpse(df_crop)## Rows: 13,075
## Columns: 14
## $ Entity <chr> "Afghanistan", "Afghanistan", "Afgh…
## $ Code <chr> "AFG", "AFG", "AFG", "AFG", "AFG", …
## $ Year <dbl> 1961, 1962, 1963, 1964, 1965, 1966,…
## $ `Wheat (tonnes per hectare)` <dbl> 1.0220, 0.9735, 0.8317, 0.9510, 0.9…
## $ `Rice (tonnes per hectare)` <dbl> 1.5190, 1.5190, 1.5190, 1.7273, 1.7…
## $ `Maize (tonnes per hectare)` <dbl> 1.4000, 1.4000, 1.4260, 1.4257, 1.4…
## $ `Soybeans (tonnes per hectare)` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Potatoes (tonnes per hectare)` <dbl> 8.6667, 7.6667, 8.1333, 8.6000, 8.8…
## $ `Beans (tonnes per hectare)` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Peas (tonnes per hectare)` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Cassava (tonnes per hectare)` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Barley (tonnes per hectare)` <dbl> 1.0800, 1.0800, 1.0800, 1.0857, 1.0…
## $ `Cocoa beans (tonnes per hectare)` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Bananas (tonnes per hectare)` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA,…1. Menampilkan kolom Entity, Year, Potatoes, dan Cassava saja.
data_view <- select(df_crop, Entity, Year, `Potatoes (tonnes per hectare)`,`Cassava (tonnes per hectare)`)
data_view## # A tibble: 13,075 × 4
## Entity Year `Potatoes (tonnes per hectare)` Cassava (tonnes per hecta…¹
## <chr> <dbl> <dbl> <dbl>
## 1 Afghanistan 1961 8.67 NA
## 2 Afghanistan 1962 7.67 NA
## 3 Afghanistan 1963 8.13 NA
## 4 Afghanistan 1964 8.6 NA
## 5 Afghanistan 1965 8.8 NA
## 6 Afghanistan 1966 9.07 NA
## 7 Afghanistan 1967 9.8 NA
## 8 Afghanistan 1968 10 NA
## 9 Afghanistan 1969 10.2 NA
## 10 Afghanistan 1970 9.54 NA
## # ℹ 13,065 more rows
## # ℹ abbreviated name: ¹`Cassava (tonnes per hectare)`2. Mengeliminasi kolom Soybeans, Beans, dan Peas dari tabel.
select(df_crop, -c(`Soybeans (tonnes per hectare)`, `Beans (tonnes per hectare)`,`Peas (tonnes per hectare)`))## # A tibble: 13,075 × 11
## Entity Code Year `Wheat (tonnes per hectare)` Rice (tonnes per hecta…¹
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Afghanistan AFG 1961 1.02 1.52
## 2 Afghanistan AFG 1962 0.974 1.52
## 3 Afghanistan AFG 1963 0.832 1.52
## 4 Afghanistan AFG 1964 0.951 1.73
## 5 Afghanistan AFG 1965 0.972 1.73
## 6 Afghanistan AFG 1966 0.867 1.52
## 7 Afghanistan AFG 1967 1.12 1.92
## 8 Afghanistan AFG 1968 1.16 1.95
## 9 Afghanistan AFG 1969 1.19 1.98
## 10 Afghanistan AFG 1970 0.956 1.81
## # ℹ 13,065 more rows
## # ℹ abbreviated name: ¹`Rice (tonnes per hectare)`
## # ℹ 6 more variables: `Maize (tonnes per hectare)` <dbl>,
## # `Potatoes (tonnes per hectare)` <dbl>,
## # `Cassava (tonnes per hectare)` <dbl>, `Barley (tonnes per hectare)` <dbl>,
## # `Cocoa beans (tonnes per hectare)` <dbl>,
## # `Bananas (tonnes per hectare)` <dbl>3. Tahun berapa saja hasil panen padi (Rice) di Indonesia yang nilainya di bawah 2 ton?
filter_year <- df_crop %>%
select(Entity, Year,`Rice (tonnes per hectare)`) %>%
filter(Entity == 'Indonesia') %>%
filter(`Rice (tonnes per hectare)` < 2)
filter_year## # A tibble: 7 × 3
## Entity Year `Rice (tonnes per hectare)`
## <chr> <dbl> <dbl>
## 1 Indonesia 1961 1.76
## 2 Indonesia 1962 1.79
## 3 Indonesia 1963 1.72
## 4 Indonesia 1964 1.76
## 5 Indonesia 1965 1.77
## 6 Indonesia 1966 1.77
## 7 Indonesia 1967 1.764. Negara apa saja yang punya hasil gandum (Wheat) di atas 5 ton pada tahun 2000 ke atas?
filter_entity1 <- filter(df_crop, `Wheat (tonnes per hectare)` > 5, Year > 2000)
select(filter_entity1, Entity, Year, `Wheat (tonnes per hectare)` )## # A tibble: 406 × 3
## Entity Year `Wheat (tonnes per hectare)`
## <chr> <dbl> <dbl>
## 1 Austria 2001 5.24
## 2 Austria 2004 5.92
## 3 Austria 2005 5.03
## 4 Austria 2008 5.69
## 5 Austria 2010 5.01
## 6 Austria 2011 5.85
## 7 Austria 2013 5.37
## 8 Austria 2014 5.92
## 9 Austria 2015 5.70
## 10 Austria 2016 6.25
## # ℹ 396 more rows5. Bagaimana cara memunculkan data negara Indonesia dan Malaysia khusus untuk tahun 2015 saja?
filter_entity2<- filter(df_crop, Entity == "Indonesia" | Entity == "Malaysia", Year == 2015)
filter_entity2## # A tibble: 2 × 14
## Entity Code Year `Wheat (tonnes per hectare)` `Rice (tonnes per hectare)`
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Indonesia IDN 2015 NA 5.34
## 2 Malaysia MYS 2015 NA 4.02
## # ℹ 9 more variables: `Maize (tonnes per hectare)` <dbl>,
## # `Soybeans (tonnes per hectare)` <dbl>,
## # `Potatoes (tonnes per hectare)` <dbl>, `Beans (tonnes per hectare)` <dbl>,
## # `Peas (tonnes per hectare)` <dbl>, `Cassava (tonnes per hectare)` <dbl>,
## # `Barley (tonnes per hectare)` <dbl>,
## # `Cocoa beans (tonnes per hectare)` <dbl>,
## # `Bananas (tonnes per hectare)` <dbl>6. Negara mana yang punya hasil jagung (Maize) paling rendah di tahun 2020?
library(magrittr) #untuk penggunaan Operator Pipe (%>%)##
## Attaching package: 'magrittr'## The following object is masked from 'package:purrr':
##
## set_names## The following object is masked from 'package:tidyr':
##
## extractdf_sum1 <- df_crop %>%
select(Entity, Year, `Maize (tonnes per hectare)`) %>%
filter(Year == 2020) %>%
arrange(`Maize (tonnes per hectare)`) %>%
head(1)
df_sum1## # A tibble: 0 × 3
## # ℹ 3 variables: Entity <chr>, Year <dbl>, Maize (tonnes per hectare) <dbl>7. Mengurutkan data Indonesia dari hasil kentang (Potatoes) yang paling tinggi.
filter_idn<- df_crop %>%
select (Entity, `Potatoes (tonnes per hectare)`) %>%
filter(Entity == 'Indonesia') %>%
arrange(desc(`Potatoes (tonnes per hectare)`))
filter_idn## # A tibble: 58 × 2
## Entity `Potatoes (tonnes per hectare)`
## <chr> <dbl>
## 1 Indonesia 18.7
## 2 Indonesia 18.3
## 3 Indonesia 18.2
## 4 Indonesia 17.7
## 5 Indonesia 16.9
## 6 Indonesia 16.7
## 7 Indonesia 16.6
## 8 Indonesia 16.6
## 9 Indonesia 16.5
## 10 Indonesia 16.4
## # ℹ 48 more rows8. Membuat kolom Rice_Status berisi teks “Tinggi” jika padi > 4 ton, dan “Rendah” jika di bawahnya.
new_col1<-df_crop %>%
mutate(Rice_Status = ifelse(`Rice (tonnes per hectare)` > 4, 'Tinggi','Rendah')) %>%
select(Entity,`Rice (tonnes per hectare)`, Rice_Status)
new_col1## # A tibble: 13,075 × 3
## Entity `Rice (tonnes per hectare)` Rice_Status
## <chr> <dbl> <chr>
## 1 Afghanistan 1.52 Rendah
## 2 Afghanistan 1.52 Rendah
## 3 Afghanistan 1.52 Rendah
## 4 Afghanistan 1.73 Rendah
## 5 Afghanistan 1.73 Rendah
## 6 Afghanistan 1.52 Rendah
## 7 Afghanistan 1.92 Rendah
## 8 Afghanistan 1.95 Rendah
## 9 Afghanistan 1.98 Rendah
## 10 Afghanistan 1.81 Rendah
## # ℹ 13,065 more rows9. Berapa rata-rata hasil panen pisang (Bananas) di Indonesia dari seluruh tahun yang ada?
df_mean <- df_crop %>%
filter(Entity == 'Indonesia') %>%
summarise(`Mean Bananas` = mean(`Bananas (tonnes per hectare)`), na.rm = TRUE)
df_mean## # A tibble: 1 × 2
## `Mean Bananas` na.rm
## <dbl> <lgl>
## 1 30.5 TRUE10.Tampilkan data jagung mulai tahun 2010, lalu menghitung simpangan baku per negara, dan mengurutkannya dari nilai yang paling besar
df_sd <- df_crop %>%
filter(Year >= 2010) %>%
group_by(Entity) %>%
summarise(`Simpangan Baku Maize` = sd(`Maize (tonnes per hectare)`, na.rm = FALSE)) %>%
arrange(desc(`Simpangan Baku Maize`))
df_sd## # A tibble: 242 × 2
## Entity `Simpangan Baku Maize`
## <chr> <dbl>
## 1 Kuwait 9.24
## 2 United Arab Emirates 9.19
## 3 Jordan 7.03
## 4 Israel 4.80
## 5 Saint Vincent and the Grenadines 2.89
## 6 Qatar 2.74
## 7 French Guiana 2.50
## 8 New Caledonia 2.29
## 9 Slovakia 1.68
## 10 Oman 1.61
## # ℹ 232 more rows