Praktikum 1
Nabila Putri
2026-05-27
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library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.5.3## Warning: package 'ggplot2' was built under R version 4.5.3## Warning: package 'tidyr' was built under R version 4.5.3## Warning: package 'purrr' was built under R version 4.5.3## Warning: package 'dplyr' was built under R version 4.5.3## Warning: package 'stringr' was built under R version 4.5.3## Warning: package 'forcats' was built under R version 4.5.3## Warning: package 'lubridate' was built under R version 4.5.3## ── 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 errorsurl <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-01/key_crop_yields.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.###1.Menampilkan kolom Entity, Year, Potatoes, dan Cassava saja###
data_select <- select(df_crop, Entity, Year, `Potatoes (tonnes per hectare)`, `Cassava (tonnes per hectare)`)
data_select## # 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###
df_crop%>%
filter(Code=="IDN", `Rice (tonnes per hectare)`<2)%>%
select(Year, Entity, `Rice (tonnes per hectare)`)## # A tibble: 7 × 3
## Year Entity `Rice (tonnes per hectare)`
## <dbl> <chr> <dbl>
## 1 1961 Indonesia 1.76
## 2 1962 Indonesia 1.79
## 3 1963 Indonesia 1.72
## 4 1964 Indonesia 1.76
## 5 1965 Indonesia 1.77
## 6 1966 Indonesia 1.77
## 7 1967 Indonesia 1.76###4.Negara apa saja yang punya hasil gandum (Wheat) di atas 5 ton pada tahun 2000 ke atas###
df_crop%>%
filter(Year>="2004", `Wheat (tonnes per hectare)`>5)%>%
select(Entity, Year, `Wheat (tonnes per hectare)`)## # A tibble: 354 × 3
## Entity Year `Wheat (tonnes per hectare)`
## <chr> <dbl> <dbl>
## 1 Austria 2004 5.92
## 2 Austria 2005 5.03
## 3 Austria 2008 5.69
## 4 Austria 2010 5.01
## 5 Austria 2011 5.85
## 6 Austria 2013 5.37
## 7 Austria 2014 5.92
## 8 Austria 2015 5.70
## 9 Austria 2016 6.25
## 10 Belgium 2004 8.98
## # ℹ 344 more rows###5.Bagaimana cara memunculkan data negara Indonesia dan Malaysia khusus untuk tahun 2015 saja###
filter(df_crop, Code=="MYS"|Code=="IDN", Year=="2015")## # 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###
df_crop%>%filter(Year=="2020")%>%
select(Entity, Year, `Maize (tonnes per hectare)`)%>%
arrange(`Maize (tonnes per hectare)`) #hasil tibble nya kosong karena tidak ada data di tahun 2020 di dataframe## # 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###
df_crop%>%
filter(Code=="IDN")%>%
arrange(desc(`Potatoes (tonnes per hectare)`))## # A tibble: 58 × 14
## Entity Code Year `Wheat (tonnes per hectare)` Rice (tonnes per hectare…¹
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Indonesia IDN 2018 NA 5.19
## 2 Indonesia IDN 2016 NA 5.24
## 3 Indonesia IDN 2015 NA 5.34
## 4 Indonesia IDN 2014 NA 5.13
## 5 Indonesia IDN 2006 NA 4.62
## 6 Indonesia IDN 2008 NA 4.89
## 7 Indonesia IDN 1995 NA 4.35
## 8 Indonesia IDN 2012 NA 5.14
## 9 Indonesia IDN 2009 NA 5.00
## 10 Indonesia IDN 2005 NA 4.57
## # ℹ 48 more rows
## # ℹ abbreviated name: ¹`Rice (tonnes per hectare)`
## # 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>, …###8.Membuat kolom Rice_Status berisi teks “Tinggi” jika padi > 4 ton, dan “Rendah” jika di bawahnya###
mutate(df_crop, `Rice_Status` = ifelse(`Rice (tonnes per hectare)` > 4, "Tinggi", "Rendah"))## # A tibble: 13,075 × 15
## 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 10 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>, …###9.Berapa rata-rata hasil panen pisang (Bananas) di Indonesia dari seluruh tahun yang ada###
df_crop%>%
filter(Code=="IDN")%>%
summarise(Rata_Rata_Pisang = mean(`Bananas (tonnes per hectare)`, na.rm=TRUE))## # A tibble: 1 × 1
## Rata_Rata_Pisang
## <dbl>
## 1 30.5###10.Tampilkan data jagung mulai tahun 2010, lalu menghitung simpangan baku per negara, dan mengurutkannya dari nilai yang paling besar###
df_crop%>%
filter(Year >=2010)%>%
group_by(Entity)%>%
summarise(Simpangan_baku_Jagung = sd(`Maize (tonnes per hectare)`, na.rm = TRUE))%>%
arrange(desc(Simpangan_baku_Jagung))## # A tibble: 242 × 2
## Entity Simpangan_baku_Jagung
## <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